MDPI - Publisher of Open Access Journals

Open AccessArticle Design of an Optimized Fuzzy Classifier for the Diagnosis of Blood Pressure with a New Computational Method for Expert Rule Optimization

by Juan Carlos Guzman, Patricia Melin and German Prado-Arechiga

Algorithms 2017, 10(3), 79; doi:10.3390/a10030079

Received: 25 May 2017 / Revised: 4 July 2017 / Accepted: 7 July 2017 / Published: 14 July 2017

Viewed by 132 | PDF Full-text (8040 KB) | HTML Full-text | XML Full-text

Abstract

A neuro fuzzy hybrid model (NFHM) is proposed as a new artificial intelligence method to classify blood pressure (BP). The NFHM uses techniques such as neural networks, fuzzy logic and evolutionary computation, and in the last case genetic algorithms (GAs) are used. The

[...] Read more.

A neuro fuzzy hybrid model (NFHM) is proposed as a new artificial intelligence method to classify blood pressure (BP). The NFHM uses techniques such as neural networks, fuzzy logic and evolutionary computation, and in the last case genetic algorithms (GAs) are used. The main goal is to model the behavior of blood pressure based on monitoring data of 24 h per patient and based on this to obtain the trend, which is classified using a fuzzy system based on rules provided by an expert, and these rules are optimized by a genetic algorithm to obtain the best possible number of rules for the classifier with the lowest classification error. Simulation results are presented to show the advantage of the proposed model. Full article

(This article belongs to the Special Issue Extensions to Type-1 Fuzzy Logic: Theory, Algorithms and Applications)

► Figures

Figure 1

Open AccessArticle Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics

by Patrick J. Trainor, Andrew P. DeFilippis and Shesh N. Rai

Metabolites 2017, 7(2), 30; doi:10.3390/metabo7020030

Received: 22 May 2017 / Revised: 13 June 2017 / Accepted: 17 June 2017 / Published: 21 June 2017

Viewed by 247 | PDF Full-text (5550 KB) | HTML Full-text | XML Full-text

Abstract

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such

[...] Read more.

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k-NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k-NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed. Full article

► Figures

Figure 1

Open AccessArticle Artificial Immune Classifier Based on ELLipsoidal Regions (AICELL) ^†

by Aris Lanaridis, Giorgos Siolas and Andreas Stafylopatis

Computation 2017, 5(2), 31; doi:10.3390/computation5020031

Received: 31 March 2017 / Revised: 5 June 2017 / Accepted: 7 June 2017 / Published: 17 June 2017

Viewed by 204 | PDF Full-text (527 KB) | HTML Full-text | XML Full-text

Abstract

Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on

[...] Read more.

Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on the other hand, classifiers using fuzzy membership functions often result in systems with fewer rules and better generalization ability. To discover an optimal set of rules, learning classifier systems have always relied on bio-inspired models, mainly genetic algorithms. In this paper we propose a classification algorithm based on an efficient bio-inspired approach, Artificial Immune Networks. The proposed algorithm encodes the patterns as antigens, and evolves a set of antibodies, representing fuzzy classification rules of ellipsoidal surface, to cover the problem space. The innate immune mechanisms of affinity maturation and diversity preservation are modified and adapted to the classification context, resulting in a classifier that combines the advantages of both incremental rule learning and fuzzy classifier systems. The algorithm is compared to a number of state-of-the-art rule-based classifiers, as well as Support Vector Machines (SVM), producing very satisfying results, particularly in problems with large number of attributes and classes. Full article

(This article belongs to the Special Issue Computational Aspects Related to Unconventional, Bio-Inspired and Quantum Methods)

► Figures

Figure 1

Open AccessArticle An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier

by Jian He, Shuang Bai and Xiaoyi Wang

Sensors 2017, 17(6), 1393; doi:10.3390/s17061393

Received: 1 April 2017 / Revised: 28 April 2017 / Accepted: 2 May 2017 / Published: 16 June 2017

Viewed by 270 | PDF Full-text (3516 KB) | HTML Full-text | XML Full-text

Abstract

Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based

[...] Read more.

Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall. Full article

(This article belongs to the Special Issue MEMS and Nano-Sensors)

► Figures

Figure 1

Open AccessArticle A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

by Qi Huang, Dapeng Yang, Li Jiang, Huajie Zhang, Hong Liu and Kiyoshi Kotani

Sensors 2017, 17(6), 1370; doi:10.3390/s17061370

Received: 15 April 2017 / Revised: 5 June 2017 / Accepted: 8 June 2017 / Published: 13 June 2017

Viewed by 306 | PDF Full-text (4615 KB) | HTML Full-text | XML Full-text

Abstract

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents

[...] Read more.

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

► Figures

Figure 1

Open AccessArticle Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

by Juan Carlos Davila, Ana-Maria Cretu and Marek Zaremba

Sensors 2017, 17(6), 1287; doi:10.3390/s17061287

Received: 31 March 2017 / Revised: 15 May 2017 / Accepted: 24 May 2017 / Published: 7 June 2017

Viewed by 374 | PDF Full-text (1890 KB) | HTML Full-text | XML Full-text

Abstract

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment,

[...] Read more.

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset. Full article

(This article belongs to the Special Issue Selected Papers from the 3rd International Electronic Conference on Sensors and Applications)

► Figures

Figure 1

Open AccessArticle Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

by Xugang Xi, Minyan Tang, Seyed M. Miran and Zhizeng Luo

Sensors 2017, 17(6), 1229; doi:10.3390/s17061229

Received: 17 March 2017 / Revised: 6 May 2017 / Accepted: 23 May 2017 / Published: 27 May 2017

Viewed by 467 | PDF Full-text (7099 KB) | HTML Full-text | XML Full-text

Abstract

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface

[...] Read more.

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection. Full article

(This article belongs to the Section Biosensors)

► Figures

Figure 1

Open AccessArticle Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy

by Joaquín Abellán and Javier G. Castellano

Entropy 2017, 19(6), 247; doi:10.3390/e19060247

Received: 24 March 2017 / Revised: 29 April 2017 / Accepted: 19 May 2017 / Published: 25 May 2017

Viewed by 556 | PDF Full-text (277 KB) | HTML Full-text | XML Full-text

Abstract

Variable selection methods play an important role in the field of attribute mining. The Naive Bayes (NB) classifier is a very simple and popular classification method that yields good results in a short processing time. Hence, it is a very appropriate classifier for

[...] Read more.

Variable selection methods play an important role in the field of attribute mining. The Naive Bayes (NB) classifier is a very simple and popular classification method that yields good results in a short processing time. Hence, it is a very appropriate classifier for very large datasets. The method has a high dependence on the relationships between the variables. The Info-Gain (IG) measure, which is based on general entropy, can be used as a quick variable selection method. This measure ranks the importance of the attribute variables on a variable under study via the information obtained from a dataset. The main drawback is that it is always non-negative and it requires setting the information threshold to select the set of most important variables for each dataset. We introduce here a new quick variable selection method that generalizes the method based on the Info-Gain measure. It uses imprecise probabilities and the maximum entropy measure to select the most informative variables without setting a threshold. This new variable selection method, combined with the Naive Bayes classifier, improves the original method and provides a valuable tool for handling datasets with a very large number of features and a huge amount of data, where more complex methods are not computationally feasible. Full article

(This article belongs to the Special Issue Maximum Entropy and Its Application II)

► Figures

Figure 1

Open AccessArticle A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers

by Xingmeng Jiang, Li Wu and Mingtao Ge

Entropy 2017, 19(5), 231; doi:10.3390/e19050231

Received: 20 March 2017 / Revised: 6 May 2017 / Accepted: 15 May 2017 / Published: 18 May 2017

Viewed by 558 | PDF Full-text (3529 KB) | HTML Full-text | XML Full-text

Abstract

According to non-stationary characteristic of the acoustic emission signal of rolling element bearings, a novel fault diagnosis method based on empirical wavelet transform (EWT) and ambiguity correlation classification (ACC) is proposed. In the proposed method, the acoustic emission signal acquired from a one-channel

[...] Read more.

According to non-stationary characteristic of the acoustic emission signal of rolling element bearings, a novel fault diagnosis method based on empirical wavelet transform (EWT) and ambiguity correlation classification (ACC) is proposed. In the proposed method, the acoustic emission signal acquired from a one-channel sensor is firstly decomposed using the EWT method, and then the mutual information of decomposed components and the original signal is computed and used to extract the noiseless component in order to obtain the reconstructed signal. Afterwards, the ambiguity correlation classifier, which has the advantages of ambiguity functions in the processing of the non-stationary signal, and the combining of correlation coefficients, is applied. Finally, multiple datasets of reconstructed signals for different operative conditions are fed to the ambiguity correlation classifier for training and testing. The proposed method was verified by experiments, and experimental results have shown that the proposed method can effectively diagnose three different operative conditions of rolling element bearings with higher detection rates than support vector machine and back-propagation (BP) neural network algorithms. Full article

(This article belongs to the Section Information Theory)

► Figures

Open AccessArticle A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

by Quang-Khanh Nguyen, Dieu Tien Bui, Nhat-Duc Hoang, Phan Trong Trinh, Viet-Ha Nguyen and Isık Yilmaz

Sustainability 2017, 9(5), 813; doi:10.3390/su9050813

Received: 14 January 2017 / Revised: 3 May 2017 / Accepted: 10 May 2017 / Published: 13 May 2017

Viewed by 437 | PDF Full-text (16314 KB) | HTML Full-text | XML Full-text

Abstract

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored

[...] Read more.

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques, i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas. Full article

► Figures

Open AccessArticle Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data

by Johanna E. Ayala-Izurieta, Carmen O. Márquez, Víctor J. García, Celso G. Recalde-Moreno, Marcos V. Rodríguez-Llerena and Diego A. Damián-Carrión

Geosciences 2017, 7(2), 34; doi:10.3390/geosciences7020034

Received: 28 February 2017 / Revised: 19 April 2017 / Accepted: 21 April 2017 / Published: 3 May 2017

Viewed by 344 | PDF Full-text (4485 KB) | HTML Full-text | XML Full-text

Abstract

We presented a methodology to accurately classify mountainous regions in the tropics. These landscapes are complex in terms of their geology, ecosystems, climate and land use. Obtaining accurate maps to assess land cover change is essential. The objectives of this study were to

[...] Read more.

We presented a methodology to accurately classify mountainous regions in the tropics. These landscapes are complex in terms of their geology, ecosystems, climate and land use. Obtaining accurate maps to assess land cover change is essential. The objectives of this study were to (1) map vegetation using the Random Forest Classifier (RFC), spectral vegetation index (SVI), and ancillar geographic data (2) identify important variables that help differentiate vegetation cover, and (3) assess the accuracy of the vegetation cover classification in hard-to-reach Ecuadorian mountain region. We used Landsat 7 ETM+ satellite images of the entire scene, a RFC algorithm, and stratified random sampling. The altitude and the two band enhanced vegetation index (EVI2) provide more information on vegetation cover than the traditional and often use normalized difference vegetation index (NDVI) in other settings. We classified the vegetation cover of mountainous areas within the 1016 km² area of study, at 30 m spatial resolution, using RFC that yielded a land cover map with an overall accuracy of 95%. The user´s accuracy and the half-width of the confidence interval for 95% of the basic map units, forest (FOR), páramo (PAR), crop (CRO) and pasture (PAS) were 95.85% ± 2.86%, 97.64% ± 1.24%, 91.53% ± 3.35% and 82.82% ± 7.74%, respectively. The overall disagreement was 4.47%, which results from adding 0.43% of quantity disagreement and 4.04% of allocation disagreement. The methodological framework presented in this paper and the combined use of SVIs, ancillary geographic data, and the RFC allowed the accurate mapping of hard-to-reach mountain landscapes as well as uncovering the underlying factors that help differentiate vegetation cover in the Ecuadorian mountain geosystem. Full article

► Figures

Figure 1

Open AccessArticle Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

by Monika Kuffer, Karin Pfeffer, Richard Sliuzas, Isa Baud and Martin van Maarseveen

Remote Sens. 2017, 9(4), 384; doi:10.3390/rs9040384

Received: 1 February 2017 / Revised: 3 April 2017 / Accepted: 13 April 2017 / Published: 19 April 2017

Viewed by 532 | PDF Full-text (9894 KB) | HTML Full-text | XML Full-text

Abstract

Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated

[...] Read more.

Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management. Full article

► Figures

Open AccessArticle Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

by Mohit Kumar, Ram Bilas Pachori and U. Rajendra Acharya

Entropy 2017, 19(3), 92; doi:10.3390/e19030092

Received: 25 January 2017 / Revised: 15 February 2017 / Accepted: 16 February 2017 / Published: 27 February 2017

Cited by 3 | Viewed by 455 | PDF Full-text (428 KB) | HTML Full-text | XML Full-text

Abstract

In the present work, an automated method to diagnose Congestive Heart Failure (CHF) using Heart Rate Variability (HRV) signals is proposed. This method is based on Flexible Analytic Wavelet Transform (FAWT), which decomposes the HRV signals into different sub-band signals. Further, Accumulated Fuzzy

[...] Read more.

In the present work, an automated method to diagnose Congestive Heart Failure (CHF) using Heart Rate Variability (HRV) signals is proposed. This method is based on Flexible Analytic Wavelet Transform (FAWT), which decomposes the HRV signals into different sub-band signals. Further, Accumulated Fuzzy Entropy (AFEnt) and Accumulated Permutation Entropy (APEnt) are computed over cumulative sums of these sub-band signals. This provides complexity analysis using fuzzy and permutation entropies at different frequency scales. We have extracted 20 features from these signals obtained at different frequency scales of HRV signals. The Bhattacharyya ranking method is used to rank the extracted features from the HRV signals of three different lengths (500, 1000 and 2000 samples). These ranked features are fed to the Least Squares Support Vector Machine (LS-SVM) classifier. Our proposed system has obtained a sensitivity of 98.07%, specificity of 98.33% and accuracy of 98.21% for the 500-sample length of HRV signals. Our system yielded a sensitivity of 97.95%, specificity of 98.07% and accuracy of 98.01% for HRV signals of a length of 1000 samples and a sensitivity of 97.76%, specificity of 97.67% and accuracy of 97.71% for signals corresponding to the 2000-sample length of HRV signals. Our automated system can aid clinicians in the accurate detection of CHF using HRV signals. It can be installed in hospitals, polyclinics and remote villages where there is no access to cardiologists. Full article

(This article belongs to the Special Issue Entropy and Cardiac Physics II)

► Figures

Figure 1

Open AccessArticle Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach

by Robin Tan and Marek Perkowski

Sensors 2017, 17(2), 410; doi:10.3390/s17020410

Received: 4 December 2016 / Revised: 17 January 2017 / Accepted: 9 February 2017 / Published: 20 February 2017

Cited by 1 | Viewed by 503 | PDF Full-text (3874 KB) | HTML Full-text | XML Full-text

Abstract

Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest

[...] Read more.

Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems. Full article

(This article belongs to the Section Biosensors)

► Figures

Figure 1

Open AccessArticle Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

by Qiang Li, Yu Gu and Jing Jia

Sensors 2017, 17(2), 272; doi:10.3390/s17020272

Received: 8 November 2016 / Accepted: 25 January 2017 / Published: 30 January 2017

Viewed by 612 | PDF Full-text (5892 KB) | HTML Full-text | XML Full-text

Abstract

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This

[...] Read more.

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors. Full article

(This article belongs to the Section Physical Sensors)

► Figures

Figure 1

Open AccessArticle Data-Filtering System to Avoid Total Data Distortion in IoT Networking

by Dae-Young Kim, Young-Sik Jeong and Seokhoon Kim

Symmetry 2017, 9(1), 16; doi:10.3390/sym9010016

Received: 30 September 2016 / Revised: 25 December 2016 / Accepted: 16 January 2017 / Published: 20 January 2017

Viewed by 479 | PDF Full-text (2554 KB) | HTML Full-text | XML Full-text

Abstract

In the Internet of Things (IoT) networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed

[...] Read more.

In the Internet of Things (IoT) networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed data from the objects. Then, the servers analyze the collected data and provide proper intelligent services to users through the results of the analysis. When the server analyzes the collected data, if there exists malfunctioning data, distortional results of the analysis will be generated. The distortional results lead to misdirection of the intelligent services, leading to poor user experience. In the analysis for intelligent services in IoT, malfunctioning data should be avoided because integrity of the collected data is crucial. Therefore, this paper proposes a data-filtering system for the server in the cloud. The proposed data-filtering system is placed in front of the server and firstly receives the sensed data from the objects. It employs the naïve Bayesian classifier and, by learning, classifies the malfunctioning data from among the collected data. Data with integrity is delivered to the server for analysis. Because the proposed system filters the malfunctioning data, the server can obtain accurate analysis results and reduce computing load. The performance of the proposed data-filtering system is evaluated through computer simulation. Through the simulation results, the efficiency of the proposed data-filtering system is shown. Full article

(This article belongs to the Special Issue Symmetry in Secure Cyber World)

► Figures

Open AccessArticle Ensemble Classification for Anomalous Propagation Echo Detection with Clustering-Based Subset-Selection Method

by Hansoo Lee and Sungshin Kim

Atmosphere 2017, 8(1), 11; doi:10.3390/atmos8010011

Received: 31 October 2016 / Revised: 6 January 2017 / Accepted: 9 January 2017 / Published: 13 January 2017

Viewed by 354 | PDF Full-text (2820 KB) | HTML Full-text | XML Full-text

Abstract

Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated

[...] Read more.

Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated atmospheric conditions. Anomalous propagation echoes should be removed because they make weather forecasting difficult. In this paper, we suggest an ensemble classification method based on an artificial neural network and a clustering-based subset-selection method. This method allows us to implement an efficient classification method when a feature space has complicated distributions. By separating the input data into atomic and non-atomic clusters, each derived cluster will receive its own base classifier. In the experiments, we compared our method with a standalone artificial neural network classifier. The suggested ensemble classifier showed 84.14% performance, which was about 2% higher than that of the k-means clustering-based ensemble classifier and about 4% higher than the standalone artificial neural network classifier. Full article

(This article belongs to the Special Issue Radar Meteorology)

► Figures

Figure 1

Open AccessArticle Application of a Classifier Based on Data Mining Techniques in Water Supply Operation

by Yi Ji, Xiaohui Lei, Siyu Cai and Xu Wang

Water 2016, 8(12), 599; doi:10.3390/w8120599

Received: 30 September 2016 / Revised: 28 November 2016 / Accepted: 12 December 2016 / Published: 16 December 2016

Viewed by 505 | PDF Full-text (2514 KB) | HTML Full-text | XML Full-text

Abstract

Data mining technology is applied to extract the water supply operation rules in this study. Five characteristic attributes—reservoir storage water, operation period number, water demand, runoff, and hydrological year—are chosen as the dataset, and these characteristic attributes are applied to build a mapping

[...] Read more.

Data mining technology is applied to extract the water supply operation rules in this study. Five characteristic attributes—reservoir storage water, operation period number, water demand, runoff, and hydrological year—are chosen as the dataset, and these characteristic attributes are applied to build a mapping relation with the optimal operation mode calculated by dynamic programming (DP). A Levenberg-Marquardt (LM) neural network and a classification and regression tree (CART) are chosen as data mining algorithms to build the LM neural network classifier and CART decision tree classifier, respectively. In order to verify the classification effect of the LM and CART, the two classifiers are applied to the operation mode recognition for the Heiquan reservoir, which is located in the Qinghai Province of China. The accuracies of the two classifiers are 73.6% and 86.9% for the training sample, and their accuracies are 65.8% and 83.3%, respectively, for the test sample, which indicates that the classification result of the CART classifier is better than that of the LM neural network classifier. Thus, the CART classifier is chosen to guide the long-series water supply operation. Compared to the operation result with the other operation scheme, the result shows that the water deficit index of the CART is mostly closest to the DP scheme, which indicates that the CART classifier can guide reservoir water supply operation effectively. Full article

► Figures

Figure 1

Open AccessArticle Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

by Lufeng Luo, Yunchao Tang, Xiangjun Zou, Chenglin Wang, Po Zhang and Wenxian Feng

Sensors 2016, 16(12), 2098; doi:10.3390/s16122098

Received: 26 September 2016 / Revised: 12 November 2016 / Accepted: 28 November 2016 / Published: 10 December 2016

Viewed by 553 | PDF Full-text (14652 KB) | HTML Full-text | XML Full-text

Abstract

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by

[...] Read more.

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination. Full article

(This article belongs to the Section Physical Sensors)

► Figures

Figure 1

Open AccessArticle A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

by Yanzhen Zhou, Junyong Wu, Zhihong Yu, Luyu Ji and Liangliang Hao

Energies 2016, 9(10), 778; doi:10.3390/en9100778

Received: 12 June 2016 / Revised: 29 August 2016 / Accepted: 8 September 2016 / Published: 27 September 2016

Viewed by 440 | PDF Full-text (8765 KB) | HTML Full-text | XML Full-text

Abstract

Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance

[...] Read more.

Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs) is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems. Full article

(This article belongs to the collection Smart Grid)

► Figures

Open AccessArticle Evaluation of Multiple Classifier Systems for Landslide Identification in LANDSAT Thematic Mapper (TM) Images

by Luiz Augusto Manfré, Rodrigo Affonso de Albuquerque Nóbrega and José Alberto Quintanilha

ISPRS Int. J. Geo-Inf. 2016, 5(9), 164; doi:10.3390/ijgi5090164

Received: 24 May 2016 / Revised: 29 July 2016 / Accepted: 1 September 2016 / Published: 13 September 2016

Cited by 1 | Viewed by 636 | PDF Full-text (4544 KB) | HTML Full-text | XML Full-text

Abstract

Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for

[...] Read more.

Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for landslide risk management, remote sensing usage does have a few drawbacks. The aforementioned events commonly occur at high steep slope regions, frequently associated with shadow occurrence in satellite images, which impairs the identification process and results in low accuracy classifications. In this sense, this paper aims to evaluate the accuracy of different ensembles of multiple classifier systems (MCSs) for landslide scar identification. A severe landslide event on a steep slope with a high rainfall rate area in the southeast region of Brazil was chosen. Ten supervised classifiers were used to identify this severe event and other possible features for the LANDSAT thematic mapper (TM) from June of 2000. The results were evaluated, and nine MCSs were constructed based on the accuracy of the classifiers. Voting was applied through the ensemble method, coupled with contextual analysis and random selection tie-breaker methods. Accuracy was evaluated for each classification ensemble, and a progressive enhancement in the ensemble accuracy was noted as the least accurate classifiers were removed. The best accuracy for landslide identification emerged from the ensemble of the three most accurate classification results. In summary, MCS application generally improved the classification quality and led to fewer omission errors, coupled with a better classification percentage for the ‘landslide’ class. However, the MCS ensemble algorithm selection must be customized to the purpose of the classification. It is crucial to assess single accuracy indicators of each algorithm to ascertain those with the most consistent performance regarding the final results. Full article

(This article belongs to the Special Issue Advances in Geo-Information for Environmental Forensics and Environmental Risk Management in the Anthropocene)

► Figures

Figure 1

Open AccessArticle A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement

by Kun Tan, Jishuai Zhu, Qian Du, Lixin Wu and Peijun Du

Remote Sens. 2016, 8(9), 749; doi:10.3390/rs8090749

Received: 27 June 2016 / Revised: 2 September 2016 / Accepted: 4 September 2016 / Published: 12 September 2016

Viewed by 649 | PDF Full-text (5163 KB) | HTML Full-text | XML Full-text

Abstract

This paper introduces a novel semi-supervised tri-training classification algorithm based on diversity measurement for hyperspectral imagery. In this algorithm, three measures of diversity, i.e., double-fault measure, disagreement metric and correlation coefficient, are applied to select the optimal classifier combination from different classifiers, e.g.,

[...] Read more.

This paper introduces a novel semi-supervised tri-training classification algorithm based on diversity measurement for hyperspectral imagery. In this algorithm, three measures of diversity, i.e., double-fault measure, disagreement metric and correlation coefficient, are applied to select the optimal classifier combination from different classifiers, e.g., support vector machine (SVM), multinomial logistic regression (MLR), extreme learning machine (ELM) and k-nearest neighbor (KNN). Then, unlabeled samples are selected using an active learning (AL) method, and consistent results of any other two classifiers combined with a spatial neighborhood information extraction strategy are employed to predict their labels. Moreover, a multi-scale homogeneity (MSH) method is utilized to refine the classification result with the highest accuracy in the classifier combination, generating the final classification result. Experiments on three real hyperspectral data indicate that the proposed approach can effectively improve classification performance. Full article

► Figures

Open AccessArticle Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

by Asamaporn Sitthi, Masahiko Nagai, Matthew Dailey and Sarawut Ninsawat

Sustainability 2016, 8(9), 921; doi:10.3390/su8090921

Received: 16 July 2016 / Revised: 29 August 2016 / Accepted: 5 September 2016 / Published: 9 September 2016

Cited by 1 | Viewed by 811 | PDF Full-text (8556 KB) | HTML Full-text | XML Full-text

Abstract

Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery

[...] Read more.

Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery containing an immense amount of information. In this paper, geotagged Flickr images are used for automatic extraction of low-level land use/land cover (LULC) features. The proposed method uses a naive Bayes classifier with color, shape, and color index descriptors. The classified images are mapped using a majority filtering approach. The classifier performance in overall accuracy, kappa coefficient, precision, recall, and f-measure was 87.94%, 82.89%, 88.20%, 87.90%, and 88%, respectively. Labeled-crowdsourced images were filtered into a spatial tile of a 30 m × 30 m resolution using the majority voting method to reduce geolocation uncertainty from the crowdsourced data. These tile datasets were used as training and validation samples to classify Landsat TM5 images. The supervised maximum likelihood method was used for the LULC classification. The results show that the geotagged Flickr images can classify LULC types with reasonable accuracy and that the proposed approach improves LULC classification efficiency if a sufficient spatial distribution of crowdsourced data exists. Full article

(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)

► Figures

Figure 1

Open AccessArticle Real-Time Tracking Framework with Adaptive Features and Constrained Labels

by Daqun Li, Tingfa Xu, Shuoyang Chen, Jizhou Zhang and Shenwang Jiang

Sensors 2016, 16(9), 1449; doi:10.3390/s16091449

Received: 5 July 2016 / Revised: 22 August 2016 / Accepted: 22 August 2016 / Published: 8 September 2016

Cited by 1 | Viewed by 581 | PDF Full-text (4334 KB) | HTML Full-text | XML Full-text

Abstract

This paper proposes a novel tracking framework with adaptive features and constrained labels (AFCL) to handle illumination variation, occlusion and appearance changes caused by the variation of positions. The novel ensemble classifier, including the Forward–Backward error and the location constraint is applied, to

[...] Read more.

This paper proposes a novel tracking framework with adaptive features and constrained labels (AFCL) to handle illumination variation, occlusion and appearance changes caused by the variation of positions. The novel ensemble classifier, including the Forward–Backward error and the location constraint is applied, to get the precise coordinates of the promising bounding boxes. The Forward–Backward error can enhance the adaptation and accuracy of the binary features, whereas the location constraint can overcome the label noise to a certain degree. We use the combiner which can evaluate the online templates and the outputs of the classifier to accommodate the complex situation. Evaluation of the widely used tracking benchmark shows that the proposed framework can significantly improve the tracking accuracy, and thus reduce the processing time. The proposed framework has been tested and implemented on the embedded system using TMS320C6416 and Cyclone Ⅲ kernel processors. The outputs show that achievable and satisfying results can be obtained. Full article

(This article belongs to the Section Physical Sensors)

► Figures

Figure 1

Open AccessArticle Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

by Xueke Li, Taixia Wu, Kai Liu, Yao Li and Lifu Zhang

Remote Sens. 2016, 8(5), 438; doi:10.3390/rs8050438

Received: 24 March 2016 / Revised: 12 May 2016 / Accepted: 18 May 2016 / Published: 21 May 2016

Cited by 2 | Viewed by 739 | PDF Full-text (2961 KB) | HTML Full-text | XML Full-text

Abstract

The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape

[...] Read more.

The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier. Full article

(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions along the Urban-Rural Gradient)

► Figures

Open AccessArticle Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor

by Jae-Neung Lee, Myung-Won Lee, Yeong-Hyeon Byeon, Won-Sik Lee and Keun-Chang Kwak

Sensors 2016, 16(5), 664; doi:10.3390/s16050664

Received: 29 February 2016 / Revised: 25 April 2016 / Accepted: 3 May 2016 / Published: 10 May 2016

Viewed by 630 | PDF Full-text (7191 KB) | HTML Full-text | XML Full-text

Abstract

In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The

[...] Read more.

In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider’s hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse’s gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider’s motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country’s top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. Full article

(This article belongs to the Section Physical Sensors)

Open AccessArticle A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

by Fernando J. Aguilar, Abderrahim Nemmaoui, Manuel A. Aguilar, Mimoun Chourak, Yassine Zarhloule and Andrés M. García Lorca

Forests 2016, 7(1), 23; doi:10.3390/f7010023

Received: 5 November 2015 / Revised: 6 January 2016 / Accepted: 7 January 2016 / Published: 15 January 2016

Viewed by 1158 | PDF Full-text (2836 KB) | HTML Full-text | XML Full-text

Abstract

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An

[...] Read more.

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region. Full article

Open AccessArticle Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

by Aitor Álvarez, Basilio Sierra, Andoni Arruti, Juan-Miguel López-Gil and Nestor Garay-Vitoria

Sensors 2016, 16(1), 21; doi:10.3390/s16010021

Received: 22 September 2015 / Revised: 9 December 2015 / Accepted: 17 December 2015 / Published: 25 December 2015

Cited by 1 | Viewed by 1004 | PDF Full-text (379 KB) | HTML Full-text | XML Full-text

Abstract

In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means

[...] Read more.

In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one. Full article

(This article belongs to the Section Physical Sensors)

Open AccessArticle Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification

by Luliang Tang, Xue Yang, Zihan Kan and Qingquan Li

ISPRS Int. J. Geo-Inf. 2015, 4(4), 2660-2680; doi:10.3390/ijgi4042660

Received: 16 June 2015 / Revised: 11 November 2015 / Accepted: 17 November 2015 / Published: 26 November 2015

Cited by 4 | Viewed by 910 | PDF Full-text (1480 KB) | HTML Full-text | XML Full-text

Abstract

In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive

[...] Read more.

In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane. Full article

Open AccessArticle Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China

by Quanlong Feng, Jianhua Gong, Jiantao Liu and Yi Li

Remote Sens. 2015, 7(9), 12539-12562; doi:10.3390/rs70912539

Received: 8 July 2015 / Accepted: 14 September 2015 / Published: 23 September 2015

Cited by 4 | Viewed by 897 | PDF Full-text (1663 KB) | HTML Full-text | XML Full-text

Abstract

Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated

[...] Read more.

Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery. Full article

(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)

► Figures

Open AccessArticle Averaged Extended Tree Augmented Naive Classifier

by Aaron Meehan and Cassio P. de Campos

Entropy 2015, 17(7), 5085-5100; doi:10.3390/e17075085

Received: 8 June 2015 / Revised: 10 June 2015 / Accepted: 17 June 2015 / Published: 21 July 2015

Cited by 1 | Viewed by 802 | PDF Full-text (915 KB) | HTML Full-text | XML Full-text

Abstract

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of

[...] Read more.

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required. Full article

(This article belongs to the Special Issue Inductive Statistical Methods)

Open AccessArticle Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier

by Hong Wang, Yu Zhao, Ruiliang Pu and Zhenzhen Zhang

Remote Sens. 2015, 7(7), 9020-9044; doi:10.3390/rs70709020

Received: 22 April 2015 / Revised: 7 July 2015 / Accepted: 9 July 2015 / Published: 16 July 2015

Cited by 6 | Viewed by 1123 | PDF Full-text (3483 KB) | HTML Full-text | XML Full-text

Abstract

The textural and spatial information extracted from very high resolution (VHR) remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM) textures and

[...] Read more.

The textural and spatial information extracted from very high resolution (VHR) remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. Specifically, eight GLCM texture features (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) were first calculated from IKONOS NIR band (Band 4) to determine an optimal window size (13 × 13) and an optimal direction (45°). Then, the optimal window size and direction were applied to the three other IKONOS MS bands (blue, green, and red) for calculating the eight GLCM textures. Next, an optimal distance value (5) and an optimal neighborhood rule (Queen’s case) were determined for calculating the four Gi features from the four IKONOS MS bands. Finally, different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 93.3% created with the all 32 GLCM features calculated from the four IKONOS MS bands with a window size of 13 × 13 and direction of 45°; (4) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen’s neighborhood rule; and (5) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The most important feature ranked by RF classifier was GLCM texture mean calculated from Band 4, followed by Gi feature calculated from Band 4. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) the IKONOS NIR band was more powerful than visible bands in quantifying varying degrees of forest crown dieback. Full article

► Figures

Open AccessArticle General and Local: Averaged k-Dependence Bayesian Classifiers

by Limin Wang, Haoyu Zhao, Minghui Sun and Yue Ning

Entropy 2015, 17(6), 4134-4154; doi:10.3390/e17064134

Received: 4 May 2015 / Revised: 2 June 2015 / Accepted: 9 June 2015 / Published: 16 June 2015

Viewed by 870 | PDF Full-text (1339 KB) | HTML Full-text | XML Full-text

Abstract

The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB) classifier can construct at arbitrary points (values of k) along the attribute dependence spectrum, it cannot identify the changes of

[...] Read more.

The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB) classifier can construct at arbitrary points (values of k) along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB) classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI) showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB), tree augmented naive Bayes (TAN), Averaged one-dependence estimators (AODE), and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance. Full article

(This article belongs to the Special Issue Inductive Statistical Methods)

Open AccessArticle Learning a Flexible K-Dependence Bayesian Classifier from the Chain Rule of Joint Probability Distribution

by Limin Wang and Haoyu Zhao

Entropy 2015, 17(6), 3766-3786; doi:10.3390/e17063766

Received: 30 November 2014 / Accepted: 3 June 2015 / Published: 8 June 2015

Viewed by 1092 | PDF Full-text (2540 KB) | HTML Full-text | XML Full-text

Abstract

As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity. The scoring functions once proposed and widely used for a Bayesian network are not appropriate for a Bayesian

[...] Read more.

As one of the most common types of graphical models, the Bayesian classifier has become an extremely popular approach to dealing with uncertainty and complexity. The scoring functions once proposed and widely used for a Bayesian network are not appropriate for a Bayesian classifier, in which class variable C is considered as a distinguished one. In this paper, we aim to clarify the working mechanism of Bayesian classifiers from the perspective of the chain rule of joint probability distribution. By establishing the mapping relationship between conditional probability distribution and mutual information, a new scoring function, Sum_MI, is derived and applied to evaluate the rationality of the Bayesian classifiers. To achieve global optimization and high dependence representation, the proposed learning algorithm, the flexible K-dependence Bayesian (FKDB) classifier, applies greedy search to extract more information from the K-dependence network structure. Meanwhile, during the learning procedure, the optimal attribute order is determined dynamically, rather than rigidly. In the experimental study, functional dependency analysis is used to improve model interpretability when the structure complexity is restricted. Full article

(This article belongs to the Section Statistical Mechanics)

Open AccessArticle Multi-Classifier Based on a Query-by-Singing/Humming System

by Gi Pyo Nam and Kang Ryoung Park

Symmetry 2015, 7(2), 994-1016; doi:10.3390/sym7020994

Received: 3 February 2015 / Revised: 25 May 2015 / Accepted: 1 June 2015 / Published: 4 June 2015

Viewed by 1097 | PDF Full-text (34569 KB) | HTML Full-text | XML Full-text

Abstract

With the increase in the number of music files on various devices, it can be difficult to locate a desired file, especially when the title of the song or the name of the singer is not known. We propose a new query-by-singing/humming (QbSH)

[...] Read more.

With the increase in the number of music files on various devices, it can be difficult to locate a desired file, especially when the title of the song or the name of the singer is not known. We propose a new query-by-singing/humming (QbSH) system that can find music files that match what the user is singing or humming. This research is novel in the following three ways: first, the Fourier descriptor (FD) method is proposed as the first classifier; it transforms the humming or music waveform into the frequency domain. Second, quantized dynamic time warping (QDTW) using symmetrical search space and quantized linear scaling (QLS) are used as the second and third classifiers, respectively, which increase the accuracy of the QbSH system compared to the conventional DTW and LS methods. Third, five classifiers, which include the three already mentioned along with the conventional DTW using symmetrical search space and LS methods, are combined using score level fusion, which further enhances performance. Experimental results with the 2009 MIR-QbSH corpus and the AFA MIDI 100 databases show that the proposed method outperforms those using a single classifier and other fusion methods. Full article

Open AccessArticle Ultrasonic Sensor Signals and Optimum Path Forest Classifier for the Microstructural Characterization of Thermally-Aged Inconel 625 Alloy

by Victor Hugo C. de Albuquerque, Cleisson V. Barbosa, Cleiton C. Silva, Elineudo P. Moura, Pedro P. Rebouças Filho, João P. Papa and João Manuel R. S. Tavares

Sensors 2015, 15(6), 12474-12497; doi:10.3390/s150612474

Received: 26 April 2015 / Accepted: 20 May 2015 / Published: 27 May 2015

Cited by 8 | Viewed by 1370 | PDF Full-text (617 KB) | HTML Full-text | XML Full-text

Abstract

Secondary phases, such as laves and carbides, are formed during the final solidification stages of nickel-based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the

[...] Read more.

Secondary phases, such as laves and carbides, are formed during the final solidification stages of nickel-based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ'' and δ phases. This work presents an evaluation of the powerful optimum path forest (OPF) classifier configured with six distance functions to classify background echo and backscattered ultrasonic signals from samples of the inconel 625 superalloy thermally aged at 650 and 950 \(^\circ\)C for 10, 100 and 200 h. The background echo and backscattered ultrasonic signals were acquired using transducers with frequencies of 4 and 5 MHz. The potentiality of ultrasonic sensor signals combined with the OPF to characterize the microstructures of an inconel 625 thermally aged and in the as-welded condition were confirmed by the results. The experimental results revealed that the OPF classifier is sufficiently fast (classification total time of 0.316 ms) and accurate (accuracy of 88.75% and harmonic mean of 89.52) for the application proposed. Full article

(This article belongs to the Section Physical Sensors)

Open AccessArticle Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble

by Hang Liu, Renzhi Chu and Zhenan Tang

Sensors 2015, 15(5), 10180-10193; doi:10.3390/s150510180

Received: 25 February 2015 / Revised: 25 April 2015 / Accepted: 28 April 2015 / Published: 30 April 2015

Cited by 2 | Viewed by 1388 | PDF Full-text (286 KB) | HTML Full-text | XML Full-text

Abstract

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate

[...] Read more.

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. Full article

(This article belongs to the Special Issue Gas Sensors—Designs and Applications)

Open AccessLetter A Speedy Cardiovascular Diseases Classifier Using Multiple Criteria Decision Analysis

by Wah Ching Lee, Faan Hei Hung, Kim Fung Tsang, Hoi Ching Tung, Wing Hong Lau, Veselin Rakocevic and Loi Lei Lai

Sensors 2015, 15(1), 1312-1320; doi:10.3390/s150101312

Received: 11 November 2014 / Accepted: 5 January 2015 / Published: 12 January 2015

Cited by 3 | Viewed by 1413 | PDF Full-text (824 KB) | HTML Full-text | XML Full-text

Abstract

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address

[...] Read more.

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented. Full article

(This article belongs to the Section Physical Sensors)

Open AccessArticle Adaptive Activity and Environment Recognition for Mobile Phones

by Jussi Parviainen, Jayaprasad Bojja, Jussi Collin, Jussi Leppänen and Antti Eronen

Sensors 2014, 14(11), 20753-20778; doi:10.3390/s141120753

Received: 10 September 2014 / Revised: 16 October 2014 / Accepted: 20 October 2014 / Published: 3 November 2014

Cited by 10 | Viewed by 1529 | PDF Full-text (767 KB) | HTML Full-text | XML Full-text

Abstract

In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these

[...] Read more.

In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy. Full article

(This article belongs to the Special Issue HCI In Smart Environments)

Open AccessArticle Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

by Daniel Planelles, Enrique Hortal, Álvaro Costa, Andrés Úbeda, Eduardo Iáez and José M. Azorín

Sensors 2014, 14(10), 18172-18186; doi:10.3390/s141018172

Received: 18 July 2014 / Revised: 16 September 2014 / Accepted: 17 September 2014 / Published: 29 September 2014

Cited by 7 | Viewed by 2133 | PDF Full-text (804 KB) | HTML Full-text | XML Full-text

Abstract

This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over

[...] Read more.

This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement generate a phenomenon called event-related desynchronization (ERD) in the mu and beta frequency bands. A novel methodology to characterize this cognitive process based on three sums of power spectral frequencies involved in ERD is presented. The main objective of this paper is to set the benchmark for classifiers and to choose the most convenient. The best results are obtained using an SVM classifier with around 72% accuracy. This classifier will be used in further research to generate the control commands to move a robotic exoskeleton that helps people suffering from motor disabilities to perform the movement. The final aim is that this brain-controlled robotic exoskeleton improves the current rehabilitation processes of disabled people. Full article

(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)

Open AccessArticle A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

by Juri Taborri, Stefano Rossi, Eduardo Palermo, Fabrizio Patanè and Paolo Cappa

Sensors 2014, 14(9), 16212-16234; doi:10.3390/s140916212

Received: 13 June 2014 / Revised: 5 August 2014 / Accepted: 26 August 2014 / Published: 2 September 2014

Cited by 18 | Viewed by 2312 | PDF Full-text (1854 KB) | HTML Full-text | XML Full-text

Abstract

In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov

[...] Read more.

In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems)

► Figures

Open AccessArticle Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

by Gang Li and Wan-Young Chung

Sensors 2013, 13(12), 16494-16511; doi:10.3390/s131216494

Received: 9 September 2013 / Revised: 4 November 2013 / Accepted: 25 November 2013 / Published: 2 December 2013

Cited by 40 | Viewed by 3510 | PDF Full-text (844 KB) | HTML Full-text | XML Full-text

Abstract

Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV

[...] Read more.

Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems)

Open AccessArticle Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems

by Yongwha Chung, Seunggeun Oh, Jonguk Lee, Daihee Park, Hong-Hee Chang and Suk Kim

Sensors 2013, 13(10), 12929-12942; doi:10.3390/s131012929

Received: 13 June 2013 / Revised: 16 September 2013 / Accepted: 22 September 2013 / Published: 25 September 2013

Cited by 9 | Viewed by 1918 | PDF Full-text (681 KB) | HTML Full-text | XML Full-text

Abstract

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an

[...] Read more.

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution. Full article

(This article belongs to the Section Physical Sensors)

Open AccessArticle Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)

by Fayçal Benrekia, Mokhtar Attari and Mounir Bouhedda

Sensors 2013, 13(3), 2967-2985; doi:10.3390/s130302967

Received: 7 January 2013 / Revised: 31 January 2013 / Accepted: 21 February 2013 / Published: 1 March 2013

Cited by 6 | Viewed by 2463 | PDF Full-text (1281 KB) | HTML Full-text | XML Full-text

Abstract

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO₂ thin film gas sensors with different selectivity patterns. The output signals are processed through a signal

[...] Read more.

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO₂ thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases. Full article

(This article belongs to the Special Issue Gas Sensors - 2013)

Open AccessArticle Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

by Giulio Reina and Annalisa Milella

Sensors 2012, 12(9), 12405-12423; doi:10.3390/s120912405

Received: 6 August 2012 / Revised: 28 August 2012 / Accepted: 30 August 2012 / Published: 12 September 2012

Cited by 28 | Viewed by 2834 | PDF Full-text (7118 KB) | HTML Full-text | XML Full-text

Abstract

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this

[...] Read more.

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy 2012)

Open AccessReview Multiple Classifier System for Remote Sensing Image Classification: A Review

by Peijun Du, Junshi Xia, Wei Zhang, Kun Tan, Yi Liu and Sicong Liu

Sensors 2012, 12(4), 4764-4792; doi:10.3390/s120404764

Received: 22 February 2012 / Revised: 22 March 2012 / Accepted: 6 April 2012 / Published: 12 April 2012

Cited by 77 | Viewed by 3721 | PDF Full-text (22962 KB) | HTML Full-text | XML Full-text

Abstract

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a

[...] Read more.

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+).Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community. Full article

(This article belongs to the Section Remote Sensors)

Open AccessArticle Prediction of Lysine Ubiquitylation with Ensemble Classifier and Feature Selection

by Xiaowei Zhao, Xiangtao Li, Zhiqiang Ma and Minghao Yin

Int. J. Mol. Sci. 2011, 12(12), 8347-8361; doi:10.3390/ijms12128347

Received: 27 July 2011 / Revised: 14 November 2011 / Accepted: 15 November 2011 / Published: 28 November 2011

Cited by 27 | Viewed by 2056 | PDF Full-text (247 KB) | HTML Full-text | XML Full-text

Abstract

Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation

[...] Read more.

Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the proposed method, 468 ubiquitylation sites from 323 proteins retrieved from the Swiss-Prot database were encoded into feature vectors by using four kinds of protein sequences information. An effective feature selection method was then applied to extract informative feature subsets. After different feature subsets were obtained by setting different starting points in the search procedure, they were used to train multiple random forests classifiers and then aggregated into a consensus classifier by majority voting. Evaluated by jackknife tests and independent tests respectively, the accuracy of the proposed predictor reached 76.82% for the training dataset and 79.16% for the test dataset, indicating that this predictor is a useful tool to predict lysine ubiquitylation sites. Furthermore, site-specific feature analysis was performed and it was shown that ubiquitylation is intimately correlated with the features of its surrounding sites in addition to features derived from the lysine site itself. The feature selection method is available upon request. Full article

(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)

Open AccessArticle A Low-Power Hardware-Friendly Binary Decision Tree Classifier for Gas Identification

by Qingzheng Li and Amine Bermak

J. Low Power Electron. Appl. 2011, 1(1), 45-58; doi:10.3390/jlpea1010045

Received: 13 December 2010 / Revised: 1 March 2011 / Accepted: 2 March 2011 / Published: 9 March 2011

Cited by 10 | Viewed by 3610 | PDF Full-text (9860 KB) | HTML Full-text | XML Full-text

Abstract

In this paper, we present a hardware friendly binary decision tree (DT) classifier for gas identification. The DT classifier is based on an axis-parallel decision tree implemented as threshold networks—one layer of threshold logic units (TLUs) followed by a programmable binary tree implemented

[...] Read more.

In this paper, we present a hardware friendly binary decision tree (DT) classifier for gas identification. The DT classifier is based on an axis-parallel decision tree implemented as threshold networks—one layer of threshold logic units (TLUs) followed by a programmable binary tree implemented using combinational logic circuits. The proposed DT classifier circuit removes the need for multiplication operation enabling up to 80% savings in terms of silicon area and power compared to oblique based-DT while achieving 91.36% classification accuracy without throughput degradation. The circuit was designed in 0.18 μm Charter CMOS process and tested using a data set acquired with in-house fabricated tin-oxide gas sensors. Full article

(This article belongs to the Special Issue Selected Topics in Low Power Design - From Circuits to Applications)

► Figures

Open AccessArticle GACEM: Genetic Algorithm Based Classifier Ensemble in a Multi-sensor System

by Rongwu Xu and Lin He

Sensors 2008, 8(10), 6203-6224; doi:10.3390/s8106203

Received: 20 April 2008 / Revised: 22 September 2008 / Accepted: 26 September 2008 / Published: 1 October 2008

Cited by 7 | Viewed by 8021 | PDF Full-text (475 KB) | HTML Full-text | XML Full-text

Abstract

Multi-sensor systems (MSS) have been increasingly applied in pattern classification while searching for the optimal classification framework is still an open problem. The development of the classifier ensemble seems to provide a promising solution. The classifier ensemble is a learning paradigm where many

[...] Read more.

Multi-sensor systems (MSS) have been increasingly applied in pattern classification while searching for the optimal classification framework is still an open problem. The development of the classifier ensemble seems to provide a promising solution. The classifier ensemble is a learning paradigm where many classifiers are jointly used to solve a problem, which has been proven an effective method for enhancing the classification ability. In this paper, by introducing the concept of Meta-feature (MF) and Trans-function (TF) for describing the relationship between the nature and the measurement of the observed phenomenon, classification in a multi-sensor system can be unified in the classifier ensemble framework. Then an approach called Genetic Algorithm based Classifier Ensemble in Multi-sensor system (GACEM) is presented, where a genetic algorithm is utilized for optimization of both the selection of features subset and the decision combination simultaneously. GACEM trains a number of classifiers based on different combinations of feature vectors at first and then selects the classifiers whose weight is higher than the pre-set threshold to make up the ensemble. An empirical study shows that, compared with the conventional feature-level voting and decision-level voting, not only can GACEM achieve better and more robust performance, but also simplify the system markedly. Full article

► Figures

Open AccessArticle Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning

by Xue Wang, Sheng Wang, Daowei Bi and Liang Ding

Sensors 2007, 7(11), 2693-2722; doi:10.3390/s7112693

Received: 24 October 2007 / Accepted: 8 November 2007 / Published: 13 November 2007

Cited by 4 | Viewed by 5955 | PDF Full-text (2331 KB) | HTML Full-text | XML Full-text

Abstract

Wireless multimedia sensor networks (WMSN) have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper

[...] Read more.

Wireless multimedia sensor networks (WMSN) have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM) based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting. Full article

(This article belongs to the Special Issue Energy Efficiency and Intelligent Signal Processing for Wireless Sensing)

MDPI Contact

Advanced Search

Search Results

MDPI Contact

Advanced Search

Search Results

Years

Subjects

Journals

Article Types

Countries