Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems

Topic Information

Dear Colleagues,

The Internet of Things (IoT) ecosystem is a vast network of complementary technologies and components that technical specialists use to reach a specific goal. From the point of view of protocol stacks, the physical layer requires sophisticated signal processing technologies to (1) extract valuable information and deliver it to an upper layer and (2) eliminate unwanted interference during data transmission. In addition, advanced data analysis is required to retrieve helpful information in the ecosystems. The purpose of this topic is to bring together state-of-the-art achievements on signal processing and data analysis and their applications on smart IoT. It discusses emerging smart IoT ecosystems and technologies from the physical layer to the application layer. We believe this topic would be a platform for colleagues to exchange novel ideas in this area. Primarily, we expect some novel approaches that fuse the idea of adaptive signal processing and machine learning techniques to open the field of intelligent signal processing. We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles on (but not limited to) signal processing in communication systems, indoor positioning, environmental monitoring, bio-signal processing, artificial intelligence, contactless vital sign monitoring, remote health care, wireless networks, and multimedia interactions. Submitted manuscripts should not have been published previously nor be under consideration for publication elsewhere. We allow extending versions of published conference proceedings papers as well.

Prof. Dr. Ying-Ren Chien
Prof. Dr. Mu Zhou
Prof. Dr. Liang-Hung Wang
Dr. Xun Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Future Internet futureinternet	2.8	7.1	2009	16.9 Days	CHF 1600
IoT IoT	-	8.5	2020	27.8 Days	CHF 1200
Journal of Sensor and Actuator Networks jsan	3.3	7.9	2012	19 Days	CHF 2000
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600

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Published Papers (21 papers)

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18 pages, 1231 KiB  

Open AccessArticle

Estimation of Wideband Multi-Component Phasors Considering Signal Damping

by Dongfang Zhao, Shisong Li, Fuping Wang, Wei Zhao and Songling Huang

Sensors 2023, 23(16), 7071; https://doi.org/10.3390/s23167071 - 10 Aug 2023

Cited by 1 | Viewed by 1262

Abstract

Harmonic and interharmonic content in power system signals is increasing with the development of renewable energy generation and power electronic devices. These multiple signal components can seriously degrade power quality, trip thermal generators, cause oscillations, and threaten system stability, especially the interharmonic tones [...] Read more.

Harmonic and interharmonic content in power system signals is increasing with the development of renewable energy generation and power electronic devices. These multiple signal components can seriously degrade power quality, trip thermal generators, cause oscillations, and threaten system stability, especially the interharmonic tones with positive damping factors. The first step to mitigate these adverse effects is to accurately and quickly monitor signal features, including frequency, damping factor, amplitude, and phase. This paper proposes a concise and robust index to identify the number of modes present in the signal using the singular values of the Hankel matrix and discusses the scope of its application by testing the influence of various factors. Next, the simplified matrix pencil theory is employed to estimate the signal component frequency and damping factor. Then their estimates are considered in the modified least-squares algorithm to extract the wideband multi-component phasors accurately. Finally, this paper designs a series of scenarios considering varying signal frequency, damping factor, amplitude, and phase to test the proposed algorithm thoroughly. The results verify that the proposed method can achieve a maximum total vector error of less than 1.5%, which is more accurate than existing phasor estimators in various signal environments. The high accuracy of the proposed method is because it considers both the estimation of the frequency number and the effect of signal damping. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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17 pages, 4598 KiB  

Open AccessArticle

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities

by Felipe Pineda-Alpizar, Sergio Arriola-Valverde, Mitzy Vado-Chacón, Diego Sossa-Rojas, Haipeng Liu and Dingchang Zheng

Sensors 2023, 23(9), 4246; https://doi.org/10.3390/s23094246 - 24 Apr 2023

Cited by 2 | Viewed by 3676

Abstract

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost [...] Read more.

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer–Ray–Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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29 pages, 1791 KiB  

Open AccessArticle

Fusion Objective Function on Progressive Super-Resolution Network

by Amir Hajian and Supavadee Aramvith

J. Sens. Actuator Netw. 2023, 12(2), 26; https://doi.org/10.3390/jsan12020026 - 20 Mar 2023

Cited by 8 | Viewed by 2555

Abstract

Recent advancements in Single-Image Super-Resolution (SISR) have explored the network architecture of deep-learning models to achieve a better perceptual quality of super-resolved images. However, the effect of the objective function, which contributes to improving the performance and perceptual quality of super-resolved images, has [...] Read more.

Recent advancements in Single-Image Super-Resolution (SISR) have explored the network architecture of deep-learning models to achieve a better perceptual quality of super-resolved images. However, the effect of the objective function, which contributes to improving the performance and perceptual quality of super-resolved images, has not gained much attention. This paper proposes a novel super-resolution architecture called Progressive Multi-Residual Fusion Network (PMRF), which fuses the learning objective functions of $L2$ and Multi-Scale SSIM in a progressively upsampling framework structure. Specifically, we propose a Residual-in-Residual Dense Blocks (RRDB) architecture on a progressively upsampling platform that reconstructs the high-resolution image during intermediate steps in our super-resolution network. Additionally, the Depth-Wise Bottleneck Projection allows high-frequency information of early network layers to be bypassed through the upsampling modules of the network. Quantitative and qualitative evaluation of benchmark datasets demonstrate that the proposed PMRF super-resolution algorithm with novel fusion objective function ($L2$ and MS-SSIM) improves our model’s perceptual quality and accuracy compared to other state-of-the-art models. Moreover, this model demonstrates robustness against noise degradation and achieves an acceptable trade-off between network efficiency and accuracy. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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20 pages, 15217 KiB  

Open AccessArticle

SingMonitor: E-bike Charging Health Monitoring Using Sound from Power Supplies

by Xiangyong Jian, Lanqing Yang, Yijie Li, Yi-Chao Chen and Guangtao Xue

Appl. Sci. 2023, 13(5), 3087; https://doi.org/10.3390/app13053087 - 27 Feb 2023

Viewed by 2692

Abstract

In recent years, fire disasters caused by charging electric bicycles/moped (e-bikes) have been increasing, causing catastrophic loss of life and property; Worse still, existing fire warning systems are costly to install and maintain, and they work after the accident occurs. Some existing works [...] Read more.

In recent years, fire disasters caused by charging electric bicycles/moped (e-bikes) have been increasing, causing catastrophic loss of life and property; Worse still, existing fire warning systems are costly to install and maintain, and they work after the accident occurs. Some existing works propose using power meters or similar sensors in the power grid to monitor e-bike charging health. However, the use of additional equipment makes them challenging to deploy. Others can use the sound or electromagnetic signals emitted by e-bikes for monitoring, but they suffer from limited monitoring distance. To solve this problem, we propose SingMonitor, a scheme to remotely monitor e-bike charging status using mobile devices’ microphones. The charging e-bike generates a unique current signal, which is then transmitted through the power grid and drives the mobile devices’ power supply to generate sound, which is then captured by a microphone. Based on this principle and the proposed template matching method, SingMonitor can identify the e-bike charging status. Experiments show SingMonitor achieves an F1 score of 0.94 in identifying 10 e-bikes’ charging status, with a detection distance of 9m+. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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13 pages, 1053 KiB  

Open AccessArticle

Adaptive Approach to Time-Frequency Analysis of AE Signals of Rocks

by Olga Lukovenkova, Yuri Marapulets and Alexandra Solodchuk

Sensors 2022, 22(24), 9798; https://doi.org/10.3390/s22249798 - 13 Dec 2022

Cited by 11 | Viewed by 2275

Abstract

The paper describes a new adaptive approach to the investigation of acoustic emission of rocks, the anomalies of which may serve as short-term precursors of strong earthquakes. The basis of the approach is complex methods for monitoring acoustic emission and for analysis of [...] Read more.

The paper describes a new adaptive approach to the investigation of acoustic emission of rocks, the anomalies of which may serve as short-term precursors of strong earthquakes. The basis of the approach is complex methods for monitoring acoustic emission and for analysis of its time-frequency content. Piezoceramic hydrophones and vector receivers, installed at the bottom of natural and artificial water bodies, as well as in boreholes with water, are used as acoustic emission sensors. To perform a time-frequency analysis of geoacoustic signals, we use a sparse approximation based on the developed Adaptive Matching Pursuit algorithm. The application of this algorithm in the analysis makes it possible to adapt to the concrete characteristics of each geoacoustic pulse. Results of the application of the developed approach for the investigation of acoustic emission anomalies, occurring before earthquakes, are presented. We analyzed the earthquakes, that occurred from 2011 to 2016 in the seismically active region of the Kamchatka peninsula, which is a part of the circum-Pacific orogenic belt also known as the “Ring of Fire”. It was discovered that geoacoustic pulse frequency content changes before a seismic event and returns to the initial state after an earthquake. That allows us to make a conclusion on the transformation of acoustic emission source scales before earthquakes. The obtained results may be useful for the development of the systems for environmental monitoring and detection of earthquake occurrences. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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27 pages, 8195 KiB  

Open AccessArticle

Uniformity Correction of CMOS Image Sensor Modules for Machine Vision Cameras

by Gabor Szedo Becker and Róbert Lovas

Sensors 2022, 22(24), 9733; https://doi.org/10.3390/s22249733 - 12 Dec 2022

Cited by 6 | Viewed by 4797

Abstract

Flat-field correction (FFC) is commonly used in image signal processing (ISP) to improve the uniformity of image sensor pixels. Image sensor nonuniformity and lens system characteristics have been known to be temperature-dependent. Some machine vision applications, such as visual odometry and single-pixel airborne [...] Read more.

Flat-field correction (FFC) is commonly used in image signal processing (ISP) to improve the uniformity of image sensor pixels. Image sensor nonuniformity and lens system characteristics have been known to be temperature-dependent. Some machine vision applications, such as visual odometry and single-pixel airborne object tracking, are extremely sensitive to pixel-to-pixel sensitivity variations. Numerous cameras, especially in the fields of infrared imaging and staring cameras, use multiple calibration images to correct for nonuniformities. This paper characterizes the temperature and analog gain dependence of the dark signal nonuniformity (DSNU) and photoresponse nonuniformity (PRNU) of two contemporary global shutter CMOS image sensors for machine vision applications. An optimized hardware architecture is proposed to compensate for nonuniformities, with optional parametric lens shading correction (LSC). Three different performance configurations are outlined for different application areas, costs, and power requirements. For most commercial applications, the correction of LSC suffices. For both DSNU and PRNU, compensation with one or multiple calibration images, captured at different gain and temperature settings are considered. For more demanding applications, the effectiveness, external memory bandwidth, power consumption, implementation, and calibration complexity, as well as the camera manufacturability of different nonuniformity correction approaches were compared. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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17 pages, 7011 KiB  

Open AccessArticle

A Method for Optimal Estimation of Shoreline in Cliff Zones Based on Point Cloud Segmentation and Centroid Calculation

by Weihua Li, Hao Liu and Changcai Qin

Appl. Sci. 2022, 12(21), 10810; https://doi.org/10.3390/app122110810 - 25 Oct 2022

Cited by 3 | Viewed by 1631

Abstract

The integrity of shoreline is disrupted by cliffs, posing obstacles to marine surveying and chart mapping, particularly in research related to the cliff section of the sea area. The present study proposes a method to solve this problem. In the proposed method, we [...] Read more.

The integrity of shoreline is disrupted by cliffs, posing obstacles to marine surveying and chart mapping, particularly in research related to the cliff section of the sea area. The present study proposes a method to solve this problem. In the proposed method, we first extract the boundary of the cliff to segment the point cloud according to a designed rule, then calculate the centroid coordinates of each point cloud block, followed by the coordinates of side points on both sides of the boundary of each block from the centroid, and finally create a side point cloud projected to the water surface corrected for elevation. The corrected point cloud is considered a point cloud dataset of the innermost shoreline. Combined with a point cloud projected from the boundary of the cliff section to the water surface, we developed a calculation method for the optimal shoreline position. Our experiment proved that the method could effectively extract the shoreline of the study area. Moreover, compared with that of the commonly used shoreline extraction method, the absolute error of the isoline tracking method was very large, up to several meters. However, the proposed method achieved smaller standard deviation and variance (0.1254 and 0.0157, respectively) than the isoline tracking method (0.9837 and 0.9677, respectively). Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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13 pages, 5935 KiB  

Open AccessArticle

Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition

by Jingwei Tang and Ying-Ren Chien

Sensors 2022, 22(19), 7414; https://doi.org/10.3390/s22197414 - 29 Sep 2022

Cited by 22 | Viewed by 2525

Abstract

Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of wind farm environmental factors and historical power [...] Read more.

Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of wind farm environmental factors and historical power on wind power generation. This paper presents a short-term wind power prediction model based on time convolution neural network (TCN) and variational mode decomposition (VMD). First, due to the non-smooth characteristics of the wind farm environmental data, this paper uses VMD to decompose the data of each environmental variable to reduce the influence of the random noise of the data on the prediction model. Then, the modal components with rich feature information are extracted according to the Pearson correlation coefficient and Maximal information coefficient (MIC) between each modal component and the power. Thirdly, a prediction model based on TCN is trained according to the preferred modal components and historical power data to achieve accurate short-term wind power prediction. In this paper, the model is trained and tested with a public wind power dataset provided by the Spanish Power Company. The simulation results show that the model has higher prediction accuracy, with MAPE and R² are 2.79% and 0.9985, respectively. Compared with the conventional long short-term neural network (LSTM) model, the model in this paper has good prediction accuracy and robustness. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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24 pages, 7513 KiB  

Open AccessArticle

EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone

by Mingcong Shu, Guoliang Chen and Zhenghua Zhang

Sensors 2022, 22(18), 6864; https://doi.org/10.3390/s22186864 - 10 Sep 2022

Cited by 4 | Viewed by 2804

Abstract

The pedestrian stride-length estimation is a crucial piece of personal behavior data for many smartphone applications, such as health monitoring and indoor location. The performance of the present stride-length algorithms is suitable for simple gaits and single scenes, but when applied to sophisticated [...] Read more.

The pedestrian stride-length estimation is a crucial piece of personal behavior data for many smartphone applications, such as health monitoring and indoor location. The performance of the present stride-length algorithms is suitable for simple gaits and single scenes, but when applied to sophisticated gaits or heterogeneous devices, their inaccuracy varies dramatically. This paper proposes an efficient learning-based stride-length estimation model using a smartphone to obtain the correct stride length. The model uses adaptive learning to extract different elements for changing and recognition tasks, including Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) modules. The direct fusion method maps the eigenvectors to the appropriate stride length after combining the features from the learning modules. We presented an online learning module to update the model to increase the SLE model’s generalization. Extensive experiments are conducted with heterogeneous devices or users, various gaits, and switched scenarios. The results confirm that the proposed method outperforms other state-of-the-art methods and achieves an average 4.26% estimation error rate in various environments. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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25 pages, 6018 KiB  

Open AccessArticle

An RFID-Based Method for Multi-Person Respiratory Monitoring

by Chaowei Zang, Chi Zhang, Min Zhang and Qiang Niu

Sensors 2022, 22(16), 6166; https://doi.org/10.3390/s22166166 - 17 Aug 2022

Cited by 5 | Viewed by 2662

Abstract

Respiratory monitoring is widely used in the field of health care. Traditional respiratory monitoring methods bring much inconvenience to users. In recent years, a great number of respiratory monitoring methods based on wireless technology have emerged, but multi-person respiratory monitoring is still very [...] Read more.

Respiratory monitoring is widely used in the field of health care. Traditional respiratory monitoring methods bring much inconvenience to users. In recent years, a great number of respiratory monitoring methods based on wireless technology have emerged, but multi-person respiratory monitoring is still very challenging; therefore, this paper explores multi-person respiratory monitoring. Firstly, the characteristics of human respiratory movement have been analyzed, and a suitable tag deployment method for respiratory monitoring is proposed. Secondly, aiming at the ambiguity and entanglement of radio frequency identification (RFID) phase data, a method of removal of phase ambiguity and phase wrapping is given. Then, in order to monitor multi-person respiration in a noisy environment, the frequency extraction method and waveform reconstruction method of multi-person respiration are proposed. Finally, the feasibility of the method is verified by experiments. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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19 pages, 1769 KiB  

Open AccessArticle

Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies

by Anita Gehlot, Praveen Kumar Malik, Rajesh Singh, Shaik Vaseem Akram and Turki Alsuwian

Appl. Sci. 2022, 12(14), 7316; https://doi.org/10.3390/app12147316 - 21 Jul 2022

Cited by 52 | Viewed by 6474

Abstract

An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the [...] Read more.

An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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14 pages, 4690 KiB  

Open AccessArticle

A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar

by Jianrong Geng, Juan He, Hongxia Ye and Bin Zhan

Appl. Sci. 2022, 12(13), 6457; https://doi.org/10.3390/app12136457 - 25 Jun 2022

Cited by 6 | Viewed by 2406

Abstract

It is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It [...] Read more.

It is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It only uses several random A-scan echoes (i.e., single channel detection echo sequence) to accurately predict the wavelet of any scene. The layered detection scenes with objects buried in different region are set for the 3D Finite-Difference Time-Domain simulator to generate radar echoes as a dataset. Additionally, the simulation echoes of different scenes are used to test the performance of the neural network. Multiple experiments indicate that the trained network can directly predict the wavelets quickly and accurately, although the simulation environment becomes quite different. Moreover, the measured data collected by the Qingdao Radio Research Institute radar and the unmanned aerial vehicle ground penetrating radar are used for test. The predicted wavelets can perfectly offset the original data. Therefore, the presented LSTM network can effectively predict the wavelets and their tailing oscillations for different detection scenes. The LSTM network has obvious advantages compared with other wavelet extraction methods in practical engineering. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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15 pages, 2258 KiB  

Open AccessArticle

A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process

by Dongting Xu, Zhisheng Zhang and Jinfei Shi

Sensors 2022, 22(11), 4042; https://doi.org/10.3390/s22114042 - 26 May 2022

Cited by 7 | Viewed by 2479

Abstract

Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings about bias of supervised [...] Read more.

Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings about bias of supervised learning models. Data collected by the multiple sensors can be regarded as multivariate time series or multi-sensor stream data. The high dimension of multi-sensor stream data makes building models even more challenging. In this study, a new and easy-to-apply data augmentation approach, namely, imbalanced multi-sensor stream data augmentation (IMSDA), is proposed for imbalanced learning. IMSDA can generate high quality of failure data for all dimensions. The generated data can keep the similar temporal property of the original multivariate time series. Both raw data and generated data are used to train the failure detection models, but the models are tested by the same real dataset. The proposed method is applied to a real-world industry case. Results show that IMSDA can not only obtain good quality failure data to reduce the imbalance level but also significantly improve the performance of supervised failure detection models. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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17 pages, 23073 KiB  

Open AccessArticle

Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach

by Alessio Martinelli, Monica Meocci, Marco Dolfi, Valentina Branzi, Simone Morosi, Fabrizio Argenti, Lorenzo Berzi and Tommaso Consumi

Sensors 2022, 22(10), 3788; https://doi.org/10.3390/s22103788 - 16 May 2022

Cited by 25 | Viewed by 5603

Abstract

Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes [...] Read more.

Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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19 pages, 2574 KiB  

Open AccessArticle

Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning

by Xiangwei Chen, Zhijin Zhao, Xueyi Ye, Shilian Zheng, Caiyi Lou and Xiaoniu Yang

Appl. Sci. 2022, 12(9), 4380; https://doi.org/10.3390/app12094380 - 26 Apr 2022

Cited by 7 | Viewed by 2585

Abstract

Interference classification plays an important role in anti-jamming communication. Although the existing interference signal recognition methods based on deep learning have a higher accuracy than traditional methods, these have poor robustness while rejecting interference signals of unknown classes in interference open-set recognition (OSR). [...] Read more.

Interference classification plays an important role in anti-jamming communication. Although the existing interference signal recognition methods based on deep learning have a higher accuracy than traditional methods, these have poor robustness while rejecting interference signals of unknown classes in interference open-set recognition (OSR). To ensure the classification accuracy of the known classes and the rejection rate of the unknown classes in interference OSR, we propose a new hollow convolution prototype learning (HCPL) in which the inner-dot-based cross-entropy loss (ICE) and the center loss are used to update prototypes to the periphery of the feature space so that the internal space is left for the unknown class samples, and the radius loss is used to reduce the impact of the prototype norm on the rejection rate of unknown classes. Then, a hybrid attention and feature reuse net (HAFRNet) for interference signal classification was designed, which contains a feature reuse structure and hybrid domain attention module (HDAM). A feature reuse structure is a simple DenseNet structure without a transition layer. An HDAM can recalibrate both time-wise and channel-wise feature responses by constructing a global attention matrix automatically. We also carried out simulation experiments on nine interference types, which include single-tone jamming, multitone jamming, periodic Gaussian pulse jamming, frequency hopping jamming, linear sweeping frequency jamming, second sweeping frequency jamming, BPSK modulation jamming, noise frequency modulation jamming and QPSK modulation jamming. The simulation results show that the proposed method has considerable classification accuracy of the known classes and rejection performance of the unknown classes. When the JNR is −10 dB, the classification accuracy of the known classes of the proposed method is 2–7% higher than other algorithms under different openness. When the openness is 0.030, the unknown class rejection performance plateau of the proposed method reaches 0.9883, while GCPL is 0.9403 and CG-Encoder is 0.9869; when the openness is 0.397, the proposed method is more than 0.89, while GCPL is 0.8102 and CG-Encoder is 0.9088. However, the rejection performance of unknown classes of CG-Encoder is much worse than that of the proposed method under low JNR. In addition, the proposed method requires less storage resources and has a lower computational complexity than CG-Encoder. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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15 pages, 1181 KiB  

Open AccessArticle

Simplify Belief Propagation and Variation Expectation Maximization for Distributed Cooperative Localization

by Xueying Wang, Yan Guo, Juliang Cao, Meiping Wu, Zhenqian Sun and Chubing Lv

Appl. Sci. 2022, 12(8), 3851; https://doi.org/10.3390/app12083851 - 11 Apr 2022

Cited by 2 | Viewed by 2120

Abstract

Only a specific location can make sensor data useful. The paper presents an simplify belief propagation and variation expectation maximization (SBPVEM) algorithm to achieve node localization by cooperating with another target node while lowering communication costs in a challenging environment where the anchor [...] Read more.

Only a specific location can make sensor data useful. The paper presents an simplify belief propagation and variation expectation maximization (SBPVEM) algorithm to achieve node localization by cooperating with another target node while lowering communication costs in a challenging environment where the anchor is sparse. A simplified belief propagation algorithm is proposed as the overall reasoning framework by modeling the cooperative localization problem as a graph model. The high-aggregation sampling and variation expectation–maximization algorithm is applied to sample and fit the complicated distribution. Experiments show that SBPVEM can obtain accurate node localization equal to NBP and SPAWN in a challenging environment while reducing bandwidth requirements. In addition, the SBPVEM has a better expressive ability than PVSPA, for SBPVEM is efficient in challenging environments. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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14 pages, 1904 KiB  

Open AccessArticle

High-Resolution Representations Network for Single Image Dehazing

by Wensheng Han, Hong Zhu, Chenghui Qi, Jingsi Li and Dengyin Zhang

Sensors 2022, 22(6), 2257; https://doi.org/10.3390/s22062257 - 15 Mar 2022

Cited by 8 | Viewed by 3325

Abstract

Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose an image dehazing network based on the high-resolution [...] Read more.

Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose an image dehazing network based on the high-resolution network, called DeHRNet. The high-resolution network originally used for human pose estimation. In this paper, we make a simple yet effective modification to the network and apply it to image dehazing. We add a new stage to the original network to make it better for image dehazing. The newly added stage collects the feature map representations of all branches of the network by up-sampling to enhance the high-resolution representations instead of only taking the feature maps of the high-resolution branches, which makes the restored clean images more natural. The final experimental results show that DeHRNet achieves superior performance over existing dehazing methods in synthesized and natural hazy images. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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12 pages, 2400 KiB  

Open AccessArticle

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

by Ioannis Saradopoulos, Ilyas Potamitis, Stavros Ntalampiras, Antonios I. Konstantaras and Emmanuel N. Antonidakis

Sensors 2022, 22(5), 2006; https://doi.org/10.3390/s22052006 - 4 Mar 2022

Cited by 12 | Viewed by 5382

Abstract

Our aim is to promote the widespread use of electronic insect traps that report captured pests to a human-controlled agency. This work reports on edge-computing as applied to camera-based insect traps. We present a low-cost device with high power autonomy and an adequate [...] Read more.

Our aim is to promote the widespread use of electronic insect traps that report captured pests to a human-controlled agency. This work reports on edge-computing as applied to camera-based insect traps. We present a low-cost device with high power autonomy and an adequate picture quality that reports an internal image of the trap to a server and counts the insects it contains based on quantized and embedded deep-learning models. The paper compares different aspects of performance of three different edge devices, namely ESP32, Raspberry Pi Model 4 (RPi), and Google Coral, running a deep learning framework (TensorFlow Lite). All edge devices were able to process images and report accuracy in counting exceeding 95%, but at different rates and power consumption. Our findings suggest that ESP32 appears to be the best choice in the context of this application according to our policy for low-cost devices. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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22 pages, 1965 KiB  

Open AccessArticle

A Study on Distortion Estimation Based on Image Gradients

by Sin Chee Chin, Chee-Onn Chow, Jeevan Kanesan and Joon Huang Chuah

Sensors 2022, 22(2), 639; https://doi.org/10.3390/s22020639 - 14 Jan 2022

Cited by 1 | Viewed by 2873

Abstract

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that [...] Read more.

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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19 pages, 6599 KiB  

Open AccessArticle

Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking

by Shuo-Yan Chou, Anindhita Dewabharata and Ferani Eva Zulvia

Sensors 2022, 22(1), 235; https://doi.org/10.3390/s22010235 - 29 Dec 2021

Cited by 14 | Viewed by 3535

Abstract

The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Although major cities have undergone considerable development in terms of transportation [...] Read more.

The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Although major cities have undergone considerable development in terms of transportation infrastructure, problems caused by a high number of moving vehicles cannot be completely resolved through the expansion of streets and facilities. This paper proposes a solution for the parking problem in cities that entails a shared parking system. The primary concept of the proposed shared parking system is to release parking lots that are open to specific groups for public usage without overriding personal usage. Open-to-specific-groups parking lots consist of parking spaces provided for particular people, such as parking buildings at universities for teachers, staff, and students. The proposed shared parking system comprises four primary steps: collecting and preprocessing data by using an Internet of Things system, predicting internal demand by using a recurrent neural network algorithm, releasing several unoccupied parking lots based on prediction results, and continuously updating the real-time data to improve future internal usage prediction. Data collection and data forecasting are performed to ensure that the system does not override personal usage. This study applied several forecasting algorithms, including seasonal ARIMA, support vector regression, multilayer perceptron, convolutional neural network, long short-term memory recurrent neural network with a many-to-one structure, and long short-term memory recurrent neural network with a many-to-many structure. The proposed system was evaluated using artificial and real datasets. Results show that the recurrent neural network with the many-to-many structure generates the most accurate prediction. Furthermore, the proposed shared parking system was evaluated for some scenarios in which different numbers of parking spaces were released. Simulation results show that the proposed shared parking system can provide parking spaces for public usage without overriding personal usage. Moreover, this system can generate new income for parking management and/or parking lot owners. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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14 pages, 7192 KiB  

Open AccessArticle

A MIMO Radar Signal Processing Algorithm for Identifying Chipless RFID Tags

by Chen Su, Chuanyun Zou, Liangyu Jiao and Qianglin Zhang

Sensors 2021, 21(24), 8314; https://doi.org/10.3390/s21248314 - 12 Dec 2021

Cited by 5 | Viewed by 3992

Abstract

In this paper, the multiple-input, multiple-output (MIMO) radar signal processing algorithm is efficiently employed as an anticollision methodology for the identification of multiple chipless radio-frequency identification (RFID) tags. Tag-identifying methods for conventional chipped RFID tags rely mostly on the processing capabilities of application-specific [...] Read more.

In this paper, the multiple-input, multiple-output (MIMO) radar signal processing algorithm is efficiently employed as an anticollision methodology for the identification of multiple chipless radio-frequency identification (RFID) tags. Tag-identifying methods for conventional chipped RFID tags rely mostly on the processing capabilities of application-specific integrated circuits (ASICs). In cases where more than one chipless tag exists in the same area, traditional methods are not sufficient to successfully read and distinguish the IDs, while the direction of each chipless tag can be obtained by applying MIMO technology to the backscattering signal. In order to read the IDs of the tags, beamforming is used to change the main beam direction of the antenna array and to receive the tag backscattered signal. On this basis, the RCS of the tags can be retrieved, and associated IDs can be identified. In the simulation, two tags with different IDs were placed away from each other. The IDs of the tags were successfully identified using the presented algorithm. The simulation result shows that tags with a distance of 0.88 m in azimuth can be read by a MIMO reader with eight antennas from 3 m away. Full article

(This article belongs to the Topic Advanced Signal Processing and Data Analysis for Smart IoT Ecosystems)

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