Sensors — Open Access Journal

Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy. By comparing statistical features with deep features extracted by a Deep Belief Network (DBN), we attempt to find the best features to identify the emotion status for speech. We propose a novel classification method that combines DBN and SVM (support vector machine) instead of using only one of them. In addition, a conjugate gradient method is applied to train DBN in order to speed up the training process. Gender-dependent experiments are conducted using an emotional speech database created by the Chinese Academy of Sciences. The results show that DBN features can reflect emotion status better than artificial features, and our new classification approach achieves an accuracy of 95.8%, which is higher than using either DBN or SVM separately. Results also show that DBN can work very well for small training databases if it is properly designed. Full article

Localization in Wireless Sensor Networks (WSNs) has been an active topic for more than two decades. A variety of algorithms were proposed to improve the localization accuracy. However, they are either limited to two-dimensional (2D) space, or require specific sensor deployment for proper operations. In this paper, we proposed a three-dimensional (3D) localization scheme for WSNs based on the well-known parametric Loop division (PLD) algorithm. The proposed scheme localizes a sensor node in a region bounded by a network of anchor nodes. By iteratively shrinking that region towards its center point, the proposed scheme provides better localization accuracy as compared to existing schemes. Furthermore, it is cost-effective and independent of environmental irregularity. We provide an analytical framework for the proposed scheme and find its lower bound accuracy. Simulation results shows that the proposed algorithm provides an average localization accuracy of 0.89 m with a standard deviation of 1.2 m. Full article

Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system. Full article

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient. Full article

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. Full article

Wearable technologies for posture monitoring and posture correction are emerging as a way to support and enhance physical therapy treatment, e.g., for motor control training in neurological disorders or for treating musculoskeletal disorders, such as shoulder, neck, or lower back pain. Among the various technological options for posture monitoring, wearable systems offer potential advantages regarding mobility, use in different contexts and sustained tracking in daily life. We describe the design of a smart garment named Zishi to monitor compensatory movements and evaluate its applicability for shoulder motor control training in a clinical setting. Five physiotherapists and eight patients with musculoskeletal shoulder pain participated in the study. The attitudes of patients and therapists towards the system were measured using standardized survey instruments. The results indicate that patients and their therapists consider Zishi a credible aid for rehabilitation and patients expect it will help towards their recovery. The system was perceived as highly usable and patients were motivated to train with the system. Future research efforts on the improvement of the customization of feedback location and modality, and on the evaluation of Zishi as support for motor learning in shoulder patients, should be made. Full article

The focusing capabilities of a pinhole zone plate lens are presented and compared with those of a conventional Fresnel zone plate lens. The focusing properties are examined both experimentally and numerically. The results confirm that a pinhole zone plate lens can be an alternative to a Fresnel lens. A smooth filtering effect is created in pinhole zone plate lenses, giving rise to a reduction of the side lobes around the principal focus associated with the conventional Fresnel zone plate lens. The manufacturing technique of the pinhole zone plate lens allows the designing and constructing of lenses for different focal lengths quickly and economically and without the need to drill new plates. Full article

In this paper, a novel 3D reconstruction framework is proposed to recover the 3D structural model of a space object from its multi-view images captured by a visible sensor. Given an image sequence, this framework first estimates the relative camera poses and recovers the depths of the surface points by the structure from motion (SFM) method, then the patch-based multi-view stereo (PMVS) algorithm is utilized to generate a dense 3D point cloud. To resolve the wrong matches arising from the symmetric structure and repeated textures of space objects, a new strategy is introduced, in which images are added to SFM in imaging order. Meanwhile, a refining process exploiting the structural prior knowledge that most sub-components of artificial space objects are composed of basic geometric shapes is proposed and applied to the recovered point cloud. The proposed reconstruction framework is tested on both simulated image datasets and real image datasets. Experimental results illustrate that the recovered point cloud models of space objects are accurate and have a complete coverage of the surface. Moreover, outliers and points with severe noise are effectively filtered out by the refinement, resulting in an distinct improvement of the structure and visualization of the recovered points. Full article

Accurate information acquisition is of vital importance for wireless sensor array network (WSAN) direction of arrival (DOA) estimation. However, due to the lossy nature of low-power wireless links, data loss, especially block data loss induced by adopting a large packet size, has a catastrophic effect on DOA estimation performance in WSAN. In this paper, we propose a double-layer compressive sensing (CS) framework to eliminate the hazards of block data loss, to achieve high accuracy and efficient DOA estimation. In addition to modeling the random packet loss during transmission as a passive CS process, an active CS procedure is introduced at each array sensor to further enhance the robustness of transmission. Furthermore, to avoid the error propagation from signal recovery to DOA estimation in conventional methods, we propose a direct DOA estimation technique under the double-layer CS framework. Leveraging a joint frequency and spatial domain sparse representation of the sensor array data, the fusion center (FC) can directly obtain the DOA estimation results according to the received data packets, skipping the phase of signal recovery. Extensive simulations demonstrate that the double-layer CS framework can eliminate the adverse effects induced by block data loss and yield a superior DOA estimation performance in WSAN. Full article

GyroWheel is an integrated device that can provide three-axis control torques and two-axis angular rate sensing for small spacecrafts. Large tilt angle of its rotor and de-tuned spin rate lead to a complex and non-linear dynamics as well as difficulties in measuring angular rates. In this paper, the problem of angular rate sensing with the GyroWheel is investigated. Firstly, a simplified rate sensing equation is introduced, and the error characteristics of the method are analyzed. According to the analysis results, a rate sensing principle based on torque balance theory is developed, and a practical way to estimate the angular rates within the whole operating range of GyroWheel is provided by using explicit genetic algorithm optimized neural networks. The angular rates can be determined by the measurable values of the GyroWheel (including tilt angles, spin rate and torque coil currents), the weights and the biases of the neural networks. Finally, the simulation results are presented to illustrate the effectiveness of the proposed angular rate sensing method with GyroWheel. Full article

The main causes of building defects are errors in the design and the construction phases. These causes related to construction are mainly due to the general lack of control of construction work and represent approximately 75% of the anomalies. In particular, one of the main causes of such anomalies, which end in building defects, is the lack of control over the physical variables of the work environment during the execution of tasks. Therefore, the high percentage of defects detected in buildings that have the root cause in the construction phase could be avoidable with a more accurate and efficient control of the process. The present work proposes a novel integration model based on information and communications technologies for the automation of both construction work and its management at the execution phase, specifically focused on the flat roof construction process. Roofs represent the second area where more defects are claimed. The proposed model is based on a Web system, supported by a service oriented architecture, for the integral management of tasks through the Last Planner System methodology, but incorporating the management of task restrictions from the physical environment variables by designing specific sensing systems. Likewise, all workers are integrated into the management process by Internet-of-Things solutions that guide them throughout the execution process in a non-intrusive and transparent way. Full article

Embedding optical fibers sensors into composite structures for Structural Health Monitoring purposes is not just one of the most attractive solutions contributing to smart structures, but also the optimum integration approach that insures maximum protection and integrity of the fibers. Nevertheless this intended integration level still remains an industrial challenge since today there is no mature integration process in composite plants matching all necessary requirements. This article describes the process developed to integrate optical fiber sensors in the Production cycle of a test specimen. The sensors, Bragg gratings, were integrated into the laminate during automatic tape lay-up and also by a secondary bonding process, both in the Airbus Composite Plant. The test specimen, completely representative of the root joint of the lower wing cover of a real aircraft, is comprised of a structural skin panel with the associated stringer run out. The ingress-egress was achieved through the precise design and integration of miniaturized optical connectors compatible with the manufacturing conditions and operational test requirements. After production, the specimen was trimmed, assembled and bolted to metallic plates to represent the real triform and buttstrap, and eventually installed into the structural test rig. The interrogation of the sensors proves the effectiveness of the integration process; the analysis of the strain results demonstrate the good correlation between fiber sensors and electrical gauges in those locations where they are installed nearby, and the curvature and load transfer analysis in the bolted stringer run out area enable demonstration of the consistency of the fiber sensors measurements. In conclusion, this work presents strong evidence of the performance of embedded optical sensors for structural health monitoring purposes, where in addition and most importantly, the fibers were integrated in a real production environment and the ingress-egress issue was solved by the design and integration of miniaturized connectors compatible with the manufacturing and structural test phases. Full article

Ubiquitous sensing allows smart cities to take control of many parameters (e.g., road traffic, air or noise pollution levels, etc.). An inexpensive Wireless Mesh Network can be used as an efficient way to transport sensed data. When that mesh is autonomously powered (e.g., solar powered), it constitutes an ideal portable network system which can be deployed when needed. Nevertheless, its power consumption must be restrained to extend its operational cycle and for preserving the environment. To this end, our strategy fosters wireless interface deactivation among nodes which do not participate in any route. As we show, this contributes to a significant power saving for the mesh. Furthermore, our strategy is wireless-friendly, meaning that it gives priority to deactivation of nodes receiving (and also causing) interferences from (to) the rest of the smart city. We also show that a routing protocol can adapt to this strategy in which certain nodes deactivate their own wireless interfaces. Full article

All empirical water column correction methods have consistently been reported to require existing depth sounding data for the purpose of calibrating a simple depth retrieval model; they yield poor results over very bright or very dark bottoms. In contrast, we set out to (i) use only the relative radiance data in the image along with published data, and several new assumptions; (ii) in order to specify and operate the simplified radiative transfer equation (RTE); (iii) for the purpose of retrieving both the satellite derived bathymetry (SDB) and the water column corrected spectral reflectance over shallow seabeds. Sea truth regressions show that SDB depths retrieved by the method only need tide correction. Therefore it shall be demonstrated that, under such new assumptions, there is no need for (i) formal atmospheric correction; (ii) conversion of relative radiance into calibrated reflectance; or (iii) existing depth sounding data, to specify the simplified RTE and produce both SDB and spectral water column corrected radiance ready for bottom typing. Moreover, the use of the panchromatic band for that purpose is introduced. Altogether, we named this process the Self-Calibrated Supervised Spectral Shallow-sea Modeler (4SM). This approach requires a trained practitioner, though, to produce its results within hours of downloading the raw image. The ideal raw image should be a “near-nadir” view, exhibit homogeneous atmosphere and water column, include some coverage of optically deep waters and bare land, and lend itself to quality removal of haze, atmospheric adjacency effect, and sun/sky glint. Full article

Dibutyl phthalate (DBP) is a widely used plasticizer which has been found to be a reproductive and developmental toxicant and ubiquitously existing in the air. A highly sensitive method for DBP monitoring in the environment is urgently needed. A DBP sensor based on a homemade wireless-electrodeless quartz crystal microbalance with dissipation (QCM-D) coated with nano-structured nickel hydroxide is presented. With the noncontact configuration, the sensing system could work at a higher resonance frequency (the 3rd overtone) and the response of the system was even more stable compared with a conventional quartz crystal microbalance (QCM). The sensor achieved a sensitivity of 7.3 Hz/ppb to DBP in a concentration range of 0.4–40 ppb and an ultra-low detection limit of 0.4 ppb of DBP has also been achieved. Full article

Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c’s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of $83\%$ indicates the potential of the proposed methodology. Full article

The UV-to-visible rejection ratio is one of the important figure of merits of GaN-based UV photodetectors. For cost-effectiveness and large-scale fabrication of GaN devices, we tried to grow a GaN epitaxial layer on silicon substrate with complicated buffer layers for a stress-release. It is known that the structure of the buffer layers affects the performance of devices fabricated on the GaN epitaxial layers. In this study, we show that the design of a buffer layer structure can make effect on the UV-to-visible rejection ratio of GaN UV photodetectors. The GaN photodetector fabricated on GaN-on-silicon substrate with a step-graded Al_xGa_−xN buffer layer has a highly-selective photoresponse at 365-nm wavelength. The UV-to-visible rejection ratio of the GaN UV photodetector with the step-graded Al_xGa_1−xN buffer layer was an order-of-magnitude higher than that of a photodetector with a conventional GaN/AlN multi buffer layer. The maximum photoresponsivity was as high as 5 × 10⁻² A/W. This result implies that the design of buffer layer is important for photoresponse characteristics of GaN UV photodetectors as well as the crystal quality of the GaN epitaxial layers. Full article

In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime. Full article

Dense disparity map estimation from a high-resolution stereo image is a very difficult problem in terms of both matching accuracy and computation efficiency. Thus, an exhaustive disparity search at full resolution is required. In general, examining more pixels in the stereo view results in more ambiguous correspondences. When a high-resolution image is down-sampled, the high-frequency components of the fine-scaled image are at risk of disappearing in the coarse-resolution image. Furthermore, if erroneous disparity estimates caused by missing high-frequency components are propagated across scale space, ultimately, false disparity estimates are obtained. To solve these problems, we introduce an efficient hierarchical stereo matching method in two-scale space. This method applies disparity estimation to the reduced-resolution image, and the disparity result is then up-sampled to the original resolution. The disparity estimation values of the high-frequency (or edge component) regions of the full-resolution image are combined with the up-sampled disparity results. In this study, we extracted the high-frequency areas from the scale-space representation by using difference of Gaussian (DoG) or found edge components, using a Canny operator. Then, edge-aware disparity propagation was used to refine the disparity map. The experimental results show that the proposed algorithm outperforms previous methods. Full article

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner. Full article