Journal of Sensors The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Interrupt-Based Step-Counting to Extend Battery Life in an Activity Monitor Tue, 01 Dec 2015 08:59:33 +0000 Most activity monitors use an accelerometer and gyroscope sensors to characterize the wearer’s physical activity. The monitor measures the motion by polling an accelerometer or gyroscope sensor or both every 20–30 ms and frequent polling affects the battery life of a wearable device. One of the key features of a commercial daily-activity monitoring device is longer battery life so that the user can keep track of his or her activity for a week or so without recharging the battery of the monitoring device. Many low-power approaches for a step-counting system use either a polling-based algorithm or an interrupt-based algorithm. In this paper, we propose a novel approach that uses the tap interrupt of an accelerometer to count steps while consuming low power. We compared the accuracy of step counting and measured system-level power consumption to a periodic sensor-reading algorithm. Our tap interrupt approach shows a battery lifetime that is 175% longer than that of a 30 ms polling method without gyroscope. The battery lifetime can be extended up to 863% with a gyroscope by putting both the processor and the gyroscope into sleep state during the majority of operation time. Seung Young Kim and Gu-In Kwon Copyright © 2016 Seung Young Kim and Gu-In Kwon. All rights reserved. Toward Improved RPL: A Congestion Avoidance Multipath Routing Protocol with Time Factor for Wireless Sensor Networks Mon, 30 Nov 2015 14:02:45 +0000 Designing routing protocols in Low power and Lossy Networks (LLNs) imposes great challenges. In emergency scenarios, the large and rapid data traffic caused by emergencies will lead to network congestion and bring about significant packet loss and delay. Routing protocol for LLNs (RPL) is the IETF standard for IPv6 routing in LLNs. The basic version of RPL uses Expected Transmission Count (ETX) as the default routing metric; it cannot solve the problem of sudden large data traffic. In this paper, we propose a congestion avoidance multipath routing protocol which uses composite routing metrics based on RPL, named CA-RPL. A routing metric for RPL that minimized the average delay towards the DAG root is proposed, and the weight of each path is computed by four metrics. The mechanism is explained and its performance is evaluated through simulation experiments based on Contiki. Simulation results show that the proposed CA-RPL reduces the average time delay by about 30% compared to original RPL when the interpacket interval is short and has almost 20% reduction in packet loss ratio. The CA-RPL can effectively alleviate the network congestion in the network with poor link quality and large data traffic and significantly improve the performance of LLNs. Weisheng Tang, Xiaoyuan Ma, Jun Huang, and Jianming Wei Copyright © 2016 Weisheng Tang et al. All rights reserved. Energy Efficient Wireless Sensor Network Modelling Based on Complex Networks Mon, 30 Nov 2015 13:44:37 +0000 The power consumption and energy efficiency of wireless sensor network are the significant problems in Internet of Things network. In this paper, we consider the network topology optimization based on complex network theory to solve the energy efficiency problem of WSN. We propose the energy efficient model of WSN according to the basic principle of small world from complex networks. Small world network has clustering features that are similar to that of the rules of the network but also has similarity to random networks of small average path length. It can be utilized to optimize the energy efficiency of the whole network. Optimal number of multiple sink nodes of the WSN topology is proposed for optimizing energy efficiency. Then, the hierarchical clustering analysis is applied to implement this clustering of the sensor nodes and pick up the sink nodes from the sensor nodes as the clustering head. Meanwhile, the update method is proposed to determine the sink node when the death of certain sink node happened which can cause the paralysis of network. Simulation results verify the energy efficiency of the proposed model and validate the updating of the sink nodes to ensure the normal operation of the WSN. Lin Xiao, Fahui Wu, Dingcheng Yang, Tiankui Zhang, and Xiaoya Zhu Copyright © 2016 Lin Xiao et al. All rights reserved. Active Suppression of Narrowband Noise by Multiple Secondary Sources Mon, 30 Nov 2015 13:15:07 +0000 This study presents theoretical and experimental investigation on the active suppression of narrowband noise with C1, C1.5, and C2 components by using multichannel secondary sources in a duct. The quality manipulation in the duct was controlled by changing quality factors which were incorporated into a multichannel FxLMS algorithm. The algorithm is extensively investigated in both theory and real-time control experiment. After analysing the primary and secondary paths of the duct system, an acoustic narrowband signal was chosen as a primary noise and the impulse responses were implemented as the secondary path models. Control results show that the quality factors in the algorithm that was implemented in a dSPACE 1104 provide a stable and excellent response compared to before control. It is obvious that the lower quality factor cancels the more primary noise as defined in the theory although the attenuation levels are not exactly and inversely proportional to the quality factor. The results in this study can be used for practical active sound quality control systems. Seokhoon Ryu, Yun Jung Park, and Young-Sup Lee Copyright © 2016 Seokhoon Ryu et al. All rights reserved. Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques Mon, 30 Nov 2015 12:55:13 +0000 Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely, the relay and replay attacks. Such threats are conducted by malicious nodes that replicate the transmissions of a real primary user (PU), thus making them resilient to many defensive procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength (RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are presented in the paper: parametric and nonparametric. The parametric scheme is based on the likelihood ratio test (LRT) and requires the existence of a precise path loss model for the observed RSS values. On the contrary, the nonparametric procedure is not tied to any particular propagation model; so, it does not require any calibration process and is robust to changing environmental conditions. Simulations show that the nonparametric detection approach is comparable in performance to the LRT under moderate shadowing conditions, specially in case of replay attacks. Mariano García-Otero and Adrián Población-Hernández Copyright © 2016 Mariano García-Otero and Adrián Población-Hernández. All rights reserved. A Precise Lane Detection Algorithm Based on Top View Image Transformation and Least-Square Approaches Mon, 30 Nov 2015 12:34:14 +0000 The next promising key issue of the automobile development is a self-driving technique. One of the challenges for intelligent self-driving includes a lane-detecting and lane-keeping capability for advanced driver assistance systems. This paper introduces an efficient and lane detection method designed based on top view image transformation that converts an image from a front view to a top view space. After the top view image transformation, a Hough transformation technique is integrated by using a parabolic model of a curved lane in order to estimate a parametric model of the lane in the top view space. The parameters of the parabolic model are estimated by utilizing a least-square approach. The experimental results show that the newly proposed lane detection method with the top view transformation is very effective in estimating a sharp and curved lane leading to a precise self-driving capability. Byambaa Dorj and Deok Jin Lee Copyright © 2016 Byambaa Dorj and Deok Jin Lee. All rights reserved. MGRO Recognition Algorithm-Based Artificial Potential Field for Mobile Robot Navigation Mon, 30 Nov 2015 11:59:35 +0000 This paper describes a novel recognition algorithm which includes mean filter, Gaussian filter, Retinex enhancement method, and Ostu threshold segmentation method (MGRO) for the navigation of mobile robots with visual sensors. The approach includes obstacle visual recognition and navigation path planning. In the first part, a three-stage method for obstacle visual recognition is constructed. Stage 1 combines mean filtering and Gaussian filtering to remove random noise and Gauss noise in the environmental image. Stage 2 increases image contrast by using the Retinex enhancement method. Stage 3 uses the Ostu threshold segmentation method to achieve obstacle feature extraction. A navigation method based on the auxiliary visual information is constructed in the second part. The method is based on the artificial potential field (APF) method and is able to avoid falling into local minimum by changing the repulsion field function. Experimental results confirm that obstacle features can be extracted accurately and the mobile robot can avoid obstacles safely and arrive at target positions correctly. Ming Pang, Zhankai Meng, Wenbo Zhang, and Changhai Ru Copyright © 2016 Ming Pang et al. All rights reserved. Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images Mon, 30 Nov 2015 11:52:59 +0000 Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE) method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR) approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU) as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR) can achieve higher accuracies than the popular support vector machine (SVM) classifier. Chen Xing, Li Ma, and Xiaoquan Yang Copyright © 2016 Chen Xing et al. All rights reserved. Fault Reconstruction Based on Sliding Mode Observer for Current Sensors of PMSM Tue, 24 Nov 2015 09:55:51 +0000 This paper deals with a method of phase current sensor fault reconstruction for permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM two-phase stationary reference frame fault model and a sliding mode variable structure observer to reconstruct fault signals. A logic algorithm is built to isolate and identify the faulty sensor for a stator phase current fault after reconstructing the two-phase stationary reference frame fault signals, which allows the phase fault signals to be reconstructed. Simulation results are presented to illustrate the functionality of the theoretical developments. Changfan Zhang, Huijun Liao, Xiangfei Li, Jian Sun, and Jing He Copyright © 2016 Changfan Zhang et al. All rights reserved. Deep Learning for Remote Sensing Image Understanding Mon, 23 Nov 2015 07:12:45 +0000 Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Liang Lin, and Xue Cheng Tai Copyright © 2016 Liangpei Zhang et al. All rights reserved. A Tile-Based EGPU with a Fused Universal Processing Engine and Graphics Coprocessor Cluster Sun, 22 Nov 2015 14:23:35 +0000 As various applied sensors have been integrated into embedded devices, the Embedded Graphics Processing Unit (EGPU) has assumed more processing tasks, which requires an EGPU with higher performance. A tile-based EGPU is proposed that can be used in both general-purpose computing and 3D graphics rendering. With fused, scalable, and hierarchical parallelism architecture, the EGPU has the ability to address nearly 100 million vertices or fragments and achieves 1 GFLOPS per second at a clock frequency of 200 MHz. A fused and scalable architecture, constituted by Universal Processing Engine (UPE) and Graphics Coprocessor Cluster (GCC), ensures that the EGPU can adapt to various graphic processing scenes and situations, achieving more efficient rendering. Moreover, hierarchical parallelism is implemented via the UPE. Additionally, tiling brings a significant reduction in both system memory bandwidth and power consumption. A 0.18 µm technology library is used for timing and power analysis. The area of the proposed EGPU is 6.5 mm 6.5 mm, and its power consumption is approximately 349.318 mW. Experimental results demonstrate that the proposed EGPU can be used in a System on Chip (SoC) configuration connected to sensors to accelerate its processing and create a proper balance between performance and cost. Yang Wang, Li Zhou, Tao Sun, Yanhu Chen, Lei Wang, and Shaotao Sun Copyright © 2016 Yang Wang et al. All rights reserved. Facial Feature Extraction Using Frequency Map Series in PCNN Sun, 22 Nov 2015 14:22:50 +0000 Pulse coupled neural network (PCNN) has been widely used in image processing. The 3D binary map series (BMS) generated by PCNN effectively describes image feature information such as edges and regional distribution, so BMS can be treated as the basis of extracting 1D oscillation time series (OTS) for an image. However, the traditional methods using BMS did not consider the correlation of the binary sequence in BMS and the space structure for every map. By further processing for BMS, a novel facial feature extraction method is proposed. Firstly, consider the correlation among maps in BMS; a method is put forward to transform BMS into frequency map series (FMS), and the method lessens the influence of noncontinuous feature regions in binary images on OTS-BMS. Then, by computing the 2D entropy for every map in FMS, the 3D FMS is transformed into 1D OTS (OTS-FMS), which has good geometry invariance for the facial image, and contains the space structure information of the image. Finally, by analyzing the OTS-FMS, the standard Euclidean distance is used to measure the distances for OTS-FMS. Experimental results verify the effectiveness of OTS-FMS in facial recognition, and it shows better recognition performance than other feature extraction methods. Rencan Nie, Dongming Zhou, Min He, Xin Jin, and Jiefu Yu Copyright © 2016 Rencan Nie et al. All rights reserved. Evaluation of a Novel Radar Based Scanning Method Sun, 22 Nov 2015 14:20:53 +0000 The following paper introduces a novel scanning method for mapping and localization purposes in mobile robotics. Our method is based on a rotating monostatic radar network, which determines the positions of objects around the scanner via a continuously running lateration algorithm. The estimation of surfaces with ultrawideband radar networks has been studied experimentally in lab environments, especially with lateration, envelopes of spheres, and SEABED algorithms. But we do not see a link to the field of mapping and localization of mobile robots, where laser scanners are dominating. Indeed, only few research groups use radars for mapping and localization, but their applied sensor principle is based on a rotating focused radar beam. Consequently, only 2D radar scanners are known inside the robotic world and methods for 3D scanning with radars need to be investigated. This paper will derive the theoretical background of the sensor principle, which is based on a radar network on a rotating joint, and discuss its erroneous influences. We were performing first scans of standard geometries and deriving a model in order to compare theoretical and experimental measurement results. Furthermore, we present first mapping approaches and a simulation of a scanner with multiple sensors. Paul Fritsche and Bernardo Wagner Copyright © 2016 Paul Fritsche and Bernardo Wagner. All rights reserved. Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network Sun, 22 Nov 2015 13:31:37 +0000 In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy. Juan Feng, Hongwei Zhao, and Baowang Lian Copyright © 2016 Juan Feng et al. All rights reserved. Miniaturized Human Insertable Cardiac Monitoring System with Wireless Power Transmission Technique Sun, 22 Nov 2015 13:28:56 +0000 Prolonged monitoring is more likely to diagnose atrial fibrillation accurately than intermittent or short-term monitoring. In this study, an implantable electrocardiograph (ECG) sensor to monitor atrial fibrillation patients in real time was developed. The implantable sensor is composed of a micro controller unit, an analog-to-digital converter, a signal transmitter, an antenna, and two electrodes. The sensor detects ECG signals from the two electrodes and transmits these to an external receiver carried by the patient. Because the sensor continuously transmits signals, its battery consumption rate is extremely high; therefore, the sensor includes a wireless power transmission module that allows it to charge wirelessly from an external power source. The integrated sensor has the approximate dimensions 3 mm × 4 mm × 14 mm, which is small enough to be inserted into a patient without the need for major surgery. The signal and power transmission data sampling rate and frequency of the unit are 300 samples/s and 430 Hz, respectively. To validate the developed sensor, experiments were conducted on small animals. Jong-Ha Lee Copyright © 2016 Jong-Ha Lee. All rights reserved. Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering Sun, 22 Nov 2015 13:12:35 +0000 For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction. Fengjun Hu Copyright © 2016 Fengjun Hu. All rights reserved. Calibration of a Stereo Radiation Detection Camera Using Planar Homography Sun, 22 Nov 2015 13:10:55 +0000 This paper proposes a calibration technique of a stereo gamma detection camera. Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision society. However, few or no stereo calibration has been investigated in the radiation measurement research. Since no visual information can be obtained from a stereo radiation camera, it is impossible to use a general stereo calibration algorithm directly. In this paper, we develop a hybrid-type stereo system which is equipped with both radiation and vision cameras. To calibrate the stereo radiation cameras, stereo images of a calibration pattern captured from the vision cameras are transformed in the view of the radiation cameras. The homography transformation is calibrated based on the geometric relationship between visual and radiation camera coordinates. The accuracy of the stereo parameters of the radiation camera is analyzed by distance measurements to both visual light and gamma sources. The experimental results show that the measurement error is about 3%. Seung-Hae Baek, Pathum Rathnayaka, and Soon-Yong Park Copyright © 2016 Seung-Hae Baek et al. All rights reserved. Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks Sun, 22 Nov 2015 12:55:35 +0000 Nowadays many camera-based advanced driver assistance systems (ADAS) have been introduced to assist the drivers and ensure their safety under various driving conditions. One of the problems faced by drivers is the faded scene visibility and lower contrast while driving in foggy conditions. In this paper, we present a novel approach to provide a solution to this problem by employing deep neural networks. We assume that the fog in an image can be mathematically modeled by an unknown complex function and we utilize the deep neural network to approximate the corresponding mathematical model for the fog. The advantages of our technique are as follows: (i) its real-time operation and (ii) being based on minimal input, that is, a single image, and exhibiting robustness/generalization for various unseen image data. Experiments carried out on various synthetic images indicate that our proposed technique has the abilities to approximate the corresponding fog function reasonably and remove it for better visibility and safety. Farhan Hussain and Jechang Jeong Copyright © 2016 Farhan Hussain and Jechang Jeong. All rights reserved. Pedestrian Detection in Crowded Environments through Bayesian Prediction of Sequential Probability Matrices Tue, 17 Nov 2015 12:31:57 +0000 In order to safely navigate populated environments, an autonomous vehicle must be able to detect human shapes using its sensory systems, so that it can properly avoid a collision. In this paper, we introduce a Bayesian approach to the Viola-Jones algorithm, as a method to automatically detect pedestrians in image sequences. We present a probabilistic interpretation of the basic execution of the original tool and develop a technique to produce approximate convolutions of probability matrices with multiple local maxima. Javier Hernández-Aceituno, Leopoldo Acosta, and José D. Piñeiro Copyright © 2016 Javier Hernández-Aceituno et al. All rights reserved. Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm Mon, 16 Nov 2015 13:56:54 +0000 A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA), parallel coordinate descent (PCD) algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios. Shafqat Ullah Khan, Ijaz Mansoor Qureshi, Aqdas Naveed, Bilal Shoaib, and Abdul Basit Copyright © 2016 Shafqat Ullah Khan et al. All rights reserved. Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification Mon, 16 Nov 2015 11:23:06 +0000 Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published. Holger Steiner, Sebastian Sporrer, Andreas Kolb, and Norbert Jung Copyright © 2016 Holger Steiner et al. All rights reserved. Stability Control of Force-Reflected Nonlinear Multilateral Teleoperation System under Time-Varying Delays Mon, 16 Nov 2015 10:00:50 +0000 A novel control algorithm based on the modified wave-variable controllers is proposed to achieve accurate position synchronization and reasonable force tracking of the nonlinear single-master-multiple-slave teleoperation system and simultaneously guarantee overall system’s stability in the presence of large time-varying delays. The system stability in different scenarios of human and environment situations has been analyzed. The proposed method is validated through experimental work based on the 3-DOF trilateral teleoperation system consisting of three different manipulators. The experimental results clearly demonstrate the feasibility of the proposed algorithm to achieve high transparency and robust stability in nonlinear single-master-multiple-slave teleoperation system in the presence of time-varying delays. Da Sun, Fazel Naghdy, and Haiping Du Copyright © 2016 Da Sun et al. All rights reserved. Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation Mon, 16 Nov 2015 09:53:44 +0000 This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA), for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT) is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA). The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA) and empirical mode decomposition (EMD), as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis. Yuning Qian and Ruqiang Yan Copyright © 2016 Yuning Qian and Ruqiang Yan. All rights reserved. RS485 Image Sensor for Digital Cinema System Mon, 16 Nov 2015 07:22:55 +0000 To activate various devices using RS485, a repeater is generally used. In current digital cinema systems, each device is controlled with RS485 by mixing RS485 and DMX512. However, as today’s cinema equips hundreds of 4D chairs and the environmental directors, it is nearly infeasible for the legacy system to control. To this end, this paper designs and implements a new system which makes hundreds of 4D chairs and the environmental directors be controlled simultaneously exploiting RS485 network topology and its repeaters. The proposed approach is tested in a real-time system for assessing the performance by Paessler Router Traffic Grapher (PRTG) in Windows environment. Simulation results show that the tested system supports 4D chairs and their motions are well operated simultaneously with RS485. Eunju Kim, Seokhoon Kang, and Sangsoon Lee Copyright © 2016 Eunju Kim et al. All rights reserved. Multiband Polarization Imaging Mon, 16 Nov 2015 06:12:50 +0000 Multiband polarization imaging is an emerging sensing method that enables simultaneous acquisition of multiband spectral and multiangle polarization information of an object of interest in the scene. Spectral signatures of the light reflected from a target reveal the characteristics of the material composing the target while polarized light provides useful information on the surface features such as light scattering and specular reflection. In multiband spectral imaging, combined spectral and polarization information offers a comprehensive representation of an object utilizing complementary spectral and polarization information in visual sensing. Multiband polarization imaging has demonstrated a potential in the recognition of targets in challenging operating environments such as low-contrast and hazy conditions. This paper presents the concept and recent advances of multiband polarization imaging techniques, in particular, a bioinspired multiband polarization vision system. Applications of multiband polarization imaging in various fields include atmospheric observation, object detection and classification, medical diagnostics, surveillance, and 3D object reconstruction. Yongqiang Zhao, Qunnie Peng, Chen Yi, and Seong G. Kong Copyright © 2016 Yongqiang Zhao et al. All rights reserved. A Distance Compensated Approach Used in Wireless Passive Pressure Sensor Readout System for High Temperature Application Mon, 16 Nov 2015 06:12:09 +0000 This paper proposed a distance compensated measurement system for a wireless passive sensor based on the high temperature cofired ceramics (HTCC) applied to high temperature environment. The sensor model is provided and fabricated. Also, a telemetric measurement system consists of a readout instrumentation and a heat insulation unit is described due to the thickness of heat insulation material between the sensor and readout unit’s inductance coils in high temperature testing environment. Consideration of the leakage inductance and parasitic parameters which depend on the coupling distance is equivalent to the thickness of heat insulation material, and a distance compensated method is presented. The compensation is based on the mathematical feature of the testing results from readout unit which show us information about the relation between the extracted resonant frequencies. This method can be used simply and reliably in the other telemetric mutual inductance coupling readout system as a viable solution to compensate the coupling distance related error when inductive coupling is varied. It has been experimentally tested, and the results are in good agreement with those measured by a reference impedance analysis instrument. Theoretical explanations, experimental results, and discussion are reported. Yingping Hong, Ting Liang, Tingli Zheng, Qun Cao, Wendong Zhang, Wenyi Liu, Huixin Zhang, and Jijun Xiong Copyright © 2016 Yingping Hong et al. All rights reserved. Novel SGH Recognition Algorithm Based Robot Binocular Vision System for Sorting Process Sun, 15 Nov 2015 14:21:11 +0000 To achieve automatic sorting on commodity trademarks, a binocular vision system has been constructed in this paper. By adjusting camera pose, this system can obtain greater shooting perspective. In order to improve sorting accuracy, a now SGH recognition method is proposed. SGH consists of spatial color histogram (S feature), gray level cooccurrence matrix (G feature), and Hu moments (H) feature, which represent color feature, texture feature, and shaper feature, respectively. Similarity judgment function is built by using SGH. The experimental results show that SGH algorithm has a higher visual accuracy compared to single feature based recognition method. Xiaoyang Yu, Shuang Liu, Ming Pang, Jixun Zhang, and Shuchun Yu Copyright © 2016 Xiaoyang Yu et al. All rights reserved. Development of a Gastric Cancer Diagnostic Support System with a Pattern Recognition Method Using a Hyperspectral Camera Sun, 15 Nov 2015 14:06:34 +0000 Gastric cancer is a completely curable cancer when it can be detected at its early stage. Thus, because early detection of gastric cancer is important, cancer screening by gastroscopy is performed. Recently, the hyperspectral camera (HSC), which can observe gastric cancer at a variety of wavelengths, has received attention as a gastroscope. HSC permits the discerning of the slight color variations of gastric cancer, and we considered its applicability to a gastric cancer diagnostic support system. In this paper, after correcting reflectance to absorb the individual variations in the reflectance of the HSC, a gastric cancer diagnostic support system was designed using the corrected reflectance. In system design, the problems of selecting the optimum wavelength and optimizing the cutoff value of a classifier are solved as a pattern recognition problem by the use of training samples alone. Using the hold-out method with 104 cases of gastric cancer as samples, design and evaluation of the system were independently repeated 30 times. After analyzing the performance in 30 trials, the mean sensitivity was 72.2% and the mean specificity was 98.8%. The results showed that the proposed system was effective in supporting gastric cancer screening. Hiroyuki Ogihara, Yoshihiko Hamamoto, Yusuke Fujita, Atsushi Goto, Jun Nishikawa, and Isao Sakaida Copyright © 2016 Hiroyuki Ogihara et al. All rights reserved. Camera Space Particle Filter for the Robust and Precise Indoor Localization of a Wheelchair Sun, 15 Nov 2015 13:54:16 +0000 This paper presents the theoretical development and experimental implementation of a sensing technique for the robust and precise localization of a robotic wheelchair. Estimates of the vehicle’s position and orientation are obtained, based on camera observations of visual markers located at discrete positions within the environment. A novel implementation of a particle filter on camera sensor space (Camera-Space Particle Filter) is used to combine visual observations with sensed wheel rotations mapped onto a camera space through an observation function. The camera space particle filter fuses the odometry and vision sensors information within camera space, resulting in a precise update of the wheelchair’s pose. Using this approach, an inexpensive implementation on an electric wheelchair is presented. Experimental results within three structured scenarios and comparative performance using an Extended Kalman Filter (EKF) and Camera-Space Particle Filter (CSPF) implementations are discussed. The CSPF was found to be more precise in the pose of the wheelchair than the EKF since the former does not require the assumption of a linear system affected by zero-mean Gaussian noise. Furthermore, the time for computational processing for both implementations is of the same order of magnitude. Raul Chavez-Romero, Antonio Cardenas, Mauro Maya, Alejandra Sanchez, and Davide Piovesan Copyright © 2016 Raul Chavez-Romero et al. All rights reserved. Research on Multifeature Segmentation Method of Remote Sensing Images Based on Graph Theory Sun, 15 Nov 2015 13:47:17 +0000 According to the characteristics of high-resolution remote sensing (RS) images, a new multifeature segmentation method of high-resolution remote sensing images combining the spectrum, shape, and texture features based on graph theory is presented in the paper. Firstly, the quadtree segmentation method is used to partition the original image. Secondly, the spectrum, shape, and texture weight components are calculated all based on the constructed graph. The matching degree between pixels and the texture is computed similarity. Finally, the ratio cut standards combination of the spectrum, shape, and texture weight components is used for the final segmentation. The experimental results show that this method can obtain more ideal results and higher segmentation accuracy applied to RS image than those traditional methods. Wenxing Bao and Xiuhong Yao Copyright © 2016 Wenxing Bao and Xiuhong Yao. All rights reserved.