Showing papers in "IEEE Sensors Journal in 2014"

High-Sensitivity Metamaterial-Inspired Sensor for Microfluidic Dielectric Characterization

Abstract: A new metamaterial-inspired microwave microfluidic sensor is proposed in this paper. The main part of the device is a microstrip coupled complementary split-ring resonator (CSRR). At resonance, a strong electric field will be established along the sides of CSRR producing a very sensitive area to a change in the nearby dielectric material. A micro-channel is positioned over this area for microfluidic sensing. The liquid sample flowing inside the channel modifies the resonance frequency and peak attenuation of the CSRR resonance. The dielectric properties of the liquid sample can be estimated by establishing an empirical relation between the resonance characteristics and the sample complex permittivity. The designed microfluidic sensor requires a very small amount of sample for testing since the cross-sectional area of the sensing channel is over five orders of magnitude smaller than the square of the wavelength. The proposed microfluidic sensing concept is compatible with lab-on-a-chip platforms owing to its compactness.

Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks

Abstract: Network lifetime plays an integral role in setting up an efficient wireless sensor network. The objective of this paper is twofold. The first one is to deploy sensor nodes at optimal locations such that the theoretically computed network lifetime is maximum. The second is to schedule these sensor nodes such that the network attains the maximum lifetime. Thus, the overall objective of this paper is to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network lifetime is maximum with the required coverage level. Since the upper bound of the network lifetime for a given network can be computed mathematically, we use this knowledge to compute locations of deployment such that the network lifetime is maximum. Further, the nodes are scheduled to achieve this upper bound. In this paper, we use artificial bee colony algorithm and particle swarm optimization for sensor deployment problem followed by a heuristic for scheduling. A comparative study shows that artificial bee colony algorithm performs better for sensor deployment problem. The proposed heuristic was able to achieve the theoretical upper bound in all the experimented cases.

A Low-Cost Sensor Network for Real-Time Monitoring and Contamination Detection in Drinking Water Distribution Systems

Abstract: This paper presents a low cost and holistic approach to the water quality monitoring problem for drinking water distribution systems as well as for consumer sites. Our approach is based on the development of low cost sensor nodes for real time and in-pipe monitoring and assessment of water quality on the fly. The main sensor node consists of several in-pipe electrochemical and optical sensors and emphasis is given on low cost, lightweight implementation, and reliable long time operation. Such implementation is suitable for large scale deployments enabling a sensor network approach for providing spatiotemporally rich data to water consumers, water companies, and authorities. Extensive literature and market research are performed to identify low cost sensors that can reliably monitor several parameters, which can be used to infer the water quality. Based on selected parameters, a sensor array is developed along with several microsystems for analog signal conditioning, processing, logging, and remote presentation of data. Finally, algorithms for fusing online multisensor measurements at local level are developed to assess the water contamination risk. Experiments are performed to evaluate and validate these algorithms on intentional contamination events of various concentrations of escherichia coli bacteria and heavy metals (arsenic). Experimental results indicate that this inexpensive system is capable of detecting these high impact contaminants at fairly low concentrations. The results demonstrate that this system satisfies the online, in-pipe, low deployment-operation cost, and good detection accuracy criteria of an ideal early warning system.

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Abstract: The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating “things” or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.

Multifocus Image Fusion Based on NSCT and Focused Area Detection

Abstract: To overcome the difficulties of sub-band coefficients selection in multiscale transform domain-based image fusion and solve the problem of block effects suffered by spatial domain-based image fusion, this paper presents a novel hybrid multifocus image fusion method. First, the source multifocus images are decomposed using the nonsubsampled contourlet transform (NSCT). The low-frequency sub-band coefficients are fused by the sum-modified-Laplacian-based local visual contrast, whereas the high-frequency sub-band coefficients are fused by the local Log-Gabor energy. The initial fused image is subsequently reconstructed based on the inverse NSCT with the fused coefficients. Second, after analyzing the similarity between the previous fused image and the source images, the initial focus area detection map is obtained, which is used for achieving the decision map obtained by employing a mathematical morphology postprocessing technique. Finally, based on the decision map, the final fused image is obtained by selecting the pixels in the focus areas and retaining the pixels in the focus region boundary as their corresponding pixels in the initial fused image. Experimental results demonstrate that the proposed method is better than various existing transform-based fusion methods, including gradient pyramid transform, discrete wavelet transform, NSCT, and a spatial-based method, in terms of both subjective and objective evaluations.

ISSAQ: An Integrated Sensing Systems for Real-Time Indoor Air Quality Monitoring

Abstract: With growing transportation and population density, increasing global warming and sudden climate change, air quality is one of the critical measures that is needed to be monitored closely on a real-time basis in today's urban ecosystems. This paper examines the issues, infrastructure, information processing, and challenges of designing and implementing an integrated sensing system for real-time indoor air quality monitoring. The system aims to detect the level of seven gases, ozone (O 3 ), particulate matter, carbon monoxide (CO), nitrogen oxides (NO 2 ), sulfur dioxide (SO 2 ), volatile organic compound, and carbon dioxide (CO 2 ), on a real-time basis and provides overall air quality alert timely. Experiments are conducted to validate and support the development of the system for real-time monitoring and alerting.

SEECH: Scalable Energy Efficient Clustering Hierarchy Protocol in Wireless Sensor Networks

Abstract: The energy efficiency is an important issue for employ distributed wireless sensor networks in smart space and extreme environments. The cluster-based communication protocols play a considerable role for energy saving in hierarchical wireless sensor networks. In most of traditional clustering algorithms, a cluster head (CH) simultaneously serves as a relay sensor node to transmit its cluster/other clusters data packet(s) to the data sink. As a result, each node would have CH role as many as relay role during network lifetime. In our view, this is inefficient from an energy efficiency perspective because in lots of cases, a node due to its position in the network comparatively is more proper to work as a CH and/a relay. This paper proposes a new distributed algorithm named scalable energy efficient clustering hierarchy (SEECH), which selects CHs and relays separately and based on nodes eligibilities. In this way, high and low degree nodes are, respectively, employed as CHs and relays. In only a few past researches, CHs and relays are different, but their goal was mainly mitigation of CHs energy burden which is intrinsically satisfied by the proposed mechanism. To consider uniformity of CHs to balance clusters, SEECH uses a new distance-based algorithm. Comparisons with LEACH and TCAC protocols show obvious better performance of SEECH in term of lifetime. To evaluate the scalability of SEECH strategy, simulations are conducted in three different network size scenarios.

Development of a Low Cost Printable Chipless RFID Humidity Sensor

Abstract: A novel low-cost chipless radio frequency identification tag sensor is presented. The tag sensor provides identification data as well as monitors relative humidity (RH) of tagged objects. The tag sensor is made of passive microwave circuit that uses humidity sensitive polymer material for RH sensing. The aim of this paper is to investigate RF sensing properties of moisture absorbing polymer polyvinyl alcohol at microwave frequency. In addition, frequency shifting technique is used to encode data bits for high data capacity. The overall size of the proposed tag sensor is 15 mm × 6.8 mm and has 6 bit data capacity for ID generation and single bit for humidity sensing. Results presented here show ~ 607 MHz frequency deviation for 50% RH increase. The tag sensor has potential to be printed on flexible laminates such as plastic and paper for ultra-low cost item level tagging and ubiquitous sensing.

Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition

Abstract: This paper presents the first attempt at fusing data from inertial and vision depth sensors within the framework of a hidden Markov model for the application of hand gesture recognition. The data fusion approach introduced in this paper is general purpose in the sense that it can be used for recognition of various body movements. It is shown that the fusion of data from the vision depth and inertial sensors act in a complementary manner leading to a more robust recognition outcome compared with the situations when each sensor is used individually on its own. The obtained recognition rates for the single hand gestures in the Microsoft MSR data set indicate that our fusion approach provides improved recognition in real-time and under realistic conditions.

A Realistic Energy Consumption Model for TSCH Networks

Abstract: Time slotted channel hopping (TSCH) is the highly reliable and ultra-low power medium access control technology at the heart of the IEEE802.15.4e-2012 amendment to the IEEE802.15.4-2011 standard. TSCH networks are deterministic in nature; the actions that occur at each time slot are well known. This paper presents an energy consumption model of these networks, obtained by slot-based “step-by-step” modeling and experimental validation on real devices running the OpenWSN protocol stack. This model is applied to different network scenarios to understand the potential effects of several network optimization. The model shows the impact of keep-alive and advertisement loads and discusses network configuration choices. Presented results show average current in the order of 570 μA on OpenWSN hardware and duty cycles 1% in network relays in both real and simulated networks. Leaf nodes show 0.46% duty cycle with data rates close to 10 packets per minute. In addition, the model is used to analyze the impact on energy consumption and data rate by overprovisioning slots to compensate for the lossy nature of these networks.

A Network Lifetime Enhancement Method for Sink Relocation and Its Analysis in Wireless Sensor Networks

Abstract: Recent advances in micromanufacturing technology have enabled the development of low-cost, low-power, multifunctional sensor nodes for wireless communication. Diverse sensing applications have also become a reality as a result. These include environmental monitoring, intrusion detection, battlefield surveillance, and so on. In a wireless sensor network (WSN), how to conserve the limited power resources of sensors to extend the network lifetime of the WSN as long as possible while performing the sensing and sensed data reporting tasks, is the most critical issue in the network design. In a WSN, sensor nodes deliver sensed data back to the sink via multihopping. The sensor nodes near the sink will generally consume more battery power than others; consequently, these nodes will quickly drain out their battery energy and shorten the network lifetime of the WSN. Sink relocation is an efficient network lifetime extension method, which avoids consuming too much battery energy for a specific group of sensor nodes. In this paper, we propose a moving strategy called energy-aware sink relocation (EASR) for mobile sinks in WSNs. The proposed mechanism uses information related to the residual battery energy of sensor nodes to adaptively adjust the transmission range of sensor nodes and the relocating scheme for the sink. Some theoretical and numerical analyze are given to show that the EASR method can extend the network lifetime of the WSN significantly.

Intelligent fusion of wi-fi and inertial sensor-based positioning systems for indoor pedestrian navigation

Abstract: Indoor positioning systems based on wireless local area networks are growing rapidly in importance and gaining commercial interest. Pedestrian dead reckoning (PDR) systems, which rely on inertial sensors, such as accelerometers, gyroscopes, or even magnetometers to estimate users' movement, have also been widely adopted for real-time indoor pedestrian location tracking. Since both kinds of systems have their own advantages and disadvantages, a maximum likelihood-based fusion algorithm that integrates a typical Wi-Fi indoor positioning system with a PDR system is proposed in this paper. The strength of the PDR system should eliminate the weakness of the Wi-Fi positioning system and vice versa. The intelligent fusion algorithm can retrieve the initial user location and moving direction information without requiring any user intervention. Experimental results show that the proposed positioning system has better positioning accuracy than the PDR system or Wi-Fi positioning system alone.

Characterizations of Noise in Kinect Depth Images: A Review

Abstract: In this paper, we characterize the noise in Kinect depth images based on multiple factors and introduce a uniform nomenclature for the types of noise. In the process, we briefly survey the noise models of Kinect and relate these to the factors of characterization. We also deal with the noise in multi-Kinect set-ups and summarize the techniques for the minimization of interference noise. Studies on noise in Kinect depth images are distributed over several publications and there is no comprehensive treatise on it. This paper, to the best of our knowledge, is the maiden attempt to characterize the noise behavior of Kinect depth images in a structured manner. The characterization would help to selectively eliminate noise from depth images either by filtering or by adopting appropriate methodologies for image capture. In addition to the characterization based on the results reported by others, we also conduct independent experiments in a number of cases to fill up the gaps in characterization and to validate the reported results.

Metamaterial-Inspired Rotation Sensor With Wide Dynamic Range

Abstract: A rotation sensor with a wide dynamic range is designed based on tapered U-shaped resonators. The proposed device is composed of a rounded microstrip transmission line that couples to two meandered resonators that are stacked on top of each other. By rotating the upper resonator, the overlapping area between the two resonators is increased causing a stronger coupling that shifts down the resonance frequency of the device. This frequency shift can be read out in the transmission response from which the rotation angle is determined. The operation principle of the sensor is explained in detail by using a circuit model. A sensor prototype is designed for the microwave frequency range and an experiment is presented for validating the proposed sensing approach. This sensing device is well suited for further miniaturization using microelectromechanical systems technology.

Thickness and Permittivity Measurement in Multi-Layered Dielectric Structures Using Complementary Split-Ring Resonators

Abstract: This paper presents a non-invasive microwave method based on a square-shaped complementary split-ring resonator (CSRR) to measure the thickness and permittivity of multilayer dielectric structures. The CSRR sensor is etched on the ground plane of a microstrip line. The change of resonance frequency depends on the thickness and permittivity of the multilayer dielectric sample below the ground plane. For resolution analysis, the resonance frequency shifts caused by a variation of permittivity (Δe = 0.01) and thickness (Δd=0.01 mm) in the detection layer were compared across various design dimensions. Sensor size optimization improved the resolution in permittivity and thickness measurement by 66% and 37%, respectively. Subsequently, the permittivity and thickness resolution was improved by 28% and 16%, respectively, by optimizing the separation of the etched CSRRs. The analysis results show that a CSRR sensor can be designed with excellent resolution in core layer permittivity changes and thickness resolution in multilayered dielectric structures.

Task Allocation for Wireless Sensor Network Using Modified Binary Particle Swarm Optimization

Abstract: Many applications of wireless sensor network (WSN) require the execution of several computationally intense in-network processing tasks. Collaborative in-network processing among multiple nodes is essential when executing such a task due to the strictly constrained energy and resources in single node. Task allocation is essential to allocate the workload of each task to proper nodes in an efficient manner. In this paper, a modified version of binary particle swarm optimization (MBPSO), which adopts a different transfer function and a new position updating procedure with mutation, is proposed for the task allocation problem to obtain the best solution. Each particle in MBPSO is encoded to represent a complete potential solution for task allocation. The task workload and connectivity are ensured by taking them as constraints for the problem. Multiple metrics, including task execution time, energy consumption, and network lifetime, are considered a whole by designing a hybrid fitness function to achieve the best overall performance. Simulation results show the feasibility of the proposed MBPSO-based approach for task allocation problem in WSN. The proposed MBPSO-based approach also outperforms the approaches based on genetic algorithm and BPSO in the comparative analysis.

Superior Path Planning Mechanism for Mobile Beacon-Assisted Localization in Wireless Sensor Networks

Abstract: In many wireless sensor network applications, such as warning systems or healthcare services, it is necessary to update the captured data with location information. A promising solution for statically deployed sensors is to benefit from mobile beacon-assisted localization. The main challenge is to design and develop an optimum path planning mechanism for a mobile beacon to decrease the required time for determining location, increase the accuracy of the estimated position, and increase the coverage. In this paper, we propose a novel superior path planning mechanism called Z-curve. Our proposed trajectory can successfully localize all deployed sensors with high precision and the shortest required time for localization. We also introduce critical metrics, including the ineffective position rate for further evaluation of mobile beacon trajectories. In addition, we consider an accurate and reliable channel model, which helps to provide more realistic evaluation. Z-curve is compared with five existing path planning schemes based on three different localization techniques such as weighted centroid localization and trilateration with time priority and accuracy priority. Furthermore, the performance of the Z-curve is evaluated at the presence of obstacles and Z-curve obstacle-handling trajectory is proposed to mitigate the obstacle problem on localization. Simulation results show the advantages of our proposed path planning scheme over the existing schemes.

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Abstract: As the heart of an aircraft, the aircraft engine's condition directly affects the safety, reliability, and operation of the aircraft. Prognostics and health management for aircraft engines can provide advance warning of failure and estimate the remaining useful life. However, aircraft engine systems are complex with both intangible and uncertain factors, it is difficult to model the complex degradation process, and no single prognostic approach can effectively solve this critical and complicated problem. Thus, fusion prognostics is conducted to obtain more accurate prognostics results. In this paper, a prognostics and health management-oriented integrated fusion prognostic framework is developed to improve the system state forecasting accuracy. This framework strategically fuses the monitoring sensor data and integrates the strengths of the data-driven prognostics approach and the experience-based approach while reducing their respective limitations. As an application example, this developed fusion prognostics framework is employed to predict the remaining useful life of an aircraft gas turbine engine based on sensor data. The results demonstrate that the proposed fusion prognostics framework is an effective prognostics tool, which can provide a more accurate and robust remaining useful life estimation than any single prognostics method.

Implementation of Tactile Sensing for Palpation in Robot-Assisted Minimally Invasive Surgery: A Review

Abstract: Robot-assisted minimally invasive surgery (RMIS) made it possible to perform a number of medical manipulations with reduced patient trauma and better accuracy. Various devices, including tactile sensors, have been developed in recent years to enhance the quality of this procedure. The objective of this paper is to review the latest advancements and challenges in the development of tactile sensing devices designed for surgical applications. In particular, the focus is on palpation and probing devices that can be potentially used in RMIS. In addition, we explore the aspects that should be taken into account when designing tactile sensors for RMIS, incorporating biological inspiration of tactile sensing, features of manual palpation, requirements of RMIS. We provide an overview of recommendations for the development of tactile sensing devices, especially in the context of RMIS.

Angular Displacement and Velocity Sensors Based on Electric-LC (ELC) Loaded Microstrip Lines

Abstract: Planar microwave angular displacement and angular velocity sensors implemented in microstrip technology are proposed. The transducer element is a circularly shaped divider/combiner, whereas the sensing element is an electric-LC resonator, attached to the rotating object and magnetically coupled to the circular (active) region of the transducer. The angular variables are measured by inspection of the transmission characteristics, which are modulated by the magnetic coupling between the resonator and the divider/combiner. The degree of coupling is hence sensitive to the angular position of the resonator. As compared with coplanar waveguide angular displacement and velocity sensors, the proposed microstrip sensors do not require air bridges, and the ground plane provides backside isolation.

Cluster Head Election for Energy and Delay Constraint Applications of Wireless Sensor Network

Abstract: Designing of a multi-hop Wireless Sensor Network (WSN) depends upon the requirements of the underlying sensing application. The main objective of WSNs is to monitor physical phenomenon of interest in a given region of interest using sensors and provide collected data to sink. The WSN is made of a large number of energy, communication, and computational constraint nodes, to overcome energy constrains, replacing or recharging the batteries of the WSN nodes is an impossible task, once they are deployed in a hostile environment. Therefore, to keep the network alive as long as possible, communication between the WSN nodes must be done with load balancing. Time critical applications like forest fire detection, battle field monitoring demands reception of data by the sink with the bounded delay to avoid disasters. Hence, there is a need to design a protocol which enhances the network lifetime and provides information to the sink with a bounded delay. This paper will address this problem and solution. In this paper, a routing algorithm is proposed by introducing Energy Delay Index for Trade-off (EDIT) to optimize both objectives-energy and delay. The EDIT is used to select cluster heads and “next hop” by considering energy and/or delay requirements of a given application. Proposed approach is derived using two different aspects of distances between a node and the sink named Euclidean distance and Hop-count, and further prove using realistic parameters of radio to get data closest to the test bed implementation. The results aspire to give sufficient insights to others before doing test bed implementation.

Data Density Correlation Degree Clustering Method for Data Aggregation in WSN

Abstract: One data aggregation method in a wireless sensor network (WSN) is sending local representative data to the sink node based on the spatial-correlation of sampled data. In this paper, we highlight the problem that the recent spatial correlation models of sensor nodes' data are not appropriate for measuring the correlation in a complex environment. In addition, the representative data are inaccurate when compared with real data. Thus, we propose the data density correlation degree, which is necessary to resolve this problem. The proposed correlation degree is a spatial correlation measurement that measures the correlation between a sensor node's data and its neighboring sensor nodes' data. Based on this correlation degree, a data density correlation degree (DDCD) clustering method is presented in detail so that the representative data have a low distortion on their correlated data in a WSN. In addition, simulation experiments with two real data sets are presented to evaluate the performance of the DDCD clustering method. The experimental results show that the resulting representative data achieved using the proposed method have a lower data distortion than those achieved using the Pearson correlation coefficient based clustering method or the α-local spatial clustering method. Moreover, the shape of clusters obtained by DDCD clustering method can be adapted to the environment.

CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment

Abstract: Time-of-arrival (TOA)-based indoor geolocation suffer from huge distance measurement error caused by multipath and nonline-of-sight (NLOS) conditions. In this paper, we presented a new distance mitigation algorithm based on channel classification and Kalman filter to enhanced TOA performance in multipath and NLOS indoor extreme environment. This algorithm could significantly reduce the ranging error caused by the extreme channel condition in indoor area. We compared the performance of our algorithm with the traditional TOA distance mitigation algorithms, such as Kalman filter, biased Kalman filter, binary hypothesis testing, and ANN, using a commercially available TOA-based geolocation system in typical indoor and underground environments. Results show the performance of our algorithm is much superior to the others.

Toward Accurate Human Tracking: Modeling Time-of-Arrival for Wireless Wearable Sensors in Multipath Environment

Abstract: For time-of-arrival-(TOA)-based indoor human tracking system, the wireless channel between human body surface and external reference node can be regarded as the source of inaccuracy. Since only the arrival time of direct path provides accurate range estimate, the nonline of sight caused by human body leads to undetectable direct path condition and thus results in a significant distance measurement error. In this paper, we measured TOA ranging error for indoor human tracking applications inside a typical office environment. A large number of TOA ranging samples was obtained and statistically analyzed. The TOA ranging error was modeled as a Gaussian random variable with the parameters, including position of target sensor, angle between human facing direction, and direction of transmitter–receiver, signal-to-noise ratio, and bandwidth of the system. As a validation of proposed model, excellent agreement has been found between empirical measurement and model-based software simulation.

A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy

Abstract: A novel hyperspectral measurement system for unmanned aerial vehicles (UAVs) in the visible to near infrared (VIS/NIR) range (350-800 nm) was developed based on the Ocean Optics STS microspectrometer. The ultralight device relies on small open source electronics and weighs a ready-to-fly 216 g. The airborne spectrometer is wirelessly synchronized to a second spectrometer on the ground for simultaneous white reference collection. In this paper, the performance of the system is investigated and specific issues such as dark current correction or second order effects are addressed. Full width at half maximum was between 2.4 and 3.0 nm depending on the spectral band. The functional system was tested in flight at a 10-m altitude against a current field spectroscopy gold standard device Analytical Spectral Devices Field Spec 4 over an agricultural site. A highly significant correlation was found in reflection comparing both measurement approaches. Furthermore, the aerial measurements have a six times smaller standard deviation than the hand held measurements. Thus, the present spectrometer opens a possibility for low-cost but high-precision field spectroscopy from UAVs.

Autonomous Wearable Sensor Nodes With Flexible Energy Harvesting

Abstract: Distributed wearable wireless sensors are widely employed in wireless body sensor network for various physiological monitoring purposes like health or performance related monitoring applications. The real challenges in employing these wearable wireless sensors on human subjects include: 1) bulky and rigid system design thus, it is difficult to conform to human body contour and 2) limited operational lifespan of batteries with finite energy supply. In this paper, an autonomous body-worn wireless sensor node with flexible energy harvesting (FEH) mechanism, able to conform to body contour, is proposed for biometric monitoring. To be totally sustainable and compact, the FEH mechanism is equipped with an ultralow power management circuit (PMC) specially designed on a flexible PCB. The flexible PMC is able to transfer near maximum electrical power from the input solar energy source to store in the supercapacitor for powering the wireless sensor node. The power consumption of the flexible PMC is only 32.86 $\mu{\rm W}$ . Experimental results show that under indoor condition, typical average lighting intensity of 320 lux, the wearable sensor node is able to continuously monitor the temperature of the wearer, read, and transmit back to the base node in a wireless manner, without the need of any battery. In addition, the designed FEH sensor node flexed onto the wearer body contour at an angle of 30 $^{\circ}$ generates 56 $\mu{\rm W}$ of electrical power, sufficient to sustain its operation for ${>}{\rm 15}~{\rm h}$ .

Nonuniform Frequency Diverse Array for Range-Angle Imaging of Targets

Abstract: Although phased-array antennas are widely used in communication, radar, and navigation systems, its beampattern is a function of angle only and thus there is no range information. To circumvent this limitation, we design a frequency diverse array (FDA) antenna system for range-angle imaging of targets. Our approach exploits the nonuniform FDA as the transmitter to provide range-dependent beampattern and the uniform phased-array as the receiver which results in angle-dependent beampattern. Range-angle imaging of targets is achieved from the cooperative transmit-receive beamforming. The imaging performance measures including spatial resolution and system processing gain are analyzed. In addition, several design specifications are discussed. The effectiveness of the proposed approach is verified by simulation results.

Impact Damage Detection and Identification Using Eddy Current Pulsed Thermography Through Integration of PCA and ICA

Abstract: Eddy current pulsed thermography (ECPT) is implemented for detection and separation of impact damage and resulting damages in carbon fiber reinforced plastic (CFRP) samples. Complexity and nonhomogeneity of fiber texture as well as multiple defects limit detection identification and characterization from transient images of the ECPT. In this paper, an integration of principal component analysis (PCA) and independent component analysis (ICA) on transient thermal videos has been proposed. This method enables spatial and temporal patterns to be extracted according to the transient response behavior without any training knowledge. In the first step, using the PCA, the data is transformed to orthogonal principal component subspace and the dimension is reduced. Multichannel morphological component analysis, as an ICA method, is then implemented to deal with the sparse and independence property for detecting and separating the influences of different layers, defects, and their combination information in the CFRP. Because different transient behaviors exist, multiple types of defects can be identified and separated by calculating the cross-correlation of the estimated mixing vectors between impact the ECPT sequences and nondefect ECPT sequences.

Energy efficient environment monitoring system based on the IEEE 802.15.4 standard for low cost requirements

Abstract: Power consumption, portability, and system cost are important parameters in designing pervasive measurement systems. With these parameters in mind, wireless environment monitoring system with a capability to monitor greenhouse gases, such as CO, CO 2 , SO x , NO x , O 2 with environmental parameter is developed. In order to achieve the target design goals, the communication module, the wireless smart transducer interface module, and wireless network capable application processor module were developed based on the IEEE 802.15.4, IEEE 1451.2, and IEEE 1451.1 standards, respectively. The low cost and energy efficient gas sensing modules were successfully developed with improved tolerance to EMF/RFI noise. We defined recalibration of the system at time intervals to ensure that the desired accuracy is maintained. This paper presents the undertaken design detailing solutions to issues raised in previous research.

Adaptive Electrical Capacitance Volume Tomography

Abstract: Electrical capacitance volume tomography (ECVT) has shown to be an effective low-cost and high-speed imaging technique suitable for many applications, including 3-D reconstruction of multiphase flow systems. In this paper, we introduce the concept of adaptive ECVT based upon the combination of a large number of small individual sensor segments to comprise synthetic capacitance plates of different (and possibly noncontiguous) shapes while still satisfying a minimum plate area criterion set by a given SNR. The response from different segments is combined electronically in a reconfigurable fashion. The proposed adaptive concept paves the way for ECVT to be applicable in scenarios requiring higher resolution and dynamic imaging reconstruction.