Showing papers in "IEEE Sensors Journal in 2012"

Design and Fabrication of Soft Artificial Skin Using Embedded Microchannels and Liquid Conductors

Abstract: We describe the design, fabrication, and calibration of a highly compliant artificial skin sensor. The sensor consists of multilayered mircochannels in an elastomer matrix filled with a conductive liquid, capable of detecting multiaxis strains and contact pressure. A novel manufacturing method comprised of layered molding and casting processes is demonstrated to fabricate the multilayered soft sensor circuit. Silicone rubber layers with channel patterns, cast with 3-D printed molds, are bonded to create embedded microchannels, and a conductive liquid is injected into the microchannels. The channel dimensions are 200 μm (width) × 300 μm (height). The size of the sensor is 25 mm × 25 mm, and the thickness is approximately 3.5 mm. The prototype is tested with a materials tester and showed linearity in strain sensing and nonlinearity in pressure sensing. The sensor signal is repeatable in both cases. The characteristic modulus of the skin prototype is approximately 63 kPa. The sensor is functional up to strains of approximately 250%.

Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication

Abstract: In order to collect information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. Clustering provides an effective way to prolong the lifetime of WSNs. Current clustering approaches often use two methods: selecting cluster heads with more residual energy, and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, most of the previous algorithms have not considered the expected residual energy, which is the predicated remaining energy for being selected as a cluster head and running a round. In this paper, a fuzzy-logic-based clustering approach with an extension to the energy predication has been proposed to prolong the lifetime of WSNs by evenly distributing the workload. The simulation results show that the proposed approach is more efficient than other distributed algorithms. It is believed that the technique presented in this paper could be further applied to large-scale wireless sensor networks.

Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly

Abstract: Wireless-sensor-network-based home monitoring system for elderly activity behavior involves functional assessment of daily activities. In this paper, we reported a mechanism for estimation of elderly well-being condition based on usage of house-hold appliances connected through various sensing units. We defined two new wellness functions to determine the status of the elderly on performing essential daily activities. The developed system for monitoring and evaluation of essential daily activities was tested at the homes of four different elderly persons living alone and the results are encouraging in determining wellness of the elderly.

Distributed Temperature Sensing: Review of Technology and Applications

Abstract: Distributed temperature sensors (DTS) measure temperatures by means of optical fibers. Those optoelectronic devices provide a continuous profile of the temperature distribution along the cable. Initiated in the 1980s, DTS systems have undergone significant improvements in the technology and the application scenario over the last decades. The main measuring principles are based on detecting the back-scattering of light, e.g., detecting via Rayleigh, Raman, and Brillouin principles. The application domains span from traditional applications in the distributed temperature or strain sensing in the cables, to the latest “smart grid” initiative in the power systems, etc. In this paper, we present comparative reviews of the different DTS technologies, different applications, standard, and upcoming, different manufacturers.

Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review

Abstract: Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing.

Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls

Abstract: The rapid aging of the world's population, along with an increase in the prevalence of chronic illnesses and obesity, requires adaption and modification of current healthcare models. One such approach involves telehealth applications, many of which are based on sensor technologies for unobtrusive monitoring. Recent technological advances, in particular, involving microelectromechnical systems, have resulted in miniaturized wearable devices that can be used for a range of applications. One of the leading areas for utilization of body-fixed sensors is the monitoring of human movement. An overview of common ambulatory sensors is presented, followed by a summary of the developments in this field, with an emphasis on the clinical applications of falls detection, falls risk assessment, and energy expenditure. The importance of these applications is considerable in light of the global demographic trends and the resultant rise in the occurrence of injurious falls and the decrease of physical activity. The potential of using such monitors in an unsupervised manner for community-dwelling individuals is immense, but entails an array of challenges with regards to design c onsiderations, implementation protocols, and signal analysis processes. Some limitations of the research to date and suggestions for future research are also discussed.

MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition

Abstract: This paper presents three different gesture recognition models which are capable of recognizing seven hand gestures, i.e., up, down, left, right, tick, circle, and cross, based on the input signals from MEMS 3-axes accelerometers. The accelerations of a hand in motion in three perpendicular directions are detected by three accelerometers respectively and transmitted to a PC via Bluetooth wireless protocol. An automatic gesture segmentation algorithm is developed to identify individual gestures in a sequence. To compress data and to minimize the influence of variations resulted from gestures made by different users, a basic feature based on sign sequence of gesture acceleration is extracted. This method reduces hundreds of data values of a single gesture to a gesture code of 8 numbers. Finally, the gesture is recognized by comparing the gesture code with the stored templates. Results based on 72 experiments, each containing a sequence of hand gestures (totaling 628 gestures), show that the best of the three models discussed in this paper achieves an overall recognition accuracy of 95.6%, with the correct recognition accuracy of each gesture ranging from 91% to 100%. We conclude that a recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.

A Zigbee-Based Wearable Physiological Parameters Monitoring System

Abstract: The design and development of a Zigbee smart noninvasive wearable physiological parameters monitoring device has been developed and reported in this paper The system can be used to monitor physiological parameters, such as temperature and heart rate, of a human subject The system consists of an electronic device which is worn on the wrist and finger, by an at-risk person Using several sensors to measure different vital signs, the person is wirelessly monitored within his own home An impact sensor has been used to detect falls The device detects if a person is medically distressed and sends an alarm to a receiver unit that is connected to a computer This sets off an alarm, allowing help to be provided to the user The device is battery powered for use outdoors The device can be easily adapted to monitor athletes and infants The low cost of the device will help to lower the cost of home monitoring of patients recovering from illness A prototype of the device has been fabricated and extensively tested with very good results

Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA

Abstract: Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with noncerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces. Artifact rejection is, thus, a key analysis for both visual inspection and digital processing of EEG. Automatic artifact rejection is needed for effective real time inspection because manual rejection is time consuming. In this paper, a novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented. Through AWICA we claim to improve the performance and fully automate the process of artifact removal from scalp EEG. AWICA is based on the joint use of the Wavelet Transform and of ICA: it consists of a two-step procedure relying on the concepts of kurtosis and Renyi's entropy. Both synthesized and real EEG data are processed by AWICA and the results achieved were compared to the ones obtained by applying to the same data the “wavelet enhanced” ICA method recently proposed by other authors. Simulations illustrate that AWICA compares favorably to the other technique. The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.

Chemical Sensing in Robotic Applications: A Review

Abstract: Robots are generally equipped with at least several different modalities of sensors. Vision and range sensors are the most popular, especially in mobile robots. On the other hand, olfaction (or chemical sensing in general) had long been ignored in the robotics community because of the technical difficulties involved in realizing artificial olfaction on robotic platforms. Over the past two decades, however, various attempts are made to use chemical sensors in robotic applications. With the help of chemical sensors, mobile robots can follow chemical trails laid on the ground, track chemical plumes to find their sources, and build distribution maps of chemical substances. This paper is intended to present a brief history and the current trends of the research in this emerging field.

Consensus Clock Synchronization for Wireless Sensor Networks

Abstract: Wireless sensor networks (WSN) are emerging as a valuable tool in many fields of science and industry. Time synchronization is an important issue for WSN's due to the collaborative and distributed nature of the tasks they perform. This paper describes a new technique for clock synchronization in WSNs called consensus clock synchronization that provides internal synchronization to a virtual consensus clock. It is sensitive to the limited resources available to sensor nodes and is robust to many of the challenges faced in dynamic ad-hoc networks. Simulations are presented to analyse the effectiveness of the synchronization protocol in a mesh network.

Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

Abstract: Inertial measurement units (IMUs) are widely used in motion measurement. However, the drift of IMUs results in significant accumulated errors for long-term position and orientation measurement. This paper reports a method to compensate the drift of inertial sensors with the assist of ultrasonic sensors and magnetometers. The magnetometer is combined with a three-axis accelerometer as a digital compass for orientation determination in static state by measuring the gravity and the earth's magnetic field vectors. A three-axis gyroscope is used to measure the orientation in dynamic state to complement the digital compass. Displacement determination is implemented using the accelerometer through double integration, and an ultrasonic sensor is employed to periodically calibrate the accumulated errors of the accelerometer. The redundant data from the multi-sensors are fused using extended Kalman filter (EKF), in which the position measured by the ultrasonic sensor and the orientation measured by the digital compass are defined as the observation values, and the position, velocity, and orientation are included in the state vector. Experimental results show that the accumulated errors of inertial sensors are reduced by ultrasonic sensors and magnetometers, and EKF improves the accuracy of orientation and position measurements.

A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing

Abstract: Objective and automatic sensor systems to monitor ingestive behavior of individuals arise as a potential solution to replace inaccurate method of self-report. This paper presents a simple sensor system and related signal processing and pattern recognition methodologies to detect periods of food intake based on non-invasive monitoring of chewing. A piezoelectric strain gauge sensor was used to capture movement of the lower jaw from 20 volunteers during periods of quiet sitting, talking and food consumption. These signals were segmented into non-overlapping epochs of fixed length and processed to extract a set of 250 time and frequency domain features for each epoch. A forward feature selection procedure was implemented to choose the most relevant features, identifying from 4 to 11 features most critical for food intake detection. Support vector machine classifiers were trained to create food intake detection models. Twenty-fold cross-validation demonstrated per-epoch classification accuracy of 80.98% and a fine time resolution of 30 s. The simplicity of the chewing strain sensor may result in a less intrusive and simpler way to detect food intake. The proposed methodology could lead to the development of a wearable sensor system to assess eating behaviors of individuals.

Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals

Abstract: Driver drowsiness is among the leading causal factors in traffic accidents occurring worldwide. This paper describes a method to monitor driver safety by analyzing information related to fatigue using two distinct methods: eye movement monitoring and bio-signal processing. A monitoring system is designed in Android-based smartphone where it receives sensory data via wireless sensor network and further processes the data to indicate the current driving aptitude of the driver. It is critical that several sensors are integrated and synchronized for a more realistic evaluation of the driver behavior. The sensors applied include a video sensor to capture the driver image and a bio-signal sensor to gather the driver photoplethysmograph signal. A dynamic Bayesian network framework is used for the driver fatigue evaluation. A warning alarm is sounded if driver fatigue is believed to reach a defined threshold. The manifold testing of the system demonstrates the practical use of multiple features, particularly with discrete methods, and their fusion enables a more authentic and ample fatigue detection.

Steel Corrosion Characterization Using Pulsed Eddy Current Systems

Abstract: A pulsed eddy current (PEC) system has been used to characterize atmospheric corrosion on steel samples. International Paint has supplied coated and uncoated mild steel (S275) samples with marine atmospheric corrosion (exposure time between 1 and 10 months). The PEC response due to corrosion is a complex mix of many factors, including conductivity, permeability and material thickness variation, which are all taken into account through experimental studies and the extraction of signal features. Considering the conductivity and permeability variation in the corrosion layer or the actual rust region, two time-domain features, each representing the conductivity and permeability, are extracted and used to characterize corrosion. The relationship between PEC features and exposure time has been derived, which can be useful for corrosion rate measurement and early-stage corrosion evaluation and prediction. In conclusion, PEC testing has the potential for corrosion characterization and monitoring in areas such as the marine industry.

Lifetime Enhancement in Wireless Sensor Networks Using Fuzzy Approach and A-Star Algorithm

Abstract: Wireless sensor networks (WSNs) are used in many applications to gather sensitive information which is then forwarded to an analysis center. Resource limitations have to be taken into account when designing a WSN infrastructure. Unbalanced energy consumption is an inherent problem in WSNs, characterized by multihop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. This paper proposes a new routing method for WSNs to extend network lifetime using a combination of a fuzzy approach and an A-star algorithm. The proposal is to determine an optimal routing path from the source to the destination by favoring the highest remaining battery power, minimum number of hops, and minimum traffic loads. To demonstrate the effectiveness of the proposed method in terms of balancing energy consumption and maximization of network lifetime, we compare our approach with the A-star search algorithm and fuzzy approach using the same routing criteria in two different topographical areas. Simulation results demonstrate that the network lifetime achieved by the proposed method could be increased by nearly 25% more than that obtained by the A-star algorithm and by nearly 20% more than that obtained by the fuzzy approach.

Medical Textiles With Embedded Fiber Optic Sensors for Monitoring of Respiratory Movement

Abstract: For patients under Magnetic Resonance Imaging (MRI) spontaneous respiration is constantly at risk of being impaired by anesthetic drugs or by upper airway obstruction. Therefore, continuous monitoring of the breathing activity is needed to assess adequate ventilation or to detect specific obstruction patterns. The paper describes three MRI compatible respiration sensors based on pure optical technologies developed within the EU FP6 project OFSETH. The sensors are based on fiber Bragg gratings, optical time-domain reflectometry and macrobending effects. The developed smart medical textiles can sense elongation up to 3% while maintaining the stretching properties of the textile substrates for patient's comfort. The OFSETH harness allows a continuous measurement of abdominal and thoracic respiration movement while all vitals organs are free for medical staff actions. The sensors were tested in MRI environment and on healthy adults.

Target Detection and Classification Using Seismic and PIR Sensors

Abstract: Unattended ground sensors (UGS) are widely used to monitor human activities, such as pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is often limited by high false alarm rates, possibly due to inadequacies of the underlying algorithms and limitations of onboard computation. In this regard, this paper presents a wavelet-based method for target detection and classification. The proposed method has been validated on data sets of seismic and passive infrared sensors for target detection and classification, as well as for payload and movement type identification of the targets. The proposed method has the advantages of fast execution time and low memory requirements and is potentially well-suited for real-time implementation with onboard UGS systems.

Comparative Study of Silicon-Based Ultraviolet Photodetectors

Abstract: This review article presents a comparative study of different silicon-based ultraviolet (UV) photodetector technologies. After a brief introduction and classification of UV photodetectors, a general comparison is made between the two most popular UV-detector solid-state materials: silicon and wide-band gap semiconductors (diamond, SiC, III-nitrides, and some III-V compounds). Particularly, the advantages of the Si-based technologies are discussed. Further in this paper, the analyses are restricted to silicon UV photodetectors. The theoretically attainable sensitivity in the UV spectral range of Si-based photodetectors is discussed. Different device structures and their working principle are shortly reviewed, followed by a comparison of the state-of-the-art Si-based UV photodetectors. By linking the device structure to the reported optical performance, the advantages and drawbacks of different structures are detailed. Finally, a number of key factors for designing high performance Si-based photodetectors are proposed.

Dynamic Power Management in Wireless Sensor Networks: State-of-the-Art

Abstract: In the last few years, interest in wireless sensor networks has increased considerably. These networks can be useful for a large number of applications, including habitat monitoring, structural health monitoring, pipeline monitoring, transportation, precision agriculture, supply chain management, and many more. Typically, a wireless sensor network consists of a large number of simple nodes which operate with exhaustible batteries, unattended. Manual replacement or recharging the batteries is not an easy or desirable task. Hence, how energy is utilized by the various hardware subsystems of individual nodes directly affects the scope and usefulness of the entire network. This paper provides a comprehensive assessment of state-of-the-art of dynamic power management (DPM) in wireless sensor networks. It investigates aspects of power dissipation in a node and analyses the strength and limitations of selective switching, dynamic frequency, and voltage scaling.

Design Strategies for Eddy-Current Displacement Sensor Systems: Review and Recommendations

Abstract: This paper presents a comprehensive study of the design aspects of eddy-current displacement sensor (ECS) systems. In accordance with the sensor analysis presented in this paper, design strategies to compensate for important sensor imperfections are recommended. To this end, the challenges that are associated with ECS interfaces are identified, with focus on advanced industrial applications. This paper also provides a technical overview of the design advances of ECS interfaces proposed in the last decade and evaluates their pros and cons. Recently reported interface solutions for demanding industrial applications with respect to high resolution, stability, bandwidth, and low power consumption, at a sufficiently high excitation frequency, are addressed in more detail.

A Review of Palladium-Based Fiber-Optic Sensors for Molecular Hydrogen Detection

Abstract: Palladium-based fiber-optic sensors have been one of the most promising configurations for hydrogen sensing. In the latest decade, fiber-optic sensors have indeed earned a strong interest owing to their ability to monitor molecular hydrogen at specific spatial points-either as a sensing tip device or in large areas via multiple sensing regions distributed along the optical fiber. This review focuses on the various types of optical fiber hydrogen sensors, containing specifically palladium as active element. Three distinct working principles are described, viz. interferometric-, in tensity-, and fiber grating-based sensors; their characteristics and sensing performances are critically overviewed.

Real-Time Monitoring of Railway Traffic Using Fiber Bragg Grating Sensors

Abstract: In this work, we present field tests concerning the application of fiber Bragg grating (FBG) sensors for the monitoring of railway traffic. The test campaigns are performed on the Spanish high-speed line Madrid-Barcelona, with different types of trains (S-102 TALGO-BOMBARDIER, S-103 SIEMENS-VELARO, and S-120 CAF). We located the FBG sensors in the rail track at 70 km from Madrid in the country side, where the trains primarily are tested during commercial operation with maximum speeds between 250-300 km/h. The FBG sensor interrogation system used allows the simultaneous monitoring of four FBG sensors at 8000 samples/s. The different position of the FBG sensors in relation with the rail can be used for different purposes such as train identification, axle counting, speed and acceleration detection, wheel imperfections monitoring, and dynamic load calculation.

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Abstract: Microwave doppler radar offers significant improvements for unobtrusive heart and respiration measurement. Radar monitoring enables non-contact measurement, through clothing, of heart and respiration rate, which is desired in several applications ranging from medical sleep laboratory measurements to home health care measurements and stress monitoring. The use of high frequency radar (>; 10 GHz) instead of lower frequencies (~2.4 GHz) increases the signal-to-noise-ratio of the signal and enables the utilization of commercial radar modules. However, if high frequency radar is used, linear combining of quadrature radar channels is inadequate. Instead, a nonlinear channel combining algorithm is needed. The combining can be performed with an arctangent function if center, amplitude error, and phase error are estimated accurately and corrected. In this paper, we show that the Levenberg-Marquardt (LM) center estimation algorithm outperforms the state-of-the-art center estimation algorithm precision-wise and is computationally less complex. The simulated results show that the root mean squared error with the LM method is always less than 1%, while it is around 5%-13% with the compared method, depending on the breathing signal model used. In addition, the computational complexity of the LM method stays almost constant as the size of the data set increases, whereas with the reference method, it increases exponentially. In this paper, the LM method is validated both with simulations and with real data.

Humidity Sensing by Polymer-Loaded UHF RFID Antennas

Abstract: Passive ultra high-frequency radio frequency identification tags, besides item labeling, are also able to exploit capability to sense the physical state of the tagged object as well as of the surrounding environment. Here, a new family of polymer-doped tags are proposed and fully characterized for the detection of ambient humidity. A sensitive chemical species based on PEDOT:PSS is used to load a shaped slot, carved into a folded-like patch tag. The communication and sensing capabilities of the resulting radio-sensor are investigated by means of simulation and measurements that show how to control and balance above opposite requirements by a proper deposition of the sensitive material. The device could have interesting applications in the assessment of the air quality within living and controlled rooms, in the monitoring of the conservation state of foods, in the preservation of walls, and even in the medical field, e.g., to monitor the healing of wounds.

Wearable Low-Cost System for Human Joint Movements Monitoring Based on Fiber-Optic Curvature Sensor

Abstract: In this paper, a measurement system for human joint movements monitoring based on a simple and low-cost intensity modulated fiber-optic curvature sensor is presented. The implemented curvature sensor is made up of a plastic optical fiber, which is optimized for small curvature measurements, and has a high sensitivity in a wide measuring range. The sensor sensitivity and resolution, in the measurement range, are 20 mV/° and 1°, respectively. The fabrication process of proposed curvature sensor is also given. The implemented sensor is wearable, noninvasive, nonintrusive, and completely harmless. In this paper, the characteristics of the sensor measured in laboratory conditions as well as measurements of the human knee joint movements are given. Wireless electronics based on ZigBee are also presented. Therefore, the sensor has the possibility of wireless measurement. The main advantages of this sensor are simplicity, lightness, and flexibility. This sensor is also electrically safe and immune to electromagnetic interference. The application, which provides a possibility of remote human joints monitoring over the internet is implemented in the LabVIEW software package.

A Passive Radio-Frequency pH-Sensing Tag for Wireless Food-Quality Monitoring

Abstract: We present a new method, suitable for food quality management by wirelessly monitoring pH level changes in food with a flexible pH sensor embedded in a batteryless radio-frequency (RF) transponder. The wireless sensor tag includes a flexible pH sensor based on miniature iridium oxide (IrOx) and silver chloride (AgCl) sensing electrodes integrated on a deformable substrate, and batteryless wireless communication circuitry. The sensor tag and reader system is designed to achieve convenient, long-term, and on-demand wireless in situ monitoring of food quality, especially for large-quantity applications and continuous monitoring from place of production to retail stores. Low-cost IrOx sol-gel fabrication process was applied on polymeric substrates to form the flexible sensing films, and a sensitivity of -49.7 mV/pH was achieved. Inducting coupling provides electromagnetic energy from the reader to drive the transponder circuits that retransmit the sensor-data modulated signals back to the reader. The electrochemical potential created by the IrOx/AgCl sensing electrodes is converted to a modulated frequency and the system achieves a sensitivity of 633 Hz/pH. The wireless pH sensing system was tested for in situ monitoring of the spoilage processes in fish meats continuously for over 18 h. The feasibility of wirelessly monitoring pH values in fish meats that could be used to identify spoilage remotely has been demonstrated.

Analyses of Triaxial Accelerometer Calibration Algorithms

Abstract: This paper proposes a calibration procedure in order to minimize the process time and cost. It relies on the suggestion of optimal positions, in which the calibration procedure takes place, and on position number optimization. Furthermore, this paper describes and compares three useful calibration algorithms applicable on triaxial accelerometer to determine its mathematical error model without a need to use an expensive and precise calibration means, which is commonly required. The sensor error model (SEM) of triaxial accelerometer consists of three scale-factor errors, three nonorthogonality angles, and three offsets. For purposes of calibration, two algorithms were tested-the Levenberg-Marquardt and the Thin-Shell algorithm. Both were then related to algorithm based on Matlab fminunc function to analyze their efficiency and results. The proposed calibration procedure and applied algorithms were experimentally verified on accelerometers available on market. We performed various analyses of proposed procedure and proved its capability to estimate the parameters of SEM without a need of precise calibration means, with minimum number of iteration, both saving time, workload, and costs.

Packaged Optical Sensors Based on Regenerated Fiber Bragg Gratings for High Temperature Applications

Abstract: We report on temperature sensors based on regenerated fiber Bragg gratings (RFBGs) for measurements up to 1100. The annealing process required to regenerate such gratings makes the optical fiber too delicate, thus making an adequate packaging necessary. Prior to the grating regeneration the optical fiber is protected with a ceramic tube which in turn is shielded with a thick metal casing. We have performed a thorough characterization of the thermo-optical response of both packaged and unpackaged RFBG sensors placing especial attention on possible residual hysteresis after several temperature cycling tests. The response and recovery times of packaged sensors were found to be, respectively, ~9 s and ~ 22 s.

Phase-Based UHF RFID Tracking With Nonlinear Kalman Filtering and Smoothing

Abstract: In this paper, we present an UHF RFID location tracking system, which is based on measuring the phases of back scattered signals from RFID tag using multiple spatially distributed antennas at a single carrier frequency. The wavelength ambiguity of the phase measurements is resolved by using the extended Kalman filter (EKF) and the Rauch-Tung-Striebel (RTS) smoother, where the state includes the position, velocity and the phase offsets of antennas. The performance of the method is experimentally verified at 890 MHz using a commercially available RFID reader.