Showing papers in "IEEE Transactions on Instrumentation and Measurement in 2011"

Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques

Abstract: This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture.

State-of-the-Art Predictive Maintenance Techniques

Abstract: Condition-based maintenance techniques for industrial equipment and processes are described in this paper together with examples of their use and discussion of their benefits. These techniques are divided here into three categories. The first category uses signals from existing process sensors, such as resistance temperature detectors (RTDs), thermocouples, or pressure transmitters, to help verify the performance of the sensors and process-to-sensor interfaces and also to identify problems in the process. The second category depends on signals from test sensors (e.g., accelerometers) that are installed on plant equipment (e.g., rotating machinery) in order to measure such parameters as vibration amplitude. The vibration amplitude is then trended to identify the onset of degradation or failure. This second category also includes the use of wireless sensors to provide additional points for collection of data or allow plants to measure multiple parameters to cover not only vibration amplitude but also ambient temperature, pressure, humidity, etc. With each additional parameter that can be measured and correlated with equipment condition, the diagnostic capabilities of the category can increase exponentially. The first and second categories just mentioned are passive, which means that they do not involve any perturbation of the equipment or the process being monitored. In contrast, the third category is active. That is, the third category involves injecting a test signal into the equipment (sensors, cables, etc.) to measure its response and thereby diagnose its performance. For example, the response time of temperature sensors (RTDs and thermocouples) can be measured by the application of the step current signal to the sensor and analysis of the sensor response to the application of the step current. Cable anomalies can be located by a similar procedure referred to as the time domain reflectometry (TDR). This test involves a signal that is sent through the cable to the end device. Its reflection is then recorded and compared to a baseline to identify impedance changes along the cable and thereby identify and locate anomalies. Combined with measurement of cable inductance (L), capacitance (C), and loop resistance (R), or LCR testing, the TDR method can identify and locate anomalies along a cable, identify moisture in a cable or end device, and even reveal gross problems in the cable insulation material. There are also frequency domain reflectometry (FDR) methods, reverse TDR, trending of insulation resistance (IR) measurement, and other techniques which can be used in addition to or instead of TDR and LCR to provide a wide spectrum of tools for cable condition monitoring. The three categories of techniques described in this paper are the subject of current research and development projects conducted by the author and his colleagues at the AMS Corporation with funding from the U.S. Department of Energy (DOE) under the Small Business Innovation Research (SBIR) program.

Magnetic Maps for Indoor Navigation

Abstract: Magnetic field fluctuations and anomalies inside buildings tend to have a great effect on the compass, which is one of the simplest navigation devices. Alternative navigation requires landmark identification, so those landmarks can be used as guideposts in assisting individuals. By employing a mobile phone with built_in magnetometer, an extensive data set of 2000 measurements was collected. Using these fields, we identify landmarks and guideposts and create magnetic maps for multiple corridors of a floor in a building. Different phones are used at different sensitivity rates, which effectively portray similar results. Magnetic signatures are used for identifying locations and rooms and are independent of the person, the phone, and the sensitivity of the sensor being used. Magnetic field behavior is demonstrated and compared with theoretical distributions of these fields. The developed magnetic maps can complement existing visual maps for location tracking and navigation of autonomous robots indoors. These maps are particularly useful during limited visual feedback in poor lighting conditions. Moreover, building designers could include this landmark and guidepost information when developing the architecture of a building, which could in turn help people or robots navigate during disasters and emergency evacuations.

Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation

Abstract: In this paper, a new time-frequency analysis method known as the polynomial chirplet transform (PCT) is developed by extending the conventional chirplet transform (CT). By using a polynomial function instead of the linear chirp kernel in the CT, the PCT can produce a time-frequency distribution with excellent concentration for a wide range of signals with a continuous instantaneous frequency (IF). In addition, an effective IF estimation algorithm is proposed based on the PCT, and the effectiveness of this algorithm is validated by applying it to estimate the IF of a signal with a nonlinear chirp component and seriously contaminated by a Gaussian noise and a vibration signal collected from a rotor test rig.

Prognosis of Defect Propagation Based on Recurrent Neural Networks

Abstract: Incremental training is commonly applied to training recurrent neural networks (RNNs) for applications involving prognosis. As the number of prognostic time-step increases, the accuracy of prognosis generally decreases, as often seen in long-term prognosis. Revision of the training techniques is therefore necessary to improve the accuracy in long-term prognosis. This paper presents a competitive learning-based approach to long-term prognosis of machine health status. Specifically, vibration signals from a defect-seeded rolling bearing are preprocessed using continuous wavelet transform (CWT). Statistical parameters computed from both the raw data and the preprocessed data are then utilized as candidate inputs to an RNN. Based on the principle of competitive learning, input data were clustered for effective representation of similar stages of defect propagation of the bearing being monitored. Analysis has shown that the developed technique is more accurate in predicting bearing defect progression than the incremental training technique.

Inkjet-Printed Humidity Sensor for Passive UHF RFID Systems

Abstract: This paper presents a novel inkjet-printed humidity sensor tag for passive radio-frequency identification (RFID) systems operating at ultrahigh frequencies (UHFs). During recent years, various humidity sensors have been developed by researchers around the world for HF and UHF RFID systems. However, to our best knowledge, the humidity sensor presented in this paper is one of the first passive UHF RFID humidity sensor tags fabricated using inkjet technology. This paper describes the structure and operation principle of the sensor tag as well as discusses the method of performing humidity measurements in practice. Furthermore, measurement results are presented, which include air humidity-sensitivity characterization and tag identification performance measurements.

Study on Innovation Adaptive EKF for In-Flight Alignment of Airborne POS

Abstract: The Position and Orientation measurement System (POS) is a dedicated Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) integrated system for airborne remote sensing. In-flight alignment (IFA) is an effective way to improve the accuracy and speed of initial alignment for an airborne POS. During IFA, the GPS provides the position and velocity references for the SINS, so the alignment accuracy will be degraded by unstable GPS measurements. To improve the alignment accuracy under unstable GPS measurement, an adaptive filtering algorithm of an extended Kalman filter (EKF) combined with innovation-based adaptive estimation is proposed, which introduces the calculated innovation covariance into the computation of the filter gain matrix directly. Then, this innovation adaptive EKF algorithm is used for the IFA of the POS with a large initial heading error. Moreover, it is optimized by blocked matrix multiplication to reduce the computational burden and improve the real-time performance. To validate the proposed algorithm, the car-mounted IFA experiment is carried out for the prototype of the airborne POS (TX-D10) under a turning maneuver, taking Applanix's POS/AV510 as a reference and changing the GPS measurement artificially. The experiment results demonstrate that the proposed algorithm can reach a better alignment accuracy than the EKF under unknown GPS measurement noises.

A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm

Abstract: Digital image libraries and other multimedia databases have been dramatically expanded in recent years. In order to effectively and precisely retrieve the desired images from a large image database, the development of a content-based image retrieval (CBIR) system has become an important research issue. However, most of the proposed approaches emphasize on finding the best representation for different image features. Furthermore, very few of the representative works well consider the user's subjectivity and preferences in the retrieval process. In this paper, a user-oriented mechanism for CBIR method based on an interactive genetic algorithm (IGA) is proposed. Color attributes like the mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histogram of an image are also considered as the texture features. Furthermore, to reduce the gap between the retrieval results and the users' expectation, the IGA is employed to help the users identify the images that are most satisfied to the users' need. Experimental results and comparisons demonstrate the feasibility of the proposed approach.

Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment

Abstract: In this paper, we analyze the accuracy of indoor localization measurement based on a wireless sensor network. The position estimation procedure is based on the received-signal-strength measurements collected in a real indoor environment. Two different classes of low-computational-effort algorithms based on the centroid concept are considered, i.e., the weighted centroid localization method and the relative-span exponential weighted localization method. In particular, different sources of measurement uncertainty are analyzed by means of theoretical simulations and experimental results.

Indoor Elliptical Localization Based on Asynchronous UWB Range Measurement

Abstract: An asynchronous position measurement system is proposed for indoor localization in this paper. The demonstrated system consists of a UWB transmitter and several energy detection receivers whose positions are known. The position measurement process starts with the locator emitting a UWB pulse. Upon arrival, the pulse is amplified and retransmitted by the target to be located. Signals from both the locator and the target are captured by the receivers. No synchronization mechanism is implemented. Instead, the proposed system measures the differential TOA between the direct coupling signal of the locator and the target signal. Together with the knowledge of the locator transmitter and receiver positions, the absolute range that the pulse travels can be calculated. The sum of transmitter-target range and target-receiver range defines an ellipse and the target resides on the intersections of several such ellipses. It is shown via the Cramer-Rao lower bound that the absolute-range-based elliptical localization is potentially more accurate than the relative-range-based hyperbolic localization. Our proposed system is able to achieve positioning error bound comparable to synchronous absolute-range-based localization systems while eliminating the cost of synchronization.

A Novel Calibration Method of Magnetic Compass Based on Ellipsoid Fitting

Abstract: Magnetic compass is widely used to indicate the heading of vehicle by measuring the Earth's magnetic field. However, it suffers from local magnetic interferences; thus, the calibration of the magnetic compass is very essential before it is used. The traditional calibration methods require reference information and special requirements such as keeping the magnetic compass level during calibration, which is very difficult to manage outdoor. This paper presents an efficient method for calibrating the magnetic compass without the aforementioned traditional requirements. This method is based on the fact that the error model of magnetic compass is an ellipsoid, and a constraint least-square method is adopted to estimate the parameters of an ellipsoid by rotating the magnetic compass in various (random) orientations. This method can estimate all the parameters of the error model and compensate errors caused by sensor defects, hard-iron interferences, and soft-iron interferences. Although the calibration parameters are relative values, it does not have any influence on the heading calculated. The experimental results show that this method is effective in calibrating the magnetic compass, and the heading precision of the magnetic compass acquired after calibration is better than 0.4°.

Coarse Alignment of a Ship's Strapdown Inertial Attitude Reference System Using Velocity Loci

Abstract: This paper presents a novel estimation method for fast initial coarse alignment of a ship's strapdown inertial attitude reference system using only inertial measurement unit (IMU) measurements for quasi-static alignment and IMU measurements with GPS aiding for moving-base alignment. Unlike several current techniques, the presented estimation method is effective with any initial attitude error. The estimator is based on the decomposition of the attitude quaternion into separate Earth motion, inertial rate, and alignment quaternions. The alignment quaternion is estimated using a minimum variance fit between loci of body- and navigation-frame velocity vectors using solutions to Wahba's problem. One set of vectors is derived from time integrals of measured vehicle motions, and the second set is derived from Earth motion and GPS data (when moving). For the case of quasi-static alignment, an algebraic expression for the covariance of the attitude estimate as a function of the variance of navigation-frame velocity disturbances is developed. It is shown that, by averaging and interleaving the velocity vectors, the resulting attitude estimate is improved over sequential sampling techniques. It is further shown, for a maneuvering vessel, that a continuous estimate of the attitude error covariance can be generated from the IMU data. This latter feature allows direct initialization of a follow-on fine-alignment stochastic estimator's covariance matrix. Results are presented for quasi-static alignment using inertial sensors only and for full in-motion alignment using navigation-frame GPS velocity and position aiding.

Dynamic Harmonic Analysis Through Taylor–Fourier Transform

Abstract: A new dynamic harmonic estimator is presented as an extension of the fast Fourier transform (FFT), which assumes a fluctuating complex envelope at each harmonic. This estimator is able to estimate harmonics that are time varying inside the observation window. The extension receives the name “Taylor-Fourier transform (TFT)” since it is based on the McLaurin series expansion of each complex envelope. Better estimates of the dynamic harmonics are obtained due to the fact that the Fourier subspace is contained in the subspace generated by the Taylor-Fourier basis. The coefficients of the TFT have a physical meaning: they represent instantaneous samples of the first derivatives of the complex envelope, with all of them calculated at once through a linear transform. The Taylor-Fourier estimator can be seen as a bank of maximally flat finite-impulse-response filters, with the frequency response of ideal differentiators about each harmonic frequency. In addition to cleaner harmonic phasor estimates under dynamic conditions, among the new estimates are the instantaneous frequency and first derivatives of each harmonic. Two examples are presented to evaluate the performance of the proposed estimator.

Self-Autonomous Wireless Sensor Nodes With Wind Energy Harvesting for Remote Sensing of Wind-Driven Wildfire Spread

Abstract: The satellite-based remote sensing technique has been widely used in monitoring wildfire spread. There are two prominent drawbacks with this approach of using satellites located in space: (1) very low sampling rate (temporal resolution problem) and (2) lack of accuracy (spatial and spectral resolution problem). To address these challenges, a wireless sensor network deployed at ground level with high-fidelity and low-altitude atmospheric sensing for wind speed of local wildfire spread has been used. An indirect approach in sensing wind speed has been proposed in this paper as an alternative to the bulky conventional wind anemometer to save cost and space. The wind speed is sensed by measuring the equivalent electrical output voltage of the wind turbine generator (WTG). The percentage error in the wind speed measurement using the proposed indirect method is measured to be well within the ±4% limit with respect to wind anemometer accuracy. The same WTG also functions as a wind energy harvesting (WEH) system to convert the available wind energy into electrical energy to sustain the operation of the wireless sensor node. The experimental results show that the designed WEH system is able to harvest an average electrical power of 7.7 mW at an average wind speed of 3.62 m/s for powering the operation of the wireless sensor node that consumes 3.5 mW for predicting the wildfire spread. Based on the sensed wind speed information, the fire control management system determines the spreading condition of the wildfire, and an adequate fire suppression action can be performed by the fire-fighting experts.

MusicJacket—Combining Motion Capture and Vibrotactile Feedback to Teach Violin Bowing

Abstract: We describe MusicJacket, which is a wearable system to support the teaching of good posture and bowing technique to novice violin players. The system uses an inertial motion capture system to track the following in real time: 1) whether the player is holding the violin correctly and 2) the player's bowing action and whether it deviates from a target trajectory. We provide the musicians with vibrotactile feedback about their bowing and posture using vibration motors that are positioned on their arms and torso. We describe a user study with novice violin players that compared a group who was trained using vibrotactile feedback with a control group who only received conventional teaching. We found that vibrotactile feedback is effective at improving novices' straight bowing technique and that half of these subjects continued to show improved bowing technique even when they no longer received vibrotactile feedback. None of the control subjects who received the same number of training sessions using conventional teaching techniques showed a comparable improvement.

Performance Analysis of Kalman-Filter-Based Clock Synchronization in IEEE 1588 Networks

Abstract: Performances in network-based synchronization depend on several related factors, including the instability of local clocks, the rate at which timing information is exchanged, and the accuracy of the resulting correction estimates. This paper analyzes these effects and their relationships, showing how these may affect the design of an IEEE 1588 Precision Time Protocol synchronization scheme. This paper introduces a state-variable clock model for which realistic parameters can be obtained for different kinds of clocks from experimental measurements of Allan variance plots. A Kalman-filter-based clock servo employing this model is developed, and a simulation analysis of the behavior of clock regulation and the effect of parameter variations on its performances is presented.

Characterization and Testing of a Tool for Photovoltaic Panel Modeling

Abstract: This paper presents the evaluation of the performance, in terms of uncertainty, of a tool designed to estimate the main parameters of a model of a photovoltaic panel (PVP) under real and/or simulated working conditions. The presented tool permits the characterization of the panel, and it is useful to predict its behavior in whatever working condition; in this way, it is possible to compare the actual and expected performance to prevent any decrease in the output power, so permitting the replacement of the monitored module before it goes out of order or its efficiency falls under a given threshold. The well-known two-diode model is used to estimate the parameters of the electrical equivalent circuit of the PVP and to simulate the I -V and P-V characteristic curves in any given environmental condition of irradiance and/or temperature. The model and the estimation algorithm are implemented with MATLAB functions, whereas data acquisition and result presentation are managed by a LabVIEW graphics user interface. The presented tool has been validated against an experimentally characterized PVP. The environmental parameters of the model such as irradiance and temperature have been set (with their respective uncertainties) during simulations or directly measured during the outdoor tests, whereas the others parameters have been evaluated using a best-fit algorithm on the measured data. The estimation is based on the minimization of a new objective function and on a modified expression of the model resistances, which differ from those mentioned in the available literature. After a review of the state of the art, this paper provides the description of the estimation technique and its validation by means of simulations and experiments. Some results are also provided to illustrate the performance of the proposed test method.

Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification

Abstract: Identification and online prediction of lifetime of cutting tools using cheap sensors is crucial to reduce production costs and downtime in industrial machines. In this paper, we use the acoustic emission from an embedded sensor for computation of features and prediction of tool wear. Acoustic sensors are cheap and nonintrusive, coupled with fast dynamic responses as compared with conventional force measurements using dynamometers. A reduced feature subset, which is optimal in both estimation and clustering least squares errors, is then selected using a new dominant-feature identification algorithm to reduce the signal processing and number of sensors required. Tool wear is then predicted using an Auto-Regressive Moving Average with eXogenous inputs model based on the reduced features. Our experimental results on a ball nose cutter in a high-speed milling machine show the effectiveness in predicting the tool wear using only the dominant features. A reduction in 16.83% of mean relative error is observed when compared to the other methods proposed in the literature.

Self-Powered Wireless Sensor for Air Temperature and Velocity Measurements With Energy Harvesting Capability

Abstract: Air temperature and velocity measurements are important parameters in many applications. A self-powered sensor placed in a duct and powered by an electromechanical generator scavenging energy from the airflow has been designed and tested. It periodically transmits the measured air temperature and velocity to a receiving unit. The system basically consists of two macroblocks, respectively: the self-power wireless sensor and the receiving unit. The self-powered sensor has a section devoted to the energy harvesting, exploiting the movement of an airscrew shaft keyed to a dc motor. The self-powered sensor adopts integrated devices in low-power technology, including a microcontroller, an integrated temperature sensor, and a radio-frequency transmitter at 433 MHz. The data transmission is realized in Manchester encoding, with amplitude-shift-keying modulation at 433 MHz, allowing covering a distance between the sensor and the reader on the order of 4-5 m, depending on the power supplied in transmission. The air velocity is measured through the rotor frequency of the electromechanical generator, whereas, for the temperature, a commercial low-power sensor is used. An experimental system has been designed and fabricated, demonstrating that the airflow harvester can power the self-powered wireless sensor permitting air temperature and velocity measurements. The system can be used for real-time monitoring of temperature and velocity. The sensor module placed into the duct does not require any batteries.

A Hybrid Wired/Wireless Networking Infrastructure for Greenhouse Management

Abstract: In this paper, the problems related to the management of a farm made up of several greenhouses are discussed. The management of this kind of farms requires data acquisition in each greenhouse and their transfer to a control unit which is usually located in a control room, separated from the production area. At present, the data transfer between the greenhouses and the control system is mainly provided by a suitable wired communication system, such as a fieldbus. In such contexts, even though the replacement of the wired system with a fully wireless one can appear very attractive, a fully wireless system can introduce some disadvantages. A solution based on a hybrid wired/wireless network, where Controller Area Network and ZigBee protocols are used, is presented along with all the related problems that this integration involves. In particular, in order to integrate at the Data Link Layer the wireless section with the wired one, a suitable multiprotocol bridge has been implemented. Moreover, at the Application Layer, porting of Smart Distributed System services on ZigBee, called ZSDS, allows one to access the network resources independently from the network segment they are connected to.

Wireless Sensing of Human Respiratory Parameters by Low-Power Ultrawideband Impulse Radio Radar

Abstract: The simultaneous tracking of the chest respiratory rate and amplitude of human beings using a low-power ultrawideband (UWB) impulse radio signal is investigated for the application of sleep apnea monitoring. The measurement of respiratory amplitude, in addition to the breathing rate, is crucial for sleep apnea assessment which requires, among other things, the accurate estimation of tidal volume per minute. Backscattered UWB impulse radio signals from a human subject are detected in the time domain to calculate the chest displacements in this paper. Since the pulse disposition in time is linearly related to the chest movement, the amplitude of the chest movement can be extracted accurately without calculation approximations normally used in many of the existing methods. The multipeak detection of the pulse disposition method, instead of using only the single pulse peak detection, is also proposed to improve the accuracy of the calculation further. The experiments are carried out on four human subjects with different sizes and genders. The correlation of the chest movements' amplitude and breathing rate between the simultaneously measured results obtained by our method and by the respiratory chest band is very good.

Fabric Texture Analysis Using Computer Vision Techniques

Abstract: This paper presents inexpensive computer vision techniques allowing to measure the texture characteristics of woven fabric, such as weave repeat and yarn counts, and the surface roughness. First, we discuss the automatic recognition of weave pattern and the accurate measurement of yarn counts by analyzing fabric sample images. We propose a surface roughness indicator FDFFT, which is the 3-D surface fractal dimension measurement calculated from the 2-D fast Fourier transform of high-resolution 3-D surface scan. The proposed weave pattern recognition method was validated by using computer-simulated woven samples and real woven fabric images. All weave patterns of the tested fabric samples were successfully recognized, and computed yarn counts were consistent with the manual counts. The rotation invariance and scale invariance of FDFFT were validated with fractal Brownian images. Moreover, to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from the 3-D fabric surface. According to the test results, we demonstrated that FDFFT is a fast and reliable parameter for fabric roughness measurement based on 3-D surface data.

Detection of Suspicious Lesions by Adaptive Thresholding Based on Multiresolution Analysis in Mammograms

Abstract: Mammography is the most effective procedure for the early detection of breast cancer. In this paper, we develop a novel algorithm to detect suspicious lesions in mammograms. The algorithm utilizes the combination of adaptive global thresholding segmentation and adaptive local thresholding segmentation on a multiresolution representation of the original mammogram. The algorithm has been verified with 170 mammograms in the Mammographic Image Analysis Society MiniMammographic database. The experimental results show that the detection method has a sensitivity of 91.3% at 0.71 false positives per image.

An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks

Abstract: Energy-based multisource localization is an important research problem in wireless sensor networks (WSNs). Existing algorithms for this problem, such as multiresolution (MR) search and exhaustive search methods, are of either high computational complexity or low estimation accuracy. In this paper, an efficient expectation-maximization (EM) algorithm for maximum-likelihood (ML) estimation is presented for energy-based multisource localization in WSNs using acoustic sensors. The basic idea of the algorithm is to decompose each sensor's energy measurement, which is a superimposition of energy signals emitted from multiple sources, into components, each of which corresponds to an individual source, and then estimate the source parameters, such as source energy and location, as well as the decay factor of the signal during propagation. An efficient sequential dominant-source (SDS) initialization scheme and an incremental parameterized search refinement scheme are introduced to speed up the algorithm and improve the estimation accuracy. Theoretic analyses on the algorithm convergence rate, the Cramer-Rao lower bound (CRLB) for localization accuracy, and the computational complexity of the algorithm are also given. The simulation results show that the proposed EM algorithm provides a good tradeoff between estimation accuracy and computational complexity.

Online State-of-Health Assessment for Battery Management Systems

Abstract: Battery-powered embedded systems have known a rapid evolution in recent years, as nickel-metal hydride (Ni-MH) battery technology has enabled important reductions in size and proportional increases in total capacity over the older nickel-cadmium (Ni-Cd) and lead-acid battery types. This paper addresses the problem of state-of-health (SoH) estimation and prediction for use in resource-constrained Ni-MH-battery-powered embedded systems. We propose a novel SoH prediction methodology, presenting both a theoretical analysis of the estimation algorithm and the detailed description of hardware and software implementation. Two versions of estimation algorithms are proposed, along with the analysis of their performances in terms of prediction accuracy and required processing power, as the SoH prediction is designed to run online, being part of an embedded battery management system.

Selecting a Reference High Resolution for Fingerprint Recognition Using Minutiae and Pores

Abstract: High-resolution automated fingerprint recognition systems (AFRSs) offer higher security because they are able to make use of level-3 features, such as pores, that are not available in lower resolution ( <; 500-dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and performance of an AFRS is the choice of image resolution. In this paper, we identify the optimal resolution for an AFRS using the two most representative fingerprint features: minutiae and pores. We first designed a multiresolution fingerprint acquisition device to collect fingerprint images at multiple resolutions and captured fingerprints at various resolutions but at a fixed image size. We then carried out a theoretical analysis to identify the minimum required resolution for fingerprint recognition using minutiae and pores. After experiments on our collected fingerprint images and applying three requirements for the proportions of minutiae and pores that must be retained in a fingerprint image, we recommend a reference resolution of 800 dpi. Subsequent tests have further confirmed the proposed reference resolution.

A Wireless Sensor Readout System—Circuit Concept, Simulation, and Accuracy

Abstract: In this paper, we present an electronic readout system for wireless passive sensors based on inductively coupled LC resonant circuits. The proposed system consists of a reader coil inductively coupled to the sensor circuit, an analog frontend circuit, and a digital signal processing unit. The analog frontend circuit generates a dc voltage representing the sensor resonance curve. The frequency of the reader coil driving signal is continuously readjusted by the digital signal processing unit. Based on analytical calculation and system simulation, we derive a model for the achievable accuracy of the overall sensor and readout system. The accuracy is limited by noise and systematic errors due to the measurement principle. We show how to design the digital signal processing system for optimal insensitivity to voltage noise. The noise sensitivity of the measurement system is inversely proportional to the square of the quality factor of the LC sensor. This means that minimizing the losses of the sensor is of crucial importance to obtain a wireless measurement system with a high range and a good insensitivity to noise. Subsequently, we outline an approach to calculate the sensor resonance frequency, quality factor, and inductive coupling factor from the available voltage signals in the signal processing unit using linear fitting functions. The accuracy of our approach is exemplified by a system simulation for typical sensor parameters. For the system studied, we show that the relative linearization error of the sensor resonance frequency measurement is below 0.02%. Taking the general models presented for both the noise sensitivity and linearization error into account, it is possible to estimate the maximum distance and accuracy for any wireless sensor system based on an inductively coupled LC resonator.

A New Method for Power Current Measurement Using a Coreless Hall Effect Current Transformer

Abstract: A new method for performing power current measurement is proposed; this method involves the use of Hall sensors without iron cores. A new apparatus for implementing this method, called coreless Hall-effect current transformer (HCT), has been developed. The HCT consists of four Hall sensors connected to a weighted adder. Four Hall sensors are symmetrically attached to an electric conducting cable, and the output of the sensors is connected to the weighted adder. The weighted adder performs the “average” operation, thereby obtaining the average voltage. Since the average Hall voltage is obtained, the HCT can eliminate the ambient interference. A measurement framework is designed and implemented to compare a series of waveforms obtained by HCTs with those measured by traditional current transformers (CTs) and linear CTs. Current measurement results show that an HCT can measure current with greater accuracy than traditional CTs. Moreover, in the presence of faults, HCTs do not encounter problems related to saturation that exist in traditional CTs.

NIST 10 V Programmable Josephson Voltage Standard System

Abstract: The National Institute of Standards and Technology has developed and implemented a new programmable Josephson voltage standard (PJVS) that operates at 10 V. This next-generation system is optimized for both dc metrology and stepwise-approximated ac voltage measurements for frequencies up to a few hundreds of hertz. The nonhysteretic Josephson junctions produce intrinsically stable voltages and are designed to operate in the 18-20 GHz frequency range. The most recent 10 V PJVS circuits have total output current ranges greater than 1 mA.