Showing papers in "IEEE Transactions on Instrumentation and Measurement in 2014"

Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

Abstract: An automated irrigation system was developed to optimize water use for agricultural crops. The system has a distributed wireless network of soil-moisture and temperature sensors placed in the root zone of the plants. In addition, a gateway unit handles sensor information, triggers actuators, and transmits data to a web application. An algorithm was developed with threshold values of temperature and soil moisture that was programmed into a microcontroller-based gateway to control water quantity. The system was powered by photovoltaic panels and had a duplex communication link based on a cellular-Internet interface that allowed for data inspection and irrigation scheduling to be programmed through a web page. The automated system was tested in a sage crop field for 136 days and water savings of up to 90% compared with traditional irrigation practices of the agricultural zone were achieved. Three replicas of the automated system have been used successfully in other places for 18 months. Because of its energy autonomy and low cost, the system has the potential to be useful in water limited geographically isolated areas.

Microwave System for Head Imaging

Abstract: A wideband microwave system for head imaging is presented. The system includes an array of 16 corrugated tapered slot antennas that are installed on an adjustable platform. A switching device is used to enable the antennas to sequentially send a wideband 1-4 GHz microwave signal and capture the backscattered signals. Those signals are recorded using suitably designed virtual instrument software architecture. To test the capability of the system to detect brain injuries, a low-cost mixture of materials that emulate the frequency-dispersive electrical properties of the major brain tissues across the frequency band 1-4 GHz are used to construct a realistic-shape head phantom. A target that emulates a realistic hemorrhage stroke is fabricated and inserted in two different locations inside the fabricated head phantom. A preprocessing algorithm that utilizes the symmetry of the two halves of human head is used to extract the target response from the background reflections. A post-processing confocal algorithm is used to get an image of the phantom and to accurately detect the presence and location of the stroke.

Enhanced Interpolated-DFT for Synchrophasor Estimation in FPGAs: Theory, Implementation, and Validation of a PMU Prototype

Abstract: The literature on the subject of synchrophasor estimation (SE) algorithms has discussed the use of interpolated discrete Fourier transform (IpDFT) as an approach capable to find an optimal tradeoff between SE accuracy, response time, and computational complexity. Within this category of algorithms, this paper proposes three contributions: 1) the formulation of an enhanced-IpDFT (e-IpDFT) algorithm that iteratively compensates the effects of the spectral interference produced by the negative image of the main spectrum tone; 2) the assessment of the influence of the e-IpDFT parameters on the SE accuracy; and 3) the discussion of the deployment of IpDFT- based SE algorithms into field programmable gate arrays, with particular reference to the compensation of the error introduced by the free-running clock of A/D converters with respect to the global positioning system (GPS) time reference. The paper finally presents the experimental validation of the proposed approach where the e-IpDFT performances are compared with those of a classical IpDFT approach and to the accuracy requirements of both P and M-class phasor measurement units defined in the IEEE Std. C37.118-2011. Index Terms— Discrete Fourier transform (DFT), field programmable gate array (FPGA), IEEE Std. C37.118, interpolated discrete Fourier transform (IpDFT), phasor measurement unit (PMU), synchrophasor.

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

Abstract: In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.

Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks

Abstract: This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.

Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems

Abstract: The detection of fastener defects is an important task in railway inspection systems, and it is frequently performed to ensure the safety of train traffic. Traditional inspection is usually operated by trained workers who walk along railway lines to search for potential risks. However, the manual inspection is very slow, costly, and dangerous. This paper proposes an automatic visual inspection system for detecting partially worn and completely missing fasteners using probabilistic topic model. Specifically, our method is able to simultaneously model diverse types of fasteners with different orientations and illumination conditions using unlabeled data. To assess the damages, the test fasteners are compared with the trained models and automatically ranked into three levels based on the likelihood probability. The experimental results demonstrate the effectiveness of this method.

Noncontact Distance and Amplitude-Independent Vibration Measurement Based on an Extended DACM Algorithm

Abstract: Utilizing microwave continuous-wave Doppler radars to wirelessly detect mechanical vibrations have been attracting more and more interests in recent years. In this paper, aiming to solve the null point and nonlinear issues in small-angle approximation-based Doppler radar sensors and eliminate the codomain restriction in the arctangent demodulation approach, we propose and investigate an extended differentiate and cross-multiply (DACM) algorithm. With an additional accumulator, the noise performance of the original DACM algorithm is improved. Moreover, the amplitude information of the vibration can be directly retrieved from accumulation without involving any distance-dependent issue. Experimental validations show that the proposed algorithm can fully recover the vibration patterns with the measured noncalibrated amplitude agreeing well with the known precalibrated data. Application examples of mechanical fault detection and human vital sign detection are demonstrated, showing a wide range of potential applications of this algorithm.

Measuring Calorie and Nutrition From Food Image

Abstract: As people across the globe are becoming more interested in watching their weight, eating more healthy, and avoiding obesity, a system that can measure calories and nutrition in every day meals can be very useful. In this paper, we propose a food calorie and nutrition measurement system that can help patients and dietitians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it to measure the consumption of calorie and nutrient components. Our results show that the accuracy of our system is acceptable and it will greatly improve and facilitate current manual calorie measurement techniques.

A Fast and Accurate PMU Algorithm for P+M Class Measurement of Synchrophasor and Frequency

Abstract: The IEEE Standard C37.118.1 defines two performance classes, P and M, for phasor measurement units (PMUs), respectively for protection and monitoring oriented applications. The goal of this paper is to define an algorithm that allows the requirements of both classes to be met simultaneously, thus avoiding an a priori selection of either the fast response time of class P or the accuracy of the class M. The designed PMU consists of two digital channels that process in parallel the acquired samples with different algorithms: the first one allows accurate measurements of steady state signals, while the second one is better suited to follow the fast signal changes. Then, a detector identifies the possible presence of dynamic situations and selects the most appropriate output for the actual operating condition. The validation of the solution, performed by means of simulations in all the static and dynamic conditions defined in the standard, confirms that the method is able to comply with all the limits indicated for both classes for synchrophasor and frequency measurement. As for the rate of change of frequency, again the compliance to P-class is fully verified, while for the M-class the only exception is represented by the tests with out-of-band disturbances.

EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs

Abstract: This paper introduces a new signal-filtering, which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature.

Impact of Skin–Electrode Interface on Electrocardiogram Measurements Using Conductive Textile Electrodes

Abstract: Physicians' understanding of biosignals as measured with medical instruments becomes the foundation of their decisions and diagnoses of patients, as they rely strongly on what the instruments show. Thus, it is critical and very important to ensure that the instruments' recordings exactly reflect what is happening in the patient's body so that the acquired signal is the real one or at least as close to the real in-body signal as possible. This is such an important issue that sometimes physicians use invasive measurements to obtain the real biosignal. Generating an in-body signal from what a measurement device shows is called “signal purification” or “reconstruction” and can be done only when we have adequate information about the interface between the body and the monitoring device. In this paper, first, we present a device that we developed for electrocardiogram (ECG) acquisition and transfer to PC. To evaluate the performance of the device, we use it to measure ECG and apply conductive textile as our ECG electrode. Then, we evaluate ECG signals captured by different electrodes, specifically traditional gel Ag/AgCl and dry golden plate electrodes, and compare the results, allowing us to investigate if ECG measured with the device is proper for applications where no skin preparation is allowed, such as ECG-assisted blood pressure monitoring devices. Next, we propose a method to reconstruct the ECG signal from the signal acquired by our device, with respect to the interface characteristics and their relation to the ECG. The interface in this paper is skin–electrode interface for conductive textiles. In the last stage of this paper, we explore the effects of pressure on skin–electrode interface impedance and its parametrical variation.

Optimal Meter Placement for Robust Measurement Systems in Active Distribution Grids

Abstract: Future active distribution grids are characterized by rapid and significant changes of operation and behavior due to, for example, intermittent power injections from renewable sources and the load-generation characteristic of the so-called prosumers. The design of a robust measurement infrastructure is critical for safe and effective grid control and operation. We had earlier proposed a placement procedure that allows finding an optimal robust measurement location incorporating phasor measurement units and smart metering devices for distribution system state estimation. In this paper, the lack of detailed information on distributed generation is also considered in the optimal meter placement procedure, so that the distributed measurement system can provide accurate estimates even with limited knowledge of the profile of the injected power. Possible non-Gaussian distribution of the distributed power generation has been taken into account. With this aim, the Gaussian mixture model has been incorporated into the placement optimization by means of the so-called Gaussian component combination method. The occurrence of either loss of data or degradation of metrological performance of the measurement devices is also considered. Tests performed on a UKGDS 16-bus distribution network are presented and discussed.

Automatic Defect Identification of Eddy Current Pulsed Thermography Using Single Channel Blind Source Separation

Abstract: Eddy current pulsed thermography (ECPT) is an emerging nondestructive testing and evaluation (NDT&E) technique and has been applied for a wide range of conductive materials. In this paper, a single-channel blind source separation is proposed to process the ECPT image sequences. The proposed method enables: 1) automatically extract valuable spatial and time patterns according to the whole transient response behavior without any training knowledge, 2) automatically identify defect patterns and quantify the defects, and 3) to provide guidelines of choosing the optimal contrast functions that can improve the separation results. In this paper, both mathematical and physical models are discussed and linked. The basis of the selection of separated spatial and time patterns is also presented. In addition, an artificial slot and a thermal fatigue natural crack are applied to validate the proposed method.

Bearing Degradation Evaluation Using Recurrence Quantification Analysis and Kalman Filter

Abstract: This paper presents an integrated approach, which combines recurrence quantification analysis (RQA) with the Kalman filter, for bearing degradation evaluation. The RQA, a nonlinear signal processing method, is applied to extracting recurrence plot entropy features from vibration signals as input to build an autoregression (AR) model. This AR model is used to estimate parameters of the dynamic model of the bearing, and the Kalman filter is then utilized to obtain optimal prediction results on the bearing degradation state from its dynamic model. Case studies performed on two test-to-failure experiments indicate that the presented approach can predict occurrence of the bearing failure 50 min in advance.

Development of an Improved Time–Frequency Analysis-Based Nonintrusive Load Monitor for Load Demand Identification

Abstract: In a smart house connected to a smart grid via advanced metering infrastructure, a nonintrusive load monitor (NILM) that identifies individual appliances by disaggregating composite electric load signal from the minimal number of sensors installed at the main distribution board in the field can be regarded as a part of a home/building energy management system. This type of load monitoring technique, not only for domestic but also for industrial applications, is relevant to electricity energy management and conservation issues. In this paper, an improved time–frequency analysis-based NILM composed of three system components, including data acquisition, transient feature extraction, and load identification, is proposed. The improved NILM proposed in this paper incorporates a multiresolution S-transform-based transient feature extraction scheme with a modified 0–1 multidimensional knapsack algorithm-based load identification method to identify individual household appliances that may either be energized simultaneously or be identified under similar real power consumption. For the load identification process, an ant colony optimization algorithm is employed to perform combinatorial search that is formulated as a modified 0–1 multidimensional knapsack problem. As shown from the experimental results, the improved NILM strategy proposed in this paper is confirmed to be feasible.

RAMSES: RFID Augmented Module for Smart Environmental Sensing

Abstract: This paper presents a radio frequency identification (RFID) augmented module for smart environmental sensing (RAMSES), which is a fully passive device with sensing and computation capabilities conceived to explore novel and unconventional RFID applications. RAMSES implements an RF energy-harvesting circuit enhanced by a dc-dc voltage booster in silicon-on-insulator technology, an ultralow-power microcontroller, temperature, light, and acceleration sensors, and a new-generation ${\rm I}^{2}{\rm C}$ -RFID chip to wirelessly deliver sensor data to standard RFID EPCglobal Class-1 Generation-2 readers. A preliminary RAMSES prototype, fabricated on a printed circuit board using low-cost off-the-shelf discrete components, has been extensively tested through experiments conducted both in lab and real-world application scenarios. The achieved results have demonstrated the ability of RAMSES to harvest the RF energy emitted by an interrogator placed up to 10 m of distance and autonomously perform sensing, computation, and data communication. To our knowledge, this is the longest range ever reported for fully passive RFID sensors. Furthermore, for applications requiring larger operating distances, RAMSES provides also a battery-assisted passive mode yielding up to 22-m communication range.

Power System Dynamic State Estimation With Synchronized Phasor Measurements

Abstract: The dynamic state estimation (DSE) applied to power systems with synchrophasor measurements would estimate the system's true state based on measurements and predictions. In this application, as phasor measurement units (PMUs) are not deployed at all power system buses, state predictions would enhance the redundancy of DSE input data. The significance of predicted and measured data in DSE is affected by their confidence levels, which are inversely proportional to the corresponding variances. In practice, power system states may undergo drastic changes during hourly load fluctuations, component outages, or network switchings. In such conditions, the inclusion of predicted values could degrade the power system state estimation. This paper presents a mixed-integer programming formulation of DSE that is capable of simultaneously discarding predicted values whenever sudden changes in the system state are detected. This feature enhances the DSE computation and will not require iterative executions. The proposed model accommodates system-wide synchronized measurements of PMUs, which could be of interest to smart grid applications in energy management systems. The voltage phasors at buses without PMUs are calculated via voltage and current measurements of adjacent buses, which are referred to as indirect measurements. The guide to the expression of uncertainty in measurement is used for computing the confidence level of indirect measurements based on uncertainties associated with PMU measurements as well as with transmission line parameters. Simulation studies are conducted on an illustrative three-bus example and the IEEE 57-bus power system, and the performance of the proposed model is thoroughly discussed.

Prognostics of Lithium-Ion Batteries Based on the Verhulst Model, Particle Swarm Optimization and Particle Filter

Abstract: A novel data-driven prognostic approach for lithium-ion batteries remaining useful life (RUL) based on the Verhulst model, particle swarm optimization (PSO) and particle filter (PF) is proposed. First, the Verhulst model based on the capacity fade trends of lithium-ion batteries is proposed, which is used as the fitting model and predicting model, respectively. Second, the PSO is applied to improve the fitting model. Third, the improved fitting model combined with the Euclidean distance is employed to determine the upper and lower bounds of the predicting model parameters. Fourth, to estimate the predicting model, the PSO is exploited based on the upper and lower bounds of parameters. Then, to compensate the prediction error, the PF is used to update the predicting model. Finally, the RUL prediction can be made by extrapolating the updated predicting model to the acceptable performance threshold. Four case studies are conducted to validate the proposed approach. The experimental results show the following: 1) the proposed prognostic approach has high prediction accuracy and 2) the proposed model needs fewer parameters than the traditional empirical models.

Performance Study of Multilayer Perceptrons in a Low-Cost Electronic Nose

Abstract: Nonselective gas sensor array has different sensitivities to different chemicals in which each gas sensor will also produce different voltage signals when exposed to an analyte with different concentrations. Therefore, the characteristics of cross sensitivities and broad spectrum of nonselective chemical sensors promote the fast development of portable and low-cost electronic nose (E-nose). Simultaneous concentration estimation of multiple kinds of chemicals is always a challengeable task in E-nose. Multilayer perceptron (MLP) neural network, as one of the most popular pattern recognition algorithms in E-nose, has been studied further in this paper. Two structures of single multiple inputs multiple outputs (SMIMO) and multiple multiple inputs single output (MMISO)-based MLP with parameters optimization in neural network learning processing using eight computational intelligence optimization algorithms are presented in this paper for detection of six kinds of indoor air contaminants. Experiments prove that the performance in accuracy and convergence of MMISO structure-based MLP are much better than SMIMO structure in concentration estimation for more general use of E-nose.

Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement

Abstract: The arterial blood pressure (ABP) is one of the most important physiological parameters for health monitoring. Most of the blood measurement devices in the market determine the ABP through the inflation and the deflation of a cuff controlled by a bladder. This method is very uncomfortable for most of the users and may even cause anxiety, which in turn can affect the blood pressure (BP) (white coat syndrome). This paper investigates a cuffless nonintrusive approach to estimate the BP. The main idea is to measure the pulse transit time (PTT), i.e., the delay between the R-peak of the electrocardiogram (ECG) signal and the following peak of the finger photoplethysmograph (PPG) signal. The main problem of this approach is that when the dicrotic notch of the PPG signal is unobservable, the position and the amplitude of the main peak of the PPG signal will be changed. As a result, the correlation between the BP and the PTT can be affected. To overcome this problem, three types of secondary peak detection methods are designed to reveal the secondary peak from the original PPG signal. Actual ECG, PPG, and the BP measurements extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database that contains clinical signal data reflecting real measurements are used. The results verify that the proposed detection methods improve the correlation relationship between the BP and the PTT, and demonstrate that the adjusted PTT can be used as an indicator of the ABP by removing the dicrotic notch impact on the PPG signal.

Effects of Measurements and Pseudomeasurements Correlation in Distribution System State Estimation

Abstract: Distribution system state estimation (DSSE) is one of the key elements of the monitoring activity of an active distribution network, and is the basis for every control and management application. The DSSE relies on real measurements collected by the distributed measurement system and on other available information, mainly obtained from historical data that help in obtaining observability. This prior information is necessary to derive the so called pseudomeasurements. Accurate input data are fundamental for an accurate estimation, as well as knowledge on possible correlation in the measured and pseudomeasured data. A degree of correlation can exist in the measured data, due to measurement devices, and among power consumptions or generations of some particular nodes. This paper presents an exhaustive analysis on the influence of correlations on the quality of the estimation. The importance of including correlation in the weighted least square estimation approach is discussed using both traditional and synchronized measurements. Results obtained on a 95-bus distribution network are presented and discussed.

A Generalized Rectangular Cavity Approach for Determination of Complex Permittivity of Materials

Abstract: A novel cavity-based unified approach to measure the complex permittivity of dielectric samples placed in the E-plane of a rectangular cavity is presented. The proposed generalized cavity method is not limited to test specimens of smaller electrical dimensions, and requires two basic steps. The first step modifies the conventional cavity perturbation technique, where the effects of possible air gap between the cavity slot and the test specimen are also considered. The second step of the proposed approach employs a numerical optimization scheme, where the actual 3-D geometry of the fabricated cavity is simulated using the numerical field simulator, the Computer Simulation Technology (CST) Microwave Studio. The dielectric properties of the test specimen in this case are determined with the help of a MATLAB-based optimization routine, which calls the CST modules over the component object model interface and minimizes the error between the measured and the simulated scattering coefficients. The permittivity of the test specimen determined using the first step is provided as the initial guess to improve the convergence of the numerical optimization scheme. The proposed unified approach is validated by designing two rectangular cavities having different slot sizes operating in the TE107 mode. A number of standard dielectric samples are measured with the help of a vector network analyzer, and a very good agreement is observed between the measured permittivity values and the published data available in the literature having a typical error of less than 2% for samples of even larger dimensions.

A Frequency-Domain Algorithm for Dynamic Synchrophasor and Frequency Estimation

Abstract: Next-generation phasor measurement units (PMUs) are expected to play a key role for monitoring the behavior of future smart grids. While most of the PMUs used nowadays in transmission networks rely on static phasor models, more sophisticated representations and stricter accuracy requirements are needed to track amplitude, phase, and frequency changes of power waveforms in strongly dynamic scenarios as those expected in future distribution systems. In this paper, a discrete Fourier transform (DFT)-based algorithm based on a dynamic phasor model (referred to as interpolated dynamic DFT-based synchrophasor estimator) is used to estimate not only amplitude and phase of the collected waveforms, but also their frequency and rate of change of frequency. The performances of the proposed method are evaluated through multiple simulations in different steady-state and transient conditions described in the Standard IEEE C37.118.1-2011.

Automated biosignal quality analysis for electromyography using a one-class support vector machine

Abstract: This paper introduces the importance of biosignal quality assessment and presents a pattern classification approach to differentiate clean from contaminated electromyography (EMG) signals. Alternatively to traditional bottom-up approaches, which examine specific contaminants only, we present a top-down approach using a one-class support vector machine (SVM) trained on clean EMG and tested on artificially contaminated EMG. Both simulated and real EMG are used. Results are evaluated for each contaminant: 1) power line interference; 2) motion artifact; 3) ECG interference; 4) quantization noise; 5) analog-to-digital converter clipping; and 6) amplifier saturation, as a function of the level of signal contamination. Results show that different ranges of contamination can be detected in the EMG depending on the type of contaminant. At high levels of contamination, the SVM classifies all EMG signals as contaminated, whereas at low levels of contamination, it classifies the majority of EMG signals as contaminant free. A transition point for each contaminant is identified, where the classification accuracy drops and variance in classification increases. In some cases, contamination can be detected with the SVM when it is not visually discernible. This method is shown to be successful in detecting problems due to single contaminants but is generic to all forms of contamination in EMG.

Flexible Indoor Localization and Tracking Based on a Wearable Platform and Sensor Data Fusion

Abstract: Indoor localization and tracking of moving human targets is a task of recognized importance and difficulty. In this paper, we describe a position measurement technique based on the fusion of various sensor data collected using a wearable embedded platform. Since the accumulated measurement uncertainty affecting inertial data (especially due to the on-board accelerometer) usually makes the measured position values drift away quickly, a heuristic approach is used to keep velocity estimation uncertainty in the order of a few percent. As a result, unlike other solutions proposed in the literature, localization accuracy is good when the wearable platform is worn at the waist. Unbounded uncertainty growth is prevented by injecting the position values collected at a very low rate from the nodes of an external fixed infrastructure (e.g., based on cameras) into an extended Kalman filter. If the adjustment rate is in the order of several seconds and if such corrections are performed only when the user is detected to be in movement, the infrastructure remains idle most of time with evident benefits in terms of scalability. In fact, multiple platforms could work simultaneously in the same environment without saturating the communication channels.

An Improved Model-Based Maximum Power Point Tracker for Photovoltaic Panels

Abstract: It is well known that in a photovoltaic (PV) plant, the modules are connected to switch-mode power converters to enhance the power output in every environmental condition. This task is performed by the maximum power point tracker (MPPT), which provides a current or voltage reference to the converter. Traditional perturb and observe or incremental conductance algorithms are not efficient in rapidly changing conditions, whereas a model-based (MB) MPPT offers a better dynamic performance. Because it is relatively easy to obtain an accurate model of a single PV panel, thus predicting the maximum power point voltage for given environmental conditions, MB MPPTs seemed to be attractive for employing in module integrated converters. Conventional MB MPPT algorithms, however, usually require an expensive pyranometer to properly operate. In this paper, starting from a new set of equations modeling a PV module, a novel MB MPPT technique, which does not require the direct measurement of the solar radiation, is proposed and experimentally validated.

Electrical Signal Source Separation Via Nonnegative Tensor Factorization Using On Site Measurements in a Smart Home

Abstract: Measuring the electrical consumption of individual appliances in a household has recently received renewed interest in the area of energy efficiency research and sustainable development. The unambiguous acquisition of information by a single monitoring point of the whole house's electrical signal is known as energy disaggregation or nonintrusive load monitoring. A novel way to look into the issue of energy disaggregation is to interpret it as a single-channel source separation problem. To this end, we analyze the performance of source modeling based on multiway arrays and the corresponding decomposition or tensor factorization. First, with the proviso that a tensor composed of the data for the several devices in the house is given, nonnegative tensor factorization is performed in order to extract the most relevant components. Second, the outcome is later embedded in the test step, where only the measured consumption over the whole home is available. Finally, the disaggregated data by the device is obtained by factorizing the associated matrix considering the learned models. In this paper, we compare this method with a recent approach based on sparse coding. The results are obtained using real-world data from household electrical consumption measurements. The analysis of the comparison results illustrates the relevance of the multiway array-based approach in terms of accurate disaggregation, as further endorsed by the statistical analysis performed.

A Novel Texture Sensor for Fabric Texture Measurement and Classification

Abstract: Surface texture is one of the important cues for human beings to identify different fabrics. This paper presents a novel design of a surface texture sensor by imitating human active texture perception by touch. A thin polyvinylidene fluoride (PVDF) film is used as the sensitive element to fabricate a high-accuracy, high-speed-response fabric surface texture sensor, and a mechanism is designed to produce the relative motion at a certain speed between the texture sensor and the surface of the perceived fabric with constant contact force. Thus, the surface texture property can be measured as the output charge of the PVDF film of the sensor induced by the small height/depth variation of the moving fabric surface. A texture feature extraction method by compressing the zero value spectral lines in frequency domain is proposed. In addition, a radial basis function (RBF) neural networks based on unsupervised K-means clustering algorithm is used as classifier for texture recognition. The experiments show that the proposed texture sensor is effective in detecting the feature signals of fabric surface textures, which are suitable for the RBF networks to classify the different fabrics.

Novel Methods for Noncontact Heart Rate Measurement: A Feasibility Study

Abstract: The following paper investigates four novel methods for noncontact measurement of heart rate (HR) and consequently its derivate HR variability, an important marker of autonomic activity proven to be predictive of likelihood of future health related events. Feasibility study of basic principles is focused on measurements of signal-to-noise ratio with respect to the distance between the subject and HR sensor/apparatus. The discussed methods are divided into the following two groups: the methods measuring electromagnetic energy generated by the bioelectrical activity within the cardiac muscle (referred to as direct methods), and the methods measuring displacement of a part of the subject's body caused by the periodic physical contractions of the heart (referred to as indirect methods). The first group is represented by a measuring device which detects changes in surrounding electric field, whereas the second group consists of measuring devices that use the Doppler effect phenomena (microwave radar, ultrasound radar) and audio signal acquired by a condenser microphone. All measuring devices were assembled and put to test. The results indicate that noncontact measuring of HR is possible, especially for distances of less than 50 cm meeting essential requirements for HR diagnostic purposes.

Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

Abstract: Multiscale noise tuning stochastic resonance (MSTSR) has been proved to be an effective method for enhanced fault diagnosis by taking advantage of noise to detect the incipient faults of the bearings and gearbox. This paper addresses a sequential algorithm for the MSTSR method to detect the train bearing faults in an embedded system through the acoustic signal analysis. Specifically, the energy operator, digital filter array, and fourth rank Runge-Kutta equation methods are designed to realize the signal demodulation, multiscale noise tuning, and bistable stochastic resonance in sequence. The merit of the sequential algorithm is that it reduces the memory consumption and decreases the computation complexity, so that it can be efficiently implemented in the embedded system based on a low-cost, low-power hardware platform. After the sequential algorithm, the real-valued fast Fourier transform is used to calculate the power spectrum of the analyzed signal. The proposed method has been verified in algorithm performance and hardware implementation by three kinds of practical acoustic signals from defective train bearings. An enhanced performance of the proposed fault diagnosis method is confirmed as compared with several traditional methods, and the hardware performance is also validated.