Elderly-falling detection using distributed direction-sensitive pyroelectric infrared sensor arrays

This paper presents a new distributed direction-sensitive infrared sensing approach for fall detection in elderly healthcare applications. Pyroelectric infrared (PIR) sensors are employed in sensing human activities. For capturing the characteristics of human normal and abnormal activities, three modules of a direction-sensitive PIR sensor are organized using a distributed sensing structure. The advantage of using the distributed sensing paradigm is that the synergistic motion patterns of head, upper-limb and lower-limb can be efficiently encoded and thus the more discriminative features can be captured. This is the new consideration of using PIR sensors in building a full detection system. In addition, a two-layer hidden Markov model is developed for recognizing a fall event based on the multidimensional signals of the distributed infrared sensing system. Experimental studies are conducted to validate the proposed method.     

Detection of solvents using distributed fibre optic sensor

A fibre optic sensor that is capable of distributed detection of liquid solvents is presented. Sensor interrogation using optical time domain reflectometry (OTDR) provides the capability of locating solvent spills to a precision of /spl plusmn/2 m over a total sensor length that may extend to 20 km.     

Detection and estimation in sensor arrays using weighted subspacefitting

The problem of signal parameter estimation of narrowband emitter signals impinging on an array of sensors is addressed. A multidimensional estimation procedure that applies to arbitrary array structures and signal correlation is proposed. The method is based on the recently introduced weighted subspace fitting (WSF) criterion and includes schemes for both detecting the number of sources and estimating the signal parameters. A Gauss-Newton-type method is presented for solving the multidimensional WSF and maximum-likelihood optimization problems. The global and local properties of the search procedure are investigated through computer simulations. Most methods require knowledge of the number of coherent/noncoherent signals present. A scheme for consistently estimating this is proposed based on an asymptotic analysis of the WSF cost function. The performance of the detection scheme is also investigated through simulations     

Source localization with distributed sensor arrays and partial spatial coherence

Multiple sensor arrays provide the means for highly accurate localization of the (x,y) position of a source. In some applications, such as microphone arrays receiving aeroacoustic signals from ground vehicles, random fluctuations in the air lead to frequency-selective coherence losses in the signals that arrive at widely separated sensors. We present performance analysis for localization of a wideband source using multiple, distributed sensor arrays. The wavefronts are modeled with perfect spatial coherence over individual arrays and frequency-selective coherence between distinct arrays, and the sensor signals are modeled as wideband, Gaussian random processes. Analysis of the Cramer-Rao bound (CRB) on source localization accuracy reveals that a distributed processing scheme involving bearing estimation at the individual arrays and time-delay estimation (TDE) between sensors on different arrays performs nearly as well as the optimum scheme while requiring less communication bandwidth with a central processing node. We develop Ziv-Zakai bounds for TDE with partially coherent signals in order to study the achievability of the CRB. This analysis shows that a threshold value of coherence is required in order to achieve accurate time-delay estimates, and the threshold coherence value depends on the source signal bandwidth, the additive noise level, and the observation time. Results are included based on processing measured aeroacoustic data from ground vehicles to illustrate the frequency-dependent signal coherence and the TDE performance.     

Reduced-rank adaptive detection of distributed sources using subarrays

We introduce a framework for exploring array detection problems in a reduced-dimensional space. This involves calculating a structured subarray transformation matrix for the detection of a distributed signal using large aperture linear arrays. We study the performance of the adaptive subarray detector and evaluate its potential improvement in detection performance compared with the full array detector with finite data samples. One would expect that processing on subarrays may result in performance loss in that smaller number of degrees of freedom is utilized. However, it also leads to a better estimation accuracy for the interference and noise covariance matrix with finite data samples, which will yield some gain in performance. By studying the subarray detector for general linear arrays, we identify this gain under various scenarios. We show that when the number of samples is small, the subarray detectors have a significant gain over the full array detector. In addition, the subarray processing can also be successfully applied to the problem of detecting moving sources in an underwater acoustic scenario. We validate our results by computer simulations.     

Novel fiber sensor arrays using erbium-doped fiber amplifiers

We examine the signal-to-noise ratio (SNR) performance of a novel type of time domain multiplexed sensor arrays in which low gain (1-10 dB) fiber amplifiers are incorporated to compensate for splitting losses between sensors. The system noise figure for passive and amplified sensor arrays is presented, along with expressions to optimize the array parameters for high SNRs. We show that practical amplified sensor arrays exhibit low system noise figures that allow much larger arrays (hundreds of sensors) than passive arrays     

Detection of sources using bootstrap techniques

Source detection in array processing can be viewed as a test for equality of eigenvalues. Such a test is proposed, based on a multiple test procedure that considers all pairwise comparisons between eigenvalues. Using the bootstrap to estimate the null distributions of the test statistics results in a procedure with minimal assumptions on the nature of the signal. Simulations show that the proposed test is superior to information theoretic criteria such as the MDL, which are based on Gaussian signals and large sample sizes. Performance in most cases exceeds the more powerful sphericity test     

基于双平行线阵的相干分布源二维DOA估计

针对现有相干分布源二维波达方向(DOA)估计算法存在的一些问题,基于双平行均匀线阵提出了一种相干分布源二维DOA估计新算法。利用旋转不变的思想并结合传播算子法来估计相干分布源的二维中心DOA。无需谱搜索和对样本协方差矩阵做特征分解,和传统算法相比,其计算复杂度更低。此外,还给出了详细的参数配对过程,因而能够应用于多源场合。算法在小角度扩展条件下估计性能良好,其性能甚至接近于一维交替搜索算法。算法还是一种对角分布先验知识盲的估计。仿真结果证实了算法的有效性。     

Detection of particle sources with directional detector arrays and a mean-difference test

In this paper, the problem of detecting far-field particle sources, such as nuclear, radioactive, optical, or cosmic, is considered. This problem arises in applications including security, surveillance, visual systems, and astronomy. The authors propose a mean-difference test (MDT) with cubic and spherical detector arrays, assuming Poisson distributed measurement models. Through performance analysis, including computing the probability of detection for a given probability of false alarm, the authors show that the MDT has a number of advantages over the generalized likelihood-ratio test (GLRT), such as computational efficiency, higher probability of detection, asymptotic constant false-alarm rate (CFAR), and applicability to low signal-to-noise ratio (SNR). For each array, the authors also present an estimator to find the source direction. Finally, Monte Carlo numerical examples are conducted that confirm the analytical results.     

Data extraction from wireless sensor networks using distributed fountain codes

A Wireless Sensor Network (WSN) observes a natural field and aims to recreate it with sufficient fidelity at a, perhaps distant, Fusion Center (FC) using a wireless communication channel of arbitrary capacity. We propose a universal and power efficient method for such data extraction, based on Digital Fountain Codes (DFCs) and joint-source channel decoding. Our method implements a distributed `rate-lessiquest DFC which automatically tunes the number of transmissions to the channel capacity. Furthermore, instead of directly compressing the WSN data, we achieve rate reduction by treating the spatiotemporal dependencies in the field as an outer code, and jointly decoding this concatenation at the FC using a multi-stage iterative decoder. We demonstrate that a power efficiency close to the capacity-rate-distortion limit is achieved at moderate distortion levels, irrespective of the channel capacity or field dependencies. As compared to the traditional approach of source-channel separation, the proposed data extraction scheme is particularly attractive for WSN applications due its computationally simple encoding procedure, low latency and the ability to seamlessly trade-off fidelity of reconstruction for power consumption.     

A quasi distributed fiber optic weight-displacement sensor using macro bends

A quasi distributed fiber optic weight - displacement sensor using Optical Time Domain Reflectometer (OTDR) is presented. Specially made triangular corrugations on aluminum block are used to create macro bends in a single mode fiber. The power loss created by the macro bend is measured by an OTDR. Initially corrugation of 5 mm pitch is used to study the weight-loss and displacement-loss characteristics. This set up is extended for the distributed studies in which three identical corrugations of 5 mm pitch are used. It is also observed that the loss introduced by the macro bend is sensitive to the period of corrugation and there is an optimum period of corrugation which gives highest sensitivity. The experiments are carried out at a wavelength of 1550 nm. Investigations exhibit higher sensitivity at 1550 nm compared to 1310 nm. The effect of number corrugation on the measurement is also a part our investigation.     

Resource optimization in distributed biometric recognition using wireless sensor network

Distributed, autonomous and energy efficient protocols are best suited for wireless sensor networks, where network needs and events are dynamic. In this paper, a wireless face recognition system with limited resources such as energy, memory and bandwidth is analyzed. The performance of the applications is influenced by the protocol adaptability and quality of information. Performance comparisons are completed using 1. In-network image compression using Contourlet and Wavelet transforms versus raw image data transmission, 2. longevity of sensors versus network throughput. Balancing resource constraints, maintaining network lifetime and throughput is a non-deterministic polynomial computation time problem. Thus a meta-heuristic combinatorial algorithm, based on swarm intelligence, forms the cognitive routing protocol. The network efficiency considers energy consumption, response time, probability of correct acceptance, processing and computation time for wireless image transmission.

分布式光纤传感器开关柜温度监测探讨

阐述了分布式光纤温度传感器的工作原理,根据电力设备温度监测的特点,探讨了用于高压开关柜温度在线监测的传感系统方案.设计了基于全分布式和准分布式光纤传感技术的高压开关柜温度监测系统,比较了不同监测系统的特点,指出了适用于电力系统高压开关柜温度测量的方法.     

Detection of sensor decalibration using the asymptotic localapproach

A novel method for the detection of linear decalibration of sensors is proposed. The presence of a fault is indicated as a change in the mean of a white noise sequence. A simulation example is described which shows the success of the technique     

Parametric localization of distributed sources

Most array processing algorithms are based on the assumption that the signals are generated by point sources. This is a mathematical constraint that is not satisfied in many applications. In this paper, we consider situations where the sources are distributed in space with a parametric angular cross-correlation kernel. We propose an algorithm that estimates the parameters of this model using a generalization of the MUSIC algorithm. The method involves maximizing a cost function that depends on a matrix array manifold and the noise eigenvectors. We study two particular cases: coherent and incoherent spatial source distributions. The spatial correlation function for a uniformly distributed signal is derived. From this, we find the array gain and show that (in contrast to point sources) it does not increase linearly with the number of sources. We compare our method to the conventional (point source) MUSIC algorithm. The simulation studies show that the new method outperforms the MUSIC algorithm by reducing the estimation bias and the standard deviation for scenarios with distributed sources. It is also shown that the threshold signal-to-noise ratio required for resolving two closely spaced distributed sources is considerably smaller for the new method     

Fault diagnosis and test of DNA sensor arrays by using IFA approach

This paper presents a fault analysis applied to a novel optical, label-free sensors array for DNA detection. The inductive fault analysis approach to extract and model the possible defects has been used. A critical equivalent resistance for the possible faults has been defined and it allowed defining the threshold values of current to discriminate the occurrence of the failures mechanisms. Particularly critical is the shorts occurrence: in this failure mode the current changes can generate a wrong information that can be confused with the current reduction due to DNA detection. At the end a test strategy for structural test is proposed.     

Smart CMOS image sensor arrays

In this paper, we present several smart image sensor arrays intended for various applications. We discuss the realization of image sensors in CMOS technology and show some examples of one-dimensional (1-D) and two-dimensional (2-D) smart image arrays     

Low-complexity estimators for distributed sources

In antenna array applications, the propagation environment is often more complicated than the ordinarily assumed model of plane wavefronts. Here, a low-complexity algorithm is suggested for estimating both the DOA and the spread angle of a source subject to local scattering, using a uniform linear array. The parameters are calculated from the estimates obtained using a standard algorithm such as root-MUSIC to fit a two-ray model to the data. The algorithm is shown to give consistent estimates, and the statistical performance is studied analytically and through simulations     

Maximum likelihood direction-of-arrival estimation in unknown noise fields using sparse sensor arrays

We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise.     

Two-DOF magnetic orientation sensor using distributed multipole models for spherical wheel motor

This paper presents a new method for measuring a two degree-of-freedom (DOF) orientation of a permanent magnet (PM) based system using magnetic field measurements. The method exploits distributed multipole (DMP) modeling method to accurately predict a magnetic field, and provides a rational basis to inversely solve for the orientation of the PM from measured data. The PM-based magnetic sensor along with the ability to characterize the magnetic field in real-time offers advantages in sensing and control such as contact-free measurements eliminating frictional wears commonly encountered in existing designs with a combination of single-axis encoders, and high-speed sampling rate thus offering a higher bandwidth than methods based on imaging sensors. This paper demonstrates the efficient method capable of measuring the orientation of the PM by implementing it on a spherical wheel motor (SWM), where the two-DOF orientation is measured. Sensor performance has been studied both analytically and experimentally to validate the DMP-based sensor model. The results can offer valuable insights for optimizing contact-free sensor designs.