Showing papers in "IEEE Transactions on Instrumentation and Measurement in 2008"

Design of an RFID-Based Battery-Free Programmable Sensing Platform

Abstract: This paper presents the wireless identification and sensing platform (WISP), which is a programmable battery-free sensing and computational platform designed to explore sensor-enhanced radio frequency identification (RFID) applications. WISP uses a 16-bit ultralow-power microcontroller to perform sensing and computation while exclusively operating from harvested RF energy. Sensors that have successfully been integrated into the WISP platform to date include temperature, ambient light, rectified voltage, and orientation. The microcontroller encodes measurements into an electronic product code (EPC) Class 1 Generation 1 compliant ID and dynamically computes the required 16-bit cyclical redundancy checking (CRC). Finally, WISP emulates the EPC protocol to communicate the ID to the RFID reader. To the authors' knowledge, WISP is the first fully programmable computing platform that can operate using power transmitted from a long-range (UHF) RFID reader and communicate arbitrary multibit data in a single response packet.

Analysis and Modeling of Inertial Sensors Using Allan Variance

Abstract: It is well known that inertial navigation systems can provide high-accuracy position, velocity, and attitude information over short time periods. However, their accuracy rapidly degrades with time. The requirements for an accurate estimation of navigation information necessitate the modeling of the sensors' error components. Several variance techniques have been devised for stochastic modeling of the error of inertial sensors. They are basically very similar and primarily differ in that various signal processings, by way of weighting functions, window functions, etc., are incorporated into the analysis algorithms in order to achieve a particular desired result for improving the model characterizations. The simplest is the Allan variance. The Allan variance is a method of representing the root means square (RMS) random-drift error as a function of averaging time. It is simple to compute and relatively simple to interpret and understand. The Allan variance method can be used to determine the characteristics of the underlying random processes that give rise to the data noise. This technique can be used to characterize various types of error terms in the inertial-sensor data by performing certain operations on the entire length of data. In this paper, the Allan variance technique will be used in analyzing and modeling the error of the inertial sensors used in different grades of the inertial measurement units. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data. Being a directly measurable quantity, the Allan variance can provide information on the types and magnitude of the various error terms. This paper covers both the theoretical basis for the Allan variance for modeling the inertial sensors' error terms and its implementation in modeling different grades of inertial sensors.

Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network

Abstract: Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.

Wavelet Distance Measure for Person Identification Using Electrocardiograms

Abstract: In this paper, the authors present an evaluation of a new biometric based on electrocardiogram (ECG) waveforms. ECG data were collected from 50 subjects during three data-recording sessions on different days using a simple user interface, where subjects held two electrodes on the pads of their thumbs using their thumb and index fingers. Data from session 1 were used to establish an enrolled database, and data from the remaining two sessions were used as test cases. Classification was performed using three different quantitative measures: percent residual difference, correlation coefficient, and a novel distance measure based on wavelet transform. The wavelet distance measure has a classification accuracy of 89%, outperforming the other methods by nearly 10%. This ECG person-identification modality would be a useful supplement for conventional biometrics, such as fingerprint and palm recognition systems.

A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements

Abstract: Orientation of a static or slow-moving rigid body can be determined from the measured gravity and local magnetic field vectors. Some formulation of the QUaternion ESTimator (QUEST) algorithm is commonly used to solve this problem. Triads of accelerometers and magnetometers are used to measure gravity and local magnetic field vectors in sensor coordinates. In the QUEST algorithm, local magnetic field measurements affect not only the estimation of yaw but also that of roll and pitch. Due to the deviations in the direction of the magnetic field vector between locations, it is not desirable to use magnetic data in calculations that are related to the determination of roll and pitch. This paper presents a geometrically intuitive 3-degree-of-freedom (3-DOF) orientation estimation algorithm with physical meaning [which is called the factored quaternion algorithm (FQA)], which restricts the use of magnetic data to the determination of the rotation about the vertical axis. The algorithm produces a quaternion output to represent the orientation. Through a derivation based on half-angle formulas and due to the use of quaternions, the computational cost of evaluating trigonometric functions is avoided. Experimental results demonstrate that the proposed algorithm has an overall accuracy that is essentially identical to that of the QUEST algorithm and is computationally more efficient. Additionally, magnetic variations cause only azimuth errors in FQA attitude estimation. A singularity avoidance method is introduced, which allows the algorithm to track through all orientations.

Nonintrusive Load Monitoring and Diagnostics in Power Systems

Abstract: This paper describes a transient event classification scheme, system identification techniques, and implementation for use in nonintrusive load monitoring. Together, these techniques form a system that can determine the operating schedule and find parameters of physical models of loads that are connected to an AC or DC power distribution system. The monitoring system requires only off-the-shelf hardware and recognizes individual transients by disaggregating the signal from a minimal number of sensors that are installed at a central location in the distribution system. Implementation details and field tests for AC and DC systems are presented.

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

Abstract: Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.

Measurement of the Sensitivity Function in a Time-Domain Atomic Interferometer

Abstract: We present here an analysis of the sensitivity of a time-domain atomic interferometer to the phase noise of the lasers used to manipulate the atomic wave packets. The sensitivity function is calculated in the case of a three-pulse Mach-Zehnder interferometer, which is the configuration of the two inertial sensors we are building at the Laboratoire National de Metrologie et d'Essais-Systeme de References Temps-Espace. We successfully compare this calculation to experimental measurements. The sensitivity of the interferometer is limited by the phase noise of the lasers as well as by residual vibrations. We evaluate the performance that could be obtained with state-of-the-art quartz oscillators, as well as the impact of the residual phase noise of the phase-locked loop. Requirements on the level of vibrations are derived from the same formalism.

Experimental Study of Coexistence Issues Between IEEE 802.11b and IEEE 802.15.4 Wireless Networks

Abstract: Coexistence issues between IEEE 802.11b wireless communication networks and IEEE 802.15.4 wireless sensor networks, operating over the 2.4-GHz industrial, scientific, and medical band, are assessed. In particular, meaningful experiments that are performed through a suitable testbed are presented. Such experiments involve both the physical layer, through measurements of channel power and the SIR, and the network/transport layer, by means of packet loss ratio estimations. Different configurations of the testbed are considered; major characteristics, such as the packet rate, the packet size, the SIR, and the network topology, are varied. The purpose of this paper is to gain helpful information and hints to efficiently face coexistence problems between such networks and optimize their setup in some real-life conditions. Details concerning the testbed, the measurement procedure, and the performed experiments are provided.

Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar

Abstract: This paper proposes a new approach to solve the problem of real-time vision-based hand gesture recognition with the combination of statistical and syntactic analyses. The fundamental idea is to divide the recognition problem into two levels according to the hierarchical property of hand gestures. The lower level of the approach implements the posture detection with a statistical method based on Haar-like features and the AdaBoost learning algorithm. With this method, a group of hand postures can be detected in real time with high recognition accuracy. The higher level of the approach implements the hand gesture recognition using the syntactic analysis based on a stochastic context-free grammar. The postures that are detected by the lower level are converted into a sequence of terminal strings according to the grammar. Based on the probability that is associated with each production rule, given an input string, the corresponding gesture can be identified by looking for the production rule that has the highest probability of generating the input string.

Analysis of Harmonics in Power Systems Using the Wavelet-Packet Transform

Abstract: This paper proposes a new algorithm based on the wavelet-packet transform for the analysis of harmonics in power systems. The proposed algorithm decomposes the voltage/current waveforms into the uniform frequency bands corresponding to the odd-harmonic components of the signal and uses a method to reduce the spectral leakage due to the imperfect frequency response of the used wavelet filter bank. This paper studies the selection of the mother wavelet, the sampling frequency, and the frequency characteristics of the wavelet filter bank for the two most common wavelet functions used for harmonic analysis and compares the performance of the proposed method with the results obtained using the discrete Fourier transform (DFT) analysis and the harmonic-group concept introduced by the International Electrotechnical Commission (IEC) under different measurement conditions.

Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks

Abstract: Tea is an extensively consumed beverage worldwide with an expanding market. The major quality attributes of tea are flavor, aroma, color, and strength. Out of these, flavor and aroma are the most important attributes. Human experts called ldquotea tastersrdquo conventionally evaluate tea quality, and they usually assign scores to samples of tea that are under evaluation on a scale of 1 to 10, depending on the flavor, the aroma, and the taste of the sample. This paper presents a study where, first, the selection of appropriate sensors was carried out based on sensitivity with the major aroma-producing chemicals of black tea. Then, this sensor array was exposed to black tea samples that were collected from the tea gardens in India, and the computational model has been developed based on artificial neural network methods to correlate the measurements with the tea taster's scores. With unknown tea samples, encouraging results have been obtained with a more than 90% classification rate.

An Intelligent Mobile Robot Navigation Technique Using RFID Technology

Abstract: This paper presents an innovative mobile robot navigation technique using radio frequency identification (RFID) technology. Navigation based on processing some analog features of an RFID signal is a promising alternative to different types of navigation methods in the state of the art. The main idea is to exploit the ability of a mobile robot to navigate a priori unknown environments without a vision system and without building an approximate map of the robot workspace, as is the case in most other navigation algorithms. This paper discusses how this is achieved by placing RFID tags in the 3-D space so that the lines linking their projections on the ground define the ldquofree waysrdquo along which the robot can (or is desired to) move. The suggested algorithm is capable of reaching a target point in its a priori unknown workspace, as well as tracking a desired trajectory with a high precision. The proposed solution offers a modular, computationally efficient, and cost-effective alternative to other navigation techniques for a large number of mobile robot applications, particularly for service robots, such as, for instance, in large offices and assembly lines. The effectiveness of the proposed approach is illustrated through a number of computer simulations considering testbeds of various complexities.

Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

Abstract: This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node's dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme.

A Multipurpose Portable Instrument for Determining Ripeness in Wine Grapes Using NIR Spectroscopy

Abstract: This paper describes the development of a noninvasive instrument that is designed to measure three parameters of ripeness in wine grapes, i.e., sugar (Brix), pH, and anthocyanin concentration. The instrument is based on near-infrared spectroscopy, and it comes in contact with the berry in the cluster without altering its ripening process. A thorough description of the calibration process for the instrument is done for the different grape varieties, e.g., Cabernet Sauvignon, Carmenere, Merlot, Pinot Noir, and Chardonnay. Samples from vineyards located in the Maipo Valley (Chile) taken during the 2003 season were processed to develop calibration models using partial least squares techniques. The models were validated in terms of root mean square error of validation and R2 indices. The results show the great potential of this technique regarding Brix and pH measurements. For the anthocyanin concentration measurements, the results are promising but require an accurate procedure to obtain reference values for model calibration. The instrument can be useful for sampling strategies that look for optimum harvest schedules according to grape maturity in terms of not only sugar content but also pH and anthocyanin concentration.

Digital Lock-In Detection for Discriminating Multiple Modulation Frequencies With High Accuracy and Computational Efficiency

Abstract: We introduce a novel digital lock-in detection technique for simultaneously measuring the amplitude and phase of multiple amplitude-modulated signals. Using particular modulation and sampling constraints and averaging filters, we achieve optimal noise reduction and discrimination between sources of different modulation frequencies. Furthermore, it is shown that the digital lock-in technique can be performed as a simple matrix multiplication, which considerably reduces the computation time. The digital lock-in algorithm is described and analyzed under certain sampling and modulation conditions, and results are shown for both numerical and experimental data.

A Broadband CMOS Amplitude Detector for On-Chip RF Measurements

Abstract: This paper presents a CMOS RF amplitude detector as a practical integrated test device and demonstrates its application for on-chip testing. The proposed circuit performs full-wave rectification and generates a dc voltage proportional to the amplitude of an RF signal over a wide frequency range. The design considerations and analysis of operation for the employed class-AB rectifier are described. Fabricated in a standard 0.35-mum CMOS process, the RF detector uses only 0.031 of area and presents an equivalent input capacitance of 13 fF. Measurements show that this RF test device has a detection dynamic range of 30 dB from 900 MHz to 2.4 GHz. Experimental results for the application of the RF amplitude detector in the built-in measurement of the gain and compression of a 1.6-GHz low-noise amplifier fabricated in the same chip are also presented.

One-Way Delay Measurement: State of the Art

Abstract: Nowadays, the evaluation of performance measurement in computer networks is an important issue. To ensure the quality of service of the network communication, one of the most important network performance parameters is the one-way delay (OWD). For accurate OWD estimation, it is essential to consider some parameters that can influence the measure, such as the operating system and, in particular, the threads, which are concurrent with the measurement application. Moreover, OWD estimation is not an easy task, because it can be affected by synchronization uncertainties. This paper aims to review the different solutions proposed in the scientific literature for OWD measurement. These solutions adopt different methods to guarantee a reasonable clock synchronization based on the Network Time Protocol, the Global Positioning System, and the IEEE 1588 Standard. These different approaches are critically reviewed, showing their advantages and disadvantages.

Crack Shape Reconstruction in Eddy Current Testing Using Machine Learning Systems for Regression

Abstract: Nondestructive testing techniques for the diagnosis of defects in solid materials can follow three steps, i.e., detection, location, and characterization. The solutions currently on the market allow for good detection and location of defects, but their characterization in terms of the exact determination of defect shape and dimensions is still an open question. This paper proposes a method for the reliable estimation of crack shape and dimensions in conductive materials using a suitable nondestructive instrument based on the eddy current principle and machine learning system postprocessing. After the design and tuning stages, a performance comparison between the two machine learning systems [artificial neural network (ANN) and support vector machine (SVM)] was carried out. An experimental validation carried out on a number of specimens with different known cracks confirmed the suitability of the proposed approach for defect characterization.

PQ Monitoring System for Real-Time Detection and Classification of Disturbances in a Single-Phase Power System

Abstract: This paper presents a system for detection and classification of power quality (PQ) voltage disturbances. The proposed system applies the following methods to detect and classify PQ disturbances: digital filtering and mathematical morphology are used to detect and classify transients and waveform distortions, whereas for short- and long-duration disturbances (such as sags, swells, and interruptions), the analysis of the root-mean-square (RMS) value of the voltage is employed. The proposed combined approach identifies the type of disturbance and its parameters such as time localization, duration, and magnitude. The proposed system is suitable for real-time monitoring of the power system and implementation on a digital signal processor (DSP).

A Flexible GPS-Based System for Synchronized Phasor Measurement in Electric Distribution Networks

Abstract: Large-scale distributed measurement systems are the object of several applications and research. The goal of this paper is to develop, by employing global positioning system (GPS) receivers, measurement techniques that are suited to the continuous monitoring of the electrical quantities in distribution networks in terms of synchronized phasors. The proposed measurement procedures, differently from commercially available phasor measurement units, are based on general-purpose acquisition hardware and processing software, thus guaranteeing the possibility of being easily reconfigured and reprogrammed according to the specific requirements of different possible fields of application and to their future developments.

Experimental Characterization of Wireless Sensor Networks for Industrial Applications

Abstract: The effects of interference in the setup of wireless sensor networks (WSNs) represent a critical issue, and as such, it needs to be carefully addressed. To this aim, helpful information can be achieved through measurements to be carried out in advance on suitable prototypes and testbeds. In this paper, the measurement of industrial WSN performance is dealt with. In particular, a suitable testbed enlisting IEEE 802.15.4 wireless sensor nodes is presented along with the results of some experiments carried out even in the presence of interference. The purpose is to show how to evaluate some specific parameters of a WSN employed for industrial applications to obtain useful information for its setup optimization in the presence of interference. The analysis will show that from the measurement of these parameters (number of failed pollings, polling round-trip time, experimental cycle time, and alarm latency), interference effects can effectively be recognized, and the network setup can be optimized.

Photonic Liquid Crystal Fibers for Sensing Applications

Abstract: The paper presents our latest experimental results on the influence of temperature, an external electric field, and hydrostatic pressure on propagation properties of the photonic crystal fibers infiltrated with liquid crystals of low and medium material anisotropies. Measurand-induced shifts of the photonic bandgap wavelengths give information about the value of temperature, voltage, and pressure. Moreover, temperature-dependent positions of the photonic bandgap wavelengths in the transmission spectrum can serve to determine the thermal characteristics of the liquid crystal ordinary refractive index.

Haptic Virtual Rehabilitation Exercises for Poststroke Diagnosis

Abstract: Nowadays, stroke is one of the most frequent causes of severe adult disability in the world. Virtual reality and haptic technologies have emerged as promising assistive tools for effective diagnosis and rehabilitation intervention. The objective of this paper is to develop and test a set of five virtual exercises on top of a framework, which is designed for the diagnosis and rehabilitation of patients with hand impairments. We have implemented task-oriented exercises based on well-established and common exercises, namely the Jebsen Test of Hand Function and the Box and Block Test. These include moving a cup, arranging blocks, navigating a maze, training with a dumbbell, and grasping a rubber ball. Furthermore, key performance measures (metrics) are proposed for each exercise to quantitatively evaluate and judge the performance of stroke patients. Our evaluation of these exercises shows promising potential to define ldquogoldenrdquo reference metrics for healthy subjects, against which the performance of a patient is compared. This will facilitate the ability of occupational therapists to assess the patient's progress.

Energy Consumption Estimation in Embedded Systems

Abstract: This paper presents an energy consumption modeling technique for embedded systems based on a microcontroller. The software tasks that run on the embedded system are profiled, and their characteristics are analyzed. The type of executed assembly instructions, as well as the number of accesses to the memory and the analog-to-digital converter, is the required information for the derivation of the proposed model. An appropriate instrumentation setup has been developed for measuring and modeling the energy consumption in the corresponding digital circuits.

A Four-Terminal Water-Quality-Monitoring Conductivity Sensor

Abstract: In this paper, a new four-electrode sensor for water conductivity measurements is presented. In addition to the sensor itself, all signal conditioning is implemented together with signal processing of the sensor outputs to determine the water conductivity. The sensor is designed for conductivity measurements in the range from 50 mS/m up to 5 S/m through the correct placement of the four electrodes inside the tube where the water flows. The implemented prototype is capable of supplying the sensor with the necessary current at the measurement frequency, acquiring the sine signals across the voltage electrodes of the sensor and across a sampling impedance to determine the current. A temperature sensor is also included in the system to measure the water temperature and, thus, compensate the water-conductivity temperature dependence. The main advantages of the proposed conductivity sensor include a wide measurement range, an intrinsic capability to minimize errors caused by fouling and polarization effects, and an automatic compensation of conductivity measurements caused by temperature variations.

A Novel Sensing and Data Fusion System for 3-D Arm Motion Tracking in Telerehabilitation

Abstract: In this paper, we present a novel sensing and data fusion system to track 3-D arm motion in a telerehabilitation program. A particle filter (PF) algorithm is adopted in the system to fuse data from inertial and visual sensors in a probabilistic manner. It is able to propagate multimodal distributions of system states based on an ldquoimportance samplingrdquo technique by using sets of weighted particles. To avoid the problem of conventional PF algorithms that suffer from particle degeneracy and perform poorly in a narrow distribution situation, we adopt two strategies in our system, namely state space pruning and an arm physical geometry constraint. Experimental results show that the proposed PF framework outperforms other fusion methods and provides accurate results in comparison to the ground truth.

Use of LCD Panel for Calibrating Structured-Light-Based Range Sensing System

Abstract: Calibration is a crucial step in structured-light-based range sensing device. The step involves the determination of the intrinsic parameters of both the camera and the projector that constitute the device and the extrinsic parameters between the two instruments. The traditional solution requires the use of an external calibration object with an accurately measured pattern printed on it. This paper presents a calibration design that makes use of a liquid-crystal display (LCD) panel as the calibration object. The LCD panel's planarity is of industrial grade and is thus dependable. The pattern shown on the LCD panel is programmable and is thus convenient to produce in high precision. We show that, with the design, the projector-and-camera system parameters can be calibrated with far fewer images with much higher accuracy. Extensive experiments are shown to illustrate the dramatic improvement in performance.

Three-Dimensional Password for More Secure Authentication

Abstract: Current authentication systems suffer from many weaknesses. Textual passwords are commonly used; however, users do not follow their requirements. Users tend to choose meaningful words from dictionaries, which make textual passwords easy to break and vulnerable to dictionary or brute force attacks. Many available graphical passwords have a password space that is less than or equal to the textual password space. Smart cards or tokens can be stolen. Many biometric authentications have been proposed; however, users tend to resist using biometrics because of their intrusiveness and the effect on their privacy. Moreover, biometrics cannot be revoked. In this paper, we present and evaluate our contribution, i.e., the 3-D password. The 3-D password is a multifactor authentication scheme. To be authenticated, we present a 3-D virtual environment where the user navigates and interacts with various objects. The sequence of actions and interactions toward the objects inside the 3-D environment constructs the user's 3-D password. The 3-D password can combine most existing authentication schemes such as textual passwords, graphical passwords, and various types of biometrics into a 3-D virtual environment. The design of the 3-D virtual environment and the type of objects selected determine the 3-D password key space.

A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms

Abstract: A systematic method based on a neural network that utilizes a genetic algorithm (GNN) and the deviation space to diagnose faulty behavior in analog circuits under test (CUTs) is presented in the paper. To reduce the computational requirement of network simulations, we derive a unified fault feature, which can be extracted from measurable voltage deviation in the deviation space. The extracted unified feature vectors for single, double, and triple faults are characterized on the basis of measurable voltage deviation in the deviation space. Then, the faults can be classified by applying a neural network (NN) whose inputs are extracted from independent measurements - the transfer impedances at accessible nodes or the corresponding feature of various faults. It is applicable to linear circuits as well as nonlinear ones. The method presented minimizes the online measurements and offline computation. Illustrative examples verify the effectiveness of the proposed method.