<![CDATA[ IEEE Transactions on Instrumentation and Measurement - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 19 2019April 18<![CDATA[Table of contents]]>685C11234215<![CDATA[IEEE Transactions on Instrumentation and Measurement publication information]]>685C2C2118<![CDATA[Special Issue for I<sup>2</sup>MTC 2018]]>2MTC) was held in Houston, TX, USA, on May 22–25, 2018. I^{2}MTC is the flagship conference of the IEEE Instrumentation and Measurement Society held every year since 1986. It is dedicated to advances in measurement methodologies, measurement systems, instrumentation, and sensors in all areas of science and technology. The I^{2}MTC is proposed as a catalyst to promote interactions between industry and academia; a wide spectrum of academic research results is presented, with potential practical applications in current industrial technology, as well as industry and application-driven developments.]]>68512351237743<![CDATA[Doppler Distortion Removal in Wayside Circular Microphone Array Signals]]>685123812512320<![CDATA[Locating Defects in Anisotropic CFRP Plates Using ToF-Based Probability Matrix and Neural Networks]]>685125212603003<![CDATA[Design and Analysis of a Relaxation Oscillator-Based Interface Circuit for LVDT]]>685126112702093<![CDATA[Parameter Measurement of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-Ary PSK Signals With Finite Rate of Innovation]]>$M$ -ary phase shift keying (MPSK) signals is significant for radar and communication systems. Traditional parameter measurement methods require very high sampling rates and heavy processing to estimate the key parameters of amplitude, carrier frequency, phases, and discontinuity locations. We propose a multichannel cooperative sampling system using the finite rate of innovation sampling theorem to address this problem. In our two-channel system, the main channel estimates the MPSK amplitude and carrier frequency using a nonlinear process and time-staggered sampling structure, while the cooperative channel determines phases and discontinuity locations with an annihilating filter using the main channel’s estimated carrier frequency. Our system enables recovery of these parameters from as few as $2K+6$ samples from MPSK signals with $K$ segments. We also present a hardware platform implementing the structure. The simulation and experimental results demonstrate the effectiveness and robustness of the proposed method.]]>685127112832318<![CDATA[Dynamic Offset Correction of Electromagnetic Flowmeters]]>685128412932712<![CDATA[Partial Discharge Localization in Insulated Switchgears by Eigenfunction Expansion Method]]>a priori information coming directly from the physics of the phenomenon. By processing the a priori information, it is possible to locate the PD sources in small (order of meters) and not homogeneous closed volumes and, at the same time, reduce the complexity of the measurement system and improve its accuracy. Here, the focus is restricted on electrical devices, such as metal-enclosed insulated switchgears (ISs). The proposed approach is based on the measurements of the electromagnetic (EM) radiation due to PDs, picked-up by few probes inside the IS volume. The a priori information concerning the EM wave propagation physics is introduced and dealt with adopting a proper eigenfunction basis defined inside the IS volume.]]>685129413014100<![CDATA[Effects of Real Instrument on Performance of an Energy Detection-Based Spectrum Sensing Method]]>de facto measurement instruments, so it becomes crucial to analyze how real instrumentation peculiarities could affect performance in spectral measurements (i.e., SS performance). In this paper, a commercial software-defined radio (SDR) is adopted as cognitive device, and its capability to accurately measure occupied spectrum is analyzed in comparison also with a simulation setup, where the same analog-to-digital converter (ADC) is modeled and simulated, and equivalent signal-to-noise ratios (SNRs) are replicated. To this aim, a suitable experimental setup has been designed, metrologically characterized, and tuned. Experimental results prove how nominal SNR typically adopted for testing the performance of SS methods is not sufficient to model the impairments of a real acquisition chain, even jointly with a modeling of the digitization process. To assure a reliable SS method validation, new approaches have to be considered for enhancing the accordance between the performance predicted in the simulation environment and the experimental one reachable on real devices. Since different architectures of CRs are present on the market, the experimental tests should be preferred for a reliable SS performance assessment.]]>685130213121749<![CDATA[Space Vector Taylor–Fourier Models for Synchrophasor, Frequency, and ROCOF Measurements in Three-Phase Systems]]>685131313212271<![CDATA[Compensation of Nonlinearity of Voltage and Current Instrument Transformers]]>685132213323550<![CDATA[Decentralized Load Estimation for Distribution Systems Using Artificial Neural Networks]]>685133313423023<![CDATA[A Proposal for a More User-Oriented GUM]]>685134313522397<![CDATA[An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks]]>685135313613849<![CDATA[Online Fault Detection of Rear Stroke Suspension Sensor in Motorcycle]]>685136213722425<![CDATA[Comparison Study of Different Features for Pocket Length Quantification of Angular Defects Using Eddy Current Pulsed Thermography]]>$R^{2}$ and 2-norm of the residual) are used. Through comparisons, three features’ strengths and limitations are summarized. Furthermore, a real RCF specimen is used to verify the comparison results.]]>685137313812208<![CDATA[Effects of Aperture Diameter on Image Blur of CMOS Image Sensor With Pixel Apertures]]>$mu text{m}$ CIS process. The image blur as a function of the aperture diameter was measured using the fabricated sensor. In addition, the effects of the $F$ -number of the camera lens, which is defined as the ratio of focal length to the diameter of the camera lens, and variation in the output of the CIS with the angle of incidence were investigated.]]>685138213883434<![CDATA[A Thermal Infrared Face Database With Facial Landmarks and Emotion Labels]]>https://github.com/marcinkopaczka/thermalfaceproject.]]>6851389140111866<![CDATA[Coaxial Probe for Dielectric Measurements of Aerated Pulverized Materials]]>685140214111879<![CDATA[A Statistical Approach for Improving the Accuracy of Dry Friction Coefficient Measurement]]>685141214232180<![CDATA[A Novel Approach for Microbial Corrosion Assessment]]>685142414314737<![CDATA[Finger Displacement Sensing: FEM Simulation and Model Prediction of a Three-Layer Electrode Design]]>685143214401773<![CDATA[A DC-Coupled High Dynamic Range Biomedical Radar Sensor With Fast-Settling Analog DC Offset Cancelation]]>685144114502747<![CDATA[A Movement-Tremors Recorder for Patients of Neurodegenerative Diseases]]>685145114571812<![CDATA[Signal Processing for Capacitive Ice Sensing: Electrode Topology and Algorithm Design]]>685145814663546<![CDATA[Vibration Measurement of an Unbalanced Metallic Shaft Using Electrostatic Sensors]]>685146714762543<![CDATA[A Fluxgate-Based Approach for Ion Beam Current Measurement in ECRIS Beamline: Design and Preliminary Investigations]]>$5.0~mu text{A}$ , equal to 3.07 nT. To assess the solution, an experimental setup, miming real magnetic flux intensity inside ECRIS beamlines, has been realized. The measuring strategy has a magnetic resolution of 1 nT and a current resolution of $1.6~mu text{A}$ , that is, in line with requirements. Results obtained demonstrate the suitability of the measurement system for the specific application addressed in this paper, both in terms of operating range and resolution.]]>685147714843714<![CDATA[Moving Photovoltaic Installations: Impacts of the Sampling Rate on Maximum Power Point Tracking Algorithms]]>685148514934814<![CDATA[Calibration and Characterization of a Magnetic Positioning System Using a Robotic Arm]]>685149415023015<![CDATA[Using Blockchains to Implement Distributed Measuring Systems]]>685150315141053<![CDATA[Determination of Frequency-Independent Component of AC–DC Transfer Difference of SUT Calculable AC Voltage Standards]]>$0.0~mu text{V}$ /V with combined standard uncertainty ($k = 1$ ) equal to $0.3~mu text{V}$ /V. The obtained results were validated with fast-reversed direct current method and will be used to supplement the uncertainty budgets of these standards. The presented method does not require expensive quantum ac voltage standards.]]>685151515211604<![CDATA[Using the Cloud to Improve Sensor Availability and Reliability in Remote Monitoring]]>685152215322679<![CDATA[Eddy Current Testing Probe Based on Double-Coil Excitation and GMR Sensor]]>685153315424209<![CDATA[Distributed Measurement of Polarization Characteristics for a Multifunctional Integrated Optical Chip: A Review]]>685154315533062<![CDATA[Theoretical Analysis of Sensitivity Enhancement by Graphene Usage in Optical Fiber Surface Plasmon Resonance Sensors]]>−1. The deposition of graphene layers over the Au-coated probes causes an increase in the sensitivity from 2581 nm/RIU to 4201 nm/RIU.]]>685155415602556<![CDATA[Wearable Inverse Light-Emitting Diode Sensor for Measuring Light Intensity at Specific Wavelengths in Light Therapy]]>685156115743896<![CDATA[Fiber Bragg Grating-Based Oil-Film Pressure Measurement in Journal Bearings]]>685157515812357<![CDATA[A Novel Sensing Methodology to Detect Furfural in Water, Exploiting MIPs, and Inkjet-Printed Optical Waveguides]]>685158215893520<![CDATA[Gas–Liquid Flow Pattern Analysis Based on Graph Connectivity and Graph-Variate Dynamic Connectivity of ERT]]>685159016015264<![CDATA[A Capacitive-Coupled Noncontact Probe for the Measurement of Conductivity of Liquids]]>685160216102154<![CDATA[Behavioral Representation of a Bridge Rectifier Using Simplified Volterra Models]]>685161116181891<![CDATA[Modeling a Nonlinear Harvester for Low Energy Vibrations]]>$400~mu text{W}$ with the optimal resistive load of 15 $text{k}Omega $ and a monotonic input with root-mean-square accelerations of 13.35 m/s^{2}. The power generated is suitable for powering low-power measurement systems or sensor nodes. This paper focuses on a methodology for the modeling of the mechanical dynamical behavior of the device subject to a periodic impulsive signal. A measurement protocol for the evaluation of system’s hidden quantities, the beam’s restoring force, and the beam’s displacement between its stable states is introduced. A second-order behavioral model with a nonlinear term representing the beam’s restoring force has been used. In order to model the nonlinearity, two different potential energy functions have been compared by fitting the models to the experimental data with different constrains, through a performance index evaluating the fitting error.]]>685161916272318<![CDATA[Wiener–Hammerstein System Identification: A Fast Approach Through Spearman Correlation]]>685162816361912<![CDATA[Low-order Nonlinear Finite-Impulse Response Soft Sensors for Ionic Electroactive Actuators Based on Deep Learning]]>685163716463873<![CDATA[IEEE Transactions on Instrumentation and Measurement information for authors]]>6851647164790<![CDATA[Introducing IEEE Collabratec]]>68516481648548<![CDATA[IEEE Instrumentation and Measurement Society Information]]>685C3C3111<![CDATA[IEEE Transactions on Instrumentation and Measurement]]>685C4C4319