Chaolong Zhang

A CMOS Humidity Sensor for Passive RFID Sensing Applications

This paper presents a low-cost low-power CMOS humidity sensor for passive RFID sensing applications. The humidity sensing element is implemented in standard CMOS technology without any further post-processing, which results in low fabrication costs. The interface of this humidity sensor employs a PLL-based architecture transferring sensor signal processing from the voltage domain to the frequency domain. Therefore this architecture allows the use of a fully digital circuit, which can operate on ultra-low supply voltage and thus achieves low-power consumption. The proposed humidity sensor has been fabricated in the TSMC 0.18 μm CMOS process. The measurements show this humidity sensor exhibits excellent linearity and stability within the relative humidity range. The sensor interface circuit consumes only 1.05 μW at 0.5 V supply voltage and reduces it at least by an order of magnitude compared to previous designs.

A Novel Approach for Analog Circuit Fault Prognostics Based on Improved RVM

In order to estimate the remaining useful performance (RUP) of analog circuits precisely in real time, an analog circuit fault prognostics framework is proposed in the paper. Output voltages are extracted from circuit responses as features to calculate cosine distance which can reflect the health condition of analog circuits. Relevance vector machine (RVM) which has been improved by particle swarm optimization (PSO) algorithm is applied to estimate the RUP. Twelve case studies involving bandpass filter, highpass filter and nonlinear circuit have validated the predict performance of the approach. Simulation results demonstrate that the proposed approach has higher prediction precision.

A Novel Approach for Diagnosis of Analog Circuit Fault by Using GMKL-SVM and PSO

This paper presents a novel analog circuit fault diagnosis approach using generalized multiple kernel learning-support vector machine (GMKL-SVM) method and particle swarm optimization (PSO) algorithm. First, the wavelet coefficients’ energies of impulse responses are generated as features. Then, a diagnosis model is constructed by using GMKL-SVM method based on features. Meanwhile, the PSO algorithm yields parameters for the GMKL-SVM method. Sallen-Key bandpass filter and two-stage four-op-amp biquad lowpass filter fault diagnosis simulations are given to demonstrate the proposed diagnose procedure, and the comparison simulations reveal that the proposed approach has higher diagnosis precision than the referenced methods.

Self-Powered RFID Sensor Tag for Fault Diagnosis and Prognosis of Transformer Winding

This paper presents a transformer winding fault diagnosis and prognosis method based on self-powered radio frequency identification (RFID) sensor tag. The proposed RFID sensor tag, which consists of RFID tag, power management circuit, MCU, and accelerometer, can acquire the vibration signals of transformer winding from the tank by accelerometer, and then wirelessly transmit the signals to the RFID reader. An inductive energy harvester utilizing surrounding magnetic field is optimized as power supply for the proposed sensor tag, including the MCU and the accelerometer. A customized ac–dc converter together with a low-dropout voltage regulator is designed to provide stable dc voltage for the proposed sensor tag. The RFID reader compiles the data from all the RFID sensor tags and then transmits them to the remote monitoring software which is developed to display the diagnosis results and alert messages. The experimental results show that the proposed energy harvester can provide 197-

A new fault diagnosis approach for analog circuits based on spectrum image and feature weighted kernel Fisher discriminant analysis

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Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

power in a 50-Hz magnetic field, and the ac–dc converter is capable of providing 2.5-V dc voltage to power the circuitry. The measured maximum power consumption of the proposed sensor tag is 147

Construction and analysis of multi-path propagation model for indoor short range Ultra-wideband signal based on time domain ray tracing method

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An Improved Quantum-Inspired Evolutionary Algorithm for Knapsack Problems

. Furthermore, the achieved reliable communication distance is 13 m in the test scenario. The experimental results show that the proposed method is effective in term of the diagnosis and prognosis of transformer winding.

Portable device for acetone detection based on cataluminescence sensor utilizing wireless communication technique

Analog circuits are one of the most commonly used components in industrial equipment and, therefore, circuit failure may lead to significant causalities and even huge financial losses. To address this problem, this work presents a fault diagnosis method based on spectrum images for analog circuits. Unlike traditional analysis methods in a one-dimensional space, this study employs a computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed approach mainly involves the following steps. First, the sampling signals are converted into spectrum images by utilizing cross-wavelet transform, which can be further processed by the following image-based feature extraction method. Then, Krawtchouk moment is applied to extract both the global and local features of the spectrum images and finally form the feature vector. Feature weighted kernel Fisher discriminant analysis is then introduced for locating faults. Two typical analog circuits, video amplifier circuit and opamp high-pass filter circuit, are chosen to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed approach based on spectrum images achieves a high accuracy, thus providing a highly effective means to fault diagnosis for analog circuits.

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits’ defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.