A. Amalin Prince

Feature selection and classification methodology for the detection of knee-joint disorders

Vibroarthographic (VAG) signals emitted from the knee joint disorder provides an early diagnostic tool. The nonstationary and nonlinear nature of VAG signal makes an important aspect for feature extraction. In this work, we investigate VAG signals by proposing a wavelet based decomposition. The VAG signals are decomposed into sub-band signals of different frequencies. Nonlinear features such as recurrence quantification analysis (RQA), approximate entropy (ApEn) and sample entropy (SampEn) are extracted as features of VAG signal. A total of twenty-four features form a vector to characterize a VAG signal. Two feature selection (FS) techniques, apriori algorithm and genetic algorithm (GA) selects six and four features as the most significant features. Least square support vector machines (LS-SVM) and random forest are proposed as classifiers to evaluate the performance of FS techniques. Results indicate that the classification accuracy was more prominent with features selected from FS algorithms. Results convey that LS-SVM using the apriori algorithm gives the highest accuracy of 94.31% with false discovery rate (FDR) of 0.0892. The proposed work also provided better classification accuracy than those reported in the previous studies which gave an accuracy of 88%. This work can enhance the performance of existing technology for accurately distinguishing normal and abnormal VAG signals. And the proposed methodology could provide an effective non-invasive diagnostic tool for knee joint disorders.

Efficient architecture for real time implementation of Hilbert Transform in FPGA

This paper presents an architecture for real time hardware implementation of Hilbert Transform (HT) using Fast Fourier Transform (FFT). HT is studied and its various application areas are discussed in the paper. Two different architectures are proposed using Fast Fourier Transform (FFT) for the implementation. Implementation of HT using the proposed FFT based architectures are compared with the implementations using Discrete Fourier Transform (DFT) and Discrete Hartley Transform (DHT). The proposed FFT based architectures are implemented on Xilinx Kintex- 7 based FPGA and the results acquired are presented in comparison to results obtained through MATLAB simulations. The architecture implemented supports transform length of 8192 points as a demonstrator to the idea using 24 bit fixed point arithmetic. Detailed comparison study in terms of resource utilization and timing analysis is also carried out and the results are reported.

A framework for remote and adaptive partial reconfiguration of SoC based data acquisition systems under Linux

Data acquisition forms the most integral part of industrial systems. Control and monitoring of parameters in hazardous areas have to be performed remotely. In this paper a framework for dynamically reconfiguring data acquisition system has been proposed. The main features of this framework are its ability to be controlled, partially reconfigured and monitored remotely. The proposed framework is capable of detecting the identity of the sensor connected to a partial reconfigurable region. The system uses embedded Linux to implement the configuration, control logic and the device drivers to communicate with the partial reconfigurable region. The proposed framework is tested with an accelerometer ADXL362 as a sensor connected to XC7Z020 SoC from the Xilinx Zynq-7000 family. The system is able to detect the presence and absence of the accelerometer. Partial reconfiguration time of 10 ms and the full reconfiguration time of 310 ms is achieved.

Investigation of Microelectromechanical Switches for Next Generation DC Power Distribution System

Abstract This paper investigates the application of microelectromechanical system (MEMS) switches for DC power distribution system. Traditional electromechanical switches, solid state switches and solid state switch array are studied and simulated to understand their characteristics. Performance and characteristics of MEMS switches are reviewed and identified that electrostatically actuated MEMS switches are suitable for DC power applications. Scalable total cross tied (TCT) array configuration using MEMS switches has been proposed. The proposed configuration is suitable for variable voltage/current rating. Arc-less behavior of the switch configuration is analyzed using modified Paschen’s curve. 400 V/6 A system is considered for the simulation and comparative study. The simulated result of the proposed MEMS switch array configuration is compared with the traditional switches. The comparative study shows that the proposed switch array configuration gives better performance in terms of voltage drop, leakage current, power loss, arc and size. This can be used in DC power system protection, circuit breaking, battery protection and smart grid load switching applications.

Coding, evaluation, comparison, ranking and optimum selection of Micro-Electro-Mechanical System (MEMS) products

To address the issues involved in the optimum selection of high performance Micro-Electro-Mechanical Systems (MEMS) products for typical applications, different subsystems of MEMS products are defined. An n-digit alphanumeric coding scheme is proposed. The coding scheme is a nomenclature and characterises the MEMS products on the basis of n-attributes. A typical Multiple Attribute decision Making (MADM) approach is used for evaluation, comparison, ranking and optimum selection. A 3-stage selection procedure is proposed to make the methodology commercially feasible and vibrant. Graphical procedures consisting of line diagram and spider diagram are also proposed. Methodology has been demonstrated by illustrative examples. A step-by-step procedure is proposed for implementation of the methodology by the industry.

Effect of electric field on electrical breakdown arc behavior of micro contact gaps: A 3D approach

This paper presents the electric breakdown arc behavior of micro electrical contact with respect to electric field across contact gap. The breakdown electric field characteristics of Al, Cu, Fe, Ni, and Pt are reported. The 3D plane-plane micro electrical contact has been considered and maximum electric field is analyzed mathematically. Also the considered electrode has been simulated using COMSOL FEA tool for the contact gap from 1 to 30μm. The influence of contact gap and contact size has been simulated and results are compared with the numerical results. The arc behavior of the micro electrical contact for various voltages and contact size are simulated and results are presented. It is found that increasing the size of micro electrical contact reduces the electric field considerably and it can switch higher voltage without arcing. The effect of thickness on electric field and off-state isolation of micro electrical contact is reported. These results can be considered while designing arc-less micro electrical switches, micro relays and micro circuit breakers which can be applicable to the future DC electric power distribution and protection systems.

Modeling and Analysis of Scalable Arcless Micromechanical Switch for Battery Powered Electrical System

Objective: In this paper, electrostatically actuated micromechanical switch for battery powered electrical system has been presented. An electrostatically actuated micromechanical switch has been designed and the electromechanical characteristics have been discussed. Methods: The switching characteristics, power loss and leakage current of the switch have been obtained for 12 V/0.2 A electrical system. In order to meet the high power rating, the designed arcless micromechanical switches have been connected in a scalable cross-tied array configuration and the switching characteristics were obtained for 144 V/3 A electrical system. Result: The arc existing parts of the micromechanical switch have been identified and the arcless switching has been discussed. The reliability of the switch has been presented based on electromechanical behavior, arcless switching and scalability. The discharging characteristics of battery have been obtained for the circuit having solid-state and micromechanical switch. Conclusion: The result shows significant improvement in the power loss, battery discharging characteristics and is promising application for battery operated electrical system. A R T I C L E H I S T O R Y Received: April 21, 2018 Revised: June 09, 2018 Accepted: June 14, 2018 DOI: 10.2174/1876402910666180622094024

Time-frequency based feature extraction for the analysis of vibroarthographic signals

Abstract In this study, we propose to develop a computer-aided diagnostic (CAD) system based on time-frequency analysis for the diagnosis of knee-joint disorders. Two methodologies based on nonstationary signal processing techniques have been proposed. We propose to use smoothed pseudo Wigner–Ville distribution (SPWVD) and a modified version of Hilbert–Huang transform (HHT) for the analysis of vibroarthographic (VAG) signals. Traditional HHT consists of empirical mode decomposition (EMD) for computing intrinsic mode functions (IMFs) and Hilbert transform (HT). But we propose to use complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for computing IMFs. The time-frequency representation of the proposed methods is considered as a time-frequency image. Statistical features such as mean, standard deviation, skewness and kurtosis are extracted. A pattern classification is carried out using Least square support vector machine (LS-SVM) to compare performance. Results concluded that highest classification accuracy of 88.76% was obtained by features extracted from CEEMDAN-HHT.

Real time cascaded moving average filter for detrending of electroencephalogram signals

Electroencephalogram signals are widely used in computer aided diagnosis of patients suffering from seizures and in detection of emotions. The signals are measured by placing electrodes on the scalp. The major sources of baseline wander in EEG signals are because of the poor contact of the electrodes and change in impedance of the electrode which can be attributed to perspiration. For effective analysis of such signals, denoising methods have to be developed. This paper proposes a low complexity method to remove baseline wander and detrend EEG signals. Dedicated handheld devices require a computationally simpler algorithm to pre-process EEG signals. This paper proposes a cascaded moving average filter as a simple and computationally efficient methodology for detrending of EEG signals. To indicate the simplicity, this work has concentrated on real-time implementation by implementing it on Xilinx XC7Z020-CLG484-1. Parameters have been derived indicating the efficiency of the proposed methodology for detrending of EEG signals. The filter discussed in this work has its uses in preprocessing of EEG signals for applications such as Brain-Computer Interface and epileptic seizure detection.

Efficient implementation of empirical mode decomposition in FPGA Using Xilinx System Generator

Empirical Mode Decomposition (EMD) is used to decompose the signal into its principal constituents called Intrinsic Mode Function (IMF). Although it is one of the most widely used research method with applications seen across number of fields, real-time hardware implementation of EMD is challenging due to its computational intensive loop structure. In this paper we propose a module based approach for hardware implementation of the EMD in FPGA. The proposed architecture uses hardware compatible stopping criteria and a way to mitigate end effects. Xilinx System Generator (XSG), a model based industry standard tool has been proposed to implement the architecture on Xilinx 7-series FPGA (XC7K325T-2FFG900C). Root mean square error of 2.35×10-4 to 2.597×10-4 and correlation coefficient of 0.9963 to 0.9981 has been observed for IMF 1 when compared with the Matlab simulation results.