Design and implementation of a realtime co-processor for denoising Fiber Optic Gyroscope signal

The amount of noise present in the Fiber Optic Gyroscope (FOG) signal limits its applications and has a negative impact on navigation system. Existing algorithms such as Discrete Wavelet Transform (DWT), Kalman Filter (KF) denoise the FOG signal under static environment, however denoising fails in dynamic environment. Therefore in this paper an Adaptive Moving Average Dual Mode Kalman Filter (AMADMKF) is developed for denoising the FOG signal under both the static and dynamic environments. Performance of the proposed algorithm is compared with DWT and KF techniques. Further, a hardware Intellectual Property (IP) of the algorithm is developed for System on Chip (SoC) implementation using Xilinx Virtex-5 Field Programmable Gate Array (Virtex-5FX70T-1136). The developed IP is interfaced as a Co-processor/ Auxiliary Processing Unit (APU) with the PowerPC (PPC440) embedded processor of the FPGA. It is proved that the proposed system is an efficient solution for denoising the FOG signal in real-time environment. Hardware acceleration of developed Co-processor is 65× with respect to its equivalent software implementation of AMADMKF algorithm in the PPC440 embedded processor.     

Synthesis and Characterisation of Polyamides and Polyimides from Amino/Methylene bis[Benzenamine] Styryl Pyridines

Polyamides and polyimides containing diamines, with potential non-linear optical characteristics, were prepared using (E)-4,4′-[[[2-(4-pyridinyl)ethenyl]phenyl]amino]bis[benzenamine] and (E)-4-4′-[[[2-(4-pyridinyl)ethenyl]2-methyl phenyl]amino]bis[benzenamine] condensed with pyromellitic dianhydride to obtain poly(amic acid)s. The poly(amic acid)s were soluble in polar aprotic solvents, such as dimethylformamide, dimethylsulphoxide and dimethylacetamide, and could be cast into transparent, tough, flexible films. Amorphous thermally stable polyimides were formed by cyclodehydration. Similarly, (E)-4,4′-[[[2-(4-pyridinyl)ethenyl]phenyl]methylene]bis[benzenamine] and (E)-4,4′-[[[2-(4-pyridinyl)ethenyl]phenyl]methylene]bis[N-ethylbenzenamine] were condensed with 3-methyladipoyl chloride to obtain other new polyamides. Characterisation using infra-red and nuclear magnetic resonance spectroscopy, X-ray diffraction and thermogravimetric analysis are reported. © 1997 SCI.     

FPGA‐based implementation for improved control scheme of grid‐connected PV system with 3‐phase 3‐level NPC‐VSI

The large scale penetration of renewable energy resources has boosted the need of using improved control technique and modular power electronic converter structures for efficient and reliable operation of grid‐connected systems. This study investigates the performance of a grid‐connected 3‐phase 3‐level neutral‐point clamped voltage source inverter for renewable energy integration by using improved current control technique. For medium or high‐voltage grid interfacing, the multilevel inverter structure is generally used to reduce the voltage stress across the switching device as well as the harmonic distortion. The neutral‐point clamped voltage source inverter is controlled by using decoupling technique along with the proper grid synchronization via moving average filter–based phase‐locked loop. The moving average filter–based phase‐locked loop is used to reduce the delay in grid angle estimation under balanced as well as distorted grid conditions. A Lyapunov‐based approach for analysing the stability of the system has also been discussed. In this study, the hardware‐in‐loop (HIL) simulation of the control algorithm and the grid synchronization technique is realized using Virtex‐6 FPGA ML605 evaluation kit. The performance of the system is analyzed by conducting a time‐domain simulation in the Matlab/Simulink platform and its performance is examined in the HIL environment. The simulation and the hardware cosimulation results are presented to validate the effectiveness of the proposed control scheme.     

SoC based floating point implementation of differential evolution algorithm using FPGA

This paper presents floating point design and implementation of System on Chip (SoC) based Differential Evolution (DE) algorithm using Xilinx Virtex-5 Field Programmable Gate Array (FPGA). The hardware implementation is carried out to enhance the execution speed of the embedded applications. Intellectual Property (IP) of DE algorithm is developed and interfaced with the 32-bit PowerPC 440 processor using processor local bus (PLB) of Xilinx Virtex-5 FPGA. In the proposed architecture the algorithmic parameters of DE are scalable. The software and hardware implementation of the DE algorithm is carried out in PowerPC embedded processor and hardware IP respectively. The optimization of numerical benchmark functions and system identification in control systems are implemented to verify the proposed hardware SoC platform. The performance of the IP is measured in terms of acceleration gain of the DE algorithm. The optimization problems are solved by using floating point arithmetic in both embedded processor and hardware. The experimental result concludes that the hardware DE IP accelerates the execution speed approximately by 200 times compared to equivalent software implementation of DE algorithm on PowerPC 440 processor. Further, as a case study an Infinite Impulse Response (IIR) based system identification task on SoC using the developed hardware accelerator is implemented.     

FPGA‐based embedded platform for fiber optic gyroscope signal denoising

This paper presents System on Chip (SoC) implementation of a proposed denoising algorithm for fiber optic gyroscope (FOG) signal. The SoC is developed using an Auxillary Processing Unit of the proposed algorithm and implemented in the Xilinx Virtex‐5‐FXT‐1136 field programmable gate array. SoC implementation of this application is first of its kind. The proposed algorithm namely adaptive moving average‐based dual‐mode Kalman filter (AMADMKF) is a hybrid of adaptive moving average and Kalman filter (KF) technique. The performance of the proposed AMADMKF algorithm is compared with the discrete wavelet transform and KF of different gains. Allan variance analysis, standard deviation and signal to noise ratio (SNR) are used to measure the efficiency of the algorithm. The experimental result shows that AMADMKF algorithm reduces the standard deviation or drift of the signal by an order of 100 and improves the SNR approximately by 80 dB. The Allan variance analysis result shows that this algorithm also reduces different types of random errors of the signal significantly. The proposed algorithm is found to be the best suited algorithm for denoising the FOG signal in both the static and dynamic environments. Copyright © 2013 John Wiley & Sons, Ltd.