Electrocardiogram (ECG/EKG) using FPGA

FPGAs (Field Programmable Gate Arrays) are finding wide acceptance in medical systems for their ability for rapid prototyping of a concept that requires hardware/software co-design, for performing custom processing in parallel at high data rates and be programmed in the field after manufacturing. Based on the market demand, the FPGA design can be changed and no new hardware needs to be purchased as was the case with ASICs (Application Specific Integrated Circuit) and CPLDs (Complex Programmable Logic Device). Medical companies can now move over to FPGAs saving cost and delivering highly-efficient upgradable systems. ECG (Electrocardiogram) is considered to be a must have feature for a medical diagnostic imaging system. This project attempts at implementing ECG heart-rate computation in an FPGA. This project gave me exposure to hardware engineering, learning about the low level chips like Atmel UC3A3256 micro-controller on an Atmel EVK1105 board which is used as a simulator for generating the ECG signal, the operational amplifiers for amplifying and level-shifting the ECG signal, the A/D converter chip for analog to digital conversion of the ECG signal, the internal workings of FPGA, how different hardware components communicate with each other on the system and finally some signal processing to calculate the heart rate value from the ECG signal

Real-time human action recognition on an embedded, reconfigurable video processing architecture

Copyright @ 2008 Springer-Verlag.In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. “motion history image”) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments.DTI and Broadcom Ltd

Real-Time Fault Detection and Diagnosis System for Analog and Mixed-Signal Circuits of Acousto-Magnetic EAS Devices

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The paper discusses fault diagnosis of the electronic circuit board, part of acousto-magnetic electronic article surveillance detection devices. The aim is that the end-user can run the fault diagnosis in real time using a portable FPGA-based platform so as to gain insight into the failures that have occurred.Peer reviewe

Electronics and data acquisition demonstrator for a kinetic inductance camera

A prototype of digital frequency multiplexing electronics allowing the real time monitoring of kinetic inductance detector (KIDs) arrays for mm-wave astronomy has been developed. It requires only 2 coaxial cables for instrumenting a large array. For that, an excitation comb of frequencies is generated and fed through the detector. The direct frequency synthesis and the data acquisition relies heavily on a large FPGA using parallelized and pipelined processing. The prototype can instrument 128 resonators (pixels) over a bandwidth of 125 MHz. This paper describes the technical solution chosen, the algorithm used and the results obtained

FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture

In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine(SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. ``motion history image") class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments