Efficient QRS complex detection algorithm implementation on SOC-based embedded system

This paper studies two different Electrocardiography ( ECG ) preprocessing algorithms , namely Pan and Tompkins (PT) and Derivative Based (DB) algorithm, which is crucial of QRS complex detection in cardiovascular disease detection . Both algorithms are compared in terms of QRS detection accuracy and computation timing performance , with implementation on System - on - C hip (SoC) based embedded system that prototype on Altera DE2 - 115 Field Programmable Gate Array (FPGA) platform as embedded software . Both algorithm s are tested with 30 minutes ECG data from each of 48 different patient records obtain from MIT - BIH arrhythmia database. Results show that PT algorithm achieve 98.15% accuracy with 56. 33 seconds computation while DB algorithm achieve 96.74% with only 22. 14 seconds processing time. Based on the study, an optimized PT algorithm with improvement on Moving Windows Integrator (MWI) has been proposed to accelerate its computation. Result show s that the proposed optimized Moving Windows Integrator algorithm achieve s 9.5 times speed up than original MWI while retaining its QRS detection accuracy

Convolutional Neural Network for Face Recognition with Pose and Illumination Variation

Face recognition remains a challenging problem till today. The main challenge is how to improve the recognition performance when affected by the variability of non-linear effects that include illumination variances, poses, facial expressions, occlusions, etc. In this paper, a robust 4-layer Convolutional Neural Network (CNN) architecture is proposed for the face recognition problem, with a solution that is capable of handling facial images that contain occlusions, poses, facial expressions and varying illumination. Experimental results show that the proposed CNN solution outperforms existing works, achieving 99.5% recognition accuracy on AR database. The test on the 35-subjects of FERET database achieves an accuracy of 85.13%, which is in the similar range of performance as the best result of previous works. More significantly, our proposed system completes the facial recognition process in less than 0.01 seconds

Gender classification: a convolutional neural network approach

An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition

ARRHYTHMIA DETECTION BASED ON HERMITE POLYNOMIAL EXPANSION AND MULTILAYER PERCEPTRON ON SYSTEM-ON-CHIP IMPLEMENTATION

ABSTRACT As the number of health issues caused by heart problems is on the rise worldwide, the need for an efficient and portable device for detecting heart arrhythmia is needed. This work proposes a Premature Ventricular Contraction detection system, which is one of the most common arrhythmia, based on Hermite Polynomial Expansion and Artificial Neural Network Algorithm. The algorithm is implemented as a System-On-Chip on Altera DE2-115 FPGA board to form a portable, lightweight and cost effective biomedical embedded system to serve for arrhythmia screening and monitoring purposes. The complete Premature Ventricular Contraction classification computation includes pre-processing, segmentation, morphological information extraction based on Hermite Polynomial Expansion and classification based on artificial Neural Network algorithm. The MIT-BIH Database containing 48 patients' ECG records was used for training and testing purposes and Multilayer Perceptron training is performed using back propagation algorithm. Results show that the algorithm can detect the PVC arrhythmia for 48 different patients with 92.1% accuracy

Finger-Vein Biometric Identification Using Convolutional Neural Network

A novel approach using a convolutional neural network (CNN) for finger-vein biometric identification is presented in this paper. Unlike existing biometric techniques such as fingerprint and face, vein patterns are inside the body, making them virtually impossible to replicate. This also makes finger-vein biometrics a more secure alternative without being susceptible to forgery, damage, or change with time. In conventional finger-vein recognition methods, complex image processing is required to remove noise and extract and enhance the features before the image classification can be performed in order to achieve high performance accuracy. In this regard, a significant advantage of the CNN over conventional approaches is its ability to simultaneously extract features, reduce data dimensionality, and classify in one network structure. In addition, the method requires only minimal image preprocessing since the CNN is robust to noise and small misalignments of the acquired images. In this paper, a reduced-complexity four-layer CNN with fused convolutional-subsampling architecture is proposed for finger-vein recognition. For network training, we have modified and applied the stochastic diagonal Levenberg-Marquardt algorithm, which results in a faster convergence time. The proposed CNN is tested on a finger-vein database developed in-house that contains 50 subjects with 10 samples from each finger. An identification rate of 100.00% is achieved, with an 80/20 percent ratio for separation of training and test samples, respectively. An additional number of subjects have also been tested, in which for 81 subjects an accuracy of 99.38% is achieved

Microprocessor-based athlete health monitoring device based on heart rate and stride length calculation

Abnormal heart rate or low heart rate during exercise or recovery has been known to cause cardiac arrest and even sudden death in some cases. Similarly, research has shown that low step rate while running may be the causal factor for running injuries due to the force impact exerted and the extra loadings on the lower body joints. Commercial electronic devices used by athletes typically use either accelerometers or coil springs to estimate the step rate resulting in low accuracy. This paper describes the design a low-cost, wearable device that can help athletes monitor their physical activity while running or walking and report step rate, heart rate, distance covered, time elapsed and calories burnt with high accuracy. The system calculates the step rate by analyzing the signal generated from two Force Sensitive Resistors (FSRs) inserted above the insole of a running shoe which is connected to a microcontroller strapped to the athlete’s ankle. According to the experimental results, the prototype was found to have an average accuracy of 97% in measuring the distance covered

Gender Classification: A Convolutional Neural Network Approach

An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition

Heart sound localization through non-linear time delay calculation method

This paper proposes a sound localization method for heart sounds recorded from subjects in normal noise environment. Various sound localization methods were investigated to discover a method suitable for heart sounds. Electronic stethoscopes were used to record real heart sounds from healthy subjects under normal environment and sound localization was performed. Part of the research work included determining the right sound velocity value for the human chest through experimentation. Preliminary results consist of sound localization done on human heart sound to locate source of the first heart sound (S1)

A biometric encryption system algorithm development and system level design

Biometric encryption (BE) is a security scheme enhancement that overcomes the exploitable vulnerabilities of biometric authentication systems and the key storage issues of cryptographic schemes by combining both those systems. The practical application of security schemes often requires them to be stand-alone devices with tamper-resistant hardware implementation such as a System-on-Chip (SoC). Therefore, it is suitable to design a BE system architecture that is enhanced for speed and performance in a resource-constrained environment. This thesis proposes a novel algorithm for chaff generation (CG), which is a highly computeintensive algorithm in the BE system. The proposed CG algorithm is suitable for hardware implementation in an SoC because it is proven to have lower algorithmic complexity of O(n2) compared to the existing Clancy’s CG algorithm that has O(n3) complexity. Experimental results have shown that the proposed algorithm is about 150 times faster. Furthermore the proposed CG algorithm overcomes the security vulnerability detected in Clancy’s CG algorithm. The design of such a complex system, which contains many compute-intensive algorithmic blocks, requires consideration of multiple options for system architecture, optimal hardwaresoftware partitioning and early design verification. Hence, state-of-the-art systemlevel modeling using SystemC is applied in the design of the proposed BE system. In this thesis, the system-level design process has been enhanced by adding the Algorithmic Model level on top of the existing design abstraction levels. To verify the functionality of the BE design, appropriate testbenches must be generated and refined along with the system model throughout the different design abstraction levels. For this purpose, a new verification framework with a testbench generation methodology is also proposed, which generates testbench at the algorithmic level using MATLAB and incrementally refines it for use at lower levels of the design abstraction. This framework is applied in the early system-level verification of the proposed BE system. Experiments conducted have also shown that the proposed verification framework that integrates MATLAB testbenches with SystemC facilitates the verification process and reduces verification time through testbench refinement

Biometric encryption using fingerprint fuzzy vault for FPGA-based embedded systems

This paper discusses a biometric encryption system using fuzzy vault scheme implemented on FPGA development board. Cryptographic algorithms are very secure overall but have a weak point in terms of the storage of the crypto keys. Biometric authentication systems have many exploitable weak points that can be used to compromise the system. Biometric encryption is a security scheme that combines strong cryptographic algorithms with biometric authentication to provide better security. This paper discusses a simple implementation of a biometric encryption system as a stand-alone embedded device. Fuzzy vault scheme is used as the method to bind the crypto key and biometrics. The system processes were implemented as software blocks run on the firmware