Smart wearable stress monitoring device for autistic children

Vital sign monitoring is the process of recording human physiological signals in order to determine the mental stress level. High stress levels can prove tobe dangerous especially for certain individuals such as autistic children who are not able to express mounting levels of stress before it leads to a full anxiety attack. This paper presents the prototype design of a real-time embedded device that accurately measures heart rate and galvanic skin response (GSR) in a non-invasive and non-intrusive way which is then used by the intelligent decision making module that uses fuzzy logic to determine the stress level of the user. Such a device could be used with autistic children in order to give early warning of an impending anxiety attack and help adults to prevent it from happening. The prototype was designed using Arduino mega platform and tested with 35 clinical patients in three experimental settings targeted to induce low stress, medium stress and high stress response. Initial results have shown that the device is capable of detecting and displaying the various stress levels efficiently

SoC-based biomedical embedded system design of arrhythmia detector

Arrhythmia is an irregular heartbeat where the blood may not be delivered effectively throughout the body and cause sudden cardiac arrest (SCA). Immediate treatment is required to prevent SCA. However, most of the existing electrocardiogram (ECG) monitoring devices are bulky, cost expensive and lack arrhythmia detection and classification system. This paper proposes a front-end on-board graphical interface design of System-on-Chip (SoC) based arrhythmia detector which can be used as a first screening device for cardiac disease patient. The system consists of a knowledge-based arrhythmia classifier which is able to identify three types of arrhythmias which are ventricular fibrillation (VF), premature ventricular contractions (PVCs) and second-degree atrioventricular (AV) block. The system has been evaluated and benchmarked with ECG data from MIT-BIH arrhythmia database. The results show that its accuracy is up to 99.25% with a computation time of 6.385 seconds. It is highly portable and relatively inexpensive for installation in small clinics and home monitoring

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

Convolutional neural networks with fused layers applied to face recognition

In this paper, we propose an effective convolutional neural network (CNN) model to the problem of face recognition. The proposed CNN architecture applies fused convolution/subsampling layers that result in a simpler model with fewer network parameters; that is, a smaller number of neurons, trainable parameters, and connections. In addition, it does not require any complex or costly image preprocessing steps that are typical in existing face recognizer systems. In this work, we enhance the stochastic diagonal Levenberg–Marquardt algorithm, a second-order back-propagation algorithm to obtain faster network convergence and better generalization ability. Experimental work completed on the ORL database shows that a recognition accuracy of 100% is achieved, with the network converging within 15 epochs. The average processing time of the proposed CNN face recognition solution, executed on a 2.5 GHz Intel i5 quad-core processor, is 3 s per epoch, with a recognition speed of less than 0.003 s. These results show that the proposed CNN model is a computationally efficient architecture that exhibits faster processing and learning times, and also produces higher recognition accuracy, outperforming other existing work on face recognizers based on neural networks

Distributed B-SDLM: accelerating the training convergence of deep neural networks through parallelism

This paper proposes an efficient asynchronous stochastic second order learning algorithm for distributed learning of neural networks (NNs). The proposed algorithm, named distributed bounded stochastic diagonal Levenberg-Marquardt (distributed B-SDLM), is based on the B-SDLM algorithm that converges fast and requires only minimal computational overhead than the stochastic gradient descent (SGD) method. The proposed algorithm is implemented based on the parameter server thread model in the MPICH implementation. Experiments on the MNIST dataset have shown that training using the distributed B-SDLM on a 16-core CPU cluster allows the convolutional neural network (CNN) model to reach the convergence state very fast, with speedups of 6.03× and 12.28× to reach 0.01 training and 0.08 testing loss values, respectively. This also results in significantly less time taken to reach a certain classification accuracy (5.67× and 8.72× faster to reach 99% training and 98% testing accuracies on the MNIST dataset, respectively)

OpenCL-based hardware-software co-design methodology for image processing implementation on heterogeneous FPGA platform

Recently, the OpenCL hardware-software co-design methodology has gained traction in realizing effective parallel architecture designs in heterogeneous FPGA platforms. In fact, the portability of OpenCL on hardware ready platforms such as GPU or multicore CPU enables ease of design verification. This is true especially for parallel algorithms before implementing them using cumbersome HDL-based RTL design. In this paper we employed OpenCL programming platform based on Altera SDK for OpenCL (AOCL) to implement a Sobel filter algorithm as an image processing test case on a Cyclone V FPGA board. Using the portability of this platform, the performance of the kernel code is benchmarked against that of the GPU and multicore CPU implementations for different image and kernel sizes. Different optimization strategies are also applied for each platform. We found that increasing the Sobel filter kernel size from 3×3 to 5×5 results in only 11.3% increase in computation time for FPGA, while the effect was much more significant where the execution time was as high as 23.6% and 85.7% for CPU and GPU, respectively

Bounded activation functions for enhanced training stability of deep neural networks on visual pattern recognition problems

This paper focuses on the enhancement of the generalization ability and training stability of deep neural networks (DNNs). New activation functions that we call bounded rectified linear unit (ReLU), bounded leaky ReLU, and bounded bi-firing are proposed. These activation functions are defined based on the desired properties of the universal approximation theorem (UAT). An additional work on providing a new set of coefficient values for the scaled hyperbolic tangent function is also presented. These works result in improved classification performances and training stability in DNNs. Experimental works using the multilayer perceptron (MLP) and convolutional neural network (CNN) models have shown that the proposed activation functions outperforms their respective original forms in regards to the classification accuracies and numerical stability. Tests on MNIST, mnist-rot-bg-img handwritten digit, and AR Purdue face databases show that significant improvements of 17.31, 9.19, and 74.99 can be achieved in terms of the testing misclassification error rates (MCRs), applying both mean squared error (MSE) and cross-entropy (CE) loss functions This is done without sacrificing the computational efficiency. With the MNIST dataset, bounding the output of an activation function results in a 78.58 reduction in numerical instability, and with the mnist-rot-bg-img and AR Purdue databases the problem is completely eliminated. Thus, this work has demonstrated the significance of bounding an activation function in helping to alleviate the training instability problem when training a DNN model (particularly CNN)

An optimized second order stochastic learning algorithm for neural network training

This paper proposes an improved stochastic second order learning algorithm for supervised neural network training. The proposed algorithm, named bounded stochastic diagonal Levenberg-Marquardt (B-SDLM), utilizes both gradient and curvature information to achieve fast convergence while requiring only minimal computational overhead than the stochastic gradient descent (SGD) method. B-SDLM has only a single hyperparameter as opposed to most other learning algorithms that suffer from the hyperparameter overfitting problem due to having more hyperparameters to be tuned. Experiments using the multilayer perceptron (MLP) and convolutional neural network (CNN) models have shown that B-SDLM outperforms other learning algorithms with regard to the classification accuracies and computational efficiency (about 5.3% faster than SGD on the mnist-rot-bg-img database). It can classify all testing samples correctly on the face recognition case study based on AR Purdue database. In addition, experiments on handwritten digit classification case studies show that significant improvements of 19.6% on MNIST database and 17.5% on mnist-rot-bg-img database can be achieved in terms of the testing misclassification error rates (MCRs). The computationally expensive Hessian calculations are kept to a minimum by using just 0.05% of the training samples in its estimation or updating the learning rates once per two training epochs, while maintaining or even achieving lower testing MCRs. It is also shown that B-SDLM works well in the mini-batch learning mode, and we are able to achieve 3.32× performance speedup when deploying the proposed algorithm in a distributed learning environment with a quad-core processor

A real-time near infrared image acquisition system based on image quality assessment

This paper presents a real-time image acquisition system with an improved image quality assessment module to acquire high-quality near infrared (NIR) images. Thermal imaging plays a vital role in a wide range of medical and military applications. The demand for high-throughput image acquisition and image processing has continuously increased especially for critical medical and military purposes where executions under real-time constraints are required. This work implements an NIR image quality assessment module, which utilizes improved two-dimensional entropy and mask-based edge detection algorithms. The effectiveness of the proposed image quality assessment algorithms is demonstrated through the implementation of a complete finger-vein biometric system. The proposed model is implemented as an embedded system on a field programmable gate array prototyping platform. By including the image quality assessment module, the proposed system is able to achieve a recognition accuracy of 0.87 % equal error rate, and can handle real-time processing at 15 frames/s (live video rate). This is achieved through hardware acceleration of the proposed image quality assessment algorithms via a novel streaming architecture