scholarly journals System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 348
Author(s):  
Francisco de Melo ◽  
Horácio C. Neto ◽  
Hugo Plácido da Silva

Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily “fooled” by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today’s demanding security systems—mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC’s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.

2021 ◽  
Author(s):  
Malte Oeljeklaus

This thesis investigates methods for traffic scene perception with monocular cameras for a basic environment model in the context of automated vehicles. The developed approach is designed with special attention to the computational limitations present in practical systems. For this purpose, three different scene representations are investigated. These consist of the prevalent road topology as the global scene context, the drivable road area and the detection and spatial reconstruction of other road users. An approach is developed that allows for the simultaneous perception of all environment representations based on a multi-task convolutional neural network. The obtained results demonstrate the efficiency of the multi-task approach. In particular, the effects of shareable image features for the perception of the individual scene representations were found to improve the computational performance. Contents Nomenclature VII 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Related Work and Fundamental Background 8 2.1 Advances in CNN...


2020 ◽  
Author(s):  
Luis H. S. Vogado ◽  
Rodrigo M. S. Veras ◽  
Kelson R. T. Aires

Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.


2020 ◽  
Vol 20 (15) ◽  
pp. 8287-8296 ◽  
Author(s):  
Siliang Lu ◽  
Gang Qian ◽  
Qingbo He ◽  
Fang Liu ◽  
Yongbin Liu ◽  
...  

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.


2019 ◽  
Vol 16 (6) ◽  
pp. 7982-7994
Author(s):  
Siyu Chen ◽  
◽  
Yin Zhang ◽  
Yuhang Zhang ◽  
Jiajia Yu ◽  
...  

2018 ◽  
Vol 232 ◽  
pp. 01061
Author(s):  
Danhua Li ◽  
Xiaofeng Di ◽  
Xuan Qu ◽  
Yunfei Zhao ◽  
Honggang Kong

Pedestrian detection aims to localize and recognize every pedestrian instance in an image with a bounding box. The current state-of-the-art method is Faster RCNN, which is such a network that uses a region proposal network (RPN) to generate high quality region proposals, while Fast RCNN is used to classifiers extract features into corresponding categories. The contribution of this paper is integrated low-level features and high-level features into a Faster RCNN-based pedestrian detection framework, which efficiently increase the capacity of the feature. Through our experiments, we comprehensively evaluate our framework, on the Caltech pedestrian detection benchmark and our methods achieve state-of-the-art accuracy and present a competitive result on Caltech dataset.


2020 ◽  
Vol 10 (4) ◽  
pp. 142-148
Author(s):  
Ahmad Reda ◽  
Tareq Alshoufi ◽  
Ahmed Bouzid ◽  
József Vásárhelyi

With a view to create an intelligent remote control for robot movements, this article treats the study case of dataset creation using RSG (Reference Signal Generator). Using artificial intelligence, the device recognizes the gestures of an operator. Indeed, a neural network can classify time series data coming from accelerometers, and for a beginning 4 gestures are taken into consideration. The most challenging work is to build a reference dataset that is necessary for the learning process. To train the neural network, a huge amount of reference data should be created (hundreds of thousands of time-series vectors per gesture per sensor), which cannot be done manually by an operator. To overcome the issue, an RSG is created. This article also describes how a 1-DoF arm has been designed to emulate the behavior of the human arm doing gestures as well as the data acquisition system. The system is based on a software/hardware co-design implemented on Programmable System on Chip (PSoC).


Author(s):  
K. Rahmani ◽  
H. Mayer

In this paper we present a pipeline for high quality semantic segmentation of building facades using Structured Random Forest (SRF), Region Proposal Network (RPN) based on a Convolutional Neural Network (CNN) as well as rectangular fitting optimization. Our main contribution is that we employ features created by the RPN as channels in the SRF.We empirically show that this is very effective especially for doors and windows. Our pipeline is evaluated on two datasets where we outperform current state-of-the-art methods. Additionally, we quantify the contribution of the RPN and the rectangular fitting optimization on the accuracy of the result.


2020 ◽  
Vol 41 (Supplement_1) ◽  
Author(s):  
Y W Hau ◽  
H W Lim ◽  
C W Lim ◽  
S Kasim

Abstract Stroke is one of the most severe cardiovascular disease which can potentially cause permanent disability. Atrial Fibrillation (AF) is one of the major risk factors of stroke that can be detected from electrocardiogram (ECG) monitoring.  Objective This study proposed an AF detection algorithm based on stationary wavelet transform (SWT) and artificial neural network (ANN) for screening purpose. The algorithm is aimed for embedded System-on-Chip (SoC) technology deployment as a standalone AF classifier for community in rural area where the internet infrastructure may not well established. Methods After standard ECG signal pre-processing, SWT is applied to filtered ECG and produces 12 sets of primary features in time-frequency domain. The power spectral density (PSD) and log energy entropy (LogEn) were calculated from these 12 sets of primary features, to measure atrial activity fall in frequency range of 4 to 9 Hz, and the randomness of an ECG signal caused by AF, respectively. Finally, the ANN classifier recognizes the pattern of AF based on high atrial activity and randomness of ECG signal. Algorithm exploration is carried out to determine the optimum parameter value which can yield the best classification and suitable to be implemented in embedded SoC technology for real-time computation performance. ECG training and testing datasets of the proposed AF detection algorithm were extracted from MIT-BIH Atrial Fibrillation Database which consists of 23 ECG record with each record contains a 10 hours ECG data.  Results AF detection accuracy is 95.3% which was able to classify an ECG signal into categories of AF, sinus rhythm, and other arrhythmia.  Conclusion The proposed AF detection algorithm based on combination of SWT and ANN can achieve high accuracy and is suitable to be implemented as a standalone AF classifier based on embedded SoC technology targeted for early detection of AF in the community.


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