The use of bio-signal is very crucial, providing enormous information concerning health and well-being of the individual. such signals can be measured and monitored by specialized devices to each bio-signal, for instance, the electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). Due to use of such devices, these signals could be utilized for several objectives. As it is observed in the devices of medical detection and Human to Machine Interactions (HCI). This paper presents a low-cost bio-signal collection device which is having the ability to record ECG, EMG, and EOG signals. Furthermore, STM32F103C8 system is used in Analog to Digital Conversion (ADC), with its particular application. An application has been developed in order to allow admins to observe and save the data signal simultaneously. This application has been developed by using  C++ programming language and MATLAB’s code. The data signal is recorded in a format of mat file, which can be studied in details in the proposed system. This system is capitalized on Universal Serial Bus (USB)  wired communication link, which is used to transmit the bio-signal through, that guarantees the safety ,avoid noise and interference. The system shows its compatiblity with various operating systems, such as, Windows, Linux, and Mac.

The COVID-19 pandemic has been one of the most challenging crises attacking the world in the last three years. Many systems have been introduced in the field of COVID-19 detection. In this research, machine learning and deep learning models for the detection of COVID-19 with a probability of the presence of COVID-19 are proposed. In the machine learning scenario, the COVID-19 dataset is split into 70% training and 30% testing, and a segmentation process is applied to the CT images in order to get the lung ROI only. The features of CT images are then extracted using Gabor-Wavelet and deep-based features. The SVM classifier is then trained and evaluated. For the deep learning model, the CT images are fed into the model without feature extraction, and three different DL models (CNN, GoogleNet, and ResNet50) are trained and evaluated. Other scenarios are proposed in which the SVM Gabor-Wavelet and deep features are fused, and the three deep learning models are also fused to get better performance. The experiments show that the best model is the deep-based fusion model by which the system achieved 96.4156%, 96.1905%, and 96.1905% for accuracy, precision, and recall, respectively.

No abstract

This paper focuses on the antenna synthesis of uniformly spaced linear phase array using artificial neural network (ANN) based on Particle Swarm Optimization (PSO). The weights of the Artificial Neural Networks (ANN) are trained by Particle Swarm Optimization (PSO). Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the "global best" ANNs for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. Chebyshev method is used to compare with this approach. Although, Chebyshev method is able to generate perfectly leveled side lobes, PSONN does not have the phenomena of up-swing in edges amplitude of the excitation and grating lobes does not appear in PSONN when the distances between elements are increased.  The basic rule is to alter the weights (current distributions of elements) such that the error between the output values and the target values (desired values) is minimized. In this paper, single layer feed forward neural network with PSO training is used.

No abstract