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Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an adaptive threshold function to detect QRS complexes. The algorithm was validated and results were checked with experimental data based on sensitivity test.

Through human development and technological expansion, it has become apparent that the potential lies within each individual to have an essential part in the transcendence of society and the community. People less privileged than others may need more strength and determination to surpass their current resources to overcome normal and natural obstacles in order to simulate an environment where productivity and creativity exist. This paper aims to study an approach that will assist the elderly and people of determination in one of the most essential activities practiced by individuals: shopping. The study focuses on facilitating the acquirement of items from shelves and skipping the cashier line. The proposed system is a service robot supported by a robotic arm and a linear actuator as a lifting mechanism, controlled by a remote joystick to help the elderly or disabled people reach items on high shelves. The scanning system is based on barcode detection, using transfer learning. The network was designed using YOLOv2 layers connected to TinyYOLO as feature extraction layers. This network has proven to be the most practical, with 86.4% accuracy and real-time operation with 27 FPS in comparison to using the YOLOv2 layers with DarkNet or VGG19 as feature extraction layers. An anti-theft system is integrated into the robot to improve the reliability of the self-checkout feature. The system uses computer vision GMM and Kalman filter for item detection inside the cart, and the item is validated to be the one that has been scanned, using SURF for structural features, HSV for color, and load-sensors mounted to the base of the cart to measure the item’s weight.

The existence of shadows in natural scenes cause challenges in computer vision applications such as object detection. In this paper, a proposed approach is introduced for shadow detection in single images. Unlike other approaches, our algorithm uses the variation in the RGB components in order to locate the drop in intensity and analyze it. The input image would be subjected to noise reduction using a Gaussian filter then vertical scanning is applied where the pixels at every column of the image are collected, grouped using a threshold to obtain smooth knee points through the variation of the intensity levels and analyzed. Shadow removal is done and then horizontal scanning is carried out following the same procedure. The analysis and optimization process was done on 300 images and the final testing was done on 4000 images from the SBU shadow images dataset. The results have shown the success of our algorithm to detect high accuracy of shadows than other approaches.

Social distancing is a necessary precaution measure taken in order to have more control over the outbreak of infectious diseases such as COVID-19. Most of Social distancing monitoring approaches are based on Bluetooth and mobile phones that require an app to be downloaded on all phones. This paper proposes a different approach to monitor social distancing, using cameras, and combining different computer vision algorithms. The approach utilizes the concept of inverse perspective mapping (IPM) together with the camera’s intrinsic information to produce a bird’s eye view with real-world coordinates of the frame being processed from a video source. The process starts with image enhancement, foreground detection using Gaussian Mixture Model (GMM) background subtraction, tracking using Kalman filter, computing real-world distance measurements between individuals, and detecting those who have been in less than 2 meters apart as they are considered to be in contact. This tool could assist the efforts of the governments to contain the virus. It can be implemented in closed areas or institutions, monitor the extent of people’s commitment, and provide analysis and a faster approach to detect possibly corona suspicion cases. The approach is tested on the task decomposition data set, which included frames of closed areas and the camera’s intrinsic parameters. Another data set was created with different scenarios to increase the confidence level of our algorithm. The results showed the success of our approach in detecting the violation in social distancing with accurate measures of the real-world coordinates.

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.

In this paper, a discrimination system, using a neural network for electromyogram (EMG) externally controlled Arm is proposed. In this system, the Artificial Neural Network (ANN) is used to learn the relation between the power spectrum of EMG signal analysed by Fast Fourier Transform (FFT) and the performance desired by handicapped people. The Neural Network can discriminate 4 performances of the EMG signals simultaneously. The digital signal processing was realized using MATLAB and LabVIEW software.

In rotating machinery, rolling element bearings are one of the most critical components and a large majority of system failures arise from faulty bearings. Hence, there is an increasing demand to find an effective and reliable condition monitoring technique. In this paper, a procedure for detecting various types of bearing faults using thermal imaging is presented and assessed. Five different fault cases are tested: No Fault (NF), Line Fault (LF), Small Circle Fault (SCF), Double Line Fault (DLF), and Large Circle Fault (LCF). Experiments were conducted on the BENTLY NEVADA RK4 Rotor Kit. A novel signal processing algorithm is proposed to detect the faults which involves utilizing the Discrete Wavelet Transform (DWT). The HAAR wavelet is used as the mother wavelet and a decomposition level of 7 is used. The inverse of HAAR is applied on the decomposed signal and the envelop spectrum is plotted. A classifier is then created to identify the fault that utilizes the Support Vector Machine (SVM) classifier to extract the features and a confusion matrix is developed to detect the prediction accuracy.

Flight maneuvers can generate nonlinear and unsteady aerodynamic loading. Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Logic Inference System (ANFIS) are developed to model and predict aerodynamics coefficient under different values for angle of attack. The system is developed to model the unsteady aerodynamic normal force and the pitch moment coefficients. Based on the extensive analysis of the proposed identification methods, the artificial intelligence models showed low mean square errors in modeling and prediction results.

Six Degree of Freedom (6 DOF) parallel manipulators have been utilized for many applications. In this paper, a 6 DOF manipulator is used as a robust vehicle stabilizer in order to fix the orientation of the upper platform. The paper starts with kinematic analysis of a Stewart platform. This analysis is formulated to cope with the design of vehicle stabilizer. The design and selection of mechanical components including primary joints based on comprehensive dynamic simulation. After validating simulation results of proposed design, they were implemented in to build a physical model. To improve system accuracy and performance, and to eliminate associated vibrations, a linear regression model of ground rise is embedded in the system to estimate and predict upcoming elevations. This has lowered the percentage error of platform orientation, made the system more stable.

An efficient automotive security system is implemented for anti-theft using an embedded system occupied with a Global Positioning System (GPS) and a Global System of Mobile (GSM). The client interacts through this system with vehicles and determines their current locations and status using Google Earth. The user can track the position of targeted vehicles on Google Earth. Using GPS locator, the target current location is determined and sent, along with various parameters received by vehicle's data port, via Short Message Service (SMS) through GSM networks to a GSM modem that is connected to PC or laptop. The GPS coordinates are corrected using a discrete Kalman filter. To secure the vehicle, the user of a group of users can turn off any vehicle of the fleet if any intruders try to run it by blocking the gas feeding line. This system is very safe and efficient to report emergency situations as crash reporting or engine failure.

Al-Khedher, M. A. (2012). Hybrid GPS-GSM localization of automobile tracking system. arXiv preprint arXiv:1201.2630., An integrated GPS-GSM system is proposed to track vehicles using Google Earth application. The remote module has a GPS mounted on the moving vehicle to identify its current position, and to be transferred by GSM with other parameters acquired by the automobile's data port as an SMS to a recipient station. The received GPS coordinates are filtered using a Kalman filter to enhance the accuracy of measured position. After data processing, Google Earth application is used to view the current location and status of each vehicle. This goal of this system is to manage fleet, police automobiles distribution and car theft cautions.