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With the development in the areas such as high performance graphic processing units (GPU), sensor technologies and artificial networks the goal of self-driving vehicles has never been closer. With the promise of massive decrease in the road accidents, self-driving cars have been the center of attention for the past few years. In this paper we discuss the major components of a self-driving vehicles along with the methodologies required to produce a self-driving vehicle. With the added advent of the 3D printed body our aim is to rapid prototype a 3D printed Electric Vehicle with the goal to test and improve the current self-driving algorithms. The chassis of the car, with an initial body design was simulated and tested along with some basic parameters such as vehicle speed acceleration and traction force. The paper also presents the simulated results of an end-to-end approach to steering vehicle using convolutional neural networks, that would later be the basis of the self-driving algorithm.

Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch&rsquo, s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification.

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

In this research human gait database is collected using different possible methods such as Wearable sensors, Smartphone and Cameras. For a gait recognition accelerometer data from wearable shimmer modules and smartphone are used. Data from different sensors location is compared to know which sensor location have better recognition rate. Different walking scenarios like slow, normal and fast walk were investigated. Wearable sensors and smartphone data are compared to know whether mobile phones can be used for gait recognition or not. Also effects of age, height, weight on gait recognition are also studied. The obtained results of gait biometric matrices like Genuine Recognition Rate (GRR), Total Recognition Rate (TRR) and Equal Error Rate (EER) showed better results. EER in different walking scenarios ranged from 0.17% to 2.27% for the five wearable sensors at different locations, whereas EER results of smartphone data ranged from 1.23% to 4.07%. For sensors located at leg, pocket and hand the average GRR value falls with increase in age group, while for sensors located at upper pocket and bag, the GRR value doesn’t follow any trend. Moreover GRR results on all sensors show no significance regarding height or weight variations.

Virtual Reality (VR) technology has become one of the most advanced techniques that is used currently in many fields. The role of education is extremely important in every society; therefore, it should always be updated to be in line with new technologies and lifestyles. Applying technology in education enhances the way of teaching and learning. This paper clarifies a virtual reality application for educational resolutions. The application demonstrates a virtual educational environment that is seen through a Virtual Reality headset, and it is controlled by a motion controller. It allows the user to perform scientific experiments, attend online live 360° lectures, watch pre-recorded lectures, have a campus tour, and visit informative labs virtually. The application helps to overcome many educational issues including hazardous experiments, lack of equipment, and limited mobility of students with special needs.

Distraction, drowsiness, and fatigue are the main factors of car accidents recently. To solve such problems, an Eye-tracking system based on camera is proposed in this paper. The system detects the driver’s Distraction or sleepiness and gives an alert to the driver as an assistance system. The camera best position is chosen to be on the dashboard without distracting the driver. The system will detect the driver’s face and eyes by using Viola-Jones Algorithm that includes Haar Classifiers that showed significant advantages regarding processing time and correct detection algorithms. A prepared scenario is tested in a designed simulator that is used to simulate real driving conditions in an indoor environment. The system is added in real-vehicle and tested in an outdoor environment. Whenever the system detects the distraction or sleepiness of the driver, the driver will be alerted through a displayed message on a screen and an audible sound for more attention. The results show the accuracy of the system with a correct detection rate of 82% for indoor tests and 72.8 % for the outdoor environment.

In this research paper, customizable wearable robust 3D printed bionic arm been designed and implemented for a right arm. An experimental test has been conducted for a person who has been born without a right arm. A control of a 9 DOF hand is accomplished successfully using EMG signals acquired by MYO armband. Different gestures are recognized and mapped to control different movements of the hand. Basic daily activities have been accomplished after training of that person. The arm is a lightweight for daily use for long time. The cost of the arm is cheap compared to available solutions available on the market.

Recent advances in the field of neurorobotics are surveyed with emphasis on the areas of brain-computer interface systems, brain-based robots and the human brain project. A simple proof-of-concept experiment, inspired by the mapping of the human brain into a robot, is described and constructed. The capturing of brain electroencephalogram signals was performed through an Emotiv Epoc headset. The resulting brain-computer interface was set-up to control a surrogate NAO humanoid robot, while feeding sensory data back to the subject. The various applications of this emerging technology is discussed while emphasising its research and pedagogical value.

The rise in popularity of humanoid robots will eventually lead to including them in all of our daily lives. Since these robots are opted to have the ability to move, act, and communicate like humans, eventually they will be used to replace humans in certain minimalist tasks or hazardous situation. In this paper, we propose a review of recent project discussing utilization of humanoid robots as service robots. Moreover, a scenario framework will be proposed where a humanoid robot (Pepper) will be performing certain service tasks at AUM reception.

Speech recognition is projected to play an increasingly important role in the future of human/machine interfacing. The objective of this study is to engineer a speech recognition system capable of deployment onto a general humanoid robot. A MATLAB based program for speech extraction and identification is constructed. Although there are many different algorithms used in speech recognition, the utilization of artificial neural networks (ANNs) was found to be adequate for the Arabic language, with its multitude of complexities, accents and linguistic intentionality. Furthermore, ANN is powerful and can model complex functions while offering the opportunity for additional cognitive abilities. The software tool developed converts the incoming audio signal into a two dimensional spectrogram, which is subsequently supplied to the ANN through a mel frequency cepstral coefficients (mfcc) algorithm. A software product capable of converting Arabic speech to commands was developed for controlling the “NAO” humanoid robot with 90% hit rate.

This paper proposed an initial engineering realization of an underwater remotely operated vehicle (ROV) with multisensory system for oil spills monitoring and detection in seawater. It covers hardware and software designs to tackle the purposes. Four environmental sensors, i.e. temperature, dissolved oxygen (DO), electrical conductivity (EC), and oxygen reduction potential (ORP), were attached on the ROV to monitor water quality parameters. Data from these sensors were transfered wirelessly to a ground segment to be analyzed. The ROV was also equipped with an HD camera for video imagery, an inertial measurement unit (IMU) sensor to monitor the ROV’s attitude, two LED flashlights to give extra lumens during underwater operation, and a customized 3D printed gripper. The ROV was actuated with three brushless DC motors with silicone-based coating. The software design was a binary difference image processing under MATLAB environment to identify oil spills based on video captures from the camera. Experiments for the water quality parameter monitoring and image processing have been carried out under seawater, oil contaminated seawater, and gaseous seawater samples, to show the performance of the designed hardware and software.

Abnormality of the driver's Bio-physiological parameters has great impact on the driver's capability, and the ability to control a vehicle; which may cause severe car accidents. In this paper, a novel system was developed to monitor the driver's health status through a Wearable Bio-Sensors Bracelet that detects heath emergency threatening situations. The system collects live bio-signals data through a Wearable Bio-Sensors Bracelet that is embedded with multiple Bio-sensors such as Electromyography (EMG), Electrocardiography (ECG), Electrodermal Activity (EDA), Accelerometer (ACC) and Temperature. A robotics rover car model has been used as a testing vehicle scenario. The developed system detects an emergency and takes control over the rover maneuver when the driver lost his capacity, and automatically avoids any obstacle by sensing the surrounding area. The system has been demonstrated in a prepared emergency scenario and verified the potential. This paper describes the design concept, system configuration, and the future prospect of the study.

With the huge development and the latest technological advancement in mechatronics, prosthetic devices have acquired interest in many different fields such as medical and industrial fields. A prosthetic device can be an external wearable mobile machine that covers the body or part of it. It is generated by pneumatics and electric motors. It can be installed on an upper and lower limb. Moreover, it can be used for different purposes such as rehabilitation, power assistance, diagnostics, monitoring, ergonomics, etc. Most of the existing wearable devices face different problems in terms of size, cost and weight; they are huge, expensive and heavy. Therefore, the goal of this project is to design a portable, lightweight and low-cost rehabilitation system for people with a paralyzed hand. The wearable device allows a user to perform specific movements and exercises to train the patient's impaired hand. Thus, the user gradually starts to restore the functionality of his hand.

The purpose of this experimental study was to define the soft and bony tissues changes as the elbow joint dislocates posteriorly in a primate model. Sixty-six fresh arms were used in this study and were divided into two groups where manual and automated procedures were performed to address the mechanism of elbow dislocation. The first group called IA (50 arms) was tested using a special designed apparatus and was used for Instron machine whereas second group IB (16 arms) a manual dislocation by hyper-extending the elbow at the end of the tabletop was performed. An axial compressive load was applied on the distal forearm at a constant rate of 10 mm/min in group IA. The humerus was rigidly secured on a humeral plate at 90 degrees (3), 45 degrees (17), 30 degrees (13) and 0 degrees (17) of elbow flexion. Photographs and computer data recorded the changes in the soft tissue and bone at the elbow. It required on average 1960 N to dislocate the elbow in pronation with flexion (45, 30 degrees) compared to 1030 N for supination and the elbow flexion (45, 30 degrees). Three reproducible stages of dislocation from initiation to complete failure were observed when the elbow was flexed at 45 degrees or 30 degrees with forearm pronated or supinated.