Using SDN as a Technology Enabler for Distance Learning Applications

The number of students who obtained degrees via distance learning has grown considerably in the last few years. Services provided by distance learning systems are expected to be delivered in a fast and reliable way. However, as the number of users increases, so does the stress on the network. Software-Defined Networking, on the other hand, is a new technology that provides a rapid response to the ever-evolving requirements of today’s businesses. The technology is expected to enhance the overall performance of cloud services, including those provided by distance learning. This paper investigates the benefits of employing such a technology by educational institutions to provide quality services to the users. The results of the experiments show an improvement in performance of up to 11%, when utilizing the technology. In addition, we show how resource reservation features can be utilized to provide quality service to users depending on their role in the distance learning system

Design of intelligent thruster decision-making system for USVs

Marine environmental surveys using unmanned surface vessels (USVs) can be a challenging task, especially if the surveyed area takes several weeks to cover. There is also the constant risk of depleting the battery before the mission is completed, which is associated with the challenge of vehicle power management. Thrusters in unmanned vehicles are the main power drainers. Waves, currents, and wind unpredictable behavior have a great influence on the motion of the vehicle and, hence, affect whether the vehicle is to be able to fulfill its mission in the allocated time. The primary objective of the present research is to design an algorithm that optimize USV\u27s power consumption by predicting the amount of power devoted to the thruster as a function of time. Thruster power predictions were performed by a genetic algorithm that uses battery, vehicle speed, solar power, and wave height as well as wave period information to forecast generated and consumed electric power. The Wave Glider was utilized as the USV of study in this work. Simulation results showed that the presented algorithm outperforms a human pilot in reducing thruster power utilization per unit distance by 17%, producing semi-consistent thruster activation plan that satisfy mission objectives as well as constraints

A novel drone-based system for accurate human temperature measurement and disease symptoms detection using thermography and AI

The world continues to witness several waves of COVID-19 spread due to the emergence of new variants of the SARS-CoV-2 virus. Stopping the spread requires synergistic efforts that include the use of technologies such as unmanned aerial vehicles and machine learning. This paper presents a novel system for detecting disease symptoms from a distance using unmanned aerial vehicles equipped with thermal and visual image sensors. A hardware/software system that uses thermography to accurately calculate the skin temperature of targeted individuals using thermal cameras is developed. In addition, machine vision algorithms are developed to recognize human actions such as coughing and sneezing, which are paramount symptoms of respiratory infections. The proposed system is implemented and tested in outdoor environments. The results of experiments showed that the system can determine the skin temperature of multiple targeted individuals simultaneously with an error of less than 1 °C. The field experiments showed that the developed system is capable of simultaneously measuring the temperature of more than 10 individuals in less than 5 seconds. Just to give a perspective, it takes at least 3 seconds to measure one individual\u27s temperature if this was done using traditional methods. Furthermore, the results showed that the system has accurately detected actions such as coughing and sneezing with almost 96% accuracy at a real-time performance of 28 frames/second

Customized Hardware Crypto Engine for Wireless Sensor Networks

Nowadays, managing for optimal security to wireless sensor networks (WSNs) has emerged as an active research area. The challenging topics in this active research involve various issues such as energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, and efficiency. Despite the open problems in WSNs, already a high number of applications available shows the activeness of emerging research in this area. Through this paper, authors propose an alternative routing algorithmic approach that accelerate the existing algorithms in sense to develop a power-efficient crypto system to provide the desired level of security on a smaller footprint, while maintaining real-time performance and mapping them to customized hardware. To achieve this goal, the algorithms have been first analyzed and then profiled to recognize their computational structure that is to be mapped into hardware accelerators in platform of reconfigurable computing devices. An intensive set of experiments have been conducted and the obtained results show that the performance of the proposed architecture based on algorithms implementation outperforms the software implementation running on contemporary CPU in terms of the power consumption and throughput

3d gpu architecture using cache stacking: Performance, cost, power and thermal analysis

Abstract—Graphics Processing Units (GPUs) offer tremendous computational and processing power. The architecture requires high communication bandwidth and lower latency between computation units and caches. 3D die-stacking technology is a promising approach to meet such requirements. To the best of our knowledge no other study has investigated the implementation of 3D technology in GPUs. In this paper, we study the impact of stacking caches using the 3D technology on GPU performance. We also investigate the benefits of using 3D stacked MRAM on GPUs. Our work includes cost, power, and thermal analysis of the proposed architectural designs. Our results show a 53 % geometric mean performance speedup for iso-cycle time architectures and about 19 % for iso-cost architectures. I

Accelerating Neuromorphic Vision Algorithms for Recognition

ABSTRACT Video analytics introduce new levels of intelligence to automated scene understanding. Neuromorphic algorithms, such as HMAX, are proposed as robust and accurate algorithms that mimic the processing in the visual cortex of the brain. HMAX, for instance, is a versatile algorithm that can be repurposed to target several visual recognition applications. This paper presents the design and evaluation of hardware accelerators for extracting visual features for universal recognition. The recognition applications include object recognition, face identification, facial expression recognition, and action recognition. These accelerators were validated on a multi-FPGA platform and significant performance enhancement and power efficiencies were demonstrated when compared to CMP and GPU platforms. Results demonstrate as much as 7.6X speedup and 12.8X more power-efficient performance when compared to those platforms