Upgrading of a Data Communication and Computer Networks Course in Engineering Technology Program

Data network communications is traditionally a course offered by computer engineering technology curricula, with the primary objective to introduce to the fundamental concepts in data communication and computer networks, as well as some level of hands-on component related to this area. Typical topics in such courses are the layered model of data communication, specifically the OSI seven-layered model, Internet routing, communication standards, protocols and technologies, and learning methods used to design the network and send data over the network in a secure manner. In the last decades, the data communication and applications have grown and become ubiquitous in both industry and people\u27s everyday life, alongside with increasing data rates and emerging broadband technologies, i.e., Internet access technologies. The ability to connect with other computers, remote systems, and mobile devices is also contributing to the increased number of applications in our daily life. Consequently, courses related to computer networks become imperative for students in engineering technology programs, as they are essential in preparing the students for the level of technology required on the current job market. However, it is challenging to keep up in classrooms with today\u27s industry requirements for graduates in terms of both content and hands-on activities. Firstly, the course content should be updated with emerging technologies, such as the Internet of Things (IoT), cloud computing, 5G, cybersecurity, etc. The knowledge of emerging communication technologies is crucial for student\u27s awareness of new trends and to prepare them for the industry, especially telecommunication and Information Technology (IT) sectors. Secondly, the course should cover hands-on activities that are aligned with the theoretical upgrades introduced in the class. Such activities should include the use of network analyzer tools for network analysis and communications protocol development, as well as a network simulator to provide students with a technology development environment for network design, troubleshooting, and protocol modeling in a simulated environment. Following these considerations, this paper presents the way the Data Network Communications course was updated as part of an overall curriculum revision in an Electrical Engineering Technology program. The paper discusses the course topics, the course objectives, and the software tools introduced to support the hands-on activities in the class, including the Wireshark software tool, for network troubleshooting, profiling network traffic and analyzing packets. The paper also presents the way the course was received by students, as well as lessons learned after the first semester of offering it in the new format and the modifications planned for future semesters

Senior Elective Communications Systems Courses as Pathways to Capstone Projects in Electrical Engineering Technology Program

In any engineering program the capstone project is the most comprehensive work completed by the students, and is regarded as the pinnacle of their engineering studies, with all their course work culminating with this major design, implementation and reporting product. Coming up with the actual topic of the project is sometimes the most difficult part of the project, especially in programs where the project topics are not solely proposed by the faculty, and they are for the student and advisor to develop together. This is especially the case of engineering technology programs, where a large percentage of students have work background (either from military training or industry, as interns or full-time employees) to which they can relate their senior projects, and the programs allow and encourage them to apply their coursework studies to application areas where they have strong hands-on skills. While core courses of any curriculum provide the foundation of the engineering education, the elective courses give the students the chance to refine their education path and focus on the area of their interest. Senior elective courses are defining the areas of specialization within a major, and they may also serve as grounds for the students to explore potential options for the capstone project, and to have the opportunity to get a good starting point for it, ahead of the capstone semester. In this paper, the senior level courses specific to communication systems area of concentration within an electrical engineering technology program are discussed, their course content and the term projects included, and how they offer venues to capstone project choices. The paper presents specific examples of how these course projects gave students successful pathways for capstone projects. The course content that can be covered by the curriculum of an undergraduate technology program is somehow limited, especially for a broad field such as communication systems, and beyond the fundamental theories, the courses can go in more details only on very few narrow areas. Therefore, with a term project in an elective course, students have the opportunity of a semester of deeper study of a topic of their choice, and the learnings and new skills developed can be later applied for the completion of a capstone project. The paper also discusses students’ opinions on the option of developing initial results or skills as part of a course project and continuing such project into a senior project, as well as how their topic selection is related to their background, previous experience and future goals

Role of Artificial Intelligence in the Internet of Things (IoT) Cybersecurity

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these three topics with the purpose of comprehensively presenting and summarizing relevant literature in these fields

Development of a Smart Grid Course in an Electrical Engineering Technology Program

Electric power systems courses have been traditionally offered by electrical engineering technology programs for a long time, with the main objective to introduce students to the fundamental concepts in the field of electric power systems and electrical to mechanical energy conversion. A typical electric power systems course covers a variety of topics, such as general aspects of electric power system design, electric generators, components of transmission and distribution systems, power flow analysis, system operation, and performance measures. In the last decades, electric power systems have significantly modernized alongside with requirement of improvement in system efficiency, reliability, cybersecurity, and environmental sustainability. The current modernized grid is called “Smart Grid,” which integrates advanced sensing technologies, control methods using machine learning approaches, and integrated communications into current electric power systems. Consequently, offered electric power systems courses are required to update in electrical engineering technology as well, to meet the industry needs of a workforce prepared to integrate smart grid technologies, such as advanced sensing, control, monitoring, communication, renewable energy, storage, computing, cybersecurity, etc. However, such updates of the course content are not always easy to implement due to the complexity of smart grid technologies and the limited number of instructors having knowledge of those technologies. In addition, smart grid courses should include a hands-on component aligned with the theoretical upgrades introduced in the course in the form of term projects. Such projects can be on a variety of topics, such as smart home/building, smart meter, smart distribution system, microgrid, communication infrastructure, Distributed energy resources (DERs) (e.g., rooftop solar photovoltaics (PV), wind), electric vehicle (EV), customer engagement, energy generation forecasting, load forecasting, and others. This paper will discuss the details of introducing a new course on smart grids in an electrical engineering technology program, including detailed examples of project selection

An Overview of Bidirectional Electric Vehicles Charging System as a Vehicle to Anything (V2X) Under Cyber–Physical Power System (CPPS)

Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much larger and smarter system with the help of emerging digitalization technologies, such as Artificial Intelligence (AI), Distributed Ledger Technology (DLT), and the Internet of Things (IoT). This study introduces a multi-layer Cyber–Physical Power Systems (CPPS) framework to explore the potential of V2X technologies allowing bidirectional charging. In addition, the impact of e-mobility is discussed from the V2X perspective. V2X has the potential to provide more practical use of electric vehicles and to bring advantages to the user in terms of both economy and comfort, thus accelerating the transformation of e-mobility and making it easier to accept

Simultaneous Wound Border Segmentation and Tissue Classification Using a Conditional Generative Adversarial Network

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs

Gaining Insight into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools

Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a ‘‘black-box’’ due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using XAI tools, such as LIME, SHAP, and ELI5, which can contribute to adopting XAI tools for smart grid applications. Understanding the inner workings of a prediction model based on AI can give insights into the application field. Such insight can provide improvements to the solar PV forecasting models and point out relevant parameters

A Comparison of Deep Learning Algorithms on Image Data for Detecting Floodwater on Roadways

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show that the proposed Mask-R-CNN-based floodwater detection and segmentation outperform previous studies, whereas the GAN-based model has a straightforward implementation compared to other models

WG\u3csup\u3e2\u3c/sup\u3eAN: Synthetic Wound Image Generation Using Generative Adversarial Network

In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various sizes taken from real hospital environments. Hyperparameters such as epoch count and dataset size for training tasks are studied to find optimum training conditions as well. The performance of the developed model was evaluated through a mean squared error (MSE) metric to determine the similarity between generated and actual wounds. Visual inspection is performed to examine generated wound images. The results show that the proposed synthetic wound image generation (WG2AN) model has great potential to be used in medical training and performs well in producing synthetic wound images with a 1000-image training dataset and 200 epochs of training

BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers to this challenge. Using homomorphic encryption, this research presents a privacy-preserving federated learning system for medical data. The proposed technique employs a secure multi-party computation protocol to safeguard the deep learning model from adversaries. The proposed approach is tested in terms of model performance using a real-world medical dataset in this paper