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检索条件"机构=Dept. of Computer and Electrical Engineering and Computer Science"

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A High Performance Switched-Capacitor Based Single-Phase Transformerless Inverter

A High Performance Switched-Capacitor Based Single-Phase Tra...

引用

2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Biswas, Mohua Biswas, Shuvra Prokash Mondal, Sudipto Islam, Anas Bin Rabiul Islam, Md. Fekih, Afef Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Deakin University School of Engineering VIC3216 Australia University of Wollongong School of Electrical Computer & Telecommunications Engineering NSW2522 Australia University of Louisiana at Lafayette Dept. of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350301571

This paper presents a new circuit structure of five-level switched-capacitor (SC) based transformerless inverter which is ideal for grid-integrated photovoltaic (PV) applications. The main objective of the proposed multilevel inverter (MLI) is to suppress the leakage current close to zero meanwhile maintaining lower number of semiconductor device count. Self-voltage balancing and voltage boosting capability of SCs are also ensured while designing the proposed MLI. Moreover, optimum switching operation ensures low power dissipation of the semiconductor devices while achieving greater efficiency. The proposed MLI along with the control strategy is developed and validated through MATLAB/Simulink computer simulation environment while an extensive loss analysis is conducted in PLECS simulation platform to ensure a strong feasibility of the proposed MLI. © 2023 IEEE.

关键词： leakage current suppression multilevel inverter photovoltiac (PV) power loss Switched capacitor (SC) voltage boosting

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Exploring the Capabilities and Limitations of Generative AI Applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Gulbir Meenakshi Sharma, Poonam Bhardwaj, Anjul Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nanital India Mmict&bm Mullana Haryana Ambala India Divine Institute of Management & Studies Dept. of Computer Application Ziradei Bihar Siwan India

ISBN: (纸本)9798331508685

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible. © 2025 IEEE.

关键词： Generative adversarial networks

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Cross-Domain Federated Computation Offloading for Age of Information Minimization in Satellite-Airborne-Terrestrial Networks

Cross-Domain Federated Computation Offloading for Age of Inf...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： X. Xia H. H. Esmat K. Dyer B. Lorenzo L. Guo Dept. Electrical and Computer Engineering University of Massachusetts Amherst USA Dept. Electrical and Computer Engineering Clemson University USA

Satellite-Airborne-Terrestrial Networks (SATNs) are expected to provide communication and edge-computing services for a plethora of IoT applications. However, preserving the freshness of information is challenging since it requires timely data collection, bandwidth, and offloading decisions across different administrative domains. In this paper, we aim to optimize the age of information (AoI) and energy consumption tradeoff when serving multiple traffic classes in SATNs. A cross-domain federated computation offloading algorithm (Fed-SATEC-Off) is presented in which different service providers (SPs) collaborate to allocate the bandwidth while unmanned aerial vehicles (UAVs) and satellites make decisions to collect, relay, and offload the computing tasks. Given the requirements of each traffic class, the optimum collaborative strategies between SPs, UAVs, and satellites are obtained together with the computation offloading topology. Our algorithm is based on multi-agent actor-critic and incorporates federated learning to improve the convergence of the learning process. The numerical results show that Fed-SATEC-Off reduces the AoI by factor 4 and achieves faster convergence than existing approaches.

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SubQUBO Annealing Based on Efficient Binary Variable Selection for Combinatorial Optimization Problems with One-Hot Constraints

SubQUBO Annealing Based on Efficient Binary Variable Selecti...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Noguchi, Tatsuya Fukada, Keisuke Bao, Siya Togawa, Nozomu Dept. of Computer Science and Communications Engineering Waseda University Japan

ISBN: (纸本)9798331521165

In combinatorial optimization problems with one-hot constraints, the number of binary variables that have the value of one in the feasible solution becomes constant and small, so it is important to always consider them to escalate the solution searching process and obtain a more converged solution in annealing. In this paper, we propose a method to preferably select binary variables with the value of one in the subQUBO annealing process using Ising machines such as quantum annealers. Evaluation experiments show that the proposed method obtains a solution with lower energy than a conventional subQUBO method and a full QUBO method. © 2025 IEEE.

关键词： Combinatorial optimization

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods 9

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Elmazi, Donald Karras, Dimitrios A. Alkholidi, Abdulsalam Capari, Klea Faculty of Engineering Dept. of Electronics and Telecommunications Tirana Albania Dept. S/W Engineering Tirana Albania Dept. General School of Science Athens Greece Tirana Albania Faculty of Engineering Sana'a University Yemen University of Rijeka Faculty of Engineering Dept. of Computer Engineering Croatia Faculty of Engineering Cit Dept. of Software Engineering Tirana Albania

ISBN: (纸本)9798350333794

Presently, the global proliferation of Internet of Things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. The special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. The statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

关键词： Internet of things

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Stomach Cancer Prediction and Detection using Deep Learning: A Review

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Sharandeep Kaur Paurav Goel Nitika Kapoor dept. of Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100 % in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates.

关键词： Deep learning Stomach Accuracy Reviews Soft sensors Mortality Predictive models Cancer Biomedical imaging Tumors

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Machine Learning Approach for Skin Melanoma Classification

Machine Learning Approach for Skin Melanoma Classification

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2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Sanvordekar, Manjusha Kuppili, Venkatanareshbabu Modi, Chirag National Institute of Technology Dept. of Computer Science & Engineering Goa Ponda India

ISBN: (纸本)9781665454995

Melanoma is the most fatal type of malignant skin cancer, posing a serious threat to people's physical health. If melanoma of the skin is detected early, the chances of survival are very high. Dermoscopic images can be used to make an early diagnosis. Dermoscopy magnifies the skin, allowing dermatologists to better evaluate morphological features that are not easy to see with the naked eye. Machine learning is now an important technique for detecting various types of skin cancers. Convolutional Neural Network (CNN) has the potential to greatly assist dermatologists in the diagnosis of melanoma accurately. This paper uses a machine learning based approach to detect benign and malignant forms of skin cancer in dermoscopic images. The EfficientNet CNN model used here can design the appropriate network architecture to extract features with greater better accuracy and efficiency. The model is evaluated using dataset from the ISIC Archive. © 2022 IEEE.

关键词： Convolutional neural networks

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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A Systematic Study of Deep Q-Networks and Its Variations 2

A Systematic Study of Deep Q-Networks and Its Variations

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Jain, Aditya Mehrotra, Aditya Rewariya, Akshansh Kumar, Sanjay Delhi Technological University Science and Engineering Dept. of Computer New Delhi India

ISBN: (纸本)9781665437899

Recently, reinforcement learning has been at the forefront of the automation revolution. Reinforcement learning has proved to be a particularly powerful technique since it can understand the actions that will lead to ultimate success in an unfamiliar environment without any of the intervention of a supervisor. Due to the advent of Neural Networks, the unison of Deep learning and Reinforcement learning has led to unprecedented scaling of previously thought computationally expensive algorithms. This has caused an emergence of autonomous systems that can play games and automate robotics using visual cues, to name a few. In this paper, we intend to analyse a particular reinforcement learning algorithm called Deep Q-Networks and its numerous other variants. We cover several approaches including Improved Replay Memory, Distributional Outlook of Deep Q-Networks. Also, we discuss their advantages and disadvantages and conclude by detailing the current area of research in this field and future progress in sight. © 2022 IEEE.

关键词： Reinforcement learning

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Near-Threshold-at-Gate based Test for Stuck-on Fault in Scan-chain Testing

Near-Threshold-at-Gate based Test for Stuck-on Fault in Scan...

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International Conference on VLSI Design

作者： R S Haripriya Soumitro Vyapari Jaynarayan T Tudu Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science & Engineering Indian Institute of Technology Tirupati India

Scan integrity test has become inevitable for the design manufacturing that demands low DPPM. As the current technology has already moved towards nanometer realm, the manufacturing process begin to see newer defects at the level of cell. Such defect doesn’t seems to be detectable either by stuck-at or transition delay test alone. The newer kind of defects which are becoming prominent are rather subtle requiring a newer test methodology that would involve finer control of voltage and current at the transistor itself. In this work we have addressed the fault of kind which can be classified as Stuck-on fault in NMOS transistor. The specific problem that we attempt to solve is that how do we test the stuck-on fault in the NMOS transistor on the scan path? The solution we propose is to find out a newer scan integrity test methodology to ensure the correctness of scan chain before it can be used for design test. The proposed solution provides a scan integrity test technique that involves a careful control of gate voltage at near threshold value. This near threshold voltage at input sensitizes the Stuck-on fault which is then propagated to the scan-out by the process of flushing shift. The proposed methodology has been experimentally verified for the standard scan cell design using 16nm technology as well as at the logic level it has been implemented on ISCAS circuit to demonstrate the sensitization and propagation of fault effect. The proposed methodology does not requires any additional DFT area.

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