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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Madhura, R. Krishnappa, Krutthika Hirebasur Shashidhar, R. Shwetha, G. Yashaswini, K.P. Sandya, G.R. Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Southern University and A&M College Dept. of Computer Science Baton RougeLA United States JSS Science and Technology University Department of Electronics and Communication Engineering Mysuru India

ISBN: (纸本)9798350317022

Verification plays vital role in chip design and takes up more than 70% of the time. Thus, building a reliable and reusable verification environment is necessary. UVM, or universal verification methodology, offers a potential remedy for these issues. In this paper, we demonstrate the digital design and verification of the ARINC 429 data bus which is frequently used in avionics applications using the Universal Verification Methodology (UVM) and DO-254 procedures. This paper entails designing the transmitter and receiver blocks in accordance with standard requirements and focusses on a thorough analysis of the ARINC 429 bus protocol. Next, a UVM test bench that complies with DO-254 standards and uses constrained random verification to confirm the design. In the creation of hardware for safety-critical tasks, DO-254 places a strong emphasis on the application of formal methods and requirements traceability. Multiple test cases were written to improve functional coverage and ensure thorough verification, resulting in a functional coverage of 98.43%. © 2023 IEEE.

关键词： ARINC 429 avionic systems DO-254 procedures System Verilog

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EEG Data Analysis for Stress Detection using K-Nearest Neighbor

EEG Data Analysis for Stress Detection using K-Nearest Neigh...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Shashidhar, R. Kadakol, Pramod Sreeniketh, D. Patil, Preetam Krishnappa, Krutthika Hirebasur Madhura, R. Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Southern University A&m College Dept. of Computer Science Baton RougeLA United States Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350315455

Stress is a impactful condition affecting individuals' well-being, making its objective assessment and management vital. In recent years, electroencephalography (EEG) has emerged as a promising tool for real-time stress measurement. This paper presents a comprehensive review of EEG-based stress detection methods and their applications. Notably, the proposed model showcased remarkable performance, achieving 100% accuracy in classifying stress and relaxation levels using EEG signals. The review underscores the identification of distinct EEG patterns associated with stress, such as elevated beta and theta power alongside decreased alpha and beta coherence. Various feature extraction techniques have been leveraged to extract pertinent information from EEG signals. Machine learning algorithms like K-Nearest Neighbor (KNN) have been instrumental in accurately classifying stress levels. This model when compared to the CIS-KNORAU DES model described in the research article with reference number of 17 as in the references. © 2023 IEEE.

关键词： Beta and theta power Electroencephalography (EEG) Feature Extraction Machine Learning Stress detection

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Efficient Deep Learning Approach for Breast Cancer Detection

Efficient Deep Learning Approach for Breast Cancer Detection

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Kasem Khalil Tamador Mohaidat Mahmoud Darwich Ashok Kumar Magdy Bayoumi Department of Electrical and Computer Engineering University of Mississippi Mississippi USA Department of Mathematics and Computer Science University of Mount Union Alliance OH USA The Center for Advanced Computer Studies University of Louisiana at Lafayette LA USA Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA USA

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Breast cancer detection saves lives by enabling early diagnosis, timely intervention, and treatment. Early detection and treatment reduce patients' mortality rates and reduce the overall burden of the disease on affected individuals and healthcare systems. A scientific research challenge for breast cancer detection is achieving as high accuracy as possible. Machine learning is providing greater hope for achieving high accuracy. This paper proposes a high-accuracy breast cancer detection method utilizing feature correlation and a reconfigurable neural network (RNN). The proposed method has been comprehensively tested using breast cancer detection datasets and is compared against Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) methods. The results demonstrate the superior performance of the proposed method, achieving an accuracy of 97.2% and sensitivity of 97.8 %, surpassing known traditional methods. The proposed method is implemented using VHDL on Xilinx Virtex-7 FPGA and incurs a lower power consumption of 0.984 W with acceptable resource utilization.

关键词： Support vector machines Accuracy VHDL Power demand Nearest neighbor methods Feature extraction Breast cancer

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Physics-Inspired Distributed Radio Map Estimation

arXiv

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arXiv 2025年

作者： Yang, Dong Wang, Yue Zhang, Songyang Li, Yingshu Cai, Zhipeng Department of Computer Science Georgia State University AtlantaGA United States Department of Electrical and Computer Engineering University of Louisiana LafayetteLA United States

To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME). While existing deep learning based methods conduct RME given spectrum measurements gathered from dispersed sensors in the region of interest, they rely on centralized data at a fusion center, which however raises critical concerns on data privacy leakages and high communication overloads. Federated learning (FL) enhance data security and communication efficiency in RME by allowing multiple clients to collaborate in model training without directly sharing local data. However, the performance of the FL-based RME can be hindered by the problem of task heterogeneity across clients due to their unavailable or inaccurate landscaping information. To fill this gap, in this paper, we propose a physics-inspired distributed RME solution in the absence of landscaping information. The main idea is to develop a novel distributed RME framework empowered by leveraging the domain knowledge of radio propagation models, and by designing a new distributed learning approach that splits the entire RME model into two modules. A global autoencoder module is shared among clients to capture the common pathloss influence on radio propagation pattern, while a client-specific autoencoder module focuses on learning the individual features produced by local shadowing effects from the unique building distributions in local environment. Simulation results show that our proposed method outperforms the benchmarks in achieving higher performance. © 2025, CC BY.

关键词： Information leakage

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A 3-Phase Supercapacitor Based Single-Source MLI with Self-Voltage Balancing and Boosting Ability

A 3-Phase Supercapacitor Based Single-Source MLI with Self-V...

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

作者： Mondal, Sudipto Biswas, Shuvra Prokash Islam, Md. Sabbir Bin Islam, Md. Rabiul Fekih, Afef Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Bangladesh Army University of Science and Technology Saidpur5310 Bangladesh 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 3-phase multilevel inverter (MLI) based on a supercapacitor (SC) unit, which is ideal for medium and low-voltage grid-feeding photovoltaic (PV) applications since it is built to deliver nine different output voltage (line-to-line) levels from a single isolated dc source. Two SC units are utilized in each phase to create virtual dc sources while providing self-voltage balancing for the SCs. As a result, no extra control complexity is required compared to conventional neutral point clamped (NPC), flying capacitor (FC) or any other series connected capacitor-based inverter topologies. The proposed MLI is designed such a way that, the SCs could boost the output voltage up to four times than the input dc voltage. The MATlaB/Simulink computer simulation environment and a preliminary experimental prototype setup are used to verify the performance of the proposed MLI. © 2023 IEEE.

关键词： autoboost Multilevel inverter (MLI) photovoltiac (PV) self-voltage balancing single source inverter supercapacitor

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Visual ScanPath Transformer: Guiding computers to See the World

Visual ScanPath Transformer: Guiding Computers to See the Wo...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Mengyu Qiu Quan Rong Dong Liang Huawei Tu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Information Technology La Trobe University Melbourne Australia

We propose to exploit the scanpath prediction technology to simulate human visual system to automatically generate gaze scanpaths for VR/AR applications, to alleviate the equipment and computational cost in foveated rendering. Specifically, we propose a novel deep learning-based scanpath prediction model called Visual ScanPath Transformer (VSPT), to predict human gaze scanpaths in both free viewing and task-driven viewing situations, based on which the VR/AR systems can execute foveated rendering rapidly and cheaply. The proposed VSPT first extracts highly task-related image features from the visual scene, and then explores the global dependency relationships among all the image regions to generate each image region a global feature. Next, VSPT simulates the human visual working memory to consider all the previous fixations’ influences when predicting each fixation. Experimental findings confirm that our model exhibits adherence to classical visual principles during saccadic decision-making, surpassing the current state-of-the-art performance in free-viewing and task-driven (goal-driven and question-driven) visual scenarios.

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Machine Learning Algorithm-based Prediction Models for Adolescent Suicide Attempt

Machine Learning Algorithm-based Prediction Models for Adole...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jinwoo Jung Seulmi Kang Myeonghwan Go Sieon Lee Seokheon Cho Department of Computer Science and Engineering Konkuk University Seoul South Korea Department of Computer Science and Engineering Yonsei University Seoul South Korea Department of AI Computer Engineering Kyonggi University Suwon South Korea Qualcomm Institute University of California San Diego La Jolla USA

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper presents a system aimed at preventing the serious social issue of adolescent suicide by identifying at-risk adolescents early and providing appropriate support. To achieve this, we propose machine learning models based on data from the Korea Youth Risk Behavior Web-Based Survey (KYRBWS), targeting South Korean adolescents and considering various factors to predict suicide attempts. The models incorporate variables such as adolescents' behavioral patterns, emotional states, and family and social support systems. We applied Random Forest for feature selection to identify important features from the original dataset. The core algorithms include Logistic Regression, Random Forest, and XGBoost. To ensure robust performance, we employed K-fold cross-validation. Additionally, to address the challenge of data imbalance, we applied various sampling techniques to develop reliable, high-performance models for predicting suicide attempts. By detecting adolescents at high risk of suicide attempts, we aim to contribute to suicide prevention efforts and promote adolescent health and well-being.

关键词： Surveys Logistic regression Machine learning algorithms Prevention and mitigation Predictive models Prediction algorithms Sampling methods Feature extraction Data models Random forests

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An Efficient Hardware Design of CoAP Protocol for The Internet of Things

An Efficient Hardware Design of CoAP Protocol for The Intern...

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Proceedings of the IEEE Dallas Circuits and Systems Workshop (DCAS)

ISBN: (数字)9798350349535

ISBN: (纸本)9798350349542

CoAP (Constrained Application Protocol) plays a crucial role in IoT by providing a lightweight and efficient communication protocol tailored for resource-constrained devices, facilitating seamless and reliable data exchange. This paper proposes a novel hardware accelerator for CoAP to address critical performance challenges in the context of the Internet of Things (IoT). The proposed method focuses on optimizing two key parameters, namely latency and execution time, through a carefully designed hardware setup. Implemented using VHDL on Xilinx Virtex-7 FPGA, the study demonstrates through extensive evaluations that the proposed method outperforms traditional software-based CoAP implementations. It achieves reduced execution times and lower latency across varying payloads and sampling rates, with a power consumption of 4.63 mW. These results signify a notable improvement in CoAP functionality, positioning the proposed hardware acceleration approach as a promising candidate for enhancing communication protocols in IoT environments.

关键词： Performance evaluation Energy consumption Protocols VHDL Power demand Internet of Things Reliability

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YoNet: A Neural Network for Yoga Pose Classification

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SN computer science 2023年第2期4卷 198页

作者： Ashraf, Faisal Bin Islam, Muhammad Usama Kabir, Md Rayhan Uddin, Jasim Department of Computer Science and Engineering University of California Riverside CA United States School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA United States Department of Computing Science University of Alberta Edmonton AB Canada Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff Wales United Kingdom

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision. © 2023, The Author(s).

关键词： Deep learning Image classification Neural network Pose recognition Yoga pose

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Inspecting Mega Solar Plants through computer Vision and Drone Technologies

Inspecting Mega Solar Plants through Computer Vision and Dro...

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Ahmed, Syed Umaid Affan, Muhammad Raza, Muhammad Ilyas Harris Hashmi, Muhammad National University of Computer and Emerging Sciences Department of Computer Sciences Karachi Pakistan University of Toulon SeaTech School of Engineering La Garde France Vision Innopolis University Institute of Robotics and Computer Innopolis Russia University of Alabama at Birmingham Department of Computer Science Birmingham United States

ISBN: (纸本)9798350345933

This research presents a unique approach for monitoring the large-scale grid-connected photovoltaic modules in solar power plants using state-of-art object detection YOLOv5 algorithm and classical image processing techniques. We have highlighted an integral part of the fully automated system in which a drone takes flight over the solar park and shoot the videos. Videos are preprocessed and used for trained YOLOv5 model to recognize the clean and dirty panels. The process is defined for a selected site and can be implemented using a Raspberry Pi. This system processes the images taken by drones, generates a report, and sends it to the concerned department automatically via email every day so that timely maintenance can be done for the long-life and safe operation of solar arrays. The inspection timeline for the same process was about one hundred and twenty hours, reduced to five minutes. It means that 99.93% of the time is saved through vision and robust automation techniques. © 2022 IEEE.

关键词： Drones

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