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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Mourya, Jigisha John, Navya Kumar, Krishan Sharda University Dept. of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798331532956

This paper addresses the challenge of improving user interaction with autonomous vehicles by integrating voice command recognition with real-time visual feedback. The study employs Google's speech recognition Application Programming Interface to transcribe spoken commands and the turtle graphics library to simulate the corresponding vehicle actions. The system is designed to accurately recognize and process voice commands, translating them into visual simulations that mirror the intended vehicle movements. The research method involved testing the system with various commands to assess its performance and effectiveness. The findings suggest that this integrated approach has significant potential for enhancing the usability and accessibility of autonomous vehicle systems. © 2024 IEEE.

关键词： Speech recognition

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Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks 24

Carotid plaque stroke risk assessment using multiscale AM-FM...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Constantinou, Kyriacos P. Constantinou, Ioannis P. Pattichis, Marios S. Kyriacou, Efthyvoulos Pattichis, Constantinos S. University of Cyprus Dept. of Computer Science Nicosia Cyprus Istognosis Ltd Nicosia Cyprus University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States Cyprus University of Technology Dept. of Electrical Engineering Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350339598

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%. © 2023 IEEE.

关键词： Frequency modulation

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Analysis on Short-Term Degradation of Oxide Thin-Film Transistors and Degradation Modeling for Prediction

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Transactions of the Korean Institute of electrical Engineers 2023年第3期72卷 402-405页

作者： Park, Ji-Hwan Kang, Kyeong-Soo Park, Junhyeong Lee, Soo-Yeon Dept. of Electrical and Computer Engineering SOFT Foundry Institute Seoul National University Seoul Korea Republic of

Gate bias stress can change the threshold voltage of amorphous indium gallium zinc oxide (a-IGZO) thin-film transistor (TFT). Especially, short-term degradation of threshold voltage should be compensated since it causes motion artifacts in the display. Therefore, a new short-term degradation model was proposed based on the stretched-exponential model. Moreover, the accuracy of the proposed model was confirmed by calculating the coefficient of determination between the measured data and the fitted data. Copyright © The Korean Institute of electrical Engineers.

关键词： Thin film transistors

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A Conformal Microwave Measurement System for Imaging Buried Anomalies

A Conformal Microwave Measurement System for Imaging Buried ...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Islam, M. Shifatul Islam, M. Asiful Kiourti, Asimina Dept. of Electrical and Computer Engineering The Ohio State University ColumbusOH43212 United States Dept. of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9781946815200

Microwave imaging has long been reported for locating buried anomalies inside a bulk object as relying upon external measurements of microwave data. The approach is noninvasive, low-cost, portable, and the use of non-ionizing radiation makes it popular for biomedical applications. In this work, we explore the experimental feasibility of a conformal microwave imaging system, where the antennas are mounted on or very close to the imaging object. Our proof of concept setup entails 12 antennas surrounding a plastic cylindrical frame in free space with two dielectric cylinders used as anomalies. S-parameters were measured using a network analyzer and a 12-port switching matrix. For imaging, we used the Born Iterative Method, where the data equation was replaced with the S-parameter sensitivity formula. Simulation and measurement results show good quality image reconstruction, asserting the feasibility of the proposed conformal imaging setup. Future work will incorporate tissue-emulating materials to ultimately develop a stable and wearable imaging system. © 2025 USNC-URSI.

关键词： Cylinders (shapes)

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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Eccher, Davide Casari, Paolo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331540081

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker. © 2024 IEEE.

关键词： Underwater acoustics

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Gait Recognition: A Cross - Dataset Evaluation of Deep Learning Models

Gait Recognition: A Cross - Dataset Evaluation of Deep Learn...

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2024 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2024

作者： Singloo, Hemal Kumar, Manoj Namdev, Simran Sonbawne, Kinsukh Suni Indoria, Nishant Vit Bhopal University Dept. Computer Science and Engineering Sehore India

ISBN: (纸本)9798350348460

This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional neural networks (CNN) and long-term memory networks (LSTM), focusing on comparative analysis across multiple datasets to gain insight and contribute to different data analyses. The gait level dataset contains important movement patterns as a main area of research. Additionally, sensor-based data identifies various information needs, and complete analysis, for greater understanding and use. These comprehensive experimental results provide several observations on the variability and performance of CNNs and LSTMs across various datasets. The applicability of these models in recognizing activities in different products is presented, and their advantages and limitations in model recognition, display, and feature extraction are seen. This study distinguishes between different types of deep learning models and demonstrates their suitability for handling different types of data. These results lead to decisions regarding model selection and application, contributing to advances in analytics, biometrics, and human recognition applications. © 2024 IEEE.

关键词： CNN Deep Learning Algorithms Gait data LSTM Machine Learning

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Image Recognition-Based Indoor Positioning System Using Federated Learning 100

Image Recognition-Based Indoor Positioning System Using Fede...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Yu, Heng-Shao Jhuang, Yu-Chiao Leu, Jenq-Shiou University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798331517786

With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy. © 2024 IEEE.

关键词： Federated learning

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Balancing Safety and Security in Autonomous Driving Systems: A Machine Learning Approach with Safety-First Prioritization 40

Balancing Safety and Security in Autonomous Driving Systems:...

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40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Hasani, Afshin Nia, Mehran Alidoost Atani, Reza Ebrahimi University of Guilan Dept. of Computer Engineering Rasht Iran Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9798331532819

In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-Attacks such as GPS spoofing and man-in-The-middle. This paper presents a novel reinforcement learning (RL)-based framework that prioritizes safety over security concerns in AVs, ensuring that life-critical tasks such as collision avoidance and speed control are always maintained, even in the presence of security threats. Our system leverages the MAPE-K loop to dynamically adapt to changing conditions, using RL in the Analyzing phase to make real-Time decisions that maximize cumulative safety rewards while addressing security risks that directly affect operational integrity. We demonstrate how our RL-based self-Adaptive system mitigates security threats while maintaining safe driving behaviors through various simulations and experiments. The proposed system shows significant improvements in collision avoidance and threat detection, ultimately ensuring that the AV's safety is preserved in even the most challenging scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Prosecutorial Outcome Predication with LoRA and QLoRA 37

Prosecutorial Outcome Predication with LoRA and QLoRA

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37th Annual Conference on Legal Knowledge and Information Systems, JURIX 2024

作者： Chien, Kuo-Chun Chang, Chia-Hui Huang, Huai-Hsuan Kung, Jo-Chi Dept. of Computer Science & Information Engineering National Central University Taiwan

ISBN: (纸本)9781643685625

Our study on Legal Judgment Prediction (LJP) focuses on indictments, designing innovative tasks for prosecutors to predict reasons, imprisonment, fines, and penalty types. We investigated multi-task learning (MTL), Low-Rank Adaptation (LoRA), and its quantized version (QLoRA). Results show that the MTL framework, combined with LoRA and QLoRA, excels in prediction while optimizing resource use. Error analysis in the Reason field of TWPJD dataset was conducted to guide future predictions. Future work will focus on balancing sub-task performance and collecting more investigative data to enhance legal AI applications. © 2024 The Authors.

关键词： Multi-task learning

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Exploring Machine Learning Techniques for Accurate Heart Disease Detection: A Comprehensive Study

Exploring Machine Learning Techniques for Accurate Heart Dis...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of Data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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