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Intelligent Mode-Locking Enabled by real-time Reinforcement Learning

Intelligent Mode-Locking Enabled by Real-Time Reinforcement ...

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2023 Asia Communications and Photonics conference/2023 international Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Wang, Jiajin Pu, Guoqing Fang, Zhiwei Luo, Chao Wu, Yong Yi, Lilin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University State Key Lab of Advanced Communication Systems and Networks Shanghai China

ISBN: (纸本)9798350312614

A real-time embedded approach to realize intelligent mode-locked fiber lasers based on reinforcement learning is proposed. The reinforcement learning is deployed in a Field programmable gate array (FPGA) to complete the inference process and real-time control. Benefited from the dedicated quantization and deployment strategy, the FPGA finishes a single forward inference in 6.4 us, which is about 32-times faster than an Nvidia RTX-3050 GPU, and the proposed solution manifests ultrahigh power efficiency. © 2023 ieee.

关键词： FPGA intelligent mode-locked fiber lasers real-time control reinforcement learning

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Optimizing Multimodal Transformers for Medical Image Captioning: Enhancing Automated Descriptions via AI systems 6

Optimizing Multimodal Transformers for Medical Image Caption...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Arman, Mithila Jahan, Md. Khurshid Dhrubo, Ahmed Faizul Haque Rhaman, Md. Mahfuzur Choya, Sumaya Binte Zilani Dohan, Din Mohammad Ul Islam Sajid, Md. Ashiq Rabiul Alam, Md. Golam BRAC University Dept. of CSE Dhaka Bangladesh North South University Dept. of ECE Dhaka Bangladesh George Mason University Dept. of Computational Data Science FairfaxVA United States American International University-Bangladesh Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798331506520

In contemporary diagnostic workflows, medical image captioning has emerged as a pivotal advancement, combining deep learning methodologies and transformer architectures to enhance accuracy and efficiency in medical interpretations. This paper proposes the optimization of multimodal transformers for automated medical image captioning, focusing on integrating Vision Transformers (ViT) and Bidirectional Auto-Regressive Transformers (BART) with novel variations such as Swin Transformers and GPT-2. We use a robust multimodal AI framework to explore how these architectures synergize to generate coherent and diagnostically relevant captions for radiological images. We assess the performance of multiple transformer models by employing the ROCO dataset, containing paired X-ray images and expert-generated reports. Our findings demonstrate that the ViT + BART combination yields the most stable and accurate captions, minimizing training and validation loss. In contrast, DEiT + MBART displayed instability, highlighting the need for further hyperparameter tuning. Through this comparative analysis, we underscore the critical role of transformer-based models in reducing the cognitive load on medical professionals, enhancing diagnostic accuracy, and promoting real-time, automated medical image interpretation. © 2025 ieee.

关键词： Medical imaging

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Frontend Subsystem Development In Traditional Market Digitalization E-Commerce applications 18

Frontend Subsystem Development In Traditional Market Digital...

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18th international conference on Telecommunication systems, Services, and applications, TSSA 2024

作者： Zakaria, Usman Juhana, Tutun School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331527686

Traditional market e-commerce applications aim to expand the market reach and potential buyers of traders in traditional markets, develop low-cost and sustainable solutions, and facilitate the dissemination of real-time data information about products and buyers' desires that are easy to see, and able to communicate directly. between traders and buyers. The Virtual Market application is designed to fulfill general activities that occur in traditional markets, such as buying and selling activities, conversations and advertising. The main characteristic of shopping in traditional markets is that there is bargaining between traders and buyers, so this application presents a bargaining feature that will facilitate this activity. This application will go through a testing and verification stage to determine that the application created is in accordance with the specified final assignment objectives. In this paper, the frontend system will be designed and designed as a whole, starting from designing the application by considering the user interface and user experience to implementing the design on mobile devices using the chosen programming language. © 2024 ieee.

关键词： Marketplaces

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4th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and applications, ICMISC 2023

4th International Conference on Recent Trends in Machine Lea...

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4th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and applications, ICMISC 2023

ISBN: (纸本)9789819994410

The proceedings contain 69 papers. The special focus in this conference is on Recent Trends in Machine Learning. The topics include: Implementation of Dual-Band Dielectric Resonator Antenna for 5G applications;defect Detection in Metal Surfaces Using Computer Vision;liver Cirrhosis Prediction Using Machine Learning Classification Techniques;a Recent Survey on Risk Factors Affecting the Blood Pressure in India;real-time Monitoring System for Breakdown Analysis and OEE in the Wire Drawing Industry;tooth Sensitivity Device—Detection and Diagnosing of Sensitivity in the Dental Pulp;recognition of Skin Cancer;ioT-Based Smart Street Lighting Surveillance System;rock Segmentation of real Martian Scenes Using Dual Attention Mechanism-Based U-Net;IAAS: IoT-Based Automatic Attendance System with Photo Face Recognition in Smart Campus;hardware Implementation of Moving Object Detection Using Background Subtraction Algorithm;crime Pattern Identification and Prediction Using Machine Learning;IMICE: An Improved Missing Data Imputation Using Machine Learning;analyzing Students’ Opinion on E-Learning—Indian Students’ Perspective;rash Driving Detection and Alerting System;Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images;Comparative Study of CNNs for Camouflaged Object Detection;3D Avatar Reconstruction Using Multi-level Pixel-Aligned Implicit Function;Helmet Detection Using YOLO-v5 and Paddle OCR for embedded systems;defogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss;Text-to-Image Generation Model with DNN Architecture and Computer Vision for embedded Devices Using Quantization Technique;one-Shot Learning for Archaeological Site Data Using Deep Neural Network on embedded systems;enhanceNet: A Deep Neural Network for Low-Light Image Enhancement with Image Restoration;intelligent Prediction of Cardiac Abnormality.

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real-time Crop Health Assessment with MobileNet for Agricultural applications 3

Real-Time Crop Health Assessment with MobileNet for Agricult...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Saidulu, Bukya Pushpalatha, R. Rajvanshi, Saurabh Alsalami, Zaid Singh, K. Ranjith Rani, L. Pattathu Department of Cse CMR Technical Campus Telangana Hyderabad India Department Of Computer Science Tamil Nadu Erode India Uttaranchal University Dehradun India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Kcg College of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331542375

The growth of sustainable and efficient farming systems has necessitated the use of automated crop health monitoring systems. In the current study, a real-time crop health assessment is proposed using a deep learning algorithm called MobileNet based on a lightweight CNN. The method under consideration includes using the camera of a smartphone to take detailed photographs of crops growing in the fields, and with the help of the MobileNet model, distinguish diseases, nutrient deficiencies, and other pathologies. To provide some resistance to variations in environmental circumstances, the current images are preprocessed through resizing, normalization, and augmentation. MobileNet model fine-tuning is made using a richer dataset that includes both healthy and disease crop images The model achieves an accuracy of 94.5% and micro-average of precision, recall, and F1-score of 95.3%, 93.7%, and 94.5% respectively. real-time efficiency tests are performed on disparate mobile devices and show that the system took less than 2 seconds on average to generate results and had a model size of 12 MB, suitable for usage on even minimalresource devices. Moreover, the trained model demonstrates relatively irgendeepstance to image brightness and orientation, which is rather advantageous for its practical application on the agricultural fields. This paper presents a possible practical and effective approach to field use, which will allow for improving precision agriculture, as well as the preservation of the environment and agricultural lands. © 2025 ieee.

关键词： convolutional neural network deep learning disease detection mobile devices MobileNet precision agriculture real-time crop health assessment

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applications of Machine Learning Techniques for Renewable Resource Management 2

Applications of Machine Learning Techniques for Renewable Re...

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2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications systems, ICMACC 2024

作者： Khot, Suhas Takawale, Amruta Kulkarni, Nikita Kulkarni, Rohan Nalawade, Himanshu S. Barfe, Rahul KJCOEMR EnTC Engineering Department Pune India KJCOEMR Electrical Engineering Department Pune India KJCOEMR Computer Engineering Departemnt Pune India

ISBN: (纸本)9798350366570

Renewable resource management plays a pivotal role in advancing sustainable development, particularly concerning energy production and utilization. The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques has significantly bolstered the efficiency and effectiveness of managing renewable resources. This survey paper offers a comprehensive overview of recent advancements in AI and ML applications within the realm of renewable resource management. It delves into areas such as renewable energy prediction, resource allocation, optimization strategies, and decision-making processes. Through this exploration, we aim to dissect various AI and ML algorithms, their associated benefits, challenges, and outline potential future research directions within the renewable resource domain. Our study centers on conducting an extensive survey of AI and ML techniques as applied to renewable resource management, practical analyses using Linear and Polynomial Regression models are performed on a real-world dataset to showcase the applicability and benefits of AI and ML in renewable resource management. The overarching goal is to unravel how AI and ML methodologies can elevate decision-making processes, finetune resource utilization strategies, and foster sustainable energy practices within the renewable resource sector. This paper offers a detailed examination of the transformative impact of AI and ML techniques on the renewable energy landscape. It delves into the pivotal role played by predictive modeling, optimization algorithms, and data analytics in surmounting the challenges inherent in renewable resource management. © 2024 ieee.

关键词： Adversarial machine learning

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Sign Language Recognition systems: A Comprehensive Review 1

Sign Language Recognition Systems: A Comprehensive Review

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Aggarwal, Rahul Meena, Anjali Kaur, Navleen Singh, Rajveer Patel, Suryamani National Institute of Technology Jalandhar Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350365016

Recently, emphasis has been placed on the applications of ISL since they assist people with HIs in their day- to-day lives in diversely populated India. This review paper aims to present years of development in real-time ISL recognition systems, the progress they brought, the issues they face, and the other ways it is being utilized in this ever-evolving area. We critically review the approaches for real-time ISL recognition from two aspects - the method and the assessment procedure based on the literature survey and recent developments and tendencies in the ISL research field. The current review accrues previous literature research to identify critical deficits and future suggestions to enrich the literature on assistive technologies for the deaf and hard-of-hearing people in India. © 2024 ieee.

关键词： Adversarial machine learning

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Metamaterial/Metasurface-Based Antenna with Improved Gain for Wireless Power Transfer systems 4

Metamaterial/Metasurface-Based Antenna with Improved Gain fo...

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4th ieee China international Youth conference on Electrical Engineering, CIYCEE 2023

作者： Islam, Tanvir Basherlou, Haleh Jahanbakhsh Ojaroudi Parchin, Naser University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Edinburgh Napier University School of Computing Engineering and the Built Environment Edinburgh United Kingdom

ISBN: (纸本)9798350305562

This research presents a novel ultra-wideband (UWB) antenna designed for future wireless power transfer systems. The antenna features improved radiation capabilities and incorporates a metamaterial-based structure. Its design includes a modified triangular patch as the main radiation element, complemented by three embedded split-ring metamaterial resonators, inspired by a compact 4×4 metasurface unit cells layout, all integrated into an FR4 substrate. The study demonstrates the antenna's effective operation within the frequency range of 2.8-12 GHz, covering the UWB spectra of 3.1-10.6 GHz. The findings confirm that the proposed design successfully achieves the desired frequency response, radiation characteristics, and gain levels, rendering it suitable for applications in ultra-wideband and wireless power-transferring systems. © 2023 ieee.

关键词： Resonators

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Architecting Efficient Multi-modal AIoT systems 23

Architecting Efficient Multi-modal AIoT Systems

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50th Annual international Symposium on Computer Architecture (ISCA)

作者： Hou, Xiaofeng Liu, Jiacheng Tang, Xuehan Li, Chao Chen, Jia Liang, Luhong Cheng, Kwang-Ting Guo, Minyi InnoHK Ctr ACCESS AI Chip Ctr Emerging Smart Syst Hong Kong Sci Pk Hong Kong Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn 800 Donchuan Rd Shanghai Peoples R China

ISBN: (纸本)9798400700958

Multi-modal computing ((MC)-C-2) has recently exhibited impressive accuracy improvements in numerous autonomous artificial intelligence of things (AIoT) systems. However, this accuracy gain is often tethered to an incredible increase in energy consumption. Particularly, various highly-developed modality sensors devour most of the energy budget, which would make the deployment of (MC)-C-2 for real-world AIoT applications a difficult challenge. To address the above issue, we propose AMG, an innovative HW/SW co-design solution tailored to multi-modal AIoT systems. The key behind AMG is modality gating (throttling) that allows for adaptively sensing and computing modalities for different tasks. This is non-trivial since we must balance situational awareness, energy conservation, and execution latency. AMG achieves our goal with two first-of-its-kind designs. 1) It introduces a novel decoupled modality sensor architecture to support partial throttling of modality sensors. Doing so allows one to greatly save AIoT power but maintains sensor data flow. 2) AMG also features a smart power management strategy based on the new architecture, allowing the device to initialize and tune itself with the optimal configuration. It can predict whether a reasonable degree of accuracy will be satisfied during runtime, and react proactively to remediate the gating process. Extensive evaluation based on our prototype system confirms that AMG improves the AIoT lifespan by 74.5% to 133.7% with the same energy budget while meeting the performance requirements.

关键词： Edge Artificial Intelligence Autonomous embedded systems Multimodal computing Modality Gating Energy Efficiency

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Design of circular-shaped two-element MIMO antenna for 5G communication

Design of circular-shaped two-element MIMO antenna for 5G co...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Subashini, V. Aasitha, S. Brindha, V. Madhumitaa, K. Rajalakshmi, B. Sri Sai Ram Institute Of Technology Department Of Electronics And Communication Engineering Chennai India Sri Sai Ram Engineering College Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

MIMO-Multiple Input Multiple Output antenna system has attained significant attention in modern wireless communication due to its ability to enhance spectral efficiency, improve data rates, and mitigate the effects of fading and interference. This paper presents an overview of MIMO antenna technology, focusing on its principles, design considerations, and applications in wireless communication systems. The fundamental concepts of MIMO, such as spatial multiplexing and diversity techniques, are discussed, highlighting their advantages in achieving higher data throughput and reliability. Additionally, various MIMO antenna design architectures, including single and multi-element antennas, beamforming, and array configurations, are explored in detail, emphasizing their role in achieving spatial diversity and exploiting multipath propagation. Furthermore, real-world applications of MIMO antennas in wireless standards like 4G LTE, 5G, and beyond are examined to illustrate their significance in meeting the increasing demands for high-speed and reliable communication. This circular shaped micro strip patch antenna operates over 28 GHz (5G band) range with the radiating efficiency of 81% and gain value of 7.28dBi. Simulated VSWR value is © 2024 ieee.

关键词： MIMO systems

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