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Deep Reinforcement Learning based Traffic signal Control: A Comparative Analysis 14

Deep Reinforcement Learning Based Traffic Signal Control: A ...

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14th international conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th international conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Wu, Chunliang Kim, Inhi Ma, Zhenliang Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology Daejeon34051 Korea Republic of Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm11428 Sweden

More recently, the advancement of deep learning techniques enables reinforcement learning (RL) to handle high-dimensional decision-making problems. It brings increasing attention in transport areas. Some work has applied them to solve the intractable traffic signal control (TSC) problem and achieved promising results. However, very few research comprehensively investigates the impacts of key design elements, including state definitions, reward functions and deep reinforcement learning (DRL) methods, on TSC performance. To fill this research gap, this paper first selects commonly used design elements from existing literature. Then, we compare their learning stability and control performance at an isolated intersection under different scenarios via simulation experiments. The experimental results show that the quantitative state (e.g., the number of vehicles on lanes) and image-like state (e.g., vehicle position and speed) have no significant impact on the performance under different traffic demands. However, the impact of various reward functions on performance is vivid, especially under high traffic demand. Also, high-resolution vehicular network data may not be possible to improve control performance versus ordinary camera data. In addition, the value-based DRL algorithms outperform the policy-based algorithm and traditional TSC control methods. The findings of this research would provide insights and guidance for transport engineers to design an efficient DRL-based TSC system in a real traffic environment. © 2023 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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A Lightweight image Entropy-based Divide-and-Conquer Network for Low-Light image Enhancement

A Lightweight Image Entropy-Based Divide-and-Conquer Network...

引用

2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Wu, Hongjun Qi, Haoran Luo, Jingzhou Li, Yining Jin, Zhi School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

images captured in low-light conditions usually suffer from degradation problems. based on the observation, we found that different image regions have different enhancement difficulties and can be processed by networks with different capacities. Hence, in this work, we propose a lightweight image entropy-based divide-and-conquer network called IEDCN for low-light image enhancement. Our network consists of Pre-processing, Enhancement, and Refinement three stages. In the Pre-processing Stage, we crop the low-light image into patches, and classify them into 'simple', 'medium' and 'hard' groups according to their image entropy. Then patches in each group are enhanced separately by corresponding branches with the divide-and-conquer strategy in the Enhancement Stage. Finally, the combined segments from the branches are refined by the last stage as the final output. Compared with other state-of-the-art methods, our IEDCN with only 0.73M parameters can effectively improve the quality of enhanced images, while saving up to 53% Flops on the LOL dataset. © 2022 IEEE.

关键词： image enhancement

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Validation of machine learning for Clinical Assessment of lumbar spine pathologies Using Graphic User Interface 7

Validation of machine learning for Clinical Assessment of lu...

引用

7th international conference on image and signal Processing and their Applications, ISPA 2022

作者： Boudjella, Aissa Arab, Sarah Bellebna, Bachir Guerbouz, Rabeh Bircham International University Dept. Electrical Engineering Miami United States Etablissement Hospitalo Universitaire Service Neurochirurgie Oran Algeria

ISBN: (纸本)9781665480420

The vertebral column degenerative disc clinical disease repressent an important part of the activity in neurospine surgery, in particular, the lumbar pathologies. Humans have certain limitations in terms of accuracy and effective time to perform the diagnostic prediction. the diagnosis by the radiologist is not always as accurate as the use of computer-aided technology (CAT). Nowadays, CAT becomes necessary to overcome these limitations in diagnosing diseases. It will help a neurosurgeon to make rapid and effective decisions. The purpose of our work is to introduce machine learning computer-aided technology methods to perform the diagnostic of pathology on the column vertebral based on the K-Nearest Neighbor classifier. Several patients with lumbar spine disease will be presented to evaluate and validate the performance of the proposed technique. It can help to diagnose a patient effectively based on the statistics performance metrics: Accuracy, precision score, sensitivity, F1-score, and specificity. The simulation results show that the proposed technique can correctly predict the pathologies with a significant higher recognition rate (100%), with the training accuracy and the test accuracy in the range of (82.6%~100.0%), (70.2%~95.2%), respectively. To conclude, the proposed model works better, indicating a pretty good prediction and a high recognition rate in detecting a new patient disease effectively. This will result in reducing the heavy physician workloads and diagnostic time. The proposed technique can be recommended to be integrated for clinical support decision systems and will serve as a primary screening and diagnosis guidline. © 2022 IEEE.

关键词： Pathology

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Generative AI Meets IoT: A Bibliometric Mapping of Research and Development

Generative AI Meets IoT: A Bibliometric Mapping of Research ...

引用

international conference on signal Processing and Communication (ICSC)

作者： Parneet Kaur Deepali Gupta Mudita Uppal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Computer technology has transformed the way people interact online within society. The integration of (GIoT) combines Artificial Intelligence (AI) and internet of Things (IoT) technology, offering numerous benefits in many fields for innovation and growth. GIoT helps with real-time applications for data prediction and alerts. based on the authors' understanding and searches in scholarly databases, less bibliometric analysis has yet been performed on Generative Artificial Intelligence (GAI) for the IoT, even though such analyses are crucial for research development in this field. This study conducted a thorough bibliometric examination of GAI applications in the IoT over the period from 2014 to 2024. The analysis is based on a dataset of 296 documents sourced from the Scopus database. The bibliometric review was conducted using the Biblioshiny application, a tool available within the Bibliometric package in the R programming language. Top literature sources, major fields of study, nations, well-known authors, popular subjects, authorship, citations, author-keywords, and co-keywords were all subjected to bibliometric analysis.

关键词： Technological innovation Computer languages Generative AI Databases Reviews Bibliometrics signal processing Real-time systems internet of Things Research and development

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Comparison of Multi-Modal Large Language Models with Deep Learning Models for Medical image Classification

Comparison of Multi-Modal Large Language Models with Deep Le...

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IEEE international conference on signal and image Processing Applications (ICSIPA)

作者： Joel Chia Ming Than Wan Tze Vong Kelvin Sheng Chek Yong School of Information Computing and Technology Swinburne University of Technology Sarawak Campus Kuching Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

In recent years, the advancement of large language models (LLMs) such as GPT-4 and Gemini has opened new avenues for artificial intelligence applications in various domains, including medical image classification. This study aims to compare the performance of multi-modal LLMs with state-of-the-art deep learning networks in classifying tumour and non-tumour images. The performance of four multi-modal LLMs and four conventional deep learning methods were evaluated using several performance measures. The results demonstrate the strengths and limitations of both approaches, providing insights into their applicability and potential integration in clinical practice. Gemini 1.5 Pro performs the best out of the eight models evaluated. This comparison underscores the evolving role of AI in enhancing diagnostic accuracy and supporting medical professionals in disease detection especially when training data is scarce.

关键词： Deep learning Heating systems Accuracy Large language models image processing Training data internet

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Mathematical Model of the OFDM System that Fends Off the Consequences of Failures Using Modular Codes

Mathematical Model of the OFDM System that Fends Off the Con...

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international Russian Automation conference (RusAutoCon)

作者： Igor A. Kalmykov Alersandr A. Olenev Nikita K. Chistousov Department of Information Security of Automated Systems North-Caucasus Federal University Stavropol Russia Department of Information Security of Automated Systems Department of Mathematics Computer Science and Digital Educational Technologies North-Caucasus Federal University State Pedagogical Institute Stavropol Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The expansion of the scope of industrial internet of Things (IIoT) technology is largely due to the advent of low-orbit satellite internet (LEO SI). LEO SI uses OFDM technology to provide broadband access and data exchange between subscribers. One of the methods that allows to increase the speed of information transfer in OFDM systems is based on replacing the fast Fourier transform (FFT) with discrete wavelet transforms (DWT). The use of parallel DWT calculations based on modular residue class codes (MCRC) makes it possible to increase the speed of orthogonal signal transformations. This result is achieved due to the parallel and independent execution of arithmetic operations based on MCRC bases. However, failures and malfunctions of the digital signal processing computing device may occur during signal processing. This leads to errors in the calculation process. When redundancy is introduced, modular codes are able to detect and correct such errors. Thus, the use of MCRC makes it possible to increase the fault tolerance of the OFDM system by fending off the consequences of failures. Therefore, the development of a mathematical model of the OFDM system that fends off the consequences of failures using modular codes is an urgent task.

关键词： Codes Satellites Fast Fourier transforms OFDM Simulation signal processing algorithms Transforms Mathematical models Discrete wavelet transforms Industrial internet of Things

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Low-light image enhancement based on variational Retinex model 13

Low-light image enhancement based on variational Retinex mod...

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13th international conference on signal Processing systems, ICSPS 2021

作者： Li, Xuesong Zhang, Hongyi Pan, Jinfeng Li, Qilei Zou, Guofeng Gao, Mingliang School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

The low-light image enhancement plays a crucial role in computer vision and multimedia applications. However, it is still a challenging task, as the degraded images reduce the visual naturalness and visibility. To address this problem, we build a novel variational Retinex model to accurately estimate the illumination and reflectance components. The illumination and reflectance are jointly updated by alternating optimization algorithm. Experimental results on several public datasets demonstrate that the proposed method outperforms the state-of-the-art methods in Retinex decomposition and illumination adjustment. © COPYRIGHT SPIE.

关键词： image enhancement

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4th international conference on Communication, Devices and Computing (ICCDC 2023)

4th International Conference on Communication, Devices and C...

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4th international conference on Communication, Devices and Computing (ICCDC 2023)

ISBN: (纸本)9789819927098

The proceedings contain 55 papers. The special focus in this conference is on Communication, Devices and Computing. The topics include: Design of a Cost-Effective Remote Health Monitoring System Using IoT;current Exploration in Microfluidics-based Point-Of-Care Biosensor Applications: A Review;a Review on Memristor-based Reactance-Less Relaxation Oscillator;Optimal Control and Voltage Sag Compensation in a PV/FC Integrated Microgrid Through Biography based Optimization Technique;Performance Comparisons of MPEDE-based Integer and Fractional-Order PID Controllers for a Cruise Control System;Novel CMOS 1-Digit BCD-Adder Correction Circuit;digital image Tampering Detection Using Deep Learning: A Survey;nanorod Shaped TiO2 Photoanode and Mixed Halide Absorber-based Perovskite Solar Cell Device Fabrication;GA-based IMC Fractional PI Controller Design for Dissolved Oxygen Control with a Non-integer Order Biological Wastewater Treatment Plant;a Gray Wolf Optimization-Inspired Hybrid Method for Disease Identification;a Comprehensive Study for Maximum Power Point Tracking Methodologies for Wind Power systems;deep Learning Approach to Recognize Yoga Posture for the Ailment of the Low Back Pain;Multicrops Disease Identification and Classification System Using Deep MobileNetV2 CNN Architecture;machine Learning and Deep Learning-based Smart City Infrastructure to Connect Intelligent Domain Using internet of Things;Experimental Investigation on Spectrum Sensing Testbed Using GNU Radio and SDR;harris Hawks Optimization-based Multilevel Thresholding Segmentation of Magnetic Resonance Brain images;applications in Medical technology for Optimized Convolutional Neural Network Using Differential Evolutionary Algorithm;Optimization of Different Parameters of One Bit Full Adder Using QCA technology;A Robust PID Design for Load Frequency Control Using AEPSO Algorithm with Changing Loads;bilayer Graphene Nanoribbon Transistor for Butane Gas Detection;sentiment-based Simplification of Legal

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Research on image Encryption Algorithm based on Chaotic System

Research on Image Encryption Algorithm Based on Chaotic Syst...

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Data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Wu Yihang Sun Yanling Li Yingqi School of Mechanical Engineering Shenyang Jianzhu University Shenyang China Department of Science Shenyang Jianzhu University Shenyang China Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

With the development of the internet, social software and media have become ubiquitous, making images increasingly important in daily life. The dissemination of a large number of images on social networks has made information security issues increasingly prominent, and image encryption technology has become increasingly important. This article mainly studies image encryption algorithms based on chaotic systems. Firstly, the background and history of image encryption were introduced, followed by an explanation of the basic concepts based on chaotic systems. Then, several chaos based image encryption schemes were comprehensively compared, and their advantages and disadvantages were analyzed. Subsequently, several common measures for evaluating the effectiveness of encryption schemes were introduced. Finally, the future development of image encryption algorithms based on chaotic systems is discussed.

关键词： Computers Quantum computing Social networking (online) Software algorithms Information security Media Software Robustness Encryption History

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2022 IEEE international conference on Communications Workshops, ICC Workshops 2022

2022 IEEE International Conference on Communications Worksho...

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2022 IEEE international conference on Communications Workshops, ICC Workshops 2022

ISBN: (纸本)9781665426718

The proceedings contain 205 papers. The topics discussed include: implementation of ultra-fast polar decoders;research on the statistical orbital angular momentum coverage based on quasi-talbot effect in mobile network;on the performance of wireless optical communication systems in underwater channels;multi-agent deep reinforcement learning for uplink power control in multi-cell systems;on the secrecy performance of UAV-assisted wireless communication system with hardware impairments and protected zone;a unified view on semantic information and communication: a probabilistic logic approach;a reinforcement learning based service scheduling algorithm for internet of drones;security tradeoffs in rate splitting multiple access: optimal signal splitting vs revealing;intelligent requests orchestration for microservice management based on blockchain in software defined networking: a security guarantee;multi-objective intelligent handover in satellite-terrestrial integrated networks;autoencoder-based semantic communication systems with relay channels;machine learning for detecting security attacks on blockchain using software defined networking;achieving two-level age by free-ride coding in preemptive mission-critical networks;and iterative spectral image reconstruction-based display field communication using advanced receiver.

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