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Energy Efficient joint user association and power allocation using Parameterized Deep DQN 9

Energy Efficient joint user association and power allocation...

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9th international conference on computer and Communication engineering, ICCCE 2023

作者： Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul Sunway University Dept. of Computing and Information Systems Petaling Jaya Malaysia University of Turku Dept. of Computing Turku Finland Cardiff Metropolitan University Dept. of Electronics and Computer Systems Engineering Cardiff United Kingdom International Islamic University Dept. of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems. © 2023 IEEE.

关键词： Energy efficiency

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MVAD: monocular vision-based autonomous driving distance perception system 3

MVAD: monocular vision-based autonomous driving distance per...

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3rd international conference on computer Vision and Data Mining, ICCVDM 2022

作者： Wei, Xiaoyun Xiao, Chaosui Wuhan research institute of posts and telecommunic Wuhan China

ISBN: (纸本)9781510661363

In recent years, with the development of social economy and the unprecedented development of computer hardware computing power, artificial intelligence technology has developed rapidly, the era of big computing has come, and breakthroughs in deep learning algorithms have brought new opportunities for human-computer interaction and autonomous vehicles. Autonomous driving is divided into three parts: perception, planning and control. The perception part is the most widely used technical field of computer vision, and the camera, as one of the indispensable sensors in intelligent driving vehicles, provides important image information for self-driving vehicle systems. The automatic driving system can obtain the location and distance information of various vehicles and pedestrians appearing in front of the car, providing accurate road information for drivers, avoiding traffic accidents, reducing casualties and property *** paper studies the object detection task and depth estimation task based on monocular vision in the visual distance perception system of autonomous driving. The emergence of deep learning has promoted the rapid development of the field of computer vision, and the application of deep convolutional neural networks has enabled vision-based object detection and depth estimation. The estimation accuracy has been greatly improved, but the computational load of the deep convolutional neural network is very large. Since the automatic driving system needs to control the car in real time, it requires the visual perception algorithm to run in real time, and the on-board computing platform of the self-driving car is required. It belongs to the mobile computing platform, and its computing power is very limited. Therefore, this paper designs a multi-task deep convolutional neural network to detect the targets appearing in the field of view of the autonomous vehicle in real time based on monocular images, and obtain the front The distance between the target veh

关键词： Deep neural networks

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A Kernel Affine Projection Maximum Correntropy Criterion Algorithm Based on the q-Rényi Kernel

A Kernel Affine Projection Maximum Correntropy Criterion Alg...

引用

2023 IEEE international conference on Electrical, Automation and computer engineering, ICEACE 2023

作者： Wu, Qishuai Zhou, Wenwen Xu, Lin Liu, Jiahui Jia, Peisong Chen, Wenjian Yantai Research Institute Harbin Engineering University Yantai China Wuhan Second Ship Design and Research Institute Wuhan China Yantai Research Institute and Graduate School Harbin Engineering University Yantai China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350309614

This work, a new kernel affine projection algorithm called q-Rényi kernel-affine-projection-maximum correntropy-criterion (qKAPMCC) is proposed. The qKAPMCC algorithm is different from the traditional kernel affine projection algorithm: the cost-function for learning algorithm is derived by the correntropy-criterion, and the q-Rényi kernel function replaces the Gaussian kernel in RKHS. The qKAPCC have an outperformance in the NCE under the impulse-noise. Simulation in different non-Gaussian noise environments express high performance of the proposed qKAPMCC algorithm compared to other kernel adaptive filters. © 2023 IEEE.

关键词： kernel affine projection maximum correntropy non-Gaussian noise q-Rényi RKHS

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Intelligent Systems Context-Aware Adaptive Reinforcement learning Approach for Autonomous Vehicles 2

Intelligent Systems Context-Aware Adaptive Reinforcement Lea...

引用

2nd international conference on computer Technologies, ICCTech 2023

作者： Sakthivel, Chandrasekaran Chandrasekar, V. Systems Engineering Department Colorado State University Fort CollinsCO United States Colorado State University Fort CollinsCO United States

ISBN: (纸本)9781665455824

Autonomous vehicles are replacing humans in the driver's seat. It requires intelligent systems. Therefore, it is crucial to have cognitive and context-aware Adaptive Reinforcement learning for highly sensitive, safety, and security-driven autonomous systems architecture design. The system must know any context as part of the machine learning outcome. Therefore, it is important to bring the dynamic, safety-driven algorithms and their hardware components together to co-work based on context-aware adaptive concepts. The context part of the Reinforcement learning system is used as input parameters to the Context Aware Machine learning decision support mechanism that inference controls the operations in an autonomous system. Therefore, it is essential to have the system adaptive in nature and dynamic at runtime to update the learning-based output, and it can be further inference based on context. This paper explores the context specification allowing it to change dynamically as the hardware applies the algorithm. A context that changes dynamically thus provides a modified set of input parameters to the operations control mechanism so that the forward-looking behavior of the autonomous system can be driven adaptively. Our research results prove that the hardware dependency to adopt such a cognitive and context-aware adaptive machine learning algorithm for context-aware autonomous driving systems is important. © 2023 IEEE.

关键词： Intelligent systems

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Bearing and distance Formation Control of Rigid Bodies in SE(3) with Bearing and distance Constraints 9

Bearing and Distance Formation Control of Rigid Bodies in SE...

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9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Mansourinasab, Sara Sojoodi, Mahdi Moghadasi, S. Reza Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran Sharif University of Technology Department of Mathematical Sciences Tehran Iran

ISBN: (纸本)9798350311402

Rigidity of the interaction graph is a fundamental condition for achieving the desired formation which can be defined in terms of distance or bearing constraints between agents. In this paper, for reaching a unique formation with the same scaling and orientation as the target formation, both distance and bearing constraints are considered for defining the desired formation. Besides, both distance and bearing measurements are also available. Each agent is able to gather the measurements with respect to other agents in its own body frame. So, the agents are coordinated-free concerning a global reference frame. On the other hand, the framework is embedded in SE(3). The control signal is designed based on a gradient descent method by introducing a cost function. Firstly, the formation problem is considered for bearing-only constraints in SE(3) configuration. Then, the formation control is expressed for the general case of both bearing and distance constraints. Furthermore, the essential conditions that guarantee reaching the desired formation is discussed. Finally, the validity of the proposed formation control is verified by numerical simulations. © 2023 IEEE.

关键词： Cost functions

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Effect of Dimensionality Reduction on Uncertainty Quantification in Trustworthy Machine learning

Effect of Dimensionality Reduction on Uncertainty Quantifica...

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2023 international conference on Machine learning and Cybernetics, ICMLC 2023

作者： Li, Yen-Chen Zhan, Justin University of Cincinanti Department of Electrical and Computer Engineering CincinnatiOH United States University of Cincinanti Department of Computer Science CincinnatiOH United States

ISBN: (纸本)9798350303780

Machine learning (ML) is a commonly employed computer-assisted tool for ECG diagnosis with above 85 % correct. However, the interpretability of the prediction has become a barrier for the clinical use. To enhance the trustworthiness of ML systems, this paper integrates the trust score framework with the existing ECG classification. At present, the trust score framework is effective for low-dimensional data, but it does not offer significant enhancement over the model confidence for feature spaces with high dimensions. Moreover, the ECG data is a time series data with duration about one or half minute with sampling time about 3 ms. In other words, each signal usually contains several thousand or more sample points. As a result, this paper investigated effect of six popular dimensionality reduction (DR) techniques, including linear and kernel-based principal component analysis (KPCA), independent component analysis (ICA), Autoencoder, uniform manifold Approximation and projection (UMAP), and local linear embedded (LLE). The experimental results showed that UMAP with 2 dimensions can achieve result as well as ICA with 25 dimensions in tracking the model tendency and the other four DR methods has better performance with 25 dimensions. © 2023 IEEE.

关键词： Independent component analysis

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Indian Sign Language Recognition Using Classical and Machine learning Techniques - A Review 2

Indian Sign Language Recognition Using Classical and Machine...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, Machine learning and Signal Processing, PCEMS 2023

作者： Kalava, Mani Deepika Kadiyala, Likhita Kommineni, Sai Atla, Ramana Reddy Thadikemalla, Venkata Sainath Gupta Electronics and Communication Engineering V R Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350310719

Humans need to communicate in order to emphasize their thoughts and feelings, collaborate with others, and elevate society as a whole. A hearing-impaired person uses sign language to communicate and this language develops naturally within them. However, the non-signer community doesn't somehow acknowledge it and hence this remains as a significant barrier that negatively impacts living quality. To bridge the gap, effective sign-language recognition (SLR) system is required and is still an unsolved research issue. New technologies have been developed for the past few years to counter the problem of recognition and were mainly developed using sensors and hardware equipment based on gloves. As contrary to earlier technology, this review presents that, there is no need for expensive and complex hardware in order to recognize sign language, only a modern device with a camera is sufficient. This is accomplished by using Google's MediaPipe framework and machine learning techniques. In this paper, we had presented various techniques developed for Indian sign language and our future goal is to deliver a reliable SLR system with computer vision and AI due to its self-learning capabilities and increased accuracy. © 2023 IEEE.

关键词： Audition

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Deep learning Based Vehicle Target Detection Algorithm Research 3

Deep Learning Based Vehicle Target Detection Algorithm Resea...

引用

3rd international conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Lv, Tianbao Zang, Jingfeng Changchun University of Science and Technology Department of Communications Engineering Changchun China Changchun University of Science and Technology Department of Electronic Information Engineering Changchun China

ISBN: (纸本)9798350316674

Vehicle detection is an indispensable and key part in autonomous driving technology. To address the problems of low recognition accuracy, poor real-time performance, lack of robustness of target detection algorithms in the realm of autonomous driving at this stage, This research paper suggests an enhanced YOLOv7 network for the study of vehicle recognition in the process of autonomous driving. The algorithm embeds a CBAM attention module in the backbone network of YOLOv7 to augment the capacity for extracting characteristics of the algorithm. The initial IOU-LOSS is substituted with EIOU-LOSS, and the loss component related to the aspect ratio is split into the disparity between the estimated width and height and the minimum outer frame width and height, respectively, to accelerate the rate of convergence. And YOLOv5s and YOLOv7 are introduced for comparison experiments. The empirical findings demonstrate that the improved YOLOv7-CBAM network exhibits a notable enhancement in vehicle target detection and outperforms the YOLOv5s and YOLOv7 algorithms. © 2023 IEEE.

关键词： CBAM attention module EIOU-LOSS Vehicle detection YOLOv7

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Coronary Artery Disease Prediction Using Enhanced Multi Layer DCNN 10

Coronary Artery Disease Prediction Using Enhanced Multi Laye...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Malik, Varun Sharma, Smita Mittal, Ruchi Rao, A. Kakoli Martin, R. John Khan, Akhilesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity School of Engineering and Technology Amity University Department of Electronics and Communication Engineering Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India Jazan University Faculty of Computer Science and Information Technology Saudi Arabia Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

Coronary Artery Disease(CAD) is a prevalent and life-threatening cardiovascular condition affecting millions of individuals globally. Early detection and accurate prediction of CAD are vital for timely intervention and prevention of severe cardiac events. Convolutional Neural Networks (CNNs) and other deep learning approaches have recently demonstrated amazing effectiveness in a number of medical applications, including illness prediction. This research proposes an innovative approach, Enhanced Multi-Layer Deep Convolutional Neural Network (EML-DCNN), for the prediction of Coronary Artery Disease. The proposed EML-DCNN architecture integrates advanced deep learning concepts, feature engineering, and data augmentation techniques to enhance the model's predictive performance. The multi-layer structure of EML-DCNN enables the automatic extraction of intricate patterns and features from medical imaging data, specifically focusing on coronary artery images. Additionally, the model incorporates attention mechanisms and residual connections, enhancing its ability to capture subtle details and relationships within the data. To validate the efficacy of the proposed EML-DCNN model, extensive experiments were conducted on a large and diverse dataset comprising coronary artery images obtained from various medical sources. The results demonstrate superior predictive accuracy, sensitivity, and specificity compared to existing CAD prediction models. Moreover, the model's interpretability is enhanced through visualization techniques, providing valuable insights into the features influencing the prediction process. © 2023 IEEE.

关键词： Coronary Artery Disease deep learning disease prediction Enhanced Multi-Layer Deep Convolutional Neural Network

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Optimal Water Utilization in the State of Odisha using Precision Agriculture 3

Optimal Water Utilization in the State of Odisha using Preci...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Mohanty, Sibani Singh, Dhanpratap Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Lovely Professionl University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350399264

Agriculture is the important and foremost role player in the life of human beings and thereby increases the economic growth of the country. A great headache and a challenging task of every country head is the increase in population which in turn affects the climatic conditions and less agricultural lands. Resources are also in deficit condition. Food requirement is increasing and due to lack of less production and supply of food, hunger conditions are increasing. To get relief from the said crisis, Precision agriculture plays an important role which is a new way of attaining sustainable development goals. It is otherwise known as Smart Farming. In India, few states have already started implementing this new technology. Proper training had been given to farmers. Water plays an important role in agriculture. Farmers used to water the crops in spite of high humidity in air and soil, which leads to low growth and death of crops and also leads to high yield. Precision Agriculture will take care of it. Machine learning technology drives the smart farming to help farmers in high growth and high yield. Soil Sampling needs to be done to study the parameters of soil properly and along with that Climatic parameters. Among many ML algorithms Random forest performance is best with 98.5 accuracy proposed by Kelasivi © 2023 IEEE.

关键词： Machine learning

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