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Applicability of Deep Learning Model Trainings on Embedded GPU Devices: An Empirical Study

Applicability of Deep Learning Model Trainings on Embedded G...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Chou, Po-Hsuan Wang, Chao Mei, Chih-Shuo National Taiwan Normal University Dept. of Computer Science and Information Engineering Taipei City Taiwan

ISBN: (纸本)9798350322910

The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU. © 2023 IEEE.

关键词： Embedded systems

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Impact of Machine Learning-Driven Predictive Models on Patient Outcomes in Modern Healthcare Systems 1

Impact of Machine Learning-Driven Predictive Models on Patie...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Madhuri Gautam, Rishikesh Kumar Shukla, Medhavi Kumar, Jitendra Singh, Ajay Pal Yaminipriya, S. CHRI Campus Dept. of Life Sciences Gwalior India Madhav University Dept. of Humanities & S.Science Pindwara India J.S. University Dept. of Computer Science Shikohabad India Lingayas Vidyapeeth Dept. of Pharmacy Faridabad India Dhanalakshmi Srinivasan College Dept. Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350356816

Traditional healthcare systems often struggle to meet the diverse needs of patients, leading to inefficiencies and less-than-optimal outcomes. The integration of machine learning (ML) is transforming healthcare by focusing on value-based care and enabling personalized, effective treatments. Modern healthcare devices are equipped with applications that collect and store comprehensive patient data, forming the basis for ML-driven predictive *** research investigates the transformative impact of ML in healthcare, highlighting its ability to improve patient care and optimize resource allocation. A proposed predictive model utilizes patient data and key parameters to forecast diseases, leveraging extensive datasets from diverse populations. These tools empower healthcare providers to identify and manage high-risk cases proactively, improving outcomes while streamlining care delivery *** study also explores the broader implications of ML adoption in value-based healthcare systems. By emphasizing personalized and proactive interventions, it demonstrates how ML-powered models can revolutionize traditional practices. The integration of ML in healthcare offers efficient, patient-centric solutions, enhancing the responsiveness and capacity of healthcare systems to meet growing patient demands and address complex medical challenges effectively. © 2024 IEEE.

关键词： ML Modern healthcare Patient data Predictive models Value- based treatment

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Analysis of Prediction of Pneumonia from Chest X-ray images using CNN and Transfer Learning 5

Analysis of Prediction of Pneumonia from Chest X-ray images ...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Vinay, K. Shashanka Kodipalli, Ashwini Swetha, P. Kumaraswamy, S. Dept. of Computer Science and Engineering Global Academy of Technology Karnataka Bengaluru India Dept of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350361155

A good design for image classification problems is the CNN architecture you presented, which consists of 5 convolution blocks followed by 4 fully connected layers. From the input X-ray images, the convolutional blocks extract pertinent features, and the fully connected layers assist in determining the final classification based on those learned features. You have integrated various approaches to improve the performance of your model. The inputs to each layer are normalized through batch normalization, which can speed up training and enhance generalization. By removing certain neurons at random during training, dynamic dropout helps avoid overfitting. L2 regularization weight decay and learning rate decay are two efficient strategies for preventing overfitting and enhancing the model's capacity to expand to new data. Popular optimization algorithm Adam optimizer effectively neural network training. For binary classification problems like the diagnosis of pneumonia, the loss function for binary Cross-Entropy is the best option. To determine your model's efficacy, you must validate it using benchmark datasets that are available to the general public. You can evaluate your model's effectiveness by comparing its performance to that of current methods by conducting experimental investigations on these datasets. Your model performs well as evidenced by accuracy scores of 90.93%, 89.17% for multi-class classification and binary classification. tasks. Automated methods, such as the one you suggested, might help medical practitioners recognize pneumonia and spot diseased spots in chest X-ray pictures. However, it's crucial to remember that automated systems shouldn't take the place of professional radiologists' and doctors' skills and judgment;rather, they should be used as supportive tools. Medical To ensure accurate diagnosis and suitable patient care, specialists should always review and interpret the system's data. It's also crucial to take into account potential drawback

关键词： Convolutional neural networks

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A Reinforcement-Learning-Based Adaptive Sliding Mode Controller for Robotic Manipulators 6

A Reinforcement-Learning-Based Adaptive Sliding Mode Control...

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6th International Conference on Robotics, Control and Automation engineering, RCAE 2023

作者： Luo, Di Cai, Zhiqin Jiang, Da Qiu, Xiaolu Peng, Haijun Dalian University of Technology Dept. of Engineering Mechanics Dalian China China North Vehicle Research Institute National Engineering Laboratory for Wheeled Vehicle Beijing China Dalian University of Technology State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dept. of Engineering Mechanics Dalian China

ISBN: (纸本)9798350315301

In this paper, a reinforcement-learning-based adaptive sliding mode controller (RLASMC) is proposed to achieve more precise tracking control in robotic manipulator systems with nonlinear friction, modeling errors, and external disturbances. In this controller, a robust term is designed to compensate for the external disturbance, system uncertainty, and joint friction. Furthermore, the dynamic information of the robotic manipulator is employed as input to a reinforcement learning agent, enabling the agent to optimize the parameters of the sliding mode controller within a continuous action space. Simulation studies are implemented to validate the effectiveness of the proposed controller. © 2023 IEEE.

关键词： Controllers

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Event Detection from Cricket Videos Using Video-Based CNN Classification of Umpire Signals 4

Event Detection from Cricket Videos Using Video-Based CNN Cl...

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4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Sakib, Shadman Haque Mridha, Md. Afranul Hasan, Nahid Habib, Md. Toasin Mahbub, Md. Shahriar Ahsanullah University of Science and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665490450

Cricket is a long duration game. It can last for several hours or even days. Due to cricket's lengthy nature, it becomes indispensable for viewers to have the option of watching selected interesting events from a cricket match. As cricket videos are unscripted in nature, detecting key events from cricket videos is a big challenge. In this paper, we propose a method to detect 5 key events, namely FOUR, SIX, OUT, NO BALL and WIDE from cricket videos by recognizing the umpire's signal. Analysis of umpire signals is performed using a pretrained Convolutional Neural Network (CNN) architecture, namely I3D. A new dataset, containing 504 videos and 2000 images from cricket matches, is introduced in this work for the detection of key events in cricket. For the tasks of umpire frame detection and umpire signal recognition, test accuracies of 97.76% and 86.14% are achieved respectively. Employing the proposed framework on official cricket match videos, we achieve precision, recall and F1 scores of 95.23%, 86.95% and 90.90% respectively. Furthermore, by evaluating performances of both proposed video-based approach and existing image-based approach on novel cricket videos, we can affirm that the proposed approach outperforms the image-based one. © 2022 IEEE.

关键词： Convolutional neural networks

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Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks

Keep It Simple: CNN Model Complexity Studies for Interferenc...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Oyedare, Taiwo Shah, Vijay K. Jakubisin, Daniel J. Reed, Jeffrey H. Virginia Tech Bradley Dept. of Electrical and Computer Engineering Blacksburg United States George Mason University Dept. of Cybersecurity Engineering Fairfax United States Virginia Tech National Security Institute BlacksburgVA United States

ISBN: (纸本)9781665494274

The growing number of devices using the wireless spectrum makes it important to find ways to minimize interference and optimize the use of the spectrum. Deep learning models, such as convolutional neural networks (CNNs), have been widely utilized to identify, classify, or mitigate interference due to their ability to learn from the data directly. However, there have been limited research on the complexity of such deep learning models. The major focus of deep learning-based wireless classification literature has been on improving classification accuracy, often at the expense of model complexity. This may not be practical for many wireless devices, such as, internet of things (IoT) devices, which usually have very limited computational resources and cannot handle very complex models. Thus, it becomes important to account for model complexity when designing deep learning based models for interference classification. To address this, we conduct an analysis of CNN based wireless classification that explores the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty: namely, interference classification, heterogeneous transmitter classification, and homogeneous transmitter classification. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model, providing important insights into the use of CNNs in computationally constrained applications. © 2023 IEEE.

关键词： Classification (of information)

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Modeling of OWC System Employing Linear MMF 9

Modeling of OWC System Employing Linear MMF

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Kumari, Meet Sharma, Neha Chauhan, Rahul Joshi, Kireet Kumar, Amit Uie Chandigarh University Dept. of Ece Mohali Gharuan140413 India Chitkara University Institute of Engineering And Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350394474

Since optical wireless communication (OWC) can provide secure, affordable access solutions for wireless transmission while also relieving pressure on the extremely congested radio-frequency spectrum, it has become a substitute technology to radio communication. For the beyond 5G networks, hybrid OWC-multimode fiber (MMF) link is proposed to raise the time/frequency resource utilization. A 4×10Gbps OWC system employing linear MMF is modeled. Results depict that faithful MMF range of 400m is obtained with fixed 10km OWC range. Also, faithful OWC range of 90km is obtained with 100-200m fixed MMF range. A comparative performance indicates the superiority of the model w.r.t. existing ones. © 2024 IEEE.

关键词： Multimode fibers

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Evaluating GAN Image Generation Quality and Diversity 4

Evaluating GAN Image Generation Quality and Diversity

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kaushik, Priyanka Barot, Kaushal Ansari, Lubna Panda, Sucheta Kapoor, Sandeep Krishnan, Neethu CSE-APEX Chandigarh University Dept. of Cse Chandigarh India Madhav University Dept. of Cse Sirohi India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Computing & It Ranchi India Lingayas Vidyapeeth University Dept. of Commerce Faridabad India Dhanalakshmi Srinivasan University Dept. of Ece Coimbatore India

ISBN: (纸本)9798350364729

Image generation has become increasingly prevalent in recent times, with image-to-image translation emerging as a rapidly growing field. Generative Adversarial Networks (GANs) are commonly used in this context, where the aim is to generate images that closely resemble given input images. In this approach, the Generator and Discriminator, revolutionizing generative models in image synthesis. a Generator creates new images based on a probability distribution, while a Discriminator attempts to distinguish between real and generated images. This paper provides a comprehensive examination of four major GAN architectures introduced since the inception of GANs in 2014. The objective is to learn a mapping function G: X -¿ Y, such that the distribution Y is indistinguishable from the input distribution G(X). The paper includes quantitative comparisons against baseline models, demonstrating the superiority of our approach. © 2024 IEEE.

关键词： Generative adversarial networks

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Ethereum Powered E-Tendering System

Ethereum Powered E-Tendering System

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： K Ananthajothi P K Jaisuriya S Haribala Dept. of computer science and engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

The use of information technology to trade information, deliver services, and other things is becoming more and more commonplace in the era of technological advancement. Tendering is one such area where the public and private sectors collaborate. E-tendering is a procurement technique that uses the internet and digital technologies to automate and simplify the whole bidding or tendering process. This makes it easier for the bidders to browse through all of the open tenders and select one to proceed with. But it frequently results in a number of challenges that both bids and bidders must deal with. The system is inefficient because of scale and transparency problems brought on by its centralized architecture. Additionally, the system frequently shares information about tenders with other outside organizations, which compromises the confidentiality and integrity of the tenders. To manage these issues, a decentralized method is used in the deployment of the Blockchain e-tendering system. Consequently, the procurement procedures will have the opportunity to leverage the potential of blockchain technology to enhance security and transparency. This research aims to provide an electronic tendering system that is transparent, liquidate, and safe. It will manage tender allocation and use smart contracts to automate the underlying processes with the least amount of human interaction possible.

关键词： Procurement Electric potential Smart contracts Organizations Blockchains Telecommunication computing Security

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Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios

Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenar...

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2024 Work-in-Progress in Hardware and software for Location Computation, WIPHAL 2024

作者： Spanos, Thodoris Fabra, Fran López-Salcedo, José A. Seco-Granados, Gonzalo Kanistras, Nikos Lapin, Ivan Paliouras, Vassilis Dept. of Electrical and Computer Engineering University of Patras Greece Loctio Greece Spain Radio Navigation Systems and Techniques Section European Space Agency Netherlands

This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and with actual measurements. Leveraging 5G capabilities, we investigate AoA algorithms for single-base station positioning. The study includes simulations and practical tests on a developed dedicated testbed featuring a base station equipped with a three-element Uniform Linear Array (ULA), considering Line of Sight conditions in an open environment. The testbed, employing Ettus E312 as the transmitter and Ettus N310 as the receiver, details waveform structures and reception processes. Additionally, our study examines the performance of Angle of Arrival (AoA) estimation algorithms, such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT), and Joint Angle and Delay Estimation (JADE) ESPRIT. A MATLAB ray tracing propagation model of the environment where the measurements are conducted, has been constructed. Simulation results using this model are presented, along with the actual measurements. The obtained results affirm the effectiveness of our implementation. © 2024 Copyright for this paper by its authors.

关键词： Multiple signal classification

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