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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 854-867页

作者： Guo, Wenzhe Fouda, Mohammed E. Eltawil, Ahmed M. Salama, Khaled Nabil King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia University of California Center for Embedded and Cyber-Physical Systems IrvineCA92612 United States

The restricted training pattern in the standard backpropagation (BP) requires end-to-end error propagation, causing large memory costs and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacles by completely cutting off the backward path between modules and isolating their gradients. These methods prevent information exchange between modules and result in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces intermodule backward dependence and facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, our method can surpass the conventional greedy local training method by 6.45% in accuracy in ResNet32 classifying CIFAR100 and recent work by 2.58% in ResNet110 classifying STL-10 with much lower complexity. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms toward better efficiency for real-time learning applications. © 2023 IEEE.

关键词： Deep neural networks

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3D-SLAM Implementation on Commercial UAV: Challenges and Future Insights 16

3D-SLAM Implementation on Commercial UAV: Challenges and Fut...

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16th International Conference on Developments in esystems engineering, DeSE 2023

作者： Shullar, Yaman Baroudi, Uthman King Fahd University of Petroleum & Minerals Interdisciplinary Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia

ISBN: (纸本)9798350381344

In recent years, the field of programming Unmanned Arial Vehicles (UAVs) has gained significant attention from researchers due to their substantial potential in various applications, including surveillance, inspection, and critical situations like examining buildings that are burning or collapsing. For this purpose, drones that have several sensors installed would be of good use in constructing a 3D map and localizing most of the objects within a certain area or room, which is also referred to as Simultaneous Localization and Mapping (SLAM). However, installing these sensors would harden the mission of the drone since it would mean more power consumption, more computations, and less navigation flexibility. For this reason, monocular visual SLAM has become the trend, which refers to using a sole camera to build the map and locate the objects in each scene. This approach introduces new challenges, one of the most crucial challenges is estimating the depth (i.e., distances within an image) of each scene from a 2D image. For this task, Deep Learning (DL) models have been considered as a solution for this problem, and with the continuous development in DL and the computational resources that can carry out the expensive training of DL models, it was shown that a 3D map reconstruction is possible utilizing 2D images. This work investigates the performance of a combination of different SLAM and depth estimation models implemented on a commercial drone. The main goal is to carry out a comparison between different methodologies of depth estimation that support monocular 3D SLAM algorithms. © 2023 IEEE.

关键词： Drones

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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC systems

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 34-48页

作者： Hammadi, Ahmed Al Bariah, Lina Muhaidat, Sami Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Debbah, Merouane Technology Innovation Institute Abu Dhabi United Arab Emirates Khalifa University Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaK1S 5B6 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere33014 Finland Khalifa University KU 6G Center Abu Dhabi United Arab Emirates

Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain's Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance. © 2023 The Authors.

关键词： Spectrum efficiency

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Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

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Nano-Micro Letters 2024年第3期16卷 254-302页

作者： Hyeonseok Kim Bruno Rigo Gabriella Wong Yoon Jae Lee Woon‑Hong Yeo IEN Center for Wearable Intelligent Systems and Healthcare Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGA 30332USA Parker H.Petit Institute for Bioengineering and Biosciences Institute for MaterialsInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health *** considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human *** addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and *** wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease ***,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

关键词： Implantable electronics Biomedical systems Batteryless devices Wireless electronics Physiological signal monitoring

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Regression based prediction of higher heating value for refuse-derived fuel using convolutional neural networks predicted elemental data and spectrographic measurements

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Waste Disposal and Sustainable Energy 2024年第3期6卷 429-437页

作者： Baki Osman Bekgoz Zerrin Günkaya Kemal Ozkan Metin Ozkan Aysun Ozkan Müfide Banar Department of Computer Engineering Meselik CampusEskisehir Osmangazi University26480EskisehirTürkiye Department of Environmental Engineering Iki Eylul CampusEskisehir Technical University26555EskisehirTürkiye Center of Intelligent Systems Applications Research Meselik CampusEskisehir Osmangazi University26480EskisehirTürkiye

Higher heating value(HHV)is the key parameter for replacing Refuse-Derived Fuel(RDF)with fossil fuels in the cement *** can be measured with a bomb calorimeter or predicted from direct elemental data by using regression *** methods require the continuous use of special laboratory equipment and are time *** overcome these limitations,this study aims to predict the HHV value of RDF from predicted elemental data by using regression ***,once the predicted elemental data are generated,there will be no need to have continuous elemental data to predict *** elemental data were generated from direct elemental data and Near Infrared(NIR)camera-based spectrometric data by using a deep learning model.A convolutional neural networks(CNN)model was used for deep learning and was trained with 10,500 NIR image samples,each of which was 28×28×*** regression models(Linear,Tree,Support-Vector Machine,Ensemble and Gaussian process)were applied for HHV *** to these results,higher R2 values(>0.85)were obtained with Gaussian process models(except for the Rational Quadratic model)for the predicted elemental *** the Gaussian models,the highest R2(0.95)but the lowest Root Mean Square Error(RMSE)(0.0563),Mean Squared Error(MSE)(0.0317)and Mean Absolute Error(MAE)(0.0431)were obtained with the Mattern 5/2 *** results of predictions from predicted elemental data were compared to predictions from direct elemental *** results show that the regression from predicted elemental data has an adequate prediction(R2=0.95)compared to the prediction from the direct elemental data(R^(2)=0.99).

关键词： Deep learning Higher heating value Refuse-derived fuel Regression Spectrographic measurement

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Federated Learning for Lidar Super Resolution on Automotive Scenes 24

Federated Learning for Lidar Super Resolution on Automotive ...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Gkillas, Alexandros Arvanitis, Gerasimos Lalos, Aris S. Moustakas, Konstantinos Industrial Systems Institute Athena Research Center Patras Greece Electrical and Computer Engineering University of Patras Greece

ISBN: (纸本)9798350339598

In this paper, the problem of lidar super-resolution is explored under a federated learning perspective. The high cost of high-resolution lidar sensors is a major obstacle to the widespread adoption of connected and autonomous vehicles (CAVs). To reduce the cost, this study investigates the use of low-cost sensors in conjunction with super-resolution algorithms. Unlike previous studies that approach this problem with centralized solutions, this work employs federated learning to leverage private lidar data from different autonomous vehicles under different environmental conditions, resulting in more robust and diverse deep learning model. Extensive experiments on a real-world lidar odometry dataset highlight the merits and applicability of the proposed lidar super-resolution method with federated learning. © 2023 IEEE.

关键词： Optical radar

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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ZCD Circuit Improvement and Novel Signal Processing Method for GaN-Based CRM PWM Converters

ZCD Circuit Improvement and Novel Signal Processing Method f...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Chen, Xingyu Yu, Gibum Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798350376067

This paper introduces a design guidance for zero current detection (ZCD) circuit in Gallium-Nitride (GaN) device based critical conduction mode (CRM) pulse-width-modulation (PWM) converters to reduce the sensing delay, avoid false trigger and prevent trigger missing. A ZCD signal processing logic is also proposed for CRM PWM converters to limit the maximum switching frequency while keeping a simple closed loop control structure. The proposed ZCD circuit design and ZCD signal processing method are verified in the simulation and on a GaN based CRM buck converter. © 2024 IEEE.

关键词： Pulse width modulation

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Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conversion for High-Performance Server Boards

Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conv...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Xu, Xinmiao Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350376067

This paper presents an extended-conversion-ratio modulation for a two-phase symmetric series-capacitor buck (SSCB) converter. With the proposed scheme, highly efficient and regulated 48V-to-12V conversion can be realized with the SSCB topology. A versatile regulated first stage streamlines power delivery architectures for server boards in data centers, because regulated 12-V buses not only serve loads requiring well-controlled 12-V input but also simplify design and optimization of a 12V-to-1V second stage for processors. Based on this idea, a highly efficient and regulated power architecture is proposed for 48-V server boards. A prototype is built with gallium nitride (GaN) transistors and minimal high-frequency loop inductance, operating with a relatively wide input voltage range from 40 V to 60 V and generating a regulated 12-V output. In 48V-to-12V regulated conversion, the GaN-based prototype achieves 98.88% peak efficiency, 98.11% full-load efficiency (including gating loss) and 787 W/inch2 power density with 80-A output current, which is beyond the performance of state-of-the-art commercially available solutions. © 2024 IEEE.

关键词： Gallium nitride

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Integrated Direct/Indirect Adaptive Robust Control for electrical Driven Injection Machine Mold Closing with Accurate Parameter Estimations 16th

Integrated Direct/Indirect Adaptive Robust Control for Elec...

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16th International Conference on intelligent Robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhu, Shiqiang Meng, Qiwei Song, Wei Gu, Jason Research Center for Intelligent Robotics Zhejiang Engineering Research Center for Intelligent Robotics Zhejiang Lab Hangzhou Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

In this paper, the control issue of the mold closing system of Injection machine is studied. In actual application, there are two major control problems needed to be addressed in controller design: to ensure the quality of products, the precision of mold needs to be guaranteed;the operation parameters of the machines are willing to be recorded for analysis, such as health monitoring and prognosis. However, the existing controllers usually rely on the servo motor, where the dynamics of the mold closing system is not taken into consideration, that may lead to degraded tracking performances. Moreover, the uncertain model parameters are usually ignored or simply treated as disturbances. Instead in this paper, the nonlinear dynamic model of mold closing system is first proposed including parameter uncertainties and disturbances for controller design. Subsequently, an integrated direct/indirect adaptive robust control law(DIARC) is proposed that not only achieves excellent tracking performance but also attenuates the parameter uncertainties with accurate parameter learning. Comparative experiments are carried out to verify our proposed approach and the results demonstrate the effectiveness of the integrated direct/indirect adaptive robust control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robust control

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