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arXiv 2022年

作者： Qian, Jiachen Duan, Peihu Duan, Zhisheng Shi, Ling State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

The coupled Riccati equations (CREs) are a set of multiple Riccati-like equations whose solutions are coupled with each other through matrix means. They are a fundamental mathematical tool to depict the inherent dynamics of many complex systems, including Markovian systems or multi-agent systems. This paper investigates a new kind of CREs called harmonic-coupled Riccati equations (HCREs), whose solutions are coupled using harmonic means. We first introduce the specific form of HCREs and then analyze the existence and uniqueness of its solutions under the conditions of collective observability and primitiveness of coupling matrices. Additionally, we ensure the uniqueness of HCREs solutions with several mild conditions. Based on this newly established theory, we greatly simplify the steady-state estimation error covariance of consensus-on-information-based distributed filtering (CIDF) into the solutions to a discrete-time Lyapunov equation (DLE). This leads to a significant conservativeness reduction of traditional performance evaluation techniques for CIDF. The obtained results are remarkable since they not only enrich the theory of CREs, but also provide a novel insight into the synthesis and analysis of CIDF algorithms. We finally validate our theoretical findings through several numerical experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion

arXiv

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arXiv 2024年

作者： Chen, Junzhou Huang, Heqiang Zhang, Ronghui Lyu, Nengchao Guo, Yanyong Dai, Hong-Ning Yan, Hong Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China School of Transportation Southeast University Nanjing210097 China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, they often compromise between speed and accuracy. To address this issue, we present a novel real-time and efficient road sign detection network, YOLO-TS. This network significantly improves performance by optimizing the receptive fields of multi-scale feature maps to align more closely with the size distribution of traffic signs in various datasets. Moreover, our innovative feature-fusion strategy, leveraging the flexibility of Anchor-Free methods, allows for multiscale object detection on a high-resolution feature map abundant in contextual information, achieving remarkable enhancements in both accuracy and speed. To mitigate the adverse effects of the grid pattern caused by dilated convolutions on the detection of smaller objects, we have devised a unique module that not only mitigates this grid effect but also widens the receptive field to encompass an extensive range of spatial contextual information, thus boosting the efficiency of information usage. Evaluation on challenging public datasets, TT100K and CCTSDB2021, demonstrates that YOLO-TS surpasses existing state-of-the-art methods in terms of both accuracy and speed. The code for our method will be available. Copyright © 2024, The Authors. All rights reserved.

关键词： Advanced driver assistance systems

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On the potential of lateral BJTs and SiGe HBTs in advanced CMOS technologies

On the potential of lateral BJTs and SiGe HBTs in advanced C...

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Pacific Rim Meeting on Electrochemical and Solid State Science 2020, PRiME 200

作者： Suligoj, Tomislav Žilak, Josip Osrečki, Željko Koričić, Marko Micro and Nano Electronics Laboratory Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Croatia

ISBN: (纸本)9781607689003

The potential of lateral bipolar transistors, based on Horizontal Current Bipolar Transistor (HCBT), in scaled CMOS technologies are examined by TCAD simulations. Thorough and consistent simulations show that SiGe HCBT can achieve fT/fmax of 358/490 GHz outperforming standard vertical-current HBT with fT/fmax of 348/423 GHz, assuming the same intrinsic transistor doping profile. By optimizing the extrinsic base shape and width, the characteristics of SiGe HCBT can be further improved. SiGe HCBTs in bulk silicon substrates result in better performance comparing to the transistors in SOI substrates. © The Electrochemical Society

关键词： Si-Ge alloys

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A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building Applications

A Wideband WiFi/NB-IoT On-Glass Antenna for Smart Building A...

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European Conference on Antennas and Propagation, EuCAP

作者： Muhammad Uzair Juin Acharjee Akkarat Boonpoonga Dirk Heberling Suramate Chalermwisutkul High Freqeuncy Systems Laboratory King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electronics and Communication Engineering St. Thomas' College of Engineering and Technology Kolkata India Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Institute of High Frequency Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR Wachberg Germany

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this work, an on-glass antenna is proposed for IoT and smart building applications. The dielectric properties of five common glass types used in architecture were investigated. Clear glass, 3 mm thick, was selected as the substrate. The antenna is a rectangular monopole with a coplanar waveguide feed and an asymmetric ground. It was designed and simulated using Ansys HFSS, then fabricated using clear glass and copper tape. Measurements of the reflection coefficient and radiation pattern showed good agreement with simulations. The antenna exhibits a bidirectional radiation pattern, with 2.8 dBi gain and 94.82% radiation efficiency at 2.1 GHz and 3.9 dBi gain and 95.72% radiation efficiency at 2.4 GHz, respectively. A −10 dB bandwidth of 650 MHz (29.3%) centered at 2.215 GHz was achieved. The proposed antenna shows promise for integration with architectural glass elements in smart building applications, such as smart glass doors and windows.

关键词： Antenna measurements Smart buildings Indium tin oxide Glass Reflection coefficient Internet of Things Substrates Wideband Gain Antenna radiation patterns

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How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and parallel Intelligence

arXiv

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arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Portfolio Risk Management via a CVaR and Stochastic Dominance Hybrid Approach

Portfolio Risk Management via a CVaR and Stochastic Dominanc...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Alashery, Mohamed Kareem Qiao, Wei Qu, Liyan University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588-0511 United States

ISBN: (纸本)9781728155081

Risk management is a crucial aspect in decision making under uncertainty. This paper proposes a hybrid risk management approach for stochastic programming-based portfolio optimization problems. The proposed approach consolidates the classical Markowitz mean-conditional value at risk (CVaR) model, which is the most commonly used approach in power and energy applications, and the stochastic dominance (SD) concept. A case study on the risk management of a wind power producer's stochastic bidding strategy in the electricity market is performed to demonstrate the superiority of the proposed approach over the commonly used mean-CVaR and the SD-based risk management approaches. © 2020 IEEE.

关键词： Stochastic programming

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Non-volatile Phase-only Transmissive Spatial Light Modulators

arXiv

引用

arXiv 2023年

作者： Fang, Zhuoran Chen, Rui Fröch, Johannes E. Tanguy, Quentin A.A. Khan, Asir Intisar Wu, Xiangjin Tara, Virat Manna, Arnab Sharp, David Munley, Christopher Miller, Forrest Zhao, Yang Geiger, Sarah J. Böhringer, Karl F. Reynolds, Matthew Pop, Eric Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Physics University of Washington SeattleWA98195 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States The Charles Stark Draper Laboratory CambridgeMA02139 United States University of Washington Institute for Nano-engineered Systems SeattleWA United States

Free-space modulation of light is crucial for many applications, from light detection and ranging to virtual or augmented reality. Traditional means of modulating free-space light involves spatial light modulators based on liquid crystals and microelectromechanical systems, which are bulky, have large pixel areas (~10μm × 10μm), and require high driving voltage. Recent progress in meta-optics has shown promise to circumvent some of the limitations. By integrating active materials with subwavelength pixels in a meta-optic, the power consumption can be dramatically reduced while achieving a faster speed. However, these reconfiguration methods are volatile and hence require constant application of control signals, leading to phase jitter and crosstalk. Additionally, to control a large number of pixels, it is essential to implement a memory within each pixel to have a tractable number of control signals. Here, we develop a device with nonvolatile, electrically programmable, phase-only modulation of free-space infrared radiation in transmission using the low-loss phase-change material (PCM) Sb2Se3. By coupling an ultra-thin PCM layer to a high quality (Q)-factor (Q~406) diatomic metasurface, we demonstrate a phase-only modulation of ~0.25π (~0.2π) in simulation (experiment), ten times larger than a bare PCM layer of the same thickness. The device shows excellent endurance over 1,000 switching cycles. We then advance the device geometry, to enable independent control of 17 meta-molecules, achieving ten deterministic resonance levels with a 2π phase shift. By independently controlling the phase delay of pixels, we further show tunable far-field beam shaping. Our work paves the way to realizing non-volatile transmissive phase-only spatial light modulators. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Mixture of Experts for Intelligent Networks: A Large Languag...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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Bridging the Artificial Intelligence Knowledge and Skill Gaps in Africa: a Case of the 3rd Google Tensorflow Bootcamp and FEDGEN Mini-Workshop

Bridging the Artificial Intelligence Knowledge and Skill Gap...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Emmanuel Adetiba John S. Wejin Oluwadamilola Oshin Ayodele H. Ifijeh Comfort Lawal Surendra Colin Thakur Ayokunle A. Awelewa Raymond Jules Kala Priscilla O. Ajayi Matthew B. Akanle Faith O. Sweetwiliams Uche Nnaji Emmanuel Owolabi Olabode Idowu-Bismark Gabriel Sobola Department of Electrical and Information Engineering & Covenant Applied Informatics & Communication African Center of Excellene (CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Taraba State University Jalingo Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN E-Skills Co-Laboratory Durban University of Technology Durban South Africa Department of Electrical and Information Engineering Covenant University Ota Nigeria International University of Grand Bassam Grand Bassam Ivory Coast Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Smartyou Integral (PTY) LTD Pretoria Gauteng South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

In transiting from one civilization to another, technology has played a vital and positive role. In the 21 st century, one of the digital developments that is paving ways for human life improvement is machine-assisted technology using Artificial Intelligence (AI). Artificial Intelligence has successfully enhanced man’s capacity in solving complex problems and processes. However, as developed nations continue to reap from the adoption of AI in various fields of human endeavors, the continent of Africa has remained behind, especially in AI-based skills and research. Various governments in developing nations have encouraged the adoption of AI, especially in institutions of learning. However, theoretical adoption without practical experience has remained an ineffective way of bridging the digital divide. In this paper we present the outcome of a practical approach to bridging the AI divide among students and researchers in Africa through funding support from the Google TensorFlow College Outreach Award. A 3-day hybrid bootcamp was organized (11 th to 13 th December, 2023) using the Google funding in order to equip postgraduate students and researchers with AI and collaborative research skills. A pre-survey method was employed to ascertain the knowledge level of the bootcamp participants. From the pre-surveyed feedback, training sessions on various AI domains were presented, and participant equipped with practical AI skills using a deployed AI-based cloud programming platform running on the private Federated Genomic Cloud (FEDGEN) infrastructure at Covenant University. A post-survey feedback was used to ascertain the effectiveness of this approach. A comparative analysis of the pre-survey and post-survey reveals a 70% improvement of AI skills among participants. This shows that having continuous training session for students and researchers is an effective method in closing the AI skills gap between developed and developing nations.

关键词： Training Surveys Knowledge based systems Government Genomics Africa Programming

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