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IEEE Conference on Decision and Control

作者： Sang-ik An Gyunghoon Park Dongheui Lee School of Electrical and Computer Engineering University of Seoul Faculty of Electrical Engineering and Information Technology Autonomous Systems at Institute of Computer Technology TU Wien Austria German Aerospace Center (DLR) Institute of Robotics and Mechatronics

The prioritized control problem is a process to find a control strategy for a dynamical system with prioritized multiple outputs, so that it can operate outside its nonsingular domain. Singularity typically leads to imperfect inversion in the prioritized control problem, which in turn results in imperfect input-output feedback linearization. In this paper, we propose a method based on the Kalman-Yakubovich-Popov lemma that compensates nonlinear feedback terms caused by the imperfect inversion of the prioritized control problem. In order to realize this idea, we prove existence of a feedback gain matrix that gives a strictly positive real transfer function whose output matrix is identical to the feedback gain matrix. Our proof is constructive so that a set of such matrices can be found. Also, we provide a numerical approach that gives a larger set of feedback gain matrices and validate the result with numerical examples.

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A2D: Anywhere Anytime Drumming

arXiv

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arXiv 2023年

作者： Yadid, Harel Algranti, Almog Levin, Mark Taitler, Ayal Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Haifa Israel Technion - Israel Institute of Technology Faculty of Computer Science Haifa Israel University of Toronto Department of Mechanical and and Industrial Engineering TorontoON Canada

The drum kit, which has only been around for around 100 years, is a popular instrument in many music genres such as pop, rock, and jazz. However, the road to owning a kit is expensive, both financially and space-wise. Also, drums are more difficult to move around compared to other instruments, as they do not fit into a single bag. We propose a no-drums approach that uses only two sticks and a smartphone or a webcam to provide an air-drumming experience. The detection algorithm combines deep learning tools with tracking methods for an enhanced user experience. Based on both quantitative and qualitative testing with humans-in-the-loop, we show that our system has zero misses for beginner level play and negligible misses for advanced level play. Additionally, our limited human trials suggest potential directions for future research. © 2023, CC BY.

关键词： Deep learning

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A New Look for Outage and Ergodic Improvements in Coordinated IoT Paradigms with Hybrid Multiple Access

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IEEE Transactions on Vehicular Technology 2025年

作者： Vu, Thai-Hoc Le, Anh-Tu Huynh-The, Thien da Costa, Daniel Benevides Kim, Sunghwan Kyonggi University School of Electronic Engineering Korea Republic of VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science 17. Listopadu 2172/15 Ostrava708 00 Czech Republic Ho Chi Minh City University of Technology and Education Department of Computer and Communications Engineering Viet Nam Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia

As spectrum utilization becomes increasingly scarce due to the exponential growth of intelligent connected devices in the Internet of Things (IoT), developing efficient communication protocols with simultaneous improvements in energy and spectrum usage is critical in response to the demands of cost-effective IoT devices. In this work, we propose a hybrid orthogonal and non-orthogonal multiple-access protocol in coordinated direct uplink/downlink and relay transmission for low-cost IoT paradigms, where a source node engages in direct transmission with proximate IoT users and relies on a relay node for distant connections. This protocol offers a successive interference cancellation–free approach and does not require any channel disparity for power allocation. Exact and asymptotic analytical expressions for the outage probability (OP) and ergodic capacity (EC) are derived. Numerical results demonstrate that the proposed protocol significantly improves OP–by 8 folds for the respective proximate users while maintaining the same OP for distant users over classical coordinated non-orthogonal multiple access–and delivers superior EC performance for proximate IoT users at low to moderate signal-to-noise ratios (SNRs) and distant users at most SNR. Moreover, critical tasks for minimizing the maximal OP and maximizing the ergodic sum capacity have been undertaken to achieve outage fairness and enhance the overall system's capacity. These non-convex problems are efficiently addressed by applying the analytical asymptotic-inspired alternative optimization. © 1967-2012 IEEE.

关键词： Signal to noise ratio

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DMCNET: Data-Driven Multi-Pilot Convolution Neural Network for Mimo-Ofdm Receiver 8

DMCNET: Data-Driven Multi-Pilot Convolution Neural Network f...

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8th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2023

作者： Xin, Yutong Peng, Jingyi Lu, Zejian Lee, Yong Yang, Yang China Industrial Control Systems Cyber Emergency Response Team 100029 China China Academic of Electronics and Information Technology 100041 China School of Artificial Intelligence Beijing University of Posts & Telecommunications 100876 China Collage of Electrical and Computer Engineering Sungkyunkwan University 16419 Korea Republic of

ISBN: (纸本)9798350317923

This paper focuses on studying the deep learning (DL) application of neural networks to solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna scenarios, the channel model is more complex compared to single-antenna cases. By leveraging the characteristics of DL, such as automatic learning of parameters using deep neural networks, we treat the reception process of MIMOOFDM signals as a black box and utilize neural networks to accomplish the signal reception task. Moreover, we propose the data-driven multi-pilot convolution neural network for MIMO-OFDM receiver (DMCNet). By incorporating complex convolution and complex fully connected structures, we design a receiver network to recover the transmitted signals from the received signals. We validate the accuracy and robustness of DMCNet under different channel conditions, comparing the bit error rates with different schemes. Additionally, we discuss the factors influencing various channel effects. Experimental results demonstrate that the DL-based reception scheme exhibits promising feasibility in terms of accuracy and interference resistance when compared to traditional approaches. © 2023 IEEE.

关键词： MIMO systems

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Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

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computers, Materials & Continua 2023年第5期75卷 3239-3255页

作者： Majdy M.Eltahir Adil Yousif Fadwa Alrowais Mohamed K.Nour Radwa Marzouk Hatim Dafaalla Asma Abbas Hassan Elnour Amira Sayed A.Aziz Manar Ahmed Hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Applied SciencesKing Khalid UniversityMuhayil63772Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event *** is especially applicable in the case of elderly or disabled people who live self-reliantly in their *** sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during *** is one of the most important problems confronted by older people and people with movement *** previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled ***,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor ***,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s *** this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)*** presented IWODL-FDDP model aims to identify the fall events to assist disabled *** presented IWODLFDDP model applies an image filtering approach to pre-process the ***,the EfficientNet-B0 model is utilized to generate valuable feature vector ***,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall ***,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the *** experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

关键词： Fall detection disabled people deep learning improved whale optimization assisted living

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Compact Design of an S-Band Rat Race Hybrid Coupler with Enhanced Phase Balance Using Parallel Coupled-Lines

Compact Design of an S-Band Rat Race Hybrid Coupler with Enh...

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International Conference on Information Technology (INCIT)

作者： Somkuan Srisawat Ravee Phromloungsri Apichan Kanjanavapastit Kritsanapong Somsuk Somchat Sonasang Mitchai Chongcheawchamnan Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Electrical Elements and Wireless Devices Unit (EEWDU) Faculty Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Electrical Engineering Prince of Songkla University Songkhla Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This paper presents the design and implementation of an S-band Rat Race Hybrid Coupler operating at 2.2-2.3 GHz, integrated with parallel coupled-line phase shifters to reduce circuit size for satellite communication systems. The proposed design improves critical performance parameters, including phase balance, insertion loss, isolation, and bandwidth. The design achieves enhanced isolation and bandwidth by incorporating the coupled-line phase shifter into the traditional rat-race structure, making it suitable for applications in signal processing, antenna-feeding networks, and radar systems. Simulations using a 70.7-Ω microstrip line at 2.25 GHz demonstrate significant improvements in phase shift accuracy and return loss. Measurement results validate the simulations, confirming the compact and efficient nature of the design, which offers promising potential for modern microwave communication technologies.

关键词： Microwave measurement Microwave antennas Antenna measurements Spaceborne radar Phase shifters Couplers Rats Microwave communication Radar antennas Microwave circuits

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On the Systematic Construction of Lyapunov Functions for Polynomial systems

On the Systematic Construction of Lyapunov Functions for Pol...

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92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)

作者： Natkowski, Lars Gerbet, Daniel Robenack, Klaus Fraunhofer Institute for Transportation and Infrastructure Systems IVI ZeunerstraBe 38 Dresden01069 Germany Institute of Control Theory Faculty of Electrical and Computer Engineering TU Dresden Georg-Schumann-StraBe 7A Dresden01187 Germany

Lyapunov functions are a widely used tool to evaluate stability properties of nonlinear dynamical systems’ equilibria. In this paper quantifier elimination is used to construct Lyapunov functions for polynomial systems from a parametric ansatz. Because the existing algorithms for quantifier elimination are inherently computationally expensive, a strategy is to simplify the quantifier elimination problem beforehand. A method is presented that may help simplifying the problem of finding suitable Lyapunov functions by deriving easier to evaluate necessary conditions. Finally, the introduced method is applied to an example system to show local asymptotic stability of an equilibrium point by constructing a Lyapunov function. © 2023 The Authors.

关键词： Lyapunov functions

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Synthetic Image Generation for Visual Particle Size Distribution Estimation based on U-NET Convolutional Networks

Synthetic Image Generation for Visual Particle Size Distribu...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Akshay Chavan Antonius Schmidt Hendrik Klopries Dominik Aufderheide Industrial Measurement Section (iMe) Faculty of Electrical Engineering South Westphalia University of Applied Sciences Soest Germany Automation Technology and Learning Systems Faculty of Electrical Engineering South Westphalia University of Applied Sciences Soest Germany

Many applications in the field of bulk material handling require the determination of the Particle Size Distribution (PSD) of the handled materials. Typically, this can only be achieved by manual sampling and a subsequent laboratory analysis. However, the availability of novel image segmentation algorithms based on Convolutional Neural Networks (CNNs) leads to the possibility for an implementation of visual PSD estimation techniques. One prominent example of such approaches is the U-Net image segmentation architecture. However, one fundamental problem for the training of the underlying CNN is the difficulty to gather ground truth data, since the exact correspondence between a set of images and the underlying particle sizes cannot be achieved in practice. This paper introduces a novel synthetic image generation procedure for the visual PSD estimation by using the U-NET structure. The overall approach is validated and verified by using a prototypic implementation of a U-NET segmentation pipeline. It is shown, that the proposed synthetic image dataset generation can be applied for all visual PSD estimation based on machine learning algorithms.

关键词： Training Visualization Image segmentation Machine learning algorithms Image synthesis Pipelines Estimation

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Scalable Multiport DC-DC Converter for Bidirectional EV charging in DC Traction Grids

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IEEE Transactions on Circuits and systems II: Express Briefs 2025年

作者： Lukianov, Mykola Romero-Cadaval, Enrique Kasprowicz, Andrzej Husev, Oleksandr Strzelecki, Ryszard Department of Power Electronics and Electrical Machines Gdañsk University of Technology Gdañsk80-222 Poland Power Electrical and Electronic Systems R and D Group School of Industrial Engineering University of Extremadura Badajoz06006 Spain Gdynia Maritime University Faculty of Electrical Engineering Gdynia Poland Warsaw University of Technology Institute of Control and Industrial Electronics Koszykowa 75 Warsaw00-662 Poland

The limited availability of public Electric Vehicle (EV) charging infrastructure poses a significant challenge to EV adoption in urban areas, that requires significant economic expenses. One potential solution to mitigate the problem is utilizing the existing traction DC grid infrastructure for EV charger connections. Such solution allows exploiting already existing traction substation equipment for both EV charging and powering DC traction grids, potentially reducing charging station installation costs. At the same time, bidirectional EV charger operation allows providing services such as DC grid voltage stabilization and traction transport braking energy recuperation. Therefore, this paper examines a scalable multiport DC-DC converter, suitable for multiterminal EV charging stations, located in the proximity to DC traction networks. The use of a multiport converter allows for sharing electric components across multiple ports, reducing the total number of elements required, compared to conventional solutions, where each EV charger relies on a separate converter. © 2004-2012 IEEE.

关键词： Charging stations

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Impact of Image Sensor Input Faults on Pruned Neural Networks for Object Detection

Impact of Image Sensor Input Faults on Pruned Neural Network...

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IEEE International Symposium on Defect and Fault Tolerance in VLSI systems

作者： Yizhi Chen Wenyao Zhu Dejiu Chen Omar Mohammed Parthib Khound Zhonghai Lu School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Industrial Engineering and Management KTH Royal Institute of Technology Stockholm Sweden Chair of Reliability of Technical Systems and Electrical Measurement University of Siegen Siegen Germany

ISBN: (数字)9798350366884

ISBN: (纸本)9798350366891

Object detection is one of the most fundamental problems in computer vision, and image sensors are commonly used for this. In this paper, we present the impact of image sensor faults on pruned neural networks for object detection. We compare the error sensitivities of networks after network slimming, networks after magnitude-based pruning, and native compact models. We also explore different spatial fault types with three intensities. Furthermore, we have developed a temporal error model based on realistic aging image sensor faults. The results illuminate that the performance on clean images is important as the mean Average Precision (mAP) experiences a decrease with an increase in injected faults. Additionally, we demonstrate that the size of the model does not invariably yield a decisive impact on error tolerance when comparing small models such as pruned models and native compact models.

关键词： Image sensors Performance evaluation Fault tolerance Sensitivity Neural networks Fault tolerant systems Object detection Aging Very large scale integration Transformers

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