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IEEE Conference on Vehicular Technology (VTC)

作者： Jaehyun Park Joohwan Chun Dongkyun Nam Cheol Hoon Park Scientific Computing Lab Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Daejeon South Korea Neural Networks and Machine Intelligence Lab Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (纸本)0780385217

This paper is about the optimization of beamforming weights in the nonlinear array antenna system. We consider the multi-objective optimization using the evolutionary algorithm. First, we constrain two variables - the width of the main-lobe, the level of the side-lobe. Second, we constrain an additional variable, the energy of beamforming weights, on the former system. In each case, we compute the costs of beamforming weights in each generation. At the end of generations, we can get the Pareto frontier which means the optimal solution set. To demonstrate the performance of the proposed optimization, we compare the results between the proposed algorithm and the conventional window method.

关键词： Antenna arrays Evolutionary computation Array signal processing Linear antenna arrays Phased arrays Optimization methods Antennas and propagation Paper technology Telephony Electronic mail

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Opportunistic strategies for lightweight signal processing for body sensor networks 10

Opportunistic strategies for lightweight signal processing f...

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3rd International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2010

作者： Seto, Edmund Martin, Eladio Yang, Allen Yan, Posu Gravina, Raffaele Lin, Irving Wang, Curtis Roy, Michael Shia, Victor Bajcsy, Ruzena School of Public Health University of California Berkeley CA 94720 United States Electrical Engineering and Computer Sciences University of California Berkeley CA 94720 United States Wireless Sensor Networks Lab Telecom Italia Berkeley CA 94720 United States Department of Electronics Informatics and Systems University of Calabria Rende Italy

ISBN: (纸本)9781450300711

We present a mobile platform for body sensor networking based on a smartphone for lightweight signal processing of sensor mote data. The platform allows for local processing of data at both the sensor mote and smartphone levels, reducing the overhead of data transmission to remote services. We discuss how the smartphone platform not only provides the ability for wearable signal processing, but it allows for opportunistic sensing strategies, in which many of the onboard sensors and capabilities of modern smartphones may be collected and fused with body sensor data to provide environmental and social context. We propose that this can help refine data reduction at the local level. We describe three examples related to health and wellness, to which our system has been *** © 2010 ACM.

关键词： Smartphones

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Parameter Estimation of Synchronous Generator Using Neural Controlled Differential Equations

Parameter Estimation of Synchronous Generator Using Neural C...

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International Conference on Control and Automation (ICCA)

作者： Zhun Yin Hong Wang Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper introduces a synchronous generator modeling method based on neural controlled differential equations (neural CDEs) using online sampled data. This method begins with a fifth-order generator model, where every trainable parameter is regarded as one parameter of the fifth-order generator model. The objective is to use the real-time data to learn these parameters. A training algorithm has been formulated, and it has been shown that the combination of the proposed neural CDEs and the fifth-order model can produce desired online parameter estimations for the synchronous generator. The simulation results show that the proposed method can generate a very accurate estimation and model predictions with the mean absolute percentage error of 0.04638%.

关键词： Training Parameter estimation Accuracy Voltage Differential equations Predictive models Mathematical models

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Data-Driven Optimal Control of Traffic Signals for Urban Road networks

Data-Driven Optimal Control of Traffic Signals for Urban Roa...

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IEEE Conference on Decision and Control

作者： Tong Liu Hong Wang Zhong-Ping Jiang Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper studies the issue of data-driven optimal control design for traffic signals of oversaturated urban road networks. The signal control system based on the store and forward model is generally uncontrollable for which the controllable decomposition is needed. Instead of identifying the unknown parameters like saturation rates and turning ratios, a finite number of measured trajectories can be used to parametrize the system and help directly construct a transformation matrix for Kalman controllable decomposition through the fundamental lemma of J. C. Willems. On top of that, an infinite-horizon linear quadratic regulator (LQR) problem is formulated considering the constraints of green times for traffic signals. The problem can be solved through a two-phase data-driven learning process, where one solves an infinite-horizon unconstrained LQR problem and the other solves a finite-horizon constrained LQR problem. The simulation result shows the theoretical analysis is effective and the proposed data-driven controller can yield desired performance for reducing traffic congestion.

关键词： Regulators Roads Simulation Process control Optimal control Turning Trajectory

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The Original Method of Controlling a computer Using Distance Sensors 16

The Original Method of Controlling a Computer Using Distance...

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2022

作者： Savaryn, Pavlo Strekha, Vadym Brych, Mykola Brych, Lesia Kabak, Vitalii Polishchuk, Mykola Lutsk National Technical University Department of Digital Education Technologies Lutsk Ukraine Lutsk National Technical University Department of Computer Science Lutsk Ukraine Internet of Things and Software Defined Networks Lab Lviv Polytechnic National University Lviv Ukraine Centre for International Education Lviv Polytechnic National University Lviv Ukraine Lutsk National Technical University Department of Computer Engineering and Cybersecurity Lutsk Ukraine

ISBN: (纸本)9781665468619

The article provides a brief overview of human-computer interaction methods and systems, and the research made in this area. We have designed the architecture of the system and tested a method of controlling a computer using ultrasonic distance sensors. In the course of the experiment, the following problems have been solved: obtaining and processing data from several distance sensors, data transfer to gesture recognition algorithm, uncontrolled oscillations of sensor indicators. The suggested method of controlling a computer can be used in ubiquitous computing. © 2022 IEEE.

关键词： Human computer interaction

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Strategic Demand-Planning in Wireless networks: Can Generative-AI Save Spectrum and Energy?

arXiv

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arXiv 2024年

作者： Çiloğlu, Berk Koç, Görkem Berkay Shamsabadi, Afsoon Alidadi Ozturk, Metin Yanikomeroglu, Halim Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Non-Terrestrial Networks Lab Systems and Computer Engineering Carleton University Ottawa Canada

Generative-AI (GenAI), a novel technology capable of producing various types of outputs, including text, images, and videos, offers significant potential for wireless communications. This article introduces the concept of strategic demand-planning through demand-labeling, demand-shaping, and demand-rescheduling. Accordingly, GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks. More specifically, GenAI is used to compress and convert the content of various types (e.g., from a higher bandwidth mode to a lower one, such as from a video to text), which subsequently enhances performance of wireless networks in various usage scenarios, such as cell-switching, user association and load balancing, interference management, as well as disasters and unusual gatherings. Therefore, GenAI can serve a function in saving energy and spectrum in wireless networks. With recent advancements in AI, including sophisticated algorithms like large language models and the development of more powerful hardware built exclusively for AI tasks, such as AI accelerators, the concept of demand-planning, particularly demand-shaping through GenAI, becomes increasingly relevant. Furthermore, recent efforts to make GenAI accessible on devices, such as user terminals, make the implementation of this concept even more straightforward and feasible. Copyright © 2024, The Authors. All rights reserved.

关键词： Demand response

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Self-triggered Robust Output Feedback Model Predictive Control of Constrained Linear Systems

Self-triggered Robust Output Feedback Model Predictive Contr...

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American Control Conference

作者： Jingyuan Zhan Xiang Li Zhong-Ping Jiang Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Engineering Fudan University Shanghai 200433 China Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn 11201 USA

ISBN: (纸本)9781509045839

This paper presents a self-triggered model predictive control (MPC) algorithm for constrained linear discrete-time systems in the presence of bounded state and output disturbances, when only the system output can be measured at triggering instants. The proposed algorithm mainly relies on a state estimator whose estimation error is bounded by an invariant set, and on the self-triggered robust model predictive control of a nominal system, in which the cost function penalizes the nominal system state and control effort as well as the triggering interval. The proposed algorithm is proved to drive the system to an invariant set with respect to the disturbances. Numerical examples are also provided to validate the proposed algorithm and to further illustrate better performance compared with the periodic MPC.

关键词： Model predictive control (MPC) Event-triggered Self-triggered Output feedback Robust control

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GENp2-2: The Simplicity Project and its Demonstrator: Improving Ease of Use and Personalization of ICT Services

GENp2-2: The Simplicity Project and its Demonstrator: Improv...

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IEEE Globecom 2006

作者： G. Bartolomeo N. Blefari Melazzi F. Martire S. Salsano S. Kapellaki E. Koutsoloukas G. N. Prezerakos N.D. Tselikas I. S. Venieris DIE Universita di Roma Tor Vergata Italy Intelligent Communications & Broadband Networks Lab School of Electrical & Computer Engineering National and Technical University of Athens Greece

As technology develops, people are using an ever broader and heterogeneous range of ICT devices and network-based services. The result is an enormous burden of complexity on the shoulders of users, service providers and network operators. The goal of the Simplicity project, supported by the European Union, is to reduce this complexity by: (i) providing automatic customization of user access to services and the network; (ii) automatically adapting services to terminal characteristics and user preferences; (iii) orchestrating network capabilities. The Simplicity approach is based on a personalization device, the so-called Simplicity device, which, together with a brokerage framework, offers transparent service configuration and runtime adaptation, according to user preferences and computing/networking context conditions. This paper presents the Simplicity project, briefly discusses its architecture and introduces the Simplicity demonstrator: a proof of concept implementation used to verify our approach, through diverse usage scenarios. In addition, we introduce the main aspects of a follow-up project of Simplicity, named SMS.

关键词： computer networks Context-aware services Intelligent agent Intelligent networks Broadband communication Network interfaces Java Universal Serial Bus Software agents Runtime

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Loss temporal dependency characteristic inference in wireless sensor network

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Journal of Computational Information Systems 2008年第5期4卷 2303-2308页

作者： Li, Yongjun Ji, Wenli Dai, Yafei Computer Networks and Distributed Systems Laboratory Peking University Beijing 100871 China School of Computer Science Northwestern Polytechnical University Xi'an 710072 China School of Communication Engineering Xi'an University of Science and Technology Xi'an 710075 China State Key Lab. for Adv. Opt. Commun Syst and Networks Beijing 1000871 China

The internal link loss characteristic inference has become an increasingly important issue for operating and evaluating a wireless sensor network. Due to the inherent stringent bandwidth and energy constraints, it is usually impractical to directly collect link loss statistical data from each node in sensor network. Here we consider the problem of inferring the internal link loss characteristics from end-to-end measurement. We use the Gilbert error model to model the sensor link losses, formulate the problem of link loss characteristics estimation as a Bayesian inference problem, and propose a Markov Chain Monte Carlo (MCMC) algorithm to solve it. The simulation shows that the link loss performance parameters can be inferred accurately, and the proposed algorithm scales well according to the sensor network size. © 2008 Binary Information Press.

关键词： Inference engines

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Interpretable and transferable models to understand the impact of lockdown measures on local air quality

arXiv

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arXiv 2020年

作者： Einsiedler, Johanna Cheng, Yun Papst, Franz Saukh, Olga Vienna University of Business Economics CSH Vienna Austria Graz University of Technology CSH Vienna Austria Computer Engineering and Networks Lab. ETH Zurich Switzerland

The COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality and how much pollution reduction potential can the society offer through digitalization and mobility-limiting policies. In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations, training a long-term prediction model and comparing its predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland, Austria and in China: evaluate up to -29.4% / +9.8% and -22.6% / +11.5%, change in NO2 / PM10 in Eastern Switzerland and Lower Austria respectively;-28.1%, / -10.8 % and -52.8 % / -50.0 % in NO2 / PM2.5 in Beijing and Wuhan respectively. Our reduction estimates are consistent with recent publications, yet in contrast to prior works, our method takes local weather into account. What can we learn from pollution emissions during lockdown? The lockdown period was too short to train meaningful models from scratch. To tackle this problem, we use transfer learning to newly fit only traffic-dependent variables. We show that the resulting models are accurate, suitable for an analysis of the post-lockdown period and capable of estimating the future air pollution reduction potential. © 2020, CC BY.

关键词： Air quality

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