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Wireless Acoustic Sensor Networks and Edge computing for Rapid Acoustic Monitoring

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

arXiv

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arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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An Accurate Reconstruction Method for Indoor Bioaerosol Concentration Field from Asynchronous and Sparse LiDAR Measurements Based on Latent Diffusion Models

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Building and Environment 2025年

作者： Xuqiang Shao Mingyu Li Lei Wang Hao Han Zhijian Liu Jiancai Huang Zaishan Qi Department of Computer Science North China Electric Power University Baoding Hebei 071003 PR China State Key Laboratory of Chemistry for NBC Hazards Protection Beijing 100025 China Department of Power Engineering North China Electric Power University Baoding Hebei 071003 PR China Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding Hebei 071003 PR China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding Hebei 071003 PR China

Bioaerosols are the primary route for pathogen transmission, and have a serious negative impact on human health. Accurately measuring the distribution of bioaerosol concentrations in the indoor environment holds significant importance. Current methodologies are constrained by limitations in measurement resolution and speed, and unable to obtain the global 3D concentration fields directly. We propose a Sparse Reconstruction Latent Diffusion Model (SR-LDM) that reconstructs the 3D global concentration field from the asynchronous sparse bioaerosol concentration data measured by LiDAR. The concentration field reconstruction is formulated as a conditional generation task, leveraging the powerful generative capabilities of the Latent Diffusion Models (LDM) to infer global distributions from sparse observational data. We employ a contrastive learning strategy to train a dedicated condition encoder, which extracts information from spatiotemporal sparse data measured at different locations and times to guide the generation of the global concentration field. We use a validated Computational Fluid Dynamics (CFD) solver to construct a dataset of aerosol concentration fields in an empty channel. The results indicate that our SR-LDM model effectively reconstructs the global concentration field formed by a single release source, using historical sparse measurement data that accounts for 1%–3.2% of the total number of units. In most cases, high-quality results are obtained after 6-16 iterations, and the similarity with CFD data reaches 80%.

关键词： Latent Diffusion Models (LDM) Field reconstruction Bioaerosols dispersion Indoor air pollution Computational Fluid Dynamics (CFD)

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Communication Lower Bound in Convolution Accelerators

Communication Lower Bound in Convolution Accelerators

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IEEE Symposium on High-Performance computer Architecture

作者： Xiaoming Chen Yinhe Han Yu Wang Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9781728161495

ISBN: (纸本)9781728161501

In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communication for CNN accelerators. For the off-chip communication, we derive the theoretical lower bound for any convolutional layer and propose a dataflow to reach the lower bound. This fundamental problem has never been solved by prior studies. The on-chip communication is minimized based on an elaborate workload and storage mapping scheme. We in addition design a communication-optimal CNN accelerator architecture. Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a three-level memory hierarchy and it is computation dominant. The gap between the energy efficiency of our accelerator and the theoretical best value is only 37-87%.

关键词： System-on-chip Convolution Random access memory Convolutional codes Memory management Microsoft Windows Kernel

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Unlocking Integrated Wireless Powered Sensing and Communication Networks using Reconfigurable Intelligent Surface

引用

IEEE Transactions on Wireless Communications 2025年

作者： Zhu, Zhengyu Guo, Kaixuan Chu, Zheng Mi, De Mu, Junsheng Muhaidat, Sami Wong, Kai-Kit Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Xidian University State Key Laboratory of Integrated Services Networks 710126 China University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Ningbo China Next-Generation Internet of Everything Laboratory Ningbo315100 China Birmingham City University College of Computing BirminghamB4 7XG United Kingdom Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Khalifa University KU6G Research Center Department of Computer and Communication Engineering Abu Dhabi127788 United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Laboratory Seoul03722 Korea Republic of

A novel integrated wireless powered sensing and communication (IWPSAC) framework is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to harvest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). Our setup further considers a reconfigurable intelligent surface (RIS) to integrate sensing, wireless energy transfer (WET) and wireless information transfer (WIT) by optimizing the phase shifts. We formulate an optimization problem to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the energy beamforming, transmission time scheduling and RIS phase shifts. The presence of multiple coupled variables in the formulated problem renders the optimization problem non-jointly convex. To address its non-convexity, we first derive a closed-form expression for the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem iteratively. Concretely, this involves alternating the design of the energy beamforming and the RIS phase shifts for sensing/WET by leveraging the semidefinite programming (SDP) relaxation method. To overcome the high complexity introduced by the SDP, we introduce a low complexity AO algorithm that derives the optimal solutions for energy beamforming, transmission time scheduling, and sensing/WET phase shift using successive convex approximation (SCA), Lagrangian duality methods, Karush-Kuhn-Tucker (KKT) conditions, and the element-wise block coordinate descent (EBCD) approach. Simulation results demonstrate the performance of the proposed algorithms and underscore the superior benefits of the RIS compared to baseline schemes. © 2002-2012 IEEE.

关键词： Lagrange multipliers

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Applying the FAIR Principles to computational workflows

arXiv

引用

arXiv 2024年

作者： Wilkinson, Sean R. Aloqalaa, Meznah Belhajjame, Khalid Crusoe, Michael R. de Paula Kinoshita, Bruno Gadelha, Luiz Garijo, Daniel Ragnar Gustafsson, Ove Johan Juty, Nick Kanwal, Sehrish Khan, Farah Zaib Köster, Johannes Gehlen, Karsten Petersvon Pouchard, Line Rannow, Randy K. Soiland-Reyes, Stian Soranzo, Nicola Sufi, Shoaib Sun, Ziheng Vilne, Baiba Wouters, Merridee A. Yuen, Denis Goble, Carole Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN United States Department of Computer Science University of Manchester Manchester United Kingdom LAMSADE PSL Paris Dauphine University Paris France Berlin Germany Earth Sciences Barcelona Supercomputing Center Barcelona Spain Heidelberg Germany Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Australian BioCommons University of Melbourne MelbourneVIC Australia ParkvilleVIC Australia University Medicine Essen University of Duisburg-Essen Essen Germany Data Management Department Deutsches Klimarechenzentrum GmbH Hamburg Germany Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Silverdraft Supercomputing BoiseID United States Informatics Institute University of Amsterdam Amsterdam Netherlands Earlham Institute Norwich United Kingdom Center for Spatial Information Science and Systems Department of Geography and Geoinformation Science George Mason University FairfaxVA United States Bioinformatics Group Riga Stradins University Riga Latvia School of Clinical Medicine University of New South Wales KensingtonNSW Australia Ontario Institute for Cancer Research TorontoON Canada

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community. © 2024, CC BY.

关键词： Application programs

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An IoT Edge computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

引用

2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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Implementing two-qubit gates at the quantum speed limit

arXiv

引用

arXiv 2022年

作者： Howard, Joel Lidiak, Alexander Jameson, Casey Basyildiz, Bora Clark, Kyle Zhao, Tongyu Bal, Mustafa Long, Junling Pappas, David P. Singh, Meenakshi Gong, Zhexuan Department of Physics Colorado School of Mines GoldenCO80401 United States Rigetti Computing 775 Heinz Ave BerkeleyCA94701 United States Department of Computer Science Colorado School of Mines GoldenCO80401 United States Department of Physics University of Colorado BoulderCO80309 United States Superconducting Quantum Materials and Systems Center Fermi National Accelerator Laboratory BataviaIL60510 United States National Institute of Standards and Technology BoulderCO80305 United States

The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine learning inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high fidelity. Thus, the method is applicable to a variety of platforms including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions. We expect our method to offer significant speedups for non-native two-qubit gates that are typically achieved with a long sequence of single-qubit and native two-qubit gates. Copyright © 2022, The Authors. All rights reserved.

关键词： Logic gates

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Predicting extreme events from data using deep machine learning: When and where

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Physical Review Research 2022年第2期4卷 023028-023028页

作者： Junjie Jiang Zi-Gang Huang Celso Grebogi Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi 710049 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University Tempe Arizona 85287 USA

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

关键词： Machine learning Spatiotemporal chaos Artificial neural networks Extreme event statistics

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Meta auxiliary learning for facial action unit detection

arXiv

引用

arXiv 2021年

作者： Li, Yong Shan, Shiguang Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Cas Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cas Center for Excellence in Brain Science and Intelligence Technology

Despite the success of deep neural networks on facial action unit (AU) detection, better performance depends on a large number of training images with accurate AU annotations. However, labeling AU is time-consuming, expensive, and error-prone. Considering AU detection and facial expression recognition (FER) are two highly correlated tasks, and facial expression (FE) is relatively easy to annotate, we consider learning AU detection and FER in a multi-task manner. However, the performance of the AU detection task cannot be always enhanced due to the negative transfer in the multi-task scenario. To alleviate this issue, we propose a Meta Auxiliary Learning method (MAL) that automatically selects highly related FE samples by learning adaptative weights for the training FE samples in a meta learning manner. The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties. Experimental results on several popular AU datasets demonstrate MAL consistently improves the AU detection performance compared with the state-of-the-art multi-task and auxiliary learning methods. MAL automatically estimates adaptive weights for the auxiliary FE samples according to their semantic relevance with the primary AU detection task. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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