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Optimal Status Updates for Minimizing Age of Correlated Information in IoT Networks with Energy Harvesting Sensors

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arXiv 2023年

作者： Xu, Chao Zhang, Xinyan Yang, Howard H. Wang, Xijun Pappas, Nikolaos Niyato, Dusit Quek, Tony Q.S. The School of Information Engineering Northwest A&F University Shaanxi China The Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Northwest A&F University Yangling712100 China The Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining China The School of Electronics and Information Technology SYSU Guangzhou China The Department of Computer and Information Science Linköping University Linköping Sweden The School of Computer Science and Engineering Nanyang Technological University Singapore The Singapore University of Technology and Design Singapore487372 Singapore The Yonsei Frontier Lab Yonsei University Korea Republic of

Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) capability. We optimize the average AoCI at the data fusion center (DFC) by appropriately managing the energy harvested by sensors, whose true battery states are unobservable during the decision-making process. Particularly, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. In order to address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors’ true battery states, and large-scale discrete action space, we devise a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of the soft actor-critic and long short-term memory techniques. Meanwhile, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis 16th

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Collaborative Learning of Gesture Recognition and 3D Hand Po...

16th European Conference on computer Vision, ECCV 2020

作者： Yang, Siyuan Liu, Jun Lu, Shijian Er, Meng Hwa Kot, Alex C. Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783030585792

Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features. In addition, a novel multi-order multi-stream feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art. © 2020, Springer Nature Switzerland AG.

关键词： Gesture recognition

Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for travel behavior analysis?

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arXiv 2023年

作者： Wang, Qingyi Wang, Shenhao Zheng, Yunhan Lin, Hongzhou Zhang, Xiaohu Zhao, Jinhua Walker, Joan Department of Civil and Environmental Engineering Department of Urban Studies and Planning United States Computer Science and Artificial Intelligence Laboratory United States Media Lab Massachusetts Institute of Technology CambridgeMA United States Civil and Environmental Engineering University of California at Berkeley BerkeleyCA United States Urban Planning and Design The University of Hong Kong Hong Kong Department of Urban and Regional Planning University of Florida GainesvilleFL United States

Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by both numeric data and urban imagery, thus necessitating a synergetic framework to combine them. This study creates a theoretical framework of deep hybrid models consisting of a mixing operator and a behavioral predictor, thus integrating the numeric and imagery data for travel behavior analysis. Empirically, this framework is applied to analyze travel mode choice using the Chicago MyDailyTravel Survey as the numeric inputs and the satellite images as the imagery inputs. We found that deep hybrid models significantly outperform both classical demand models and deep learning models in predicting aggregate and disaggregate travel behavior. The deep hybrid models can reveal spatial clusters with meaningful sociodemographic associations in the latent space. The models can also generate new satellite images that do not exist in reality and compute the corresponding economic information, such as substitution patterns and social welfare. Overall, the deep hybrid models demonstrate the complementarity between the low-dimensional numeric and high-dimensional imagery data and between the traditional demand modeling and recent deep learning. They enrich the family of hybrid demand models by using deep architecture as the latent space and enabling researchers to conduct associative analysis for sociodemographics, travel decisions, and generated satellite imagery. Future research could address the limitations in interpretability, robustness, and transferability, and propose new methods to further enrich the deep hybrid models. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control

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arXiv 2025年

作者： Gu, Hankang Wang, Shangbo Jia, Dongyao Zhang, Yuli Luo, Yanrong Mao, Guoqiang Wang, Jianping Lim, Eng Gee School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Computer Science University of Liverpool LiverpoolL69 3GJ United Kingdom Department of Engineering and Design University of Sussex BrightonBN1 9RH United Kingdom School of Advanced Technology China International Business School Suzhou Xi’an Jiaotong-Liverpool University Suzhou215123 China ISN State Key Lab Xidian University Xi’an710126 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute China

Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off between scalability and optimality. Most existing RTSC approaches partition a traffic network into several disjoint regions, followed by applying centralized reinforcement learning techniques to each region. However, the pursuit of cooperation among RTSC agents still remains an open issue and no communication strategy for RTSC agents has been investigated. In this paper, we propose communication strategies to capture the correlation of micro-traffic states among lanes and the correlation of macro-traffic states among intersections. We first justify the evolution equation of the RTSC process is Markovian via a system of store-and-forward queues. Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules—GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states. While GA2-Naive only considers the movements at each intersection, GA2-Aug also considers the lane-changing behavior of vehicles. Two proposed communication modules are then aggregated into two existing novel RTSC frameworks—RegionLight and Regional-DRL. Experimental results demonstrate that both GA2-Naive and GA2-Aug effectively improve the performance of existing RTSC frameworks under both real and synthetic scenarios. Hyperparameter testing also reveals the robustness and potential of our communication modules in large-scale traffic networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Climate Change Resilience of Freshwater Supply on Small Islands: Research Gaps and Strategies for a Case Study in Grand Bahama

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SSRN

SSRN 2024年

作者： Imig, Anne Welsh, Kristen Klausner, Sophia Hotta, Carolina Iwane McKenzie, Zeko Perosa, Francesca Stephens, Mark Turner, Amber Thomas, John Chaves, Henrique Leite Bowen-O'Connor, Clare Moxey, Andrew Dokou, Zoi Rein, Arno Hydrogeology School of Engineering and Design Technical University of Munich Germany Small Island Sustainability Programme University of The Bahamas Nassau Bahamas Geosciences Department Oberlin College OberlinOH United States Hydrology and River Basin Management School of Engineering and Design Technical University of Munich Germany Department of Earth and Biological Sciences Loma Linda University Loma LindaCA United States School of Chemistry Environmental and Life Sciences University of The Bahamas Nassau Bahamas University of the West Indies Barbados Strata Environmental Nassau Bahamas Watershed Management Lab EFL – School of Technology University of Brasilia DF Brasilia Brazil University of Trinidad and Tobago Trinidad and Tobago School of Chemistry Environmental and Life Sciences University of The Bahamas Freeport Bahamas Department of Civil Engineering College of Engineering and Computer Science California State University SacramentoCA United States

Study Region: Grand Bahama Island, the Commonwealth of The Bahamas. Study Focus: In Grand Bahama, due to its low elevation, sea level rise and flooding during hurricane events has led to the salinization of groundwater, which is the only source of potable water for the island. In this study, we assessed available literature of groundwater resources in Grand Bahamas to identify key challenges and knowledge gaps for the protection of the potable water supply. A literature review guided our assessment of various sustainable groundwater management approaches for the island. Understanding the hydrogeology and identifying strategies to maintain the freshwater lenses in Grand Bahama are key to ensure the island remains *** Hydrological Insights for the Region: Existing hydrogeological data highlight the urgent need to improve the island’s water supply system and to identify safe groundwater yields to sustain its drinking water supply. Based on analysis of these data, we propose the implementation of nature-based solutions, such as managed aquifer recharge and reforestation measures, to restore the groundwater ecosystem service of freshwater supply. Furthermore, to ensure effective water supply management, legislation and regulations should be adapted to actively involve both institutions and the public in the decision-making process. The results of this review are applicable to other small island nations around the world that are facing the effects of climate change and seawater intrusion. © 2024, The Authors. All rights reserved.

关键词： Groundwater resources

What's bothering developers in code review?

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What's bothering developers in code review?

IEEE/ACM International Conference on Software engineering: Software engineering in Practice Track (ICSE-SEIP)

作者： Emma Sö derberg Luke Church Jü rgen Bö rstler Diederick C. Niehorster Christofer Rydenfä lt Dept. of Computer Science Lund University Sweden Lund University University of Cambridge Sweden United Kingdom Dept. of Software Engineering Blekinge Institute of Technology Sweden Dept. of Psychology Humanities Lab Lund University Sweden Dept. of Design Sciences Lund University Sweden

The practice of code review is widely adopted in industry and has been studied to an increasing degree in the research community. However, the developer experience of code review has received limited attention. Here, ...

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Smartphone camera oximetry in an induced hypoxemia study

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arXiv 2021年

作者： Hoffman, Jason S. Viswanath, Varun Ding, Xinyi Thompson, Matthew J. Larson, Eric C. Patel, Shwetak N. Wang, Edward Paul G. Allen School of Computer Science and Engineering University of Washington United States Department of Electrical and Computer Engineering University of California San Diego United States The Design Lab University of California San Diego United States Department of Computer Science Southern Methodist University United States Department of Family Medicine University of Washington United States Department of Electrical and Computer Engineering University of Washington United States

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO2) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health, as well as improve physicians’ ability to remotely diagnose and treat respiratory conditions. In this work, we take a step towards this goal by performing the first clinical development validation on a smartphone-based SpO2 sensing system using a varied fraction of inspired oxygen (FiO2) protocol, creating a clinically relevant validation dataset for solely smartphone-based methods on a wide range of SpO2 values (70%-100%) for the first time. This contrasts with previous studies, which evaluated performance on a far smaller range (85%-100%). We build a deep learning model using this data to demonstrate accurate reporting of SpO2 level with an overall MAE=5.00% SpO2 and identifying positive cases of low SpO22 monitoring, and we provide the data from the FiO2 study in open-source format, so that others may build on this work. © 2021, CC BY-NC-ND.

关键词： Smartphones

The Stretchy Strap: supporting encumbered interaction with guitars

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Interface 2023年第1期52卷

作者： Juan Martinez Avila Adrian Hazzard Chris Greenhalgh Steve Benford Andrew McPherson a Mixed Reality Laboratory School of Computer Science The University of Nottingham Nottingham UK b Augmented Instruments Lab School of Electronic Engineering and Computer Science Queen Mary University of London. Dyson School of Design Engineering Imperial College London London UK

Guitarists struggle to play their instruments while simultaneously using additional computing devices (i.e., encumbered interaction). We explored designs with guitarists through co-design and somaesthetic design workshops, learning that they (unsurprisingly) preferred to focus on playing their guitars and keeping their instruments’ material integrity intact. Subsequently, we devised an interactive guitar strap controller, which guitarists found promising for tackling encumbered interaction during instrumental transcription, learning and practice. Our design process highlights three strategies: considering postural interaction, applying somaesthetic design to interactive music technology development, and augmenting guitar accessories.

关键词： Augmented guitars Encumbered interaction Somaesthetic design User-centred design E-textiles Embodied interaction Postural interaction Learning and practicing music

DenseNet for Breast Tumor Classification in Mammographic Images 1st

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DenseNet for Breast Tumor Classification in Mammographic Ima...

1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Jiménez Gaona, Yuliana Rodriguez-Alvarez, María José Espino-Morato, Hector Castillo Malla, Darwin Lakshminarayanan, Vasudevan Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja San Cayetano Alto s/n CP1101608 Loja Ecuador Valencia46022 Spain Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science Waterloo Canada Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada Valencia46022 Spain

ISBN: (纸本)9783030881627

Breast cancer screening is an efficient method to detect breast lesions early. The common screening techniques are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload for pathologists, and hence is prone to diagnostic errors. Thus, the aim of this study was to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to automate RoI segmentation. Then feature extraction, selection and classification were carried out by the DenseNet architecture. Finally, the precision and accuracy of the model was evaluated by the AUC, accuracy and precision metrics. To summarize, the findings of this study show that the methodology may improve the diagnosis and efficiency in automatic tumor localization through medical image classification. © 2021, Springer Nature Switzerland AG.

关键词： Mammography