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arXiv 2023年

作者： Kwon, Hyeokhyen Hegde, Chaitra Kiarashi, Yashar Madala, Venkata Siva Krishna Singh, Ratan Nakum, ArjunSinh Tweedy, Robert Tonetto, Leandro Miletto Zimring, Craig M. Doiron, Matthew Rodriguez, Amy D. Levey, Allan I. Clifford, Gari D. The Department of Biomedical Informatics School of Medicine Emory University AtlantaGA30322 United States The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States The School of Industrial Design College of Design Georgia Institute of Technology AtlantaGA30332 United States The School of Architecture College of Design Georgia Institute of Technology AtlantaGA30332 United States The Department of Neurology School of Medicine Emory University AtlantaGA30322 United States The Department of Biomedical Informatics Emory University School of Medicine AtlantaGA30322 United States The Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30322 United States

Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. In this paper, we present a feasibility study and systematic analysis of a camera-based indoor localization and multi-person tracking system implemented on edge computing devices within a large indoor space. To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation and tracking of multiple individuals within a large therapeutic space spanning 1700m2, all while maintaining a strong focus on preserving privacy. Our pipeline consists of 39 edge computing camera systems equipped with Tensor Processing Units (TPUs) placed in the indoor space’s ceiling. To ensure the privacy of individuals, a real-time multi-person pose estimation algorithm runs on the TPU of the computing camera system. This algorithm extracts poses and bounding boxes, which are utilized for indoor localization, body orientation estimation, and multi-person tracking 1. Our pipeline demonstrated an average localization error of 1.41 meters, a multiple-object tracking accuracy score of 88.6%, and a mean absolute body orientation error of 29°. These results shows that localization and tracking of individuals in a large indoor space is feasible even with the privacy constrains. © 2023, CC BY-NC-SA.

关键词： computer vision

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Wideband Gap-Waveguide Phase Shifter Based on a Glide-Symmetric Ridge

TechRxiv

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TechRxiv 2021年

作者： Palomares-Caballero, Ángel Megías, Carlos Molero, Carlos Alex-Amor, Antonio Padilla, Pablo Granada18071 Spain The Department of Computer Architecture and Technology CITIC ETSIIT University of Granada Granada18071 Spain The Information Technologies Department Universidad CEU San Pablo Madrid28003 Spain

This letter presents a gap-waveguide phase shifter based on ridged unit cells with glide-symmetric configuration. The proposed unit cell design provides a higher stable phase shift compared to a conventional ridged unit cell whose ridge height and waveguide width are tuned to achieve a stable phase shift. Through the insertion of glide-symmetric holes with semicircle base in the ridged waveguide, a stable phase shift in a wide frequency range is achieved. Depending on the radii of the holes, the operating frequency range of the phase shifter can be tuned. A 90o phase shifter in millimeter-wave range is designed and manufactured to validate the analysis. The experimental results reveal a 85o± 5o phase shift from 33 GHz to 43 GHz (26.3% bandwidth). In this bandwidth, the reflection coefficient is below -10 dB with a maximum insertion loss of 0.7 dB. © 2021, CC BY-NC-SA.

关键词： Phase shifters

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Recommendation System Towards Residential Energy Saving Based on Anomaly Detection

Recommendation System Towards Residential Energy Saving Base...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Shadi Atalla Yassine Himeur Wathiq Mansoor Abbes Amira Fodil Fadli Abigail Copiaco Shahab Saquib Sohail College of Engineering and IT University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah UAE Dept. of Architecture & Urban Planning Qatar University Doha 2713 Qatar School of Engineering Sciences & Technology Jamia Hamdard New Delhi 110062 India

ISBN: (纸本)9781665492669

This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time residential data. Specifically, the data stream includes disaggregated power consumption, context, and weather conditions data. Internally the system converts time-series data streams into discrete ordered data points, which serve as inputs for training ML models. This method is used to predict power consumption anomalies. Gradually, the system helps to shape its users’ activities into more energy-efficient ones. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the K-neighbors neighbors’ algorithm is used to classify the features extracted with interleaving current with the previous data points. The performance assessment of the machine learning algorithms shows the suitability of our implementation for Edge and Fog platforms in terms of accuracy, latency, and model size.

关键词： Training Power demand Machine learning algorithms Shape Signal processing algorithms Signal processing Feature extraction

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TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Atoms Driven by Deep Learning on a New Generation of Sunway Supercomputer

TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Ato...

引用

Supercomputing Conference

作者： Honghui Shang Xin Chen Xingyu Gao Rongfen Lin Lifang Wang Fang Li Qian Xiao Lei Xu Qiang Sun Leilei Zhu Fei Wang Yunquan Zhang Haifeng Song SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Applied Physics and Computational Mathematics Beijing China Department of Computer Science and Technology Tsinghua University Wuxin China National Supercomputer Center in Wuxi Wuxi China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.

关键词： Technological innovation Monte Carlo methods Codes Quantum chemistry High performance computing Optimization methods Artificial neural networks

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The State of the Art in Visual Analytics for 3D Urban Data

arXiv

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arXiv 2024年

作者： Miranda, Fabio Ortner, Thomas Moreira, Gustavo Hosseini, Maryam Vuckovic, Milena Biljecki, Filip Silva, Claudio T. Lage, Marcos Ferreira, Nivan Department of Computer Science University of Illinois Chicago United States City Form Lab Massachusetts Institute of Technology United States VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH Austria Department of Architecture Department of Real Estate National University of Singapore Singapore Tandon School of Engineering Center for Data Science New York University United States Institudo de Computação Universidade Federal Fluminense Brazil Centro de Informática Universidade Federal de Pernambuco Brazil

Urbanization has amplified the importance of three-dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine-grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

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Expanding the Underwater Communication Capabilities of Seafloor Ecosystem Monitoring Stand-Alone Platforms using Pop-Up Buoys

Expanding the Underwater Communication Capabilities of Seafl...

引用

OCEANS

作者： Matias Carandell Steve Hernández Daniel Mihai Toma Enoc Martínez Marc Nogueras Jacopo Aguzzi Joaquín del Río SARTI-MAR Research Group Universitat Politècnica de Catalunya Vilanova i la Geltrú Spain Department of Computer Architecture and Technology Universitat de Girona Girona Spain Instituto de Ciencias del Mar Consejo Superior de Investigaciones Científicas Barcelona Spain Stazione Zoologica Anton Dohrn Villa Comunale Napoli Italia

The Platform for Long-lasting Observation of Marine Ecosystems (PLOME) project aims to improve the communication capabilities of stand-alone seafloor platforms for monitoring marine ecosystems. PLOME project involves building a cooperative network that integrates several seafloor remote stations with AUVs and USVs to collect and transmit the data. One of the innovative ideas studied in PLOME is the integration of pop-up buoys with sensing, processing, and communication capabilities. These buoys are periodically released untethered from the seafloor node with part of its collected data, and become oceanic drifters that transmit this data and their position using a satellite link. This article describes the design of the pop-up project and discusses the technological challenges related to wireless underwater communication, automatic release systems, and robust satellite communication links. Preliminary tests demonstrate the feasibility of the project's implementation.

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End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement

arXiv

引用

arXiv 2022年

作者： Liu, Jiachen Xue, Yuan Duarte, Jose Shekhawat, Krishnendra Zhou, Zihan Huang, Xiaolei College of Information Sciences and Technology The Pennsylvania State University United States Department of Electrical and Computer Engineering Johns Hopkins University United States College of Arts and Architecture The Pennsylvania State University United States Department of Mathematics BITS Pilani United States Manycore Tech Inc. United States

The automatic generation of floorplans given user inputs has great potential in architectural design and has recently been explored in the computer vision community. However, the majority of existing methods synthesize floorplans in the format of rasterized images, which are difficult to edit or customize. In this paper, we aim to synthesize floorplans as sequences of 1-D vectors, which eases user interaction and design customization. To generate high fidelity vectorized floorplans, we propose a novel two-stage framework, including a draft stage and a multi-round refining stage. In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence. To polish the initial design and generate more visually appealing floorplans, we further propose a novel panoptic refinement network (PRN) composed of a GCN and a transformer network. The PRN takes the initial generated sequence as input and refines the floorplan design while encouraging the correct room connectivity with our proposed geometric loss. We have conducted extensive experiments on a real-world floorplan dataset, and the results show that our method achieves state-of-the-art performance under different settings and evaluation metrics. Copyright © 2022, The Authors. All rights reserved.

关键词： User interfaces

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A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning

arXiv

引用

arXiv 2023年

作者： Abouheaf, Mohammed Gueaieb, Wail Spinello, Davide Al-Sharhan, Salah College of Technology Architecture & Applied Engineering Bowling Green State University Bowling GreenOH43402 United States School of Electrical Engineering and Computer Science Department of Mechanical Engineering University of Ottawa OttawaONK1N 6N5 Canada Machine Intelligence Research Labs AuburnWA98071-2259 United States

Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise adaptive control strategies. The resulting solutions are often challenged by the complexity of the reference model and those of the derived control strategies. Additionally, the explicit dependence of the control strategies on the process dynamics and reference dynamical models may contribute in degrading their efficiency in the face of uncertain or unknown dynamics. A model-reference adaptive solution is developed here for autonomous systems where it solves the Hamilton-Jacobi-Bellman equation of an error-based structure. The proposed approach describes the process with an integral temporal difference equation and solves it using an integral reinforcement learning mechanism. This is done in real-time without knowing or employing the dynamics of either the process or reference model in the control strategies. A class of aircraft is adopted to validate the proposed technique. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Data Fusion in Neuromarketing: Multimodal Analysis of Biosignals, Lifecycle Stages, Current Advances, Datasets, Trends, and Challenges

arXiv

引用

arXiv 2022年

作者： Pérez, Mario Quiles Beltrán, Enrique Tomás Martínez Bernal, Sergio López Prat, Eduardo Horna Del Campo, Luis Montesano Maimó, Lorenzo Fernández Celdrán, Alberto Huertas Department of Information and Communications Engineering University of Murcia Murcia30100 Spain Bit&Brain Technologies S.L Zaragoza50006 Spain Department of Computer Architecture and Technology University of Murcia Murcia30100 Spain Communication Systems Group CSG Department of Informatics IfI University of Zurich UZH ZürichCH-8050 Switzerland

The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhance the promotion of products and generate a greater acceptance on potential buyers. Traditionally, neuromarketing studies have relied on a single biosignal to obtain feedback from presented stimuli. However, thanks to new devices and technological advances studying this area of knowledge, recent trends indicate a shift towards the fusion of diverse biosignals. An example is the usage of electroencephalography for understanding the impact of an advertisement at the neural level and visual tracking to identify the stimuli that induce such impacts. This emerging pattern determines which biosignals to employ for achieving specific neuromarketing objectives. Furthermore, the fusion of data from multiple sources demands advanced processing methodologies. Despite these complexities, there is a lack of literature that adequately collates and organizes the various data sources and the applied processing techniques for the research objectives pursued. To address these challenges, the current paper conducts a comprehensive analysis of the objectives, biosignals, and data processing techniques employed in neuromarketing research. This study provides both the technical definition and a graphical distribution of the elements under revision. Additionally, it presents a categorization based on research objectives and provides an overview of the combinatory methodologies employed. After this, the paper examines primary public datasets designed for neuromarketing research together with others whose main purpose is not neuromarketing, but can be used for this matter. Ultimately, this work provides a historical perspective on the evolution of techniques across various phases over recent years and enumerates key lessons learned. © 2022, CC BY-NC-SA.

关键词： Life cycle

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