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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Licheng Wang Yi Li Hanying Zhao Yuan Shen Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China School of Electronic Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In multi-agent systems, the hybrid active-silent relative localization framework is widely employed, where only active nodes transmit signals. The selection of active nodes, known as node activation, significantly impacts the positioning accuracy. This paper investigates the node activation in anchor-free localization systems. First, the constrained Cramér-Rao lower bound (CRLB) is derived to evaluate the localization error. Then the combinatorial optimization problem on node activation is presented and approximately solved using the difference of convex programming (DCP) method. Moreover, to reduce computational complexity, we propose a geometry-based greedy iterative (GBGI) algorithm which leverages a geometry metric to evaluate and iteratively refine the selection of active nodes. Finally, simulation results demonstrate the performance of proposed algorithms. Especially the GBGI algorithm closely approaches the optimal solution.

关键词： Location awareness Measurement Geometry Accuracy Simulation Signal processing algorithms Signal processing Approximation algorithms Speech processing Optimization

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An application development for smart monitoring of COVID patients using six stage microbiological health systems

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Journal of Intelligent and Fuzzy Systems 2023年第3期45卷 4383-4393页

作者： Prasad, Padavala Sai Nair, Prabha Shreeraj Patil, Anagha Patil, Nilesh Madhukar Chaturvedi, Abhay Taqui, Syed Noeman Almoallim, Hesham S. Alharbi, Sulaiman Ali Raghavan, S.S. India Uttar Pradesh Greater Noida India Maharashtra Vasai India Department of Computer Engineering SVKM's D J Sanghvi College of Engineering Vile Parle West Maharashtra India Department of Electronics Communication Engineering GLA University Uttar Pradesh Mathura India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Oral and Maxillofacial Surgery College of Dentistry King Saud University Riyadh Saudi Arabia Department of Botany and Microbiology College of Science King Saud University Riyadh Saudi Arabia Department of Biology University of Tennessee Health Science Center Memphis United States

For many, Covid-19 is a short-term, mildly debilitating disease. But some people are still struggling with monthly symptoms with persistent inflammation, chronic pain and shortness of breath. The situation of 'long-term cowardice' has become so debilitating that it is now common for some to say that they are tired even if they walk a short distance. So far, the focus has been on saving lives from the plague. But now there are growing concerns about people facing the long-term consequences of the COVID epidemic. The fundamental question, with the uncertainty of whether those with chronic goiter, or all those affected, will fully recover is raised. In this paper a smart monitoring model was proposed to keep monitoring the COVID patient's health conditions. The smart method keep on watching the different changes reflected in the body conditions and ensure the changes in the database. In case any emergency is raised, then these smart monitoring tools inform the information to the doctors. This can very much helpful for the patients to communicate with the doctors. © 2023 - IOS Press. All rights reserved.

关键词： Pathology

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Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

arXiv

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arXiv 2024年

作者： Zhang, Yongheng Chen, Qiguang Zhou, Jingxuan Wang, Peng Si, Jiasheng Wang, Jin Lu, Wenpeng Qin, Libo School of Computer Science and Engineering Central South University China Research Center for SCIR Harbin Institute of Technology Harbin China China Yunnan University Kunming China

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Single verification method: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: The traditional paradigm directly ignores wrong information during reasoning and refines the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks. © 2024, CC BY-NC-SA.

关键词： Direct process refining

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Non-Uniform 3D Massive MIMO Arrays Topology Optimization for Near-Field Communications

Non-Uniform 3D Massive MIMO Arrays Topology Optimization for...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Yunhui Guo Yang Zhang Lihua Pang Yuanwei Liu Zhiguo Ding State Key Laboratory of Integrated Service Networks Xidian University Xi’an China School of Electronic Engineering and Computer Science Queen Mary University of London London U.K School of Information Engineering Xian University of Science and Technology Xian China Department of Computer and Communication Engineering 6G Center Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

In this paper, the design of non-uniform antenna topology for 3D massive MIMO arrays in near-field communications is investigated. Specifically, the near-field spherical wavefront radiation characteristics are considered to accurately model the variations of signal phase across array elements. Subsequently, the closed-form expressions of the per-user signal-to-interference noise ratio (SINR) and achievable sum rate for a multi-user MIMO system with maximum-ratio transmission (MRT) precoding are derived. The focus is on the maximization of the achievable sum rate by optimizing the non-uniform 3D antenna array topology. Since the optimization problem exhibits highly nonlinear and nonconvex characteristics, an enhanced particle swarm optimization (EPSO) algorithm is proposed to effectively solve it. Numerical results demonstrate the superiority of the proposed non-uniform 3D array topology in near-field communications for enhancing the achievable sum rate.

关键词： Three-dimensional displays Simulation Precoding Massive MIMO Interference Mobile antennas Topology Optimization Antenna arrays Signal to noise ratio

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Enhancement of superconductivity in the Fibonacci chain

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Physical Review B 2024年第13期109卷 134504-134504页

作者： Meng Sun Tilen Čadež Igor Yurkevich Alexei Andreanov School of Physics and Optoelectronic Engineering Beijing University of Technology Beijing 100124 China Center for Theoretical Physics of Complex Systems Institute for Basic Science Daejeon 34126 Republic of Korea School of Computer Science and Digital Technologies Aston University Birmingham B4 7ET United Kingdom Basic Science Program Korea University of Science and Technology Daejeon 34113 Republic of Korea

We study the interplay between quasiperiodic disorder and superconductivity in a one-dimensional tight-binding model with the quasiperiodic modulation of on-site energies that follow the Fibonacci rule, and all the eigenstates are multifractal. As a signature of multifractality, we observe the power-law dependence of the correlation between different single-particle eigenstates as a function of their energy difference. We numerically compute the mean-field superconducting transition temperature for every realization of a Fibonacci chain of a given size and find the distribution of critical temperatures, analyze their statistics, and estimate the mean value and variance of critical temperatures for various regimes of the attractive coupling strength and quasiperiodic disorder. We find an enhancement of the critical temperature compared to the analytical results that are based on strong assumptions of the absence of correlations and self-averaging of multiple characteristics of the system, which are not justified for the Fibonacci chain. For the very weak coupling regime, we observe a crossover where the self-averaging of the critical temperature breaks down completely and strong sample-to-sample fluctuations emerge.

关键词： Superconductivity Fractals Mean field theory Methods in superconductivity Tight-binding model

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ReLeaPS : Reinforcement Learning-based Illumination Planning for Generalized Photometric Stereo

ReLeaPS : Reinforcement Learning-based Illumination Planning...

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International Conference on computer Vision (ICCV)

作者： Jun Hoong Chan Bohan Yu Heng Guo Jieji Ren Zongqing Lu Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University School of Artificial Intelligence Beijing University of Posts and Telecommunications Osaka University School of Mechanical Engineering Shanghai Jiao Tong University

Illumination planning in photometric stereo aims to find a balance between surface normal estimation accuracy and image capturing efficiency by selecting optimal light configurations. It depends on factors such as the unknown shape and general reflectance of the target object, global illumination, and the choice of photometric stereo backbones, which are too complex to be handled by existing methods based on handcrafted illumination planning rules. This paper proposes a learning-based illumination planning method that jointly considers these factors via integrating a neural network and a generalized image formation model. As it is impractical to supervise illumination planning due to the enormous search space for ground truth light configurations, we formulate illumination planning using reinforcement learning, which explores the light space in a photometric stereo-aware and reward-driven manner. Experiments on synthetic and real-world datasets demonstrate that photometric stereo under the 20-light configurations from our method is comparable to, or even surpasses that of using lights from all available directions.

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Transfer Learning Based Effective Approach for Classification of Flowers

Transfer Learning Based Effective Approach for Classificatio...

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International Conference on Computational science and Technology (ICCST)

作者： Mattapally Sai Nithin Avesha Shaik A Balasundaram Kovvuri Uday Surya Deveswar Reddy Lakshmi Sai Ram kakarla A Selvakumar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Center for Cyber Physical Systems Vellore Institute of Technology Chennai India

ISBN: (纸本)9781665476560

Deep learning algorithms are being used to do complex tasks like extracting meaningful features, segmenting, and semantic classification of images. In recent years, these methodologies have had a substantial impact on the classification of flower types. Deep neural networks are a type of image recognition system that has been widely used in computer vision applications because of the similarities between classes and intraclass variance. Due to a greater number of flower species that are similar in shape, color, and visualization, classifying flowers is a tough task. Classification of flowers is used for different purposes, like Recognition of medicinal plants. In this paper, we implemented VGG19 and EfficientNetV2L architecture for the classification of flowers. We have fine-tuned our model compared to other methods to obtain higher accuracy. By normalizing and scaling our flower images, we underwent pre-processing. Then we input the pre-trained model of the images. we divided our flower dataset into train, test, and validation. We achieved an accuracy of 88.21 for VGG19 for 20 epochs and 96.28 for 20 epochs which provides the best accuracy than other proposed architectures in the Kaggle dataset.

关键词： Deep learning Shape Image color analysis Transfer learning Neural networks Taxonomy Semantics

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SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving

arXiv

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arXiv 2023年

作者： Wei, Yi Zhao, Linqing Zheng, Wenzhao Zhu, Zheng Zhou, Jie Lu, Jiwen Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China School of Electrical and Information Engineering Tianjin University China PhiGent Robotics

3D scene understanding plays a vital role in vision-based autonomous driving. While most existing methods focus on 3D object detection, they have difficulty describing real-world objects of arbitrary shapes and infinite classes. Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images. We first extract multi-scale features for each image and adopt spatial 2D-3D attention to lift them to the 3D volume space. Then we apply 3D convolutions to progressively upsample the volume features and impose supervision on multiple levels. To obtain dense occupancy prediction, we design a pipeline to generate dense occupancy ground truth without expansive occupancy annotations. Specifically, we fuse multi-frame LiDAR scans of dynamic objects and static scenes separately. Then we adopt Poisson Reconstruction to fill the holes and voxelize the mesh to get dense occupancy labels. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our method. Code and dataset are available at https://***/weiyithu/SurroundOcc. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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FluidPlaying: Efficient Adaptive Simulation for Highly Dynamic Fluid

FluidPlaying: Efficient Adaptive Simulation for Highly Dynam...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Sinuo Liu Xiaojuan Ban Sheng Li Haokai Zeng Xiaokun Wang Yanrui Xu Fei Zhu Guoping Wang Graphics & Interaction Lab National Biomedical Imaging Center School of Computer Science Peking University School of Intelligence Science and Technology University of Science and Technology Beijing Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

We present FliudPlaying, a novel dynamic level-based spatially adaptive simulation method that can handle highly dynamic fluid efficiently. To capture the subtle detail of the fluid surface, the high-resolution simulation is performed not only at the free surface but also at those regions with high vorticity levels and velocity difference levels. To minimize the density error, an online optimization scheme is used when increasing the resolution by particle splitting. We also proposed a neighbor-based splash enhancement to compensate for the loss of dynamic details. Compared with the high-resolution simulation baseline, our method can achieve over 3× speedups while consuming only less than 10% computational resources. Furthermore, our method can make up for the loss of high-frequency details caused by the spatial adaptation, and provide more realistic dynamics in particle-based fluid simulation.

关键词： Solid modeling Adaptation models Three-dimensional displays Fluid dynamics Computational modeling Conferences Virtual reality

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Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

arXiv

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arXiv 2023年

作者： Chen, Longfei Liu, Qianyu Zhang, Chenyang Huang, Yangkun Peng, Zhenhui Zeng, Haipeng Sun, Zhida Ma, Xiaojuan Li, Quan The School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging China The Department of Computer Science University of Illinois Urbana-Champaign United States Tandon School of Engineering New York University United States The School of Artificial Intelligence Sun Yat-Sen University China The School of Intelligent Systems Engineering Sun Yat-Sen University China The College of Computer Science and Software Engineering Shenzhen University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations. © 2023, CC BY.

关键词： Music

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