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arXiv 2023年

作者： Ma, Caoyuan Liu, Yu-Lun Wang, Zhixiang Liu, Wu Liu, Xinchen Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China National Yang Ming Chiao Tung University Taiwan The University of Tokyo Japan National Institute of Informatics Japan School of Information Science and Technology University of Science and Technology of China China JD Explore Academy

We present HumanNeRF-SE, a simple yet effective method that synthesizes diverse novel pose images with simple input. Previous HumanNeRF works require a large number of optimizable parameters to fit the human images. Instead, we reload these approaches by combining explicit and implicit human representations to design both generalized rigid deformation and specific non-rigid deformation. Our key insight is that explicit shape can reduce the sampling points used to fit implicit representation, and frozen blending weights from SMPL constructing a generalized rigid deformation can effectively avoid overfitting and improve pose generalization performance. Our architecture involving both explicit and implicit representation is simple yet effective. Experiments demonstrate our model can synthesize images under arbitrary poses with few-shot input and increase the speed of synthesizing images by 15 times through a reduction in computational complexity without using any existing acceleration modules. Compared to the state-of-the-art HumanNeRF studies, HumanNeRF-SE achieves better performance with fewer learnable parameters and less training time. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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An Overview on IRS-Enabled Sensing and Communications for 6G: Architectures, Fundamental Limits, and Joint Beamforming Designs

arXiv

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arXiv 2024年

作者： Song, Xianxin Fang, Yuan Wang, Feng Ren, Zixiang Yu, Xianghao Zhang, Ye Liu, Fan Xu, Jie Ng, Derrick Wing Kwan Zhang, Rui Cui, Shuguang Guangdong518172 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Information Engineering Guangdong University of Technology Guangzhou510006 China Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Hefei230027 China Computer School Beijing Information Science and Technology University Beijing100096 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station’s (BS’s) non-line-of-sight (NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication (ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Pattern-Aware Motion Tracking and Estimation From Low-Resolution Using Deep Learning

Pattern-Aware Motion Tracking and Estimation From Low-Resolu...

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International Conference on Computing and Networking technology (ICCNT)

作者： Dharmesh Dhabliya M. Vigenesh Hitesh Kalra R Srisainath Amritpal Sidhu K Lakshman Department of Information Technology Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Sample-conscious motion monitoring and estimation from low-decision using deep studying is an exploratory studies have a look at that makes use of convolutional neural networks (CNNs) and deep mastering strategies for actual-time movement estimation and monitoring from low-decision photo sequences. Particularly, the study’s objectives are to increase a CNN version to examine the input-output correlation of excessive-resolution motion supply sequences and coffee-resolution check sequences, after which use the found out model to appropriately song the motion of targets in low-resolution photograph sequences. The model is then evaluated over a large set of actual-world photo sequences, with promising effects showing accurate monitoring of goals from low-decision pictures. This technique can be used to facilitate actual-time movement tracking in imaging programs along with surveillance, independent riding, and augmented truth.

关键词： Deep learning Accuracy Target tracking Correlation Surveillance Computational modeling Estimation Imaging Complexity theory Convolutional neural networks

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EEG based Emotion Recognition: A Tutorial and Review

arXiv

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arXiv 2022年

作者： Li, Xiang Zhang, Yazhou Tiwari, Prayag Song, Dawei Hu, Bin Yang, Meihong Zhao, Zhigang Kumar, Neeraj Marttinen, Pekka Qilu University of Technology Shandong Academy of Sciences Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Software Engineering College Zhengzhou University of Light Industry Zhengzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Aalto University Finland School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Engineering Medicine Beijing Institute of Technology Beijing China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Deemed University Punjab Patiala India

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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Research on Robust Aircraft's Trajectory Tracking using Adaptive Controller 1

Research on Robust Aircraft's Trajectory Tracking using Adap...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Shreepad, Sarange Deena, S. Raman, Ramakrishnan Shastri, Dhiresh S. Rajesh, A.S. Krishnakumar, S.K. Department of Mechanical Engineering Ajeenkya DY Patil School of Engineering Maharashtra Pune412105 India Department of Computer Science Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai601103 India University Maharashtra Pune412115 India Department of Mechanical Engineering Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune411038 India Department of Mechanical Engineering JSS Science & Technology University Karnataka Mysuru570006 India Department of Mechanical Engineering Academy of Maritime Education and Training Tamil Nadu Chennai600119 India

ISBN: (纸本)9781665460842

Military, naval, army, etc., rely heavily on aircraft as their primary mode of transportation. In comparison to other types of transportation, aircraft have more freedom of motion. Tracking an aircraft's trajectory in the sky, is an extremely non-linear process. Movement control is crucial to the success of trajectory tracking. The prevention of crashes, damage, and other mishaps relies heavily on the accurate direction and movement controls in the aviation sector. Both stability and control are crucial in aircraft. Yaw, roll, and pitch are the three angles that can be employed to stabilize and control the movement. In this study, an Adaptive Fuzzy PID Controller is developed to regulate all three parameters. The study begins with a model of the aircraft, then moves on to the design of a conventional PID controller and an Adaptive Fuzzy PID controller, and finally concludes with an evaluation of the two controllers through an examination of their output responses at varying degrees of set angle and error metrics like the Integral Squared Error (ISE) and the Integral Absolute Error (IAE). This comparison highlights the advantage of the Adaptive Fuzzy PID controller in trajectory tracking with minimal rising, settling, and overshoot. Among the three angle-tracking systems, Adaptive Fuzzy PID gives the lowest error values. © 2022 IEEE

关键词： Controllers

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Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation

Introducing Shape Prior Module in Diffusion Model for Medica...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Zhiqing Zhang Guojia Fan Tianyong Liu Nan Li Yuyang Liu Ziyu Liu Canwei Dong Shoujun Zhou Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Shenzhen China College Of Information Science and Engineering Northeastern University (Of Affiliation) Shenzhen China College Of Geosciences Northeast Petroleum University Shenzhen China Department of Computer Science City University of Hong Kong Shenzhen China Shenyang Institute of Automation University of Chinese Academy of Sciences Shenzhen China Faculty of Science and Engineering Chinese Academy of Sciences University of Nottingham Ningbo China Ningbo China College Of Information Science And Engineering Northeastern University (Of Affiliation) Shenzhen China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

Medical image segmentation is critical for diagnosing and treating spinal disorders. However, the presence of high noise, ambiguity, and uncertainty makes this task highly challenging. Factors such as unclear anatomical boundaries, inter-class similarities, and irrational annotations contribute to this challenge. Achieving both accurate and diverse segmentation templates is essential to support radiologists in clinical practice. In recent years, denoising diffusion probabilistic modeling (DDPM) has emerged as a prominent research topic in computer vision. It has demonstrated effectiveness in various vision tasks, including image deblurring, super-resolution, anomaly detection, and even semantic representation generation at the pixel level. Despite the robustness of existing diffusion models in visual generation tasks, they still struggle with discrete masks and their various effects. To address the need for accurate and diverse spine medical image segmentation templates, we propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM). Our approach integrates the diffusion model into a standard U-shaped architecture. At each step, we combine the noise-added image with the labeled mask to guide the diffusion direction accurately towards the target region. Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module. This module efficiently extracts structural semantic information from the input spine images. We evaluate our method on a single dataset of spine images acquired through X-ray imaging. Our results demonstrate that VerseDiff-UNet significantly outperforms other state-of-the-art methods in terms of accuracy while preserving the natural features and variations of anatomy. The generality and validity of our proposed model are confirmed through extensive experiments. By enabling more precise segmentation of anatomical structures, our framew

关键词： Image segmentation Visualization Accuracy Shape Semantics Noise reduction Anatomical structure Diffusion models Probabilistic logic X-ray imaging

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Causality-driven hierarchical structure discovery for reinforcement learning 22

Causality-driven hierarchical structure discovery for reinfo...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shaohui Peng Xing Hu Rui Zhang Ke Tang Jiaming Guo Qi Yi Ruizhi Chen Xishan Zhang Zidong Du Ling Li Qi Guo Yunji Chen SKL of Processors Institute of Computing Technology CAS and University of Chinese Academy of Sciences and Cambricon Technologies SKL of Processors Institute of Computing Technology CAS SKL of Processors Institute of Computing Technology CAS and Cambricon Technologies Department of Computer Science and Engineering Southern University of Science and Technology SKL of Processors Institute of Computing Technology CAS and Cambricon Technologies and University of Science and Technology of China University of Chinese Academy of Sciences and SKL of Computer Science Institute of Software CAS SKL of Processors Institute of Computing Technology CAS and University of Chinese Academy of Sciences

ISBN: (纸本)9781713871088

Hierarchical reinforcement learning (HRL) effectively improve agents' exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e.g., subgoals or options). However, how to automatically discover high-quality hierarchical structures is still a great challenge. Previous HRL methods can hardly discover the hierarchical structures in complex environments due to the low exploration efficiency by exploiting the randomness-driven exploration paradigm. To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments. The key insight is that the causalities among environment variables are naturally fit for modeling reachable subgoals and their dependencies and can perfectly guide to build high-quality hierarchical structures. The results in two complex environments, 2D-Minecraft and Eden, show that CDHRL significantly boosts exploration efficiency with the causality-driven paradigm.

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Improving Iris Recognition Systems With CNN Based Image Processing and Machine Learning Approach

Improving Iris Recognition Systems With CNN Based Image Proc...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Shaleen Bhatnagar S.Pravinth Raja Amirtha Preeya Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of CSE & IS Presidency University Bengaluru Karnataka India School of CSE & IS Presidency University Bangalore India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The principal advancement in iris biometric acknowledgment is the extraction of these striking highlights. The outcome of profound learning innovations can assist us with settling the issues we had with PC vision. The Iris acknowledgment framework has viewed the elements found through CNN as of extraordinary use. In most of validation and personality situations, biometric security confirmation is fundamental. Because of its predictable and astounding surface variety, iris acknowledgment is believed to be the most reliable biometric acknowledgment. While utilizing iris acknowledgment to recognize the people who need an elevated degree of safety, the particular examples are utilized. To work on the viability of acknowledgment, this examination researches a valuable strategy that utilizes support vector machines (SVM) and convolutional brain organizations (CNN) for highlight extraction and characterization, separately. The uniqueness and security of the iris design have made iris acknowledgment frameworks a hotly debated issue in biometric confirmation. This theoretical spotlights on creating iris distinguishing proof frameworks using machine learning and CNN-based image processing.

关键词： Support vector machines Technological innovation Reviews Machine learning Trademarks Safety Convolutional neural networks

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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Efficient Reconstruction of Spatial Features for Remote Sensing Image-Text Retrieval

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Transactions of Nanjing University of Aeronautics and Astronautics 2025年第1期42卷 101-111页

作者： ZHANG Weihang CHEN Jialiang ZHANG Wenkai LI Xinming GAO Xin SUN Xian Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100190P.R.China Key Laboratory of Target Cognition and Application Technology(TCAT) Chinese Academy of SciencesBeijing 100190P.R.China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100190P.R.China School of Computer Science and Artificial Intelligence Aerospace Information Technology UniversityJinan 250299P.R.China

Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention ***,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage ***,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity *** address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial *** spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel *** conduct quantitative and qualitative experiments on two different commonly used RSCIR *** with traditional methods,our approach achieves an improvement of 3%-11% in sumR *** with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.

关键词： remote sensing cross-modal image-text retrieval(RSCIR) spatial features channel features contrastive learning parameter effective transfer learning

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