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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 309-318页

作者： Tao Zhuang Jia-Xin Zhong Guilin Ma Feng Niu Jing Lu Key Laboratory of Modern Acoustics Nanjing University Nanjing China NJU-Horizon Intelligent Audio Lab Horizon Robotics Beijing China Graduate Program in Acoustics Pennsylvania State University University Park PA USA Audfly Technology Suzhou Company Ltd. Suzhou China Division of Mechanics and Acoustics National Institute of Metrology Beijing China

Parametric array loudspeakers (PALs) that utilize the phased array technique are receiving considerable attention because of their ability to electronically control and manipulate highly directional audio beams with great flexibility. However, accurately modeling the phased array PAL poses significant challenges due to the complex nature of the nonlinear processes involved, which results in the demodulated audio sound generated by the array not simply being a superposition of that generated by each individual array element. In addition, the nonlinear interaction between the ultrasound generated by any two distinct elements creates a coupled audio sound component that also contributes to the total audio sound field. Based on the quasilinear solution of the Westervelt equation as well as the spherical wave expansion method, this paper proposed a computationally efficient and accurate method to include the coupled audio sound component. Both simulations and experiments for a uniform linear PAL array are conducted to validate the method. The proposed method paved the way for simulating and designing phased array PAL systems.

关键词： Ultrasonic imaging Phased arrays Mathematical models Loudspeakers Harmonic analysis Field emitter arrays Acoustic beams Speech processing Computational modeling Transducers

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery

PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wendi Yang Zihang Jiang Shang Zhao S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

With the recent advancements in single-image-based 3D human pose and shape estimation (3DHPSE), there is a growing amount of works that can achieve good results on standard benchmarks but struggle to yield accurate hu-man mesh in extreme scenarios like occlusion. Previous works propose to leverage 2D poses to help 3D HPSE model improve performance under occlusion, but usually rely on manual design to integrate 2D poses and only aim for specific kinds of occlusion. In this paper, we present PostoMETRO (Pose token enhanced MEsh TRansfOrmer), which integrates 2D pose prior knowledge as tokens into transformers to improve model's performance under occlusion. Using a VQ- VAE-based pose tokenizer; we efficiently represent 2D poses as tokens andfeed them to transformers together with image tokens. Subsequently, these tokens are queried by vertex andJoint tokens to decode 3D coordinates of mesh vertices and human Joints. Our proposed 2D poses integration strategy is manual-design-free and suitable for various kinds of occlusion. Experiments on both standard and occlusion-specific benchmarks demonstrate the effectiveness of PostoMETRO. Code will be made available 1 1 https://***/PostoMETRO/PostoMETRO-Paper.

关键词： Solid modeling Computer vision Three-dimensional displays Shape Estimation Manuals Benchmark testing Transformers Robustness Standards

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

arXiv

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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MS-Glance: Bio-Inspired Non-Semantic Context Vectors and Their Applications in Supervising Image Reconstruction

MS-Glance: Bio-Inspired Non-Semantic Context Vectors and The...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Ziqi Gao Wendi Yang Yujia Li Lei Xing S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China Department of Radiation Oncology Stanford University Stanford CA USA Key Laboratory of Precision and Intelligent Chemistry USTC CAS Hefei Anhui

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.

关键词： Computer vision Visualization Magnetic resonance imaging Fitting Vectors Image restoration Indexes Image reconstruction Biomedical imaging Visual perception

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Adapting NIST Aerial Drone Tests for Thermal Identification, Inspection, and Suppression of Wildfires

Adapting NIST Aerial Drone Tests for Thermal Identification,...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Alex Fraley Ethan Nowery McCarthy Devine Adam Jacoff Paul Y. Oh Robotics Research Engineer for the Emergency Response Robots (Ground Aerial Aquatic) Project in the Intelligent Systems Division of the Engineering Laboratory National Institute of Standards and Technology (NIST) Gaithersburg MD USA Department of Mechanical Engineering University of Nevada Las Vegas (UNLV) Las Vegas NV USA Department of Aerospace Engineering University of Colorado Boulder (CU Boulder) Boulder CO USA Project Leader for Emergency Response Robots (Ground Aerial Aquatic) in the Intelligent Systems Division of the Engineering Laboratory National Institute of Standards and Technology (NIST) Gaithersburg MD USA Faculty of Mechanical Engineering University of Nevada Las Vegas

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

The National Institute of Standards and technology (NIST) has developed standard test methods to evaluate the capabilities of aerial drones, ground robots, and aquatic vehicles, as well as the proficiency of their operators. The aerial drone tests measure key capabilities such as maneuverability, visual acuity, and thermal acuity. With a rising demand for aerial drones among first responders, these tests are essential for assessing system performance, mitigating risk, and improving operational effectiveness to ensure the safety of responders and the public. Given the increasing prominence of wildfire threats and the widespread destruction they cause, aerial drones are being adapted to help detect, inspect, and contain them. This paper outlines how NIST-developed aerial drone tests can be adapted to simulate wildfire threats and provide a framework of reproducible tests that measure the system capabilities of aerial drones and the proficiency of operators in mitigating these threats.

关键词： Heating systems Visualization Wildfires System performance NIST Inspection Writing Object recognition Underwater vehicles Drones

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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Multimodal layer surveillance map based on anomaly detection using multi-agents for smart city security

Multimodal layer surveillance map based on anomaly detection...

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作者： Shin, Hochul Na, Ki-In Chang, Jiho Uhm, Taeyoung Department of Intelligent Robotics Electronics and Telecommunications of Research Institute Daejeon Korea Republic of Intelligent Robotics R&D Division Korea Institute of Robotics and Technology Convergence Pohang Korea Republic of

Smart cities are expected to provide residents with convenience via various agents such as CCTV, delivery robots, security robots, and unmanned shuttles. Environmental data collected by various agents can be used for various purposes, including advertising and security monitoring. This study suggests a surveillance map data framework for efficient and integrated multimodal data representation from multi-agents. The suggested surveillance map is a multi-layered global information grid, which is integrated from the multimodal data of each agent. To confirm this, we collected surveillance map data for 4 months, and the behavior patterns of humans and vehicles, distribution changes of elevation, and temperature were analyzed. Moreover, we represent an anomaly detection algorithm based on a surveillance map for security service. A two-stage anomaly detection algorithm for unusual situations was developed. With this, abnormal situations such as unusual crowds and pedestrians, vehicle movement, unusual objects, and temperature change were detected. Because the surveillance map enables efficient and integrated processing of large multimodal data from a multi-agent, the suggested data framework can be used for various applications in the smart city. 1225-6463/$ © 2022 ETRI.

关键词： Modal analysis

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Windoro: The world's first commercialized window cleaning robot for domestic use

Windoro: The world's first commercialized window cleaning ro...

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2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2011

作者： Choi, Young-Ho Jung, Kwang-Mok Applied Technology Division Pohang Institute of Intelligent Robotics Kyungbuk 790-784 Korea Republic of

ISBN: (纸本)9781457707223

This paper describes the world's first commercialized window cleaning robot named windoro, which is a fully autonomous intelligent window cleaning robot. The windoro adheres to the surface of window glass by means of four permanent magnet pairs between an inner unit and an outer unit rather than using vacuum suckers for the energy efficiency and safety. An inner unit and an outer unit are in charge of navigation and cleaning, respectively. To do this, an inner unit has two driving wheels equipped with silicon tire with the big friction coefficient and an outer unit has four motorized rolling disks with a superfine fiber pad. For the convenience of user, the windoro can recognize the initial starting position and return to that position after finishing cleaning. In addition to this, an inner unit has mechanism for adjusting magnetic force according to the thickness of window glass. Due to this, the windoro is applicable to various kinds of windows. From the several severe experiments, the windoro demonstrated system stability, good mobility, cleaning performance and safety. © 2011 IEEE.

关键词： Cleaning

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