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Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

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IEEE Transactions on Medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

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arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

BLTESTI: Benchmarking Lightweight TinyJAMBU on Embedded systems for Trusted IoT

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BLTESTI: Benchmarking Lightweight TinyJAMBU on Embedded Syst...

IEEE International SOC Conference

作者： Mohamed El-Hadedy Russell Hua Shahzman Saqib Kazutomo Yoshii Wen-Mei Hwu Martin Margala Department of Electrical and Computer Engineering California State Polytechnic University Pomona USA Coordinated Science Laboratory University of Illinois at Urbana-Champaign USA College of Computing University of Louisiana at Lafayette Louisiana USA Argonne National Laboratory Lemont Illinois USA

Embedded systems face an increasing number of attacks, necessitating improved security measures. However, the physical constraints of these devices often limit the use of traditional cryptographic techniques. To address this challenge, NIST organized a competition focused on lightweight cryptographic algorithms (LWC) tailored for embedded systems, aiming to minimize resource usage in low-power devices. This paper benchmarks the Rust, C, and Golang programming languages, considering their advantages in memory safety, portability, and concurrency, to implement the LWC algorithm finalist named TinyJAMBU. TinyJAMBU utilizes the non-linear feedback shift register (NLFSR) model as a core security element. The evaluation is conducted on embedded systems and FPGAs to determine the optimal strategy for mapping LWC cryptographic algorithms onto resource-constrained devices. Among the three languages, the C-based implementation achieves the highest runtime efficiency, while TinyGo demonstrates the lowest power consumption. Rust falls between the two, indicating trade-offs associated with each language. Despite its larger memory footprint of 73kB, the C-based implementation of TinyJAMBU outperforms other language implementations, achieving a faster runtime of ≈ 81 seconds for the same algorithm and 4-byte message size. Golang exhibits the smallest memory footprint of 19kB, while Rust’s memory footprint falls between the other two at 66kB. Furthermore, implementing TinyJAMBU on an FPGA using software-hardware partitioning with MicroBlaze takes ≈ 6.30 seconds to complete 10,000 encryption and decryption cycles, consuming around 1.09W of power. Although employing a proposed NLFSR co-processor with MicroBlaze for the 128-bit version of TinyJAMBU may not be the fastest approach, the reconfigurable nature of FPGAs allows for replicating the co-processor multiple times, offering significant advantages over the current state-of-the-art architecture of embedded systems.

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RECO-LFSR: Reconfigurable Low-power Cryptographic processor based on LFSR for Trusted IoT platforms

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RECO-LFSR: Reconfigurable Low-power Cryptographic processor ...

IEEE International Symposium on Quality Electronic Design

作者： Mohamed El-Hadedy Russell Hua Kazutomo Yoshii Wen-Mei Hwu Martin Margala Department of Electrical and Computer Engineering California State Polytechnic University Pomona USA Coordinated Science Laboratory University of Illinois at Urbana-Champaign USA College of Computing University of Louisiana at Lafayette Louisiana USA Argonne National Laboratory Lemont Illinois USA

Today we see lightweight computer hardware utilized in large volumes, especially with the growing use of IoT devices in homes. However, such devices often ignore security until it is too late and sensitive data breaches have *** here, the importance of finding lightweight cryptographic primitives to secure IoT devices is exponentially increasing, while not impacting the limited resources and limitation of the battery lifetime. In the search for a lightweight cryptographic standard, one must consider how to implement such algorithms optimally. For example, certain parts of an algorithm might be faster in hardware than in software and vice *** paper presents a hardware extension supporting the MicroBlaze softcore processor to efficiently implement one of the Lightweight Cryptography (LWC) finalists (TinyJAMBU) on Digilent Nexys A7-100T. The proposed hardware extension consists of a reconfigurable Non-Linear Feedback Shift Register (NLFSR), the central computing part for the authenticated encryption with associated data (AEAD) TinyJAMBU. The proposed NLFSR can run different variants of TinyJAMBU while only consuming 186 mWh in just ten minutes at 100 MHz. The total resources needed to host the proposed NLFSR on the FPGA are 610 LUT and 505 Flip-Flops while executable the binary size is 352 bytes smaller. Therefore, the proposed solution based on the hardware extension is x2.17 times faster than the pure software implementation of the whole TinyJAMBU using MicroBlaze while consuming six mWh more. To our knowledge, this is the first implementation of TinyJAMBU using software/hardware partitioning on FPGA with the softcore processor MicroBlaze.

关键词： Microprocessors Software algorithms Shift registers Hardware Software Table lookup Internet of Things

FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

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arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay

A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multi-player non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

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Multimodal Emotion Recognition Based on Multi-Scale Facial F...

26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

Learning Eco-Driving Strategies at Signalized Intersections

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arXiv 2022年

作者： Jayawardana, Vindula Wu, Cathy MIT Laboratory for Information & Decision Systems Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Laboratory for Information & Decision Systems Department of Civil and Environmental Engineering Institute of Data Systems and Society CambridgeMA02139 United States

Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits. Copyright © 2022, The Authors. All rights reserved.

关键词： Travel time

Multi-User Probabilistic Semantic Communication with Semantic Compression Ratio Optimization

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Multi-User Probabilistic Semantic Communication with Semanti...

IEEE International Conference on Communications Workshops, ICC

作者： Zhouxiang Zhao Zhaohui Yang Mingzhe Chen Changsheng You Qianqian Yang Wei Xu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized raw data before transmitting them to a multi-antenna base station (BS). Our model represents the raw data through comprehensive knowledge graphs, utilizing shared probability graphs between users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of equivalent rate of all users, considering total power budget constraint. This joint optimization problem inherently addresses the delicate balance between transmission efficiency and computational complexity. To address this optimization challenge, we present an iterative algorithm in which the optimal solution for the semantic compression ratio of a specific user is determined at each iteration. Numerical results validate the effectiveness of the proposed scheme.

关键词： Conferences Computational modeling Semantics Knowledge graphs Probabilistic logic Information retrieval Data models