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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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Using Large Language Models to Mitigate Human-Induced Bias in SMS Spam: An Empirical Approach

Using Large Language Models to Mitigate Human-Induced Bias i...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Gerard Shu Fuhnwi Matthew Revelle Bradley Whitaker Clemente Izurieta Gianforte School of Computing Montana State University Bozeman MT USA Electrical & Computer Engineering Montana State University Bozeman MT USA Gianforte School of Computing Montana State University Pacific Northwest National Laboratory Idaho National Laboratory Bozeman MT USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Short Message Service (SMS) is a widely used text messaging feature on both basic and smartphones. SMS spam detection is a crucial task. Traditional machine learning approaches often struggle in this domain due to their reliance on manually crafted features, such as keyword detection, which can result in overly simplistic patterns and misclassification of more complex messages. With this shortcoming, these models can amplify human-induced biases if the training data contains inconsistent labeling or subjective interpretations, leading to unfair treatment of specific keywords or contexts. Conversely, advanced LLMs present effective approaches to addressing such issues, as they can more accurately capture linguistic patterns, contextual nuances, and textual ambiguities than traditional models, representing a substantial advancement in improving label accuracy. This paper proposes utilizing LLMs to address humaninduced labeling bias in spam detection and applying different prompt design methods to guide the process. In text classification, we surveyed two leading-edge LLMs, ChatGPT and Gemini, and evaluated them on the English SMS spam dataset source from UC Irvine’s Machine Learning Repository. We explored the highest-performing prompt designs using approaches like in-context learning. The findings indicate that in-context techniques for prompting improve model effectiveness by reducing human-induced (contextual) labeling bias in SMS spam detection with a Balanced Accuracy of 82% $\mathbf{97 \%}$ and an Equal Opportunity Difference (EOD) of precisely zero, indicating LLMs’ trustworthiness (fairness) in reducing this bias compared to traditional machine learning approaches. Our results also suggested that expanding the sample size can decrease LLMs’ ability to reduce human-induced labeling bias in spam detection. In general, this study provides information on the strengths and limitations of LLMs and suggestions for methods to minimize human-induced labeling bias in sp

关键词： Accuracy Large language models Text categorization Training data Machine learning Linguistics Message services Labeling Smart phones Context modeling

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Reinforcement Learning-Based Optimal Control for Continuous-Time Quantized systems via Adaptive Dynamic Programming

Reinforcement Learning-Based Optimal Control for Continuous-...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Omar Qasem Weinan Gao Omar R. Daoud Electrical and Computer Engineering Department School of Engineering and Computing American International University Al-Jahra Kuwait State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Lianning China

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the conditions derived by the small-gain theorem. The study begins by deriving explicit bounds on the quantizer parameters required for maintaining system stability. Building on this foundation, an optimal controller is designed using the linear quadratic regulator (LQR) framework, providing an efficient data-driven control strategy. To further enhance the system's performance, an adaptive dynamic programming (ADP) algorithm, referred to as the hybrid iteration (HI) method, is developed. This algorithm effectively learns the optimal control policy by leveraging the trajectories of the quantized states and inputs, thereby addressing the challenges posed by quantization constraints. The proposed HI approach combines the advantages of adaptive learning and optimization, making it well-suited for continuous-time systems with limited information. The simulation results confirm that the ADP approach with the provided conditions not only stabilizes the quantized system but also achieves optimal control performance under the specified quantization conditions. This study offers valuable insights and a robust methodological framework for addressing stability and control challenges, with insights to be expanded to continuous-time nonlinear quantized systems, with potential applications in various engineering domains, such as networked systems, robotics and autonomous systems.

关键词： Quantization (signal) Adaptive systems Regulators Simulation Heuristic algorithms Optimal control Stability analysis Dynamic programming Trajectory Thermal stability

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Internet of Digital Twin: Framework, Applications and Enabling Technologies

引用

IEEE Communications Surveys and Tutorials 2025年

作者： Liu, Ruhan Luan, Tom H. Qu, Youyang Xiang, Yong Gao, Longxiang Zhao, Dongmei Deakin University School of Info Technology MelbourneVIC3125 Australia Xi'an Jiaotong University School of Cyber Science and Engineering Shaanxi Xi'an710049 China Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China McMaster University Department of Electrical and Computer Engineering HamiltonON Canada

Intelligent physical systems, such as smart vehicles and robotic arms, are increasingly integrated into both industrial and everyday applications. However, the systems typically face hardware limitations that constrain their computational capacities. Digital twin systems offer a solution by creating real-time digital replicas of physical systems that enhance computational efficiency, overcoming physical limitations. Moreover, multiple digital twins that hold complementary knowledge can conveniently collaborate to share information and computational resources, further improving the performance of physical systems by forming an Internet of Digital Twin (IoDT). This paper presents a comprehensive investigation of the digital twin network, tracing the evolution of digital twins and providing a classification of the key technologies, functional frameworks, and application domains of IoDT. This paper delves into the IoDT communication framework by studying the fundamental communication modes of IoDT, exploring its integration with advanced technologies such as edge computing, blockchain, 5G/6G networks, and machine learning to facilitate data transmission, interaction, and omni-directional sensing. By offering a broad perspective, the paper aims to deepen stakeholders' understanding of current research and potential future developments, encouraging further exploration of IoDT technologies and their evolution. © 1998-2012 IEEE.

关键词： 5G mobile communication systems

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Disturbance Rejection Control for PMSGs Based on Novel Sliding Mode Control Law and Unknown System Disturbance Estimator

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IEEE Transactions on Energy Conversion 2025年

作者： Wei, Chun Yin, Tianqi Qiao, Wei Zhu, Ying Zhejiang University of Technology College of Information Engineering Zhejiang Hangzhou310023 China University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588 United States Hohai University School of Electrical and Power Engineering Nanjing211100 China

This paper proposes a novel terminal sliding mode control strategy for the speed control of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs) to improve wind power generation efficiency. Firstly, a novel terminal sliding mode function based on exponential convergence is constructed, and a controller is designed accordingly, effectively addressing the system's nonlinear characteristics while achieving high precision tracking and chattering suppression with rapid convergence. Secondly, an unknown system dynamic estimator (USDE) is designed to estimate disturbances caused by external environments and inaccurate model parameters, further enhancing the system's disturbance rejection capability. Finally, the proposed USDE-based novel exponential sliding mode control is applied in a 1000 W PMSG-based WECS hardware simulator. Experimental results indicate that compared to the full-order sliding mode controller, the proposed control method exhibits stronger speed tracking, chattering suppression, and disturbance rejection capabilities. © 1986-2012 IEEE.

关键词： Sliding mode control

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ChainPIM: A ReRAM-Based Processing-in-Memory Accelerator for HGNNs via Chain Structure

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Xiao, Wenjing Wang, Jianyu Chen, Dan Shi, Chenglong Ling, Xin Chen, Min Wu, Thomas School of Computer Electronics and Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China School of Computer Electronics and Information Guangxi University Nanning530004 China School of Computing National University of Singapore 119077 Singapore School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Pazhou Laboratory Guangzhou510330 China School of Electrical Engineering Guangxi University Nanning530004 China

Heterogeneous graph neural networks (HGNNs) have recently demonstrated significant advantages of capturing powerful structural and semantic information in heterogeneous graphs. Different from homogeneous graph neural networks directly aggregating information based on neighbors, HGNNs aggregate information based on complex metapaths. ReRAM-based processing-in-memory (PIM) architecture can reduce data movement and compute matrix-vector multiplication (MVM) in analog. It can be well used to accelerate HGNNs. However, the complex metapath-based aggregation of HGNNs makes it challenging to efficiently utilize the parallelism of ReRAM and vertices data reuse. To this end, we propose ChainPIM, the first ReRAM-based processing-in-memory accelerator for HGNNs featuring high computing parallelism and vertices data reuse. Specifically, we introduce R-Chain, which is based on a chain structure to build related metapath instances together. We can efficiently reuse vertices through R-Chain and process different R-Chains in parallel. Then, we further design an efficient storage format for storing R-Chains, which reduces a lot of repeated vertices storage. Finally, a specialized ReRAM-based architecture is developed to pipeline different types of aggregations in HGNNs, fully exploiting the huge potential of multi-level parallelism in HGNNs. Our experiments show that ChainPIM achieves an average memory space reduction of 47.86% and performance improvement by 128.29× compared to NVIDIA Tesla V100 GPU. © 1982-2012 IEEE.

关键词： Random access storage

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Self-Supervised Reflective Learning Through Self-Distillation and Online Clustering for Speaker Representation Learning

引用

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1535-1550页

作者： Danwei Cai Zexin Cai Ze Li Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Digital Innovation Research Center Duke Kunshan University Kunshan China School of Computer Science Wuhan University Wuhan China

Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage iterative frameworks, while effective, suffer from significant computational overhead due to repeated rounds of clustering and training. They also struggle with noisy pseudo labels that can impair model learning. This paper introduces self-supervised reflective learning (SSRL), an improved framework that addresses these limitations by enabling continuous refinement of pseudo labels during training. Through a teacher-student architecture and online clustering mechanism, SSRL eliminates the need for iterative training rounds. To handle label noise, we incorporate noisy label modeling and pseudo label queues that maintain temporal consistency. Experiments on VoxCeleb show SSRL's superiority over current two-stage iterative approaches, surpassing the performance of a 5-round method in just a single training round. Ablation studies validate the contributions of key components like noisy label modeling and pseudo label queues. Moreover, consistent improvements in pseudo labeling and the convergence of cluster counts demonstrate SSRL's effectiveness in deciphering unlabeled data. This work marks an important advancement in efficient and accurate self-supervised speaker representation learning through the novel reflective learning paradigm.

关键词： Training Representation learning Iterative methods Noise Adaptation models Noise measurement Predictive models Speech processing Robustness Convergence

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RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

arXiv

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arXiv 2025年

作者： Shi, Jianrui Zhao, Yong Cui, Zeyang Shen, Xiaoming Zeng, Minhang Liu, Xiaojie Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing and Data Science The University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods. © 2025, CC BY.

关键词： Computation offloading

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