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Conference on computer vision and Pattern Recognition (CVPR)

作者： Bangyan Liao Delin Qu Yifei Xue Huiqing Zhang Yizhen Lao College of Computer Science and Electronic Engineering Hunan University College of Electrical and Information Engineering Hunan University Shanghai AI Laboratory Jiangxi Provincial Natural Resources Cause Development Center

We propose an accurate and fast bundle adjustment (BA) solution that estimates the 6-DoF pose with an independent RS model of the camera and the geometry of the environment based on measurements from a rolling shutter (RS) camera. This tackles the challenges in the existing works, namely, relying on high frame rate video as input, restrictive assumptions on camera motion and poor efficiency. To this end, we first verify the positive influence of the image point normalization to RSBA. Then we present a novel visual residual covariance model to standardize the reprojection error during RSBA, which consequently improves the overall accuracy. Besides, we demonstrate the combination of Normalization and covariance standardization Weighting in RSBA (NW-RSBA) can avoid common planar degeneracy without the need to constrain the filming manner. Finally, we propose an acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix and Schur complement. The extensive synthetic and real data experiments verify the effectiveness and efficiency of the proposed solution over the state-of-the-art works.

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Supervisor Alignment Framework: Enhancing LLM Alignment with Query-Ignoring Strategy and Multi-Agent Interaction

Supervisor Alignment Framework: Enhancing LLM Alignment with...

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

作者： Ziqun Bao Yu Ji Wen Wu Xi Chen Liang He School of Computer Science and Technology East China Normal University Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The increasing focus on value alignment in Large Language Models (LLMs) underscores the need to ensure alignment with human morals and avoid biased or harmful outputs. However, LLMs aligned using existing methods are still easily affected by adversarial prompt attacks. Inspired by psychology, this paper introduces a Supervisor Alignment framework, which innovatively incorporates a query-ignoring strategy. This strategy ensures that the supervisor does not receive user queries, preventing it from being influenced by potential adversarial prompts. Meanwhile, the study compares the efficacy of a single supervisor versus a team of supervisors in value alignment tasks. While our designed single-agent supervisor approach utilizes a standalone agent or integrates with Retrieval-Augmented Generation (RAG) techniques, the team approach we proposed emphasizes multi-agent collaboration through voting, cooperation, and debate strategies. Extensive experiments demonstrate that the Supervisor Alignment framework we designed, incorporating the query-ignoring strategy and multi-agent collaboration, effectively defends against adversarial prompts and enhances its performance in value alignment tasks.

关键词： Ethics Large language models Retrieval augmented generation Collaboration Psychology Signal processing Acoustics Speech processing Protection

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Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

arXiv

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arXiv 2024年

作者： Peng, Zhangjie Zhu, Jianchen Pan, Cunhua Zhang, Zaichen da Costa, Daniel Benevides Elkashlan, Maged Karagiannidis, George K. The College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Lebanon

Active reconfigurable intelligent surface (RIS) has attracted significant attention as a recently proposed RIS architecture. Owing to its capability to amplify the incident signals, active RIS can mitigate the multiplicative fading effect inherent in the passive RIS-aided system. In this paper, we consider an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system in the presence of phase noise at the active RIS. Specifically, we employ a two-timescale scheme, where the beamforming at the base station (BS) is adjusted based on the instantaneous aggregated channel state information (CSI) and the statistical CSI serves as the basis for designing the phase shifts at the active RIS, so that the feedback overhead and computational complexity can be significantly reduced. The aggregated channel composed of the cascaded and direct channels is estimated by utilizing the linear minimum mean square error (LMMSE) technique. Based on the estimated channel, we derive the analytical closed-form expression of a lower bound of the achievable rate. The power scaling laws in the active RIS-aided system are investigated based on the theoretical expressions. When the transmit power of each user is scaled down by the number of BS antennas M or reflecting elements N, we find that the thermal noise will cause the lower bound of the achievable rate to approach zero, as the number of M or N increases to infinity. Moreover, an optimization approach based on genetic algorithms (GA) is introduced to tackle the phase shift optimization problem. Numerical results reveal that the active RIS can greatly enhance the performance of the considered system under various settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Attention-based Open RAN Slice Management using Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Lotfi, Fatemeh Afghah, Fatemeh Ashdown, Jonathan Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC United States Air Force Research Laboratory RomeNY13441 United States

As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different service requirements. However, managing network slices while maintaining quality of services (QoS) in dynamic environments is a challenging task. Utilizing machine learning (ML) approaches for optimal control of dynamic networks can enhance network performance by preventing Service Level Agreement (SLA) violations. This is critical for dependable decision-making and satisfying the needs of emerging networks. Although RL-based control methods are effective for real-time monitoring and controlling network QoS, generalization is necessary to improve decision-making reliability. This paper introduces an innovative attention-based deep RL (ADRL) technique that leverages the ORAN disaggregated modules and distributed agent cooperation to achieve better performance through effective information extraction and implementing generalization. The proposed method introduces a value-attention network between distributed agents to enable reliable and optimal decision-making. Simulation results demonstrate significant improvements in network performance compared to other DRL baseline methods. © 2023, CC BY.

关键词： Decision making

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Improving Prediction of GNSS Satellite Visibility in Urban Canyon Based on Graph Transformer 36

Improving Prediction of GNSS Satellite Visibility in Urban C...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zheng, Shaolong Li, Zhenni Wang, Qianming Xie, Kan Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Rsearch and Innovation Center of Excellence Nicosia Cyprus

ISBN: (纸本)9780936406350

Signals from global navigation satellite systems (GNSS) suffer from serious multipath errors in urban areas caused by building blockages and reflections. The use of deep neural networks offer great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first is the inability to exploit effectively the irregular GNSS dataset because of inconsistent numbers of visible satellites in different epochs. The second is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network for predicting satellite visibility that effectively learns environment representations from the irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from the irregular GNSS measurements, a sky satellite graph is constructed as the input to a graph neural network by using the satellites captured in the same epoch, which can represent the spatial relationships between the satellites and enhance the model to enable learning of satellite-related features sufficiently well. To improve generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites for extracting the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, we develop a novel graph transformer neural network (GTNN) for predicting satellite visibility, which can not only handle irregular GNSS measurements but also learn an environment representation via graph attention. Comparative experiments were carried out on real-world GNSS meas

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Design of Metasurface Antenna for 5G Applications

Design of Metasurface Antenna for 5G Applications

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Artificial Intelligence and Signal Processing (AISP), INDIA, International Conference on

作者： Bashar S. Bashar Basim Ghalib Mejbel Z.A. Rhazali Halina Misran Marwa M. Ismail T. A. Oleiwi Department College of Engineering Electrical & Electronic Engineering Universiti Tenaga Nasional Kajang Malaysia Department of Computer Techniques Engineering Al Hikma University Baghdad Iraq Department of Mechanical Engineering Nanoarchitectonic Laboratory Universiti Tenaga Nasional Kajang Malaysia Computer technologies Engineering Al-Nisour University College Baghdad Iraq Islamic University Centre for Scientific Research The Islamic University Najaf Iraq

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

This research suggests a multiband antenna-based Metasurface for improved gains and beam splitting. Here, the development of a smart antenna for contemporary wireless applications through the use of a Metasurface technology is the main goal. The initial patch of the proposed antenna is square in shape and is positioned on Taconic substrate. The suggested patch appears to be U-shaped at the end of the design process to guarantee that the antenna beam is splitting at the intended frequency ranges. Furthermore, the first patch is excited by conduction using a 50 Ω discrete port, whereas the second patch is excited via capacitive coupling. Moreover, to boost the antenna gain in the bore-sight direction, a metasurface layer is created and affixed to the second patch as a superstrate. The outcomes indicate a maximum gain. The results indicate a maximum size of 280× 280mm2 and a maximum gain of 8 dBi at 4.2 GHz. Furthermore, this antenna has a minimum reflection coefficient of −18.5 dB, 11.1 dB, −25.2 dB, −25.2 dB, −17.4 dB, and −11.5 dB, respectively, and operates at additional frequency bands (1.9 GHz, 3.3 GHz, 3.8 GHz, 4, 4.5 GHz, and 5.9 GHz). The CST MWS simulator is used in the design and analysis of the suggested antenna.

关键词： Wireless communication Surface impedance Slot antennas Shape Signal processing Metasurfaces Reflection coefficient Antennas Gain Substrates

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H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

H-GCN: A Graph Convolutional Network Accelerator on Versal A...

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International Conference on Field Programmable Logic and Applications

作者： Chengming Zhang Tong Geng Anqi Guo Jiannan Tian Martin Herbordt Ang Lit Dingwen Tao School of Electrical Engineering and Computer Science Washington State University Pullman WA USA School of Informatics Computing and Engineering Indiana University Bloomington IN USA Mathematics and Computer Science Division Pacific Northwest National Laboratory Richland WA USA Department of Electrical and Computer Engineering University of Rochester Rochester NY USA Department of Electrical and Computer Engineering Boston University Boston MA USA

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs' irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1~2.3x.

关键词： Tensors Adaptive systems Machine learning computer architecture Systolic arrays Graph neural networks Sparse matrices

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A Stable Lithium-ion Battery SOH Estimation Framework for Suppressing Measurement Noise with Unknown Distribution

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IEEE Transactions on Transportation Electrification 2025年

作者： Ma, Wentao Xue, Jingsong Li, Yang Guo, Peng Liu, Xinghua Wei, Zhongbao Wang, Yiwen Chen, Badong Xi’an University of Technology School of Electrical Engineering 710048 China Chalmers University of Technology Department of Electrical Engineering Gothenburg41296 Sweden Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute of artificial intelligence and robotics Xi’an Jiaotong University Xi’an710048 China

Existing methods for estimating the state of health (SOH) of lithium-ion battery (LIB) typically rely on the assumption that the distribution of noise (or outliers) in the measurement data is known. However, this assumption rarely holds true for LIB operating under real-word conditions. This article proposes a stable framework for accurate SOH estimation that accommodates noises with unknown distribution in both measurement data and label values. The framework combines generalized correntropy loss (GCL) with Savitzky-Golay (SG) filter and extreme learning machine (ELM) to obtain measurement data filter named SG-GCL and SOH estimator named generalized ELM (GELM), respectively. The SG-GCL filtering of the measurement data keeps the root mean square error (RMSE) within 0.0365%, and Pearson correlation between extracted feature and SOH improves by 0.4963, which in turn leads to the reduction of the RMSE metrics of the ELM for the estimation of the SOH by 43.69%. From the filtering results, feature extraction and estimation results proved its necessity and effectiveness. GELM effectively suppresses the influence of label value noise on the model in the training process, which reduces the SOH estimation RMSE index by more than 0.66%. The results from experiments with different distributional noise conditions show that the proposed SOH estimation framework has excellent and stable performance. © 2015 IEEE.

关键词： Mean square error

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Maritime Path Planning Using Heuristic Evaluation Functions for Weather Parameters

Maritime Path Planning Using Heuristic Evaluation Functions ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Adam Bienkowski Krishna R. Pattipati David Sidoti Department of Electrical and Computer Engineering University of Connecticut Storrs CT U.S.A US Naval Research Laboratory Monterey CA U.S.A

Planning ship routes that take into account meteorological and oceanographic conditions is a salient problem for both commercial and Naval applications. The A* algorithm is a very common method for finding the minimum cost path in a graph. Finding appropriate heuristics for a cost function is critical to the computational efficiency and memory requirements of the A* algorithm. We propose heuristic evaluation functions (HEFs) based on the minimum, mean, median, and mode of the cost function values over the reachable nodes given time constraints. Only the HEF based on the minimum is admissible, but we show that the other heuristics are able to find near-optimal solutions in substantially shorter times than the Dijkstra's algorithm, which does not use a heuristic. We evaluate these heuristics over many scenarios and show that overall the HEF based on the mean value performs the best. This HEF can be used in time-critical applications where an occasional loss of optimality is sacrificed for faster run time, such as real-time planning and control, or as part of a multi-objective shortest path algorithm for planning and execution.

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Aperiodicity and disorder as systematic spectral tuning mechanisms for plasmonic nanostructures

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Optics Express 2025年第11期33卷 23227-23243页

作者： Ekin Gunes Ozaktas Sreyas Chintapalli Eliza O’Reilly Susanna M Thon Department of Electrical Engineering and Ginzton Laboratory Stanford University USA Department of Electrical and Computer Engineering Johns Hopkins University USA Department of Physics and Astronomy Johns Hopkins University USA Department of Applied Mathematics and Statistics Johns Hopkins University USA eoreill2@jh.edu Department of Materials Science and Engineering Johns Hopkins University USA susanna.thon@jhu.edu

We demonstrate how aperiodicity and disorder can be used as quantifiable mechanisms for tuning the spectral response of plasmonic nanostructure arrays. We tune the extinction spectra of these arrays using deterministically aperiodic (quasicrystal), perturbed lattice (Bernoulli point process, frozen phonon disorder, long-range frozen phonon disorder), negatively correlated (Strauss point process), and positively correlated (Log Gaussian Cox point process) assemblies. We quantify this tuning by considering the local variance of the extinction spectra, demonstrating two orders of magnitude of tunability. Our structures have potential applications in plasmonic or waveguide-based optoelectronic devices such as photovoltaics and photosensing, where spectral tuning is critical to performance.

关键词： Electron beam lithography Fourier transforms Second harmonic generation Spectral properties Surface plasmon polaritons Surface plasmon resonance

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