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13th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2024

作者： Imaz-Lueje, Borja Martinez-De-Rioja, Daniel Vaquero, Alvaro F. Lopez-Fernandez, Jesus A. Pino, Marcos R. Arrebola, Manuel Universidad Rey Juan Carlos Department of Signal Theory and Communications and Telematic Systems and Computing Fuenlabrada Spain Universidad Politécnica de Madrid Information Processing and Telecommunications Center Madrid Spain Universidad de Oviedo Signal Theory and Communications Group Gijón Spain

ISBN: (纸本)9798350360776

Modern satellite communications rely on high-gain antennas on board the satellite to establish deep-space communications, provide high-throughput links, or perform high-resolution radiometry. Conventional reflector antennas provide high gain beams, although their large aperture and doubly curved surface make them difficult to manufacture, mount on the satellite, and fold and deploy after the satellite launch. © 2024 IEEE.

关键词： Satellite antennas

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Overview of Variable Rate Coding in JPEG AI

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Jia, Panqi Brand, Fabian Yu, Dequan Karabutov, Alexander Alshina, Elena Kaup, Andre Friedrich-Alexander University Erlangen-Nürnberg Department of Electrical-Electronic-Communication Engineering Chair of Multimedia Communications and Signal Processing Erlangen91058 Germany Huawei Munich Research Center Munich80992 Germany

Empirical evidence has demonstrated that learning-based image compression can outperform classical compression frameworks. This has led to the ongoing standardization of learned-based image codecs, namely Joint Photographic Experts Group (JPEG) AI. The objective of JPEG AI is to enhance compression efficiency and provide a software and hardware-friendly solution. Based on our research, JPEG AI represents the first standardization that can facilitate the implementation of a learned image codec on a mobile device. This article presents an overview of the variable rate coding functionality in JPEG AI, which includes three variable rate adaptations: a three-dimensional quality map, a fast bit rate matching algorithm, and a training strategy. The variable rate adaptations offer a continuous rate function up to 2.0 bpp, exhibiting a high level of performance, a flexible bit allocation between different color components, and a region of interest function for the specified use case. The evaluation of performance encompasses both objective and subjective results. With regard to the objective bit rate matching, the main profile with low complexity yielded a 13.1% BD-rate gain over VVC intra, while the high profile with high complexity achieved a 19.2% BD-rate gain over VVC intra. The BD-rate result is calculated as the mean of the seven perceptual metrics defined in the JPEG AI common test conditions. With respect to subjective results, the example of improving the quality of the region of interest is illustrated. © 1991-2012 IEEE.

关键词： Image compression

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Per-RMAP: Feasibility-Seeking and Superiorization Methods for Floorplanning with I/O Assignment

Per-RMAP: Feasibility-Seeking and Superiorization Methods fo...

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2023 International Symposium of Electronics Design Automation, ISEDA 2023

作者： Yu, Shan Censor, Yair Jiang, Ming Luo, Guojie School of Mathematical Sciences Peking University Department of Information and Computational Sciences Beijing China University of Haifa Department of Mathematics Haifa Israel School of Computer Science Peking University National Key Laboratory for Multimedia Information Processing Beijing China Center for Energy-efficient Computing and Applications Peking University Beijing China

ISBN: (纸本)9798350304510

The feasibility-seeking approach provides a systematic scheme to manage and solve complex constraints for continuous problems, and we explore it for the floorplanning problems with increasingly heterogeneous constraints. The classic legality constraints can be formulated as the union of convex sets. However, the convergence of conventional projection-based algorithms is not guaranteed when the constraints sets are non-convex, which is the case with unions of convex sets. In this work, we propose a resetting strategy to greatly eliminate the divergence issue of the projection-based algorithm for the feasibility-seeking formulation. Furthermore, the superiorization methodology (SM), which lies between feasibility-seeking and constrained optimization, is firstly applied to floorplanning. The SM uses perturbations to steer the iterates of a feasibility-seeking algorithm to a feasible solution with shorter total wirelength. The proposed algorithmic flow is extendable to tackle various constraints and variants of floorplanning problems, e.g., floorplanning with I/O assignment problems. We have evaluated the proposed algorithm on the MCNC benchmarks. We can obtain legal floorplans only two times slower than the branch-And-bound method in its current prototype using MATLAB, with only 3 % wirelength inferior to the optimal results. We evaluate the effectiveness of the algorithmic flow by considering the constraints of I/O assignment, and our algorithm achieves 8 % improvement on wirelength. © 2023 IEEE.

关键词： Branch and bound method

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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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Polarizing Reflectarray Antenna in K and Ka Bands for Small Satellites

Polarizing Reflectarray Antenna in K and Ka Bands for Small ...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Juan Ruiz-Casado Daniel Martinez-De-Rioja Eduardo Martinez-De-Rioja Jose A. Encinar Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain Department of Signal Theory and Communications and Telematic Systems and Computing Universidad Rey Juan Carlos Fuenlabrada Spain

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This contribution describes a dual-band polarizing reflectarray antenna for small-satellite communications. The proposed antenna has been designed to generate a high-gain beam while converting the incident dual-linearly polarized field into dual-circular polarization in K and Ka bands. The unit-cell, based on orthogonal groups of dipoles, comprises two layers of metallization, where each layer is designed to independently operate at a different frequency. The polarization conversion process has been generalized to operate with non-stacked dual-polarization elements for each frequency, which allows to use a single layer per operating frequency. The simulated results show a maximum gain larger than 30 dBi and an axial ratio below 1.5 dB at both design frequencies (19.7 and 29.5 GHz).

关键词： Satellite antennas Dipole antennas Metallization Conferences Dual band Small satellites Reflector antennas

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Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference 41

Causal-IQA: Towards the Generalization of Image Quality Asse...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhong, Yan Wu, Xingyu Zhang, Li Yang, Chenxi Jiang, Tingting School of Mathematical Sciences Peking University Beijing China National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China Hefei China National Biomedical Imaging Center Peking University Beijing China

Due to the high cost of Image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA *** address this, this paper proposes a novel end-to-end blind IQA method: ***, we first analyze the causal mechanisms in IQA tasks and construct a causal graph to understand the interplay and confounding effects between distortion types, image contents, and subjective human ***, through shifting the focus from correlations to causality, Causal-IQA aims to improve the estimation accuracy of image quality scores by mitigating the confounding effects using a causality-based optimization *** optimization strategy is implemented on the sample subsets constructed by a Counterfactual Division process based on the Backdoor *** experiments illustrate the superiority of Causal-IQA. Copyright 2024 by the author(s)

关键词： Image correlation

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Train rolling stock video segmentation and classification for bogie part inspection automation: a deep learning approach

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Journal of Engineering and Applied Science 2022年第1期69卷 1-24页

作者： Krishnamohan, Kaja Prasad, Ch. Raghava Kishore, P.V.V. Department of Electronics and Communications Engineering K.L. University Green Fields Vaddeswaram Andhra Pradesh Guntur DT522 502 India Image Speech and Signal Processing Research Group Department of Electronics and Communications Engineering Biomechanics and Vision Computing Research Center K.L. University Green Fields Vaddeswaram Andhra Pradesh Guntur DT522 502 India

Train rolling stock examination (TRSE) is a physical procedure for inspecting the bogie parts during transit at a little over 30 kmph. Currently, this process is manually performed across many railway networks across the world. This work proposes to automate the process of TRSE using artificial intelligence techniques. The previous works have proposed active contour-based models for the segmentation of bogie parts. Though accurate, the models require manual intervention and are found to be iterative making them unsuitable for real-time operations. In this work, we propose a segmentation model followed by a deep learning classifier that can accurately increase the deployability of such systems in real time. We apply the UNet model for the segmentation of bogie parts which are further classified using an attention-based convolutional neural network (CNN) classifier. In this work, we propose a shape deformable attention model to identify shape variations occurring in the video sequence due to viewpoint changes during the train movement. The TRSNet is trained and tested on the high-speed train bogie videos captured across four different trains. The results of the experimentation have been shown to improve the recognition accuracy of the proposed system by 6% over the state-of-the-art classifiers previously developed for TRSE. © 2022, The Author(s).

关键词： Iterative methods

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Can Large Multimodal Models Uncover Deep Semantics Behind Images?

arXiv

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arXiv 2024年

作者： Yang, Yixin Li, Zheng Dong, Qingxiu Xia, Heming Sui, Zhifang State Key Laboratory of Multimedia Information Processing Peking University China Department of Computing The Hong Kong Polytechnic University Hong Kong Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University China

Understanding the deep semantics of images is essential in the era dominated by social media. However, current research works primarily on the superficial description of images, revealing a notable deficiency in the systematic investigation of the inherent deep semantics. In this work, we introduce DEEPEVAL, a comprehensive benchmark to assess Large Multimodal Models' (LMMs) capacities of visual deep semantics. DEEPEVAL includes human-annotated dataset and three progressive subtasks: fine-grained description selection, in-depth title matching, and deep semantics understanding. Utilizing DEEPEVAL, we evaluate 9 open-source LMMs and GPT-4V(ision). Our evaluation demonstrates a substantial gap between the deep semantic comprehension capabilities of existing LMMs and humans. For example, GPT-4V is 30% behind humans in understanding deep semantics, even though it achieves human-comparable performance in image description. Further analysis reveals that LMM performance on DEEPEVAL varies according to the specific facets of deep semantics explored, indicating the fundamental challenges remaining in developing LMMs. © 2024, CC BY.

关键词： Semantics

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Design of Multiband Large Reflectarray Antennas for Space Applications using Support Vector Machines

Design of Multiband Large Reflectarray Antennas for Space Ap...

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IEEE-APS Conference on Antennas and Propagation for Wireless Communications

作者： Borja Imaz-Lueje Daniel Martinez-de-Rioja Álvaro F. Vaquero Jesús A. López-Fernández Marcos R. Pino Manuel Arrebola Department of Signal Theory and Communications and Telematic Systems and Computing Universidad Rey Juan Carlos Fuenlabrada Spain Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain Signal Theory and Communications Group Universidad de Oviedo Gijón Spain

ISBN: (数字)9798350360776

ISBN: (纸本)9798350360783

关键词： Wireless communication Support vector machines Satellite antennas Satellite broadcasting Reflector antennas Radiometry Antennas and propagation Aperture antennas Deep-space communications

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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