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A Polarization-Diversity Omnidirectional Glass Dielectric Resonator Antenna with Gain and Bandwidth Enhancements

IEEE Transactions on Antennas and Propagation 2025年

作者： Yu, Bo Hao Leung, Kwok Wa Hu, Peng Fei Lee, Mike W. K. Luk, Kwai Man City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Hong Kong CityU Shenzhen Research Institute Shenzhen Key Laboratory of Millimeter Wave and Wideband Wireless Communications Information and Communication Technology Centre Shenzhen518057 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China

A polarization-diversity omnidirectional glass dielectric resonator antenna (DRA) with gain and bandwidth enhancements is presented in this communication. It employs high-order TM01δ+2 and TE01δ+3 modes to improve the antenna gains of the vertical and horizontal polarizations, respectively. However, their high quality factors result in narrow bandwidths. When the modes of feeding circuits are combined with them, the overlapping bandwidth of the two polarizations is only 6.5%. To overcome this, an air gap is introduced into the dielectric resonator (DR), modifying its shape from cylindrical to I-shaped. This reduces the effective dielectric constant and quality factor, increasing the overlapped bandwidth from 6.5% to 16.0%. In addition, the peak gain is increased from 2.66 to 4.62 dBi by reducing the back lobe. To verify the idea, a DRA made of K9 glass is designed for the 3.3-3.6 GHz WiMAX band. The prototype antenna has a measured overlapping bandwidth of 13.8% and a peak gain of 4.14 dBi. © 2025 IEEE.

关键词： Bandwidth

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Fine-Grained Spatial-Temporal Framework for Engagement Prediction 2nd

Fine-Grained Spatial-Temporal Framework for Engagement Pred...

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2nd CSIG Conference on Emotional Intelligence, CEI 2024

作者： Wang, Shanshan Zheng, Yong Wang, keyang Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Beijing China Institutes of Physical Science and Information Technology Anhui University Anhui China Zhejiang Dahua Technology Co. Ltd. Zhejiang China School of Computer Science and Technology Anhui University Anhui China

ISBN: (纸本)9789819650835

The engagement prediction task aims to identify the current level of involvement of individuals based on the information presented in video clips. In recent years, engagement prediction has attracted considerable attention due to its significance in real-life scenarios. We observed that an individual’s engagement status is often primarily manifested in the facial region. This observation prompted us to solve the engagement prediction problem by exploiting facial information to learn features related to engagement. In this paper, we propose a novel model for learning fine-grained features, called Fine-Grained Spatial-Temporal Framework (FGST). Specifically, we consider both spatial and temporal aspects. In the spatial domain, we propose an entropy-weighted method based on Fourier transform. By applying Fourier transform to video frames and introducing the concept of information entropy, we can segregate engagement-related information from engagement-unrelated information and mining difficult samples. This aids in enabling the model to learn features conducive to engagement prediction. In the temporal domain, we calculate the differences between adjacent frames and sum these differences to obtain regions of pixel variation between video frames, thereby generates saliency weight and improving model performance. Experiments on several public datasets demonstrate the proposed model can outperform the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Engagement prediction Fourier transform Frame difference information entropy

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Low-light Image Enhancement through Learning a Simplified Inverse Rendering Model

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wu, Wenhui Pang, Jia Gao, Shuaibo Zhang, Pingping Chen, Zhe Qiu, Guoping Shenzhen University College of Electronics and Information Engineering China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China City University of Hong Kong Department of Computer Science Hong Kong University of Nottingham School of Computer Science United Kingdom

It remains to be extremely difficult to capture high quality photographs of low-light scenes. Low light causes the low signal-to-noise ratio (SNR) problem which makes the image noisy. Such scenes almost always have the high dynamic range (HDR) problem caused by uneven lighting where a small area surrounding the light source is very bright while the rest of the scene is very dark, making it very difficult to simultaneously obtain high quality signals in both the dark and bright areas. This paper presents a new image restoration method for tackling the problems in low-light scenes. Fundamentally differing from existing approaches, the new method borrows ideas from inverse graphics rendering and re-renders the image with a canonical light source thus correcting the image from first principle. A deep learning based simplified inverse rendering model (SIRM) featuring implicit regularization is first developed for correcting uneven lighting and then an end-to-end convolutional neural network is constructed for reducing noise. Extensive experimental results are presented to demonstrate that the new method outperforms state-of-the-art methods, and is capable of effectively brightening up dark image regions while at the same time preserving details and color consistency. Our code is available at: https://***/pj0927/SIRNet. © 2025 IEEE.

关键词： Rendering (computer graphics)

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Domain Knowledge Decomposition for Cross-Domain Few-Shot Scene Classification from Remote Sensing Imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Li, Can Chen, He Zhuang, Yin Chen, Liang Li, Lianlin Beijing Institute of Technology National Key Laboratory of Space-Born Intelligent Information Processing Beijing100081 China Peking University School of Electronic Engineering and Computer Science Beijing100871 China

Cross-domain few-shot scene classification (CDFSSC) aims to tackle the challenge of classifying target domain data with limited labeled samples under distribution shift and category mismatch in remote sensing imagery classification. Existing methods primarily focus on extracting domain-common knowledge while overlooking domain-specific knowledge, which is insufficient for effective cross-domain representation learning. Additionally, the category mismatch between the source and target domain further hinders the model's ability to adapt and perform effectively on few-shot tasks in the target domain. Hence, in this article, a novel CDFSSC method called domain knowledge decomposition (DKD) framework is proposed to effectively exploit domain-common and domain-specific knowledge from the pseudo-labels of target samples, improve the certainty of cross-domain representation learning, and enhance the model's adaptability to target domain. First, a cross-domain pseudo-label decomposed learning structure is proposed for DKD to facilitate domain-common knowledge transfer and correct interference with domain-specific knowledge. It decomposes the pseudo-labels at the logit level to separately exploit two types of knowledge, ensuring a more effective and robust cross-domain representation learning. Second, a certainty-enhanced dynamic loss is designed to strengthen the certainty in cross-domain representation learning by minimizing the self-entropy of predictions and dynamic loss re-weighting principle. Third, in target domain adaptation and few-shot evaluation stage, a target-domain-specific adapter is designed to improve the model's adaptability to target domain few-shot tasks, while addressing category mismatch by classifier fine-tuning. Extensive experimental results on 12 RS cross-domain scenarios exhibit impressive performance of the proposed DKD framework. © 2008-2012 IEEE.

关键词： Labeled data

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TEMPO: Temporal Preference Optimization of Video LLMs via Difficulty Scheduling and Pre-SFT Alignment

arXiv

引用

arXiv 2025年

作者： Li, Shicheng Li, Lei Ouyang, Kun Ren, Shuhuai Liu, Yuanxin Zhang, Yuanxing Zhang, Fuzheng Kong, Lingpeng Liu, Qi Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China The University of Hong Kong Hong Kong Kuaishou Technology China

Video Large Language Models (Video LLMs) have achieved significant success by leveraging a two-stage paradigm: pretraining on large-scale video-text data for vision-language alignment, followed by supervised fine-tuning (SFT) for task-specific capabilities. However, existing approaches struggle with temporal reasoning due to weak temporal correspondence in the data and reliance on the next-token prediction paradigm during training. To address these limitations, we propose TEMPO (TEMporal Preference Optimization), a systematic framework that enhances Video LLMs’ temporal reasoning capabilities through Direct Preference Optimization (DPO). To facilitate this, we introduce an automated preference data generation pipeline that systematically constructs preference pairs by selecting videos that are rich in temporal information, designing video-specific perturbation strategies, and finally evaluating model responses on clean and perturbed video inputs. Our temporal alignment features two key innovations: curriculum learning which that progressively increases perturbation difficulty to improve model robustness and adaptability;and "Pre-SFT Alignment", applying preference optimization before instruction tuning to prioritize fine-grained temporal comprehension. Extensive experiments demonstrate that our approach consistently improves Video LLM performance across multiple benchmarks with a relatively small set of self-generated DPO data. We further analyze the transferability of DPO data across architectures and the role of difficulty scheduling in optimization. Our findings highlight our TEMPO as a scalable and efficient complement to SFT-based methods, paving the way for developing reliable Video LLMs. © 2025, CC BY.

关键词： Visual languages

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Towards Multi-Label Open Set Diagnosis for Medical Images

Towards Multi-Label Open Set Diagnosis for Medical Images

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IEEE International Symposium on Biomedical Imaging

作者： Shijie Pang Yuze Zhao Rui-Wei Zhao Rui Feng Academy for Engineering and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In real clinical scenarios, diagnosis algorithms may face the complex concurrent presence of multiple common, rare, or even unknown disease conditions. In particular, rare disease categories may be underrepresented or even absent from the class-imbalanced training set, leaving safe and trustworthy diagnostic decision-making extremely challenging. In this work, we first formalize the medical image diagnosis as a multi-label open set recognition under class-imbalanced training data and propose a novel Multi-label Open set Diagnosis (MOSD) solution to address this problem. Our MOSD first employs a Balanced Multi-label Data Augmentation (BMDA) module to augment multi-label image data to alleviate the class imbalance in model training more effectively. Then it also boasts a Multi-label Evidential Deep Learning (MEDL) module with tailored learning objectives to better estimate the uncertainty for multi-label open set prediction. Experimental results on the ODIR-5K benchmark successfully demonstrate its superiority in both close set known class classification and open set unknown class detection.

关键词： Training Uncertainty Image recognition Face recognition Training data Multi label classification Robustness Medical diagnosis Medical diagnostic imaging Diseases

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Hybrid Spatial-Frequency Attention Network For Fine-Grained Skeleton-Based Action Recognition

Hybrid Spatial-Frequency Attention Network For Fine-Grained ...

引用

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

作者： Dandan Zhang Sicong Zhan Jia Wang Zhiqing Guo School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Transformer-based methods have gained popularity in skeleton-based action recognition due to their advantages in modeling long-range dependencies. However, Transformer lacks the inductive biases towards skeletal topology and tends to capture salient features, potentially overlooking subtle inter-class variations in similar actions, leading classifications. To address these issues, we first propose Hybrid Spatial-Frequency Attention Network (HSFA-Net) with spatial and temporal structure for fine-grained action recognition. The spatial structure includes a Frequency Domain Channel Enhancement module, which utilizes the Discrete Cosine Transform to convert grouped skeleton data into frequency domain and refines it with multiple high-frequency components. Next, we propose Spatial-Frequency Attention Fusion module to integrate spatial and frequency features for a more comprehensive feature representation. In the temporal structure, we apply the concept of temporal differences to human skeletons and design a Temporal Motion-Sensitive module to highlight motion-sensitive features. Finally, we propose Temporal Frequency Attention module for richer temporal features in the frequency domain. Extensive experiments on NTU-RGB+D and NTU-RGB+D 120 datasets validate the effectiveness of HSFA-Net.

关键词： Network topology Frequency-domain analysis Speech recognition Transforms Signal processing Transformers Feature extraction Skeleton Topology Speech processing

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A Harmonic Current Self-Compensation Control Strategy of DC Active Power Filter

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Li, Gaoxiang Wu, Huanchao Chen, Jining Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical En-gineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Guangxi Uni-versity Nanning530004 China

Due to the diversity of nonlinear loads, the harmonic frequency of the DC microgrid is unpredictable, which poses a challenge to traditional DC active power filters (DC-APF). Firstly, the harmonic compensation mechanism and characteristics of the DC-APF with classical control strategy are analyzed. For the classical DC-APF, the voltage control and harmonic extraction are independent of each other, which may lead to their conflict and reduce the harmonic compensation performance. To address this issue, a harmonic current self-compensation control strategy of DC-APF is proposed, which uses a first-order inertia controller in the voltage loop and can simultaneously achieve voltage control and harmonic extraction. Then, the mechanism, characteristics, and parametric design method of the proposed harmonic current self-compensation control are introduced in detail. Compared with the classical DC-APF, the proposed DC-APF does not require any harmonic extraction filter and has better harmonic current compensation performance. Finally, the proposed control strategy and theoretical analysis are verified by simulation and experiment. © 2025 IEEE. All rights reserved.

关键词： Active filters

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Tackling Sparse Facts for Temporal Knowledge Graph Completion 34

Tackling Sparse Facts for Temporal Knowledge Graph Completio...

引用

34th ACM Web Conference, WWW 2025

作者： Zhang, Yuchao Kong, Xiangjie Ye, Kailun Shen, Guojiang Zheng, Shangfei Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Zhejiang Key Laboratory of Visual Information Intelligent Processing Hangzhou China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798400712746

Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in downstream tasks. Nonetheless, real-world knowledge—such as the progression of social network interactions and the unfolding of news events—is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. ANEL first derives initial entity embeddings through a base model and then uncovers concealed semantic relationships between entities via a latent relation module, enriching the explicit relationships within the knowledge graph. Furthermore, ANEL incorporates an adaptive latent information adjustment component, which dynamically calibrates the influence of latent information based on the entity’s relational structure: entities with fewer connections derive greater benefit from latent information, while entities with denser connections become less dependent on latent augmentation, ensuring precise and resilient representations. We conducted comprehensive experiments on four prominent benchmark datasets, and the results underscore the effectiveness and superiority of ANEL in TKGC tasks. © 2025 Copyright held by the owner/author(s).

关键词： Fact Sparsity Knowledge Representation Temporal Knowledge Graph Completion

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