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Congress on Evolutionary Computation

作者： Tianye Shu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time.

关键词： Costs Sociology Evolutionary computation Statistics Optimization Standards

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When Sign Language Meets Semantic Communications 35

When Sign Language Meets Semantic Communications

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Singh, Keshav Qi, Nan InnoCube P.C. Department of Research and Development 17th Noemvriou 79 Thessaloniki55535 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics & Telecommunications Lamia35100 Greece Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China

ISBN: (纸本)9798350362244

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system's training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

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Chinese Journal of Electronics 2023年第2期21卷 202-208页

作者： Huihui Li Xuanqin Mou Zhen Ji Hang Yu Yan Li Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Embedded System Design Shenzhen China

A novel wideband 5.8GHz CPW-fed antenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numerically using the Method of moment (MOM) and the Finite element method (FEM). With the antenna size limited to $30\times 30 \text{mm}^{2}$ , the −10dB bandwidth obtained by MOM is 3.235GHz (5.765∼9GHz) and the −9.5dB band-width obtained by FEM is 2.74GHz (5.32∼8.06GHz), corresponding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured −10dB bandwidth is 2.68GHz (5.63GHz∼8.31GHz), 46.2% of the 5.8GHz frequency. Furthermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than −10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： Antenna measurements Resonant frequency Loss measurement Frequency measurement Finite element analysis Broadband antennas RFID tags

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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Hybrid Variational Autoencoder for Collaborative Filtering 25

Hybrid Variational Autoencoder for Collaborative Filtering

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

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Optimized Design of a Haptic Unit for Vibrotactile Amplitude Modulation

Optimized Design of a Haptic Unit for Vibrotactile Amplitude...

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IEEE International Conference on Robotics and Biomimetics

作者： Jingchen Huang Yun Fang Weichao Guo Zhijian Qiao Xinjun Sheng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering and the Meta Robotics Institute Shanghai Jiao Tong University Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Communicating information to users is a crucial aspect of human-machine interaction. Vibrotactile feedback encodes information into spatiotemporal vibrations, enabling users to perceive tactile sensations. It offers advantages such as lightweight, wearability, and high stability, with broad applications in sensory substitution, virtual reality, education, and healthcare. However, existing haptic unit designs lack amplitude modulation capabilities, which limits their applications. This paper proposed an optimized design of the haptic unit from the perspective of vibration amplitude modulation. A modified elastic model was developed to describe the propagation and attenuation mechanisms of vibration in the skin. Based on the model, two types of hierarchical architectural design were proposed. The design incorporated various materials arranged in multiple layers to amplify or attenuate the vibration amplitude as it traveled through the structure. An experimental platform was built to evaluate the performance of the optimized design.

关键词： Vibrations Solid modeling Virtual reality Amplitude modulation Robot sensing systems Motors Stability analysis Skin Haptic interfaces Spatiotemporal phenomena

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Dual-demodulation large-scope high-sensitivity refractive index sensor based on twin-core PCF

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Optoelectronics Letters 2021年第4期17卷 193-198页

作者： LI Chen-yuan SONG Bin-bin WU Ji-xuan HUANG Wei WU Xu-jie JIN Chang The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information EngineeringTiangong UniversityTianjin 3003S7China

In this paper, a refractive index(RI) sensor based on the twin-core photonic crystal fiber(TC-PCF) is presented. Introducing the rectangular array in the core area makes the PCF possible to obtain high birefringence and low confinement loss over the wavelength range from 0.6 μm to 1.7 μm. Therefore, the core region can enhance the interaction between the core mode and the filling material. We studied theoretically the evolution characteristics of the birefringence and operating wavelength corresponding to the strongest polarization point under the condition of filling the rectangular array with RI matching fluid range from 1.33 to 1.41. Simulation results reveal that the proposed TC-PCF has opposite evolutions of change rates between the B and wavelength, and the maximum RI sensing sensitivities of 1.809× 10-2 B/RIU and 8 700 nm/RIU at low and high RI infill are obtained respectively, which means that the TC-PCF features of dual-parameter demodulation for the RI sensing can maintain a high refractive index sensing sensitivity within a large scope of RI ranging from 1.33 to 1.41. Compared with the results of single-parameter demodulation, it is an optimized method to improve the sensitivity of low refractive index sensors, which has great application potency in the field of biochemical sensing and detection.

关键词： polarization refractive index

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DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation

arXiv

引用

arXiv 2024年

作者： Pan, Qingtao Qiao, Wenhao Lou, Jingjiao Ji, Bing Li, Shuo School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it often suffers from error supervision from low-quality pseudo labels. Vision-Language Model (VLM) has great potential to enhance pseudo labels by introducing text prompt guided multimodal supervision information. It nevertheless faces the cross-modal problem: the obtained messages tend to correspond to multiple targets. To address aforementioned problems, we propose a Dual Semantic Similarity-Supervised VLM (DuSSS) for SSMIS. Specifically, 1) a Dual Contrastive Learning (DCL) is designed to improve cross-modal semantic consistency by capturing intrinsic representations within each modality and semantic correlations across modalities. 2) To encourage the learning of multiple semantic correspondences, a Semantic Similarity-Supervision strategy (SSS) is proposed and injected into each contrastive learning process in DCL, supervising semantic similarity via the distribution-based uncertainty levels. Furthermore, a novel VLM-based SSMIS network is designed to compensate for the quality deficiencies of pseudo-labels. It utilizes the pretrained VLM to generate text prompt guided supervision information, refining the pseudo label for better consistency regularization. Experimental results demonstrate that our DuSSS achieves outstanding performance with Dice of 82.52%, 74.61% and 78.03% on three public datasets (QaTa-COV19, BM-Seg and MoNuSeg). Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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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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Reference-Based Iterative Interaction With P2-Matching for Stereo Image Super-Resolution

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 34卷 3779-3789页

作者： Runmin Cong Rongxin Liao Feng Li Ronghui Sheng Huihui Bai Renjie Wan Sam Kwong Wei Zhang School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei China School of Computer Science and Engineering Hefei University of Technology Hefei China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Department of Computer Science Hong Kong Baptist University Hong Kong China School of Data Science Lingnan University Hong Kong China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

Stereo Image Super-Resolution (SSR) holds great promise in improving the quality of stereo images by exploiting the complementary information between left and right views. Most SSR methods primarily focus on the inter-view correspondences in low-resolution (LR) space. The potential of referencing a high-quality SR image of one view benefits the SR for the other is often overlooked, while those with abundant textures contribute to accurate correspondences. Therefore, we propose Reference-based Iterative Interaction (RIISSR), which utilizes reference-based iterative pixel-wise and patch-wise matching, dubbed $P^{2}$ -Matching, to establish cross-view and cross-resolution correspondences for SSR. Specifically, we first design the information perception block (IPB) cascaded in parallel to extract hierarchical contextualized features for different views. Pixel-wise matching is embedded between two parallel IPBs to exploit cross-view interaction in LR space. Iterative patch-wise matching is then executed by utilizing the SR stereo pair as another mutual reference, capitalizing on the cross-scale patch recurrence property to learn high-resolution (HR) correspondences for SSR performance. Moreover, we introduce the supervised side-out modulator (SSOM) to re-weight local intra-view features and produce intermediate SR images, which seamlessly bridge two matching mechanisms. Experimental results demonstrate the superiority of RIISSR against existing state-of-the-art methods.

关键词： Feature extraction Iterative methods Superresolution Electronic mail Image resolution Image reconstruction Modulation Estimation Data mining Merging

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