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arXiv 2021年

作者： Souza, Lincon S. Sogi, Naoya Gatto, Bernardo B. Kobayashi, Takumi Fukui, Kazuhiro Japan The Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Japan

This paper addresses the problem of object recognition given a set of images as input (e.g., multiple camera sources and video frames). Convolutional neural network (CNN)-based frameworks do not exploit these sets effectively, processing a pattern as observed, not capturing the underlying feature distribution as it does not consider the variance of images in the set. To address this issue, we propose the Grassmannian learning mutual subspace method (G-LMSM), a NN layer embedded on top of CNNs as a classifier, that can process image sets more effectively and can be trained in an end-to-end manner. The image set is represented by a low-dimensional input subspace;and this input subspace is matched with reference subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric. The key idea of G-LMSM is that the reference subspaces are learned as points on the Grassmann manifold, optimized with Riemannian stochastic gradient descent. This learning is stable, efficient and theoretically well-grounded. We demonstrate the effectiveness of our proposed method on hand shape recognition, face identification, and facial emotion recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients

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Brain science Advances 2023年第2期9卷 78-94页

作者： Yan Niu Nan Zhang Mengni Zhou Lan Yang Jie Sun Xueting Cheng Yanan Li Lefan Guo Jie Xiang Bin Wang College of Information and Computer Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama UniversityOkayamaJapan Research Center for Medical Artificial Intelligence Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangzhouChina

Schizophrenia(SZ)and bipolar disorder(BD)are two of themost frequent mental *** disorders exhibit similarpsychotic symptoms,making diagnosis challenging and leadingto ***,the network complexity changes drivingspontaneous brain activity in SZ and BD patients are still *** entropy(FE)is a novel way of measuring the dispersion(or spread)of functional connectivities inside the *** FE wasutilized in this study to examine the network complexity of the resting-state fMRI data of SZ and BD patients at three levels,including global,modules,and *** three levels,the FE of SZand BD patients was considerably lower than that of normal control(NC).At the intra-module level,the FE of SZ was substantially higher than that of BD in the cingulo-opercular ***,a strong negative association between FE and clinical measureswas discovered in patient ***,we classified using theFE features and attained an accuracy of 66.7%(BD *** ***)and an accuracy of 75.0%(SZ ***).These findings proposed that network connectivity’s complexity analyses using FE can provideimportant insights for the diagnosis of mental illness.

关键词： functional entropy network complexity schizophrenia bipolar disorder resting-state fMRI

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First Demonstration of HZO-LNOI Integrated Ferroelectric Electro-Optic Modulator and Memory to Enable Reconfigurable Photonic Systems

First Demonstration of HZO-LNOI Integrated Ferroelectric Ele...

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International Electron Devices Meeting (IEDM)

作者： Zefeng Xu Chun-Kuei Chen Hong-Lin Lin Yuan Gao Wei Ke Baochang Xu Pavel Dmitriev Carlan Arbiz Evgeny Zamburg Steven Touzard Xinlun Cai James Lee Suresh Venkatesan Aaron Danner Aaron Voon-Yew Thean Department of Electrical and Computer Engineering National University of Singapore (NUS) Integrative Sciences and Engineering Program NUS Graduate School Singapore Next-Generation Hybrid μ-Electronics Center (SHINE) Singapore School of Electronics and Information Technology Sun Yat-Sen University China 510275 Singapore Centre for Quantum Technologies NUS POET Technologies

We have successfully demonstrated, for the first time, an innovative back-end-of-line (BEOL) compatible electro-optic modulator and memory (EOMM) based on Lithium Niobate on Insulator (LNOI) micro-ring resonator (MRR) integrated with Ferroelectric Hafnium Zirconate Hf 0.5 Zr 0.5 O (HZO) non-volatile analog memory. High non-volatile memory and modulation performances are both achieved in a single compact device, exhibiting high extinction ratio of 13.3 dB, excellent efficiency of 66pm/V, stable nine-state switching, record-high endurance exceeding 10 9 cycles. This is accomplished by utilizing Pockels effect in LNOI, induced by electric-field effect from remnant HZO ferroelectric polarization. We studied the system implementation of reconfigurable chiplet-interposer photonic interconnect, enabled by the EOMM and EOMM with hybrid thermal-optical modulation. Our model shows a potential 70% energy efficiency improvement over conventional electrical interposer interconnect. We have also tested the integration of the EOMM with POET technologies’ 400G Tx/Rx optical interposer chip and studied a limited scale demonstration of the EOMM device.

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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

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IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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Enhancing Coarse-Grained and Fine-Grained Image-Text Retrieval Models via Multi-Scale Modal Distance Learning

Enhancing Coarse-Grained and Fine-Grained Image-Text Retriev...

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International Conference on computer Supported Cooperative Work in Design

作者： Kaili Zhou Yushui Geng Jing Zhao Yiyun Xing Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The current image-text cross-modal retrieval faces challenges due to the heterogeneous nature of different modalities. This paper proposes an improved model based on multi-scale modal distance learning to enhance the performance of coarse-grained and fine-grained image-text matching. In the image encoding part, an improved ResNet architecture is adopted, utilizing upsampling techniques to better preserve key semantic information. For text encoding, BERT and Bi-LSTM models are combined to extract shallow and deep textual features, respectively. We design a coarse-fine granularity interaction mechanism, leveraging a bidirectional stacked Transformer to achieve local similarity interaction between image regions and text words, while also using Bi-LSTM-extracted contextual features to interact with global image features for global similarity matching. Additionally, this paper introduces a novel multi-scale modal contrastive loss, which dynamically adjusts the distance between samples of different modalities, thereby improving the model's sensitivity to semantic similarity. Experimental results on the COCO 5K and Flickr30K datasets demonstrate that the proposed method outperforms existing mainstream methods in both retrieval accuracy and efficiency, achieving complementary advantages between coarse- and fine-grained features. This provides an effective solution and new research direction for cross-modal retrieval.

关键词： computer aided instruction Image coding Accuracy Sensitivity Semantics Bidirectional control Feature extraction Transformers Encoding Cross modal retrieval

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Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images

Pyramid Scene Parsing Net Model for Automated Paddy Field Ma...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Yaya Heryadi Edy Irwansyah Eka Miranda Haryono Soeparno Herlawati Kiyota Hashimoto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince of Songkla University Phuket Thailand

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops.

关键词： Training Productivity Deep learning Satellites Semantic segmentation Crops Predictive models

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A Comprehensively Introduction and Analysis of AVS3 Entropy Coding

A Comprehensively Introduction and Analysis of AVS3 Entropy ...

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Data Compression Conference (DCC)

作者： Bin Guo Jianchao Wei Shanshe Wang Hui Ma Jiaqi Zhang Shenzhen International Graduate School Tsinghua University China Konka Grp Co Ltd China BEIJING BOYA RUISHI TECHNOLOGY CO. LTD China National Engineering Research Center of Visual Technology School of Computer Science Peking University China Information Technology R&D Innovation Center of Peking University China

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

This paper elaborates the binary bin distribution of each syntax element in AVS3. The overall syntax elements in AVS3 can be divided into Context-Modeled(CM) Bin, Bypass(BPS) Bin, Weighted Context-Modeled(WCM) Bin. A comprehensive and quantitative analysis on these bitstreams are conducted and the result is shown in Fig. 1. Moreover, Fig. 2 illustrates the distribution of CM Bin, BPS Bin, and WCM Bin across various resolutions and QPs. The analysis reveals insightful correlations between the types of binary symbols and syntax elements within the AVS3 bitstream. Through this analysis, we aim to contribute to the implementation of AVS3 codecs.

关键词： Correlation Codecs Statistical analysis Symbols Data compression Syntactics Entropy coding Binary sequences

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Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction

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Chemosphere 2023年 334卷 138980页

作者： Hai, Tao Alshahri, Abdullah H. Mohammed, Amin Salih Sharma, Aman Almujibah, Hamad R. Mohammed Metwally, Ahmed Sayed Ullah, Mirzat School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Electronics and Information Engineering Ankang University Ankang China Universiti Teknologi MARA Shah Alam40450 Malaysia Department of Civil Engineering College of Engineering Taif University P.O. Box 11099 Taif City21974 Saudi Arabia Department of Software and Informatics Engineering College of Engineering Salahaddin University-Erbil Kurdistan Region Iraq Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Institute of Engineering and Technology GLA University UP Mathura281406 India Department of Mathematics College of Science King Saud University Riyadh11451 Saudi Arabia Graduate School of Economics and Management Ural Federal University Yekaterinburg620002 Russia

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h. © 2023

关键词： Gasification

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DetecVFuzz: Enhancing Security in Consumer Electronic Devices Through Scalable Vulnerability Testing of Virtual Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Xiao, Xi Guo, Yongjian Jolfaei, Alireza Chen, Chuan Haghighi, Mohammad Sayad Wen, Sheng Lin, Yuanyi Tsinghua University Shenzhen International Graduate School Shenzhen China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Peng Cheng Laboratory Guangdong Shenzhen China Flinders University College of Science and Engineering SA5042 Australia Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Ministry of Education China Qilu University of Technology Shandong Academy of Sciences National Supercomputer Center in Jinan Jinan China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

The interconnection facilitated by consumer electronics has led to the creation of a complex network of devices. Testing efficiency of such consumer electronic devices can be improved through hardware emulation. QEMU is a classical emulator for simulation and testing. Fuzzing is a promising solution to test the security of such devices, which has become pivotal in identifying device vulnerability. However, Qtest libfuzzer, the QEMU native fuzzer, requires manual writing of stub code, which makes conducting the fuzzing process difficult and yields poor performance. To address these challenges in virtual device security analysis, we propose DetecVFuzz, a novel automatic fuzzing framework based on QEMU-Qtest, aiming to find vulnerabilities in virtual PCI devices, including USB, audio, network, etc. We inject mutated inputs into nested data structures as parameters of memory-related APIs to ensure the stable operation of VM procedures. We implemented DetecVFuzz and evaluated it on different virtual devices, such as virtual memory USB stack and drivers. In preliminary evaluations on nine recent versions of the QEMU, we found 15 new memory errors in virtual devices. In general, DetecVFuzz achieves 89.37% line coverage and 78.65% branch coverage in virtual devices. We identified 26 new crashes/errors, three of which were unseen new bugs. © 1975-2011 IEEE.

关键词： Virtual addresses

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