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11th International Conference on Computational Intelligence and Communication Networks, CICN 2019

作者： Kumar, Abhishek Pandey, Bishwajeet Akbar Hussain, D.M. Atiqur Rahman, Mohammad Jain, Vishal Bahanasse, Ayoub Gyancity Research Consultancy Reseach and Development Wing Motihari India Center of Energy Excellence Gyancity Research Lab Motihari India Aalborg University Department of Energy Technology Esbjerg Denmark SYNERGY Research Development Section Dhaka Bangladesh Bharati Vidyapeeth Department of Computer Application New Delhi India Hasan II University Laboratory LTI Casablanca Morocco

ISBN: (纸本)9781538684399

UART is the most popular two-wire communication interface. It is recognized as Universal Asynchronous Receiver Transmitter. It is a one of the essential element in Communication System to communicate two micro controller based system. It is widely used in case of small distance communication. The implementation of UART's with VHDL can be unified into FPGA for the achievement of stable,reliable,and compact data transmission. Our main aim to implement and design an energy efficient model for that we have used the LVCMOS IO standards and we are comparing our design with a normal UART. In this paper,it has been illustrated that the demand for total power reduced with LVCMOS IO/Standard based UARTs in compare to default IO standards. This paper illustrates differences on the basis of the power consumption by the LVCMOS based energy efficient Universal Asynchronous Receiver Transmitter (UART) and Default IO/Standard based UARTs. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Frequency Scaling and High Speed Transceiver Logic Based Low Power UART design on 45nm FPGA 11

Frequency Scaling and High Speed Transceiver Logic Based Low...

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11th International Conference on Computational Intelligence and Communication Networks, CICN 2019

ISBN: (纸本)9781538684399

UART is a trendy two-wire communication interface. It recognized as Universal Asynchronous Receiver Transmitter. It is an important element to communicate two microcontroller based system. It is widely used in case if high-speed transmission is not required. The main aim to implement the UART on 45nm technology based Spartan-6 FPGA and achieve reliable,compact and stable data transmission. In order to make the more energy efficient UART we have used the HSTL (High-Speed Transceiver Logic) IO standards. To achieve speed and high performance of UART,we have preferred HSTL (High-Speed Transceiver Logic) IOSTANDARD. In this paper,we have used HSTL-II,HSTL-II-18,HSTL-III,and HSTL-III-18. We have also used Frequency Scaling techniques,so we can analyze the demand of different power by device at different frequencies. We have analyzed the demand of total power of different IO Standard at different frequency level so then we make UART more energy efficient. Our analysis has explained that the main reason for power consumption in UART with above IO standard are Clock and IO powers,which we have analyzed at different frequencies. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A physics-constrained deep learning framework for actual evapotranspiration estimation using ground station observations and remote sensing data

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Environmental Modelling & Software 2025年

作者： Lingyue Wang Ping Hu Hongwei Zheng Jie Bai Ying Liu Xingwen Cao Olaf Hellwich Tie Liu Anming Bao Xi Chen State Key Laboratory of Desert and Oasis Ecology Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 China University of Chinese Academy of Sciences Beijing 100049 China Research Center for Ecology and Environment of Central Asia Chinese Academy of Sciences Urumqi 830011 China Key Laboratory of RS & GIS Application Xinjiang Urumqi 830011 China School of Information Science and Engineering Xinjiang University Urumqi 830017 China Department of Computer Vision & Remote Sensing Technische Universität Berlin 10587 Berlin Germany

Evapotranspiration (ET) is a key process in the water cycle. While machine learning-based methods have been increasingly applied to ET estimation, they typically neglect physical mechanisms and ecological impacts. This study proposes a physics-constrained hybrid model (TST-PHY) that incorporates Penman-Monteith-derived physical knowledge into the time series transformer (TST). The framework integrates Bayesian optimization to automatically determine optimal weights for physical mechanisms within the data-driven modeling workflow. The arid and semi-arid regions of Northern China (ASNC) were chosen as the study area, and our results show that the proposed framework optimally balances estimation error and the physical law, with value and trend constraint weights of 0.26 and 0.41, respectively. Moreover, proper physical constraints can improve the prediction accuracy, generalization, and physical consistency of TST-PHY at various spatiotemporal scales. In conclusion, this study establishes a novel paradigm for evapotranspiration estimation in data-sparse regions through the synergistic integration of data-driven and knowledge-based models.

关键词： Evapotranspiration Hybrid model Transformer Physical mechanism Bayesian optimization

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A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

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Towards efficient processing and learning with spikes: New approaches for multi-spike learning

arXiv

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arXiv 2020年

作者： Yu, Qiang Li, Shenglan Tang, Huajin Wang, Longbiao Dang, Jianwu Chen Tan, Kay Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Technology Zhejiang University China Co. Ltd. Tianjin China Department of Computer Science City University of Hong Kong City University of Hong Kong Shenzhen Research Institute

Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing. However, efficient processing and learning of discrete spikes still remains as a challenging problem. In this paper, we make our contributions towards this direction. A simplified spiking neuron model is firstly introduced with effects of both synaptic input and firing output on membrane potential being modeled with an impulse function. An event-driven scheme is then presented to further improve the processing efficiency. Based on the neuron model, we propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks including association, classification, feature detection. In addition to efficiency, our learning rules demonstrate a high robustness against strong noise of different types. They can also be generalized to different spike coding schemes for the classification task, and notably single neuron is capable of solving multi-category classifications with our learning rules. In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented, and find a new phenomenon of losing selectivity. In contrast, our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied. Moreover, our rules can not only detect features but also discriminate them. The improved performance of our methods would contribute to neuromorphic computing as a preferable choice. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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A Convex and Global Solution for the PnP Problem in 2D Forward-Looking Sonar

arXiv

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arXiv 2025年

作者： Su, Jiayi Qian, Jingyu Yang, Liuqing Yuan, Yufan Fu, Yanbing Wu, Jie Wei, Yan Qu, Fengzhong Guangzhou511458 China Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Ocean College Zhejiang University Zhoushan316021 China Provincial Key Laboratory of Cutting-edge Scientific Instruments R&D and Application Ocean College Zhejiang University Zhoushan316021 China Hainan Institute of Zhejiang University Sanya572025 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

The perspective-n-point (PnP) problem is important for robotic pose estimation. It is well studied for optical cameras, but research is lacking for 2D forward-looking sonar (FLS) in underwater scenarios due to the vastly different imaging principles. In this paper, we demonstrate that, despite the nonlinearity inherent in sonar image formation, the PnP problem for 2D FLS can still be effectively addressed within a point-to-line (PtL) 3D registration paradigm through orthographic approximation. The registration is then resolved by a duality-based optimal solver, ensuring the global optimality. For coplanar cases, a null space analysis is conducted to retrieve the solutions from the dual formulation, enabling the methods to be applied to more general cases. Extensive simulations have been conducted to systematically evaluate the performance under different settings. Compared to non-reprojection-optimized state-of-the-art (SOTA) methods, the proposed approach achieves significantly higher precision. When both methods are optimized, ours demonstrates comparable or slightly superior precision. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Weakly supervised histopathology tissue semantic segmentation with multi-scale voting and online noise suppression

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Engineering applications of Artificial Intelligence 2025年 156卷

作者： Pan, Xipeng Zhang, Hualong Deng, Huahu Wang, Huadeng Li, Lingqiao Liu, Zhenbing Wang, Lin An, Yajun Lu, Cheng Liu, Zaiyi Han, Chu Lan, Rushi School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Southern Medical University Guangdong Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Guangzhou510080 China Department of Nasopharyngeal Carcinoma Sun Yat-sen University Cancer Center Guangzhou510060 China International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guangxi Guilin541004 China

The development of an Artificial Intelligence (AI) assisted tissue segmentation method of digital pathology images is critical for cancer diagnosis and prognosis. Excellent performance has been achieved with the current fully supervised segmentation approach, which relies on a huge number of annotated data. However, drawing dense pixel-level annotations on the giga-pixel whole slide image (WSI) is extremely time-consuming and labor-intensive. To this end, we propose a tissue segmentation method using only patch-level classification labels to reduce such annotation burden and significantly improve the quality of the pseudo-masks. We introduce a framework with two phases of classification and segmentation. In the classification phase, we propose a multi-scale voting method on the Class Activation Map (CAM) based model to obtain more stable pseudo masks. In the segmentation phase, an Online Noise Suppression Strategy (ONSS) is proposed to encourage the model to focus on more reliable signals in the pseudo mask rather than noisy signals. Extensive experiments on two weakly supervised pathology image tissue segmentation datasets Lung Adenocarcinoma (LUAD-HistoSeg) and Breast Cancer Semantic Segmentation (BCSS-WSSS) demonstrate our model outperforms state-of-the-art weakly-supervised semantic segmentation (WSSS) methods using patch-level labels. Furthermore, our method exhibits superior generalization ability compared to other models, and demonstrates promising adaptation performance on unseen domains with only small amounts of data. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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Evidence of mini-jet emission in a large emission zone from a magnetically-dominated gamma-ray burst jet

arXiv

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arXiv 2023年

作者： Yi, S.-X. Wang, C.-W. Shao, X. Moradi, R. Gao, H. Zhang, B. Xiong, S.-L. Zhang, S.-N. Tan, W.-J. Liu, J.-C. Xue, W.-C. Zhang, Y.-Q. Zheng, C. Wang, Y. Zhang, P. An, Z.-H. Cai, C. Feng, P.-Y. Gong, K. Guo, D.-Y. Huang, Y. Li, B. Li, X.-B. Li, X.-Q. Liu, X.-J. Liu, Y.-Q. Ma, X. Peng, W.-X. Qiao, R. Song, L.-M. Wang, J. Wang, P. Wang, Y. Wen, X.-Y. Xiao, S. Xu, Y.-B. Yang, S. Yi, Q.-B. Zhang, D.-L. Zhang, F. Zhang, H.-M. Zhang, J.-P. Zhang, Z. Zhao, X.-Y. Zhao, Y. Zheng, S.-J. Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Department of Astronomy Beijing Normal University Beijing100875 China Nevada Center for Astrophysics University of Nevada Las Vegas NV89154 United States Department of Physics and Astronomy University of Nevada Las Vegas NV89154 United States ICRANet Piazza della Repubblica 10 PescaraI-65122 Italy ICRA Dipartamento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 RomeI-00185 Italy INAF Osservatorio Astronomico d’Abruzzo Via M. Maggini snc TeramoI-64100 Italy College of Electronic and Information Engineering Tongji University Shanghai201804 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China School of Computer and Information Dezhou University Dezhou253023 China

The second brightest GRB in history, GRB230307A, provides an ideal laboratory to study the mechanism of GRB prompt emission thanks to its extraordinarily high photon statistics and its single episode activity. Here we demonstrate that the rapidly variable components of its prompt emission compose an overall broad single pulse-like profile. Although these individual rapid components are aligned in time across all energy bands, this overall profile conspires to show a well-defined energy-dependent behavior which is typically seen in single GRB pulses. Such a feature demonstrates that the prompt emission of this burst is from many individual emitting units that are casually linked in a emission site at a large distance from the central engine. Such a scenario is in natural consistency with the internal-collision-induced magnetic reconnection and turbulence framework, which invokes many mini-jets due to local magnetic reconnection that constantly appear and disappear in a global magnetically-dominated jet. © 2023, CC BY.

关键词： Gamma radiography

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Selective electrocatalytic hydrogenation of bio-oil to oxygenated chemicals via suppression of deoxygenation

Research Square

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research Square 2021年

作者： Peng, Tao Zhuang, Taotao Yan, Yu Qian, Jin Dick, Graham de Bueren, Jean Behaghel Hung, Sung-Fu Wang, Ziyun Wicks, Joshua De Arquer, F. Pelayo Garcia Abed, Jehad Wang, Ning Rasouli, Armin Lee, Geonhui Wang, Miao He, Daping Wang, Zhe Liang, Zhixiu Song, Liang Wang, Xue Chen, Bin Ozden, Adnan Lum, Yanwei Leow, Wan Ru Luo, Mingchuan Meira, Déboa Ip, Alexander Luterbacher, Jeremy Zhao, Wei Sargent, Edward University of Toronto Canada Lawrence Berkeley National Laboratory United States Ecole Polytechnique Fédérale de Lausanne Switzerland Shenzhen University China Hubei Engineering Research Center of RF-Microwave Technology and Application Wuhan University of Technology China Wuhan University of Technology China Brookhaven National Laboratory United States Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada Canadian Light Source Canada EPFL Switzerland

Catalytic hydrogenation of bio-oil provides an avenue to produce renewable chemicals. To this end, electrocatalytic hydrogenation is especially interesting when powered using low-carbon electricity;however, it has to date lacked the needed selectivity: when hydrogenating bio-oil to oxygenated hydrocarbons, for example, it reduces the desired oxygenated groups (-OH and -OCH3). Here we report that Rh and Au modulate electronic structure of Pt and steer intermediate energetics to favor the hydrogenation while suppressing deoxygenation using computational studies and in-situ spectroscopies. PtRhAu catalysts achieve a record 47% faradaic efficiency (FE) and a partial current density (Jp) of 28 mA·cm-2 toward oxygenated 2-methoxycyclohexanol from lignin-derived guaiacol under room temperature and ambient pressure, representing 1.5x FE and 3.5x Jp increases compared to the best prior reports. We further demonstrate an integrated lignin biorefinery where wood-derived lignin oils are selectively hydrogenated and funneled to the oxygenated 2-methoxy-4-propylcyclohexanol using PtRhAu catalysts. © 2021, CC BY.

关键词： Lignin

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Enhanced gear fault diagnosis via heterodyne downconversion: Theoretical verification and optimized ultrasonic signal acquisition

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Measurement 2025年 256卷

作者： Jingchao Zhang Shaoqun Zhang Yixue Dong Chen Li Guoqian Jiang Yingwei Li Xiaoli Li School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao 066004 China Intelligent Control System R&D Department Computer Application Technology Research Institute China Ordnance Industry Beijing 100089 China School of Electrical Engineering Yanshan University Qinhuangdao 066004 China School of Automation Science and Engineering South China University of Technology Guangzhou 510641 China Pazhou Lab Guangzhou 510330 China

Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its practical application is constrained by the requirement for extremely high sampling rates. Heterodyne downconversion offers a potential solution by translating ultrasonic spectral components to lower frequencies, though its effectiveness in preserving diagnostic features remains insufficiently validated. This study conducts a theoretical analysis of the influence of heterodyne downconversion on ultrasonic signal characteristics and proposes an optimized heterodyne-based ultrasonic acquisition system with enhanced charge amplification and frequency conversion circuits. Experimental evaluations using a gear fault test platform demonstrate that the downconverted signals preserve the envelope spectral features of the original ultrasonic signals. Furthermore, comparative analyses indicate that the proposed method achieves superior fault detection sensitivity compared to conventional vibration-based techniques, particularly under high rotational speeds. These findings validate the feasibility and diagnostic advantages of the proposed heterodyne-based approach for efficient and accurate ultrasonic condition monitoring.

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