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International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)

作者： Fumiyoshi Kuwashima Mona Jarrahi Semih Cakmakyapan Kenji Wada Masanobu Haraguchi Yuki Kawakami Takeshi Moriyasu Osamu Morikawa Kazuyoshi Kurihara Hideaki Kitahara Takashi Furuya Makoto Nakajima Masahiko Tani Dep. of Electr. and Electron. Eng. Fukui University of Technology Fukui Japan Electrical and Computer Eng. UCLA Los Angeles CA USA Dep. of Physics and Electronics Osaka Metropolitan University Osaka Japan Industrial and Social Science Grad. Sch. of Tech. Tokushima Univ. Tokushima Japan Dep. of Elec. and Inf. Eng. Natl. Inst. of Tech. Fukui Col. Fukui Japan Faculty of Engineering University of Fukui Fukui Japan Chair of Liberal Arts Japan Coast Guard Academy Kure Japan School of Education University. of Fukui Fukui Japan FIR-UF University of Fukui Fukui Japan Inst. of Laser Engineering Osaka Univ. Osaka Japan

We apply the chaos supremacy of laser chaos system to the high stabilization of THz waves. Stability was demonstrated by down converting the optical beats between laser longitudinal modes in the THz domain to the radio frequency domain and comparing a conventional free running CW laser diode to a chaotically oscillating laser diode. Because of their exceptional stability, chaotically oscillating lasers are attractive options for optical pump sources in terahertz time-domain spectroscopy systems.

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Opportunities for machine learning in scientific discovery

arXiv

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arXiv 2024年

作者： Vinuesa, Ricardo Rabault, Jean Azizpour, Hossein Bauer, Stefan Brunton, Bingni W. Elofsson, Arne Jarlebring, Elias Kjellström, Hedvig Markidis, Stefano Marlevi, David Cinnella, Paola Brunton, Steven L. FLOW Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden Stockholm Sweden IT Department Norwegian Meteorological Institute Oslo0313 Norway KTH Royal Institute of Technology Stockholm Sweden TUM School of Computation Information and Technology Technical University Munich Munich Germany Helmholtz AI Helmholtz Center Munich Munich Germany Department of Biology University of Washington SeattleWA98195 United States Dept. of Biochemistry and Biophysics Science for Life Laboratory Stockholm University Solna171 21 Sweden Dept. Mathematics KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Dept. Molecular Medicine and Surgery Karolinska Institutet Stockholm171 77 Sweden Inst. for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA02139 United States Institut Jean le Rond D’Alembert Sorbonne Université France Department of Mechanical Engineering University of Washington SeattleWA98195 United States

Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields. However, our ability to leverage ML methods for scientific discovery, i.e. to obtain fundamental and formalized knowledge about natural processes, is still in its infancy. In this review, we explore how the scientific community can increasingly leverage ML techniques to achieve scientific discoveries. We observe that the applicability and opportunity of ML depends strongly on the nature of the problem domain, and whether we have full (e.g., turbulence), partial (e.g., computational biochemistry), or no (e.g., neuroscience) a-priori knowledge about the governing equations and physical properties of the system. Although challenges remain, principled use of ML is opening up new avenues for fundamental scientific discoveries. Throughout these diverse fields, there is a theme that ML is enabling researchers to embrace complexity in observational data that was previously intractable to classic analysis and numerical investigations. © 2024, CC BY.

关键词： Deep learning

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Stochastic-deterministic multipath model for time-delay estimation 20

Stochastic-deterministic multipath model for time-delay esti...

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20th International ITG Workshop on Smart Antennas, WSA 2016

作者： Wendler, Friederike Antreich, Felix Nossek, Josef A. Lee Swindlehurst, A. Inst. for Communications and Navigation Wessling 82234 Germany Germany Dept. of Electrical Engineering and Computer Science University of California Irvine CA United States

ISBN: (纸本)9783800741779

Line-of-sight (LOS) delay estimation in multipath scenarios is a central problem in global navigation satellite systems (GNSS). Deterministic channel models can be used to describe the multipath environment, but this usually requires the estimation of several nuisance parameters. In order to avoid this effort, stochastic channel models can be used. In this case the multipath statistics have to be estimated. The correlated path (CP) model combines the two approaches, dividing the multipath signal into a part correlated with the LOS signal, and the rest as uncorrelated multipath interference. In an earlier version of the CP model the multipath interference was modeled as temporally white noise. This is not an accurate assumption however, especially in the case when a bank of correlators is used for signal compression. In this paper we derive the temporal covariance matrix of the multipath interference and show how the temporal multipath correlation can incorporated into the maximum-likelihood (ML) estimator of the CP model. Simulation results show that this new approach leads to better LOS time-delay estimation performance. © VDE VERLAG GMBH · Berlin · Offenbach, Germany.

关键词： Covariance matrix

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Multi-Center CNN-Based Spine Segmentation from T2W MRI Using Small Amounts of Data

Multi-Center CNN-Based Spine Segmentation from T2W MRI Using...

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IEEE International Symposium on Biomedical Imaging

作者： Satheshkumar Kaliyugarasan Magnhild H. Dagestad Evin I. Papalini Erling Andersen John-Anker Zwart Helena Brisby Hanna Hebelka Ansgar Espeland Kerstin M. Lagerstrand Alexander S. Lundervold Dept. of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Dept. of Radiology MMIV Haukeland University Hospital Norway Dept. of Radiology Haukeland University Hospital Norway Dept. of Clinical Medicine University of Bergen Norway Dept. of Medical Physics and Biomedical Engineering Sahlgrenska University Hospital Sweden Inst. of Clinical Sciences The Sahlgrenska Academy at the University of Gothenburg Sweden Dept. of Clinical Engineering Haukeland University Hospital Norway Faculty of Medicine University of Oslo Dept. of Research and Innovation Division of Clinical Neuroscience Oslo University Hospital Dept. of Orthopaedics Sahlgrenska University Hospital Sweden Dept. of Radiology Sahlgrenska University Hospital Gothenburg Sweden

Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, assessing performance and generalizability by external validation. Our study used 15 scans from one site (Haukeland University Hospital, HUH) and 10 scans from another (Sahlgrenska University Hospital, SUH). MRI experts manually delineated the vertebral bodies Th12-L5 on all the HUH data and a subset of six scans from SUH. We trained multiple convolutional neural networks, assessing the performance in an experimental design tailored to small-data contexts and also on external data. Our best model achieved a mean Dice score of 0.899. This is comparable to results in the literature, but our system required much less training data. 1 .

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A Whole Slide Ki-67 Proliferation Analysis System for Breast Carcinoma 12

A Whole Slide Ki-67 Proliferation Analysis System for Breast...

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12th International Conference on Ubi-Media Computing, Ubi-Media 2019

作者： Ko, Chien-Chuan Lin, Chun-Hung Chuang, Chih-Hung Chang, Chuan-Yu Chang, Shih-Hao Jiang, Ji-Han Dept. of Computer Science and Information Engineering National ChiaYi University ChaiYi City Taiwan Inst. of General Surgery Dalin TsuChi Hospital ChiaYi County Taiwan Dept. of Computer Science and Information Engineering National YunLin University of Science and Technology YunLin County Taiwan Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Dept. of Computer Science and Information Engineering National Formosa University YunLin County Taiwan

ISBN: (纸本)9781728128207

The expression of Ki-67 with IHC stain has been utilized to assess the prognosis of breast cancer, and the degree of cellular differentiation and proliferation rate. Recently, some researchers utilize the index to predict metastasis of breast carcinoma. In traditional pathological screening, manual assessment of Ki-67 proliferative index may be limited by manual evaluation from different pathologists. Especially, inconsistent biopsy staining would affect the quantitation of Ki-67 proliferation so that developing an automatic system to assess Ki-67 proliferation index poses a big challenge. The goal of this paper is to propose an automatic analysis system to evaluate the degrees of Ki-67 proliferation on IHC stained cells of breast tissue using image processing and machine intelligence techniques. The proposed system not only can assist physicians diagnose, but also provides important information of treatment and prognosis. In order to validate the evaluation performance, we compared with visual assessments by a pathologist and the ImmnuoRatio (i.e., a web-based evaluation system in Ki-67 expression) developed by Vilppu J Tuominen et al.[1] via a number of Ki-67 stained samples for patients with breast carcinoma. Experimental results also demonstrate that the proposed system can automatically, accurately and reliably assess the Ki-67 proliferation index on the breast tissue images with a precision of around 87.37%. However, the accuracy evaluating with ImmunoRatio only can reach 75.82% with the same samples. Moreover, our proposed system also provides various interaction functions including browsing, navigation, and quantitative analyses for pathologists who evaluate the expression of the Ki-67 proliferation. © 2019 IEEE.

关键词： Support vector machines

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ApproxDNN: Incentivizing DNN Approximation in Cloud

ApproxDNN: Incentivizing DNN Approximation in Cloud

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Seyed Morteza Nabavinejad Lena Mashayekhy Sherief Reda School of Computer Science Inst. for Research in Fundamental Sciences (IPM) Tehran Iran Dept. Computer and Information Sciences University of Delaware Newark DE School of Engineering Brown University Providence RI

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

Service providers leverage discounted prices of reserved inst.nces offered by cloud providers to amortize their operational costs. They reserve a certain number of inst.nces to cover a significant portion of their computing resource requirements, and further employ on-demand inst.nces to cover remaining requirements not satisfied by the reserved inst.nces. Because of the higher price of on-demand inst.nces, service providers seek to lower their usage to minimize operational costs. In this work, we propose ApproxDNN approach for Machine Learning as a Service to reduce operational costs of service providers by incentivizing approximate results, based on the capabilities of cutting-edge GPUs and a discounted pricing model. When the deadlines of jobs submitted by users are very tight, a service provider might not be able to execute all of them on reserved inst.nces under the default precision. In such cases, Ap- proxDNN leverages the reduced-precision inst.uctions to reduce the execution time of the jobs with slight reduction in their final accuracy, and consequently, to minimize the employment of on- demand inst.nces. To incentivize users to accept the approximate results of reduced-precision inst.uctions, ApproxDNN offers them a discounted price for the service based on a newly designed pricing model. Our proposed pricing model of ApproxDNN guarantees lower or equal cost for service providers compared to the conventional method that solely depends on employment of on-demand inst.nces in case of the reserved inst.nce shortage. We employ real-world traces to conduct an extensive set of experiments and evaluate the performance of our proposed approach. The results show that ApproxDNN reduces the cost of service providers by 18%, while never exceeding the cost of the conventional method and slightly affecting the accuracy by 0.14%.

关键词： Runtime Pricing Cloud computing Employment Graphics processing units Optimization Machine learning

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TKC: Mining Top-K Cross-Level High Utility Itemsets

TKC: Mining Top-K Cross-Level High Utility Itemsets

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Mourad Nouioua Ying Wang Philippe Fournier-Viger Jerry Chun-Wei Lin Jimmy Ming-Tai Wu University of Bordj Bou Arreridj Algeria School of Computer Sciences Harbin Inst. of Technology (Shenzhen) Shenzhen China School of Humanities and Social Sciences Harbin Inst. of Technology (Shenzhen) Shenzhen China Western Norway Univ. of Applied Sciences (HVL) Bergen Norway School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728190129

ISBN: (纸本)9781728190136

High utility itemset mining is a well-studied data mining task for analyzing customer transactions. The goal is to find all high utility itemsets, that is items purchased together that generate a profit equal to or greater than a user-defined minimum utility threshold. However, a limitation of traditional high utility itemset mining algorithms is that item categories (e.g. drinks, dairy products) are ignored. Recently, two algorithms were designed to find multi-level and cross-level high utility itemsets to reveal relationships between items and/or categories of items. This is achieved by considering a product taxonomy, where items are organized into a hierarchy. Though these algorithms can reveal interesting patterns, a problem is that setting the minimum utility threshold is not intuitive and greatly influences the number of patterns found and the algorithms' performance. If the user sets the threshold too low, a huge number of patterns is found and runtimes can be very long, while if the threshold is set too high, few patterns are found. Hence, a user often have to run an algorithm numerous times to find an appropriate threshold value to obtain just enough patterns. This paper addresses this issue by presenting a novel algorithm called TKC (Top-K Cross-level high utility itemset miner), which let the user directly set the number of patterns k to be discovered. TKC performs a depth-first search and include search space pruning techniques and an optimization to enhance its performance. Experiments were done on retail data with taxonomy information. Results indicate that the algorithm is efficient and the optimization improves its performance.

关键词： Runtime Itemsets Social networking (online) Taxonomy Data mining Task analysis Optimization

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Multiple Pseudo-Local Feedbacks for Controlling High-Order Oscillatory Objects 15

Multiple Pseudo-Local Feedbacks for Controlling High-Order O...

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15th International Scientific-Technical Conference on Actual Problems of Electronic inst.ument engineering, APEIE 2021

作者： Zhmud, V.A. Nosek, J. Mansurova, M.E. Semibalamut, V.M. Fomin, Yu. N. Stukach, O.V. Novosibirsk State Technical University Institute of Laser Physics Sb Ras Dept. of Automation Novosibirsk Russia Technical University of Liberec Inst. of Mechatronics and Computer Engineering Liberec Czech Republic Al-Farabi Kazakh National University Department of Artificial Intelligence and Big Data Almaty Kazakhstan Unified Geophysical Service of the Russian Academy of Sciences Siberian Branch of the Federal State Budgetary Institution of Science of the Federal Research Center Novosibirsk Russia Novosibirsk State Technical University Higher School of Economics Dept. of Information Defense Moscow Russia

ISBN: (纸本)9781665434089

Development of the Internet of things (IoT) technologies arises many additional problems as energy harvesting. In particular, a long-Time controlling object needs a device without an external power supply. The control of technical objects with a high order of mathematical model has a large difficulty if a feature of the object is a high tendency to oscillations due to the ratio of the coefficients in the model. In particular, the tendency to oscillations occurs if the object model has complex roots, or if the model has a transport delay. One such example is a model that consists of two oscillatory objects and a delay loop. Widespread controlling such an objects using a PID controller is very difficult since in the transient process in the resulting system there is a large overshoot and many oscillations. If such an object could be stabilized with additional local feedbacks, the control task would be greatly simplified, but the object does not have corresponding outputs and inputs, therefore local feedbacks cannot be used. The pseudo-local feedback approach assumes the use of virtual fragments of the object's mathematical model in the controller to form such connections that the system perceives as local ones. This paper proposed the use of several pseudo-local feedbacks to control an object with elements that increased its tendency to oscillate. The effectiveness of the proposed approach is confirmed by modeling. © 2021 IEEE.

关键词： Feedback

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Integration Design and Process of 3-D Heterogeneous 6T SRAM with Double Layer Transferred Ge/2Si CFET and IGZO Pass Gates for 42% Reduced Cell Size

Integration Design and Process of 3-D Heterogeneous 6T SRAM ...

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International Electron Devices Meeting (IEDM)

作者： X.-R. Yu M.-H. Chuang S.-W. Chang W.-H. Chang T.-C. Hong C.-H. Chiang W.-H. Lu C.-Y. Yang W.-J. Chen J.-H. Lin P.-H. Wu T.-C. Sun S. Kola Y.-S. Yang Yun Da P.-J. Sung C.-T. Wu T.-C. Cho G.-L. Luo K.-H. Kao M.-H. Chiang W. C.-Y. Ma C.-J. Su T.-S. Chao T. Maeda S. Samukawa Y. Li Y.-J. Lee W.-F. Wu J.-H. Tarng Y.-H. Wang Dept. of Electrical Engineering National Cheng Kung University Tainan Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Taiwan Semiconductor Research Institute (TSRI) Hsinchu Taiwan National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Japan Dept. of Electrophysics National Yang Ming Chiao Tung University Hsinchu Taiwan Dept. of Electrical Engineering Natl. Sun Yat-Sen University Kaohsiung Taiwan Inst. of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan Dept. of Electrical Engineering National University of Kaohsiung Kaohsiung

ISBN: (纸本)9781665489607

In this work, we propose an advanced 3-D heterogeneous 6T SRAM with a newly designed hetero-integration method. CFET inverters and IGZO pass gates are vertically stacked within a 2T footprint area. The Low-Temperature Hetero-Layers Bonding Technique (LT-HBT) process is utilized successfully to fabricate single crystalline heterogeneous Double Layer Transferred (DLT) Ge/2Si CFET-OI on an 8-inch full wafer. Furthermore, an IGZO nFET is deposited and treated as a pass gate (PG) to realize a 6T SRAM operation. The hetero-integration of IGZO PG and self-align DLT Ge/2Si CFET inverters showed improved Read Static Noise Margin (RSNM) and stand-by leakage power. The state-of-the-art 3-D heterogeneous 6T SRAM leads to 42% area reduction.

关键词： Wafer bonding Surface cleaning Three-dimensional displays Microprocessors Stacking Random access memory computer architecture

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An Automated Biometric Identification System Using CNN-Based Palm Vein Recognition

An Automated Biometric Identification System Using CNN-Based...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Sin-Ye Jhong Po-Yen Tseng Natnuntnita Siriphockpirom Chih-Hsien Hsia Ming-Shih Huang Kai-Lung Hua Yung-Yao Chen Dept. of Engineering Science National Cheng Kung University Tainan Taiwan Inst. of Automation Technology National Taipei University of Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Computer Science and Information Engineering National Ilan University Ilan Taiwan AI Fab Inc. Taipei Taiwan Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9781728198231

ISBN: (纸本)9781728198248

Recently, automated biometric identification system (ABIS) has wide applications involving automatic identification and data capture (AIDC), which includes automatic security checking, verifying personal identity to prevent information disclosure or identity fraud, and so on. With the advancement of biotechnology, identification systems based on biometrics have emerged in the market. These systems require high accuracy and ease of use. Palm vein identification is a type of biometric that identifies palm vein features. Compared with other features, palm vein recognition provides accurate results and has received considerable attention. We developed a novel high-performance and noncontact palm vein recognition system by using high-performance adaptive background filtering to obtain palm vein images of the region of interest. We then used a modified convolutional neural network to determine the best recognition model through training and testing. Finally, the developed system was implemented on the low-level embedded Raspberry Pi platform with cloud computing technology. The results showed that the system can achieve an accuracy of 96.54%.

关键词： Veins Feature extraction Convolution Cameras Electronic mail Filtering Convolutional neural networks

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