检索结果-内蒙古大学图书馆

On bubble sizing in water by ultrasound

学校读者我要写书评

暂无评论

International Journal of Interactive Mobile Technologies

International Journal of Interactive Mobile Technologies 2017年第2期11卷 128-138页

作者： Hussein, W.B. Essmat, S.A. Yoma, Nestor B. Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Electrical Engineering Universidad de Chile Santiago Chile

Classifying bubbles in liquids is a crucial problem that is demanded within multiple fields. This paper discusses a new method for classifying bubble sizes in non-contact and inexpensive approach using ultrasound analysis. Exploiting the principle of buoyancy, free rising bubbles with larger volumes elevate faster to the surface compared to the smaller ones, given that they have the same densities. An envelope detector is proposed which tracks the changes in the ultrasound signals reflected by the bubbles when they cross the ultrasound field. These changes in the reflected signals are distinctive for the sizes under consideration. Relevant spectral and linear predictive coding features that represent the distinct characteristics are extracted. These features are fed to a feed-forward artificial neural network to successfully classify air bubbles according to their sizes with an accuracy of 98.8%. This method provides promising applications to be implemented in industrial, biomedical and environmental fields.

关键词： Neural networks

Fs laser inscription of FBGs in silicon core silica clad optical fibres 5

学校读者我要写书评

暂无评论

Fs laser inscription of FBGs in silicon core silica clad opt...

5th Workshop on Specialty Optical Fibers and Their Applications, WSOF 2017

作者： Theodosiou, Antreas Fokine, Michael Song, Seunghan Hawkins, Thomas Ballato, John Gibson, Ursula J. Kalli, Kyriacos Cyprus University of Technology Department of Electrical Engineering Computer Engineering and Informatics Limassol3036 Cyprus Department of Applied Physics KTH Royal Institute of Technology Stockholm100-44 Sweden Department of Physics Norwegian University of Science and Technology TrondheimN-7491 Norway Center for Optical Materials Science and Engineering Technologies COMSET Clemson University ClemsonSC29634 United States

We present results on fs laser inscription of Bragg gratings in Si-core fibres throughmodification of the silicon-glass interface;this results in stress modulation with indications ofquasi-periodic stress relief in th... 详细信息

关键词： Stress relief

Correction: Variability of temperature measurements recorded by a wearable device by biological sex

学校读者我要写书评

暂无评论

Biology of sex differences 2023年第1期14卷 82页

作者： Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun K Viswanath Wendy Hartogensis Stephan Dilchert Frederick M Hecht Anoushka Chowdhary Claudine Anglo Leena Pandya Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E Mason Benjamin L Smarr Department of Biomedical Informatics UC San Diego Health University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. Bioinformatics and Systems Biology University of California San Diego San Diego CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Osher Center for Integrative Health University of California San Francisco San Francisco CA USA. Department of Management Zicklin School of Business Baruch College The City University of New York New York NY USA. San Diego Supercomputer Center University of California San Diego San Diego CA USA. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. bsmarr@eng.ucsd.edu. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. bsmarr@eng.ucsd.edu.

Fermion-boson many-body interplay in a frustrated kagome paramagnet

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Yin, J.-X. Shumiya, Nana Mardanya, Sougata Wang, Qi Zhang, S.S. Tien, Hung-Ju Multer, Daniel Jiang, Yuxiao Cheng, Guangming Yao, Nan Wu, Shangfei Wu, Desheng Deng, Liangzi Ye, Zhipeng He, Rui Chang, Guoqing Liu, Zhonghao Jiang, Kun Wang, Ziqiang Neupert, Titus Agarwal, Amit Chang, Tay-Rong Chu, Ching-Wu Lei, Hechang Zahid Hasan, M. Department of Physics Princeton University PrincetonNJ United States Department of Physics National Cheng Kung University Tainan Taiwan Department of Physics Beijing Key Laboratory of Opto-electronic Functional Materials&Micro-nano Devices Renmin University of China Beijing China Princeton University PrincetonNJ United States Institute of Physics Chinese Academy of Sciences Beijing China Department of Physics Texas Center for Superconductivity University of Houston HoustonTX United States Department of Electrical and Computer Engineering Texas Tech University LubbockTX United States State Key Laboratory of Functional Materials for Informatics Center for Excellence in Superconducting Electronics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Department of Physics Boston College Chestnut HillMA United States Department of Physics University of Zurich Winterthurerstrasse 190 Zurich Switzerland Department of Physics Indian Institute of Technology Kanpur Kanpur India Lawrence Berkeley National Laboratory BerkeleyCA United States

Kagome-net, appearing in areas of fundamental physics, materials, photonic and cold-atom systems, hosts frustrated fermionic and bosonic excitations1-3. However, it is extremely rare to find a system to study both fermionic and bosonic modes to gain insights into their many-body interplay. Here we use state-of-the-art scanning tunneling microscopy and spectroscopy to discover unusual electronic coupling to flat-band phonons in a layered kagome paramagnet. Our results reveal the kagome structure with unprecedented atomic resolution and observe the striking bosonic mode interacting with dispersive kagome electrons near the Fermi surface. At this mode’s energy, the fermionic quasi-particle dispersion exhibits a pronounced renormalization, signaling a giant coupling to bosons. Through a combination of self-energy analysis, first-principles calculation, and a lattice vibration model, we present evidence that this mode arises from the geometrically frustrated phonon flat-band, which is the lattice analog of kagome electron flat-band. Our findings provide the first example of kagome bosonic mode (flat-band phonon) in electronic excitations and its strong interaction with fermionic degrees of freedom in kagome-net materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Phonons

Reliable probabilistic gossip over large-scale random topologies

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Hu, Ruijing Jehl, Leander College of Informatics Huazhong Agricultural University China Department of Electrical Engineering and Computer Science University of Stavanger Norway

This paper studies reliability of probabilistic neighbor-aware gossip algorithms over three well-known large-scale random topologies, namely Bernoulli (or Erdos-Rényi) graph, the random geometric graph, and the scale-free graph. We propose a new and simple algorithm which ensures higher reliability at lower message complexity than the three families of gossip algorithms over every topology in our study. We also present a uniform approach to model the reliability of probabilistic gossip algorithms in the different random graphs, whose properties, in fact, are quite different. In our model a forwarding probability is derived with consideration of parameters in gossip algorithms and graph properties. Our simulations show that our model gives a reasonable prediction of the trade-off between reliability and message complexity for all probabilistic neighbor-aware gossip algorithms in various random networks. Therefore, it allows to fine-tune the input parameters in the gossip protocols to achieve a desirable reliability with tolerable message complexity. Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

A systematic study of cross-project defect prediction with meta-learning

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Porto, Faimison Minku, Leandro Mendes, Emilia Simao, Adenilso University of Sao Paulo Sao Carlos Brazil Department of Informatics University of Leicester University Road LeicesterLE1 7RH United Kingdom Department of Computer Science and Engineering Faculty of Computing Blekinge Institute of Technology SE-37179 Karlskrona Sweden research unit Faculty of Information Technology and Electrical Engineering University of Oulu P.O. Box 3000 90014 Finland

The prediction of defects in a target project based on data from external projects is called Cross-Project Defect Prediction (CPDP). Several methods have been proposed to improve the predictive performance of CPDP models. However, there is a lack of comparison among state-of-the-art methods. Moreover, previous work has shown that the most suitable method for a project can vary according to the project being predicted. This makes the choice of which method to use difficult. We provide an extensive experimental comparison of 31 CPDP methods derived from state-of-the-art approaches, applied to 47 versions of 15 open source software projects. Four methods stood out as presenting the best performances across datasets. However, the most suitable among these methods still varies according to the project being predicted. Therefore, we propose and evaluate a meta-learning solution designed to automatically select and recommend the most suitable CPDP method for a project. Our results show that the meta-learning solution is able to learn from previous experiences and recommend suitable methods dynamically. When compared to the base methods, however, the proposed solution presented minor difference of performance. These results provide valuable knowledge about the possibilities and limitations of a meta-learning solution applied for CPDP. Copyright © 2018, The Authors. All rights reserved.

关键词： Defects

Deep Information Theoretic Registration

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Sedghi, Alireza Luo, Jie Mehrtash, Alireza Pieper, Steve Tempany, Clare M. Kapur, Tina Mousavi, Parvin Wells, William M. Medical Informatics Laboratory Queen’s University Kingston Canada Radiology Department Brigham and Womens Hospital Harvard Medical School Boston United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge United States Graduate School of Frontier Sciences University of Tokyo Japan Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

This paper establishes an information theoretic framework for deep metric based image registration techniques. We show an exact equivalence between maximum profile likelihood and minimization of joint entropy, an important early information theoretic registration method. We further derive deep classifier-based metrics that can be used with iterated maximum likelihood to achieve Deep Information Theoretic Registration on patches rather than pixels. This alleviates a major shortcoming of previous information theoretic registration approaches, namely the implicit pixel-wise independence assumptions. Our proposed approach does not require well-registered training data;this brings previous fully supervised deep metric registration approaches to the realm of weak supervision. We evaluate our approach on several image registration tasks and show significantly better performance compared to mutual information, specifically when images have substantially different contrasts. This work enables general-purpose registration in applications where current methods are not successful. Copyright © 2018, The Authors. All rights reserved.

关键词： Image registration

Correction: Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

学校读者我要写书评

暂无评论

BMC genomics 2023年第1期24卷 388页

作者： Elena V Demidova Ilya G Serebriiskii Ramilia Vlasenkova Simon Kelow Mark D Andrake Tiffiney R Hartman Tatiana Kent James Virtucio Gail L Rosen Richard T Pomerantz Roland L Dunbrack Erica A Golemis Michael J Hall David Y T Chen Mary B Daly Sanjeevani Arora Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Kazan Federal University Kazan 420008 Russia. Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia PA 19111 USA. Department of Biochemistry and Molecular Biophysics University of Pennsylvania Philadelphia PA 19104 USA. Arcadia University Glenside PA USA. Department of Biochemistry & Molecular Biology Sidney Kimmel Cancer Center Thomas Jeferson University Philadelphia PA 19107 USA. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University Philadelphia PA 19104 USA. Department of Cancer and Cellular Biology Lewis Katz School of Medicine Philadelphia PA 19140 USA. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Department of Surgical Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu.

Clustering dycom an online cross-company software effort estimation study 13

学校读者我要写书评

暂无评论

Clustering dycom an online cross-company software effort est...

13th International Conference on Predictive Models and Data Analytics in Software engineering, PROMISE 2017

作者： Minku, Leandro L. Hou, Siqing Department of Informatics University of Leicester University Road LeicesterLE1 7RH United Kingdom Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

ISBN: (纸本)9781450353052

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom's predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom's CC subsets. © 2017 Association for Computing Machinery. All rights reserved.

关键词： Set theory