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2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence, ICUSAI 2019

作者： Li, Wen-Yong Shen, Yang Wang, Du-Jin Yang, Zhan-Kui Yang, Xin-Ting National Engineering Research Center for Information Technology in Agriculture Dept. Intelligent System Beijing China Tianjin University of Science Technology Dept. Electronic Information and Automation Tianjin China Beijing University of Technology Dept. Computer Science Beijing China

ISBN: (纸本)9781728158594

Automatic and objective body condition scoring of dairy cow has aroused great researches as a method to assist management of nutritional programs in dairy herds. This study presents a 3-dimensional surface fitting approach that provides a structure description of dairy cow's body for BCS estimation. a 3-dimensional algorithm that provides a topographical understanding of the cow's body to estimate BCS. In this study, the hypothesis tested was that the body contour of a fatter dairy cow is plumper than that of a thin dairy cow and, therefore, may better fit a paraboloid surface. dept. image processing and fitting model were investigated and consisted of three stages: (1) object location and separation of individual cow;(2) image surface fitting;and (3) parameters determination in body condition scoring (BCS) model. Compared with the previous studies, the novelty in this paper was completing the full-automation of a BCS system. The proposed model was trained and tested with the BCS value obtained manually. Pearson correlation between the proposed BCS and the manual BCS was 0.84 for the test data set. © 2019 IEEE.

关键词： computer vision

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Improving the Interference Tolerant Noise Immunity of Ambulatory Telemetry ECG Diagnostics Systems

Improving the Interference Tolerant Noise Immunity of Ambula...

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2019 International Seminar on Electron Devices Design and Production, SED 2019

作者： Kenzhaliyev, Bagdaulet Ozhikenov, Kassymbek Ozhikenova, Aiman Bodin, Oleg Krivonogov, Leonid Papshev, Dmitry Dept. of Science Satbayev University Almaty Kazakhstan Dept. of Robotics and Engineering Tools of Automation Satbayev University Almaty Kazakhstan Dept. of Public Health Kazakh-Russian Medical University Almaty Kazakhstan Dept. of Medical Cybernetics and Computer Science Penza State University Penza Russia

ISBN: (纸本)9781538665244

The article is devoted to the issues of improving interference tolerant in the creation of ambulatory telemetry system for ECG diagnostics. The conditions of functioning of such systems and problems on the way to increase noise immunity are considered. The generalized structure of the of ambulatory telemetry system for ECG diagnostics is developed. The concept of increasing the reliability of automatic conclusions in ambulatory of ambulatory telemetry system for ECG diagnostics is formed. The estimation of misleading influence of noise in such systems is carried out. Decision rule that ensures the development of a control module of interference in ECG signal is obtained. Two types of low-frequency noise estimates are investigated. The method of noise control in the registered ECS and the structure of the module noise control are developed. © 2019 IEEE.

关键词： Electrocardiograms

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Energy-Optimal Configurations for Single-Node HPC Applications

Energy-Optimal Configurations for Single-Node HPC Applicatio...

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2019 International Conference on High Performance Computing and Simulation, HPCS 2019

作者： Silva, Vitor R. G. Furtunato, Alex F. A. Georgiou, Kyriakos Sakuyama, Carlos A. V. Eder, Kerstin Xavier-De-Souza, Samuel University of Mons Dept. of Electronics and Microeletronics Mons Belgium Instituto Federal Do Rio Grande Do Norte Directorate of Information Technology Natal Brazil University of Bristol Dept. of Computer Science Bristol United Kingdom Universidade Fedral Do Rio Grande Do Norte Dept. of Computer Engineering and Automation Natal Brazil

ISBN: (纸本)9781728144849

Energy efficiency is a growing concern for modern computing, especially for HPC due to operational costs and the environmental impact, considering that processors have an important role in this energy consumption. In this work, we propose a methodology to find energy-optimal frequency and number of active cores to run single-node HPC applications using an application-agnostic power model of the architecture and an architecture-aware performance model of the application. We characterize the application performance using machine learning, specifically the 'Support Vector Regression' algorithm. Besides that, the power consumption is estimated by modeling CMOS dynamic and static power without knowledge of the application. So, The energy-optimal configuration is estimated by minimizing the product these two models outcomes, the power model and the performance model. Then, the final model can be used to find better frequency and number of cores to aim energy efficiency application execution. Results were obtained for four PARSEC applications and, with five different inputs shows that the proposed approach used substantially less energy when compared to the DVFS governor, in best cases and worst cases. © 2019 IEEE.

关键词： Energy efficiency

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Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation

arXiv

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arXiv 2020年

作者： Liu, Yanqi Calderoni, Giuseppe Bahar, R. Iris Dept.of Computer Science Brown University ProvidenceRI United States Dept.of Automation and Informatics Politecnico di Torino Torino Italy School of Engineering Dept. of Computer Science Brown University ProvidenceRI United States

Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based approaches have high computational requirements, making them unsuitable for real-time applications with tight energy constraints. In this paper, we investigate 6 degree-of-freedom (6DoF) pose estimation for robot manipulation using this method, which uses rendering combined with sequential Monte-Carlo sampling. While potentially very accurate, the significant computational complexity of the algorithm makes it less attractive for mobile robots, where runtime and energy consumption are tightly constrained. To address these challenges, we develop a novel hardware implementation of Monte-Carlo sampling on an FPGA with lower computational complexity and memory usage, while achieving high parallelism and modularization. Our results show 12X–21X improvements in energy efficiency over low-power and high-end GPU implementations, respectively. Moreover, we achieve real time performance without compromising accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Robotics

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Intellectual control system for unmanned energy crop combine 1

Intellectual control system for unmanned energy crop combine

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1st International Conference on Intellectual Systems and Information Technologies, ISIT 2019

作者： Gunchenko, Yurii Shvorov, Sergey Lukin, Volodymyr Mezhuyev, Vitalii Dept. of Mathematical Support of Computer Systems Odessa I.I.Mechnikov National University Odessa Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Malaysia

The purpose of the work is to develop methodical bases for the construction of an intelligent control system (ISC) for unmanned combines (UMs) of energy crops (EC) for biogas plants (BP). In order to achieve this goal, the functional structure of the ISC was substantiated in the work, the method of recognition and determination of the volume of biomass EC in fields with unmanned aerial vehicles (UAVs) was developed, the method of synthesis of compromise-optimal routes of the BP with the minimum length of their routes in the process of biomass collection was proposed. and taking into account passive (unmoving) and active (moving) obstacles. With the help of the proposed ISC, the following tasks are solved: monitoring of the EC cultivation process and the determination of EC volumes based on the use of UAVs;determination of the density of changes in the yield of EC, the coordinates of active and passive obstacles in the way of the UMs movement;distribution of UMs by fields and planning of optimal routes and speed parameters of their movement for EC collection. A prerequisite for efficient harvesting combine driving is the constant control of the technological process of harvesting energy crops. Deviations from the work plan in most cases occur due to malfunction of technical equipment or adverse weather conditions. However, there are situations in which the deviation of the "plan-fact" is influenced by other factors, which may lead to a failure of the plan, and vice versa. Failure to complete the plan may be due to a reduction in the intensity of the work or the assumption by the user of errors when entering the initial data into the system, which reduces the adequacy of the model to the real process. The increase in planned indicators may be due to an increase in the speed of technological operations, which may lead to additional losses of biomass or a decrease in its quality. To solve the tasks listed, the ISC is divided into a subsystem of monitoring, planning and

关键词： Biomass

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Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

Bayesian Neural Networks Uncertainty Quantification with Cub...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

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Radio-Frequency Pseudo-Null Induced by Light in an Ion Trap

arXiv

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arXiv 2025年

作者： Chung, Daun Cha, Yonghwan Shon, Hosung Park, Jeonghyun Lee, Woojun Lee, Kyungmin Cho, Beomgeun Choi, Kwangyeul Kim, Chiyoon Yoo, Seungwoo Kim, Suhan Jeong, Uihwan Kang, Jiyong You, Jaehun Kim, Taehyun Dept. of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

In a linear radio-frequency (rf) ion trap, the rf null is the point of zero electric field in the dynamic trapping potential where the ion motion is approximately harmonic. When displaced from the rf null, the ion is superimposed by fast oscillations known as micromotion, which can be probed through motion-sensitive light-atom interactions. In this work, we report on the emergence of the rf pseudo-null, a locus of points where the ion responds to light as if it were at the true rf null, despite being displaced from it. The phenomenon is fully explained by accounting for the general two-dimensional structure of micromotion and is experimentally verified under various potential configurations, with observations in great agreement with numerical simulations. The rf pseudo-null manifests as a line in a two-dimensional parameter space, determined by the geometry of the incident light and its overlap with the motional structure of the ion. The true rf null occurs uniquely at the concurrent point of the pseudo-null lines induced by different light sources. © 2025, CC BY.

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A Paper Recommendation System Based on User Interest and Citations

A Paper Recommendation System Based on User Interest and Cit...

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International Informatics and Software engineering Conference (UBMYK)

作者： Betül Bulut Buket Kaya Mehmet Kaya Dept. of Computer Engineering Fırat University Elazığ Turkey Dept. of Electronics and Automation Fırat University Elazığ Turkey

ISBN: (纸本)9781728139937

It is difficult for researchers to access useful scientific articles from an excessive number of articles. This difficulty has led to article recommendation systems. Many studies have been conducted on the article recommendation. Article recommendation studies in the literature generally do not take into account the interest of users. The innovation of this study is to propose research articles by using bibliography and creating a user profile. The proposed approach aims to personalize scientific recommendations based on article-bibliographic relationships and user profile. In our study, candidate recommendation articles were clustered by K-Means clustering method according to their bibliographies. In our study, K-NN algorithm is used to determine which cluster is closer to the user's profile. Once the cluster to which the user belongs is determined, other articles in the cluster are recommended to the user. Experimental results show that the method is successful.

关键词： Clustering algorithms Bibliographies Databases Proposals Classification algorithms Clustering methods

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Prostate cancer histopathology with label-free multispectral deep UV microscopy quantifies phenotypes of tumor grade and aggressiveness

arXiv

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arXiv 2021年

作者： Soltani, Soheil Ojaghi, Ashkan Qiao, Hui Kaza, Nischita Li, Xinyang Dai, Qionghai Osunkoya, Adeboye O. Robles, Francisco E. Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Automation Tsinghua University Beijing100084 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30313 United States Department of Pathology and Urology Emory University School of Medicine AtlantaGA30322 United States Winship Cancer Institute Emory University AtlantaGA30322 United States

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. To shed light on this problem, we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease. First, we find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that differentiates critical structures of thin tissue sections with subcellular spatial resolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and inflammation. Further, we show that this phenotypical continuum can be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly, we adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images. Agreement between the virtual H&E images and the gold standard H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis. © 2021, CC BY-NC-ND.

关键词： Diseases

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Prostate cancer histopathology with label-free multispectral deep UV microscopy quantifies phenotypes of tumor grade and aggressiveness

Research Square

引用

Research Square 2021年

作者： Soltani, Soheil Ojaghi, Ashkan Qiao, Hui Kaza, Nischita Li, Xinyang Dai, Qionghai Osunkoya, Adeboye O. Robles, Francisco E. Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Automation Tsinghua University Beijing100084 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30313 United States Departments of Pathology and Urology Emory University School of Medicine AtlantaGA30322 United States Winship Cancer Institute of Emory University AtlantaGA30322 United States

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. To help address this problem, we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease. First, we find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that differentiates critical structures of thin tissue sections with subcellular spatial resolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and inflammation. Further, we show that this phenotypical continuum can be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly, we adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images. Agreement between the virtual H&E images and the gold standard H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis. © 2021, CC BY.

关键词： Tumors

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