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IEEE International Symposium on Biomedical Imaging

作者： Zili Kang Yifan Lv Mengshen He Yiheng Liu Tianming Liu Bao Ge School of Physics & Information Technology Shaanxi Normal University Xi’an China Department of Computer Science and Bioimaging Research Center Cortical Architecture Imaging and Discovery Lab The University of Georgia GA USA

It has been of great interest to study relationship between white matter and gray matter in brain science. Previous studies have found that that the formation of gray matter is closely related to white matter. This paper attempts explore the relationship between cerebral cortex and white matter from the perspective of brain image, which tries to predict fiber connection information by using brain cortical information alone. The extensive experiments on HCP dataset demonstrate that brain cortical surface can be used to predict the presence or absence of fibers between any two cortical regions with about 80% accuracy, furtherly, it can be used to predict the fractional anisotropy (FA) value for each fiber, the experiments show that the FA value can be also predicted with a percentage error of about 20%. These results demonstrate that the brain surface can be used to predict fiber connection information, and there is a close relationship between the gray matter and white matter fiber. This work provides a new perspective for further study of the relationship between cerebral cortex and axonal fiber.

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Low-Cost Method for Effective Conductivity Improvement of Additively Manufactured All-Metal Waveguide Components

Low-Cost Method for Effective Conductivity Improvement of Ad...

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European Conference on Microwave

作者： Jakub Sorocki Ilona Piekarz Michal Baranowski Adam Lamecki Alberto Cattenone Stefania Marconi Gianluca Alaimo Nicolo Delmonte Lorenzo Silvestri Maurizio Bozzi Institute of Electronics AGH University of Krakow Poland Department of Microwave and Antenna Engineering Gdansk University of Technology Poland Department of Civil Engineering and Architecture University of Pavia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

In this paper, a low-cost method of 3D printed all-metal waveguide effective conductivity improvement is proposed and studied. The approach is a combination of internal surface polishing to reduce the roughness followed by coating a high-conductivity layer through electroplating. Both methods allow to reduce total power losses within the waveguide which are impacted by the conductivity of the metal. A set of mm-wave test vehicles was developed in WR-28 geometry (26.5 GHz to 40 GHz) being a straight and twisted transmission line section along with a narrowband filter to experimentally validate the approach. The models were 3D printed using Powder Bed Fusion out of stainless-steel powder, dry polished using glass microbeads, and then coated with copper. Up to 40% power loss reduction was obtained with respect to raw prints proving the performance of the approach.

关键词： Geometry Powders Three-dimensional displays Surface waves Masers Waveguide lasers Conductivity Surface roughness Rough surfaces Microwave filters

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Construction of personalized learning service system based on deep learning and knowledge graph

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Huang, Min Xu, Gaojian Li, Huilin School of Information and Architecture Engineering Anhui Open University Anhui Hefei230022 China School of Information and Computer Anhui Agricultural University Anhui Hefei230036 China Information Technology Center Anhui Ningguo Senior High School of China Anhui Xuancheng242399 China

With the development of information technology, it is difficult for people to find effective information from the massive Internet information, and personalized services are in short supply. This paper explores the essential association between knowledge graphs and deep learning and examines the construction process of knowledge graphs. Based on the content and collaborative filtering recommendation method, the theoretical method of personalized learning service recommendation is being studied. Knowledge graphs are introduced in collaborative filtering to accurately portray user and item vector representations. The knowledge graph is used to represent the TransR model, forming an entity space and a multivariate relationship space. The students' demand for personalized learning service systems is analyzed from the user's point of view, the personalized learning service system is constructed, and the accuracy of the personalized learning service system constructed in this paper is analyzed. The accuracy rate of DLKG is 0.1425 when K=10, which is the highest among the five models. In the effect of personalized teaching, the mean value of the metacognitive ability of the students in the experimental class is higher than that of the control class by 0.4155, 0.0261, 0.3995, 0.7967, respectively, and the experiment proves that the personalized learning service system based on the DLKG model proposed in this paper is more effective. © 2023 Min Huang et al., published by Sciendo.

关键词： Collaborative filtering

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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DESIGNING EV CHARGING STATIONS DEPLOYMENT THROUGH HOLISTIC SIMULATIONS: THE SANEVEC PROJECT 5

DESIGNING EV CHARGING STATIONS DEPLOYMENT THROUGH HOLISTIC S...

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5th Conference on Code of Practice for Electric Vehicle Charging Equipment Installation: Charging Ahead, EVI 2023

作者： Guisado-Lizar, José-Luis Jiménez-Moreno, Gabriel Diaz-Del-Rio, Fernando Sevillano-Ramos, J.L. Cagigas-Muñiz, Daniel Morón-Fernández, María José Cascado-Caballero, Daniel Jiménez-Morales, Francisco Cárdenas-Montes, Miguel Wainer, Gabriel A. Ragel-Díaz-Jara, David Rodríguez-Leal, J. Cerezuela-Escudero, E. Montes-Sánchez, J.M. Casanueva-Morato, Daniel Ayuso-Martínez, Alvaro Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain Department of Computer Architecture and Technology Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain Department of Condensed Matter Physics Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain Department of Basic Research Centro de Investigaciones Energéticas Medioambientales y Tecnológicas Madrid Spain Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781839539848

To determine the optimal location of the large number of urban public charging stations that will have to be deployed in the following years to transition to electric vehicles (EVs) is a challenging task, as it is intimately interconnected with the complexity of urban traffic and electric grid operation. The city is a complex system in which emerging patterns such as traffic jams, and instabilities or even outages in the electric grid are difficult to predict. The layout of charging stations can alter those patterns in traffic and electric grid operation, with consequences to mobility, air quality, and electric system. The present article presents the concept under the research project SANEVEC. It will build a framework for the optimal determination of the location of EV charging stations in a city based on simulation, which will be able to reproduce the feedback effects between all the variables involved: locations of the stations, traffic patterns, characteristics, and operation of the electricity grid, charging fees and air quality in the city. It will employ artificial intelligence methods to find high-quality station location solutions from the simulations and to predict the air quality in the city. © The Institution of Engineering & technology 2023.

关键词： Air quality

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NEURAL PROGRAM SYNTHESIS WITH QUERY

arXiv

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arXiv 2022年

作者： Huang, Di Zhang, Rui Hu, Xing Zhang, Xishan Jin, Pengwei Li, Nan Du, Zidong Guo, Qi Chen, Yunji SKL of Computer Architecture Institute of Computing Technology CAS China University of Chinese Academy of Sciences China University of Science and Technology of China China Cambricon Technologies China

Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few input-output examples under the training distribution. In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the query-based framework can generate informative input-output examples which achieve and even outperform well-designed input-output examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Fault Management Impacts on the Networking Systems Hardware Design 49

Fault Management Impacts on the Networking Systems Hardware ...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Vitucci, Carlo Sundmark, Daniel Jagemar, Marcus Danielsson, Jakob Larsson, Alf Nolte, Thomas Ericsson Ab Technology Management Stockholm Sweden Mälardalen University Computer Science and Software Enigineering Vasterås Sweden Ericsson Ab Sys Compute Dimensioning Stockholm Sweden Ericsson Ab Sys Architecture Stockholm Sweden Ericsson Ab Senior Specialist Observability Stockholm Sweden Mälardalen University Networked and Embedded Systems Västerås Sweden

ISBN: (纸本)9798350331820

Processing capacity distribution has become widespread in the fog computing era. End-user services have multiplied, from consumer products to Industry 5.0. In this scenario, the services must have a very high-reliability level. But in a system with such displacement of hardware, the reliability of the service necessarily passes through the hardware design. Devices shall have a high quality, but they shall also efficiently support fault management. Hardware design must take into account all fault management functions and participate in creating a fault management policy to ensure that the ultimate goal of fault management is fulfilled, namely to increase a system's reliability. Efficiently and sustainably, both in the system's performance and the product's cost. This paper analyzes the hardware design techniques that efficiently contribute to the realization of fault management and, consequently, guarantee a high level of reliability and availability for the services offered to the end customer. We describe hardware requirements and how they affect the choice of devices in the hardware design of networking systems. © 2023 IEEE.

关键词： Consumer products

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Energy efficiency in Edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification

arXiv

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arXiv 2023年

作者： Corral, José María Rodríguez Civit-Masot, Javier Luna-Perejón, Francisco Díaz-Cano, Ignacio Morgado-Estévez, Arturo Domínguez-Morales, Manuel 10 Spain Architecture and Computer Technology Dept. E.T.S. Ingeniería Informática University of Seville Avda. Reina Mercedes s/n Sevilla Spain

In this work, we evaluate the energy usage of fully embedded medical diagnosis aids based on both segmentation and classification of medical images implemented on Edge TPU and embedded GPU processors. We use glaucoma diagnosis based on color fundus images as an example to show the possibility of performing segmentation and classification in real time on embedded boards and to highlight the different energy requirements of the studied implementations. Several other works develop the use of segmentation and feature extraction techniques to detect glaucoma, among many other pathologies, with deep neural networks. Memory limitations and low processing capabilities of embedded accelerated systems (EAS) limit their use for deep network-based system training. However, including specific acceleration hardware, such as NVIDIA’s Maxwell GPU or Google’s Edge TPU, enables them to perform inferences using complex pre-trained networks in very reasonable times. In this study, we evaluate the timing and energy performance of two EAS equipped with Machine Learning (ML) accelerators executing an example diagnostic tool developed in a previous work. For optic disc (OD) and cup (OC) segmentation, the obtained prediction times per image are under 29 and 43 ms using Edge TPUs and Maxwell GPUs, respectively. Prediction times for the classification subsystem are lower than 10 and 14 ms for Edge TPUs and Maxwell GPUs, respectively. Regarding energy usage, in approximate terms, for OD segmentation Edge TPUs and Maxwell GPUs use 38 and 190 mJ per image, respectively. For fundus classification, Edge TPUs and Maxwell GPUs use 45 and 70 mJ, respectively. © 2023, CC BY-NC-ND.

关键词： Graphics processing unit

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Classifications of Single-input Lower Triangular Forms

arXiv

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arXiv 2022年

作者： Zhang, Duan Sun, Ying College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China School of Civil Engineering and Architecture Zhejiang Sci-Tech University Hangzhou310018 China

The purposes of this paper are to classify lower triangular forms and to determine under what conditions a nonlinear system is equivalent to a specific type of lower triangular forms. According to the least multi-indices and the greatest essential multi-index sets, which are introduced as new notions and can be obtained from the system equations, two classification schemes of lower triangular forms are constructed. It is verified that the type that a given lower triangular form belongs to is invariant under any lower triangular coordinate transformation. Therefore, although a nonlinear system equivalent to a lower triangular form is also equivalent to many other appropriate lower triangular forms, there is only one type that the system can be transformed into. Each of the two classifications induces a classification of all the systems that are equivalent to lower triangular forms. A new method for transforming a nonlinear system into a lower triangular form, if it is possible, is provided to find what type the system belongs to. Additionally, by using the differential geometric control theory, several necessary and sufficient conditions under which a nonlinear system is locally feedback equivalent to a given type of lower triangular form are established. An example is given to illustrate how to determine which type of lower triangular form a given nonlinear system is equivalent to without performing an equivalent transformation. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Towards Efficient Elastic Parallelism for Deep Learning Processor

Towards Efficient Elastic Parallelism for Deep Learning Proc...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jinyu Cheng Ruyi Qian Qinwen Shi Gaomei Hu Mengjuan Gao Qirun Huo Yuanchao Xu College of Information Engineering Capital Normal University Beijing China SKL of Computer Architecture CAS Institute of Computing Technology Beijing China

Resources underutilization is the most troublesome problem faced by almost all deep learning processors. To address this challenge, researchers have proposed various methods, especially in exploiting the parallelism of deep neural network models. However, some parallelisms have mutual effects, for example, intra-operator parallelism has adverse effects on inter-operator parallelism, resulting in a waste of resources. In order to make full use of chip resources, this paper applies the idea of elastic scheduling to the driver level of deep learning processor to achieve elastic parallelism. That is, if a task consists of multiple independent subtasks, it can be executed as long as the minimum resource requirements of the sub tasks are met. In addition, based on greedy scheduling algorithm, some improved methods are proposed, including subtask awareness, NUMA awareness, and cluster alignment awareness. Our analysis and preliminary evaluation demonstrate that, by reducing intra-operator parallelism, elastic parallelism can help improve chip utilization, and our proposed scheduling optimizations can also further improve the overall system performance.

关键词： Deep learning Program processors Scheduling algorithms System performance Pipelines Neural networks Optimization methods

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