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International Conference on Optical Network Design and Modeling, ONDM

作者： Fatima C. Garcia Gunning Amandeep Kaur Natalia Cañas Estrada Julie Raulin Yuliya Verbishchuk Cormac J. Sreenan Tyndall National Institute University College Cork Ireland School of Engineering and Architecture University College Cork Ireland School of Computer Science and Information Technology University College Cork Ireland

Current bandwidth demands are encouraging the exploration of optical frequency comb generators and flexible modulation formats to enable very high-capacity transmission beyond the C-band, which can be augmented with existing real-time digital electronic signal processing for pre-emphasis and post-impairment compensation at very high clock speeds. But for exploitation and deployment in commercial networks these devices will require support for remote management embedded with service providers’ operation and business-support systems. However, installed network management systems must deal with a range of dissimilar network devices. This diversity spans across electronic packet switches, optical circuit switches, and monitoring instrumentation for telemetry, ranging from legacy to state-of-the-art models. Software defined networking (SDN) flexible management approach unlocks exciting possibilities to jointly exploit the full capabilities of digital electronic packet switching and the many degrees of freedom presented by analogue optical EM carriers. In this paper, we will expose some of these possibilities, where these technologies can, through SDN, be reimagined to enable flexible high-capacity systems. We will be revisiting the potential for multi-carrier transmitters, and utilising analogue methods to monitor transmitter performance. We will also show how existing protocols can be used to support both legacy and state-of-the-art SDN-enabled devices, incorporating to management operating systems such as ONOS, showing that a smooth transition to fully automated networks with closed-loop control is now a real prospect.

关键词： Optical filters Protocols Optical switches Operating systems Optical fiber networks Optical transmitters Software defined networking

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CSI-Based Human Activity Recognition using Convolutional Neural Networks 11

CSI-Based Human Activity Recognition using Convolutional Neu...

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11th International Conference on computer Engineering and Knowledge, ICCKE 2021

作者： Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shahid Beheshti University G. C. Cognitive Telecommunication Research Group Department of Electrical Engineering Tehran Iran Standard Research Institute Faculty of Technology and Engineering Department of Electrical Engineering Alborz31745-139 Iran University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9781665402088

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as "walk", "run", and "sleep". These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks. © 2021 IEEE.

关键词： Channel state information

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Understanding the Runtime Overheads of Deep Learning Inference on Edge Devices

Understanding the Runtime Overheads of Deep Learning Inferen...

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

作者： Xiu Ma Guangli Li Lei Liu Huaxiao Liu Xiaobing Feng College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

With the growing ubiquity of the Internet of Things, in-the-edge inference of deep neural network models has been a major driver for promoting the widespread use of intelligent applications. As model inference characteristics are crucial for optimizing and deploying deep neural networks on hardware platforms, many studies focus on analyzing the performance of neural networks such as latency, accuracy, throughput, and energy consumption. However, few existing works have ever discussed the runtime overheads hidden in neural network inference, despite the overheads are non-negligible for edge applications. The lack of in-depth analysis of the overheads hinders the understanding of how hardware designs and model structures impact on-device inference performance. In this paper, we characterize the runtime overheads of deep learning inference on representative edge devices by leveraging state-of-the-art neural network models, performing a systematical analysis from the perspective of end-to-end performance, hardware platforms, memory bandwidth, and neural network model structures. Based on experimental results, the crucial insights are offered to facilitate the design and configure of resource-efficient networks and pick appropriate models on the specific platform, which provides a comprehensive view of runtime overheads of in-the- edge neural network inference for architects and developers.

关键词： Deep learning Performance evaluation Analytical models Runtime Roads Neural networks Throughput

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Symbolic Models for Interconnected Impulsive Systems

Symbolic Models for Interconnected Impulsive Systems

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IEEE Conference on Decision and Control

作者： Sadek Belamfedel Alaoui Adnane Saoud Pushpak Jagtap Abdalla Swikir School of Computer Science at Mohammed VI Polytechnical University Benguerir Morocco Centrale-Supelec University Paris-Saclay Gif-sur-Yvette France Robert Bosch Center for Cyber-Physical Systems Indian Institute of Science Bangalore Chair of Robotics and Systems Intelligence and MIRMI Technical University Munich (TUM) Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya Centre for Tactile Internet with Human-in-the-Loop (CeTI) Dresden Germany

In this paper, we present a compositional method-ology for constructing symbolic models of nonlinear interconnected impulsive systems. Our approach relies on the concept of “alternating simulation function” to establish a relation-ship between concrete subsystems and their symbolic models. Assuming some small-gain type conditions, we develop an alternating simulation function between the symbolic models of individual subsystems and those of the nonlinear interconnected impulsive systems. To construct symbolic models of nonlinear impulsive subsystems, we propose an approach that depends on incremental input-to-state stability and forward completeness properties. Finally, we demonstrate the advantages of our framework through a case study.

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Scalable and probabilistically complete planning for robotic spatial extrusion

arXiv

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arXiv 2020年

作者： Garrett, Caelan Reed Huang, Yijiang Lozano-Pérez, Tomás Mueller, Caitlin Tobin Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Department of Architecture Massachusetts Institute of Technology

There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms. The key planning challenge is, throughout the printing process, satisfying both stiffness constraints that limit the deformation of the structure and geometric constraints that ensure the robot does not collide with the structure. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware heuristic is able to successfully balance both constraints. We empirically compare our methods on a benchmark of over 40 simulated extrusion problems. Finally, we apply our approach to 3 real-world extrusion problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrusion

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Pressure induced transition from chiral charge order to time-reversal symmetry-breaking superconducting state in Nb-doped CsV3Sb5

arXiv

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arXiv 2024年

作者： Graham, J.N. Islam, S.S. Sazgari, V. Li, Y. Deng, H. Janka, G. Zhong, Y. Gerguri, O. Král, P. Doll, A. Bialo, I. Chang, J. Salman, Z. Suter, A. Prokscha, T. Yao, Y. Okazaki, K. Luetkens, H. Khasanov, R. Wang, Z. Yin, J.-X. Guguchia, Z. PSI Center for Neutron and Muon Sciences CNM Villigen5232 Switzerland Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement Ministry of Education School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Department of Physics Southern University of Science and Technology Guangdong Shenzhen518055 China Institute for Solid States Physics The University of Tokyo Kashiwa Japan Physik-Institut Universität Zürich Winterthurerstrasse 190 ZürichCH-8057 Switzerland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków30-059 Poland International Center for Quantum Materials Beijing Institute of Technology Zhuhai319000 China Trans-scale Quantum Science Institute The University of Tokyo Tokyo Japan Material Innovation Research Center The University of Tokyo Kashiwa Japan

The experimental realisation of unconventional superconductivity and charge order in kagome systems AV3Sb5 is of critical importance. We conducted a highly systematic study of Cs(V1−xNbx)3Sb5 with x=0.07 (Nb0.07-CVS) by employing a unique combination of tuning parameters such as doping, hydrostatic pressure, magnetic fields, and depth, using muon spin rotation, AC susceptibility, and STM. We uncovered tunable magnetism in the normal state of Nb0.07-CVS, which transitions to a time-reversal symmetry (TRS) breaking superconducting state under pressure. Specifically, our findings reveal that the bulk of Nb0.07-CVS (at depths >20 nm from the surface) experiences TRS breaking below T∗ = 40 K, lower than the charge order onset temperature, TCO = 58 K. However, near the surface ( © 2024, CC BY.

关键词： Magnetic susceptibility

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Regular perturbation solution of Couette flow(non-Newtonian)between two parallel porous plates: a numerical analysis with irreversibility

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Applied Mathematics and Mechanics(English Edition) 2021年第1期42卷 127-142页

作者： M.NAZEER M.I.KHAN S.KADRY Yuming CHU F.AHMAD W.ALI M.IRFAN M.SHAHEEN Department of Mathematics Institute of Arts and SciencesGovernment College UniversityChiniot CampusFaisalabad 35400Pakistan Department of Mathematics and Statistics Riphah International University I-14Islamabad 44000Pakistan Department of Mathematics and Computer Science Beirut Arab UniversityBeirut 11-5020Lebanon Department of Mathematics Huzhou UniversityHuzhou 313000Zhejiang ProvinceChina Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering Changsha University of Science&TechnologyChangsha 410114China Department of Applied Sciences National Textile UniversityFaisalabad 38000Pakistan Chair of Production Technology Faculty of Engineering TechnologyUniversity of TwenteEnschede 7500 AEThe Netherlands Department of Mathematics Riphah Inernational LniversityFaisalabad CampusFaiselabad 38000Pakistan

The unavailability of wasted energy due to the irreversibility in the process is called the entropy *** irreversible process is a process in which the entropy of the system is *** second law of thermodynamics is used to define whether the given system is reversible or ***,our focus is how to reduce the entropy of the system and maximize the capability of the *** are many methods for maximizing the capacity of heat *** constant pressure gradient or motion of the wall can be used to increase the heat transfer rate and minimize the *** objective of this study is to analyze the heat and mass transfer of an Eyring-Powell fluid in a porous *** this,we choose two different fluid models,namely,the plane and generalized Couette *** flow is generated in the channel due to a pressure gradient or with the moving of the upper *** present analysis shows the effects of the fluid parameters on the velocity,the temperature,the entropy generation,and the Bejan *** nonlinear boundary value problem of the flow problem is solved with the help of the regular perturbation *** validate the perturbation solution,a numerical solution is also obtained with the help of the built-in command NDSolve of MATHEMATICA *** velocity profile shows the shear thickening behavior via first-order Eyring-Powell *** is also observed that the profile of the Bejan number has a decreasing trend against the Brinkman ***ηi→0(i=1,2,3),the Eyring-Powell fluid is transformed into a Newtonian fluid.

关键词： Couette flow Eyring-Powell fluid entropy generation perturbation method Bejan number

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SCENIC: An Area and Energy-Efficient CNN-based Hardware Accelerator for Discernable Classification of Brain Pathologies using MRI

SCENIC: An Area and Energy-Efficient CNN-based Hardware Acce...

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International Conference on VLSI Design

作者： Bodepu Sai Tirumala Naidu Shreya Biswas Rounak Chatterjee Sayak Mandal Srijan Pratihar Ayan Chatterjee Arnab Raha Amitava Mukherjee Janet Paluh SandLogic Technologies Bangalore India Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Network Science Institute Northeastern University Boston USA Advanced Architecture Research Intel Edge.AI Intel Corporation Santa Clara USA Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India College of Nanoscale Science and Engineering Nanobioscience SUNY Polytechnic Institute Albany New York USA

ISBN: (数字)9781665485050

ISBN: (纸本)9781665496612

Biomedical brain imaging lies at the interface of visual, and spatial neuropathology and neurosurgical intervention. Future treatments are expected to require greater feature detail in imaging as well as underlying mechanisms that will be synergistically advanced. This will be made more accessible via low power embedded devices and cloud platforms through applied deep learning software and hardware analysis. Towards that end, we develop a hardware-software co-design technique referred to as SCENIC (or, Separable Convolution Enabled Non-Invasive Classification) for the identification and classification of glioma brain tumors, using physical tissue features reflected in parameter weighted MRI scan types such as-T1-w, T1-ce, T2-w and FLAIR. The high performance hardware exceeds current accuracy, resource efficiency and time consumption parameters. The proposed SCENIC-CNN Accelerator is synthesized on 45 nm process technology andit can operate at a minimum frequency of 1GHz while maintaining low-power consumption of only 0.36 W and a low chip-area size of 0.431mm 2 . Our classification accuracy achieves 98.3% in detection of the presence of a tumor pathology and 99.62% within classification of imaging modalities that relate to tissue parameters such as fat content, blood or CSF flow and tissue density. Compared to prominent state-of the-art Convolutional Neural Network (CN) models being designed for biomedical imaging, SCENIC is competitive versus XceptionNet, InceptionV3, ResNet-50, and VGG-16. With model compression techniques, SCENIC requires a memory space of less than 0.265MB. We discuss design methodology as applied to future goals to meet challenging needs to distinguish tumor origins, such as glioma and metastasized tumors, along with other neuropathologies that may be TBI, vascular or developmental. SCENIC will aid impactful, cost-effective, rapid and accurate neurosurgical intervention and treatments.

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A systematic view of leakage risks in deep neural network systems

TechRxiv

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TechRxiv 2021年

作者： Hu, Xing Liang, Ling Chen, Xiaobing Deng, Lei Ji, Yu Ding, Yufei Du, Zidong Guo, Qi Sherwood, Timothy Xie, Yuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering University of California Santa Barbara United States Department of Computer Science University of California Santa Barbara United States Tsinghua University China University of California Santa Barbara United States

As deep neural networks (DNNs) continue their reach into a wide range of application domains, the neural network architecture of DNN models becomes an increasingly sensitive subject, due to either intellectual property protection or risks of adversarial attacks. In observing the large gap between the architectural surfaces exploration and the model integrity study, this paper first presents the formulated schema of the model leakage risks. Then, we propose DeepSniffer, a learning-based model extraction framework, to obtain the complete model architecture information without any prior knowledge of the victim model. It is robust to architectural and system noises introduced by the complex memory hierarchy and diverse run-time system optimizations. Taking GPU platforms as a showcase, DeepSniffer performs model extraction by learning both the architecture-level execution features of kernels and the inter-layer temporal association information introduced by the common practice of DNN design. We demonstrate that DeepSniffer works experimentally in the context of an off-the-shelf Nvidia GPU platform running a variety of DNN models. The extracted models are directly helpful to the attempting of crafting adversarial inputs. The DeepSniffer project has been released in https://***/xinghu7788/DeepSniffer. © 2021, CC0.

关键词： Deep neural networks

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The State of the Art in Visual Analytics for 3D Urban Data

arXiv

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arXiv 2024年

作者： Miranda, Fabio Ortner, Thomas Moreira, Gustavo Hosseini, Maryam Vuckovic, Milena Biljecki, Filip Silva, Claudio T. Lage, Marcos Ferreira, Nivan Department of Computer Science University of Illinois Chicago United States City Form Lab Massachusetts Institute of Technology United States VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH Austria Department of Architecture Department of Real Estate National University of Singapore Singapore Tandon School of Engineering Center for Data Science New York University United States Institudo de Computação Universidade Federal Fluminense Brazil Centro de Informática Universidade Federal de Pernambuco Brazil

Urbanization has amplified the importance of three-dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine-grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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