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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

Functional MRI has attracted increasing attention in cognitive neuroscience and clinical mental health research. Towards understanding how brain give rises to mental phenomena, deep learning has been applied to functional MRI (fMRI) dataset to discover the physiological basis of cognitive process. Considering the unsupervised nature of fMRI due to the complex intrinsic brain activities, an encoder-decoder structure is promising to model hidden structure of latent signal sources. Inspired by the success of deep residual learning, we propose a 68-layer 3D residual autoencoder (3D ResAE) to model deep representations of fMRI in this paper. The proposed model is evaluated on the fMRI data under 3 cognitive tasks in Human Connectome Project (HCP). The experimental results have shown that the temporal representations learned by the encoder matches the task design and the spatial representations can be interpreted to be meaningful functional brain networks (FBNs), which not only include tasks based FBNs, but also intrinsic FBNs. The proposed model also outperforms a 3-layer autoencoder, showing the key factor for the performance improvement is depth. Our work demonstrates the feasibility and success of adopting 2D advanced deep residual networks in computer vision into 3D fMRI volume modeling. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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An ARQ-Based Cooperative Relaying Scheme for 5G IoT Slice

An ARQ-Based Cooperative Relaying Scheme for 5G IoT Slice

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2019 IEEE Global Conference on Internet of Things, GCIoT 2019

作者： Otaru, Musa Ajiya, Mohammed Adinoyi, Abdulkareem Aljlayl, Mohammed Yanikomeroglu, Halim Telecommunications Services Nigerian Communications Satellite Ltd Abuja Nigeria Bayero University Department of Electrical Engineering Kano Nigeria Technology Enterprise Architecture Saudi Telecommunications Company Saudi Arabia Carleton University Dept. of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781728148731

We introduce a new twist to the classical automatic repeat request (ARQ)-based cooperative relaying scheme. Here, we target a 5G slice for offering internet-of-things (IoT)-inspired services. In this 5G application segment (e.g., utility or smart metering), utility sensors or devices are expected to last for decades and, operate on tight power budgets, and the system can tolerate periodic stops to check the fidelity of previous transmissions. For this scenario, we employ a service provider-deployed relay that alleviates the IoT devices of their communication burdens. The source transmits for a certain time window or period without requiring acknowledgments (ACK) from the destination. At the end of this window, the destination sends an ACK. If negative, the relay is expected to assist. Thus, the relay transmits only intermittently, in either one transmission time slot or a few time slots, using a suitably chosen higherorder modulation constellation. If no error occurs, the source continues its transmission. The numerical results show that with just a few antennas at the relay, the new scheme provides both a reduction in the number of ARQ transmissions and a superior probability of bit error compared with a reference scheme. We adopt the two-parameter Weibull fading model to evaluate the performance of the proposed scheme. © 2019 IEEE.

关键词： Internet of things

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Two-Dimensional Error Estimation in Point Positioning with Fuzzy Logic

Two-Dimensional Error Estimation in Point Positioning with F...

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2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018

作者： Bülbül, Mehmet Akif Öztürk, Celal Ilçi, Veli Ozulu, Ibrahim Murat Computer Technology Department Hitit University Çorum Turkey Computer Engineering Department Erciyes University Kayseri Turkey Architecture and City Planning Department Hitit University Çorum Turkey

ISBN: (纸本)9781538668788

Developments in technology and computer aims to facilitate the human life. Every new technology developed by computer science is tried to integrate into systems. Rapidly developing satellite, navigation and geographic information systems also use computer science. The position information that these systems provide to people has had too much importance in recent years. The low margin of error in point position estimation has become a criterion for developed technologies. In this study, a model based on fuzzy logic is developed for estimation of point position error. With this derived model, point position error prediction has been determined and the accuracy of estimates of the system has been measured. © 2018 IEEE.

关键词： Fuzzy logic

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Eliminating NB-IoT interference to LTE system: A sparse machine learning based approach

arXiv

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arXiv 2020年

作者： Liu, Sicong Xiao, Liang Han, Zhu Tang, Yuliang Department of Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

—Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Nature Reviews Biodiversity

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Nature Reviews Biodiversity 2025年第3期1卷 166-182页

作者： Laura J. Pollock David Rolnick Justin Kitzes Sara Beery Kaitlyn M. Gaynor Marta A. Jarzyna Tanya Berger-Wolf Oisin Mac Aodha Bernd Meyer Graham W. Taylor Devis Tuia Department of Biology McGill University Montréal Quebec Canada Mila—Quebec AI Institute Montréal Quebec Canada School of Computer Science McGill University Montréal Quebec Canada Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Faculty of AI and Decision Making Massachusetts Institute of Technology Cambridge MA USA Department of Botany University of British Columbia Vancouver British Columbia Canada Department of Zoology University of British Columbia Vancouver British Columbia Canada Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH USA Translational Data Analytics Institute The Ohio State University Columbus OH USA Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Electrical & Computer Engineering The Ohio State University Columbus OH USA School of Informatics University of Edinburgh Edinburgh UK Department of Data Science and AI Monash University Clayton Victoria Australia School of Engineering University of Guelph Guelph Ontario Canada Vector Institute Toronto Ontario Canada School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Large, well described gaps exist in both what we know and what we need to know to address the biodiversity crisis. Artificial intelligence (AI) offers new potential for filling these knowledge gaps, but where the biggest and most influential gains could be made remains unclear. To date, biodiversity-related uses of AI have largely focused on tracking and monitoring of wildlife populations. Rapid progress is being made in the use of AI to build phylogenetic trees and species distribution models. However, AI also has considerable unrealized potential in the re-evaluation of important ecological questions, especially those that require the integration of disparate and inherently complex data types, such as images, video, text, audio and DNA. This Review describes the current and potential future use of AI to address seven clearly defined shortfalls in biodiversity knowledge. Recommended steps for AI-based improvements include the re-use of existing image data and the development of novel paradigms, including the collaborative generation of new testable hypotheses. The resulting expansion of biodiversity knowledge could lead to science spanning from genes to ecosystems — advances that might represent our best hope for meeting the rapidly approaching 2030 targets of the Global Biodiversity Framework.

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Practical Stabilization of Passive Nonlinear Systems with Limited Control

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IFAC-PapersOnLine 2019年第16期52卷 460-465页

作者： B. Jayawardhana M.Z. Almuzakki A. Tanwani Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen The Netherlands Department of Computer Science Faculty of Science and Computer Universitas Pertamina Jakarta Indonesia Laboratory for Analyses and Architecture of Systems (LAAS) –CNRS Université de Toulouse France

This article addresses output feedback stabilization of continuous-time nonlinear systems by choosing control actions from a finite set. Working under the assumption that the system under consideration is passive and large-time norm observable, we propose a static feedback mapping, from the output space to the finite set of control actions, which is shown to be practically stabilizing if the convex hull of certain control actions (in the chosen finite set) contains the origin in its interior. Consequently, to construct this stabilizing feedback, it suffices to have, in addition to a zero symbol, another m + 1 elements in the control set which form an m -simplex in ℝ m (the input, and output space).

关键词： Nonlinear passive systems finite control set output feedback binary control

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Monitoring System for Prediction and Detection of Epilepsy Seizure

Monitoring System for Prediction and Detection of Epilepsy S...

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Nashaat el Halabi Roy Abi Zeid Daou Roger Achkar Ali Hayek Josef Börcsök Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department of Computer Architecture and System Programming University of Kassel Germany

Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.

关键词： Electroencephalography Epilepsy Feature extraction Tools Monitoring Artificial neural networks Support vector machines

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White Rabbit: A Deterministic Time Transfer and Frequency Dissemination technology for Distrihuted Systems 2

White Rabbit: A Deterministic Time Transfer and Frequency Di...

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2nd URSI Atlantic Radio Science Meeting, AT-RASC 2018

作者： Diaz, J. Ros, E. Rodriguez-Gomez, R. University of Granada Computer Architecture and Technology Department Spain Seven Solutions S.L. Spain

ISBN: (纸本)9789082598735

Latest developments on the field of radio-communications and astrophysics rely on the development of distributed systems as a way to better sensing and optimize the utilization of the electromagnetic spectrum. From modern radio-telescopes like the Square Kilometer Array (SKA) or the Cherenkov Telescope Array (CTA), to distributed radars and communication systems, all of them require a common and highly stable frequency reference in all the antennas. This is needed to guarantee that the radio signal is sampled or emitted in a coherent way despite the location. Furthermore, all of them need a proper Pulse-Per-Second (PPS) signal. This signal is used as synchronization mechanism, key for signal timestamping as well as a mechanism to control the phase of the signal distributed at each antenna of the system. This allows the distributed instrumentation (arrays of antennas) to work as a single system, with a global time and frequency reference. © 2018 International Union of Radio Science URSI.

关键词： Antennas

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When Deep Learning Meets the Edge: Auto-Masking Deep Neural Networks for Efficient Machine Learning on Edge Devices

When Deep Learning Meets the Edge: Auto-Masking Deep Neural ...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Ning Lin Hang Lu Xing Hu Jingliang Gao Mingzhe Zhang Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering University of California Santa Barbara

Deep neural network (DNN) has demonstrated promising performance in various machine learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring DNN models directly on edge devices trends the development of intelligent systems, like self-driving cars, smart Internet-of-Things (IoTs) and autonomous robotics. The on-device DNN model is obtained by expensive training via vast volumes of high-quality training data in the cloud datacenter, and then deployed into these devices, expecting it to work effectively at the edge. However, edge device always deals with low-quality images caused by compression or environmental noise pollutions. The well-trained model, though could work perfectly on the cloud, cannot adapt to these edge-specific conditions without remarkable accuracy drop. In this paper, we propose an automated strategy, called "AutoMask", to embrace effective machine learning and accelerate DNN inference on edge devices. AutoMask comprises end-to-end trainable software strategies and cost-effective hardware accelerator architecture to improve the adaptability of the device without compromising the constrained computation and storage resources. Extensive experiments, over ImageNet dataset and various state-of-the-art DNNs, show that AutoMask achieves significant inference acceleration and storage reduction while maintains comparable accuracy level on embedded Xilinx Z7020 FPGA, as well as NVIDIA Jetson TX2.

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A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

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IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

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