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6th IEEE International Conference on Communications and Electronics, IEEE ICCE 2016

作者： Huynh, Chi-Kien Le, Thanh-Sach Hamamoto, Kazuhiko Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam Deparment of Information Media Technology School of Information and Telecommuncation Engineering Tokai University Japan

ISBN: (纸本)9781509019311

Motorbike detection is not a novel problem. However, as it is not a common vehicle in most countries in the world, there is barely enough research on vision-based methods for motorbike detection, even more so for motorbike detection in dense clusters. In this work, we proposed a detection method that is able to overcome fair occlusions in traffic images. The method is centered around a robust model: The convolutional neural network (CNN) with multiple layers. The CNN model has been proven to achieve state-of-The-Art results in multiple vision tasks. Although having a straightforward mechanic, the CNN we constructed is able to achieve positive and stable results throughout a difficult test dataset for motorbike detection. © 2016 IEEE.

关键词： Statistical tests

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SDN/NFV - A new approach of deploying network infrastructure for IoT 27

SDN/NFV - A new approach of deploying network infrastructure...

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27th Wireless and Optical Communication Conference, WOCC 2018

作者： Sinh, Do Le, Luong-Vy Lin, Bao-Shuh Paul Tung, Li-Ping Deparment of Computer Science National Chiao Tung University Hsinchu Taiwan College of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Microelectronics and Information Research Center National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781538649596

The Internet of Things (IoTs) is a recent concept in which variety of smart devices such as smartphones, tablets, and sensors can interact with one another through the Internet. Hence, a plethora of data is generated and grown rapidly. They have posed various requirements of high throughput, high availability, lower latency, and high performance in heterogeneous connectivity environments. However, existing networks often struggle with such of limitations such as the complexity of control protocols and the internetworking of a huge number of smart devices. These obstacles become substantial barriers to deploy IoT services from different providers on the same network. In this context, SDN/NFV not only are considered as the key innovations in technology and novel services but also are expected to be a new model of network infrastructure that meets the requirement of developing IoT applications. In this study, the authors focus on the following contributions: i) investigate the roles of SDN/NFV in deploying IoT services and introduce an SDN/NFV architecture for deploying IoT framework;ii) build applications to slice end-to-end multiple network segments to meet requirements of deploying IoT services from different providers;iii) build an application to recover an IoT service when it is out of order. © 2018 IEEE.

关键词： Wireless sensor networks

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Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management 4

Applying Big Data, Machine Learning, and SDN/NFV to 5G Traff...

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4th IEEE Conference on Network Softwarization and Workshops, NetSoft 2018

作者： Le, Luong-Vy Sinh, Do Lin, Bao-Shuh Paul Tung, Li-Ping National Chiao Tung University College of Electrical and Computer Engineering Hsinchu Taiwan National Chiao Tung University Deparment of Computer Science Hsinchu Taiwan Microelectronics and Information Research Center National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781538646335

Traffic clustering, forecasting, and management play a crucial role in improving network efficiency, network quality, load balancing (LB), and energy saving of mobile networks. Especially, in 5G networks, a dense heterogeneous architecture of various types of cells (macro cells and small cells) make traffic management become more complicated. Moreover, investigating and understanding traffic patterns of a huge number of cells are challenging issues, but valuable for network operators. On the other hand, big data, machine learning (ML), software-defined network (SDN), and network functions virtualization (NFV) have recently been proposed as emerging technologies and the necessary tools for empowering the SON of 5G to address the intensive computation and optimization issues. In this study, the authors applied those technologies to build a practical and powerful framework for clustering, forecasting, and managing traffic behaviors for a huge number of base stations with different statistical traffic characteristics of different types of cells (GSM, 3G, 4G). Besides, several applications based on traffic forecasting were also introduced. Finally, the performance of the proposed models was evaluated by applying them to a real dataset that collected traffic KPIs (key performance indicators) of more than 6000 cells of a real network during the years, 2016 and 2017. © 2018 IEEE.

关键词： Machine learning

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A Smartphone-Based System for Clinical Gait Assessment

A Smartphone-Based System for Clinical Gait Assessment

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International Conference on Smart Computing (SMARTCOMP)

作者： Andres A. Perez Miguel A. Labrador Deparment of Computer Science and Engineering University of South Florida Tampa Florida USA

ISBN: (纸本)9781509008995

In 2010, There were more than forty million doctor visits of people with lower joints complaints, including more than one million surgeries involving total or partial hip, knee, and ankle replacement in the US. Generally, these procedures require some level of post-op physical therapy (PT), to recover most of the walking capabilities. Therefore, it is important to have a method to assess the walking capabilities of the patients in order to track their improvement over time. This paper presents a smartphone-based system to perform the Functional Gait Analysis test to assess the balance and fall risk of patients with walking difficulties. The paper addresses most of the shortcomings of current systems for the same purpose, including: 1) low cost, as it uses one off-the-shelf smartphone and the sensors embedded in it; 2) self-care, as it does not need of a specialist or physician; 3) mobile; 4) accurate, as it eliminates the ambiguities incurred by specialists; 5) information-based, as it enhances the diagnostic including other metrics that assess the gait of individual limbs and provides historical data to assess the evolution of the patient over time; and 6) easy to use and unobtrusive.

关键词： Legged locomotion Foot Medical services Wearable sensors Signal processing algorithms Sensor systems

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Geolocation on the iPhone by automatic street sign reading

Geolocation on the iPhone by automatic street sign reading

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Joshua Oosterman Richard Green Deparment of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

In any country, roads are marked with signs. In particular, street signs are a type of road sign used to display the names of streets. Mobile devices such as smart phones are now powerful enough to serve as platforms for computer vision applications. These mobile devices are commonly equipped with high resolution cameras and increasingly with internal GPS for *** propose an accurate method of geolocation which detects, segments and reads street signs in complex natural scenes from an iPhone image. Street signs are detected using image segmentation based on edge detection and contour detection techniques. Sign candidates are selected using several heuristics, and then partitioned into individual characters. The letters are recognised using a nearest-neighbour algorithm for Optical Character Recognition (OCR). The street names finally are passed to a GeoCoding API to display a street map of the users location. We evaluate the system for a real word data set and achive 75% sign detection accuracy, 91% OCR accuracy and 55% total geolocation accuracy.

关键词： Optical character recognition software Image edge detection Geology Accuracy Algorithm design and analysis Image color analysis Cameras

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Detection and Classification of Rheumatoid Nodule using Deep Learning Models

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Procedia computer science 2023年 218卷 2401-2410页

作者： Uma N. Dulhare Azmath Mubeen Department of Computer Science & Artificial Intelligence Muffakham Jah College of Engineering & Technology Hyderabad500034 India Deparment of Computer Science and Engineering University College of Engineering Osmania University Hyderabad-500007 India

In the past few years there have been many cases in which the patient may get some form of nodule on the body and it becomes very difficult to identify the type of nodule and its relative disease. Sometimes the nodule may be a simple skin lesion or it may be cancerous cyst or it may be due to a severe condition of rheumatoid arthritis. This research work concentrates on classifying the nodules from a given data set of images and detecting the Rheumatoid Nodule (RN) and its risks. Many studies have been conducted for detection of different nodules but specific to rheumatoid nodule identification and its risks on other organs are minimum as per my cognizance. In imaging study, Deep Convolutional Neural Networks (DCNN) have shown efficient accomplishment, hence the model proposed uses dual three-dimensional Deep Convolutional Networks design for the detection of RN and an improved Res Net for classification, respectively. Rheumatoid Nodule detection was performed through Pure Deep Convolutional Networks (PDCNN) and then feature selection was done using Particle Swarm Optimization (PSO) and later classification was done using the Residual Network (Res-Net) on proficiently learned features from Particle Swarm Optimization. The Deep Learning (DL) model for Rheumatoid Nodule detection and classification, blended with medical aspects, aids in minimizing the errors and False Positive (FP) rate and help in early detection of rheumatoid nodule. The recommended system was assessed on dataset collected from different orthopaedic hospitals in the city and improved results were obtained in comparison to the prevailing approaches.

关键词： Classification Deep Learning PDCNN PSO Res-Net Rheumatoid Arthritis (RA) Rheumatoid Nodule

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Android based Recommender System (ARS) to Detect Breast Abnormalities 1

Android based Recommender System (ARS) to Detect Breast Abno...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Kavitha, M. Venkata Krishna, P. Rama Krishna, V. Digavinti, Sreenivasulu Kalyani, M. Naga Divya, Tiruluka V.S. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Deparment of Computer Science Sri Padmavathi Mahila Visvavidyalam Tirupati India Vignan's Foundation for Science Technology and Research Vadlamudi India Institute of Aeronautical Engineering Hyderabad India Department of Mathematics PACE Institute of Technology and Sciences AP Ongole India

ISBN: (纸本)9781665460842

Breast tumor is the second leading cause of death among women from the last six decades. The researchers are working intensely on identifying breast tumor symptoms at an early stage. Early detection and exact treatment of tumors will support expanding the lifetime of a patient. Doctors recommend that periodic Breast-Self Examination (BSE) is the preliminary breast cancer screening test. This test will assist the people to detect breast abnormalities at an early stage. But due to a lack of awareness and knowledge on the test procedure of BSE, people are unable to take this self-test periodically. An Android based Recommender System (ARS) to detect breast abnormalities are proposed to serve this gap. ARS is an android based mobile app developed to support all categories of people in breast health monitoring. Through this recommender system, the breast physical symptoms are assessed, and the analysis report helps the user to detect the abnormality at an early stage. © 2022 IEEE

关键词： Recommender systems

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Measurement of stereo matching on images using dissimilarity estimation

Measurement of stereo matching on images using dissimilarity...

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International Conference on Devices, Circuits and Systems (ICDCS)

作者： P. Sakthivel G. Balakrishnan Department of VLSI Deparment of computer science Indra ganesan College of Engineering Trichy

This paper proposes the dissimilarity based adaptive prior modelling for stereo images and improve the performance on images. The fast likelihood function based on rank transform implemented in matching images that replaces the intensity of a pixel with its rank among all pixels within a certain neighbourhood. Rank transform reduce the radiometric gain, bias also reduce the discriminatory power in stereo matching, by giving matching result. Adaptive prior modelling is proposed, improves the smoothness of matching result and is defined as a pixel wise energy function by using adaptive interference between neighbouring disparities. The disparity is estimated by minimizing the joined energy function which combines the likelihood matching and prior modelling. The dissimilarity based adaptive prior modelling measures the dissimilarity between joint energy function and gradient (GRAD). The optimal weight ω is determined between join energy function and gradient (GRAD) by maximizing the number of reliable correspondences that are filtered using cross checking test. Finally we consider filters like wiener, median, order static filter to improve the smoothness of stereo images.

关键词： Wiener filters Adaptation models Estimation Radiometry Maximum likelihood detection Matched filters Nonlinear filters

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Efficient Router Bypass via Hybrid Flow Control

Efficient Router Bypass via Hybrid Flow Control

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International Workshop on Network on Chip Architectures, NoCArc

作者： Ivan Perez Enrique Vallejo Ramon Beivide Deparment of Computer Science and Electronics Engineering University of Cantabria Santander Cantabria Spain

Minimizing latency and power are key goals in the design of NoC routers. Different proposals combine lookahead routing and router bypass to skip the arbitration and buffering stages of their pipeline, reducing router delay to a single-cycle. However, the conditions to use the bypass are unnecessarily conservative, requiring completely empty buffers in the intermediate routers. This restricts the amount of flits that use the bypass, increasing average latency and power .This paper introduces Non-Empty Buffer Bypass (NEBB), a mechanism that allows to bypass flits even if the buffers to bypass are not empty. The mechanism applies to wormhole and virtual-cut-through, each of them with different advantages. NEBB-Hybrid is proposed to employ the best flow control in each situation, maximizing the utilization of the bypass. The proposals have been evaluated using Booksim. Results show up to 75% reduction of the buffered flits for single-flit packets, which translates into latency and dynamic power reductions of up to 30% and 23% respectively. For bimodal traffic, these improvements are 20% and 21% respectively. Additionally, the bypass utilization is largely independent of the number of VCs when using shared buffers and very competitive with few private ones, allowing to simplify the allocation mechanisms.

关键词： Pipelines Routing Resource management Buffer storage Proposals Technological innovation Hybrid power systems

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Fundamental Spatial Parameters of Human Mobility

Fundamental Spatial Parameters of Human Mobility

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computer science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Sasha Knezevic Ivica Kostanic Zaid Matloub Deparment of Computer Science and Engineering Florida Institute of Technology Melbourne FL USA

A set of parameters for characterizing spatial aspects of human mobility are presented. The parameters are extracted from a large set of crowd-sourced cellular data which were collected in a major metropolitan area of United States. Each parameter is analyzed at three different time scales (daily, weekly, and monthly time scale). The results demonstrate that human mobility on a population scale has a high degree of statistical regularity. The mobility may be described to a great extend using a limited number of judiciously selected parameters.

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