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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Multi-Threading image processing in Single-core and Multi-core CPU using R Language 2

Multi-Threading Image Processing in Single-core and Multi-co...

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2nd IEEE International conference on Electrical, Computer and Communication Technologies (IEEE ICECCT)

作者： Ramalakshmi, E. Kompala, Neeharika Chaitanya Bharathi Inst Technol Dept Informat Technol Hyderabad Andhra Prades India

ISBN: (纸本)9781509032396

R language executes its program on a one core CPU by default, using image processing. R language requires gigantic amount of calculations which are all processed by one core itself. When there is a need to use multiple core present in our platform, special packages in R language are used and executed. image processing requires a large number of resources and processing all running simultaneously, and all the calculations are majorly done with the help of matrix pixels. In order to see how single-core and Multi-core systems affect the efficiency of image processing algorithms we execute codes on multiple platforms with varying number of codes along with varying sizes of the image. Also we use the concept of threading, and performing the same function both as a single thread process and a Multi thread process to check its efficiency. An increase on the performance as a whole can be observed when we change the number of codes, size of images and also as we choose to implement thread concepts.

关键词： R language image processing Single-core Multicore Multi-Threading

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Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration 1st ed. 2017

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丛书名： Communications in Computer and Information Science

2017年

作者： Kang Li Yusheng Xue Shumei Cui Qun Niu Zhile Yang Patrick Luk

ISBN: (数字)9789811063640

ISBN: (纸本)9789811063633

The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International conference on Life System Modeling and Simulation, LSMS 2017, and of the International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical Signal processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, algorithms and Apparatus; Modeling and Simulation of Life systems; Data Driven Analysis; image and Video processing; Advanced Fuzzy and Neural Network Theory and algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear systems; Advanced Methods for Networked systems; Control and Analysis of Transportation systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.

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Full-View 3D Imaging System for Functional and Anatomical Screening of the Breast

Full-View 3D Imaging System for Functional and Anatomical Sc...

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conference on Photons Plus Ultrasound - Imaging and Sensing

作者： Oraevsky, Alexander Su, Richard Nguyen, Ha Moore, James Lou, Yang Bhadra, Sayantan Forte, Luca Anastasio, Mark Yang, Wei TomoWave Labs 6550 Mapleridge St Ste 103-124 Houston TX 77081 USA Washington Univ 1 Brookings Dr Box 1097 St Louis MO 63130 USA Univ Texas MD Anderson Canc Ctr 1515 Holcombe Blvd Houston TX 77030 USA

ISBN: (数字)9781510614741

ISBN: (纸本)9781510614741

Laser Optoacoustic Ultrasonic Imaging System Assembly (LOUISA-3D) was developed in response to demand of diagnostic radiologists for an advanced screening system for the breast to improve on low sensitivity of x-ray based modalities of mammography and tomosynthesis in the dense and heterogeneous breast and low specificity magnetic resonance imaging. It is our working hypothesis that co-registration of quantitatively accurate functional images of the breast vasculature and microvasculature, and anatomical images of breast morphological structures will provide a clinically viable solution for the breast cancer care. Functional imaging is LOUISA-3D is enabled by the full view 3D optoacoustic images acquired at two rapidly toggling laser wavelengths in the near-infrared spectral range. 3D images of the breast anatomical background is enabled in LOUISA-3D by a sequence of B-mode ultrasound slices acquired with a transducer array rotating around the breast. This creates the possibility to visualize distributions of the total hemoglobin and blood oxygen saturation within specific morphological structures such as tumor angiogenesis microvasculature and larger vasculature in proximity of the tumor. The system has four major components: (i) a pulsed dual wavelength laser with fiberoptic light delivery system, (ii) an imaging module with two arc shaped probes (optoacoustic and ultrasonic) placed in a transparent bowl that rotates around the breast, (iii) a multichannel electronic system with analog preamplifiers and digital data acquisition boards, and (iv) computer for the system control, data processing and image reconstruction. The most important advancement of this latest system design compared with previously reported systems is the full breast illumination accomplished for each rotational step of the optoacoustic transducer array using fiberoptic illuminator rotating around the breast independently from rotation of the detector probe. We report here a pilot case studies

关键词： optoacoustic ultrasound image co-registration functional imaging breast cancer tumor microvasculature ultrawide-band ultrasonic transducer

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Multicast routing algorithm for ad hoc networks based on cognitive radio 4

Multicast routing algorithm for ad hoc networks based on cog...

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4th International conference on Fuzzy systems and Data Mining, FSDM 2018

作者： Zhang, Fan Xu, Xinyan Xie, Liguo Yuan, Qi Postdoctoral Innovation Practice Base Shandong Cable Interactive Service Ltd Jinan250014 China Department of Computer Science and Technology Shandong College of Electronic Technology Jinan250200 China School of Information Science and Engineering Shandong University Qingdao226237 China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China No.32 Jing 11 Road Jinan China

ISBN: (纸本)9781614999270

Mobile Ad hoc networks (MANETs) have the characteristics of distribution, self-organization and mobility. PUMA, as a mesh-based multicast routing protocol for MANET, has been proved to perform better than other comparable multicast algorithms because of its effectiveness and robustness. Recently, cognitive radio technology has been applied to solve the scarcity problem of radio spectrum resources. In this paper, we combine PUMA with cognitive radio technology and propose a novel multicast routing algorithm, which is named as CR-PUMA for MANET. In CR-PUMA, node detects the available channels and selects a suitable one to send data packets to its next-hop neighbor. A cross-layer approach, which goes across the physical layer, link layer and network layer, is employed in our algorithm. Simulation experiments show that CR-PUMA attains a superior performance in terms of packet delivery ratio and throughput. © 2018 The authors and IOS Press. All rights reserved.

关键词： Mobile ad hoc networks

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CVBL IRIS Gender Classification Database image processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL)

CVBL IRIS Gender Classification Database Image Processing an...

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International conference on image, Vision and Computing (ICIVC)

作者： Saeed Aryanmehr Mohsen Karimi Farsad Zamani Boroujeni Computer Department Islamic Azad University Isfahan Iran Electric Department Islamic Azad University Isfahan Iran

ISBN: (纸本)9781538649923

Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images. Considering the importance of this research area and its commercial applications, it is highly essential for researchers to make use of efficient color features in their algorithms which necessitates the production of color iris image databases. The present study introduces an iris image database for gender classification and proposes a new gender classification algorithm for its evaluation. The database consists of iris images taken from 720 subjects including 370 females and 350 males in university students. For each student, more than 6 images were taken from his/her both left and right eyes. After examining the images, 3 images from the left eye and 3 images from the right eye were selected among the most appropriate images and were included in the database. All 4320 images from this database were taken under the same condition and by the same color camera. Finally, the quality and the efficiency of the introduced database are evaluated using a new method that extract Zernike moments on spectral features and two well-known classifiers, namely, SVM and KNN. The results revealed that there is a significant improvement in gender classification compared with the similar databases.

关键词： Iris recognition Databases Feature extraction image recognition image segmentation image color analysis Classification algorithms

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Control and Security System for Classroom Access Based on Facial Recognition

Control and Security System for Classroom Access Based on Fa...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Cedric Monteiro Eduardo Ogasawara Laercio Gonçalves João R. de Toledo Quadros

ISBN: (纸本)9781728104386

This work presents a facial recognition system based on spectral analysis. With this system, it is possible to provide greater security of access to a classroom, avoiding irregularities, such as falsified signatures on the presence sheet, or use of adulterated identities. It applies an image recognition process in which it seeks to extract relevant information from an image, then encode and compare it with other facial data stored in an image database. This information of the images represents a set of characteristics that present the variations between the images of the faces collected by the system and those contained in the image database. The Facial Recognition System is composed of two processing modules: training and recognition. It was applied in a high school classroom to evaluate the usefulness and accuracy of these algorithms for people recognition.

关键词： Face recognition image databases Computational modeling Computer security Control systems Spectral analysis

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Learned D-AMP: Principled Neural Network Based Compressive image Recovery 31

Learned D-AMP: Principled Neural Network Based Compressive I...

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31st Annual conference on Neural Information processing systems (NIPS)

作者： Metzler, Christopher A. Mousavi, Ali Baraniuk, Richard G. Rice Univ Houston TX 77251 USA

Compressive image recovery is a challenging problem that requires fast and accurate algorithms. Recently, neural networks have been applied to this problem with promising results. By exploiting massively parallel GPU processing architectures and oodles of training data, they can run orders of magnitude faster than existing techniques. However, these methods are largely unprincipled black boxes that are difficult to train and often-times specific to a single measurement matrix. It was recently demonstrated that iterative sparse-signal-recovery algorithms can be "unrolled" to form interpretable deep networks. Taking inspiration from this work, we develop a novel neural network architecture that mimics the behavior of the denoising-based approximate message passing (D-AMP) algorithm. We call this new network Learned D-AMP (LDAMP). The LDAMP network is easy to train, can be applied to a variety of different measurement matrices, and comes with a state-evolution heuristic that accurately predicts its performance. Most importantly, it outperforms the state-of-the-art BM3D-AMP and NLR-CS algorithms in terms of both accuracy and run time. At high resolutions, and when used with sensing matrices that have fast implementations, LDAMP runs over 50x faster than BM3D-AMP and hundreds of times faster than NLR-CS.

关键词： Neural networks

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Automated Detection and Segmentation of Brain Tumor Using Genetic Algorithm

Automated Detection and Segmentation of Brain Tumor Using Ge...

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International conference on Smart systems and Inventive Technology (ICSSIT)

作者： V KIRUTHIKA LAKSHMI C. AMARSINGH FEROZ J ASHA JENIA MERLIN P.G Scholar/ECE Anna University Chennai Francis Xavier Engineering College Tirunelveli-627003 TamilNadu INDIA Assoc.Professor/ECE Anna University Chennai Francis Xavier Engineering College Tirunelveli-627003 Tamilnadu INDIA

ISBN: (纸本)9781538658741

Medical image processing is the most emerging and challenging field nowadays . Magnetic Resonance images act as a main source for the development of classification system. The extraction, identification and segmentation of affected region from Magnetic resonance brain image is significant but is a time consuming task for the clinical experts. To overcome this limitation, it is essential to use computer aided techniques. To improve accuracy and efficiency in medical segmentation process, the proposed tumor segmentation is based on adaptive threshold algorithm. Deep learning CNN classifier used to compare the test and trained data and produces the result for tumor. The proposed technique results have been evaluated and validated based on accuracy, sensitivity and specificity. The detection, extraction and classification of MR brain images is done by using MATLAB software.

关键词： Tumors image segmentation Genetic algorithms Feature extraction Gabor filters Wavelet transforms

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Dynamic Data Driven SAR Reconstruction on Hybrid Multicore systems

Dynamic Data Driven SAR Reconstruction on Hybrid Multicore s...

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International Green Computing conference

作者： Adeesha Wijayasiri Sanjay Ranka Sartaj Sahni Department of Computer and Information Science and Engineering University of Florida Gainesville FL 32611

ISBN: (纸本)9781538674673

The reconstruction of nxn-pixel Synthetic Aperture Radar imagery using a Backprojection algorithm is compute intensive and incurs O(n 2 · m) cost, where m is the number of pulses. As part of this research, we develop dynamic data driven multiresolution algorithms that speed up SAR backprojection on GPUs, hybrid multicore and many-core processors. Further, we performed experiments to observe improvements on a variety of architectures. The challenges in improving performance of this spatially variant reconstruction process on any architecture is load balancing, which circumvents asymmetric work assignment. On GPUs, fine tuned algorithms were developed as part of our research for improving execution time. Further, communication between processors was overlapped with computation to reduce overall execution time. We also developed parallel algorithms and software for constructing multi-resolution SAR images on hybrid multicore processors (HMPs). In particular, several load balancing algorithms were developed for optimizing performance and energy consumption on HMPs. We also developed a systematic approach for deriving the performance-energy trade-offs on HMPs while exploiting dynamic voltage and frequency scaling (DVFS) features of CPU cores and GPUs. This approach helps the user to select the right system configuration, that is, the number of processing elements of each type (cores/GPUs/etc.) and the respective clock frequencies, depending on whether performance or energy optimization is critical to the user. We evaluated performance and energy consumption of our algorithms on an Intel Knights Landing (KNL) processor as a representative of a many-core architecture. We also compared performance and energy consumption of KNL, Ivy Bridge and Tesla K40m.

关键词： image reconstruction image resolution Graphics processing units Energy resolution Radar polarimetry Synthetic aperture radar

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Smart Marketing systems for Fruits using Wireless Sensors

Smart Marketing Systems for Fruits using Wireless Sensors

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IEEE International conference on System, Computation, Automation and Networking (ICSCAN)

作者： Kirupa Ganapathy Bhuvaneswari Balachander Saveetha University Chennai India

The objective of this paper is to develop a smart marketing system for farmers cultivating mango (Mangifera indica) and papaya (Carica papaya). This research involves wireless sensor network and Information technology to facilitate smart marketing to identify the quality of fruits in order distribute among consumers/marketing agencies. The sweetness, aroma and ripening index of the fruits are collected using smell and firmness sensors. Color and shape of the fruit determined using image processing techniques. Quality quantity of the fruits are classified using machine learning algorithms based on soft computing techniques. Our main aim to focus on accurate data estimation and timely marketing in order to ensure the need of the farmers.

关键词： image color analysis Intelligent sensors Shape Wireless sensor networks Agriculture

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