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A Neural Network Based Optic Disc Segmentation

A Neural Network Based Optic Disc Segmentation

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International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： Nija K.S. Varun P. Gopi Anitha V.S. GEC Wayanad APJ Abdul Kalam Technological University Thiruvananthapuram Kerala India NIT Tiruchirappalli Tamilnadu India

Identifying the pixels belonging to the optic disc region accurately and segmenting the optic disc's ambiguous boundary in retinal images is crucial in developing systems that assist in detecting retinal diseases. Such segmentation is an arduous task to accomplish manually and eased off by computer-aided systems. The proposed method makes effective use of a convolutional neural network that utilizes the benefits of dropout regularization, skip connections, and a minimal number of fully connected layers. The network captures the context and localization information accurately. It is an image input-image output network model that is tolerant of a wide range of input image resolutions and field of views available in the datasets. The method is tested on four standard retinal image datasets, DIARETDB1, DIARETDB0, MESSIDOR, and DRIONS-DB, and achieved an accuracy of 99.26%, 99.53%, 97.86%, and 92.50%, respectively. A comparative analysis of the proposed algorithm has segmented the optic disc better than many existing algorithms.

关键词： Wireless communication Image segmentation Signal processing algorithms optical computing optical fiber networks optical imaging Retina

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Ant Colony Optimization based Medical Image Preservation and Segmentation

Ant Colony Optimization based Medical Image Preservation and...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International conference on

作者： Priya Nandihal Piyush Kumar Pareek victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging optical design Image edge detection Metaheuristics

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Arabic Offline Character Recognition Model Using Non-dominated Rank Sorting Genetic Algorithm 6th

Arabic Offline Character Recognition Model Using Non-dominat...

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6th International conference on Advanced Intelligent systems and Informatics, AISI 2020

作者： Darwish, Saad M. Hassan, Osama F. Elzoghaly, Khaled O. Department of Information Technology Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Faculty of Science Department of Mathematics Damanhour University Damanhur Egypt

ISBN: (纸本)9783030586683

In recent years, there was intensive research on Arabic optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR results are unsatisfactory and are still an evolving research area. Exploring the best feature extraction techniques and selecting an appropriate classification algorithm lead to superior recognition accuracy and low computational overhead. This paper presents a new Arabic OCR approach by integrating both of Extreme Learning Machine (ELM) and Non-dominated Rank Sorting Genetic Algorithm (NRSGA) in a unified framework with the aim of enhancing recognition accuracy. ELM is adopted as a neural network classifier that has a short processing time and avoids many difficulties faced by gradient-based learning methods such as learning epochs and local minima. NSRGA is utilized as a feature selection algorithm that has better convergence and spread of solutions. NSRGA emphasizes ranking among the solutions of the same front, along with elite preservation mechanism, and ensuring diversity through the nearest neighbor method reduces the run-time complexity using the simple principle of space-time trade-off. The Experimental results reveal the efficiency of the proposed model and demonstrated that the features selection approach increases the accuracy of the recognition process. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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3rd International conference on Computer Science, Engineering and Education Applications, ICCSEEA 2020

3rd International Conference on Computer Science, Engineerin...

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3rd International conference on Computer Science, Engineering and Education Applications, ICCSEEA 2020

ISBN: (纸本)9783030555054

The proceedings contain 59 papers. The special focus in this conference is on Computer Science, Engineering and Education Applications. The topics include: Construction of Individual Learning Scenarios;technology-Enhanced Financial Education and Sustainability Goals;health Status Recognition System for Communication Equipment Based on Data Mining;use of Sustainable Innovation in information System: Integrate Individual’s Spirit and Team Climate;relations of the Genetic Code with Algebraic Codes and Hypercomplex Double Numbers;research on Logistics Center Location-Allocation Problem Based on Two-Stage K-Means algorithms;operational Intelligence Software Concepts for Continuous Healthcare Monitoring and Consolidated Data Storage Ecosystem;method of Recognition and Indexing of People’s Faces in videos Using Model of Machine Learning;deduplication Method for Ukrainian Last Names, Medicinal Names, and Toponyms Based on Metaphone Phonetic Algorithm;two-Layer Perceptron for Voice Recognition of Speaker’s Identity;significant Parameters of the Keystroke for the Formation of the Input Field of a Convolutional Neural Network;detection of Ventricular Late Potentials in Electrocardiograms Using Machine Learning;smart Prothesis: Sensorization, Humane vibration and Processor Control;optimization of Stock Basing on Improved Grey Prediction Model: A Case Study on Garment Supply Chain;improved Post-quantum Merkle Algorithm Based on Threads;improvement of Merkle Signature Scheme by Means of optical Quantum Random Number Generators;integrated Water Management and Environmental Rehabilitation of River Basins Using a System of Non-linear Criteria;distribution of Resources Between Composite Applications in a Hyperconverged System;the Possibilistic Gustafson-Kessel Fuzzy Clustering Procedure for Online Data processing;information System Penetration Testing Using Web Attack Automated Simulation.

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A Reconfigurable Area and Energy Efficient Hardware Accelerator of Five High-order Operators for vision Sensor Based Robot systems

A Reconfigurable Area and Energy Efficient Hardware Accelera...

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2021 IEEE International conference on Integrated Circuits, Technologies and Applications, ICTA 2021

作者： Wang, Qianjin Zhan, Yi Liu, Bingqiang Wu, Jiajun Shi, Youhua Yu, Guoyi Wang, Chao Huazhong University of Science and Technology School of Optical and Electronic Information Wuhan China Waseda University Faculty of Fundamental Science and Engineering Tokyo Japan

ISBN: (纸本)9781665417471

This paper proposes a reconfigurable hardware accelerator design of five major high-order operators for vision sensor based robot systems. These five high-order operators include convolution, median filtering, Euclidean distance, vector inner-product and iToF, which are intensively used in robot vision algorithms. In this work, a reconfigurable hardware accelerator design method for multiple high-order operators is proposed. FPGA implementation results show that the proposed design has achieved area efficiency with 17.54% reduced LUTs and 44.02% reduced FFs against the baseline hardware design of the five high-order operators. Case studies of Laplace edge-detection and iToF benchmark demonstrate the energy efficiency of proposed design with 19.70% and 6.2% reduction in energy consumption, respectively. © 2021 IEEE.

关键词： System-on-chip

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Target Classification in Synthetic Aperture Radar and optical Imagery Using Loihi Neuromorphic Hardware

Target Classification in Synthetic Aperture Radar and Optica...

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IEEE High Performance Extreme Computing conference (HPEC)

作者： Barnell, Mark Raymond, Courtney Wilson, Matthew Isereau, Darrek Cicotta, Chris Air Force Res Lab Informat Directorate Rome NY 13441 USA SRC Inc Radar & Sensors Div North Syracuse NY USA

ISBN: (纸本)9781728192192

Intel's novel Loihi processing chip has been used to explore new information exploitation techniques. Specifically, we analyzed two types of data (optical and radar). These data modalities and associated machine learning algorithms were used to showcase the ability of the system to address real world problems, such as object detection and classification. Intel's fully digital Loihi design is inspired by biological processes and brain functions. Neuromorphic architectures, such as Loihi, promise to improve computational efficiency for various machine learning tasks with a realizable path toward implementation into many systems, e.g., airborne computing for intelligence, surveillance and reconnaissance systems, and/or future autonomous vehicles and household appliances. With the current software development kit, it is possible to train an artificial neural network model in a common deep learning framework such as Keras and quantize the model weights for a simplistic, direct translation onto the Loihi hardware. The radar imagery analyzed included a seven-vehicle class target set, which was processed at a rate of 9.5 images per second and with an overall accuracy of 90.1%. The optical data included a binary (two classes), and another nine-class data set. The binary classifier processed the optical data at a rate of 12.8 images per second with 94.0% accuracy. The nine classes optical data was processed at a rate 12.9 images per second and 79.7% accuracy. Lastly, the system used similar to 6 Watts of total power with similar to 0.6 Watts being utilized by the neuromorphic cores. The inferencing energy used to classify each image varied between 14.9 and 63.2 millijoules/image.

关键词： High Performance Computing Neuromorphic Computing Machine Learning Embedded Computing Synthetic Aperture Radar optical Image Exploitation Spiking Neural Networks Intelligence Surveillance and Reconnaissance

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Spatially Multiplexed Multicore Fiber Communication to Fuel the Next information Revolution 6

Spatially Multiplexed Multicore Fiber Communication to Fuel ...

引用

conference on Micro-Structured and Specialty optical Fibres vi

作者： Mishra, Jitendra K. Kumar, Atul Agarwal, Saurabh Pareek, Prakash Priye, vishnu Indian Inst Informat Technol Ranchi Dept Elect & Commun Engn Ranchi 834010 Jharkhand India Indian Inst Informat Technol Design & Mfg Jabalpu Dept Elect & Commun Engn Jabalpur India Govind Ballabh Pant Univ Agr & Technol Coll Technol Pantnagar Uttar Pradesh India Vaagdevi Engn Coll Singaram Telangana India Indian Inst Technol ISM Dhanbad Dhanbad 826004 Bihar India

ISBN: (纸本)9781510634831

Space division multiplexing (SDM) technique is proposed to augment performance of short-reach optical transmission systems by utilizing 8-core multicore fiber (MCF) that typically degrades at high data rates for higher order modulation. Intercore crosstalk (XT) and higher order modulation format are the most challenging impairments of SDM based interconnection technology for efficient usage of MCF as OI. To satisfy the capacity requirements of emerging heterogeneous and bandwidth-intensive short reach communication links a frequency interleaving scheme is applied to MCF OI transmission systems. The negative effects of spectral overlap and intercore XT is reduced by shifting channel frequencies between adjacent cores. To combat explosive growth in data volume requirement of next generation high-performance short-reach OIs transmission higher order quadrature phase shift keying (QPSK) modulation in index profiled MCF system is proposed. Digital signal processing such as multiple input multiple output (MIMO) equalization are used to evaluate the symbol error probability in QPSK modulated SDM system.

关键词： Multicore fiber optical interconnects space division multiplexing crosstalk fiber optics communications

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Iterative algorithm of optical triangulation sensors signals superposition for measuring solid deformation 6

Iterative algorithm of optical triangulation sensors signals...

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6th International conference information Technology and Nanotechnology. Session Image processing and Earth Remote Sensing, ITNT-IPERS 2020

作者： Diyazitdinov, Rinat Networks and Communication Systems Department Povolzhskiy State University of Telecommunications and Informatics Samara Russia

The article was described as the algorithms for contour superposition. The first contour is the etalon signal. The first contour output signal of the optical triangulation sensor (measured object contour). The contour superposition method was developed on the hypothesis that etalon contour consists of line equations and measured contour consists of points sequence. The superposition includes two processing stages. The first stage is dividing the source sequence point into point subsequences. The second stage is making an equation that links line equations with point subsequences. The superposition parameters are calculated from this equation. The article was shown several iterative algorithms which differ source data (the line types for etalon signal). The error measuring of solid deformation was shown in the example side wear railway rail. The developing superposition algorithms were compared with superposition by control points. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Deformation

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Advanced DSP for monitoring and mitigation in optical transport networks

Advanced DSP for monitoring and mitigation in optical transp...

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optical Fiber Communication conference, OFC 2020

作者： Hoshida, Takeshi Tanimura, Takahito Oda, Shoichiro Yoshida, Setsuo Nakashima, Hisao Huang, Guoxiu Tao, Zhenning Fujitsu Limited 1-1 Shin-ogura Saiwai-ku Kawasaki212-8510 Japan Fujitsu R&D Center No. 2A Gong Ti Bei Lu Rd. Beijing China

DSP-based transceivers with enhanced monitoring and mitigation capabilities enable highly efficient transport networking with minimized excess margin and open line systems with enhanced availability. Examples for such... 详细信息

ISBN: (数字)9781943580712

ISBN: (纸本)9781943580712

关键词： Digital signal processing

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Segmentation of medical images using U-Net++

Segmentation of medical images using U-Net++

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： T. Jahnavi DN. Vasundhara Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology

Many eye diseases can be detected by segmenting retinal vessels, and their accurate computerization is becoming increasingly important in automated retinal disease screening systems. The segmentation of retinal vessels can be performed using many algorithms, but these methods improve accuracy levels. Low contrast vessels require more attention in the segmentation process due to low sensitivity due to improper segmentation. A new pre-processing step is proposed in this paper to extract retinal blood vessels with greater precision. Observing their impact on other existing algorithms is another aspect of testing these preprocessing steps. To segment retinal blood vessels, we suggest two steps. In the first step, the preprocessing module must be implemented and validated. We then apply these preprocessing stages to the proposed binarization steps to extract retinal blood vessels. This preprocessing phase provides a much-improved segmented vessel image using the traditional image-processing method. The image coherence technique was used to binarize retinal blood vessels. Identification and localization of retinal vessels separate anatomical structures from background fundus images, such as optic discs and maculae. Due to the noninvasive fundus imaging and the crucial information in the vascular network, retinal vessel identification studies are excellent for detecting and diagnosing diabetic retinopathy, glaucoma, hypertension, and age-related macular degeneration (AMD). As computer-aided techniques for segmenting retinal vessels have developed over the past two decades, they have become increasingly important and are becoming routine in clinical practice. Additionally, retinal vessel segmentation results are evaluated and validated. A final objective assessment of retinal vessel identification techniques is presented, along with a discussion of future developments and trends.

关键词： Training Image segmentation Sensitivity Retinopathy Veins optical imaging Retinal vessels

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