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

作者： Gautam Amiya Kottaimalai Ramaraj Pallikonda Rajasekaran Murugan Vishnuvarthanan Govindaraj Muneeswaran Vasudevan Arunprasath thiyagarajan Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

Osteoporosis is a disease that causes low bone mass and bone tissue deterioration. the application of medical image segmentation in identifying the anomalies present in the multimodal medical images is utilized in this work to predict and locate the low bone mass section. the X ray dataset is downloaded from the freely available Kaggle repository contains osteoporosis and normal bone images. this paper utilizes Gabor Filter for the extraction of features and the modified U-net algorithm derived from Deep Convolutional Neural Network for segmentation. For improvement over classical U-Net, we had gone for Parametric Relu as an activation function to overcome the disadvantages of Relu while keeping the advantages of Relu intact. the performance metric parameters including Mean Squared Error (MSE), Peak signal to Noise Ratio (PSNR), Tanimoto Co-efficient index (TC), Dice Overlap Index (DOI) and computational time (T) are evaluated andthe outcomes attained are *** fuzzy logic dependent rules could be applied to the final outcome of image processing and deep learning techniques for clinical decision making and handling treatmenttherapy of those suffering from any of these orthopedic ailments in the regular clinical practices. the proposed method performs better on many aspects than the existing deep learning algorithms like Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN).

关键词： Deep learning Osteoporosis image segmentation PSNR Bones Feature extraction Convolutional neural networks

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image compression and enlargement algorithms

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International Journal of Circuits, Systems and signal processing 2021年 15卷 836-846页

作者： Burova, I.G. Dem’yanovich, Yu.K. Terekhov, A.N. Altynova, A.Yu. Satanovskiy, A.D. Babushkin, A.A. St. Petersburg State University 7/9 Universitetskaya nab St.Petersburg199034 Russia

In some cases, there are problems associated with the compression and enlargement of images. the use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. the features of increasing the size of an image when using local polynomial or non-polynomial splines are considered. the main method for enlarging an image is based on the use of splines of the second and third order of approximation. Polynomial and trigonometric splines are considered. To speed up the process of enlarging the image, we used the parallelization techniques. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： image compression

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Approximate Multiplier Using Reordered 4–2 Compressor with OR-based Error Compensation

Approximate Multiplier Using Reordered 4–2 Compressor with ...

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International conference on ASIC

作者： Yufeng Xu Yi Guo Shinji Kimura Graduate School of Information Production and System Waseda University Japan

Today, the approximate circuits have become an efficient solution for low power design on human related error-tolerant applications, such as multimedia, recognition and several signal processing. Approximate multipliers are believed to be an important key to make approximate arithmetic systems, and more area efficient multiplier is expected. the paper proposes a new area-efficient 4-2 compressor based on input reordering and OR-based error compensation. By the reordering, we can focus on just 2 of 4 inputs, and the compressor becomes very simple and less gates. Two multipliers are proposed with different accuracy based on reordered compressors with OR-based error compensation. the experimental results show that the proposed multipliers achieve high accuracy (98.7% and 97.39%) while reduce power consumption (by 44.72% and 45.95%) and area (by 31.72% and 34.85%). the proposed approximate multipliers are applied on image sharpening process and show high PSNR and SSIM.

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Ocelot: A dynamic optimization framework for bulk-synchronous applications in heterogeneous systems

Ocelot: A dynamic optimization framework for bulk-synchronou...

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International conference on Parallel Architecture and Compilation Techniques (PACT)

作者： Gregory Diamos Andrew Kerr Sudhakar Yalamanchili Nathan Clark School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia College of Computing Georgia Institute of Technology Atlanta Georgia

ISBN: (纸本)9781617824692;9781509050321

Ocelot is a dynamic compilation framework designed to map the explicitly data parallel execution model used by NVIDIA CUDA applications onto diverse multithreaded platforms. Ocelot includes a dynamic binary translator from Parallel thread eXecution ISA (PTX) to many-core processors that leverages the Low Level Virtual Machine (LLVM) code generator to target x86 and other ISAs. the dynamic compiler is able to execute existing CUDA binaries without recompilation from source and supports switching between execution on an NVIDIA GPU and a many-core CPU at runtime. It has been validated against over 130 applications taken from the CUDA SDK, the UIUC Parboil benchmarks [1], the Virginia Rodinia benchmarks [2], the GPU-VSIPL signal and image processing library [3], the thrust library [4], and several domain specific applications. this paper presents a high level overview of the implementation of the Ocelot dynamic compiler highlighting design decisions and trade-offs, and showcasing their effect on application performance. Several novel code transformations are explored that are applicable only when compiling explicitly parallel applications and traditional dynamic compiler optimizations are revisited for this new class of applications. this study is expected to inform the design of compilation tools for explicitly parallel programming models (such as OpenCL) as well as future CPU and GPU architectures.

关键词： Graphics processing units Computer architecture Dynamic compiler Instruction sets Parallel processing

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An Efficient Hardware Implementation of Walsh Hadamard Transform for JPEG XR

An Efficient Hardware Implementation of Walsh Hadamard Trans...

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IEEE India conference (INDICON)

作者： Shaik Hafizullah M. S. S. V Srikrishna Manideep Vinay Sharma PallabKumar Nath Alok Naugarhiya Shrish Verma Electronics& Telecommunication Engineering NIT RAIPUR

ISBN: (纸本)9781538682364

Walsh Hadamard Transform (WHT) is an important transformation technique used in image and signal processing applications like image compression, filtering and speech processing. the WHT becomes an automatic choice in JPEG XR image compression standard due to the simple computation technique. the kernel matrix used in WHT consists of only two values namely +1 and -1, hence input coefficients are either added or subtracted. this paper proposes a high throughput pipelined architecture for computing WHT by reusing common sub-expressions. In the proposed design, computation is performed by adding and subtracting the input data and store the intermediate results (sub-expressions) in the register stacks for reuse. the architecture is designed and implemented in XC3S700AN-4 device and maximum frequency of operation obtained is 162 MHz. the comparison of the computing speed of the proposed architecture with the previously available designs has been done based on the throughput which is defined as the time interval for computing overall results of an 8×8 image matrix. the proposed architecture consumes 395 nS time (throughput) to generate the result of an 8×8 image matrix which is 51% lesser than that of the best available design.

关键词： Computer architecture Registers Clocks Transforms image coding throughput Frequency control

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Software and Hardware Implementation of Regular Mode CABAC Decoder in VVC Standard

Software and Hardware Implementation of Regular Mode CABAC D...

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International conference on image and signal processing and their applications (ISPA)

作者： Wahiba Menasri Abdelkader Morsli Abderrezak Gacemi Amina Yahia Chouihi Lakhder Softa Khalil Research Laboratory in Renewable Energies and Materials University Yahia Fares of Medea Algeria Research Laboratory on Electricale Engenering and Automatic University Yahia Fares of Medea Algeria Research Laboratory on Electrical Engineering and Automatic University Yahia Fares of Medea Algeria University Yahia Fares of Medea Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Video compression is considered as an important step-in real-time application for 4K and 8K video contain. Actually, several techniques of compression are adopted and developed, but the Versatile Video Coding (VVC) standard is the latest one. VVC adopt many new and developed techniques such LMCS filter, motion estimation, intra prediction and Context-based Adaptive Binary Arithmetic Coding plus (CABAC + ). CABAC + is the only operation of entropy coding adopted in VVC video codec but it undergone modifications and improvement compared to that used in High Efficiency Video Coding (HEVC). CABAC is operate with three main modes: Bypass (BM), Terminate (TM) and Regular (RM). Additionally, the heavy calculation of RM results a long critical path delay and consume more resources. We propose in this work, software and material implementation of regular mode CABAC + decoder in the latest VVC standard. Firstly, the RM CABAC + algorithm is implemented on MATLAB software in order to control and manage the decoding process of syntax element. Secondly, the RM CABAC + algorithm is designed, implemented, synthesized and emulated. Finally, comparison results are carried out between that obtained with ModelSim and that obtained with Matlab in order to ensure the accuracy of our proposed design.

关键词： Software algorithms signal processing algorithms Syntactics Software Hardware Mathematical models Decoding

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An Optimization for Distance Estimation in Real-Time with DM6437

An Optimization for Distance Estimation in Real-Time with DM...

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2012 4th International conference on signal processing Systems(ICSPS 2012)

作者： He Tian-xiang FU Xiao-ning Hou Guo-qiang School of Electromechanical Engineering Xidian University

the passive ranging system on monocular has earned its bright application prospect in modern opto-electronic countermeasures and traffic monitoring. this technology is characterized by using the imaging direction and the distance-related features in the target image to estimate distance by solving a proper equation [1] . the distance-related features could be obtained by making use of complex image processing algorithms [2] . Limited by the complexity and time-consuming of the algorithm, to realize such an algorithm in real-time is much difficult. To solve this problem, in addition to using high-performance image processing chip, optimization on program body is also a quite effective method. Taking into account the hardware characteristics of DM6437, the DSP chip, a simplified [3] Scale Invariant Feature Transform (SIFT [4] ) image matching algorithm was developed. On the other hand, a lot of effort has been done for optimization subroutine itself based on the characteristics of C64x+ DSP core, such as using intermediate variables, intrinsic operations, word access to short type data, the compiler settings, software pipelining and linear assembly. It is showed by our final experiments that the operation speed has been significantly improved. For an image sequence with size of 256×256 pixels, the operation speed was improved from 4 seconds per frame to 25 frames per second, which meets the requirements of real-time location algorithm perfectly while the ranging error is less than 7%.

关键词： video image, distance estimation, DM6437, optimization, SIFT

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A Robust Large-scale Dataset for Assessing Light Field image Quality

A Robust Large-scale Dataset for Assessing Light Field Image...

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IEEE International conference on signal and image processing (ICSIP)

作者： Jian Ma Zhuochi Liu Yaozong Xu Jiajun Yu Yang Wang Yuhao Liu School of Internet Anhui University Hefei China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In recent years, light field imaging has gained significant attention in the scientific community due to its ability to provide a more immersive representation of the 3D world. However, ensuring the quality of light field images is crucial for their subsequent processing and applications. Deep learning methods, leveraging neural networks, have shown promising performance in image Quality Assessment (IQA). However, the unique characteristics of light field data pose a challenge for existing IQA methods. To address this challenge, we propose a Robust Large-scale Dataset for Assessing Light Field image Quality, named RLSD, specifically designed for evaluating the quality of light field images. the dataset comprises both real and synthetic scenes, covering a wide range of key low attributes and including three representative distortions: compression, noise, and blur. To obtain subjective evaluations, we adopt the single stimulus continuous quality evaluation (SSCQE) method and compute the Mean Opinion Score (MOS). We performed statistical analysis on the dataset and experimental results indicate that our proposed RLSD dataset includes various common scenes and distortion levels, making it suitable for designing and evaluating LF-IQA algorithms. the dataset is publicly available at the following link: “https://***/s/1kJmx4qsy8ywLPba-HwGCEg” (password: XY28).

关键词： image quality three-dimensional displays image coding Statistical analysis Noise Neural networks Imaging

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FlexWAFE - A High-end Real-Time Stream processing Library for FPGAs 07

FlexWAFE - A High-end Real-Time Stream Processing Library fo...

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Design Automation conference

作者： Amilcar do Carmo Lucas Sven Heithecker Rolf Ernst Institute for Computer and Communication Network Engineering Technical University of Braunschweig Germany

ISBN: (纸本)9781595936271

Digital film processing is characterized by a resolution of at least 2 K (2048times1536 pixels per frame at 30 bit/pixel and 24 pictures/s, data rate of 2.2 Gbit/s); higher resolutions of 4 K (8.8 Gbit/s) and even 8 K (35.2 Gbit/s) are on their way. Real-time processing at this data rate is beyond the scope of today's standard and DSP processors, and ASICs are not economically viable due to the small market volume. As an answer to these challenges, an FPGA-based approach was followed in the FlexFilm project. the multi-board, multi-FPGA hardware/software architecture is based on Xilinx Virtex-II Pro FPGAs which contain the reconfigurable image stream processing data path, large external SDRAM memories for multiple frame storage, and a PCI Express communication backbone network. Different applications are supported on a single hardware platform by using different FPGA configurations. this paper will focus on the FlexWAFE framework, a component library consisting of parameterizable modules for real-time stream processing including memory and communication controllers. Some of the library blocks' parameters are set at synthesis time via VHDL generics, while others are run-time configurable. this combination allows some flexibility without sacrificing FPGA area or speed.

关键词： Field programmable gate arrays Hardware image storage Software libraries Digital signal processing Software architecture Streaming media SDRAM Spine Application software

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T1-Weighted Contrast-Enhanced Synthesis for Multi-Contrast MRI Segmentation

T1-Weighted Contrast-Enhanced Synthesis for Multi-Contrast M...

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IEEE signal processing and Communications applications (SIU)

作者： Muzaffer Özbey Tolga Çukur Elektrik ve Elektronik Mühendisliği Bölümü Bilkent Üniversitesi Ankara Türkiye Ulusal Manyetik Rezonans Araştırma Merkezi Bilkent Üniversitesi Ankara Türkiye Sinirbilim Programı Mühendislik ve Fen Bilimleri Enstitüsü Bilkent Üniversitesi Ankara Türkiye

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

In recent years, deep learning techniques have been used in computer science as well as in many other disciplines. Successful detection of complex relationships and connections within big data enables effective use of deep learning in many areas. Deep learning with the detection of patterns and abnormalities in images is also a promising method for the field of Radiology. Detection of abnormalities in MR images enables detection of brain tumor and can automate this process. However, the deep learning models developed for brain tumor detection are sensitive to missing MR images in the input data and therefore the model is not robust enough. One of the models used for brain tumor detection requires a combined MR image in 4 different contrast and sequence; T1, T2, Flair and T1c images. In this study, it is proposed to synthesize the missing contrast image in the input data with another deep learning technique. Incomplete T1c MR image was synthesized by the generative adversarial networks (GAN) method and brain tumor detection performance was examined.

关键词： Gallium nitride image segmentation Brain modeling Deep learning Tumors three-dimensional displays Data models

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