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2017 IEEE International conference on Circuits and Systems, ICCS 2017

作者： Rakesh, S. Grace, K. S. Vijula Dept. of ECE Noorul Islam Centre for Higher Education Thuckalay KanyakumariTamil Nadu India Dept. of ECE Mangalam College of Engineering Ettumanoor KottayamKerala India

ISBN: (纸本)9781509064809

The rapid advancements in science and technology has augmented the demand for Digital signal Processors (DSPs) with high computing performance and real time processing capabilities. Multiply Accumulate (MAC) Unit is one of the most significant blocks which helps a DSP to achieve high performance. The performance of the MAC unit determines the ultimate overall performance of the system. Several MAC unit designs have been developed and implemented so far to achieve better performance parameters. This paper provides an overview of the different designs used in a MAC unit in order to improve its performance. © 2017 IEEE.

关键词： Digital signal processors

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A Flexible, High-Performance FPGA Implementation of a Feed-Forward Equalizer for Optical Interconnects up to 112 Gb/s

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JOURNAL OF signal processing SYSTEMS FOR signal image AND VIDEO TECHNOLOGY 2017年第2期88卷 107-125页

作者： Maragos, Konstantinos Spatharakis, Christos Lentaris, George Kontzilas, Panagiotis Dris, Stefanos Bakopoulos, Paraskevas Avramopoulos, Hercules Soudris, Dimitrios Natl Tech Univ Athens Microprocessors & Digital Syst Lab Zografos 15780 Greece Natl Tech Univ Athens Photon Commun Res Lab Zografos 15780 Greece

Commodity optoelectronic components and multi-level modulation formats are combined nowadays in optical networks to increase their throughput while decreasing their cost. To overcome the inherent limitations of such interconnects, research focuses on digital equalizers that compensate for the effects of the developed channels. The current paper proposes the use of FPGAs to enhance the speed, power and flexibility of digital equalization for the next generation 100 Gb/s rack-to-rack optical links in datacenters. We present the high-performance hardware architecture of a flexible feed-forward equalizer (FFE) with multiple reconfigurations. We describe parallelization techniques to accelerate FFE, accuracy analysis for various FFE scenarios, as well as a design space exploration leading to a fine-tuned and platform-dependent FFE customization. Our final implementation on a single Xilinx XC7VH580T FPGA device with GTZ transceivers can support a single link of up to 112 Gbps (56 GSa/s with PAM-4 modulation) and 2.26 . 10(-6) Bit-Error-Rate.

关键词： FPGAs Optical interconnects Feedforward equalization Parallel architectures 112 Gb/s Datacenters

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Fast Cloud Detection Using Low-Frequency Components of Satellite imagery 25

Fast Cloud Detection Using Low-Frequency Components of Satel...

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25th signal processing and Communications Applications conference (SIU)

作者： Ufuk, D. Utku Demirpolat, Caner Demirci, M. Fatih TUBITAK Uzay Teknol Arastirma Enstitusu Ankara Turkey TOBB ETU Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781509064946

Being able to rapidly detect clouds in satellite imagery is critical in terms of increasing mission efficiency of ground observation satellites. In this work, a new approach has been proposed for cloud detection involving the utilisation of lowfrequency Discrete Wavelet Transform (DWT) components whose sizes are 1/64 of the original image sizes. In the proposed method, several texture features are calculated from the low-frequency DWT components and the cloud pixels are detected by using a KNearest Neighbors (KNN) classifier. The proposed method has been tested on the Gokturk-2 images. Since the utilization of lowfrequency components leads to a significant loss in detail;the cloud detection performance have decreased to some extent. Nevertheless the obtained results were found to be sufficient for determining the cloudiness rate at a small margin of error, and at approximately 145x increased processing speed.

关键词： Cloud Detection Gokturk-2 Discrete Wavelet Transform Satellite image Texture Feature

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A Hybrid Approach to image Segmentation: Combination of BBO (Biogeography based optimization) and Histogram Based Cluster Estimation 25

A Hybrid Approach to Image Segmentation: Combination of BBO ...

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25th signal processing and Communications Applications conference (SIU)

作者： Kucukkulahli, Enver Erdogmus, Pakize Polat, Kemal Duzce Univ Bilgisayar Muhendisligi Bolumu Bilgisayar Teknol Duzce Turkey Abant Izzet Baysal Univ Elekt Elekt Muhendisligi Bolumu Bolu Turkey

ISBN: (纸本)9781509064946

image segmentation is the process of separating objects within an image. image segmentation can be considered as an important computer vision problem in image sensing where the homogeneous regions in an image can be distinguished with high accuracy. In this study, a two stage hybrid method has been proposed for image segmentation. In the first stage, the Histogram Based Cluster Estimation (HBCE) is used to determine the number of clusters on the image. In the second stage, the cluster numbers determined by the HBCE algorithm are given to the BBO (Biogeography based optimization) algorithm and then image segmentation is performed. In this study, the proposed hybrid image segmentation method was applied to 6 different images taken from Berkeley database and compared with human segmentation results obtained from the same database. To test the performance of the proposed image segmentation method, RI (Rand Index), GCE (Global Consistency Error) and run time as comparison criterion have been used. The proposed method has been compared with other hybrid methods namely HBCE-PSO (Particle Swarm Optimization) and HBCE-k means clustering. When running on 6 different images, the best Rand Index values from the results obtained for all three methods are as follows;HBCE-BBO incorporation: 0.9859, HBCE-PSO incorporation: 0.9856, HBCE-k means incorporation: 0.7570. The results have shown that the HBCE-BBO hybrid method yields better results than the other two hybrid methods used in working with 6 different image segmentations.

关键词： clustering image segmentation Biogeography based optimization Histogram Based Cluster Estimation

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design of Virtual Reality Robot Based on Android Platform 4

Design of Virtual Reality Robot Based on Android Platform

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4th International conference on image Information processing (ICIIP)

作者： Bai, Jiawang Wang, Xin Zhang, Wenbin Zhuang, Chaojian Pan, Mengdi Jilin Univ Coll Software Changchun Jilin Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781509067343

Combining the virtual reality (VR) technology with hardware, this paper proposes a remote control system for robot based on Android and Arduino platform. A binocular camera and Pan/Tilt/Zoom (PTZ) are used to build dynamic view, VR headset display device with an Android phone is used to receive video and binocular parallax is used to form stereoscopic images. The control scheme adopts the command word recognition technology based on the Auto Speech Recognition of iFLYTEK. Then it sends out the corresponding control signal by judging the input voice. The experiment results show this system feasible and reliable.

关键词： component virtual reality robot voice recognition rotation matrix

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A Novel System for Content Based Retrieval of Remote Sensing images based on Bag of Spectral Values 25

A Novel System for Content Based Retrieval of Remote Sensing...

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25th signal processing and Communications Applications conference (SIU)

作者： Dai, Osman Emre Demir, Begum Sankur, Bulent Bruzzone, Lorenzo Bogazici Univ Elekt Elekt Muhendisligi Bolumu Istanbul Turkey Trento Univ Bilgisayar Bilimleri & Bilgi Muhendisligi Bolumu Trento Italy

ISBN: (纸本)9781509064946

This paper presents a novel system for the content based retrieval in remote sensing images based on the Bag of Spectral Values. In the proposed method, the spatial and spectral information contents of RS images are treated separately by parallel pipelines. Defining a novel spectral descriptor, the Bag of Spectral Values, this approach allows for a computationally efficient extraction of features from multiband RS images. The conjunction of the features extracted from the two pipelines are then passed onto the retrieval system that computes the likelihood of any image to contain a given label based on sparse reconstruction performances of sample images. This label based approach allows the retrieval method to be suitable for single label archives as well as multi-labeled ones. Retrieved images are chosen such that their label sets are most likely to be a close match to that of the query image. Experimental results demonstrate the effectiveness of the proposed spectral description method as well the success of the retrieval system in the case of a small number training samples.

关键词： Spectral features sparse reconstruction content based image retrieval remote sensing

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Color Multilevel Thresholding using Gray-Level Co-occurrence Matrix and Differential Evolution Algorithm 6

Color Multilevel Thresholding using Gray-Level Co-occurrence...

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IEEE International conference on Communication and signal processing (ICCSP)

作者： Pare, S. Kumar, A. Singh, G. K. PDPM Indian Inst Informat Technol Design & Mfg Dept ECE Jabalpur 482005 MP India Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju South Korea Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttarakhand India

ISBN: (纸本)9781509038008

Color images such as satellite images (remote sensing images) and some natural images exhibit poor fuzziness in boundaries, complex background, and non-uniform illumination. This article presents a new approach intended to produce more sophisticated and reliable segmentation of such color images, which are based on the gray level co-occurrence matrix (GLCM) and differential evolution (DE) algorithm that upholds optimum result during iterations. The proposed methodology augments the robustness and effectiveness of the traditional GLCM algorithm. Performance of the presented technique is evaluated through different tests conducted over various natural and satellite colored images. The simulation results validate the overall accuracy obtained by the proposed GLCM-DE approach, and the methodology was found to be superior to other optimization algorithm based techniques.

关键词： Gray level co-occurrence matrix Optimization algorithm Color images Multilevel thresholding image segmentation

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OBJECT SEGMENTATION IN THE DEEP NEURAL NETWORK FEATURE DOMAIN FROM HIGHLY CLUTTERED NATURAL SCENES 24

OBJECT SEGMENTATION IN THE DEEP NEURAL NETWORK FEATURE DOMAI...

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24th IEEE International conference on image processing (ICIP)

作者： Yousif, Hayder He, Zhihai Kays, Roland Univ Missouri Columbia Dept Elect & Comp Engn Columbia MO 65211 USA North Carolina State Univ Dept Forestry & Environm Resources Raleigh NC 27695 USA

ISBN: (纸本)9781509021758

Deep convolutional neural networks (DCNNs) offer an effective hierarchical representation of images for various vision analysis tasks, including classification and detection. In this paper, we propose to study background modeling and object segmentation from highly cluttered natural scenes in the DCNN feature domain instead of traditional pixel domain Specifically, we first design and train a DCNN for animal human-background object classification, which is used to analyze the input image to generate multi-layer feature maps, representing the responses of different image regions to the animal-human-background classifier. From these feature maps, we construct the so-called deep abjectness graph for accurate animal-human object segmentation with graph cut. The segmented object regions from each image in the sequence are then verified and fused in the temporal domain using background modeling. Recognizing that the DCNN is very computation-intensive, we explore a fast and efficient design of the DCNN which finds a good trade-off between complexity and the classification-segmentation performance. Our experimental results demonstrate that our proposed method outperforms existing state-of-the-art methods on the camera-trap dataset with highly cluttered natural scenes.

关键词： Camera-trap graph cut Convolutional Neural Network image segmentation object detection

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Sigma delta analog to digital converter: design and implementation with reduction in power consumption

Sigma delta analog to digital converter: Design and implemen...

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2017 International conference on Trends in Electronics and Informatics, ICEI 2017

作者： Gandhi, Neha Shelke, Sushama D. India

ISBN: (纸本)9781509042579

Data converters play an instrumental part in all communication systems. The numerous benefits of digital signal over analog signals have necessitated the use of analog to digital(A/D) converters in the transmitter side and of digital to analog(D/A) converters in the receiver side of the communication system. The steady developments in VLSI and signal processing domain have made Sigma-Delta(ΣΔ) A/D Converter an absolute choice for data conversion due to its architectural efficiency and easy implementation. The reduced requirement of chip area, reduced power consumption and improved frequency response without affecting the compatibility and design cost have made these converters the preferred choice. But like all other systems, Sigma Delta converters are not perfect. They have their own drawbacks which need to be reduced or removed to be at par for the efficient designing of the communication system. The decimation filter being a crucial block in a ΣΔ A/D Converter, the demands for efficient design are met by improving its design. The paper highlights the benefits of ΣΔ A/D Converter over other converter technologies and some Decimation filter design architectures. A ΣΔ A/D Converter for audio bandwidth application has been designed. The designing has been done using Matlab Simulink DSP Toolbox. © 2017 IEEE.

关键词： Data handling

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A Customized Many-Core Hardware Acceleration Platform for Short Read Mapping Problems Using Distributed Memory Interface with 3D-Stacked Architecture

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JOURNAL OF signal processing SYSTEMS FOR signal image AND VIDEO TECHNOLOGY 2017年第3期87卷 327-341页

作者： Liu, Pei Hemani, Ahmed Paul, Kolin Weis, Christian Jung, Matthias Wehn, Norbert KTH Royal Instaitute Technol Sch ICT Dept Elect Syst S-16440 Stockholm Sweden Indian Inst Technol Delhi Dept Comp Sci & Engn New Delhi India Univ Kaiserslautern Microelect Syst Design Res Grp Kaiserslautern Germany

Rapidly developing Next Generation Sequencing technologies produce huge amounts of short reads that consisting randomly fragmented DNA base pair strings. Assembling of those short reads poses a challenge on the mapping of reads to a reference genome in terms of both sensitivity and execution time. In this paper, we propose a customized many-core hardware acceleration platform for short read mapping problems based on hash-index method. The processing core is highly customized to suite both 2-hit string matching and banded Smith-Waterman sequence alignment operations, while distributed memory interface with 3D-stacked architecture provides high bandwidth and low access latency for highly customized dataset partitioning and memory access scheduling. Conformal with original BFAST program, our design provides an amazingly 45,012 times speedup over software approach for single-end short reads and 21,102 times for paired-end short reads, while also beats similar single FPGA solution for 1466 times in case of single end reads. Optimized seed generation gives much better sensitivity while the performance boost is still impressive.

关键词： Accelerator architectures Application specific integrated circuits Bioinformatics Computational biology Coprocessors Three-dimensional integrated circuits

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