2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)最新文献

Satellite image processing 卫星图像处理

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085410

K. Vani

引用次数: 7

Robust audio watermarking for monitoring and information embedding 鲁棒音频水印的监测和信息嵌入

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085650

R. Subhashini, K. B. Bagan

引用次数: 8

Automatic segmentation and recognition of iris images: With special reference to twins 虹膜图像的自动分割与识别:以双胞胎为例

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085415

C. N. Devi

引用次数: 7

Literature survey on traffic-based server load balancing using SDN and open flow 基于SDN和开放流的基于流量的服务器负载均衡研究综述

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085416

S. Raghul, T. Subashri, K. Vimal

引用次数: 11

Flower species recognition system using convolution neural networks and transfer learning 花卉种类识别系统采用卷积神经网络和迁移学习

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085675

I. Gogul, V. S. Kumar

{"title":"Flower species recognition system using convolution neural networks and transfer learning","authors":"I. Gogul, V. S. Kumar","doi":"10.1109/ICSCN.2017.8085675","DOIUrl":"https://doi.org/10.1109/ICSCN.2017.8085675","url":null,"abstract":"Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While modern search engines provide methods to visually search for a query image that contains a flower, it lacks in robustness because of the intra-class variation among millions of flower species around the world. Hence in this proposed research work, a Deep learning approach using Convolutional Neural Networks (CNN) is used to recognize flower species with high accuracy. Images of the plant species are acquired using the built-in camera module of a mobile phone. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). A machine learning classifier such as Logistic Regression or Random Forest is used on top of it to yield a higher accuracy rate. This approach helps in minimizing the hardware requirement needed to perform the computationally intensive task of training a CNN. It is observed that, CNN combined with Transfer Learning approach as feature extractor outperforms all the handcrafted feature extraction methods such as Local Binary Pattern (LBP), Color Channel Statistics, Color Histograms, Haralick Texture, Hu Moments and Zernike Moments. CNN combined with Transfer Learning approach yields impressive Rank-1 accuracies of 73.05%, 93.41% and 90.60% using OverFeat, Inception-v3 and Xception architectures, respectively as Feature Extractors on FLOWERS102 dataset.","PeriodicalId":383458,"journal":{"name":"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124595520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 104

An integrator circuit using differential difference current conveyor transconductance amplifier 一种采用差动差电流输送跨导放大器的积分器电路

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085420

Ravipati Linita, V. V. Reddy, Avireni Srinivasulu

引用次数: 6

Machine condition monitoring using audio signature analysis 利用音频特征分析进行机器状态监测

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085717

B. Rubhini, P. Ranjan

引用次数: 3

Design and implementation of programmable read only memory using reversible decoder on FPGA 基于FPGA可逆解码器的可编程只读存储器的设计与实现

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085645

Gopi Chand Naguboina, K. Anusudha

{"title":"Design and implementation of programmable read only memory using reversible decoder on FPGA","authors":"Gopi Chand Naguboina, K. Anusudha","doi":"10.1109/ICSCN.2017.8085645","DOIUrl":"https://doi.org/10.1109/ICSCN.2017.8085645","url":null,"abstract":"Reversible logic is the emerging field for research in present era. The aim of this paper is to design and synthesize a Programmable Read Only Memory (PROM) using a reversible decoder which is designed using reversible logic with minimum quantum cost. The PROM is a Programmable Logic device which consists of fixed AND Gates and programmable OR gates array. Fixed AND gates can be termed as a decoder. PROM finds its applications in cell phones, RFID tags, video game consoles, medical devices, computers and other electronic devices. An n input and k output Boolean function f (a1, a2, a3, ….an) (referred as (n, k)) is said to be logically reversible if and only if, the number of inputs are equal to the number of outputs i.e., ‘n’ equals ‘k’ and the input pattern uniquely maps the output pattern. The reversible logic must run both forward and backward as well such that the inputs can also be retrieved from outputs. There are many reversible logic gates in literature like NOT gate, Feynman Gate (CNOT gate), Double Feynman Gate, Peres Gate, TR gate, Seynman Gate and many more. Fan-out and Feed-back are not allowed in Logical Reversibility. To overcome the Fan out limitation, the signals from required output lines are duplicated to desired lines using additional reversible combinational circuits. Reversible Logic owns its applications in various fields which include Quantum Computing, Optical Computing, Nano-technology, Computer Graphics, low power VLSI etc., Reversible logic is gaining its own importance in recent years largely due to its property of low power consumption and low heat dissipation. In this paper, the Programmable Read Only Memory (PROM) using reversible decoder which has less heat dissipation and low power consumption is proposed. The designed circuit is analyzed in terms of quantum cost, garbage outputs and number of gates. The Circuit has been designed and simulated using Xilinx software and implemented on FPGA SPARTAN — 3E.","PeriodicalId":383458,"journal":{"name":"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115406454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A survey on ARP cache poisoning and techniques for detection and mitigation ARP缓存中毒及其检测和缓解技术综述

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI: 10.1109/ICSCN.2017.8085417

Jitta Sai Meghana, T. Subashri, K. Vimal

引用次数: 13

Perceptual video hashing using 3D-radial projection technique 基于三维径向投影技术的感知视频散列

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-01 DOI: 10.1109/ICSCN.2017.8085727

R. Sandeep, Saksham Sharma, P. Bora

{"title":"Perceptual video hashing using 3D-radial projection technique","authors":"R. Sandeep, Saksham Sharma, P. Bora","doi":"10.1109/ICSCN.2017.8085727","DOIUrl":"https://doi.org/10.1109/ICSCN.2017.8085727","url":null,"abstract":"A function that outputs a feature vector from the perceptual contents of the input video is called a perceptual video hashing function and the output feature vector that characterizes the perceptual contents of the input video is called the perceptual video hash. This hash must be robust to the manipulations that preserves the perceptual contents of the video and fragile to the modifications that vary the perceptual contents of the video. The wide scale applications of perceptual hash in the world of multimedia, such as video authentication, video copyright protection, and video retrieval, emphasize the importance of this area of research. This work generates a perceptual hash from the video using the 3D-radial projection of the pixels and assesses the differentiating capabilities and the perceptual robustness of the hash generated. This method is the 3D extension of the 2D radial projection based image hashing. In this work, the variance of the luminance values of the projected pixels is calculated for each randomly generated sub-tensor. The variance of all the sub-tensors are averaged along the second dimension and projected onto the discrete cosine transform (DCT) basis. The performance measure of the proposed work is assessed using the receiver operating characteristic (ROC) curves. Simulation results indicate that the performance of the proposed method is satisfactory for both the content-preserving and the content changing attacks.","PeriodicalId":383458,"journal":{"name":"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125855680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10