Transmission Control Protocol for Mobile Ad Hoc Network

The Transmission Control Protocol (TCP) is a reliable protocol of transport layer which delivers data over unreliable networks. It was designed in the context of wired networks. Due to popularity of wireless communication it is made to extend TCP protocol to wireless environments where wired and wireless network can work smoothly. Although TCP work in wireless and wired-cum-wireless network, the performance is not up to the mark. In literature lot of protocols has been proposed to adopt TCP in wireless mobile ad hoc network. In this, we present an overall view on this issue and detailed discussion of the major factors involved. In addition, we survey the main proposals which aim at adapting TCP to mobile and static Ad hoc environments. Specifically, we show how TCP can be affected by mobility and its interaction with routing protocol in static and dynamic wireless ad hoc network.

Adoption of Dual Iris and Periocular Recognition for Human Identification

Personal identification from the iris images acquired under less-constrained imaging environment is highly challenging. Such environment requires the development of efficient iris segmentation approach and recognition strategy which can exploit multiple features available for the potential identification. So, along with the iris features periocular features have increasing attention in biometrics technology. For the recognition purpose iris and periocular information are collected from both the eyes of same person simultaneously. The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of image for periocular biometric is expected to require less subject cooperation. In this chapter, a dual iris based multimodal biometric system that increases the performance and accuracy of the typical iris recognition system is proposed.

HTLS Conductors

The power system has undergone multifold growth in its generation, transmission and distribution in past few decades. The types of conductors used for transmission system in India are ACSR / AAAC. These conductors have several constraints. The Ampacity of these conductors is less and hence they cannot be operated at high temperature also the losses in these type of conductors are more. To overcome the drawbacks of ACSR / AAAC conductors, this paper proposes a new approach of using High Tension Low Sag (HTLS) conductors, also a comparison is made between ACSR, AAAC and HTLS conductors on the basis of voltage drop and power loss for benefit evaluation of HTLS conductor over traditionally used conductors.

Indic Language

The aim of this research paper is to convert Kannada script to Braille, to enable the visually-impaired lead a better life by means of providing better learning aides. It proposes a possibility of facilitating the regional teachers to teach Kannada through Braille. “Braille Lipi” is instrumental in providing an able platform for the visually-impaired to habituate studying. This paper addresses the various aspects of “Braille Lipi”, it throws light on the origin and various levels, which depends on user-type (either simple, moderate or expert) explained with architecture of Braille system. Kannada to Braille Conversion Tool mainly focuses on elaborating the conversion of Kannada script to Braille script. An attempt to better understand, by a brief insight to Kannada script, Kannada alphabets is made and the whole intention of the contribution is a humble gesture to humanity. The main advantage of the model is visually-impaired can also have access to e-governance.

Cotton Leaf Disease Detection by Feature Extraction

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

Restoration Technique to Optimize Recovery Time for Efficient OSPF Network

Some high speed IP networks, which involve interior gateway protocols, such as OSPF, are not capable of finding the new routes to bypass the effect like failure in time. At the point when the failure occurs the network must converge it before the traffic has the capacity to go to and from the network segment that caused a connection disconnect. The duration of the convergence period of these protocols vary from hundred of milliseconds to 10 seconds, which creates unsteadiness and results high packet loss rate. This issue may be determined by proposing an algorithm that can rapidly react to the topology change and reduce the convergence time by providing back up path which is already stored in routing table before the failover occurs.

White Patch Detection in Brain MRI Image Using Evolutionary Clustering Algorithm

Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this paper, an attempt is taken to analyse a CT brain image. It has been segmented for a particular patch in the brain CT image that may be one of the tumours in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it.In this paper both the concept of clustering and thresholding technique with edge based segmentation methods like sobel, prewitt edge detectors is applied. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.

Face Recognition using Fast Fourier Transform

Biometrics refers to metrics related to human characteristics and Traits. Face Recognition is the process of identification of a person by their facial image. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. The objective is to authenticate a person, to have a FAR and FRR very low. This project introduces a new approach for face recognition system using FFT algorithm. The database that contains the images is named as train database and the test image which is stored in test database is compared with the created train database. For further processing RGB data is converted into grayscale, thus reduces the matrix dimension. FFT is applied to the entire database and mean value of the images is computed and the same is repeated on test database also. Based on the threshold value of the test image, face recognition is done. Performance evaluation of Biometrics is done for normal image, skin color image, ageing image and blur image using False Acceptance Rate(FAR), False Rejection Rate(FRR), Equal Error Rate(EER) and also calculated the accuracy of different images.

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

A Study on Different Facial Features Extraction Technique

Facial recognition is the most natural means of biometric identification as it deals with the measurement of a biological relevance. Since, faces varies from each and every person, therefore, it can be used for security purpose. Face recognition is a very challenging problem, where the human face changes over time, as it depends on the pose, expression, occlusion, aging, etc. It can be used in many areas such as for surveillance purposes, security, general identity verification, criminal justice system, smart cards, etc. The most important part of the face recognition is the evaluation of facial features. With the help of facial feature, the system usually looks for the position of eyes, nose and mouth and distances between them can be detected and computed. This chapter will discuss some of the techniques that can be used to extract important facial features.