ANN-based Innovative Segmentation Method for Handwritten text in Assamese

Artificial Neural Network (ANN) s has widely been used for recognition of optically scanned character, which partially emulates human thinking in the domain of the Artificial Intelligence. But prior to recognition, it is necessary to segment the character from the text to sentences, words etc. Segmentation of words into individual letters has been one of the major problems in handwriting recognition. Despite several successful works all over the work, development of such tools in specific languages is still an ongoing process especially in the Indian context. This work explores the application of ANN as an aid to segmentation of handwritten characters in Assamese- an important language in the North Eastern part of India. The work explores the performance difference obtained in applying an ANN-based dynamic segmentation algorithm compared to projection- based static segmentation. The algorithm involves, first training of an ANN with individual handwritten characters recorded from different individuals. Handwritten sentences are separated out from text using a static segmentation method. From the segmented line, individual characters are separated out by first over segmenting the entire line. Each of the segments thus obtained, next, is fed to the trained ANN. The point of segmentation at which the ANN recognizes a segment or a combination of several segments to be similar to a handwritten character, a segmentation boundary for the character is assumed to exist and segmentation performed. The segmented character is next compared to the best available match and the segmentation boundary confirmed

An electron diffraction study of the development of preferred orientations in vacuum-condensed deposits

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Artificial Neural Network (ANN) based Object Recognition Using Multiple Feature Sets

In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning

ZnO/ZnS core/shell nanostructures based gas sensor for sensing Acetone gas at room temperature

In this paper  ZnO/ZnS core/shell nanostructures are used to fabricate the gas sensor which can sense low concentration of acetone gas at room temperature. Due to its reducing properties, acetone gas releases electrons to the surface of the core/shell nanorods. Therefore a sharp increase in conductivity of the sensing material was observed when the sensor was exposed to the acetone gas. The fabricated sensor exhibited excellent sensitivity towards acetone gas at room temperature and is capable of detecting it to a minimum concentration of 10 ppm.  Â

Convolutional Coding Using Booth Algorithm For Application in Wireless Communication

Convolutional codes are preferred types of error control codes which can achieve low BERs at signal to noise ratio (SNR) very close to Shannon limit. Here, a new method of convolutional encoding is proposed using the general Booth algorithm for multiplication. This algorithm follows a fast multiplication process and achieves a significantly less computational complexity over its conventional counterparts. It can be a useful technique for use in chip design as it provides significant improvements. In this work, the performance of conventional convolutional coding with Viterbi decoding in AWGN channel, is studied and the results show the effectiveness of the work described here

Transmit Beamforming in Dense Networks-A Review

Communication technology has prospered in manifolds over the last decade. The scarcity of spectrum as well as the demand for higher data rates and increase in capacity has become a matter of concern. Newer technologies have evolved time and again, the latest of which is Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A) systems more commonly known as 4G technology. The striking feature of LTE/LTE-A is the deployment of smaller cells (femto cells) in the vicinity of a large macro cells resulting in a dense network. As a result the data rate as well as capacity has increased in manifolds but the detrimental factor is the issue of interference between the various cells. Beamforming provides a solution in removing the issues of interference in dense networks. This paper focuses on the interference scenario in LTE dense networks and gives an overview of different beamforming methods that can provide a solution to the interference problem. Further, a review of several such methods so far proposed in available literature has been presented in this paper.Keywords:LTE/LTE-A, Dense Network, Interference,Beamformin

STUDY ON THE HYDROTHERMAL GROWTH OF ZnO NANORODS FOR PIEZOTRONIC APPLICATIONS

The capability of a certain material to generate an electric charge in response to applied mechanical stress is called as piezoelectric Effect. Metal oxidesemiconductors having high piezoelectric coefficient can be cost effectively manufactured by a simple hydrothermal methods at low temperature. These nanostructures are capable of transforming mechanical deformation into electrical power. The nanostructure morphologies and dimensions can be controlled by controlling the growth conditions. When subjected to mechanical deformations, these nanostructures are capable of transforming mechanical deformation into electrical power. Due to the structural noncentralsymmetry,ZnO nanostructures exhibit anisotropic piezoelectric properties. High aspect ratio ZnO nanostructures can be merely designed using hydrothermal methods and these nanowires or nanorods show piezoelectric properties. When subjected to mechanical deformations, these nanostructures undergo a charge separation due to inherent structural asymmetry. Tapping of the separated charges and subsequent accumulation can give a manifestation of mechanical to electrical energy transformation and lead to energy harvesting

Chaotic Spreading Sequence for Spread Spectrum Modulation in Stochastic Wireless Channels

In wireless communication system, spread spectrum techniques have been widely used because of the advantages like robustness against interference and noise, low probability of intercept, realization of Code Division Multiple Access (CDMA) and so on. One of the key aspects in such methods is the generation of the spreading sequence which continues to be challenging issue. This paper proposes a scheme for generating binary sequences from chaotic logistic map for use in Direct Sequence Spread Spectrum (DS SS) system in fading environment. The main advantages of such usage are increased security of the data transmission and ease of generation of a extended numbers of chaotic sequences. Generally to spread the bandwidth of the transmitting signals, pseudo-noise (PN) sequences, Gold sequences have been used extensively. We have generated a binary spreading sequences using logistic map. A comparison between Gold sequences and proposed sequences in faded environment have been derived. It is clearly seen that our sequences are comparable and even superior to Gold sequences in several key aspects such as bit error rate (BER), computational time and mutual information for three different spreading code lengths. Therefore, the proposed sequences can be effectively used as spreading sequences in high data rate modulation schemes.Keywords: Logistic map code, Gold code, BER, DS SS.Cite as: Katyayani Kashyap, Manash Pratim Sarma, Kandarpa Kumar Sarma, “Chaotic Spreading Sequence for Spread Spectrum Modulation in Stochastic Wireless Channels†ADBU J.Engg.Tech., 1(2014) 0011402(5pp

AN ADAPTIVE SAR IMAGE DESPECKLING ALGORITHM USING STATIONARY WAVELET TRANSFORM

In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. The solution of the MAP estimator is based on the assumption that the wavelet coefficients have a known distribution. Rayleigh distribution is used for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients for the purpose of designing the MAP estimator. Rayleigh distribution is used for modeling the speckle noise since speckle noise can be well described by it. The parameters required for MAP estimator is determined by the technique used for parameter estimation after SWT. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images