This paper deals with adding ANN assisted Turbo coding and decoding to OFDM signal for transmission in the wireless channel. Channel coding improves the performance of OFDM signal, but the system ...design becomes complex due to the iterative decoding technique. The system design can be simplified and made faster by the use of ANN in the coding and decoding process. Simulation is done in MATLAB to analyse the BER performance of Turbo coded OFDM in Rayleigh and Rician fading channel.
Artificial Neural Network (ANN)s like the Multi Layer Perceptron (MLP)s with temporal characteristics demonstrate architectural complexity during estimation of Multi Input Multi Output (MIMO) ...channels for which alternative ANN options are required. The Recurrent Neural Network (RNN) with the ability to track time-dependence of input signals emerge as a choice for such applications. But a standoff surfaces regarding the approach in which RNNs are to be trained to deal with signals with real and complex components. Signals with separate real and complex components can be used to train RNNs better with the responses of each block combined and optimized with Self Organizing Map (SOM) enabling them to show satisfactory performance with tightly coupled transmissions. The present work deals with the formation of a cluster of Complex Time Delay Fully Recurrent Neural Network (CTDFRNN)s optimized with SOMs with inherent temporal characteristics to deal with MIMO channel estimation. The performance derived is superior to statistical and MLP-based approaches and provide diversity gain.
The work is related to the use of error correction coding and adaptive equalization as an aid to Maximal Ratio Combining (MRC) in order to improve bit error rate(BER)s of received signals in wireless ...channels with fading characteristics. Several modulation schemes are used in the transmission in Gaussian, multipath Rayleigh and Rician fading channels. The work adopts a few error correction codes and Least Mean Square (LMS) adaptive filter blocks as part of a MRC set-up and is tested under SNR variation between -10 to 10 dB in Gaussian and multipath slow fading channels. The results generated justify the use of the coding and adaptive equalizer block as an aid to the MRC setup.
Out of several antenna design techniques the Artificial Neural Network (ANN) based method is suitable for prediction of characteristic parameters of loop antenna by considering transmit - receive ...conditions of practical communication set-ups. The predicted set of parameters can be used to fix dimensions of a loop antenna which involves theoretical calculations. This work proposes an approach to determine the best suitable combination of conductor thickness and loop radius using Competitive Learning ANN from predicted values of antenna parameters. The proposed method uses the ANN predicted parameters to find the optimized set of conductor thickness and loop radius using Self Organizing Map (SOM) to fix the layout of a loop antenna for applications with electrically driven finite element grids.
Texture classification is an important and challenging factor in image processing system which refers to the process of partitioning a digital image into multiple constituent segments. The goal of ...segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Artificial Neural Network (ANN) Based texture classification or Segmentation is an advanced technique providing rich information of an image of interest. As a part the work, an ANN is implemented to segment the image. For that a particular type of ANN is configured and trained so that it becomes capable of segmenting an image. The current work deals with a task where an object of interest is to be segmented out of a background for processes which can be carried out as part of extended applications.
The quality and details captured in speech corpus directly affects the precision of performance in an Automatic Speech Recognition (ASR) system. The current work proposes a platform for speech corpus ...generation using an adaptive LMS filter and LPC Cepstrum, as a part of an Artificial Neural Network (ANN) based Speech Recognition System which is exclusively designed to recognize isolated numerals of Assamese language-a major language in the North Eastern part of India. The paper describes the use of an adaptive filter configured as a pre-emphasis block for generation of LPC-Cepstrum feature to apply in an ANN-based Speech Recognition System for Assamese language.
The structure of a Digital Phase Locked Loop (DPLL) based systems for dealing with Nakagami-m fading is proposed here. The emphasis of the work is the implementation of the essential components of a ...DPLL for better reception of signals with certain modulation transmitted through Nakagami-m channels. A sixth order polynomial fitting algorithm for better phase-frequency detection has been implemented, which has helped to attain optimum performance of DPLL. The results of simulation of the proposed DPLL with Nakagami-m fading and QPSK modulation shows that the proposed method provides better performance than existing systems of similar type.
The work is related to the use of Artificial Neural Network (ANN) assisted equalization as an aid to Maximal Ratio Combining (MRC) in order to improve bit error rate (BER) values of demodulated ...signals in wireless channels that have both Gaussian and multipath fading characteristics. Modulation technique used in this work is Bipolar Phase Shift Keying (BPSK) in Gaussian and multipath Rayleigh fading channels. The work considers the use of ANN block as part of a MRC set-up and is tested under SNR variation between -10 to 10 dB in Gaussian and multipath fading channels. The results generated justify the use of the ANN block as an aid to the MRC setup.
A facial recognition based verification system is a computer application for automatically identifying or verifying a person from a digital image or a video feature. Our work deals with the ...recognition of human faces. One stage of recognizing a face is to figure out how the eyes, nose and mouth are placed in the facial structure which are used as decision support entities of the system configured using a Generalized Feed Forward Artificial Neural Network (GFFANN). A specially designed feature set extracted from the eyes, nose and mouth are fed to a GFFANN which provides a decision. The other flow of the system takes the entire face and extracts its features which are applied to another GFFANN. The decision from these two GFFANNs is combined to provide a reinforced decision for verification of a person's identity. The work includes images with illumination variations. The test results obtained show a success rate of around the higher 90s.
