Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. In this paper, a neural network-based algorithm for predicting the temperature is presented. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time dataset. The results are compared with practical working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting. Real time processing of weather data indicate that the BPN based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well. Index Terms—Multi layer perception, Temperature forecasting, Back propagation, Artificial Neural Network

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An Efficient Weather Forecasting System using Artificial Neural Network

An Adaptive Thresholding algorithm of field leaf image

Problem statement: The problem inherent to any digital image is the large amount of
bandwidth required for transmission or storage. This has driven the research area of image
compression to develop algorithms that compress images to lower data rates with better quality.
Artificial neural networks are becoming attractive in image processing where high computational
performance and parallel architectures are required. Approach: In this research, a three layered
Backpropagation Neural Network (BPNN) was designed for building image
compression/decompression system. The Backpropagation neural network algorithm (BP) was used for
training the designed BPNN. Many techniques were used to speed up and improve this algorithm by
using different BPNN architecture and different values of learning rate and momentum variables.
Results: Experiments had been achieved, the results obtained, such as Compression Ratio (CR) and
peak signal to noise ratio (PSNR) are compared with the performance of BP with different BPNN
architecture and different learning parameters. The efficiency of the designed BPNN comes from
reducing the chance of error occurring during the compressed image transmission through analog or
digital channel. Conclusion: The performance of the designed BPNN image compression system can
be increased by modifying the network itself, learning parameters and weights. Practically, we can
note that the BPNN has the ability to compress untrained images but not in the same performance of
the trained images.

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Improving the Performance of Backpropagation Neural Network Algorithm for Image Compression/Decompression System

Iris recognition is a form of biometric techniques that identifies user with the unique iris patterns between the pupil and the sclera. There is an advantage that its recognition accuracy is very high due to high DOF (degree of freedom) of the iris patterns. However, it requires an iris image with high quality in order to preserve the high recognition accuracy. So, the conventional iris recognition systems have used expensive cameras with zoom lens because an iris image which is obtained from the camera without zoom lens has low resolution at a depth of focus region. To overcome this problem, we propose a new super-resolution method based on multiple MLPs (multi- layer perceptrons) for the iris recognition. The proposed method uses three MLPs considering the edge directions of the iris patterns. We produce a high resolution iris image from a single low resolution iris image by using bilinear interpolation based on the output pixel values of the three MLPs. Experimental results showed that the improvement of the recognition accuracy was obtained by using the proposed method.

Super-Resolution Method Based on Multiple Multi-Layer Perceptrons for Iris Recognition

This work presents the implementation of trainable Artificial Neural Network (ANN) chip, which can be trained to implement certain functions. Usually training of neural networks is done off-line using software tools in the computer system. The neural networks trained off-line are fixed and lack the flexibility of getting trained during usage. In order to overcome this disadvantage, training algorithm can implemented on-chip with the neural network. In this work back propagation algorithm is implemented in its gradient descent form, to train the neural network to function as basic digital gates and also for image compression. The working of back propagation algorithm to train ANN for basic gates and image compression is verified with intensive MATLAB simulations. In order to implement the hardware, verilog coding is done for ANN and training algorithm. The functionality of the verilog RTL is verified by simulations using ModelSim XE III 6.2c simulator tool. The verilog code is synthesized using Xilinx ISE 10.1 tool to get the netlist of ANN and training algorithm. Finally the netlist was mapped to FPGA and the hardware functionality was verified using Xilinx Chipscope Pro Analyzer 10.1 tool. Thus the concept of neural network chip that is trainable on-line is successfully implemented.

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Implementation of Neural Network Back Propagation Training Algorithm on FPGA

Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly. Keywords—Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.

Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function

In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Counter Propagation Neural Network, it takes longer time to converge. The reason for this is that the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbor with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative Distribution Function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used the Counter Propagation Neural Network yield high compression ratio as well as it converges quickly. Keywords—Correlation, Counter Propagation Neural Networks, Cummulative Distribution Function, Image compression.

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E. Anna Saro

Papers

Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function

Image Mapping with Cumulative Distribution Function for Quick Convergence of Counter Propagation Neural Networks in Image Compression