Digital Processing

The algorithm of the JPEG image compression based on DCT and IDCT was developed and implemented . The proposed algorithm can cope with the problem of impairing dependency of noised digital image. The result indicates that the algorithm is an effective way for grayscale image compression and the image rebuilt is acceptable. The practical experiments were done with MATLAB7.0. The algorithm doing experiments with MATLAB is simple and with little error, and it can improve the efficiency and precision of the image compression greatly.

People with disability such as visual impaired and also elderly for whom it's very hard to identify the screen text and area where the keyboard and mouse may not be an appropriate means of communication between systems, it would be a helpful to use voices to navigate and control the computer systems. Microsoft has designed an interface called SAPI (Speech Application Programming Interface) which supports dynamic speech input and output, and is integrated in our current operating systems. In this paper we have described a model which is developed for conversion of multilingual audio into multilingual editable text for continuous speech in offline mode. Automatic Speech Recognition, (ASR) is used that works with Dynamic Time Wrapping (DTW) algorithm. This text will be transmitted and displayed on computer or LCD. Software used is Microsoft Visual Studio. Coding language used is c sharp. Keywords - SAPI, ASR, DTW, Microsoft Visual Studio, C sharp.

The objective of this web application is to develop a system to conduct online tests in various domains and functional areas. The purpose is to enable a fast evaluation of applicant’s skills and abilities. Online Analytical Examination is the application useful to conduct online examination for an organization, Academic institutions and training centers. It is an excellent test management, which offers a complete solution for Computer Based Test (CBT). It keeps all the records of conducted exams, score reports and other info.The aim of “Online Analytical Examination” system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of online exam is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results for students the paper are given to students as their convenience and time and there is no need of using extra thing like paper, pen etc.

Remotely-sensed data obtained from satellites or aircraft are usually geometrically distorted due to the acquisition system and the movements of the platform. Preprocessing of satellite images prior to image classification and change detection is essential. Image Processing is a technique which is used to enhance raw images received from cameras and sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. Digital image processing is the technique of processing images in the form of discrete digital brightness quantities by means of using digital circuits or digital computers.

Underwater images have a wide range of applications in different fields. But underwater images have poor visibility, low contrast and diminishing colors. These all problems erupt as a result of the haze present in underwater images which seriously lowers the quality of underwater images. So, underwater image dehazing or haze removal is important. Haze removal is a challenging and complex problem because it is based on unknown depth information. This paper has reviewed various techniques for haze removal in underwater images. Every technique has its own advantages and limitations. This paper has also discussed the methods, advantages and limitations of various underwater image haze removal techniques.

Image processing is widely applied in various area of applications such as Medical, military, agriculture, etc.. The problem which generally occurs in image processing is the removal of noise generated due to various sources. In this paper a new approach based on neural network technique is proposed for the removal of noise. This technique follows three levels. This technique combines the advantages of filtering, neural network and bayes shrinkage technique. The noisy image is first passed through a bilateral filter and neural network is applied to the filtered image and the output of NN is then applied to bayes shrink. The proposed method outperforms other methods both visually and in case of objective quality peak-signal-to-noise ratio (PSNR) and MSE. Proposed method is verified for additive white Gaussian noise.

Wavelet transforms enable us to represent signals with a high degree of scarcity. Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. The aim of this paper is to study various thresholding techniques such as Sure Shrink, Visu-Shrink and Bayes Shrink and determine the best one for image denoising. This paper presents an overview of various threshold methods for image denoising. Wavelet transform based denoising techniques are of greater interest because of their performance over Fourier and other spatial domain techniques. Selection of optimal threshold is crucial since threshold value governs the performance of denoising algorithms. Hence it is required to tune the threshold parameter for better PSNR values. In this paper, we present various wavelet based shrinkage methods for optimal threshold selection for noise removal.

Wavelet transforms enable us to represent signals with a high degree of scarcity. Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. The aim of this paper is to study various thresholding techniques such as Sure Shrink, Visu Shrink and Bayes Shrink and determine the best one for image denoising. This paper presents an overview of various threshold methods for image denoising. Wavelet transform based denoising techniques are of greater interest because of their performance over Fourier and other spatial domain techniques. Selection of optimal threshold is crucial since threshold value governs the performance of denoising algorithms. Hence it is required to tune the threshold parameter for better PSNR values. In this paper, we present various wavelet based shrinkage methods for optimal threshold selection for noise removal.

BCI (Brain Computer Interface) roles as a machine that provides direct communication between brain and computer. These kinds of machines can help people with physical disability, does their daily tasks as well as healthy people. In these machines, the brain signals are recorded from the scalp and will be prepared for analyzing in three steps of preprocessing, feature selection and classification that what kinds of tasks have been imagined. In BCI applications a big challenge is to improve classification accuracy in parallel with the computation time. In this paper, in preprocessing level we filtered the samples of each electrode with band pass digital Butterworth filter with cutoff frequency of 0.5 to 30 HZ. In the next level, the features are extracted from some famous fractal dimension estimation of the signal. These fractal features are Katz and Higuchi. In the classification stage we used ANFIS (Adaptive Neuro-Fuzzy Inference System) classifier and compared it with three strong classifiers as FKNN (Fuzzy k-Nearest Neighbors), LDA (Linear Discriminate Analysis) and SVM (Support Vector Machine). We found ANFIS with Higuchi fractal features has the most classification accuracy (88%) among other investigated methods, but its speed is rather low among them.

Image is the combination of pixels. Pixel is the element which is present in the image in the dot form. In today’s era image is treated like as 3D form but at previous time image is in the form of 1D text or 2D text. In this paper we show the image compression on the images. We basically perform the compression on the biomedical images. Biomedical images are very huge in size. We select biomedical images because if we transfer any biomedical image from one place to another then we need compression methods.