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With the growing concern related to global warming and energy security a need for a clean source of energy has risen. PV system is one of the best renewable sources of energy. PV inverter is a part of the Photo-Voltaic system which is capable of converting AC current into DC current so that the power produced can be used in household appliances and grids. In this paper SPWM technique is used as a...
Pansharpening is a technique used for fusion of low spatial resolution multispectral images with high spatial resolution panchromatic image. The objective of pansharpening is to enhance the spatial resolution of multispectral image while preserving its spectral information. The commonly used pansharpening methods like IHS, PCA, Gram Schmidt and Wavelet based method compromise either on spatial or...
The images, captured by camera, might suffer from poor contrast, saturation artefacts or improper brightness. Hence, image enhancement becomes an important step to improve the quality of image. Images are enhanced such that there is change in intensity or saturation component, keeping hue unchanged. Often, gamut problem arises when transforming from one plane to another. In this paper, the technique...
Data hiding is the process of concealing the existence of one type of information into another. In reversible data hiding techniques, original host image can be successfully restored from the marked image after the exact extraction of the secret information. In recent years, various modifications of this technique have been proposed. In this paper, the reversible data hiding technique based on prediction...
Compressive sensing system merges sampling and compression for a given sparse signal. It can reconstruct the image accurately by using fewer linear measurements than the original measurements. Hence, it is able to achieve reduction in complexity of sampling and number of computations. Since existing algorithms for implementation of sampling for the whole image are time consuming and it requires huge...
Multimodal medical image fusion plays a crucial role in medical diagnostics and treatment. Widely used transform domain based image fusion methods like DWT, CVT, CT, NCST suffer from spatial inconsistency and high complexity. Recently proposed guided filter based spatial domain image fusion techniques are also limited by contrast reduction and halo artifacts. In this paper, the existing guided filter...
ECG signal is time varying in nature which is most common source used for the diagnosis and analysis of heart diseases present in the patient. ECG is recorded by placing electrodes at specified positions of human body. During recording, ECG is contaminated with artifacts and noises which always degrade its quality, and makes accurate and automatic interpretation more difficult. Power line interference,...
Compressed sensing is an emerging technique that reconstructs signals and images discarding the Shannon Nyquist theory of reconstruction. This paper demonstrates that if orthogonal matching pursuit is implemented in multi stages, it gives a faster recovery of an image with kth Sparsity level by taking k ln R measurements for a dimension R. The results of orthogonal matching pursuit are comparable...
In Compressive Sensing (CS), the small collections of non-adaptive linear projections of a sparse signal can efficiently help in the reconstruction of the image with the help of the image data sent to the decoder. Although number of CS measurements are small but the result obtained is acceptable because the image data is analyzed by Human Visual System (HVS) which is less sensitive to high frequency...
In the field of compressed sensing, Orthogonal least Square is a well known greedy algorithm for sparse signal reconstruction. It has been proved that this algorithm gives stable and speedy recovery as compared to L-norm minimization but at the cost of computation complexity. This paper demonstrates that by dividing orthogonal least square algorithm in sub stages, its complexity can be reduced up...
Reconstruction of a signal based on Compressed Sensing (CS) framework relies on the knowledge of the sparse basis & measurement matrix used for sensing. While most of the studies so far focus on the application of CS in fields of images, radar, astronomy etc.; wepresent our work on application of CS in field of speech/Audio processing. This work shows a comparative analysis of different sparse...
Reconstruction of a signal based on Compressed Sensing (CS) framework relies on the knowledge of the sparse basis & measurement matrix used for sensing. While most of the studies so far focus on the prominent random Gaussian, Bernoulli or Fourier matrices, we have proposed construction of efficient sensing matrix we call Grassgram Matrix using Grassmannian matrices. This work shows how to construct...
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