Kinetics of Aldehyde Oxidation on Platinum Anode In Aqueous Perchloric Acid & Sulphuric Acid

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Dimensionality Reduction Using Band Selection Technique for Kernel Based Hyperspectral Image Classification

AbstractHyperspectral images have abundant of information stored in the various spectral bands ranging from visible to infrared region in the electromagnetic spectrum. High data volume of these images have to be reduced, preserving the original information, to ensure efficient processing. In this paper, dimensionality reduction is done on Indian Pines and Salinas-A datasets using inter band block correlation coefficient technique followed by Singular Value Decomposition (SVD) and QR decomposition. The dimensionally reduced images are classified using GURLS and LibSVM. Classification accuracies of the original image is compared to that of the dimensionally reduced image. The experimental analysis shows that, for 10% training sample the overall accuracy, average accuracy and kappa coefficient of the dimensionally reduced image (about 50% of the dimension is reduced) is i)83.52%, 77.18%, 0.8110 for Indian Pines and ii)99.53%, 99.40%, 0.9941 for Salinas-A dataset which is comparable to that of original image i)84.67%, 82.28%, 0.8247 for Indian Pines and ii)99.32%, 99.18%, 0.9916 for Salinas-A dataset

LIBRARY AND INFORMATION NEEDS OF DIFFERENTLY-ABLED STUDENTS IN KERALA: A STUDY

The study was conducted to investigate the information needs of differently-abled students in the school libraries of Kerala. The study was done among students belonging to the category of visually challenged (VC), hearing and speech impaired (HI), and physically challenged (PC) from special schools and schools under Inclusive Education of the Disabled at Secondary Stage (IEDSS). The study was based on a questionnaire survey, conducted in the three districts of Kerala state ie; Thiruvananthapuram, Ernakulum & Kozhikode. The analyses revealed that the information needs of differently-abled students have become complex and problematic due to the insufficiency of adequate information sources and services and there are quite a number of challenges faced by these students in accessing information from the libraries. The overall result of the study was that, though the library services provided in the school are useful for their studies, the respondents cannot make use of them because of barriers. The study comes out with some practical suggestions to improve the library services for differently-abled student

Characteristics of Anodic Film on Aluminium in Borate Bath

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Application of Least Square Denoising to Improve ADMM Based Hyperspectral Image Classification

AbstractHyperspectral images contain a huge amount of spatial and spectral information so that, almost any type of Earth feature can be discriminated from any other feature. But, for this classification to be possible, it is to be ensured that there is as less noise as possible in the captured data. Unfortunately, noise is unavoidable in nature and most hyperspectral images need denoising before they can be processed for classification work. In this paper, we are presenting a new approach for denoising hyperspectral images based on Least Square Regularization. Then, the hyperspectral data is classified using Basis Pursuit classifier, a constrained L1 minimization problem. To improve the time requirement for classification, Alternating Direction Method of Multipliers (ADMM) solver is used instead of CVX (convex optimization) solver. The method proposed is compared with other existing denoising methods such as Legendre-Fenchel (LF), Wavelet thresholding and Total Variation (TV). It is observed that the proposed Least Square (LS) denoising method improves classification accuracy much better than other existing denoising techniques. Even with fewer training sets, the proposed denoising technique yields better classification accuracy, thus proving least square denoising to be a powerful denoising technique

ℓ1 Trend Filter for Image Denoising

AbstractThe major problem in digital image processing is the presence of unwanted frequencies(noise). In this paper ℓ1 trend filter is proposed as an image denoising technique. ℓ1-trend filter estimates the hidden trend in the data by formulating a convex optimization problem based on ℓ1 norm. The proposed method extends the application of ℓ1 trend filter from one dimensional signals to three dimensional color images. Here the filter is applied over the image in a cascade, initially filtering along the rows followed by filtering along the columns. This identifies the hidden image information from the noisy image resulting in a smooth or denoised image. The proposed method is compared with the wavelet denoising technique using the quality metrics Peak-Signal-to-Noise-Ratio(PSNR) and Structural Similarity Index(SSIM)