An Improved Face Recognition Using Neighborhood Defined Modular Phase Congruency Based Kernel PCA

A face recognition algorithm based on NMPKPCA algorithm presented in this paper. The proposed algorithm when compared with conventional Principal component analysis (PCA) algorithms has an improved recognition Rate for face images with large variations in illumination, facial expressions. In this technique, first phase congruency features are extracted from the face image so that effects due to illumination variations are avoided by considering phase component of image. Then, face images are divided into small sub images and the kernel PCA approach is applied to each of these sub images. but, dividing into small or large modules creates some problems in recognition. So a special modulation called neighborhood defined modularization approach presented in this paper, so that effects due to facial variations are avoided. Then, kernel PCA has been applied to each module to extract features. So a feature extraction technique for improving recognition accuracy of a visual image based facial recognition system presented in this paper

Lossless Linear Integer signal Resampling

This paper describes about signal resampling based on polynomial interpolation is reversible for all types of signals, i.e., the original signal can be reconstructed losslessly from the resampled data. This paper also discusses Matrix factorization method for reversible uniform shifted resampling and uniform scaled and shifted resampling. Generally, signal resampling is considered to be irreversible process except in some special cases because of strong attenuation of high frequency components. The matrix factorization method is actually a new way to compute linear transform. The factorization yields three elementary integer-reversible matrices. This method is actually a lossless integer-reversible implementation of linear transform. Some examples of lower order resampling solutions are also presented in this paper

Experimental Evidence of Time Delay Induced Death in Coupled Limit Cycle Oscillators

Experimental observations of time delay induced amplitude death in a pair of coupled nonlinear electronic circuits that are individually capable of exhibiting limit cycle oscillations are described. In particular, the existence of multiply connected death islands in the parameter space of the coupling strength and the time delay parameter for coupled identical oscillators is established. The existence of such regions was predicted earlier on theoretical grounds in [Phys. Rev. Lett. 80, 5109 (1998); Physica 129D, 15 (1999)]. The experiments also reveal the occurrence of multiple frequency states, frequency suppression of oscillations with increased time delay and the onset of both in-phase and anti-phase collective oscillations.Comment: 4 aps formatted RevTeX pages; 6 figures; to appear in Phys. Rev. Let

Effect of feeding maize fiber in wet, dry and silage form with cotton cake supplementation on intake, nutrient utilization and performance in Nellore Brown sheep

Maize fiber was evaluated in wet, dry and silage form with 200 g cotton cake supplementation in growing Nellore Brown ram lambs (24.8±0.96) using six sheep per treatment in a growth-cummetabolism trial of 90 days with collection of feed, leftover, feces and urine samples during the last ten days. Average daily gain (g), nutrient digestibility (OM, CP, NDF, ADF) tended to be higher (P = 0.07 to 0.09) and intake of OM, DOM, CP (gld) and ME (MJ!d) and nitrogen retention were significantly (P = 0. 0002 to 0. 002) higher in lambs when fed maize fiber in silage rather than in wet and dry form. Depending on input such as labor required ensilaging or drying of maize fiber seems an economically more beneficial and from a food security point of view a safer way than feeding wet maize fiber

A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN)

The management of plant diseases is vital for the economical production of food and poses important challenges to the employment of soil, water, fuel and alternative inputs for agricultural functions. In each natural and cultivated populations, plants have inherent sickness tolerance, however there also are reports of devastating impacts of plant diseases. The management of diseases, however, within reason effective for many crops. sickness management is allotted through the employment of plants that square measure bred permanently resistance to several diseases and thru approaches to plant cultivation, like crop rotation, the employment of pathogen-free seeds, the given planting date and plant density, field wetness management, and therefore the use of pesticides. so as to enhance sickness management and to stay up with changes within the impact of diseases iatrogenic by the continued evolution and movement of plant pathogens and by changes in agricultural practices, continued progress within the science of soil science is required. Plant diseases cause tremendous economic losses for farmers globally. it's calculable that in additional developed settings across massive regions and lots of crop species, diseases usually cut back plant yields by ten percent per annum, however yield loss for diseases usually exceeds twenty percent in less developed settings. Around twenty-five percent of crop losses square measure caused by pests and diseases, the Food and Agriculture Organization estimates. to unravel this, new strategies for early detection of diseases and pests square measure required, like novel sensors that sight plant odours and spectrographic analysis and bio photonics that may diagnose plant health and metabolism. In artificial neural networks, deep learning is an element of a broader family of machine learning approaches supported realistic learning. Learning is often controlled, semi-supervised or unmonitored. to handle several real-world queries, Deep Learning Approaches are normally used. so as to differentiate pictures and acknowledge their options, coevolutionary neural networks have had a larger result. This article will do a Leaf Disease Identification Survey with Deep Learning Methods. It takes Sugarcane leaf as an instance to our paper

Comparisons of ensiled maize, sorghum and pearl millet forages fed to sheep

Water-use efficient sorghum (7) and pearl millet (5) forages were compared with reference maize forage as silage tested with Nellore Brown sheep. Mean silage organic matter intake was 352, 297 and 137g!d in maize, sorghum and pearl millet silage, respectively Current pearl millet forage cultivars do not match maize forage in terms of fodder quality Of the 7 sorghum cultivars several were on par with maize though the cultivar dependent variation in intake was huge (254 to 343g!d). Anti-nutritive factors associated with sorghum like dhurrin were undetectable in the silages, although present in the fresh forage. A routine laboratory trait does not seem to describe sorghum and pearl millet forages adequately More research is required to understand the true nutritional potential of sorghum and in particular pearl millet forages. Dissemination of these forages based on only biomass yield should be discouraged