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The phonocardiography (PCG) – the art and science of recording and interpreting of heart sounds using latest digital technology has significantly helped us to understand and interpret the complex heart sounds (normal, abnormal sounds including murmurs) and in particular valvular diseases. In general, the classification and interpretation of heart sounds and murmurs is based on a adjective 0-6/6 grade scale and described by using ˵faint″, ˵soft″, ˵loud″, ˵ high pitch″, ˵clear″, ˵thrill″, ˵tremor″, ˵musical″ and others terms. The adjective scales vary among the doctors and difficult to derive a standard model for heart sound quality and correct clinical interpretation. In this work, we propose a novel framework based on psychoacoustic principles and derive psychoacoustic models for the heart sounds and murmurs. We discuss the theoretical foundations, psychoacoustic principles and derive the mathematical models for the psychoacoustic features such as loudness, sharpness, intensity, strength, roughness, tonality etc. for a set of heart sounds and murmurs. The proposed framework helps in deriving heart sound quality and also helps in Identifying heart defects. [ABSTRACT FROM AUTHOR]

Homegardens are archetypal land-use systems in the tropics. They involve diverse kinds of trees and field crops that coexist in apparent harmony and in close juxtaposition with one another (Kumar and Nair, 2004). In the peninsular India, coconut palms (Cocos nucifera) form the "nucleus" of these gardens, around which the other components are orchestrated (Jose and Shanmugaratnam, 1993). Several multipurpose trees and shrubs also occur in homegardens. These include trees scattered around the homesteads and trees planted at specific points to provide or avoid shade, necessary or harmful to different plants, besides trees providing support to vines such as Piper nigrum (Mathew et al. 1996). In addition, many farmers integrate fast growing timber species in well-fertilized plantations (e.g. coconut) in expectation of extra cash returns. Indeed, the steadily rising timber prices in the local and regional markets of peninsular India have given an impetus to such intercropping practices. [ABSTRACT FROM AUTHOR]

Practical face recognition applications may involve hundreds and thousands of people. Such large-population face recognition (LPFR) can be a real challenge when the recognition system stores one template for each subject in the database and matches each test image with a large number of templates in real-time. Such a system should also be able to handle the situation where some subjects do not have a sufficient number of training images, and it should be flexible enough to add or remove subjects from the database. In this chapter, we address the LPFR problem and introduce the correlation pattern recognition (CPR)-based algorithms/systems for LPFR. The CPR, a subset of statistical pattern recognition, is based on selecting or creating a reference signal (e.g., correlation filters designed in the frequency domain from training images) and then determining the degree to which the objects under examination resemble the reference signal. We introduce class-dependence feature analysis (CFA), a general framework that applies kernel correlation filters (KCF) to effectively handle the LPFR problem. In place of the computationally demanding one template for each subject design method, we introduce a more computationally attractive approach to deal with a large number of classes via binary coding and error control coding (ECC). In this chapter, we focus on using advanced kernel correlation filters along with the ECC concept to accomplish LPFR. We include results on the Face Recognition Grand Challenge (FRGC) database. [ABSTRACT FROM AUTHOR]

The complexity and stability of the human iris pattern make it well suited for the task of biometric verification. However, any realistically deployed iris imaging system collects images from the same iris that exhibit variation in appearance. This variation originates from external factors (e.g., changes in lighting or camera angle), as well as the subject's physiological responses (e.g., pupil motion and eyelid occlusion). Following iris segmentation and normalization, the standard iris matching algorithm measures the Hamming distance between quantized Gabor features across a range of relative eye rotations. This chapter asserts that matching performance becomes more robust when iris images are aligned with a more flexible deformation model, using distortion-tolerant similarity cues. More specifically, the responses from local distortion-tolerant correlation filters are taken as evidence of local alignments. Then this observed evidence, along with the outputs of an eyelid detector, are used to infer posterior distributions on the hidden true states of deformation and eyelid occlusion. If the estimates are accurate, this information improves the robustness of the final match score. The proposed technique is compared to the standard iris matching algorithm on two datasets: one from the NIST Iris Challenge Evaluation (ICE), and one collected by the authors at Carnegie Mellon University. In experiments on these data, the proposed technique shows improved performance across a range of match score thresholds. [ABSTRACT FROM AUTHOR]

Three bacterial species, viz. Bacillus megaterium, B. subtilis and Pseudomonas corrugata, originally isolated from temperate locations in the Indian Himalayan region, were examined for their growth promotion ability using both pot and field based assays. A local landrace of rice was used as test crop. The three bacterial species exhibited in vitro phosphate solubilizing activity in following order: P. corrugata > B. megaterium > B. subtilis. The bacterial treatments (broth based in pot and charcoal based in field experiments) resulted in improved plant performance. Out of the three treatments, B. subtilis gave best performance resulting in 1.66 and 1.55 fold increase in grain yield of rice in pot and field trials, respectively. Inoculations also stimulated the rhizosphere associated bacterial and actinomycetes populations and suppressed the fungal flora. Colonization of roots by mycorrhizal fungi improved in all the treatments. Out of the three bacterial inoculants, B. subtilis was the best in affecting these changes. Bacterial treatments also resulted in higher values for phosphorus in shoots and grains in inoculated rice plants. The study indicates that the stimulation of native bacterial flora, including mycorrhizae, in and around roots is one of the important parameters playing indirect role in improving the overall plant growth. The study suggests that charcoal based B. subtilis cultures can be developed as an efficient bioinoculant for rice fields in the mountains. [ABSTRACT FROM AUTHOR]