Quantization dimension for inhomogeneous bi-Lipschitz IFS

Let ν be a Borel probability measure on a d-dimensional Euclidean space Rd, d≥1, with a compact support, and let (p0,p1,p2,…,pN) be a probability vector with pj\u3e0 for 1≤j≤N. Let {Sj:1≤j≤N} be a set of contractive mappings on Rd. Then, a Borel probability measure μ on Rd such that μ=∑Nj=1pjμ∘S−1j+p0ν is called an inhomogeneous measure, also known as a condensation measure on Rd. For a given r∈(0,+∞), the quantization dimension of order r, if it exists, denoted by Dr(μ), of a Borel probability measure μ on Rd represents the speed at which the nth quantization error of order r approaches to zero as the number of elements n in an optimal set of n-means for μ tends to infinity. In this paper, we investigate the quantization dimension for such a condensation measure

A Review on the epidemiology and characteristics of COVID-19

In December 2019, there was a health emergency worldwide named novel coronavirus or COVID-19 by the world health organization (WHO). It originated from the Wuhan seafood market, Hubei Province, China. Till now Severe Acute Respiratory Syndrome Coronavirus-2 or SARS-CoV-2 spread over 216 countries with 177,108,695 confirmed cases and 3,840,223 confirmed death cases has been reported (5:31 pm CEST, 18 June 2021; WHO). Analyzing the risk factor of this pandemic situation, different government health organizations of all the countries including WHO are taking several preventive measures with ongoing research works, even the vaccination process started. In this study, we tried to analyze all the available information on pandemic COVID-19, which includes the origin of COVID-19, pathogenic mechanism, transmission, diagnosis, treatment, and control-preventive measures, also the additional treatment and prevention taken by the Indian government is being studied here

Fusion of gait and face for human identification

Identification of humans from arbitrary view points is an important requirement for different tasks including perceptual interfaces for intelligent environments, covert security and access control etc. For optimal performance, the system must use as many cues as possible and combine them in meaningful ways. In this paper we present fusion of face and gait cues for the single camera case. We employ a view invariant gait recognition algorithm for gait recognition. A sequential importance sampling based algorithm is used for probabilistic face recognition from video. We employ decision fusion to combine the results of our gait recognition algorithm and the face recognition algorithm. We consider two fusion scenarios: hierarchical and holistic. The first involves using the gait recognition algorithm as a filter to pass on a smaller set of candidates to the face recognition algorithm. The second involves combining the similarity scores obtained individually from the face and gait recognition algorithms Simple rules like the SUM, MIN and PRODUCT are used for combinining the scores. The results of the fusion are demonstrated on the NIST database which has outdoor gait and face data of 30 subjects. 1

A hidden markov model based framework for recognition of humans from gait sequences

In this paper we propose a generic framework based on Hidden Markov Models (HMMs) for recognition of individuals from their gait. The HMM framework is suitable, because the gait of an individual can be visualized as his adopting postures from a set, in a sequence which has an underlying structured probabilistic nature. The postures that the individual adopts can be regarded as the states of the HMM and are typical to that individual and provide a means of discrimination. The framework assumes that, during gait, the individual transitions between N discrete postures or states but it is not dependent on the particular feature vector used to represent the gait information contained in the postures. The framework, thus, provides flexibility in the selection of the feature vector. The statistical nature of the HMM lends robustness to the model. In this paper we use the binarized background-subtracted image as the feature vector and use different distance metrics, such as those based on the L1 and L2 norms of the vector difference, and the normalized inner product of the vectors, to measure the similarity between feature vectors. The results we obtain are better than the baseline recognition rates reported before. 1