双多通道虹膜识别系统的研究

在双多通道虹膜识别系统中,研究了局部区域通道对识别率的影响,提出了双向式区域划分和通道数的优化。研究了小波滤波器通道对识别能力的影响和滤波器通道的选择,给出了引入小波包通道来提升识别率的新方法。通过对WED(Weighted Euclidean Distance)分类器的改进研究,提出了相对WED分类器的新概念,进一步提高了系统的识别能力。     

Indoor human detection based on micro-Doppler features in the presence of interference from moving clutter sources

In this paper, to address the problem of detecting the presence of human in indoor environments in the presence of moving clutter sources, an indoor human detection method that utilizes random forest to process micro-Doppler signatures and a single pair of TX/RX unit is proposed. In contrast to most of the existing methods that use both distance information and micro-Doppler information assuming no interference from moving clutter sources (window curtains, blinds, table fans, etc.), our proposed method relies only on micro-Doppler information for human detection in indoor environments with curtain and fan interferences. Based on our time–frequency analyses on the measured radar data, seven features, i.e., the mean and standard deviation of the doppler centroid, the mean and standard deviation of the span of envelopes, the silhouette size, the positive peak values, and the peak spread, are extracted from spectrograms. These features are fed into a random forest classifier for categorizing the state of a room into one of the five scenarios considered in this work, namely (1) a person entering the room, (2) a person leaving the room, (3) interference from curtain/blind, (4) interference from fan, and (5) an empty room. The proposed system has been validated using real-world experiments and be able to deliver an accuracy of 97.5% in classifying the scenarios.     

Raman spectroscopy based analysis of milk using random forest classification

The development of a classification system based on the Raman spectra of milk samples is proposed in present study. Such development could be useful for nutritionists in suggesting healthy food to infants for their proper growth. Previously, molecular structures in milk samples have been exploited by Raman spectroscopy. In the current study, Raman spectral data of milk samples of different species is utilized for multi-class classification using a dimensionality reduction technique in combination with random forest (RF) classifier. Quantitative and experimental analysis is based on locally collected milk samples of different species including cow, buffalo, goat and human. This classification is based on the variations (different concentrations of the components present in milk such as proteins, milk fats, lactose etc.) in the intensities of Raman peaks of milk samples. Principal component analysis (PCA) is used as a dimensionality reduction technique in combination with RF to highlight the variations which can differentiate the Raman spectra of milk samples from different species. The proposed technique has demonstrated sufficient potential to be used for differentiation between milk samples of different species as the average accuracy of about 93.7%, precision of about 94%, specificity of about 97% and sensitivity of about 93% has been achieved.     

Dynamical behavior of solutions of a reaction–diffusion model in river network

In this paper, we study the long-time behavior of solutions of a reaction–diffusion model in a one-dimensional river network, where the river network has two branches, and the water flow speeds in each branch are the same constant $\beta$. We show the existence of two critical values $c_0$ and 2 with $0<c_0<2$, and prove that when $-c_0\le \beta <2$, the population density in every branch of the river goes to 1 as time goes to infinity; when $-2<\beta <-c_0$, then, as time goes to infinity, the population density in every river branch converges to a positive steady state strictly below 1; when $\left|\beta \right|\ge 2$, the species will be washed down the stream, and so locally the population density converges to 0. Our result indicates that only if the water-flow speed is suitably small (i.e., $\left|\beta \right|<2$), the species will survive in the long run.