Ensuring the actual presence of a genuine legitimate trait as opposed to a fake self-manufactured synthetic is a major problem in bio-metric authentication. The proposed system's objective is to improve the reliab...
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ISBN:
(纸本)9781510638624;9781510638617
Ensuring the actual presence of a genuine legitimate trait as opposed to a fake self-manufactured synthetic is a major problem in bio-metric authentication. The proposed system's objective is to improve the reliability of bio metric recognition systems through the use of image quality evaluation. The proposed technique uses general image quality features derived from a single image to distinguish between legitimate and impostor samples, making it optimal for applications with a very low degree of complexity. In the proposed method, we are using publicly available ATVS-Fir_DB dataset of iris which makes it highly competitive. We have also tested the algorithm on self-generated dataset for authenticity and rigorous testing purposes. The results acquired from the experimental phase were satisfying and authentic. The proposed method is able to achieve an averaged accuracy of 99.1% for the ATVS-Fir_DB dataset and 99.9% for the self-generated dataset.
This paper presents a novel feature-based technique for path optimization problems, in which the performance index is defined to minimize the energy consumption of a rover with consideration of terrain, kinematic, and...
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This paper presents a novel feature-based technique for path optimization problems, in which the performance index is defined to minimize the energy consumption of a rover with consideration of terrain, kinematic, and dynamic constraints. The proposed method estimates rover energy consumption by discretizing a path and by extracting statistical data for fast calculation of the performance index. The concepts of grouped data and data discretization techniques are used to analyze the energy-related data obtained from the search environment. The method improves runtime computation by statistically calculating the energy consumption of a rover for a defined path, rather than solving the dynamic equations of the rover. This technique is computationally more efficient than other energy optimization approaches when it estimates rover energy consumption with sufficient accuracy. The Genetic Algorithm (GA) solver is integrated to the approach to illustrate the efficiency of the algorithm. Additionally, a hardware-in-the-loop (HIL) simulation is developed for the validation of the rover's power flow as it traverses through the optimal path by incorporating rover hardware components within real-time simulation.
We investigate the use of fractal analysis (FA) as the basis of a system for multiclass prediction of the progression of glaucoma. FA is applied to pseudo 2-D images converted from 1-D retinal nerve fiber layer data o...
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We investigate the use of fractal analysis (FA) as the basis of a system for multiclass prediction of the progression of glaucoma. FA is applied to pseudo 2-D images converted from 1-D retinal nerve fiber layer data obtained from the eyes of normal subjects, and from subjects with progressive and nonprogressive glaucoma. FA features are obtained using a box-counting method and a multifractional Brownian motion method that incorporates texture and multiresolution analyses. Both features are used for Gaussian kernel-based multiclass classification. Sensitivity, specificity, and area under receiver operating characteristic curve (AUROC) are computed for the FA features and for metrics obtained usingwavelet-Fourier analysis(WFA) and fast-Fourier analysis (FFA). The AUROCs that predict progressors from nonprogressors based on classifiers trained using a dataset comprised of nonprogressors and ocular normal subjects are 0.70, 0.71, and 0.82 for WFA, FFA, and FA, respectively. The correct multiclass classification rates among progressors, nonprogressors, and ocular normal subjects are 0.82, 0.86, and 0.88 for WFA, FFA, and FA, respectively. Simultaneous multiclass classification among progressors, nonprogressors, and ocular normal subjects has not been previously described. The novel FA-basedfeatures achieve better performance with fewer features and less computational complexity thanWFA and FFA.
We propose a novel adaptive pipeline for continuous collision detection in cloth simulation. The pipeline consists of four components: bounding volume hierarchy (BVH) update, BVH traversal, a skipping frame session, a...
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We propose a novel adaptive pipeline for continuous collision detection in cloth simulation. The pipeline consists of four components: bounding volume hierarchy (BVH) update, BVH traversal, a skipping frame session, and elementary test processing. The skipping frame session is activated adaptively for skipping both BVH update and BVH traversal during the process of collision detection. The proposed method improves the performance of collision detection in simulating cloth models comparing to some existing feature-based collision detection techniques, as indicated from experimental results.
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