Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.

Feasibility study on the integration of urban and rural minimum living security system based on SWOT model

This paper uses SWOT analysis, feasibility analysis of integration of urban and rural residents in China, including the overall analysis of the urban and rural integration China internal conditions, mainly for its overall advantages and disadvantages are analyzed; and the external environment, the system is mainly to raise the opportunities and threats were analyzed; in these. The external conditions are analyzed based on the construction of SWOT strategic matrix, found the advantages and disadvantages of the reform process of the integration of urban and rural subsistence allowances system in coexist, opportunities outweigh the challenges, so China＇s overall urban and rural integration construction is feasible.

Non-contact mechanical and chemical analysis of single living cells by microspectroscopic techniques

Innovative label-free microspectroscopy,which can simultaneously collect Brillouin and Raman signals,is used to characterize the viscoelastic properties and chemical composition of living cells with sub-micrometric resolution.The unprecedented statistical accuracy of the data combined with the high-frequency resolution and the high contrast of the recently built experimental setup permits the study of single living cells immersed in their buffer solution by contactless measurements.The Brillouin signal is deconvoluted in the buffer and the cell components,thereby revealing the mechanical heterogeneity inside the cell.In particular,a 20%increase is observed in the elastic modulus passing from the plasmatic membrane to the nucleus as distinguished by comparison with the Raman spectroscopic marker.Brillouin line shape analysis is even more relevant for the comparison of cells under physiological and pathological conditions.Following oncogene expression,cells show an overall reduction in the elastic modulus(15%)and apparent viscosity(50%).In a proof-of-principle experiment,the ability of this spectroscopic technique to characterize subcellular compartments and distinguish cell status was successfully tested.The results strongly support the future application of this technique for fundamental issues in the biomedical field.

Tetris:A Heuristic Static Memory Management Framework for Uniform Memory Multicore Neural Network Accelerators

Uniform memory multicore neural network accelerators(UNNAs)furnish huge computing power to emerging neural network applications.Meanwhile,with neural network architectures going deeper and wider,the limited memory capacity has become a constraint to deploy models on UNNA platforms.Therefore how to efficiently manage memory space and how to reduce workload footprints are urgently significant.In this paper,we propose Tetris:a heuristic static memory management framework for UNNA platforms.Tetris reconstructs execution flows and synchronization relationships among cores to analyze each tensor’s liveness interval.Then the memory management problem is converted to a sequence permutation problem.Tetris uses a genetic algorithm to explore the permutation space to optimize the memory management strategy and reduce memory footprints.We evaluate several typical neural networks and the experimental results demonstrate that Tetris outperforms the state-of-the-art memory allocation methods,and achieves an average memory reduction ratio of 91.9%and 87.9%for a quad-core and a 16-core Cambricon-X platform,respectively.