Bio-AKA: An efficient fingerprint based two factor user authentication and key agreement scheme

The fingerprint has long been used as one of the most important biological features in the field of biometrics. It is person-specific and remain identical though out one’s lifetime. Physically uncloneable functions (PUFs) have been used in authentication protocols due to the unique physical feature of it. In this paper, we take full advantage of the inherent security features of user’s fingerprint biometrics and PUFs to design a new user authentication and key agreement scheme, namely Bio-AKA, which meets the desired security characteristics. To protect the privacy and strengthen the security of biometric data and to improve the robustness of the proposed scheme, the fuzzy extractor is employed. The scheme proposed in the paper can protect user’s anonymity without the use of password and allow mutual authentication with key agreement. The experimental results show superior robustness and the simplicity of our proposed scheme has been validated via our performance and security analysis. The scheme can be an ideal candidate for real life applications that requires remote user authentication

Local keypoint-based Faster R-CNN

Region-based Convolutional Neural Network (R-CNN) detectors have achieved state-of-the-art results on various challenging benchmarks. Although R-CNN has achieved high detection performance, the research of local information in producing candidates is insufficient. In this paper, we design a Keypoint-based Faster R-CNN (K-Faster) method for object detection. K-Faster incorporates local keypoints in Faster R-CNN to improve the detection performance. In detail, a sparse descriptor, which first detects the points of interest in a given image and then samples a local patch and describes its invariant features, is first employed to produce keypoints. All 2-combinations of the produced keypoints are second selected to generate keypoint anchors, which are helpful for object detection. The heterogeneously distributed anchors are then encoded in feature maps based on their areas and center coordinates. Finally, the keypoint anchors are coupled with the anchors produced by Faster R-CNN, and the coupled anchors are used for Region Proposal Network (RPN) training. Comparison experiments are implemented on PASCAL VOC 07/12 and MS COCO. The experimental results show that our K-Faster approach not only increases the mean Average Precision (mAP) performance but also improves the positioning precision of the detected boxes

A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Effect of Mn on hardenability of 25CrMo axle steel by an improved end-quench test

With the sixth large-scale railway speed-up, the quality of the axles is essential to the safety of the locomotive. According to the high-speed axle technical standard for the control of alloy elements in axle steel, optimization experiments of 25CrMo steel composition were performed by vacuum inductive melting. In order to study the hardenability of high-speed rail axles, an improved end-quench test was put forward. The advantage is that it enables the heat to transfer along the axial direction, thus avoiding edge effects. The hardenability of 25CrMo axle steels with Mn content of 0.60wt.% and 0.80wt.% was investigated mainly by means of optical microscopy and hardness tests. The experimental results indicate that the Mn has a pronounced effect on the hardenability of the steel. With an increase in Mn content from 0.60wt.% and 0.80wt.%, the hardenability of 25CrMo axle steel increases and the hard microstructure is maintained at an increasing distance from the quenched end. From the surface of the water quenched end to the center of the sample, the microstructure is martensite, martensite with bainite, and bainite

Exploration of Lintong-Chang’an Fault in relation to Lintong Kine of Rapid Rail Rransit in Xi’an city

In this paper, the method of combining high-density electrical exploration and shallow seismic exploration is used to explore the Lintong-chang ’an fault in relation to Lintong line of rapid rail transit in Xi’an city. The distribution and activity of the fault are determined, providing a basis for seismic fortification and seismic geological disaster evaluation in the line

Sulfur and Water Resistance of Carbon-Based Catalysts for Low-Temperature Selective Catalytic Reduction of NO<i>_x</i>: A Review

Low-temperature NH3-SCR is an efficient technology for NOx removal from flue gas. The carbon-based catalyst designed by using porous carbon material with great specific surface area and interconnected pores as the support to load the active components shows excellent NH3-SCR performance and has a broad application prospect. However, overcoming the poor resistance of H2O and SO2 poisoning for carbon-based catalysts remains a great challenge. Notably, reviews on the sulfur and water resistance of carbon-based low-temperature NH3-SCR catalysts have not been previously reported to the best of our knowledge. This review introduces the reaction mechanism of the NH3-SCR process and the poisoning mechanism of SO2 and H2O to carbon-based catalysts. Strategies to improve the SO2 and H2O resistance of carbon-based catalysts in recent years are summarized through the effect of support, modification, structure control, preparation methods and reaction conditions. Perspective for the further development of carbon-based catalysts in NOx low-temperature SCR is proposed. This study provides a new insight and guidance into the design of low-temperature SCR catalysts resistant to SO2 and H2O in the future

Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality

In order to improve the quality of fingerprint with large noise, this paper proposes a fingerprint enhancement method by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality. Multi-scale dictionary is used to balance the contradiction between the accuracy and the anti-noise ability, which has been shown to be an ideal solution to reconcile the demands of enhancement quality and computational performance. Principal component analysis (PCA)is applied in our technique for dimension reduction of multi-scale classification dictionaries. Under the quality grading scheme and multi-scale composite windows, the fingerprint patches are enhanced by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality according to their priorities. In addition, the multi-scale composite windows help the more high quality spectra diffuse into the low quality fingerprint patches and this can greatly improve the spectra quality of them. Experimental results and comparisons on FVC 2000 and FVC 2004 databases are reported.And it shows that the proposed method yields better result in terms of the robustness of fingerprint enhancement as compared with latest techniques.Moreover, the results show that the proposed algorithm can obtain better identification performanc