A rapid method for evaluating partially coherent images of 1-D periodic patterns including focal error is described. A transmission cross-coefficient approach is used. For a square source and objective, the cross-coefficient is calculated analytically. For a circular source and objective, a 1-D integration which has been transformed for rapid convergence is performed. Images of a mask with 1-microm lines and spaces for several values of defocus and various degrees of partial coherence are given for both circular and square apertures.

No abstract

The unique iris pattern of each human eye is complex, but easily be scanned or captured by a camera. However, the high cost infrared iris scanners used for acquisition causes inconvenience to users by distance related constraints. This restricts its widespread use in real-time applications such as airports and banks. The images captured by cameras under visible wavelength are obstructed by the presence of reflections and shadows which requires additional attention. The main objective of this paper is to propose a secure biometric iris authentication system by fusion of RGB channel information from the real-time data captured under visible wavelength and varying light conditions. The proposed system is adapted to a real-time noisy iris dataset. The effectiveness of this proposed system was tested on two different color iris datasets, namely, a public database UBIRISv1 and a newly created database SSNDS which contains images captured with any digital/mobile camera of minimum 5MP under unconstrained environments. This system supports the cross sensor acquisition and successful iris segmentation from these unconstrained inputs. The features from each channel are extracted using log Gabor filter and a matching is performed using hamming distance based on two thresholds (inter and intra class variations). The performance quality of the proposed biometric system leads to the feasibility of a new cost-effective approach for any real-time application, which requires authentication to ensure quality service, enhance security, eliminate fraud, and maximize effectiveness.

Summary In this article, a novel hybrid method is proposed to optimally manage the energy for a hybrid electric vehicle system. The proposed technique is the joint execution of both the Kernel Wingsuit Flying Search Algorithm and Sea Lion Optimization Algorithm, hence it is called WF2SLOA. The main objective of the WF2SLOA method is integrated in the energy management system to split the torque between the engine and electric machine. During the WF2SLOA‐based energy management development, this article performs a parametric investigation on numerous main factors, such as state types and number of states, states and action discretization, exploration and exploitation, and learning experience selection. The proposed method is implemented in MATLAB/Simulink, and the performance is assessed with the existing methods. Consequently, the outcomes illustrate that the selection of the learning experience can diminish the fuel consumption of the vehicle. Furthermore, the states and action discretization study indicates the fuel consume of the vehicle diminishes as action discretization enhances while raising the states discretization is harmful to the fuel consume. The maximizing count of states also raises the economy of fuel. Thus, the simulation outcomes show that the performance of the proposed method is more efficient than the existing methods. The mean, median, and SD of the WF2SLOA method attains 1.5420, 1.5043, and 0.0509.

Demand for high end privacy and security in human computer interaction, telecom environment is very high in the era of digital world. Multibiometric system combines information from multiple biometric traits of an individual and has an exceptional ability to address these demands with add-on customer satisfaction. It also overcomes intra class variations, non-universality, noisy data and attacks during authentication process. This paper proposes a multibiometric system suitable for secure access of data, devices and services. A database has been constructed using real time multiple biometric samples acquired from 500 individuals in an unconstrained environment. Existence of noise in the samples captured in an unconstrained environment are removed using filtering techniques, and the contrast is adjusted using dark channel priorities and scattering model. Then, the region of interest and features appropriate to each trait are extracted and fused in various forms like multiple samples, instances and traits in recognizing an individual. The proposed system is analysed by computing genuine and false acceptance rates. With the promising experimental results of various fusion schemes, the authentication is tested using transfer learning process with automatic extraction of essential features using Convolution Neural Network and classifying the target using Support Vector Machine (SVM), which outperforms in identifying an individual through fusion of biometric features acquired even in an unconstrained environment. Hence this authentication process could be modified into an effective one to identify and monitor the user interacting with a security related application in online mode with their unique available unconstrained features.

No abstract

No abstract

No abstract