Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Lattice Boltzmann Method Applied to Radiative Transport Analysis in a Planar Participating Medium

This article deals with the extension of the usage of the lattice Boltzmann method (LBM) to the analysis of radiative heat transfer with and without conduction in a one-dimensional (1-D) planar participating medium. A novel lattice needed for the calculation of the volumetric radiation spanned over the 4π spherical space has been introduced. The LBM formulation is tested for three benchmark problems, namely, radiative equilibrium, nonradiative equilibrium, and a combined mode conduction–radiation problem in a planar geometry. In the combined mode problem, with radiative information known from the proposed lattice structure, the energy equation is also formulated and solved using the LBM. The D1Q2 lattice is used in the energy equation. For validation, in problems 1 and 2, the LBM results are compared with the finite-volume method (FVM), while in problem 3, the LBM-LBM results are compared with the LBM-FVM in which FVM is used for the computation of radiative information. Comparisons are made for the effects of the governing parameters such as the extinction coefficient, the scattering albedo, and so on, on heat flux and emissive power (temperature) distributions. LBM results are found to be in excellent agreement with the benchmark results.     

Tent-Shaped Surface Morphologies of Silicon: Texturization by Metal Induced Etching

Silicon - Nano-metal/semiconductor junction dependent porosification of silicon has been studied here. The silicon nanostructures (SiNSs) have been textured on n- and p- type silicon wafers using...     

Analysis of Conduction-Radiation Heat Transfer in a 2D Enclosure Using the Lattice Boltzmann Method

Application of the lattice Boltzmann method (LBM) recently extended by Pietro et al. [P. Asinari, S. C. Mishra, and R. Borchiellini, A Lattice Boltzmann Formulation to the Analysis of Radiative Heat Transfer Problems in a Participating Medium, Numer. Heat Transfer B, 57(2), 126–146, 2010] for calculation of volumetric radiative information is extended for the analysis of a combined mode transient conduction and radiation heat transfer in a 2D rectangular enclosure containing an absorbing, emitting and scattering medium. Unlike all previous studies, with volumetric radiative information computed using the proposed LBM, the energy equation is formulated and solved using the LBM. In the combined mode conduction–radiation problem, to assess the computational advantage of computing the radiative information too using the LBM, the same problem is also solved using the LBM–finite volume method (FVM) formulation. In this LBM–FVM formulation, the FVM is used to calculate the volumetric radiative information needed for the energy equation, and the energy equation is solved using the LBM. Comparisons are made for the effects of the extinction coefficient, the scattering albedo and the conduction–radiation parameter on the temperature distributions in the medium. Although the number of iterations for the converged solution in LBM–LBM is much more than that of the LBM–FVM, computationally, the LBM–LBM is faster than the LBM–FVM.     

Gesture image segmentation with Otsu’s method based on noise adaptive angle threshold

By analyzing the essence and deficiency of the improved Otsu’s method, this paper proposes a noise adaptive angle threshold based Otsu’s method for gesture image segmentation. It first designs a two-dimensional histogram of gray value-neighborhood truncated gray mean to avoid the interference of extreme noise by discarding the extremes of the neighborhood. Then, the probability that the pixel is noise is calculated according to the actual situation, adaptive filtering is implemented to enhance the algorithm’s universal applicability. It finally converts the threshold space to an angle space from 0° to 90°, and the threshold search range is compressed to improve its efficiency. As the gesture is close to the background and the boundary is blurred, this paper combines the global and local Otsu’s method to segment the gesture images based on the angle space. On the one hand, it uses the global Otsu’s method to obtain the global threshold t₁. On the other hand, it uses the local Otsu’s method to obtain the local threshold t₂, and segments gesture images based on t₂. Experimental results show that the proposed method is effective and can accurately segment gesture images with different noises.

     

Upgrading Information Security and Protection for Palm-Print Templates

Biometric systems proven to be one of the most reliable and robust method for human identification. Integration of biometrics among the standard of living provokes the necessity to vogue secure authentication systems. The use of palm-prints for user access and authentication has increased in the last decade. To give the essential security and protection benefits, conventional neural networks (CNNs) has been bestowed during this work. The combined CNN and feature transform structure is employed for mapping palm-prints to random base-n codes. Further, secure hash algorithm (SHA-3) is used to generate secure palm-print templates. The proficiency of the proposed approach has been tested on PolyU, CASIA and IIT-Delhi palm-print datasets. The best recognition performance in terms of Equal Error Rate (EER) of 0.62% and Genuine Acceptance Rate (GAR) of 99.05% was achieved on PolyU database.

     

Detection of adulteration in milk: A review

Milk is a wholesome nutritious dairy product and is consumed by a majority of the population worldwide for drinking as such, as well as via dairy products. However, the practice of adulteration of milk invariably reduces its quality and may introduce hazardous substances into the dairy supply chain jeopardising consumers’ health. Various instances of adulteration of milk have been reported globally, wherein substances such as extraneous water, foreign proteins, whey proteins, melamine and urea, vegetable or animal fats, plus many minor constituents of milk fat have been added as potential adulterants in milk and milk products. This review focusses on the different methods of detection of these adulterants in milk using techniques such as DSC, RP‐HPLC, LC‐GC, HPTLC, immunoassays: CE, ELISA, FAMPST, FTIR, NIR spectroscopy, PAGE, IEF, DNA‐based methods and MALDI‐MS that have been developed and employed for the last 25 years. The combination of advanced IR spectroscopy and chemometrics provides a powerful tool for quality and authenticity analysis of milk. An electronic tongue is an easy and economic tool for the detection of caprine milk adulterations with bovine milk. Biosensors having the ability to furnish real‐time signals have been developed for the detection of urea in milk. An attempt has been made to give a clear understanding of the most suitable methods for the determination of various sources of adulteration.     

Role of extractives in biomethane production: characterization and comparison of different varieties of rice straw

Clean Technologies and Environmental Policy - The challenges of climate change, energy security, ever-increasing price of fossil fuels in the international market, and the detrimental effects of...     

Integrated Design of Undepressed Collector for Low Power Gyrotron

A 42 GHz, 200 kW continuous wave (CW) gyrotron, operating at TE₀₃ mode is under development for the electron cyclotron resonance plasma heating of the Indian TOKAMAK system. The gyrotron is made up of an undepressed collector. The undepressed collector is simple to design and cost effective. In this paper, a detailed design study of the undepressed collector for the 42 GHz gyrotron is presented. The EGUN code is used to analyze the spent electron beam trajectory for the maximum spread to reduce the power loading on the collector surface. To achieve wall loading ≤1 kW/cm², a collector with a length of 800 mm and a radius of 42.5 mm is designed. The design also includes the three magnet systems around the collector for maximum and uniform beam spread. The thermal and the structural analyses are done using the ANSYS code to optimize the collector structure and dimensions with tolerance.