Hiding iris data for authentication of digital images using wavelet theory

This paper introduces an efficient approach to protect the ownership by hiding an iris data into digital image for an authentication purpose. It is based on the theory of wavelets. The idea is to secretly embed biometric data (iris print) in the content of the image identifying the owner. The system is based on an empirical analysis of biometric and watermarking technologies, and it is split into several processes. The first process is based on iris image analysis, which aids the generation of the iris code (watermark); the second and the third processes deal with embedding and detecting a watermark; and the last process deals with the authentication. A new metric that measures the objective quality of the image based on the detected watermark bit is introduced, which does not require the original unmarked image for watermark detection. Simulation results show the effectiveness and efficiency of the proposed approach. The text was submitted by the author in English.     

A novel facile synthesis and characterization of molybdenum nanowires

We describe a straightforward technique to synthesize pure Mo nanowires (NWs) from Mo₆S_yI_z (8,2 <y + z ≤ 10) NWs as precursor templates. The structural transformations occur when Mo₆S_yI_z NWs are annealed in Ar/H₂ mixture leading to the formation of pure Mo NWs with similar structures as initial morphologies. Detailed microscopic characterizations show that large diameters (>15 nm) Mo NWs are highly porous, while small diameters (<7 nm) are made of solid nanocrystalline grains. We find NW of diameter 4 nm can carry up to 30 μA current without suffering structural degradation. Moreover, NWs can be elastically deformed over several cycles without signs of plastic deformation.     

Tuning the Properties of CZTS Films by Controlling the Process Parameters in Cost-Effective Non-vacuum Technique

Highly dispersive Cu₂ZnSnS₄ (CZTS) nanoparticles were successfully synthesized by a simple solvothermal route. A low cost, non-vacuum method was used to deposit CZTS nanoparticle ink on glass substrates by a doctor blade process followed by selenization in a tube furnace to form Cu₂ZnSn (S,Se)₄ (CZTSSe) layers. Different selenization conditions and particle concentrations were considered in order to improve the crystallinity and surface morphology; the annealing temperature was varied between 400°C and 550°C and the annealing time was varied between 5 min and 20 min in a selenium-nitrogen atmosphere. The influence of annealing conditions on structural, compositional, optical and electrical properties of CZTSSe thin films was studied. An improvement in the structural and surface morphology was observed with increasing of annealing temperature (up to 500°C). An enhancement in the crystallinity and surface morphology were observed for thin films annealed for 10–15 min. Absorption study revealed that the band gap energy of as-deposited CZTS thin film was approximately 1.43 eV, while for CZTSSe thin films it ranged from 1.15 eV to 1.34 eV at different annealing temperatures, and from 1.33 eV to 1.38 eV for different annealing times.     

Influence of annealing on the optical properties of 5,10,15,20-tetraphenyl-21H, 23H-porphine iron (III) chloride thin films

Optical constants (refractive index, n, and absorption index, k) of the as-deposited and annealed films of 5,10,15,20-tetraphenyl-21H, 23H-porphine iron (III) chloride (FeTPPCl) have been obtained in the wavelength range 190–2500 nm by using spectrophotometric measurements. The obtained optical constants were used to estimate the type of transition for the as-deposited and annealed films. We present a single oscillator model that describes the dispersion of refractive index. Drude model of free carriers absorption have been described for the analysis the dispersion of refractive index dispersion before and after annealing.     

Smart antenna design for high‐speed moving vehicles with minimum return loss

The Internet of Things (IoT) is a connection amongst people and applications to another dimension of machine‐to‐machine communication. IoT scenario is unequivocally related with the development of the advancement of wireless sensor systems (WSNs) and radio‐frequency identification (RFID) frameworks. Owing to the technological advances around the world, energy demand is increasing exponentially. Energy proficiency has turned out to be one of the real worries in the present life that essentially influence every single human action. In communication system, return loss is a major issue for transmission process. Owing to return loss, a huge amount of power consumption occurs. This phenomenon is contemporary with transmission process, and it will initiate a serious problem for high‐speed moving substance like aircraft, rockets, and spaceship. To overcome this problem, a four‐element cylindrical antenna (conformal) array with better axial radiation is proposed. The four U‐shaped slots are uniformly wrapped around on a cylindrical surface, which produces tilted radiation. To enhance the axial radiation, four conformal elements are reefed by a one‐ to four‐microstrip feed network. The proposed conformal design has a bandwidth of 200 MHz (narrow bandwidth) at the center frequency of 3.9 GHz, covering the range of 3 to 3.9 GHz, with the gain of 4.9 dBic, and can be suitable for unmanned aerial vehicles (UAV), wireless avionics intra‐communication (WAIC), and so forth. The proposed design is low profile and can be used for high‐speed avionic applications. Finally, machine learning technique is explored to design a model for a smart antenna with optimistic parameters to reduce return loss and enhance the transmission rate.     

Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients

Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of lymphocytes from days 1 to 15 after admission to hospital) are associated with relapse rates and evaluations of COVID-19 infections. Accordingly, the severity of COVID-19 is classified in two categories, serious and non-serious. The experimental results indicate that the proposed CQNN’s prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.