Proceedings of the National Academy of Sciences, India Section A: Physical Sciences最新文献

Wireless sensor networks are a relatively new class of networks that have recently attracted a lot of attention from academia and business. If there are enough redundant nodes available to keep the network functioning and connected in the event of node failures, the network's ability to self-organize makes it robust and fault-tolerant. The router, which plays a key role in organizing the data flow, is the brain of an on-chip network. The main components of the WSNs are the routers, which oversee sending messages and publishing-subscribe events between senders and receivers. A high level of parallelism and a fast on-chip router are both made possible by allowing routing functions for each input port and utilizing distributed arbiters. The research article focuses on the hardware chip design of mesh configured network on chip routers chip with embedding RSA cryptographic algorithm. The chip design is done in Xilinx ISE, simulated in Modelsim with different key sizes, and analyzed on Virtex-5 field programmable gate array. The performance of the network is evaluated using delay and power as the major indices with different key lengths and the design has proven the maximum frequency support of 347.00 MHz.

Ensuring the security of medical image transmission (SoMIT) is vital in healthcare settings, as it protects sensitive patient and healthcare data, enabling doctors to provide accurate and efficient treatment and optimal care. This research focuses on enhancing SoMIT over hospital communication networks using a soft computing-driven framework (Hybrid Fuzzy-AHP) method within the Multiple-Criteria Group Decision Making (MCGDM) technique. The Hybrid Fuzzy-AHP method considers five critical medical image security (MIS) parameters: integrity [MS1], authentication [MS2], confidentiality [MS3], access control [MS4], and availability [MS5]. Furthermore, this soft computing technique assigns weighted prioritization to each MIS parameter, indicating that integrity holds the highest impact, followed by authentication, confidentiality, access control, and availability. The MIS optimization outcome of 0.8075 signifies robust security according to the triangular fuzzy number (TFN) scale. These MIS security outcomes were validated using GNU Octave and MATLAB tools. Additionally, a comparison between the proposed Hybrid Fuzzy-AHP method and various MCGDM techniques shows that the proposed method outperforms other MCGDM techniques. This research significantly contributes to the development of secure and efficient medical image transmission (MIT) systems. The findings of this research have important implications for healthcare providers, enhancing the security and efficiency of MIT while improving the accuracy of patient treatments.

A technique for expressing an image in which each pixel is represented by one or more bits of the byte is known as bit plane slicing (BPS). The BPS method necessitates the use of a bit-slicing algorithm to include hidden data in any one of the eight slices. This method uses 8 bits to represent each pixel in an (8 × 8) image. The information in a grayscale image is encoded using 8 bits since each pixel's value falls between 0 and 255. Bit plane slicing is widely used in data hiding, image compression, and other related applications of image processing in security, privacy, encryption-decryption, health care data processing, and so on. The research article provides bit plane slicing concept realization in hardware chips for digital image processing in which the 4-bit and the 8-bit images are processed to estimate the different planes and corresponding values in Xilinx ISE14.7 software using VHDL programming. In the Modelsim 10.0 software, the functional simulation is carried out for these planes with various stimulation inputs to verify the functionality of the design.

Significance Statement Bit plane slicing is a widely used concept used in digital image applications. The significance of the work is that the work provides the platform to realize the concept at the hardware chip level and how parallel processing can be used to provide the outputs in different planes based on 8-bit image processing.

Within this field of research, a wind resource evaluation can identify the locations where wind-induced heat losses can have a greater impact on the overall losses of plant factories, as well as those locations where it may not be as significant. This study focuses on choosing the optimal locations for plant factories based on wind resource assessment in Davleia, Central Greece. Based on the results, suitable regions for plant factory/vertical farms locations were suggested. The study analyses the heat losses due to wind in two scenarios with various wall surfaces based on annual wind speed, direction, and temperature experimental data from a mast. The heat transfer in a wall surface with 10 m² and an annual average wind speed of 4.6 m/s, can be more than 18 MWh/yr. The investigation showed that while places outside the built-up agglomerations can face noticeably larger heat losses, there are sites within Davleia with low wind speeds that can limit heat loss. The study offers useful information for choosing suitable locations based on an analysis of the wind resource.

Fingerprint recognition has emerged as a crucial biometric authentication technology with diverse applications, such as access control, identity verification, and forensic investigations. This paper presents a comprehensive study on the application of Artificial Neural Networks (ANNs) in fingerprint recognition. ANNs, a subset of machine learning, have demonstrated remarkable potential in extracting distinctive features from fingerprint images and achieving high accuracy in fingerprint identification and verification tasks. In this paper, we delve into the theoretical foundations of ANNs, discuss their relevance in fingerprint recognition, and present an in-depth analysis of recent advancements, challenges, and prospects. Additionally, we provide insights into the key components of ANNs employed in fingerprint recognition, including data preprocessing, feature extraction, and classification, along with a review of prominent fingerprint datasets and evaluation metrics. This paper seeks to make a valuable addition to the existing knowledge in the field of fingerprint identification and stimulate additional research into improving the skills of artificial neural networks (ANNs) for biometric authentication.

The fifth generation (5G) technology is efficiently designed to perform many things for the betterment of lives, such as Artificial Intelligence, Cyber-Physical Systems, the Internet of Things, etc. To facilitate this huge amount of data very high bandwidth is needed, hence 5G extensively uses the millimeter wave (mm-Wave) to enhance bandwidth. The technology of mm-Wave communication operates at very high frequencies, typically between 30 and 300 GHz. Some of the challenges will be key to realizing the full potential of mm-Wave communication technology for high-speed wireless communication in the future. The difficulties caused by mm-Wave are directivity, propagation loss, and sensitivity to blockage. To overcome these difficulties, we surveyed existing solutions and standards and identified research gaps. As a high data rate, mm-Wave may be considered in future generation communication and propagation channel requirements for mm-Wave investigated precisely for the prior knowledge of the quality of service (QoS) parameters. Therefore, channel modeling is the key need for the estimation of QoS parameters namely delay, angle of arrival, path loss, angle of departure, etc. In this paper, an efficient data-driven model for mm-Wave 5G Channel modeling using machine learning and high-performance computing is proposed which outperformed the other state-of-the-art in terms of various performance matrices.

This study focuses on the computation of the nonlinearity acoustic parameter for binary ionic liquid mixtures, which is an important area of research in nonlinear acoustics. For the first time nonlinear acoustic parameter (B/A) of six binary ionic liquid mixture have been computed using four different methods namely Hartmann, Ballou, Johnson and thermoacoustic method of Sharma. The binary mixtures of six RTIL’s are DEAA + water, DEAS + water, TEAA + water, TEAS + water, TMAA + water and TMAS + water at 298.15 K. The experimental input data of density (ρ) and sound speed (u) have been taken from a very accurate and precise work of Venbatesu et al. (J Phys Chem 13:118, 2014. 5971). A comparative study of (B/A) values obtained from different methods has been presented.

In the present work, we have applied the group invariance method to discuss the propagation of spherical shock wave using the concept of Roche model in a non-ideal gas under the influence of gravitational and magnetic fields for the adiabatic and isothermal flows. We have obtained the similarity solution with power law shock paths in both the ideal gas and non-ideal gas cases by the different choice of the arbitrary constant values appearing in the expression for infinitesimals. Numerical solutions are obtained for both the isothermal and adiabatic flows. The effect of the gravitational parameter, shock Cowling number, non-idealness parameter and adiabatic index on the shock strength, the density ratio across the shock front, and on the flow variables are studied. It is found that an increase in the gravitational parameter or non-idealness parameter or shock Cowling number or adiabatic index increases the density ratio across the shock front and decreases the shock strength.

Influence of the two different hardeners (ammonium chloride NH₄Cl and ammonium sulphate (NH₄)₂SO₄) on the physical (density, thickness, moisture content, thickness swelling and water absorption) and mechanical (modulus of rupture, modulus of elasticity, surface soundness and internal bonding strength) properties and formaldehyde emission of the particleboard was figured out. Boards were industrially produced (at continuous press line) using urea formaldehyde resin, liquid paraffin, and a 50:40:10 mixture of Scots pine, oak and poplar chips, respectively. Except for elasticity, boards produced using ammonium chloride had better mechanical properties, but their thickness swelling and water absorption capabilities were lower (approx. 55.5% and 45.9%, respectively) than those produced with ammonium sulphate. Utilization of ammonium chloride provided 30.3% higher surface soundness. There were less than 1% differences in the thickness and density. Around 36.4% less formaldehyde emission was determined for ammonium chloride-modified boards. According to the results, only means of IB did not present statistically significant differences.

In this article, we present a description of the behaviour of shock-compressed solid materials following Geometrical Shock Dynamics (GSD) theory. GSD has been successfully applied to various gas dynamics problems, and here we have employed it to investigate the propagation of cylindrically and spherically symmetric converging shock waves in solid materials. The analytical solution of shock dynamics equations has been obtained in the strong-shock limit, assuming the solid materials to be homogeneous and isotropic and obeying the Mie-Grüneisen equation of state. The non-dimensional expressions are obtained for the velocity of shock, the pressure, the mass density, the particle velocity, the temperature, the speed of sound, the adiabatic bulk modulus, and the change-in-entropy behind the strong converging shock front. The influences as a result of changes in (i) the propagation distance r from the axis or centre ((r=0)) of convergence, (ii) the Grüneisen parameter, and (iii) the material parameter are explored on the shock velocity and the domain behind the converging shock-front. The results show that as the shock focuses at the axis or origin, the shock velocity, the pressure, the temperature, and the change-in-entropy increase in the shock-compressed titanium Ti6Al4V, stainless steel 304, aluminum 6061-T6, etc.