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Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.

The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.

In this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.

Algorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

This paper aims to target tracking in the marine environment is typically obtained by considering the measurement parameters like frequency, elevation and bearing. Marine environmental surveillance provides critical information and assistance for the exploitation and maintenance of marine resources.

With the use of intelligent sensor techniques like Hull-mounted and towed array sensors, convenient, precise and dependable three-dimensional (3D) underwater target tracking is introduced.

This research investigates a method to develop a reliable Unscented Kalman Filter (UKF) algorithm for enhanced underwater target tracking in a 3D scenario by using bearing, frequency and elevation measurements. In applications for underwater target tracking, uncertainty and inaccuracies are typically described by using Gaussian additive noise.

The proposed UKF algorithm is tested and analyzed using 100 Monte Carlo simulations with the Gaussian generated noise.

Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking.

This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF).

SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach.

The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

Nowadays advancement in acoustic technology can be explored with marine assets. The purpose of the paper is pervasive computing underwater target tracking has aroused military and civilian interest as a key component of ocean exploration. While many pervasive techniques are currently found in the literature, there is little published research on the effectiveness of these paradigms in the target tracking context.

The unscented Kalman filter (UKF) provides good results for bearing and elevation angles only tracking. Detailed methodology and mathematical modeling are carried out and used to analyze the performance of the filter based on the Monte Carlo simulation.

Due to the intricacy of maritime surroundings, tracking underwater targets using acoustic signals, without knowing the range parameter is difficult. The intention is to find out the solution in terms of standard deviation in a three-dimensional (3D) space.

A new method is found for the acceptance criteria for range, course, speed and pitch based on the standard deviation for bearing and elevation 3D target tracking using the unscented Kalman filter covariance matrix. In the Monte Carlo simulation, several scenarios are used and the results are shown.

Integrating complementary information with high-quality visual perception is essential in infrared and visible image fusion. Contrast-enhanced fusion required for target detection in military, navigation and surveillance applications, where visible images are captured at low-light conditions, is a challenging task. This paper aims to focus on the enhancement of poorly illuminated low-light images through decomposition prior to fusion, to provide high visual quality.

In this paper, a two-step process is implemented to improve the visual quality. First, the low-light visible image is decomposed to dark and bright image components. The decomposition is accomplished based on the selection of a threshold using Renyi’s entropy maximization. The decomposed dark and bright images are intensified with the stochastic resonance (SR) model. Second, texture information-based weighted average scheme for low-frequency coefficients and select maximum precept for high-frequency coefficients are used in the discrete wavelet transform (DWT) domain.

Simulations in MATLAB were carried out on various test images. The qualitative and quantitative evaluations of the proposed method show improvement in edge-based and information-based metrics compared to several existing fusion techniques.

In this work, a high-contrast, edge-preserved and brightness-improved image is obtained by the processing steps considered in this work to get good visual quality.

The purpose of this paper is to propose a three‐dimensional thermal model for friction stir welding of AISI 1018 mild steel to predict the thermal cycle, temperature distribution, the effect of welding parameters on power required, heat generation and peak temperature during the friction stir welding process.

The mathematical expressions for heat generation during the friction stir welding process were derived. The simulations for various welding and rotational speeds were carried out on ANSYS software employing temperature and radius dependent moving heat source and applying the boundary conditions.

The predicted thermal cycle, torque required and temperatures were found to be in good agreement with the experimental results. The heat generation and peak temperatures were found to be directly proportional to rotational speed and inversely proportional to welding speed. The rate of increase in heat generation and peak temperature were found to be higher at lower rotational speeds and lower at higher rotational speeds. The heat generation during friction stir welding was found to be 71.4 per cent at shoulder, 23.1 per cent at pin side and 5.5 per cent at bottom of the pin.

A new temperature dependent slip factor has been used to determine the contribution of slipping and sticking on total heat generation. A temperature and radius dependent moving heat source has been employed.

This study aims to develop a new simple and sensitive method for the microextraction of trace levels of lead in environmental samples. It is based on the use of ionic liquids based dispersive liquid–liquid microextraction (IL–DLLME) before spectrofluorometry.

Cadmium sulphide quantum dots have been synthesised using thioglycolic acid as capping agent through a one-step process with stability and excellent water-solubility, and have strong affinity for lead (Pb). This probe is based on the fluorescence quenching effect of functionalised cadmium sulphide quantum dots.

Factors affecting the extraction efficiency and fluorescence quenching of metals, such as the amount of ionic liquid, amount of metanol, microextraction and centrifugation time, volume of quantum dots and buffer pH, were investigated. Under optimum conditions, the calibration graph was linear in the range of 0.01-3 µg.L-1, with the detection limit of 0.004 µg.L-1 for Pb2+. The relative standard deviation (RSD%, n = 5) of 5.4 per cent at 1 µg.L-1 of Pb2+ was obtained.

This method for pre-concentration of the Pb ions by dispersive liquid–liquid microextraction is novel and could be used for various applications in the synthesis of a wide variety of determination of fluorescence quenching of cadmium sulphide quantum dots.

This paper aims to present a correlation study on general and accelerated corrosion of the welded structure of aluminum alloy 2219 in N₂O₄.

Corrosion experiments of the Tungsten Inert Gas (TIG)-welded aluminum alloy structure were conducted under both mild and accelerated corrosion conditions by changing the water content in N₂O₄.

The experimental results indicated that both general and accelerated corrosion processes of the TIG-welded structure of aluminum alloy 2219 in N₂O₄ followed the linear equation ΔW = A + Bt, and the corrosion products were unchanged regardless of the water content in the N₂O₄ solution.

The weight loss comparison method is used to identify the structure of the conventional aluminum welding and aluminum welding structure corrosion dinitrogen tetroxide in nitric acid accelerated corrosion relationship.

From many decades, bearings-only tracking (BOT) is the interested problem for researchers. This utilises nonlinear filtering methods for state estimation as there is only information about the target, i.e. bearing is a nonlinear measurement. The measurement bearing is tangentially related to the target state vector. There are many nonlinear filtering algorithms developed so far in the literature.

In this research work, the recently developed nonlinear filtering algorithm, i.e. shifted Rayleigh filter (SRF), is applied to BOT.

The SRF is tested for two-dimensional BOT against various scenarios. The simulation results emphasise that the SRF performs well when compared to the standard nonlinear filtering algorithm, unscented Kalman filter (UKF).

SRF utilises the nonlinearities present in the bearing measurement through the use of moment matching. The SRF is able to produce the solution in highly noisy environment, long ranges and high dimension tracking.

This study aims to identify several perspectives that affect the adoption of blockchain technology in India (BCTA) and evaluate their impact. To study the sector’s influence on adoption and the impact of BCTA on the performance of the Indian healthcare supply chain (HSCP) using BCTA as a mediating variable.

In this study, we first developed a conceptual model based on Organizational Information Processing Theory and Technology-Organization-Environment, then formulated hypotheses. Based on this, a questionnaire was developed, and data were gathered from experts in the Indian healthcare industry who were familiar with blockchain technology. AMOS 19 was used to analyze data using structural equation modelling.

All the factors have a significant positive influence on BCTA. Healthcare supply chain factors influenced the adoption most dominantly, followed by technological, environmental, organizational and record-keeping unit factors. Both the public and private sectors of HSCP benefited significantly from BCTA.

This research work is fruitful for healthcare practitioners, top management, academicians and policymakers in assessing BCTA’s impact on the HSCP.

We have attempted to evaluate the possible BCTA impact on HSCP. BCTA as a mediating variable and considering different perspectives for a holistic view of adoption in the Indian context add to this work’s originality.