Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.     

Multiple material domain‐based computational tools for ease assessment of modal parameter in photonic crystal fibers

We report here the development of two computational tools PCFPS (Photonic Crystal Fiber Parameter Study) and PCFPA (Photonic Crystal Fiber Parameter Analysis), equipped with graphical user interface (GUI) for modeling of photonic crystal fiber. The tools are based on different structural parameters, and they provide characteristic analysis of the modal parameters from the structural parameters. The main feature of PCFPS is that it enables the user to find out the values of each defining modal parameter that has an immense contribution towards the manufacture of photonic crystal fiber. Additionally, PCFPA allows the user to observe the variation in the modal parameters with respect to the changes in structural parameters (such as d, Λ, d/Λ, and λ/>Λ). Besides their ease of use, these two schemes have high computational precision and adaptability, giving a novel platform to optical engineers to modulate the microstructured fibers according to their requirement.     

An optimal linear and elliptical antenna array design using black widow optimisation for 5G communication

This research illustrates a precise linear and elliptical antenna array design for synthesising the optimal far-field radiation pattern in the fifth-generation (5G) communication spectrum using a meta-heuristic optimisation technique known as black widow optimisation (BWO). 5G communication is an emerging technology with revolutionary changes in the wireless communication system with ultra-high data rate, enhanced capacity, low latency and good quality of service. An accurate antenna array design for an ideal far-field radiation pattern synthesis with a suppressed side lobe level (SLL) value and half power beam width (HPBW) is the most crucial aspect of 5G communications. A suppressed SLL is necessary to reduce interference in the entire side lobe region, whereas a low HPBW is required for long-distance communication. Here, the BWO is employed to find the optimal feeding current to each array element to lower the SLL and the HPBW value. The BWO algorithm sustains impeccable equity between the exploration and exploitation stages to impact different potential regions of the search space and generate new solutions to attain the global optima by evading the trap of local optima. The design examples of the linear antenna array (LAA) and elliptical antenna array (EAA) are illustrated in this article by applying the optimal feeding currents to each array element. Compared to the uniform antenna array and methodologies described in the recently published literature, the results obtained utilising the BWO algorithm for designing the LAAs and EAAs demonstrate a substantial development in the reduction of SLL and HPBW.     

DEICA: A differential evolution-based improved clustering algorithm for IoT-based heterogeneous wireless sensor networks

With the evolution of technology, many modern applications like habitat monitoring, environmental monitoring, disaster prediction and management, and telehealth care have been proposed on wireless sensor networks (WSNs) with Internet of Things (IoT) integration. However, the performance of these networks is restricted because of the various constraints imposed due to the participating sensor nodes, such as nonreplaceable limited power units, constrained computation, and limited storage. Power limitation is the most severe among these restrictions. Hence, the researchers have sought schemes enabling energy-efficient network operations as the most crucial issue. A metaheuristic clustering scheme is proposed here to address this problem, which employs the differential evolution (DE) technique as a tool. The proposed scheme achieves improved network performance via the formulation of load-balanced clusters, resulting in a more scalable and adaptable network. The proposed scheme considers multiple parameters such as nodes' energy level, degree, proximity, and population for suitable network partitioning. Through various simulation results and experimentation, it establishes its efficacy over state-of-the-art schemes in respect of load-balanced cluster formation, improved network lifetime, network resource utilization, and network throughput. The proposed scheme ensures up to 57.69%, 33.16%, and 57.74% gains in network lifetime, energy utilization, and data packet delivery under varying network configurations. Besides providing the quantitative analysis, a detailed statistical analysis has also been performed that describes the acceptability of the proposed scheme under different network configurations.     

Effect of Ag doping on the electrical properties of thermally deposited CdS–La2O3 TFTs

This paper describes a new approach for repairing memories. Repair is implemented by deletion of either rows and/or columns on which faulty cells lie. These devices are commonly referred to as redundant memories, because redundant columns and rows are added. A new repair technique and an algorithm are proposed. The algorithm is based on a fault-counting technique and on a reduced-covering approach. The innovative feature is that reduced covering permits an heuristic, but efficient, criterion to be included in the selection of the rows and/or columns to be deleted. This retains independence of the repair process on the distribution of faulty cells in memory, while allowing a good repairability/ unrepairability detection. The main benefits that result by using the proposed repair algorithm are a reduction in execution time to determine the repair-solution for the device under test and its suitability for implementation in a defect analysis system. Illustrative examples and theoretical results are provided to substantiate the validity of the proposed repair technique     

Monitoring of black tea fermentation process using electronic nose

Black tea fermentation is essentially an oxidation process. After the plucked tea leaves are treated by series of processes called withering (removal of moisture by air flow), pre-conditioning and CTC (essentially maceration and cutting of leaves), the leaves are subjected to the process of fermentation by exposing them to air by laying the cut tea leaves on floor, trough or moving conveyor under controlled temperature, humidity and air-flow conditions. During this process, the leaves change colour from green to coppery brown and the grassy smell gets transformed to floral smell. It is critical that the leaves be allowed to ferment only up to the desired limit and both under and over fermentation result in deteriorated quality of black tea. Out of the two detectable parameters (colour and smell), smell is very important since a strong, very specific fragrance emanates from the leaves once leaves are optimally fermented. A new electronic nose-based approach for monitoring of tea aroma during fermentation is proposed. Two methods namely the 2-Norm method (2NM) and the Mahalanobis distance method (MDM) were tested and the results were correlated with the results of colorimetric tests and human expert evaluation.     

Identification of monofloral honey using voltammetric electronic tongue

Quality assessment of honey is often related to its floral origin which is a complex task to evaluate. Traditional technique of floral assessment is made by melissopalynological method. However, this method is quite time consuming and also often operator dependent. Thus, the fallout is a large range of error in interpretation of the result and hence there is considerable demand for instrumental methods to assess the identification of pollen in honey. In this pursuit, an electronic tongue based on cyclic voltammetry is developed to discriminate honey samples based on their floral types and is described in this paper. The technique has been investigated using platinum as the working electrode and the resultant current from the potentiostat has been considered for data analysis. The use of principal component analysis (PCA) and linear discriminate analysis (LDA) proves to be useful in clustering honey samples. Finally, classification performances are investigated using back-propagation multilayer perceptron (BP-MLP) and radial basis function (RBF) neural network models for identification of different floral origin of honey.     

Additive Manufacturing of Polymer-Based Lubrication

The fast growth of 3D printing technology gives designers many ways to make structures that are hard to see. 3D printing lets you customize complex structures in any way you want and make rapid prototypes of materials. It enables you to simulate things more effectively. So far, experiments with polymer-based lubrication have been done on atomically smooth surfaces, under dynamic conditions, and on the nano- or micro-scale. Polymer-based lubrication in 3D printing has been studied in depth, which has made it possible to make significant, multifunctional 3D structures with microscale accuracy. It is a crucial way to approach lubrication and has sparked much scientific interest. A thorough literature review is done to keep track of the latest advances in 3D printing for structural polymer-based lubrication simulation. The design and lubrication performance quality of bio-inspired, different-sized simulation structures is given much attention. The material requirements, skills, and representative applications of various 3D printing technologies are summarized. The efficient directions for future research in designing and making 3D-printed lubrication structures are also pointed out.