Showing papers by "Tripura Institute of Technology published in 2021"

Shear augmented microvascular solute transport with a two-phase model: Application in nanoparticle assisted drug delivery

Abstract: Solutal dispersion phenomena are associated with the nanoparticle-based drug delivery in the cardiovascular system to cure cardiovascular disorder. In the present problem, we explored the solutal transport for an unsteady blood flow through a microvessel with wall absorption. The rheology of blood is characterized by a two-fluid model similar to three-layer flow, namely, the core region, the intermediate region, and the peripheral region. The nature of the blood is considered as Casson fluid near the axis of the microvessel and Newtonian fluid close to the wall of the microvessel (at the intermediate and peripheral region). The peripheral region and the wall of the microvessel are permeable, and the permeability of the microvessel wall is defined by the Darcy–Brinkman model. The permeability of the inner surface of the microvessel is subjected to a slip condition at the surface. The stress-jump condition acts at the interface of the intermediate and peripheral region. The impact of the absorption parameter, velocity slip, yield stress, stress jump constant, nanoparticle volume fraction, and permeability on the velocity, exchange coefficient, convection coefficient, dispersion coefficient, and mean concentration is shown. It observed that the mean concentration boosts by the yield stress, nanoparticle volume fraction, and absorption parameters. The stress jump constant and permeability boost the convection coefficient, while the dispersion coefficient is restricted by the yield stress and absorption parameter.

Taylor dispersion in non-Darcy porous media with bulk chemical reaction: a model for drug transport in impeded blood vessels

Abstract: The present article discusses the solute transport process in steady laminar blood flow through a non-Darcy porous medium, as a model for drug movement in blood vessels containing deposits. The Darcy–Brinkman–Forchheimer drag force formulation is adopted to mimic a sparsely packed porous domain, and the vessel is approximated as an impermeable cylindrical conduit. The conservation equations are implemented in an axisymmetric system (R, Z) with suitable boundary conditions, assuming constant tortuosity and porosity of the medium. Newtonian flow is assumed, which is physically realistic for large vessels at high shear rates. The velocity field is expanded asymptotically, and the concentration field decomposed. Advection and dispersion coefficient expressions are rigorously derived. Extensive visualization of the influence of effective Peclet number, Forchheimer number, reaction parameter on velocity, asymptotic dispersion coefficient, mean concentration, and transverse concentration at different axial locations and times is provided. Increasing reaction parameter and Forchheimer number both decrease the dispersion coefficient, although the latter exhibits a linear decay. The maximum mean concentration is enhanced with greater Forchheimer numbers, although the centre of the solute cloud is displaced in the backward direction. Peak mean concentration is suppressed with the reaction parameter, although the centroid of the solute cloud remains unchanged. Peak mean concentration deteriorates over time since the dispersion process is largely controlled by diffusion at the large time, and therefore the breakthrough curve is more dispersed. A similar trend is computed with increasing Peclet number (large Peclet numbers imply diffusion-controlled transport). The computations provide some insight into a drug (pharmacological agents) reacting linearly with blood.

Natural and Mixed Convection Study of Isothermally Heated Cylinder in a Lid-Driven Square Enclosure Filled with Nanofluid

Abstract: A computational study of an isothermally heated circular cylinder which has been locomoted at different locations within a square enclosure is investigated numerically using finite volume method. The numerical work is conducted to acquire the heat transfer details through steady, laminar, natural and mixed convection in the enclosure filled with pure water and Al2O3–water nanofluid. The study is performed for different restraints such as cylinder position (CP1–CP9), nanoparticles volume fractions (0 ≤ ϕ ≤ 0.05), Richardson number (Ri = 0.1–10) and Rayleigh number (105–107). The results are exhibited qualitatively in terms of streamlines and isotherms however quantitatively by local and average Nusselt number for various constraints. The results unfolded from investigation that the average Nusselt number is significantly changed with the cylinder locations and is found maximal at the cylinder position CP2. The investigation is further explored at this position of cylinder. It is found that the average Nusselt number enhances in addition, with the increment of nanoparticle concentrations, for lower value of Richardson number and higher values of Rayleigh number.

Data Augmentation for Improved Brain Tumor Segmentation

Abstract: Deep neural networks (DNN) oblige large preprocessed samples of training annotated images for successful training, which makes the approach costly particularly in the biomedical imaging domain. The...

Detection of Early Breast Cancer Using A-Priori Rule Mining and Machine Learning Approaches

Abstract: In today’s world, breast cancer is extremely predominant in females that establishes in the breast and further extends to other locales of the body in the track of time. It is the second major ailment that causes decease. In long term, an early detection can reduce the death rate due to breast cancer appreciably. The crucial point for early prediction is to recognize the cancer cells at virgin stages. Various researches are carried out on breast cancer detection using mammography, ultrasounds, CT scans, PET, MRI, biopsy, etc. Still, these techniques are expensive, prolonged and sometimes unsuitable for young females. Hence, a fast and accurate detection system is highly demanded. In recent years, data mining and machine learning techniques are given utmost attention for early stage breast cancer detection. The aim of this paper is to present a framework for accurate and quick conclusion of breast cancer using machine learning techniques. We applied our proposed technique on SEER dataset of breast cancer and obtained highly appreciable results with accuracy of 99.9% using random forest. Various rules are also presented in support of breast cancer detection using A-priori algorithm.

An Improved ANN Model for Prediction of Solar Radiation Using Machine Learning Approach

Abstract: An accurate forecast of weather is essential for obtaining energy from Renewable sources. The objective of this paper is to present an analysis of weather parameters and comparison among different models of the weather prediction from accessible parameters and finally deriving a new technique for solar prediction in support of photovoltaic output power. Artificial Neural Network model with 5 weather parameters from NASA POWER dataset have been utilized to predict the day-ahead solar radiation and evaluated against real data measured for 4 years at Agartala, India (Latitude 23.83° N and Longitude 91.282° E). Results detailed in this work confirm the best predicting potential of the proposed method. The proposed model has been shown to predict solar radiation with accuracy of 83% shows the robustness of the system.

IoT-Based Smart Irrigation and Related Environment Parameters Monitoring: An Empirical Review

Abstract: Agriculture is the backbone of economy. The agrarian economy mainly depends on two parameters viz—weather monitoring and irrigation monitoring. Real-time weather monitoring is an important tool to visualize climate conditions prevailing in a field to solve production of crop and its yield associated problems by better understanding of surrounding weather. The main aim is to view weather conditions of any agricultural field and on-demand access of the current data of any near and remote locations of an agricultural field. Also, with discontinuous monsoon, farmers have to use other unsupervised alternate means of freshwater for the crops leading to scarcity of water. Therefore, farmers are facing challenges to make best irrigation schedules. The ever-augmenting technologies like Internet of Things (IoT) paved the way for smart weather stations and smart irrigation management with the help of wireless sensors to sense data for adapting changes of crop design, remote field site, and irrigation patterns taking into account all sort of environment constraints. IoT is capable to sense data, monitor, accurately predict climate conditions and is capable to limit water wastage, thus improving overall crop yield. This paper offers a detailed review of the broader aspect of IoT-based smart agriculture system depending on weather and irrigation.

High throughput resource efficient reconfigurable interleaver for MIMO WLAN application.

Abstract: Demand for high-speed wireless broadband internet service is ever increasing. Multiple-input-multiple-output (MIMO) Wireless LAN (WLAN) is becoming a promising solution for such high-speed internet service requirements. This paper proposes a novel algorithm to efficiently model the address generation circuitry of the MIMO WLAN interleaver. The interleaver used in the MIMO WLAN transceiver has three permutation steps involving floor function whose hardware implementation is the most challenging task due to the absence of corresponding digital hardware. In this work, we propose an algorithm with a mathematical background for the address generator, eliminating the need for floor function. The algorithm is converted into digital hardware for implementation on the reconfigurable FPGA platform. Hardware structure for the complete interleaver, including the read address generator and memory module, is designed and modeled in VHDL using Xilinx Integrated Software Environment (ISE) utilizing embedded memory and DSP blocks of Spartan 6 FPGA. The functionality of the proposed algorithm is verified through exhaustive software simulation using ModelSim software. Hardware testing is carried out on Zynq 7000 FPGA using Virtual Input Output (VIO) and Integrated Logic Analyzer (ILA) core. Comparisons with few recent similar works, including the conventional Look-Up Table (LUT) based technique, show the superiority of our proposed design in terms of maximum improvement in operating frequency by 196.83%, maximum reduction in power consumption by 74.27%, and reduction of memory occupancy by 88.9%. In the case of throughput, our design can deliver 8.35 times higher compared to IEEE 802.11n requirement.

Enhanced Colliding Bodies Optimization Towards the Solution of Economic Load Dispatch Problem

Abstract: This paper proposes application of enhanced colliding bodies optimization algorithm (ECBO) for solving the economic load dispatch problem, which aims to get minimum generation cost through economic scheduling of generating unit. The algorithm is a novel efficient optimization algorithm, and its idea is attained from collisions, which are one-dimensional between two or more bodies. Each agent involved in this collision is shaped as a body having defined mass and defined velocity. To analyze the performance of the proposed method, it is tested on four different test systems consisting of 3, 5, 13, and 18 generating units. Both convex and non-convex fuel cost function have been considered. Numerical results have been compared with various well-known algorithms. A significant improvement in the results has been observed. It has been farther noted that an average 5.1% better results were provided by ECBO in terms of generation cost compared to other algorithms. Moreover, the simulation time is also improved. Besides this, Kruskal-Wallis non-parametric test has been performed.