The Role of Modern Technology to Improve Education in Bangladesh

Modern technology in education is regularly developing day by day To realize the effects of modern technology is indeed significant for educational institutions Technology affects all the aspects of education Technology helps the instructors and learners to be more motivated to learn something very clearly Study background is discussed to understand the real perspective of modern technology and education By terms the points- significant of technology in education objective of the study literature review technological challenges of education the benefits of technology in education digital technologies in education the impact of technology in education technological transforming in education sector the impact of technology on the students traditional teaching versus virtual teaching challenges in implementing technology in the schools and colleges the importance of eLearning the ways to improve education based on technology limitations of technology in education are delineated in a straight forward way so that everyone can decipher the purpose of this articl

An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats SubChallenge. Our primary classification framework constitutes a convolutional neural network with 1D-CNN time-convolution (tConv) layers, which uses features transferred from a model trained on the 2016 Physionet Heart Sound Database. We also employ a Representation Learning (RL) approach to generate features in an unsupervised manner using Deep Recurrent Autoencoders and use Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we utilize an SVM classifier on a high-dimensional segment-level feature extracted using various functionals on short-term acoustic features, i.e., Low-Level Descriptors (LLD). An ensemble of the three different approaches provides a relative improvement of 11.13% compared to our best single sub-system in terms of the Unweighted Average Recall (UAR) performance metric on the evaluation dataset.Comment: 5 pages, 5 figures, Interspeech 2018 accepted manuscrip

Investigation of the shape transferability of nanoscale multi-tip diamond tools in the diamond turning of nanostructures

In this article, the shape transferability of using nanoscale multi-tip diamond tools in the diamond turning for scale-up manufacturing of nanostructures has been demonstrated. Atomistic multi-tip diamond tool models were built with different tool geometries in terms of the difference in the tip cross-sectional shape, tip angle, and the feature of tool tip configuration, to determine their effect on the applied forces and the machined nano-groove geometries. The quality of machined nanostructures was characterized by the thickness of the deformed layers and the dimensional accuracy achieved. Simulation results show that diamond turning using nanoscale multi-tip tools offers tremendous shape transferability in machining nanostructures. Both periodic and non-periodic nano-grooves with different cross-sectional shapes can be successfully fabricated using the multi-tip tools. A hypothesis of minimum designed ratio of tool tip distance to tip base width (L/Wf) of the nanoscale multi-tip diamond tool for the high precision machining of nanostructures was proposed based on the analytical study of the quality of the nanostructures fabricated using different types of the multi-tip tools. Nanometric cutting trials using nanoscale multi-tip diamond tools (different in L/Wf) fabricated by focused ion beam (FIB) were then conducted to verify the hypothesis. The investigations done in this work imply the potential of using the nanoscale multi-tip diamond tool for the deterministic fabrication of period and non-periodic nanostructures, which opens up the feasibility of using the process as a versatile manufacturing technique in nanotechnology

Review of intelligence for additive and subtractive manufacturing: current status and future prospects

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes

Analytical modeling, experimental investigations and performance test of mist coolant on micro end milling operations

Master'sMASTER OF ENGINEERIN

Fighting Pollution Attacks in P2P Streaming

In recent years, the demand for multimedia streaming over the Internet is soaring. Due to the lack of a centralized point of administration, Peer-to-Peer (P2P) streaming systems are vulnerable to pollution attacks, in which video segments might be altered by any peer before being shared. Among existing proposals, reputation-based defense mechanisms are the most effective and practical solutions. In this thesis, we perform a measurement study on the effectiveness of this class of solutions. We simulate a framework that allows us to simulate different variations of the reputation rating systems, from the centralized global approach to the decentralized local approach, under different parameter settings and pollution models. In order to ensure that the framework and the simulated solution is representative enough, we dissect existing proposals and simulate a flexible defense mechanism, in which different components may be enabled and disabled by simply tuning certain parameters. Our experimental results reveal that global knowledge of the reputation rating is necessary to provide the best defense against the attack. But it is often susceptible under collaborative attacks, like collusion. We also find that expelling misbehaving peers is often more useful to prevent attacks than limiting their likelihood to be connected, although this can lead to poor playback quality. Based on these key observations, we propose DRank, a fully distributed rank-based reputation system, which decentralizes the global ranking system and combines it with Bayesian reputation rating systems. Experimental results show that this technique is more flexible and robust in fighting pollution attacks

Design optimization of sandwich core

Ultralight sandwich structures comprising of low-density core with stiff facings have attracted significant research interest for their considerable weight saving applications. The aircraft industries are focusing on decreasing the structural mass to lower the manufacturing and operating costs. Design analysis of the sandwich cores using finite element analysis has been developed as a promising concept to feature sandwich structures with maximum strength, stiffness, and reduced weight. To obtain multifunctional behavior of sandwich panels, a profound investigation of geometrical and mechanical properties in the transverse plane is required because it is very susceptible to any kind loadings. Structural optimization is one of the key factors for designing lightweight structures, where the main concern is not merely to ensure an intricate design, but also to identify the limiting factors and resolve the issues by generating optimum values of the main parameters. This Thesis presents the design optimization of multifunctional sandwich panels in two chapters. The first chapter reports the shape optimization approach of four different core topologies considering three-dimensional isotropic patterns that are optimally designed for minimum weights. Additive manufacturing technology is a suitable and amenable method for the construction of sandwich structures because it ensures strong bonding between the facings and core to reduce the slipping. Fused deposition modeling method is employed to build the 3D printed structures. Short beam shear tests were carried out on the initially non-optimized structures to generate the structural response. Peak loads and deformations were recorded to compare the flexural properties. To obtain the new design of the sandwich cores with optimum stiffness and reduced weight shape optimization task is performed by ABAQUS. Stress and weight are the design variables to carry out the optimization method. Shape optimization process deals with the coordinates of surface nodes; eventually, it creates a new design of the cores that demonstrates versatile performance. Finally, based on the output of the optimization procedure new STL files are imported in the additive manufacturing machine to produce the optimized structure. Optimized panels are subjected to short beam shear test again to investigate their performance that has changed by employing shape optimization. Comparison using the mechanical properties are subsequently performed for the optimized and non-optimized panels to demonstrate the overall responses numerically. Results show that optimized structures are significantly lighter that perform decently from the strength standpoint with diverse characteristics such as ductility and brittleness. Algorithms, like a genetic algorithm, mimics natural process can be employed in the structural optimization technique. In this paper, both finite element analysis and genetic algorithm are employed to obtain the optimum result of the cross- sectional area for truss structures. The area is the main variable for this optimization technique that can be expressed by the array of binary numbers to carry out genetic algorithm operation and subsequently stress analysis is performed using the material properties. Since minimization of the weight is the objective function, so decreasing the cross-sectional areas subjected to a higher stress of the truss members and allowable stress operates as a stopping criterion for this iterative process. Finally, stress analysis and genetic algorithm create a possible solution set for areas and weight of the unit cell for the truss structure is determined. FEA is conducted by combining FEA (using ABAQUS) and genetic algorithm that is implemented in MATLAB. The findings shown in this Thesis have established appropriate weight saving technique for sandwich structures. The work provided a solid foundation for structural optimization that utilizes finite element package and a robust tool genetic algorithm which is not found in the commercial software packages