An Efficient Modified Dragonfly Optimization Based MIMO-OFDM for Enhancing QoS in Wireless Multimedia Communication

Wireless Personal Communications - Multiple inputs multiple outputs (MIMO) is a reliable technique which can manage the increased wireless data traffic in the future generation of wireless...     

Technical review: Improvement of mechanical properties and suitability towards armor applications – Alumina composites

Ceramic Matrix Composites (CMCs) have many interesting properties, mainly light weight, cost efficiency, low density, high compressive strength, high hardness and durability. Hence, they emerged as a boon to the development of personnel armors in the past. The current work aims to review various new methodologies adapted for the reinforcement of Alumina (Al₂O₃) CMCs in recent times, including some of the interesting results obtained with respect to mechanical properties, suitability of the synthesized composites for armor applications, and the upcoming reinforcement trends. Finally, studies related to reinforcement in Al₂O₃ CMCs, specifically towards armor applications have been consolidated to arrive at some of the important inferences for concluding reasonably.     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.     

Structural health monitoring of railway tracks using IoT-based multi-robot system

Neural Computing and Applications - A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to...     

Finite-element simulation of induction heat treatment

An efficient finite-element procedure has been developed for the analysis of induction heat treatment problems involving nonisothermal phase changes. The finite-element procedure first simulates the magnetic field developed when currents flow through an induction coil by solving Maxwell’s electromagnetic field equations; at the following step, it calculates the temperature distribution in the workpiece due to eddy currents induced by the magnetic field. The final stage of the simulation involves the determination of the distributions of residual stress, hardness, and microstructure in the workpiece. The finite-element analysis includes temperature-dependent material properties, changes in permeability of the workpiece at the Curie temperature, a mixed hardening rule to describe the material constitutive model, and the incorporation of time-temperature-transformation (TTT) diagram. The procedure was applied to the simulation of the induction hardening of 1080 steel bar. Firstly, the magnetic field and temperatures developed in the workpiece during (a) the induction heating of an infinitely long 1080 steel cylinder by a single encircling coil and (b) the induction heating of a semi-infinite half-space by a single coil suspended above it were calculated using the finite-element procedures. These were validated by comparing them with analytical solutions derived for these configurations using a Green’s function method. Finally, to demonstrate the predictive capability and practical applicability of the current finite-element procedure, two examples pertaining to the induction heat treatment of an infinite 1080 steel bar of square cross section and a notched finite 1080 steel cylinder of circular cross section were analyzed to predict the magnetic field, temperature, and residual stress distributions. The current finite-element procedure could be used as a powerful design tool for linking induction heat treating parameters with the mechanical property attributes of the heat treated component.     

Finite-element simulation of moving induction heat treatment

An efficient finite-element procedure with a remesh scheme has been developed for the analysis of the moving induction heat treatment process, wherein relative motion occurs between the coil and the workpiece. In this procedure, the magnetic field is first simulated by using an updated mesh that tracks the moving coil position; the moving heat source within the workpiece material is derived from the magnetic field. The heat equation is then solved to obtain the temperature field created by the heat source. The procedure has been applied to calculate the temperature distributions in 1080 carbon steel cylinders during induction heating. The calculations have been validated by comparison with analytical solutions for the temperature distribution obtained using Green’s function methods. Finally, the temperature, residual stress, and microstructure distributions in quenched 1080 steel cylinders have been obtained using the finite-element procedure. Quenching of the heated cylinders, by both a moving cooling ring and a stationary liquid bath, has been analyzed. The finite-element procedure presented incorporates temperature-dependent material properties, phase transformations occurring in the 1080 steel, the change in magnetic permeability of the 1080 steel at the Curie temperature, and an elastoplastic stress model based on a mixed hardening rule. The simulation results demonstrate that the finite-element procedure could be applied to a variety of moving induction heat treatment problems to determine the residual stress and microstructure distributions in the heat-treated component. It also could be used in the design of process parameters and coils.     

An expressive framework and efficient algorithms for the analysis of collaborative tagging

The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three components, user, item (e.g., photos in Flickr), and tags (i.e., words or phrases). Analyzing how tags are assigned by certain users to certain items has important implications in helping users search for desired information. In this paper, we develop a dual mining framework to explore tagging behavior. This framework is centered around two opposing measures, similarity and diversity, applied to one or more tagging components, and therefore enables a wide range of analysis scenarios such as characterizing similar users tagging diverse items with similar tags or diverse users tagging similar items with diverse tags. By adopting different concrete measures for similarity and diversity in the framework, we show that a wide range of concrete analysis problems can be defined and they are NP-Complete in general. We design four sets of efficient algorithms for solving many of those problems and demonstrate, through comprehensive experiments over real data, that our algorithms significantly out-perform the exact brute-force approach without compromising analysis result quality.     

Key-dependent 3D model hashing for authentication using heat kernel signature

Multimedia-based hashing is considered an important technique for achieving authentication and copy detection in digital contents. However, 3D model hashing has not been as widely used as image or video hashing. In this study, we develop a robust 3D mesh-model hashing scheme based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications. We further discuss the robustness, uniqueness, security, and spaciousness of the method for 3D model hashing. In the proposed hashing scheme, we calculate the local and global HKS coefficients of vertices through time scales and 2D cell coefficients by clustering HKS coefficients with variable bin sizes based on an estimated L² risk function, and generate the binary hash through binarization of the intermediate hash values by combining the cell values and the random values. In addition, we use two parameters, bin center points and cell amplitudes, which are obtained through an iterative refinement process, to improve the robustness, uniqueness, security, and spaciousness further, and combine them in a hash with a key. By evaluating the robustness, uniqueness, and spaciousness experimentally, and through a security analysis based on the differential entropy, we verify that our hashing scheme outperforms conventional hashing schemes.     

Design of artificial neuron controller for STATCOM in dSPACE environment

Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt–ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with PI and Artificial Neural Network Controller (ANNC). The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network requires less computation time. The ANNC has the ability to generalize and can interpolate in between the training data. The ANNC designed was tested on a 75 V, ±3KVAR STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that it was producing better results than the PI controller.