http://ieeexplore.ieee.org/iel5/5/31047/01444179.pdf

Fundamentals of acoustics

Data gathering problem with the data importance consideration in Underwater Wireless Sensor Networks

Stainless steel, titanium alloys and cobalt chromium molybdenum alloys are classified under the metallic biomaterials whereby various surgical implants, prosthesis and medical devices are manufactured to replace missing body parts which may be lost through accident, trauma, disease, or congenital conditions. Among these materials, cobalt chromium molybdenum alloys are the common cobalt base alloy used for orthopedic implants due their excellence properties which include high corrosion resistance, high strength, high hardness, high creep resistance, biocompatibility and greater wear resistance. This paper summarises the various aspects and characteristic of metallic biomaterials such as stainless steel, titanium and cobalt chromium alloys for medical applications especially for orthopedic implant. These include material properties, biocompatibility, advantages and limitations for medical implants applications.

Metallic Biomaterials for Medical Implant Applications: A Review

In this paper, a new channel estimation algorithm is proposed that exploits channel sparsity in the time domain for an orthogonal frequency division multiplexing (OFDM)-based underwater acoustical (UWA) communications systems in the presence of Rician fading. A path-based channel model is used, in which the channel is described by a limited number of paths, each characterized by a delay, Doppler scale, and attenuation factor. The resulting algorithm initially estimates the overall sparse channel tap delays and Doppler shifts using a compressed sensing approach, in the form of the orthogonal matching pursuit (OMP) algorithm. Then, a computationally efficient and novel channel estimation algorithm is developed by combining the OMP and maximum a posteriori probability (MAP) techniques for estimating the sparse complex channel path gains whose prior densities have complex Gaussian distributions with unknown mean and variance vectors, where a computationally efficient maximum likelihood algorithm is proposed for their estimation. Monte Carlo simulation results show that the mean square error and symbol error rate performances of the OMP–MAP algorithm uniformly outperforms the conventional OMP-based channel estimation algorithm, in case of uncoded OFDM-based UWA communications systems.

Sparse Channel Estimation for OFDM-Based Underwater Acoustic Systems in Rician Fading With a New OMP-MAP Algorithm

Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. This paper is a distilled tutorial on UWA channel modeling with a focus on network simulation, providing a trade-off between the flexibility of low level channel modeling via beam tracing and the convenience of automated channel modeling, e.g. via the World Ocean Simulation System (WOSS). The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.

/pdf/channel-modeling-for-underwater-acoustic-network-simulation-3j1gxu508x.pdf

Channel Modeling for Underwater Acoustic Network Simulation

Acoustic underwater sensor network (UWSN) is important in terms of business and for military applications. Optimum UWSN design provides a reliable connection with maximum connectivity, minimal multipath, low technical cost, and emphasis on energy conservation. This paper examines the channel estimation parameters, such as channel impulse response, coherence bandwidth, BER due to delay for differentially detected MSK receiver, and transmission loss impact of underwater node placement in three scenarios (ground-based, buoys-based, and horizontal point-to-point sensor node). All data (bathymetry, altimetry, temperature, salinity, and depth) are real and have been sourced from different satellite databases. The case study of this paper is the Desaru beach in the eastern shore of Johor Bahru in Malaysia. In this investigation, Acoustics Toolbox of BELLHOP through MATLAB and mathematical analyzing method has been utilized. As a result, this paper indicates the maximum received power, and the lowest delay time characteristics and TL belong to horizontal point-to-point scenario; moreover, the ground-based scenario has the highest delay time and maximum coherence bandwidth among three scenarios.

Study of Underwater Channel Estimation Based on Different Node Placement in Shallow Water

This article presents an Artificial Neural Network (ANN) architecture to model the Electrical Discharge Machining (EDM) process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data (input variables) of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate (MRR). In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.

Material Removal Rate Prediction of Electrical Discharge Machining Process Using Artificial Neural Network

PID controller of servo control system maintains the gap between Electrode and workpiece in Electrical Discharge Machining (EDM). Capability of the controller is significant since machining process is a stochastic phenomenon and physical behaviour of the discharge is unpredictable. Therefore, a Proportional Integral Derivative (PID) controller using Differential Evolution (DE) algorithm is designed and applied to an EDM servo actuator system in order to find suitable gain parameters. Simulation results verify the capabilities and effectiveness of the DE algorithm to search the best configuration of PID gain to maintain the electrode position.

Differential evolution for optimization of pid gain in electrical discharge machining control system

http://scientiairanica.sharif.edu/article_3744_48ee7b922e2ecf0e56bb094872daf83b.pdf

Review on an electrical discharge machining servomechanism system

Electrical Discharge Machining (EDM) is included in a stochastic process. So maintaining gap between electrode and workpiece is not easy. In order to control the gap, a proportional integral derivative (PID) controller is designed for EDM servo actuator system. The main goal of this work is to get PID parameters through Particle Swarm Optimization (PSO) algorithm to ensure a stable, robust and controlled system. The controller and the model for EDM die sinking are verified by simulation of the control system using MATLAB and simulink program. Simulation results verify the effectiveness of the PID controller in which its parameter determined by PSO to control the electrode position towards workpiece.

Ameruddin Baharom

Papers

Study of Underwater Channel Estimation Based on Different Node Placement in Shallow Water

Material Removal Rate Prediction of Electrical Discharge Machining Process Using Artificial Neural Network

Differential evolution for optimization of pid gain in electrical discharge machining control system

Review on an electrical discharge machining servomechanism system

PID controller tuning by particle swarm optimization on electrical discharge machining servo control system