Aini Hussain

Energy harvesting for the implantable biomedical devices: issues and challenges

The development of implanted devices is essential because of their direct effect on the lives and safety of humanity. This paper presents the current issues and challenges related to all methods used to harvest energy for implantable biomedical devices. The advantages, disadvantages, and future trends of each method are discussed. The concept of harvesting energy from environmental sources and human body motion for implantable devices has gained a new relevance. In this review, the harvesting kinetic, electromagnetic, thermal and infrared radiant energies are discussed. Current issues and challenges related to the typical applications of these methods for energy harvesting are illustrated. Suggestions and discussion of the progress of research on implantable devices are also provided. This review is expected to increase research efforts to develop the battery-less implantable devices with reduced over hole size, low power, high efficiency, high data rate, and improved reliability and feasibility. Based on current literature, we believe that the inductive coupling link is the suitable method to be used to power the battery-less devices. Therefore, in this study, the power efficiency of the inductive coupling method is validated by MATLAB based on suggested values. By further researching and improvements, in the future the implantable and portable medical devices are expected to be free of batteries.

Implementation of inverse perspective mapping algorithm for the development of an automatic lane tracking system

Vision based automatic lane tracking system requires information such as lane markings, road curvature and leading vehicle be detected before capturing the next image frame. Placing a camera on the vehicle dashboard and capturing the forward view results in a perspective view of the road image. The perspective view of the captured image somehow distorts the actual shape of the road, which involves the width, height, and depth. Respectively, these parameters represent the x, y and z components. As such, the image needs to go through a pre-processing stage to remedy the distortion using a transformation technique known as an inverse perspective mapping (IPM). This paper outlines the procedures involved.

Modulation Techniques for Biomedical Implanted Devices and Their Challenges

Implanted medical devices are very important electronic devices because of their usefulness in monitoring and diagnosis, safety and comfort for patients. Since 1950s, remarkable efforts have been undertaken for the development of bio-medical implanted and wireless telemetry bio-devices. Issues such as design of suitable modulation methods, use of power and monitoring devices, transfer energy from external to internal parts with high efficiency and high data rates and low power consumption all play an important role in the development of implantable devices. This paper provides a comprehensive survey on various modulation and demodulation techniques such as amplitude shift keying (ASK), frequency shift keying (FSK) and phase shift keying (PSK) of the existing wireless implanted devices. The details of specifications, including carrier frequency, CMOS size, data rate, power consumption and supply, chip area and application of the various modulation schemes of the implanted devices are investigated and summarized in the tables along with the corresponding key references. Current challenges and problems of the typical modulation applications of these technologies are illustrated with a brief suggestions and discussion for the progress of implanted device research in the future. It is observed that the prime requisites for the good quality of the implanted devices and their reliability are the energy transformation, data rate, CMOS size, power consumption and operation frequency. This review will hopefully lead to increasing efforts towards the development of low powered, high efficient, high data rate and reliable implanted devices.

ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

Modeling sediment load is a significant factor in water resources engineering as it affects directly the design and management of water resources. In this study, artificial neural networks (ANNs) are employed to estimate the daily sediment load. Two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for this purpose. The neural networks are trained and tested using daily sediment and flow data from Rantau Panjang station on Johor River. The results show that combining flow data with sediment load data gives an accurate model to predict sediment load. The comparison of the results indicate that the FFNN model has superior performance than the RB model in estimating daily sediment load.

Genetically optimised disassembly sequence for automotive component reuse

Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.

A review on technologies and their usage in solid waste monitoring and management systems: Issues and challenges.

In the backdrop of prompt advancement, information and communication technology (ICT) has become an inevitable part to plan and design of modern solid waste management (SWM) systems. This study presents a critical review of the existing ICTs and their usage in SWM systems to unfold the issues and challenges towards using integrated technologies based system. To plan, monitor, collect and manage solid waste, the ICTs are divided into four categories such as spatial technologies, identification technologies, data acquisition technologies and data communication technologies. The ICT based SWM systems classified in this paper are based on the first three technologies while the forth one is employed by almost every systems. This review may guide the reader about the basics of available ICTs and their application in SWM to facilitate the search for planning and design of a sustainable new system.

Determination of Transmission Reliability Margin Using Parametric Bootstrap Technique

The determination of available transfer capability (ATC) must accommodate a reasonable range of transmission reliability margin (TRM) so that the transmission network is secure from uncertainty of transfer capability that may occur during a power transfer. This paper presents a computationally accurate method in determining the TRM with large amount of transfer capability uncertainty using the parametric bootstrap technique. The parametric bootstrap technique is used to randomly generate a bootstrap sample of ATCs with large uncertainty selected at a certain percentage of bootstrap confidence interval. The bootstrap sample of ATCs in a day is used to determine the TRM at every time interval. The TRM value at a particular time interval is selected based on a certain percentage of normal cumulative distribution function (CDF). Then, a new value of ATC at the current time interval is calculated by considering the TRM at the same time interval. The effectiveness of the proposed TRM method in providing new ATC value is validated on the Malaysian power system. The results show that the proposed method provides accurate estimation of TRM in which it is relatively similar to the TRM result obtained by the standard deviation of uncertainty which is incorporated in the Monte Carlo simulation technique. Further comparisons in terms of accuracy and total computational time in estimating the TRM is made between the parametric and nonparametric bootstrap techniques.

Vulnerability control of large scale interconnected power system using neuro-fuzzy load shedding approach

Vulnerability control is becoming an essential requirement for security of power systems in the new utility environment. It is a difficult task for system operator who under economic pressure may be reluctant to take preventive action against harmful contingencies in order to guarantee providing continued service. For power systems which are operated closer to their stability limits, it is desirable to use load shedding as a form of vulnerability control strategy. This paper presents a neuro-fuzzy approach for determining the amount of load to be shed in order to avoid a cascading outage. The objective is to develop fast and accurate load shedding technique to control the vulnerability of power systems by means of using a neuro-fuzzy controller. A case study is performed on the IEEE 300-bus test system so as to validate the performance of neuro-fuzzy controller in determining the amount of load shed. Test results prove that the neuro-fuzzy controller provides accurate and faster vulnerability control action.

Feature Extraction Technique using Discrete Wavelet Transform for Image Classification

The purpose of feature extraction technique in image processing is to represent the image in its compact and unique form of single values or matrix vector. Low level feature extraction involves automatic extraction of features from an image without doing any processing method. In this paper, we consider the use of high level feature extraction technique to investigate the characteristic of narrow and broad weed by implementing the 2 dimensional discrete wavelet transform (2D-DWT) as the processing method. Most transformation techniques produce coefficient values with the same size as the original image. Further processing of the coefficient values must be applied to extract the image feature vectors. In this paper, we propose an algorithm to implement feature extraction technique using the 2D-DWT and the extracted coefficients are used to represent the image for classification of narrow and broad weed. Results obtained suggest that the extracted 2D-DWT coefficients can uniquely represents the two different weed type.

RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia

Abstract Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.