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Scheduling system is developed based on customers, individuals or organizational preferences in order to create an efficient and effective management of arrangement in prioritization, tasks, place and time. Some of scheduling systems will integrate a database for effective access of editing, viewing and recording amount of data. However, poor scheduling practices would cause undesirable time redundancy and inefficient process just to schedule a timetable for the lecturers and students. Poor scheduling also can cause a misunderstanding between lectures such as double-assignations of lectures, cancellations of presentations, clash in class schedule, prolonged postponement as well as inefficiency in terms of resources and time. A good scheduling system could generate optimal presentation of timetables and enable a direct interaction between lecturers and administration staff that will take into account all the constraints such as to check availability of time and place of events and real-time updates that will help data handling accuracy, cost effective and efficient time management. Therefore, this study will make a comparison and execute a standard process of scheduling framework for future practice in academic industry. This paper presents a practical method for making a comparison based on the studies on existing scheduling system.

The penetration of electric vehicles (EVs) in the transportation sector is increasing but conventional internal combustion engine (ICE) based vehicles dominates. To accelerate the adoption of EVs and to achieve sustainable transportation, the bottlenecks need to be elevated that mainly include the high cost EVs, range anxiety, lack of EV charging infrastructure, and the pollution of the grid due to EV chargers. The high cost of EVs is due to costly energy storage systems (ESS) with high energy density. This paper provides a comprehensive review of EV technology that mainly includes electric vehicle supply equipment (EVSE), ESS, and EV chargers. A detailed discussion is presented on the state-of-the-art of EV chargers that include on-/off-board chargers. Different topologies are discussed with low-/high-frequency transformers. The different available power levels for charging are discussed. To reduce the range anxiety the EV chargers based on inductive power transfer (IPT) are discussed. The last part of the paper focuses on the negative impact of EV chargers along with the remedies that can be adopted. The international standards decided by different institutions and adopted universally are discussed in the latter part of this paper and finally, this paper concludes with the near to future advancement in EV technology.

Multi-level inverters (MLI) are power electronic converters that convert DC power to AC power with high power-quality of output voltage waveforms. These MLI are the main streamline converters for integrating dc power of EV with microgrids. Thus, the recent interest of researchers is to investigate the MLI with a lower number of active and passive switch counts which could integrate the DC power of EV to the microgrid with the boosting ability. This chapter discusses various topologies of MLI for the integration of the DC power of EV to the grid for vehicle-to-grid (V2G) applications. MLI converts DC power to AC power with high quality of output voltage waveform. Thus, the recent interest of researchers is to investigate the MLI with a lower number of active and passive switch counts which could integrate the DC power of EV to the microgrid. Also, MLI must be capable of boosting the voltage level to meet the grid requirements. The aim of this chapter is to discuss the various topologies of MLI for the integration of DC power of EV to the grid for vehicle-to-grid (V2G) applications.

Electric vehicles (EVs) have gained popularity due to their eco-friendly nature. This paper discusses the types of EVs along with the charger types and their power levels. The battery electric vehicles (BEVs), fuel cell electric vehicles (FCHEVs) and plug-in hybrid electric vehicles (PHEVs) are discussed. Furthermore, the AC-DC converters and various DC-DC converter topologies that are essential for EV chargers are explained with their pros and consequences. A non-isolated conventional DC-DC boost converter and two-phase interleaved boost converter (IBC) is designed and analyzed in the MATLA®/Simulink environment. The result shows that the IBC has low ripples in the input current and the output voltage. Thus, they are more suitable as compared to conventional DC-DC converter.

Bridge pier scour is an important issue for safety evaluation of bridges. The existing scour depth prediction equations for bridge piers are mostly deterministic in nature, which do not incorporate...

This project involves research about software effort estimation using machine learning algorithms. Software cost and effort estimation are crucial parts of software project development. It determines the budget, time and resources needed to develop a software project. One of the well-established software project estimation models is Constructive Cost Model (COCOMO) which was developed in the 1980s. Even though such a model is being used, COCOMO has some weaknesses and software developers still facing the problem of lack of accuracy of the effort and cost estimation. Inaccuracy in the estimated effort will affect the schedule and cost of the whole project as well. The objective of this research is to use several algorithms of machine learning to estimate the effort of software project development. The best machine learning model is chosen to compare with the COCOMO.

Multi-level inverters (MLI) have improved power quality at output voltage as compared to conventional inverters. In MLI, number of switches increases with number of levels in output voltage. This increases the complexity and reduces the reliability of the MLI. In this chapter, a three-phase five-level MLI is proposed which involves lesser number of switches as compared with other topologies present in literature. The proposed MLI involves a single DC source and two capacitors along with 10 switches. For reducing total harmonic distortion (THD) in output voltage of the proposed MLI, a particle swarm optimization (PSO)-based selective harmonics elimination method is adopted. The proposed MLI topology is modeled in MATLAB®/Simulink. The simulation results obtained show the improved THD in output voltage and thus confirm the effectiveness of the proposed optimization method for the MLI. The proposed MLI can be used for the integration of renewable resources in the utility grid system.

Virtual Reality (VR) technology has become cheaper over time and makes it possible to own. However, interaction techniques in VR need to be natural as possible to be accepted by users. Compared to other computing devices (e.g., mobile and desktop), there is very few human-computer interactions (HCI) paradigms in VR and a lot of HCI issues and problems are yet to be solved. In this paper, we present a VR Welding Kit, a virtual reality welding training simulation developed for mobile smartphones using multiple marker images as an interaction method. The experimental results have revealed with comparison benchmark with high-end VR device, Oculus Quest to see the efficiency of multiple marker interaction with a current VR technology device. System Usability Scales (SUS) also used to see the acceptance between users with this new interaction. This is the first evaluation of combination VR with marker-based tracking method interaction. Simulator Sickness Questionnaire (SSQ) are used to check the cybersickness between participants toward multiple marker interaction. The results on performance shows that mobile VR have a large gap completion compared with Oculus Quest. The SUS score is 73.33, above 68 which is great. SSQ surveys result shows that participant almost feels none in simulation sickness.

In this paper a non-isolated dc-dc boost converter is described having high voltage gain ability. The proposed high gain boost converter, unlike the conventional boost converter offers high voltage conversion at low duty ratio, thus have high comparative efficiency. The proposed dc-dc boost converter is based on the voltage lift (VL) technique, in which input voltage is boosted in step-by-step manner. A detailed analysis of proposed boost dc-dc converter for non-ideal model is presented. The performance of proposed converter is compared with the conventional boost converter in term of different parameters, viz. number of elements, voltage gain ratio, and switch stress. The proposed dc-dc converter has advantages over conventional one and is suitable for renewable energy resources (RES) applications, especially solar and wind energy. The results obtained both for proposed and conventional converters in MATLAB®/Simulink environment is validated through the lab scaled developed prototype.

The aim of this paper is to introduce the new emerging technology of x-electric vehicles (xEVs); where x is a general term which stands for hybrid/plug-in/battery e.t.c. xEVs have emerged as an alternate for internal combustion (IC) engine based vehicles because of their lower dependence on fossil fuel, thus causing less environmental pollution (mainly CO and CO 2 ) and hence plays an important role in saving the earth from global warming. Moreover, xEVs are far more efficient than IC engine based vehicles. Thus, it is a wise option to switch for xEVs to IC engine based vehicles. In this paper the xEVs technology is discussed in detail which includes their charging infrastructures, charging power levels and energy storage systems. Moreover, the hurdles in the way to success of this technology are discussed and the most efficient solution to them are suggested which are easy to implement and do not need large investments.

Precision genome engineering has dramatically advanced with the development of CRISPR/Cas base editing systems that include cytosine base editors and adenine base editors (ABEs). Herein, we compare the editing profile of circularly permuted and domain-inlaid Cas9 base editors, and find that on-target editing is largely maintained following their intradomain insertion, but that structural permutation of the ABE can affect differing RNA off-target events. With this insight, structure-guided design was used to engineer an SaCas9 ABE variant (microABE I744) that has dramatically improved on-target editing efficiency and a reduced RNA-off target footprint compared to current N-terminal linked SaCas9 ABE variants. This represents one of the smallest AAV-deliverable Cas9-ABEs available, which has been optimized for robust on-target activity and RNA-fidelity based upon its stereochemistry., Off-target effects and the feasibility for AAV-mediated delivery are the major barriers impeding the clinical in vivo application of base editors. Here, the authors report the small size AAV-deliverable Cas9-ABE variant that has improved on-target editing efficiency and reduced RNA-off target footprint.

Image based target used in augmented reality application is not the same as traditional fiducial markers, data markers code and QR-codes in detection and tracking. The available features from the image detected and tracked are naturally found in the image itself. Image target commonly used in recognizing and augmented hard copy media and product for marketing campaigns, gaming, and visualizing products in the environment where the product was targeted to be used. HOwever, for mobile-based augmented reality application, image features detection and tracking still expose many challenges. This paper aims to review previous and current keypoint detection and tracking techniques for image target and address several issues face in mobile based augmented reality application. Most traditional interest-point detection and description methods are handcrafted. These methods focus on interest points in a generic setting and lack provision to adapt toward a specific dataset. Learning-based approaches can be a new solution to real-time application such mobile-based application. This project continuity work from our previous work done for mobile-based augmented reality coloring application [1].

The global agenda of sustainable development (SD) will get a major boost from the successful implementation of the Sustainable Development Goals (SDGs) in developing countries. This requires effective planning and understanding of the SDGs at the national level and developing sound SDG indicators that account for country priorities. Currently, there is no formal SDG indicator set in India, but the process for it is underway. This paper has attempted to propose a unique technique for identifying the most representative indicator set for SD measurement in India based on the SDGs. Considering the needs and preferences of the country, a three‐stage process is proposed to develop the priority indicator set. The application of the approach has been presented for SDG 7, which relates to energy. This is supported with a background on India's SD progress. The proposed technique is simple yet effective, and can be easily replicated by other developing countries.

The serious growing concern of climate change has pushed the adoption of a sustainable transportation system. In this regard, electric vehicles are the utmost feasible solution. However, the commercialization of on-road electric vehicles faces multiple challenges such as an adequate number of charging stations availability, charging time and range anxiety. To overcome the issues of charging time and range anxiety, the energy storage system plays a vital role. Thus, in this paper, the various technological advancement of energy storage system for electric vehicle application has been covered which includes the support for the superiority of the Li-ion batteries in terms of various parameters. The various aspect such as expected futurist development in EV battery technology, capacity demand, battery sizing and life cycle of Li-ion batteries are well explained. Further, the details of the Battery management system with working and operation have been explained under the cell balancing, consistency, charging/discharging control, state of charge, state of health, and improved life span. Next in the drift, the advancement in the battery thermal management system is covered. Moreover, the manuscript has covered the detailed significance of the alternative energy storage systems followed by the comparative analysis which reveals their challenges, application and limitations. In last the role of the energy storage system for sustainable development is explained.

Accurate customer churn classification is vital in any business organisation due to the higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability to produce higher classification accuracy, the ensemble method tends to produce similar or redundant classifiers. Therefore, this paper aims to achieve higher classification accuracy and at the same time, minimising ensemble classifiers by constructing a new ensemble method based on dimensionality reduction in soft set theory. The combination of ensemble classifier is calculated based on the simple majority voting algorithm. The performance measure used in determining the optimal subset of classifiers is the combination of Accuracy (ACC), True Negative Rate (TNR) and True Positive Rate (TPR). The proposed soft set ensemble methods (SSPN and SSSC) are systematically evaluated using customer churn data set taken from one of the local Telco companies in Malaysia. The selection and combination algorithm (SSSC) has proven its supremacy by producing accuracy (ACC) of 87.0% for local Telco data set and 94.0% for UCI data set, which is better than any other single classifier. This work proved that the proposed soft ensemble method could search for the minimum number of classifiers in the ensemble repository while at the same time improving the classification performance. In conclusion, the proposed soft ensemble method not only reduces the number of members of the ensemble but is also able to produce the highest classification accuracy.

Coloring activity with pens and paper is a natural activity and an important experience for children to practice and express their creative skills. To gain interest and attention of kids making these creative activities are the main challenge faced by teachers and parents. By developing augmented reality coloring to solve this problem require us to design a mobile application with suitable coloring book contents designed suit for kids. This work aims to provide guidelines developing interactive coloring book with augmented reality for kids that integrate with our previous technique. Wonderful Augmented Reality and Arts (wARna) is our previous fast texturing technique as the main core of the framework application that interactively play colored 2D coloring book page by visualizing it in 3D on a user's view of the real world. This work proposed a framework with suitable specification in creation of content so it reusable to create new coloring content that integrated with a mobile application and highlighting issues that need to be solved.

A body measurement technique for virtual apparel by using angle-based algorithm is presented. The existing virtual apparel or virtual fitting room system only focus on normal-sized human body. This paper proposes a technique that will allow the system to measure accurately and dynamically, to be used by users with various body sizes, using a depth motion sensor. This paper will also discusses about the data from the depth motion sensor, and how the data is processed using the proposed angle-based algorithm and ellipse perimeter equation. Experiments from this study have shown that the system can measure various human body size and the calculated measurement using the technique is almost the same as the actual measurement of the user's body. Potential application of this technique and future plan are also discussed.

Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.

Knowledge of energy conservation is vital to be understood by students in grasping the topic of energy science. Hence, a study has been piloted to produce energy converter which can be used practically by the students to understand about the conversion of energy from kinetic energy to electricity. In the convertible of kinetic energy to electrical energy, as these energy undergo conversion, the kinetic energy are stored in two forms of storage apparatus. The first type is synchronous flywheels which operate within the allowable range of the synchronous frequency. The second type is asynchronous flywheels whose rotational speed is independent from the system synchronous frequency and varied over a wide range and this features gives the asynchronous flywheel the ability to store and release significant amount of kinetic energy. The outcomes from this study displayed successful performance which is dynamo that act as generator for the converter as energy is being convert from the kinetic energy into the electrical energy and efficaciously can be used to charge electrical gadget.

Customer care plays an important role in a company especially in managing churn for Telecommunication Company. Churn is perceived as the behaviour of a customer to leave or to terminate a service. This behaviour causes the loss of profit to companies because acquiring new customer requires higher investment compared to retaining existing ones. Thus, it is necessary to consider an efficient classification model to reduce the rate of churn. Hence, the purpose of this paper is to propose a new classification model based on the Rough Set Theory to classify customer churn. The results of the study show that the proposed Rough Set classification model outperforms the existing models and contributes to significant accuracy improvement. Keywords : customer churn; classification model; telecommunication industry; data mining; rough set.

The advancement of augmented reality (AR) and holographic display technologies have a great potential to support and improve teaching and learning process, because the 3D images give new perspectives to the students to understand certain topic easily and intuitively. In this paper, an Interactive Holographic Display is introduced, which is aimed to ease the teachers to deliver the knowledge to the students as well as to provide self-learning for the students. This proposed solution is implemented using the AR tracking technique and fused with 3D holographic pyramid display. This makes the virtual objects can display in thin air like a real object and makes the holographic effect more realistic and interactive, as the user can interact with the virtual objects using an image target. This paper explains how the system is physically realized in term of hardware configuration and software design.

This paper presents an improved seed point placement to locate initial seed points based on magnitude value. The vector field is separated in to grid to ensure the uniformity in generating streamlines and broader streamlines coverage, thus reducing poor visualization information especially near to visualization boundary. An evaluation using Mean Square Error is conducted to evaluate the improvement in term of streamlines placement uniformity. Result shows that proposed method able to come out with lower Mean Square Error value compared to evenly-spaced in across different separation distance, resulting better streamlines control to produce uniform visualization result. Finally, the proposed method can be improved in many aspects to come out with a better visualization result in 2 and possibly 3 dimension flow visualization.

For harnessing bulk power from the solar photovoltaic (PV) cell/ array and to supply it to the utility grid, dc-to-ac converters are needed. For this purpose, both single-stage and multi-stage converters are being used which include PWM inverters. Recently, thyristor based dc-to-ac converters were reported for low-power, grid connected inverters for solar photovoltaic (PV) applications. This paper proposes a new topology for single phase grid connected line commutated inverter which gives lower harmonics in input line current as compared to conventional line commutated grid connected inverter. The proposed topology is analyzed in continuous, just continuous and discontinuous modes. The performances of conventional and proposed topologies are compared through MATLAB/Simulink based model. A laboratory prototype of the proposed converter is also developed and successfully implemented to validate the simulation results.

This study was conducted to analyse the needs of computer programming education. The research design used was a case study and a total of 30 participants has taken part in this study. All participants are among programming coach, teachers and lecturers. Data collection has been analysed using thematic analysis techniques. The findings show that each dimension of the needs analysis, research succeeds in obtaining specific themes and could be used as a foundation to develop teaching and learning programming module. The results of the analysis find that two essential elements still need further research and need to be done separately. The first requirement element requiring further and separate study is how to produce assessment rubrics that contain benchmarks equivalent to the expertise thinking of the programmer. Research that needs to be done is to explore the creative processes that occur in the programmer's mind. The second requirement element is how the instructors develop coaching skills based on technology knowledge, pedagogy and teaching content as well as the issues and challenges faced.

The use of scientific visualization technique in real-time rendering environment has great potential to enhance user interaction in visualizing blood flow; therefore the integration algorithm of chosen scientific visualization technique and real-time algorithm should be developed and implemented in virtual environment. It is a basic fact that the effectiveness of blood flow visualization are reliant on two fundamental issues. First is how to present an improved visualization of cardiovascular flow, and the second is how to interactively visualize the fluidic blood flow in gaining insight into the cardiovascular physiology. This research proposed a framework on based on idea from previous research that improve visualization of blood flow by integrates with particle based simulation method in order to allow more user interaction. Despite the variety and number of existing method, there are still demands for new improved visualization technique with a mission to provide better information. Good understanding on blood flow pattern will aid clinician, engineer and researcher during the diagnosis and prognosis of pathology as well as the assessment of risk and follow-up finding.

This paper presents the applicability of soft set theory for discovering the preference relation in multi-valued information systems. The proposed approach is based on the notion of multi-soft sets. An inclusion of objects into value set of decision class in soft set theory is used to discover the relation between objects based on preference relation. Results from the experiment shows that dominance relation based on soft theory for preference relation is able to produce a finer object classification by eliminating inconsistencies during classification process as opposed to the expert judgement classification.

Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability of producing the highest classification accuracy, ensemble methods have suffered significantly from their large volume of base classifiers. Thus, in the previous work, we have proposed a novel soft set based method to prune the classifiers from heterogeneous ensemble committee and select the best subsets of the component classifiers prior to the combination process. The results of the previous study demonstrated the ability of our proposed soft set ensemble pruning to reduce a substantial number of classifiers and at the same time producing the highest prediction accuracy. In this paper, we extended our soft set ensemble pruning on the customer churn dataset. The results of this work have proven that our proposed method of soft set ensemble pruning is able to overcome one of the drawbacks of ensemble method. Ensemble pruning based on soft set theory not only reduce the number of members of the ensemble, but able to increase the prediction accuracy of customer churn.

Loss of human limbs that are caused by traumatic accidents, vascular diseases and diabetes that lead to amputation has great impact to the well-being of the affected segment of societies. They usually require prostheses to restore the original functionality of the missing limbs so to be able to assist them in the activities of daily living. Extensive works have been reported in developing prostheses that not only could work as close as the natural limbs but also look alike the original ones. Many of these prostheses are based on the myoelectric control which requires the electromyography (EMG) signal to be measured and analyzed from the remaining nerves or muscles left after amputation. It is still a challenge however to detect, process, classify and apply the signal appropriately. In this paper, a study on the relationship of thumb-tip force related to EMG signals using a well-designed, portable thumb training system is conducted. The EMG signal is classified by using machine learning techniques when the thumb is flexed at different angles and exerts different magnitude of forces at its tip. The thumb training system is developed with compliances to fit different shape and size of human hands. The result from the analysis done using WEKA software shows that the EMG signal can be used to estimate the posture of the thumb at different flexed angles and thumb tip forces.

The large penetration of electric vehicles (EV) charging stations in existing utility grid is bringing up many power-quality problems which highly affect the load performances at the large and small consumer ends. This trend seems to grow at more pronounced rate as nowadays there is a shift from using internal combustion (IC) engine based vehicles to EV due to their superiorities. EV chargers convert the ac power to dc power for charging of EV batteries. During this conversion of power by power electronics high frequency switching converters, harmonics are injected into the grid which degrades the power-quality of line current in the grid. Such injection of harmonics causes electrical and thermal overloading of distribution transformers thus affecting their life expectancy. Other problems include drawing of excess current from the grid, unbalancing between demand and supply in utility grid, voltage deviation in the grid, etc. Thus, it is required to maintain the IEEE-519 power quality standards during charging of EV. This paper presents a survey of the fast charging stations for EV and their impacts on the exiting utility grid along with the solution to overcome these issues.

Restoring human limb that is lost due to accidents or amputation because of vascular diseases with prosthesis is one challenging issue in engineering field. The loss of human body, especially the upper and lower limbs will limit the daily activities of patient in many ways. There were many studies that had been done previously in developing prostheses which were simpler in the forms than human limb. However, the performance of these units was mostly unnatural and is damped by constraint in the model of the limbs. In this paper, the development of thumb training system that allows the measurement of electromyography (EMG) signal when the thumb is flexed at different angles with varying force is discussed. The parameters are then possible to be used with conjunction of Hill's muscle model to develop a more precise model of the thumb. The platform also features several adjustments so as to suit various size of human hands. The result from EMG measurement from the system shows that the system is able to characterize the EMG signal of thumb at different pose and force.

This paper proposes a combination of network frequencies as the supporting medium for communication within a small Search and Rescue (SAR) team to ensure stable communication, and thus command and control, in particular in a forest environment. The proposal is to implement a combination of 869 MHz and 2.4 GHz, which will support the use of smart phones for the SAR team members for audio/oral, visual and data communication as well as other facilities, the most important being location monitoring. The proposal also includes the positioning of the (mobile) base station for uninterrupted coverage.

This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of Naïve Bayes (NB) and a combination of bagging+NB algorithm displayed the highest accuracy at the same percentage (97.51%) compared to other combinations of ensemble classifiers. For the classification of the Hepatitisdata set, the result showed that the combination of stacking+Multi-Layer Perception (MLP) algorithm achieved a higher accuracy at 86.25%. By using the ensemble classifiers, the result may be improved. In future, a multi-classifier approach will be proposed by introducing a fusion at the classification level between these classifiers to obtain classification with higher accuracies.

Predicting customer churn has become the priority of every telecommunication service provider as the market is becoming more saturated and competitive. This paper presents a comparison of neural network learning algorithms for customer churn prediction. The data set used to train and test the neural network algorithms was provided by one of the leading telecommunication company in Malaysia. The Multilayer Perceptron (MLP) networks are trained using nine (9) types of learning algorithms, which are Levenberg Marquardt backpropagation (trainlm), BFGS Quasi-Newton backpropagation (trainbfg), Conjugate Gradient backpropagation with Fletcher-Reeves Updates (traincgf), Conjugate Gradient backpropagation with Polak-Ribiere Updates (traincgp), Conjugate Gradient backpropagation with Powell-Beale Restarts (traincgb), Scaled Conjugate Gradient backpropagation (trainscg), One Step Secant backpropagation (trainoss), Bayesian Regularization backpropagation (trainbr), and Resilient backpropagation (trainrp). The performance of the Neural Network is measured based on the prediction accuracy of the learning and testing phases. LM learning algorithm is found to be the optimum model of a neural network model consisting of fourteen input units, one hidden node and one output node. The best result of the experiment indicated that this model is able to produce the performance accuracy of 94.82%.

The use of prostheses is necessary to restore lost limbs to a level of functionality to enable activity of daily living. Many prostheses are now using myoelectric based control techniques to operate. However, to develop a model based controller for the system remains a challenge as accurate model is necessary. This study investigates the use of electromyogram and ultrasound signal to predict thumb tip force based on Hill’s Muscle model. The results obtained has shown a significant improvement in the prediction of thumb tip force as much as 31.45% of average RMSE over the benchmark model that leverages on biomechanics model and active marker to characterize the muscle

This chapter provides a review on fluid simulation methods that involved in blood flow simulation. Fundamental of fluid simulation methods are described briefly. The primary factors describing the popular techniques are analyzed in detail and the advantage and disadvantage are also been reported. The aim of this chapter is to categorize methodologically all the current methods used in real-time blood flow simulation. Despite the variety and number of existing methods, there are still demands for new simulation technique with a mission to provide better information. Good visual understanding about blood flow will aid clinician, engineer and researcher during the diagnosis and prognosis in clinical pathology as well as the assessment of risk and follow-up findings with blood flow related diseases.

Abstract Development decisions of oil and gas fields are based with many uncertainties within the supporting data. Uncertainties are larger in the beginning which gets reduced with time as more data is acquired and understanding of the field is improved. However, significant uncertainties persist even in mature fields which are case for redevelopment. Therefore, enormous inherent uncertainties need to be characterized, analyzed and incorporated in the simulation study to the extent possible in order to improve the decision making process. This paper discusses the relevance of reviewing and incorporating the field uncertainties related to geological and/or dynamic properties in estimation of incremental volumes in the redevelopment of a mature oilfield. Reservoir models need to be updated through history matching process to reflect actual measurements from reservoir. In this study a workflow to quantify the uncertainty of a mature oilfield of offshore Malaysia and to estimate the incremental oil potential from infill wells has been designed. During history matching process, dynamic and static parameters of uncertainty, relative permeability curve, local permeability around wells, the aquifers? volume and productivity, and contacts between gas-oil, and oil-water have been considered. An objective function is applied for the history matching which consists of the error between simulated and observed data from wells. An "Evolutionary strategy optimization", proprietary to MEPO software is used to obtain multiple equivalent models with high quality. While running an optimization for history matching, the ranges of uncertainty parameters have been narrowed and adjusted the ranges to be used for quantification of uncertainty of Estimated Ultimate Recovery (EUR). In the prediction stage, both filtered models from actual runs and sampled models from Latin Hypercube have been used. This approach not only allows to evaluate intrinsic uncertainty, but also to keep the variety and risk of prediction. Monte-Carlo simulation technique then makes it possible to acquire the distribution of EUR.

This paper presents the implementation of Alamouti's scheme on Polarizone's Field Programmable Gate Array (FPGA)-based software defined radio platform and also to conduct experiment with RF transmission. A 16-QAM encoder is implemented in this project successfully. The system manages to encode incoming data to Alamouti codes by performing baseband signal processing and schedule its timing according to Alamouti encoding scheme. Detail design specification is presented and future enhancement for decoding and channel estimation is proposed.

In statistics, the proportional hazards model (PHM) is one of a class of survival models. This model estimates the effects of different covariates influencing the time-to-event data in which the hazard function has been assumed to be the product of the baseline hazard function and a non-negative function of covariates. In this study, we investigate the hazard function, also known as the risk function or intensity function, which is employed in modelling the survival data and waiting times. The model parameters can be estimated via frequentist or Bayesian approach. However, the Bayesian approach is well known to have the advantages over frequentist methods when the data are small in size and involve censored individuals. In this paper, the PHM for right-censored data from Bayesian perspective will be discussed and the Markov Chain Monte Carlo (MCMC) method will be used to estimate the posterior distributions of the model parameters using Leukemia data.

Along a continual process of collecting data, missing recorded datum always a main problem faced by the real application. It happens due to the carelessness or the unawareness of a recorder to the importance of data documentation. In this study, a random-effects analysis which simulates data from a proposed algorithm is presented with a missing covariate. It is an improved simulation method which involves first-order autoregressive (AR(1)) process in measuring the correlation between measurements of a subject across two time sequence. Complete-case analysis and multiple imputation method are comparatively implemented for the estimation procedure. This study shows that the multiple imputation method results in estimations which fit well to the data which are not only missing completely at random (MCAR) but also missing at random (MAR). However, the complete-case analysis results in estimators which fit well to the data which are only MCAR.

Mobile devices in training offer a new horizon to help participants to get started with mobile learning in the venture to experience the learning process. Various research emphasize the opportunities and benefits of using mobile learning and simultaneously highlight the obstacles that organizations faced when designing and implementing training by mobile learning. The aim of this paper is to highlight the difficulty in getting more participation and full engagement from the generation Y staff. The traditional approach neglects to consider the different learning approaches of the Gen Y type of learners, their generational attributes and their values at the workplace. Future study should incorporate the use of other platforms such as Whatsapp™ or Instagram ™ among Gen Y.

Knowledge sharing communities like Wikipedia and automated extraction like DBpedia enable a large construction of machine processing knowledge bases with relational fact of entities. These options give a great opportunity for researcher to use it as a domain concept between low-level features and high-level concepts for image retrieval. The collection of images attached to entities, such as on-line news articles with images, are abundant on the Internet. Still, it is difficult to retrieve accurate information on these entities. Using entity names in a search engine yields large lists, but often results in imprecise and unsatisfactory outcomes. Our goal is to populate a knowledge base with on-line image news resources in the BBC sport domain. This system will yield high precision, a high recall and include diverse sports photos for specific entities. A multi-modality ontology retrieval system, with relational facts about entities for generating expanded queries, will be used to retrieve results. DBpedia will be used as a domain sport ontology description, and will be integrated with a textual description and a visual description, both generated by hand. To overcome semantic interoperability between ontologies, automated ontology alignment is used. In addition, visual similarity measures based on MPEG7 descriptions and SIFT features, are used for higher diversity in the final rankings.

Hardware occlusion queries is a method by which graphic processing unit (GPU) can gives fast response to the visibility status of a geometry object in virtual environment. Increasing number of issued queries lead to stall in CPU and starvation in GPU. However, hardware occlusion queries have improved by exploiting temporal coherence (TC). TC is proven improved hardware occlusion queries based on previous research. In driving simulation as car or camera move fast, more and more objects in scene become less significant to be rendered. Implementation of TC on driving simulation need be redesigned so allow TC suitable to faster movement. Intent of this paper is to describe how automatic parameters adaptation in temporal coherence is applied to driving simulation environment. Automatic parameters adaptation manipulated when the car or camera movement become more faster to maintain interactively rendering process and maintain realisms of complex virtual environment.

This paper explains a study on natural interaction (NI) in human body motion using depth image data. It involves about overview of NI and depth image data. Human body motion is a non-verbal part for interaction or movement that can be used to involves real world and virtual world. Furthermore, interaction with computer or machine can be more realistic as real world and becoming more important to academic researchers, game industries, and can be adapt to other field like mechanical engineering for robotics movement and surgery purpose in medical area. Functional taxonomies will show step-by-step how human body motion were detected and created a skeleton joint. Also, we discuss about technologies behind Kinect for Xbox 360 (Kinect). Recent research in this area also included.

Ontology based retrieval is gaining popularity due to the limitation posed by the conventional bag-of-words retrieval system. To semantically search for images, textual descriptions are usually used since low level features provide little meaningful information. As such conventional searching and retrieving of images are currently being employed. Complex querying and enhanced semantic search still remain the issues to be solved in image retrieval systems. We proposed an ontology driven framework for supporting image retrieval with particular emphasis on the sport news domain. Furthermore, we look into the possibility how multimodality ontology can be integrated in the framework and shows how open knowledge-bases such as the DBpedia play an important role in this area.