Preoperative to Intraoperative Space Registration for Management of Head Injuries

A registration framework for image-guided robotic surgery is proposed for three emergency neurosurgical procedures, namely Intracranial Pressure (ICP) Monitoring, External Ventricular Drainage (EVD) and evacuation of a Chronic Subdural Haematoma (CSDH). The registration paradigm uses CT and white light as modalities. This paper presents two simulation studies for a preliminary evaluation of the registration protocol: (1) The loci of the Target Registration Error (TRE) in the patient-s axial, coronal and sagittal views were simulated based on a Fiducial Localisation Error (FLE) of 5 mm and (2) Simulation of the actual framework using projected views from a surface rendered CT model to represent white light images of the patient. Craniofacial features were employed as the registration basis to map the CT space onto the simulated intraoperative space. Photogrammetry experiments on an artificial skull were also performed to benchmark the results obtained from the second simulation. The results of both simulations show that the proposed protocol can provide a 5mm accuracy for these neurosurgical procedures.

Improved Fuzzy Neural Modeling for Underwater Vehicles

The dynamics of the Autonomous Underwater Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate accurately because of the variations of these coefficients with different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line adaptive fuzzy model and adaptive neural fuzzy network (ANFN) model techniques to overcome the uncertain external disturbance and the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according to the back propagation algorithm based upon the error between the identified model and the actual output of the plant. The proposed ANFN model adopts a functional link neural network (FLNN) as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN model is a nonlinear combination of input variables. Fuzzy control system is applied to guide and control the AUV using both adaptive models and mathematical model. Simulation results show the superiority of the proposed adaptive neural fuzzy network (ANFN) model in tracking of the behavior of the AUV accurately even in the presence of noise and disturbance.

Generating Class-Based Test Cases for Interface Classes of Object-Oriented Gray-Box Frameworks

An application framework provides a reusable design and implementation for a family of software systems. Application developers extend the framework to build their particular applications using hooks. Hooks are the places identified to show how to use and customize the framework. Hooks define Framework Interface Classes (FICs) and their possible specifications, which helps in building reusable test cases for the implementations of these classes. In applications developed using gray-box frameworks, FICs inherit framework classes or use them without inheritance. In this paper, a test-case generation technique is extended to build test cases for FICs built for gray-box frameworks. A tool is developed to automate the introduced technique.

Collaborative Education Practice in a Data Structure E-Learning Course

This paper presented a collaborative education model, which consists four parts: collaborative teaching, collaborative working, collaborative training and interaction. Supported by an e-learning platform, collaborative education was practiced in a data structure e-learning course. Data collected shows that most of students accept collaborative education. This paper goes one step attempting to determine which aspects appear to be most important or helpful in collaborative education.

On the Learning of Causal Relationships between Banks in Saudi Equities Market Using Ensemble Feature Selection Methods

Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.

Cardiopulmonary Exercise Testing in Young Asthmatic Children Ages 6-10 Years Old

The aim of this study was to establish the feasibility of a minute incremental exercise testing protocol in young asthma children. Twenty-two children with clinically diagnosed mild to moderate asthma volunteered to participate. The maximum incremental exercise test was performed using a cycle ergometer with an electromagnetic braking. A warm-up unloaded for 2 minutes then the workload was started at 40 watts for 2 minutes, and then stepwise increments of 8 watts per 2 minutes were applied. The pedaling frequency was set at 50 rpm. Ventilation and gas exchange were measured with a breath-by-breath automatic metabolic measurement system. Results showed that this test was well tolerated by all asthmatic children. Most of the children reached the VO2 plateau and satisfied the criteria for maximal respiratory exchange ratio of ≥ 1. This Study demonstrated that this testing protocol was suitable for young asthmatic children.

Progressive Strategy of Milling by means of Tool Axis Inclination Angle

This work deals with problems of tool axis inclination angles in ball-end milling. Tool axis inclination angle contributes to improvement of functional surface properties (surface integrity - surface roughness, residual stress, micro hardness, etc.), decreasing cutting forces and improving production. By milling with ball-end milling tool, using standard way of cutting, when work piece and cutting tool contain right angle, we have zero cutting speed on edge. At this point cutting tool only pushes material into the work piece. Here we can observe the following undesirable effects - chip contraction, increasing of cutting temperature, increasing vibrations or creation of built-up edge. These effects have negative results – low quality of surface and decreasing of tool life (in the worse case even it is pinching out). These effects can be eliminated with the tilt of cutting tool or tilt of work piece.

Narrative and Expository Text Reading Comprehension by Fourth Grade Spanish-Speaking Children

This work aims to explore the factors that have an incidence in reading comprehension process, with different type of texts. In a recent study with 2nd, 3rd and 4th grade children, it was observed that reading comprehension of narrative texts was better than comprehension of expository texts. Nevertheless it seems that not only the type of text but also other textual factors would account for comprehension depending on the cognitive processing demands posed by the text. In order to explore this assumption, three narrative and three expository texts were elaborated with different degree of complexity. A group of 40 fourth grade Spanish-speaking children took part in the study. Children were asked to read the texts and answer orally three literal and three inferential questions for each text. The quantitative and qualitative analysis of children responses showed that children had difficulties in both, narrative and expository texts. The problem was to answer those questions that involved establishing complex relationships among information units that were present in the text or that should be activated from children’s previous knowledge to make an inference. Considering the data analysis, it could be concluded that there is some interaction between the type of text and the cognitive processing load of a specific text.

Logistics Outsourcing: Performance Models and Financial and Operational Indicators

The growing outsourcing of logistics services resulting from the ongoing current in firms of costs reduction/increased efficiency means that it is becoming more and more important for the companies doing the outsourcing to carry out a proper evaluation. The multiple definitions and measures of logistics service performance found in research on the topic create a certain degree of confusion and do not clear the way towards the proper measurement of their performance. Do a model and a specific set of indicators exist that can be considered appropriate for measuring the performance of logistics services outsourcing in industrial environments? Are said indicators in keeping with the objectives pursued by outsourcing? We aim to answer these and other research questions in the study we have initiated in the field within the framework of the international High Performance Manufacturing (HPM) project of which this paper forms part. As the first stage of this research, this paper reviews articles dealing with the topic published in the last 15 years with the aim of detecting the models most used to make this measurement and determining which performance indicators are proposed as part of said models and which are most used. The first steps are also taken in determining whether these indicators, financial and operational, cover the aims that are being pursued when outsourcing logistics services. The findings show there is a wide variety of both models and indicators used. This would seem to testify to the need to continue with our research in order to try to propose a model and a set of indicators for measuring the performance of logistics services outsourcing in industrial environments.

Dynamic Anonymity

Encryption protects communication partners from disclosure of their secret messages but cannot prevent traffic analysis and the leakage of information about “who communicates with whom". In the presence of collaborating adversaries, this linkability of actions can danger anonymity. However, reliably providing anonymity is crucial in many applications. Especially in contextaware mobile business, where mobile users equipped with PDAs request and receive services from service providers, providing anonymous communication is mission-critical and challenging at the same time. Firstly, the limited performance of mobile devices does not allow for heavy use of expensive public-key operations which are commonly used in anonymity protocols. Moreover, the demands for security depend on the application (e.g., mobile dating vs. pizza delivery service), but different users (e.g., a celebrity vs. a normal person) may even require different security levels for the same application. Considering both hardware limitations of mobile devices and different sensitivity of users, we propose an anonymity framework that is dynamically configurable according to user and application preferences. Our framework is based on Chaum-s mixnet. We explain the proposed framework, its configuration parameters for the dynamic behavior and the algorithm to enforce dynamic anonymity.

A Simplified Approach for Load Flow Analysis of Radial Distribution Network

This paper presents a simple approach for load flow analysis of a radial distribution network. The proposed approach utilizes forward and backward sweep algorithm based on Kirchoff-s current law (KCL) and Kirchoff-s voltage law (KVL) for evaluating the node voltages iteratively. In this approach, computation of branch current depends only on the current injected at the neighbouring node and the current in the adjacent branch. This approach starts from the end nodes of sub lateral line, lateral line and main line and moves towards the root node during branch current computation. The node voltage evaluation begins from the root node and moves towards the nodes located at the far end of the main, lateral and sub lateral lines. The proposed approach has been tested using four radial distribution systems of different size and configuration and found to be computationally efficient.

Optical Road Monitoring of the Future Smart Roads – Preliminary Results

It has been shown that in most accidents the driver is responsible due to being distracted or misjudging the situation. In order to solve such problems research has been dedicated to developing driver assistance systems that are able to monitor the traffic situation around the vehicle. This paper presents methods for recognizing several circumstances on a road. The methods use both the in-vehicle warning systems and the roadside infrastructure. Preliminary evaluation results for fog and ice-on-road detection are presented. The ice detection results are based on data recorded in a test track dedicated to tyre friction testing. The achieved results anticipate that ice detection could work at a performance of 70% detection with the right setup, which is a good foundation for implementation. However, the full benefit of the presented cooperative system is achieved by fusing the outputs of multiple data sources, which is the key point of discussion behind this publication.

Arabic Character Recognition using Artificial Neural Networks and Statistical Analysis

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.

Using Neural Network for Execution of Programmed Pulse Width Modulation (PPWM) Method

Application of neural networks in execution of programmed pulse width modulation (PPWM) of a voltage source inverter (VSI) is studied in this paper. Using the proposed method it is possible to cancel out the desired harmonics in output of VSI in addition to control the magnitude of fundamental harmonic, contineously. By checking the non-trained values and a performance index, the most appropriate neural network is proposed. It is shown that neural networks may solve the custom difficulties of practical utilization of PPWM such as large size of memory, complex digital circuits and controlling the magnitude of output voltage in a discrete manner.

An Effective Approach for Distribution System Power Flow Solution

An effective approach for unbalanced three-phase distribution power flow solutions is proposed in this paper. The special topological characteristics of distribution networks have been fully utilized to make the direct solution possible. Two matrices–the bus-injection to branch-current matrix and the branch-current to busvoltage matrix– and a simple matrix multiplication are used to obtain power flow solutions. Due to the distinctive solution techniques of the proposed method, the time-consuming LU decomposition and forward/backward substitution of the Jacobian matrix or admittance matrix required in the traditional power flow methods are no longer necessary. Therefore, the proposed method is robust and time-efficient. Test results demonstrate the validity of the proposed method. The proposed method shows great potential to be used in distribution automation applications.

Fingerprint Verification System Using Minutiae Extraction Technique

Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another issue still under research. Our work has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work.

Generalized Predictive Control of Batch Polymerization Reactor

This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.

Problems and Obstacles to Value Creation of Thai Monk-s Bowls: The Case Study of Ban-Baat Village, Bangkok

This research aims to study value-creation process of producing monk-s bowls, Thai traditional handicrafts, which is facing problems in adapting to the changing society. It also aims to identify problems and obstacles to value creation. This research is based on a case study of monk-s bowl manufactures from Ban-Baat Village, Bangkok. The conceptual framework is based on the model of value chain to analyze the process. The research methodology is qualitative. This research found that the value-creation process of monk-s bowls consists of eight activities contributing to adding value to the products and increasing profits to the producers in return. Five major problems and obstacles are found. The research suggests that these problems and obstacles limit the manufacturers- potential for creating more valued product and lead to business stagnation. These problems should be addressed and solved with collaboration among the government, the private sector and the manufacturers.

Analysis of a Double Pipe Heat Exchanger Performance by Use of Porous Baffles and Nanofluids

The present work is a numerical simulation of nanofluids flow in a double pipe heat exchanger provided with porous baffles. The hot nanofluid flows in the inner cylinder, whereas the cold nanofluid circulates in the annular gap. The Darcy- Brinkman-Forchheimer model is adopted to describe the flow in the porous regions, and the governing equations with the appropriate boundary conditions are solved by the finite volume method. The results reveal that the addition of metallic nanoparticles enhances the rate of heat transfer in comparison to conventional fluids but this augmentation is accompanied by an increase in pressure drop. The highest heat exchanger performances are obtained when nanoparticles are added only to the cold fluid.

Predictability Analysis on HIV/AIDS System using Hurst Exponents

Methods of contemporary mathematical physics such as chaos theory are useful for analyzing and understanding the behavior of complex biological and physiological systems. The three dimensional model of HIV/AIDS is the basis of active research since it provides a complete characterization of disease dynamics and the interaction of HIV-1 with the immune system. In this work, the behavior of the HIV system is analyzed using the three dimensional HIV model and a chaotic measure known as the Hurst exponent. Results demonstrate that Hurst exponents of CD4, CD8 cells and viral load vary nonlinearly with respect to variations in system parameters. Further, it was observed that the three dimensional HIV model can accommodate both persistent (H>0.5) and anti-persistent (H