COMPUTER PRODUCTIVITY EVALUATION

Computer productivity evaluation also known as benchmarking is the act of running a computer program, a set of programs, or other operations, in order to assess the relative performance of an object, normally by running a number of standard tests and trials against it. The term 'benchmark' is also mostly utilized for the purposes of elaborately designed benchmarking programs themselves. Benchmarking is usually associated with assessing performance characteristics of computer hardware, for example, the floating point operation performance of a CPU, but there are circumstances when the technique is also applicable to software. Software benchmarks are, for example, run against compilers or database management systems. Benchmarks provide a method of comparing the performance of various subsystems across different chip/system architectures. Test suites are a type of system intended to assess the correctness of software. [1] Many components in a system contribute to its overall performance, but the CPU/processor, memory, graphics board, and storage configuration generally play the largest roles for most users. Which of these components is the most important in a particular case depends on the individual person's usage patterns. [2

Pattern Recognition By a Scaled Corners Detection

In this paper we developed a new approach to extract points descriptor used for pattern recognition with Corner detection approach. We used scales of image, each scale was scaled by a scaling factor, detect the corners in each scale, extract the key points descriptor from these corners, and using these points descriptor as key features of recognition in the Hough Transform to classify the Descriptor to its class. We implemented and analyzed SIFT algorithm, corner detection algorithm, and the proposed approach. The experimental results using MATLAB of a proposed approach gave results of recognition with high accuracy. Keywords: Pattern Recognition; Corner Detection; SIFT; Hough Transform

Critical evaluation and novel design of a non-invasive and wearable health monitoring system

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.This study is about developing a non-invasive wearable health-monitoring system. The project aims to achieve miniaturisation as much as possible, using nanotechnology. The achieved results of the project are nothing but conceptual images of a convertible watch. The system is a non-invasive health measurement system. An important part of the study is researching the automation of blood pressure measurement by means of experiments which test the effect of exterior factors on blood pressure level. These experiments have been held to improve the automation and simplicity of BP measurements to establish a 24hr BP monitoring system. This study proposed a medical sensor that is part of the watch system, and that is most compatible with the elderly people product preferences in the UK. The “sensor strip” is in cm range, integrating a number of MEMS sensors, for the non-invasive detection of certain health aspects. The health aspects are chosen according to how close they are from the “health vital signs”, which are the first measurements executed by the doctor, if a patient is to visit him. An applied QFD study showed that the most suitable measurement technology to be used in the proposed sensor strip is the infrared technology. In addition to the sensor strip, EEG health detection is added, which is the reason why the watch is convertible. MEMS sensors, MEMS memory and an embedded processor are selected, since that this project also includes minimising the size of a device where the utilization of nanotechnology is vital. The final result of the study is only a conceptual design of a product with a carefully selected subsystems. The software design of the product will not be further developed to become a physical prototype of a consumer product

Design of a 2D MRI compatible robot for performing prostate cancer treatment using therapeutic ultrasound

Therapeutic ultrasound is a promising treatment method for many common cancers, including prostate cancer. Magnetic resonance image (MRI) guidance of therapeutic ultrasound permits targeting and monitoring of therapy. In this thesis a prototype MRI compatible positioning device for the treatment of prostate cancer using therapeutic ultrasound is presented. The accuracy, MRI compatibility and functionality of the positioning device was evaluated in in vitro experiments (using gel phantoms and in vitro). The MRI was used as the imaging guidance technique. The proposed device incorporates a portable electronic system and operates in two PC controlled stages, linear and angular (X - Θ) and one manual driven stage Z (height of the probe). The device is small and portable and can be placed on the patient’s table to any commercial MRI scanner. The proposed device was tested on two clinical MRI scanners of different manufacturers. Additionally, in this thesis a software that controls an MRI guided focus ultrasound system is presented. The software was written in C sharp and consists of the following options: a) connection with DAQ device, b) tab that controls 2D device, c) tab that controls 3D device, d) tab that controls ultrasound protocol and e) operation command history list, g) MRI compatible camera, h) open and control the DICOM images captured from the MRI scanner during the therapy, i) temperature reading of the HIFU focal point. The proposed positioning device offers approximately 20μm accuracy on linear and angular stages. It incorporates MRI compatible optical encoders as mechanical motion feedback. The accuracy measurements were taken using a digital calibre. The positioning device has range of 111mm in linear stage, ±90o on angular stage and 50mm on Z stage. The design was based on measurements that were taken by a 100 patients. The MRI compatibility and motion accuracy images were taken by scanning gel phantoms using T2W FSE on 1.5T and 3T MRI scanner

Towards an automated photogrammetry-based approach for monitoring and controlling construction site activities

The construction industry has a poor productivity record, which was predominantly ascribed to inadequate monitoring of how a project is progressing at any given time. Most available approaches do not offer key stakeholders a shared understanding of project performance in real-time, which as a result failed to identify any project slippage on the original schedule. This study reports on the development of a novel automated system for monitoring, updating and controlling construction site activities in real-time. The proposed system seeks to harness advances in close-range photogrammetry, BIM and computer vision to deliver an original approach that is capable of continuous monitoring of construction activities, with the progress status determinable, at any given time, throughout the construction stage.The research adopted a sequential mixed approach strategy pursuant to the design science standard processes in three stages. The first stage involved interviews within a focus group setting with seven carefully selected construction professionals. Their answers were analysed and provided "the informed-basis for the development of the automated system” for detecting and notifying delays in construction projects. The second stage involved development of ‘proof of the concept’ in a pilot project case study with nine potential users of the proposed automated system. Face-to-face interviews were conducted to evaluate and verify the effectiveness of the developed prototype, which as a result was continuously refined and improved according to the users’ comments and feedbacks. Within this stage the prototype to be tested and evaluated by a representative of construction professionals was developed. Subsequently a sub-stage of the system’s development sought to test and validate the final version of the system in the context of a real-life construction project in Dubai whereby an online survey is administered to 40 users, a representative sample of potential system users. The third stage addressed the conclusion, limitations and recommendations for further research studies for the proposed system.The findings of the study revealed that once the system installed and programmed, it does not require any expertise or manual intervention. This is mainly due to all the processes of the system being fully automated and the data collection, interpretations, analysis and notifications are automatically processed without any human intervention. Consequently, human errors and subjectivity are eliminated, and accordingly the system achieved a significantly high level of accuracy, automation and reliability. The system achieved a level of accuracy of 99.97% for horizontal construction elements and exceeded 99.70% for vertical elements. The findings also highlighted that this developed system is inexpensive, easy to operate and its accuracy excels that of current systems sought to automate monitoring and updating of progress status’ for construction projects. The distinctive features of the proposed system assisted the site team to complete the project 61 days ahead of its contractual completion date with a 9% time saving and 3% cost saving.The proposed system has the potential to identify any deviation from as-planned construction schedules, and prompt actions taken in response to the automatic notification system, which informs decision-makers via emails and SMS

Multiscale Machine Learning and Numerical Investigation of Ageing in Infrastructures

Infrastructure is a critical component of a country’s economic growth. Interaction with extreme service environments can adversely affect the long-term performance of infrastructure and accelerate ageing. This research focuses on using machine learning to improve the efficiency of analysing the multiscale ageing impact on infrastructure. First, a data-driven campaign is developed to analyse the condition of an ageing infrastructure. A machine learning-based framework is proposed to predict the state of various assets across a railway system. The ageing of the bond in fibre-reinforced polymer (FRP)-strengthened concrete elements is investigated using machine learning. Different machine learning models are developed to characterise the long-term performance of the bond. The environmental ageing of composite materials is investigated by a micromechanics-based machine learning model. A mathematical framework is developed to automatically generate microstructures. The microstructures are analysed by the finite element (FE) method. The generated data is used to develop a machine learning model to study the degradation of the transverse performance of composites under humid conditions. Finally, a multiscale FE and machine learning framework is developed to expand the understanding of composite material ageing. A moisture diffusion analysis is performed to simulate the water uptake of composites under water immersion conditions. The results are downscaled to obtain micromodel stress fields. Numerical homogenisation is used to obtain the composite transverse behaviour. A machine learning model is developed based on the multiscale simulation results to model the ageing process of composites under water immersion. The frameworks developed in this thesis demonstrate how machine learning improves the analysis of ageing across multiple scales of infrastructure. The resulting understanding can help develop more efficient strategies for the rehabilitation of ageing infrastructure

Proceedings of the International Workshop "Innovation Information Technologies: Theory and Practice": Dresden, Germany, September 06-10.2010

This International Workshop is a high quality seminar providing a forum for the exchange of scientific achievements between research communities of different universities and research institutes in the area of innovation information technologies. It is a continuation of the Russian-German Workshops that have been organized by the universities in Dresden, Karlsruhe and Ufa before. The workshop was arranged in 9 sessions covering the major topics: Modern Trends in Information Technology, Knowledge Based Systems and Semantic Modelling, Software Technology and High Performance Computing, Geo-Information Systems and Virtual Reality, System and Process Engineering, Process Control and Management and Corporate Information Systems

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010