A robust digital image watermarking using repetition codes against common attacks

Digital watermarking is hiding the information inside a digital media to protect for such documents against malicious intentions to change such documents or even claim the rights of such documents. Currently the capability of repetition codes on various attacks in not sufficiently studied. In this project, a robust frequency domain watermarking scheme has been implemented using Discrete Cosine Transform (DCT). The idea of this scheme is to embed an encoded watermark using repetition code (3, 1) inside the cover image pixels based on Discrete Cosine Transform (DCT) embedding technique. The proposed methods have undergone several simulation attacks tests in order to check up and compare their robustness against various attacks, like salt and pepper, speckle, compress, Gaussian, image contrast, resizing and cropping attack. The robustness of the watermarking scheme has been calculated using Peak Signal-To-Noise Ratio (PSNR), Mean Squared Error (MSE) and Normalized Correlations (NC). In our experiments, the results show that the robustness of a watermark with repetition codes is much better than without repetition code

Passive wireless tags for tongue controlled assistive technology interfaces

Tongue control with low profile, passive mouth tags is demonstrated as a human–device interface by communicating values of tongue-tag separation over a wireless link. Confusion matrices are provided to demonstrate user accuracy in targeting by tongue position. Accuracy is found to increase dramatically after short training sequences with errors falling close to 1% in magnitude with zero missed targets. The rate at which users are able to learn accurate targeting with high accuracy indicates that this is an intuitive device to operate. The significance of the work is that innovative very unobtrusive, wireless tags can be used to provide intuitive human–computer interfaces based on low cost and disposable mouth mounted technology. With the development of an appropriate reading system, control of assistive devices such as computer mice or wheelchairs could be possible for tetraplegics and others who retain fine motor control capability of their tongues. The tags contain no battery and are intended to fit directly on the hard palate, detecting tongue position in the mouth with no need for tongue piercings

Methodology for Testing RFID Applications

Radio Frequency Identification (RFID) is a promising technology for process automation and beyond that capable of identifying objects without the need for a line-of-sight. However, the trend towards automatic identification of objects also increases the demand for high quality RFID applications. Therefore, research on testing RFID systems and methodical approaches for testing are needed. This thesis presents a novel methodology for the system level test of RFID applications. The approach called ITERA, allows for the automatic generation of tests, defines a semantic model of the RFID system and provides a test environment for RFID applications. The method introduced can be used to gradually transform use cases into a semi-formal test specification. Test cases are then systematically generated, in order to execute them in the test environment. It applies the principle of model based testing from a black-box perspective in combination with a virtual environment for automatic test execution. The presence of RFID tags in an area, monitored by an RFID reader, can be modelled by time-based sets using set-theory and discrete events. Furthermore, the proposed description and semantics can be used to specify RFID systems and their applications, which might also be used for other purposes than testing. The approach uses the Unified Modelling Language to model the characteristics of the system under test. Based on the ITERA meta model test execution paths are extracted directly from activity diagrams and RFID specific test cases are generated. The approach introduced in this thesis allows to reduce the efforts for RFID application testing by systematically generating test cases and the automatic test execution. In combination with meta model and by considering additional parameters, like unreliability factors, it not only satisfies functional testing aspects, but also increases the confidence in the robustness of the tested application. Mixed with the instantly available virtual readers, it has the potential to speed up the development process and decrease the costs - even during the early development phases. ITERA can be used for highly automated testing, reproducible tests and because of the instantly available readers, even before the real environment is deployed. Furthermore, the total control of the RFID environment enables to test applications which might be difficult to test manually. This thesis will explain the motivation and objectives of this new RFID application test methodology. Based on a RFID system analysis it proposes a practical solution on the identified issues. Further, it gives a literature review on testing fundamentals, model based test case generation, the typical components of a RFID system and RFID standards used in industry.Integrative Test-Methodology for RFID Applications (ITERA) - Project: Eurostars!5516 ITERA, FKZ 01QE1105

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

ENHANCING USERS’ EXPERIENCE WITH SMART MOBILE TECHNOLOGY

The aim of this thesis is to investigate mobile guides for use with smartphones. Mobile guides have been successfully used to provide information, personalisation and navigation for the user. The researcher also wanted to ascertain how and in what ways mobile guides can enhance users' experience. This research involved designing and developing web based applications to run on smartphones. Four studies were conducted, two of which involved testing of the particular application. The applications tested were a museum mobile guide application and a university mobile guide mapping application. Initial testing examined the prototype work for the ‘Chronology of His Majesty Sultan Haji Hassanal Bolkiah’ application. The results were used to assess the potential of using similar mobile guides in Brunei Darussalam’s museums. The second study involved testing of the ‘Kent LiveMap’ application for use at the University of Kent. Students at the university tested this mapping application, which uses crowdsourcing of information to provide live data. The results were promising and indicate that users' experience was enhanced when using the application. Overall results from testing and using the two applications that were developed as part of this thesis show that mobile guides have the potential to be implemented in Brunei Darussalam’s museums and on campus at the University of Kent. However, modifications to both applications are required to fulfil their potential and take them beyond the prototype stage in order to be fully functioning and commercially viable

Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.Comment: 6 pages, 4 figure