Automatic message annotation and semantic interface for context aware mobile computing

This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.The Ministry of Higher Education and Scientific Research (IRAQ

A Survey of Protocol-Level Challenges and Solutions for Distributed Energy Resource Cyber-Physical Security

The increasing proliferation of distributed energy resources (DERs) on the smart grid has made distributed solar and wind two key contributors to the expanding attack surface of the network; however, there is a lack of proper understanding and enforcement of DER communications security requirements. With vendors employing proprietary methods to mitigate hosts of attacks, the literature currently lacks a clear organization of the protocol-level vulnerabilities, attacks, and solutions mapped to each layer of the logical model such as the OSI stack. To bridge this gap and pave the way for future research by the authors in determining key DER security requirements, this paper conducts a comprehensive review of the key vulnerabilities, attacks, and potential solutions for solar and wind DERs at the protocol level. In doing so, this paper serves as a starting point for utilities, vendors, aggregators, and other industry stakeholders to develop a clear understanding of the DER security challenges and solutions, which are key precursors to comprehending security requirements

ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Automatic message annotation and semantic interface for context aware mobile computing

In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.EThOS - Electronic Theses Online ServiceMinistry of Higher Education and Scientific Research (Iraq)GBUnited Kingdo

A trajectory-tracking controller for improving the safety and stability of four-wheel steering autonomous vehicles

To achieve anti-crosswind, anti-sideslip, and anti-rollover in trajectory-tracking for Four-Wheel Steering (4WS) autonomous vehicles, a trajectory-tracking controller based on a four-channel Active Disturbance Rejection Control (ADRC) was used to track the desired lateral displacement, longitudinal displacement, yaw angle, and roll angle, and minimize the tracking errors between the actual output values and the desired values through static decoupling steering and braking systems. In addition, the anti-crosswind, anti-sideslip, and anti-rollover simulations were implemented with CarSim®. Finally, the simulation results showed that the 4WS autonomous vehicle with the controller still has good anti-crosswind, anti-sideslip, and anti-rollover performance in path tracking, even under a small turning radius or lowadhesion curved roads. First published online 12 March 202

Human face detection techniques: A comprehensive review and future research directions

Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection

Survey of maps of dynamics for mobile robots

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area

Consumer behaviour in the context of SMS-based marketing

YesGiven the potential benefits such as time and location-based advertisements, the scope of interactivity and customisation through one-to-one target marketing, SMS-based (Short Messaging Service) advertisement, is now being considered as an important alternative marketing channel. This has raised a real challenge for marketers to harness the potential of this alternative channel by overcoming several limiting issues such as security, privacy, authenticity, and irritation of unknown senders. The main objective of this study is to identify and review relevant research issues faced by wireless marketing (particularly via SMS-based advertisement) as an aide to identify potential research gaps and for developing future guiding principles. The review and analysis of literature suggest that there is scope of undertaking further research on issues related to different cultures reflecting ubiquitous traits of mobile phone, SMS advertisement, market segmentation, location and time sensitive delivery, content and context of advertisement, language and appropriate delivery

Fuzzy optimisation based symbolic grounding for service robots

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySymbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field