Managing uncertainty in sound based control for an autonomous helicopter

In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper ). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Leadership capability of team leaders in construction industry

This research was conducted to identify the important leadership capabilities for Malaysia construction industry team leaders. This research used exploratory sequential mix-method research design which is qualitative followed by quantitative research method. In the qualitative phase, semi-structured in-depth interview was selected and purposive sampling was employed in selecting 15 research participants involving team leaders and Human Resource Managers. Qualitative data was analysed using content and thematic analyses. Quantitative data was collected using survey questionnaire involving 171 randomly selected team leaders as respondents. The data was analyzed using descriptive and inferential statistics consisting of t-test, One-way Analysis of Variance (ANOVA), Pearson Correlation, Multiple Regression and Structured Equation Modeling (SEM). This study found that personal integrity, working within industry, customer focus and quality, communication and interpersonal skill, developing and empowering people and working as a team were needed leadership capabilities among construction industry team leaders. The research was also able to prove that leadership skill is a key element to develop leadership capability. A framework was developed based on the results of this study, which can be used as a guide by employers and relevant agencies in enhancing leadership capability of Malaysia construction industry team leade

A note on the robust stability of uncertain stochastic fuzzy systems with time-delays

Copyright [2004] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Takagi-Sugeno (T-S) fuzzy models are now often used to describe complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear submodels. In this note, the T-S fuzzy model approach is exploited to establish stability criteria for a class of nonlinear stochastic systems with time delay. Sufficient conditions are derived in the format of linear matrix inequalities (LMIs), such that for all admissible parameter uncertainties, the overall fuzzy system is stochastically exponentially stable in the mean square, independent of the time delay. Therefore, with the numerically attractive Matlab LMI toolbox, the robust stability of the uncertain stochastic fuzzy systems with time delays can be easily checked

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

An Overview of Vertical Handoff Decision Algorithms in NGWNs and a new Scheme for Providing Optimized Performance in Heterogeneous Wireless Networks

Because the increasingly development and use of wireless networks and mobile technologies, was implemented the idea that users of mobile terminals must have access in different wireless networks simultaneously. Therefore one of the main interest points of Next Generation Wireless Networks (NGWNs), refers to the ability to support wireless network access equipment to ensure a high rate of services between different wireless networks. To solve these problems it was necessary to have decision algorithms to decide for each user of mobile terminal, which is the best network at some point, for a service or a specific application that the user needs. Therefore to make these things, different algorithms use the vertical handoff technique. Below are presented a series of algorithms based on vertical handoff technique with a classification of the different existing vertical handoff decision strategies, which tries to solve these issues of wireless network selection at a given time for a specific application of an user. Based on our synthesis on vertical handoff decision strategies given below, we build our strategy based on solutions presented below, taking the most interesting aspect of each one.Vertical Handoff, Genetic Algorithms, Fuzzy Logic, Neural Networks, AHP