Mohammed J. Alhaddad

Towards a linear general type-2 fuzzy logic based approach for computing with words

Within the last two decades, the paradigm of Computing With Words (CWW) has been gaining more attention. Mainly, CWW has an exciting vision which tries to tackle the problem of human intelligence by taking the human mind as a role model. The human intelligence has been investigated by various disciplines including psychology, philosophy, neuroscience, linguistics, computer science, and cognitive sciences. Notably, it is not a straightforward task to map the human’s brain reasoning into computer processes. In this paper, we propose to facilitate such mapping by investigating a key element, which is to identify the step-by-step formation of perceptual judgments. Herein, we first introduce an approach that employs general type-2 fuzzy logic to dynamically model the human perceptions based on the human experience. This approach can be regarded as a step to enable the CWW vision. We have deployed the proposed approach in real-world settings and we will present two sets of real-world experiments which were conducted in the intelligent apartment (iSpace) in the University of Essex. The first set of experiments demonstrates the results of the proposed approach for the adaptive modeling of ambient luminance perception. In the second set of experiments, we show that our approach performs better in the rule base evaluation processing time and in output accuracy with comparison to an interval type-2 fuzzy logic system.

Towards the Next Generation of Learning Environments: An InterReality Learning Portal and Model

Advances in technology are enabling different and interesting ways of experiencing education outside the traditional classroom. For example using network technology it is possible for people to follow courses remotely via technologies such as eLearning, pod casts and 3D virtual learning environments. This paper describes research towards the integration of some cutting edge concepts, such as mixed reality, learning design, cloud learning and 3D environments that we have utilised to form a novel InterReality portal and associated pedagogical model. Our InterReality model applies Problem-based Learning (PBL) pedagogy, including co-creative learning, to the realization of a mixed reality laboratory environment for teaching embedded-computing and emerging computing applications such as the Internet-of-Things. Moreover our model proposes the use of learning design to structure the tasks and activities, their assignment to roles, and their workflow within a unit of learning (UoL) approach to allow a standardised learning activity construction, and re-use. The main contributions of this paper are the InterReality model and architecture and the supporting pedagogical analysis and rationale.

Remote Mixed Reality Collaborative Laboratory Activities: Learning Activities within the InterReality Portal

Technology is changing our way to experience education from one-dimensional (physical) to multi-dimensional (physical and virtual) education using a diversity of resources such as web-based platforms (eLearning), videoconferences, eBooks and innovative technologies (e.g. mixed reality, virtual worlds, immersive technology, etc.). This represents bigger opportunities for universities and educational institutions to collaborate with partners from around the world and to be part of todays knowledge economy. This also enables greater opportunities to experience distance learning, modifying our experience of both space and time, changing specific spatial locations to ubiquitous locations and time as asynchronous/synchronous according to our necessities. The use of virtual and remote laboratory activities is an example of the application of some of these concepts. In this work-in-progress paper we propose a different approach to the integration of the physical and virtual world by creating remote mixed reality collaborative laboratory activities within an Inter Reality Portal learning environment, thereby extending our previous progress towards these goals. The learning goal of our mixed reality lab activity is to produce Internet-of-Things-based computer projects using combinations of Cross-Reality (xReality) and Virtual objects based on co-creative and collaborative interaction between geographically dispersed students.

A Type-2 Fuzzy Logic based system for linguistic summarization of video monitoring in indoor intelligent environments

Video monitoring can provide vital context awareness information from indoor intelligent environments where privacy is not a limitation. However, there is a need to develop linguistic summarization tools which are capable of summarizing in a layman language the information of interest within long video sequences. The key module which can enable the linguistic summarization of video monitoring is human activity/behaviour recognition. However, human behavior recognition is an important yet challenging task due to the behavior uncertainty, activity ambiguity, and uncertain factors such as position, orientation and speed, etc. In order to handle such high levels of uncertainties in activity analysis, we introduce a system based on Interval Type-2 Fuzzy Logic Systems (IT2FLSs) whose parameters are optimized by the Big Bang-Big Crunch (BB-BC) algorithm which allows for robust behaviour recognition using 3D machine vision techniques in intelligent environments. We present several experiments which were performed in real-world intelligent environments to fairly make comparisons with the state-of-the-art algorithms. The experimental results demonstrate that the proposed BB-BC paradigm is effective in tuning the parameters of the membership functions and the rule base of the IT2FLSs to improve the recognition accuracy. It will be shown through real-world experiments that the proposed IT2FLSs outperformed the Type-1 FLSs (TIFLSs) counterpart as well as other traditional non-fuzzy based systems. Based on the recognition results, higher-level applications will presented including video linguistic summarizations event searching and activity retrieval/playback.

An experience based linear general type-2 fuzzy logic approach for Computing With Words

In this paper, we present an approach to interpret the Computing With Words (CWWs) paradigm merging the advancements from neuroscience, psychology and artificial intelligence. The presented approach will incorporate fuzzy composite concepts (FCCs), a special case of linguistic weighted average (LWA) and case-based reasoning (CBR). The focus of the paper is on the inception of the CWWs paradigm to bridge the gap between the human and machine intelligence. The investigation of FCCs processing is performed using linear general type-2 (LGT2) and interval type-2 (IT2) fuzzy sets. The results show that LGT2 fuzzy sets outperform IT2 fuzzy sets in the processing time of complete rule base evaluation, in providing better modeling of the human perceptual judgment, and in producing richer range of output intervals.

A general type-2 fuzzy logic approach for adaptive modeling of perceptions for Computing With Words

In a broad sense, the Computing With Words (CWW) vision tries to tackle the problem of human intelligence by taking the human mind as a role model. A key element to facilitate the mapping of the humans brain reasoning into computer processes is to identify the step-by-step formation of perceptual judgments. In this paper, we present an approach that employs general type-2 fuzzy logic which enables the human perceptions to be dynamically modeled and adapted depending on the human experience. This approach can be regarded as a step to enable the CWW vision. We will present real-world experiments which were conducted with real users in the intelligent apartment (iSpace) in the University of Essex. The experiments demonstrate the results of the proposed approach for the adaptive modeling of ambient luminance perception.

An Interval Type-2 Fuzzy Logic System for Human Silhouette Extraction in Dynamic Environments

In this paper, we present a type-2 fuzzy logic based system for robustly extracting the human silhouette which is a fundamental and important procedure for advanced video processing applications, such as pedestrian tracking, human activity analysis and event detection. The presented interval type-2 fuzzy logic system is able to detach moving objects from extracted human silhouette in dynamic environments. Our real-world experimental results demonstrate that the proposed interval type-2 fuzzy logic system works effectively and efficiently for moving objects detachment where the type-2 approach outperforms the type-1 fuzzy system while significantly reducing the misclassification when compared to the type-1 fuzzy system.

Using Mixed-Reality to Develop Smart Environments

Smart homes, smart cars, smart classrooms are now a reality as the world becomes increasingly interconnected by ubiquitous computing technology. The next step is to interconnect such environments, however there are a number of significant barriers to advancing research in this area, most notably the lack of available environments, standards and tools etc. A possible solution is the use of simulated spaces, nevertheless as realistic as strive to make them, they are, at best, only approximations to the real spaces, with important differences such as utilising idealised rather than noisy sensor data. In this respect, an improvement to simulation is emulation, which uses specially adapted physical components to imitate real systems and environments. In this paper we present our work-in-progress towards the creation of a development tool for intelligent environments based on the interconnection of simulated, emulated and real intelligent spaces using a distributed model of mixed reality. To do so, we propose the use of physical/virtual components (xReality objects) able to be combined through a 3D graphical user interface, sharing real-time information. We present three scenarios of interconnected real and emulated spaces, used for education, achieving integration between real and virtual worlds.

A Computing with Words Framework for Ambient Intelligence

One of the challenges for the fast advancing ambient intelligence vision is to maintain the perception of the future home being a safe place where the inhabitants relax, enjoy and feel comfortable. In a home environment, the role of technology is approached skeptically, hence, there is a need to provide high-level communication between the users and the intelligent space so that the users get accustomed to what technology has to offer. In this paper, we introduce a Computing with Words (CWWs) framework which provides human-like reasoning via abstraction and high-level description of thoughts, feelings, etc. This framework can be considered as an exocortex as it aids the human thinking outside the bio-brain. The CWWs paradigm aims to establish high level home-human communication, which is necessary for people to perceive the technology as a cooperative guide and an improvement on their life styles.

Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing

BackgroundThe signals acquired in brain-computer interface (BCI) experiments usually involve several complicated sampling, artifact and noise conditions. This mandated the use of several strategies as preprocessing to allow the extraction of meaningful components of the measured signals to be passed along to further processing steps. In spite of the success present preprocessing methods have to improve the reliability of BCI, there is still room for further improvement to boost the performance even more.MethodsA new preprocessing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks is presented. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing preprocessing and allowing low channel counts to be used.ResultsThe new method is verified using experimental data and compared to the classification results of the same data without denoising and with denoising using present wavelet shrinkage based technique. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed.ConclusionThe new preprocessing method based on spectral subtraction denoising offer superior performance to existing methods and has potential for practical utility as a new standard preprocessing block in BCI signal processing.