A Survey on the Project in title

In this paper we present a survey of work that has been done in the project ldquo;Unsupervised Adaptive P300 BCI in the framework of chaotic theory and stochastic theoryrdquo;we summarised the following papers, (Mohammed J Alhaddad amp; 2011), (Mohammed J. Alhaddad amp; Kamel M, 2012), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2013), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2013), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2014), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2014), (Mohammed J Alhaddad, Kamel, amp; Kadah, 2014), (Mohammed J Alhaddad, Kamel, Makary, Hargas, amp; Kadah, 2014), (Mohammed J Alhaddad, Mohammed, Kamel, amp; Hagras, 2015).We developed a new pre-processing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks. 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 pre-processing and allowing low channel counts to be used. We also developed a novel approach for brain-computer interface data that requires no prior training. The proposed approach is based on interval type-2 fuzzy logic based classifier which is able to handle the usersrsquo; uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximize the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. The basic principle of this new class of techniques is that the trial with true activation signal within each block has to be different from the rest of the trials within that block. Hence, a measure that is sensitive to this dissimilarity can be used to make a decision based on a single block without any prior training. The new methods were verified using various experiments which were performed on standard data sets and using real-data sets obtained from real subjects experiments performed in the BCI lab in King Abdulaziz University. The results were compared to the classification results of the same data using previous methods. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed. It will be shown that the produced type-2 fuzzy logic based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets

Interactions within distributed mixed reality collaborative environments

Traditionally virtual worlds have been regarded as standalone entities. However, the world moves fast towards a mixed reality collective environment, joining virtual and real world by incorporating accessible ubiquitous computing for people. Mobile and wearable computers act as a door to connect people to virtuality, e.g. The use of fitness/activity trackers, which collect real world information helping users to complement reality with virtuality improving their health and fitness. A different example is the use of mobile devices to connect people that do not share the same physical location in a virtual way, thought phone calls, videoconferences, chat and social media applications. These examples show that currently we live in two realities, processing information of both worlds in real time. Our video submission presents a work-in-progress research prototype towards the creation of a Blended Reality Distributed System, complementing the paper [1] submitted to the main track of the conference. The test bed scenario proposed is a mixed reality collaborative laboratory activity, performed by learners within geographically dispersed locations. The goal of the activity is to construct a small robot emphasising computing fundamentals. The video is available at: http://youtu.be/akKPHnDY9bw

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

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 today's 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. © 2012 IEEE

Employing an Enhanced Interval Approach to encode words into Linear General Type-2 fuzzy sets for Computing With Words applications

In 1996, Zadeh coined Computing With Words (CWWs) to be a methodology in which words are used instead of numbers for computing and reasoning. One of the main challenges which faced the CWWs paradigm has been modelling words adequately. Mendel has pointed out that the CWWs paradigm should employ type-2 fuzzy logic to model words. This paper proposes employing an Enhanced Interval Approach (EIA) to create Linear General Type-2 (LGT2) fuzzy sets from Interval Type-2 (IT2) fuzzy sets to encode words for CWWs applications. We have performed experiments on 18 words belonging to 3 different linguistic variables (having 6 linguistic terms each). Interval data has been collected from 17 subjects and 18 linguistic terms have been modeled with IT2 fuzzy sets using EIA. The proposed conversion approach uses several key points within the parameters of IT2 fuzzy sets to redesign the linguistic variable using LGT2 fuzzy sets. Both IT2 and LGT2 fuzzy sets have been evaluated within a CWWs Framework, which aims to mimic the ability of humans to communicate and manipulate perceptions via words. The comparison results show that LGT2 fuzzy sets can be better than IT2 fuzzy sets in mimicking human reasoning as well as learning and adaptation since the progressive Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for LGT2 based CWWs Framework converge faster and are lower than those for IT2 based CWWs Framework

Utilising networked workstations to accelerate database queries

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things

With the unprecedented growing demand for communication between Internet of Things (IoT) devices, the upcoming 5G and 6G technologies will pave the path to a widespread use of ultra-reliable low-latency applications in such networks. However, with most of the sensitive data being transmitted over wireless links, security, privacy and trust management are emerging as big challenges to handle. IoT applications vary, from self-driving vehicles, drone deliveries, online shopping, IoT smart cities, e-healthcare and robotic assisted surgery, with many applications focused on Voice over IP (VoIP) and require securing data from potential eavesdroppers and attackers. One well-known technique is a hidden exchange of secret data between the devices for which security can be achieved with audio steganography. Audio steganography is an efficient, reliable and low-latency mechanism used for securely communicating sensitive data over wireless links. MPEG-1 Audio Layer 3’s (MP3’s) bit rate falls within the acceptable sound quality required for audio. Its low level of noise distortion does not affect its sound quality, which makes it a good carrier medium for steganography and watermarking. The strength of any embedding technique lies with its undetectability measure. Although there are many detection techniques available for both steganography and watermarking, the detection accuracy of secret data has been proven erroneous. It has yet to be confirmed whether different bit rates or a constant sampling rate for embedding eases detection. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work