Random Neural Networks and Optimisation

In this thesis we introduce new models and learning algorithms for the Random Neural Network (RNN), and we develop RNN-based and other approaches for the solution of emergency management optimisation problems. With respect to RNN developments, two novel supervised learning algorithms are proposed. The first, is a gradient descent algorithm for an RNN extension model that we have introduced, the RNN with synchronised interactions (RNNSI), which was inspired from the synchronised firing activity observed in brain neural circuits. The second algorithm is based on modelling the signal-flow equations in RNN as a nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory quasi-Newton algorithm specifically designed for the RNN case. Regarding the investigation of emergency management optimisation problems, we examine combinatorial assignment problems that require fast, distributed and close to optimal solution, under information uncertainty. We consider three different problems with the above characteristics associated with the assignment of emergency units to incidents with injured civilians (AEUI), the assignment of assets to tasks under execution uncertainty (ATAU), and the deployment of a robotic network to establish communication with trapped civilians (DRNCTC). AEUI is solved by training an RNN tool with instances of the optimisation problem and then using the trained RNN for decision making; training is achieved using the developed learning algorithms. For the solution of ATAU problem, we introduce two different approaches. The first is based on mapping parameters of the optimisation problem to RNN parameters, and the second on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer linear programming formulation, which is based on network flows. Finally, we design and implement distributed heuristic algorithms for the deployment of robots when the civilian locations are known or uncertain

Machine Learning Prediction of Mechanical and Durability Properties of Recycled Aggregates Concrete

Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the effects of RA and several types of binders on the carbonation depth of RAC. The ML models developed in this study demonstrated robust performance to predict diverse properties of RAC

Performance improvement for mobile ad hoc cognitive packets network

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn this thesis, focusing on the quality of service (QoS) improvement using per-packet power control algorithm in Ad Hoc Cognitive Packet Networks (AHCPN). A power control mechanism creates as a network-assisted function of ad hoc cognitive packet-based routing and aims at reducing both energy consumption in nodes and QoS requirements. The suggested models facilitate transmission power adjustments while also taking into account the effects on network performance. The thesis concentrate on three main contributions. Firstly, a power control algorithm, namely the adaptive Distributed Power management algorithm (DISPOW) was adopted. Performance of DISPOW was compared to existing mechanisms and the results showed 27, 13, 9, and 40 percent improvements in terms of Delay, Throughput, Packet Loss, and Energy Consumption respectively. Secondly, the DISPOW algorithm was enhanced, namely a Link Expiration Time Aware Distributed Power management algorithm (LETPOW). This approach periodically checks connectivity, transmission power, interference level, routing overhead and Node Mobility in AHCPN. The results show that LETPOW algorithm improves the performance of system. Results show further improvement from DISPOW by 30,25,30,42 percent in terms of delay, packet loss ratio , path lengths and energy consumption respectively. Finally,Hybrid Power Control Algorithm (HLPCA) has presented is a combination of Link Expiration Time Aware Distributed Power management algorithm (LETPOW) and Load Power Control Algorithm (LOADPOW); deal with cross-layer power control applied for transmitting information across the various intermediate layers. LOADPOW emphasis on the concept of transmission Power, Received Signal Strength Indication (RSSI), and the suitable distance between the receiver and the sender. The proposed algorithm outperforms DISPOW and LETPOW by 31,15,35,34,44 percent in terms of Delay, Throughput, Packet Loss,path length and Energy Consumption respectively. From this work, it can be concluded that optimized power control algorithm applied to Ad-hoc cognitive packet network results in significant improvement in terms of energy consumption and QoS

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

On-the-fly dense 3D surface reconstruction for geometry-aware augmented reality.

Augmented Reality (AR) is an emerging technology that makes seamless connections between virtual space and the real world by superimposing computer-generated information onto the real-world environment. AR can provide additional information in a more intuitive and natural way than any other information-delivery method that a human has ever in- vented. Camera tracking is the enabling technology for AR and has been well studied for the last few decades. Apart from the tracking problems, sensing and perception of the surrounding environment are also very important and challenging problems. Although there are existing hardware solutions such as Microsoft Kinect and HoloLens that can sense and build the environmental structure, they are either too bulky or too expensive for AR. In this thesis, the challenging real-time dense 3D surface reconstruction technologies are studied and reformulated for the reinvention of basic position-aware AR towards geometry-aware and the outlook of context- aware AR. We initially propose to reconstruct the dense environmental surface using the sparse point from Simultaneous Localisation and Map- ping (SLAM), but this approach is prone to fail in challenging Minimally Invasive Surgery (MIS) scenes such as the presence of deformation and surgical smoke. We subsequently adopt stereo vision with SLAM for more accurate and robust results. With the success of deep learning technology in recent years, we present learning based single image re- construction and achieve the state-of-the-art results. Moreover, we pro- posed context-aware AR, one step further from purely geometry-aware AR towards the high-level conceptual interaction modelling in complex AR environment for enhanced user experience. Finally, a learning-based smoke removal method is proposed to ensure an accurate and robust reconstruction under extreme conditions such as the presence of surgical smoke

Special oils for halal and safe cosmetics

Three types of non conventional oils were extracted, analyzed and tested for toxicity. Date palm kernel oil (DPKO), mango kernel oil (MKO) and Ramputan seed oil (RSO). Oil content for tow cultivars of dates Deglect Noor and Moshkan was 9.67% and 7.30%, respectively. The three varieties of mango were found to contain about 10% oil in average. The red yellow types of Ramputan were found to have 11 and 14% oil, respectively. The phenolic compounds in DPKO, MKO and RSO were 0.98, 0.88 and 0.78 mg/ml Gallic acid equivalent, respectively. Oils were analyzed for their fatty acid composition and they are rich in oleic acid C18:1 and showed the presence of (dodecanoic acid) lauric acid C12:0, which reported to appear some antimicrobial activities. All extracted oils, DPKO, MKO and RSO showed no toxic effect using prime shrimp bioassay. Since these oils are stable, melt at skin temperature, have good lubricity and are great source of essential fatty acids; they could be used as highly moisturizing, cleansing and nourishing oils because of high oleic acid content. They are ideal for use in such halal cosmetics such as Science, Engineering and Technology 75 skin care and massage, hair-care, soap and shampoo products

Pathogenesis of Encephalitis

Many infectious agents, such as viruses, bacteria, and parasites, can cause inflammation of the central nervous system (CNS). Encephalitis is an inflammation of the brain parenchyma, which may result in a more advanced and serious disease meningoencephalitis. To establish accurate diagnosis and develop effective vaccines and drugs to overcome this disease, it is important to understand and elucidate the mechanism of its pathogenesis. This book, which is divided into four sections, provides comprehensive commentaries on encephalitis. The first section (6 chapters) covers diagnosis and clinical symptoms of encephalitis with some neurological disorders. The second section (5 chapters) reviews some virus infections with the outlines of inflammatory and chemokine responses. The third section (7 chapters) deals with the non-viral causative agents of encephalitis. The last section (4 chapters) discusses the experimental model of encephalitis. The different chapters of this book provide valuable and important information not only to the researchers, but also to the physician and health care workers