Homogeneous Einstein Metrics on SU(n) Manifolds, Hoop Conjecture for Black Rings, and Ergoregions in Magnetised Black Hole Spacetimes

This Dissertation covers three aspects of General Relativity: inequivalent Einstein metrics on Lie Group Manifolds, proving the Hoop Conjecture for Black Rings, and investigating ergoregions in magnetised black hole spacetimes. A number of analytical and numerical techniques are employed to that end. It is known that every compact simple Lie Group admits a bi-invariant homogeneous Einstein metric. We use two ansatze to probe the existence of additional inequivalent Einstein metrics on the Lie Group SU (n). We provide an explicit construction of 2k + 1 and 2k inequivalent Einstein metrics on SU (2k) and SU (2k + 1) respectively. We prove the Hoop Conjecture for neutral and charged, singly and doubly rotating black rings. This allows one to determine whether a rotating mass distribution has an event horizon, that it is in fact a black ring. We investigate ergoregions in magnetised black hole spacetimes. We show that, in general, rotating charged black holes (Kerr-Newman) immersed in an external magnetic field have ergoregions that extend to infinity near the central axis unless we restrict the charge to q = amB and keep B below a maximal value. Additionally, we show that as B is increased from zero the ergoregion adjacent to the event horizon shrinks, vanishing altogether at a critical value, before reappearing and growing until it is no longer bounded as B becomes greater than the maximal value

Countering Active Attacks on RAFT-based IoT Blockchain Networks

This paper considers an Internet of Thing (IoT) blockchain network consisting of a leader node and various follower nodes which together implement the RAFT consensus protocol to verify a blockchain transaction, as requested by a blockchain client. Further, two kinds of active attacks, i.e., jamming and impersonation, are considered on the IoT blockchain network due to the presence of multiple {\it active} malicious nodes in the close vicinity. When the IoT network is under the jamming attack, we utilize the stochastic geometry tool to derive the closed-form expressions for the coverage probabilities for both uplink and downlink IoT transmissions. On the other hand, when the IoT network is under the impersonation attack, we propose a novel method that enables a receive IoT node to exploit the pathloss of a transmit IoT node as its fingerprint to implement a binary hypothesis test for transmit node identification. To this end, we also provide the closed-form expressions for the probabilities of false alarm, missed detection and miss-classification. Finally, we present detailed simulation results that indicate the following: i) the coverage probability improves as the jammers' locations move away from the IoT network, ii) the three error probabilities decrease as a function of the link quality

Dynamic modelling and simulation of industrial scale multistage flash desalination process

YesMultistage Flash (MSF) desalination process is still a dominant process, especially in the Gulf region, to produce high quality freshwater. Although there has been energy price surge in recent years, MSF process will continue to operate in that region for some foreseeable future. The key challenge is how to make such processes still profitable. Understanding the dynamics of any processes under uncertainty and disturbances is very important to make a process operationally feasible and profitable. The main aim of this work is to understand the dynamics of industrial scale MSF process using high fidelity and reliable process model. For this purpose, a detailed dynamic model for the MSF process incorporating key and new features is developed and validated against the actual data of a large-scale seawater desalination plant. The model is then used to study the behaviour of large scale MSF processes for disturbances in steam temperature, feed temperature and the recycle brine flow rate. The simulation results show that the last stage requires a longer time to settle compared to the preceding stages. In addition, steam temperature shows insignificant influence on the performance ratio compared to the inlet seawater temperature and recycle brine flow rate. Furthermore, it is found that the productivity of plant can increase in the winter compared to that in the summer. However, this benefit comes at the expense of increased steam consumption in the winter, resulting in a low performance ratio

EHHR: an efficient evolutionary hyper-heuristic based recommender framework for short-text classifier selection

With various machine learning heuristics, it becomes difficult to choose an appropriate heuristic to classify short-text emerging from various social media sources in the form of tweets and reviews. The No Free Lunch theorem asserts that no heuristic applies to all problems indiscriminately. Regardless of their success, the available classifier recommendation algorithms only deal with numeric data. To cater to these limitations, an umbrella classifier recommender must determine the best heuristic for short-text data. This paper presents an efficient reminisce-enabled classifier recommender framework to recommend a heuristic for new short-text data classification. The proposed framework, “Efficient Evolutionary Hyper-heuristic based Recommender Framework for Short-text Classifier Selection (EHHR),” reuses the previous solutions to predict the performance of various heuristics for an unseen problem. The Hybrid Adaptive Genetic Algorithm (HAGA) in EHHR facilitates dataset-level feature optimization and performance prediction. HAGA reveals that the influential features for recommending the best short-text heuristic are the average entropy, mean length of the word string, adjective variation, verb variation II, and average hard examples. The experimental results show that HAGA is 80% more accurate when compared to the standard Genetic Algorithm (GA). Additionally, EHHR clusters datasets and rank heuristics cluster-wise. EHHR clusters 9 out of 10 problems correctly

Non-Contact Monitoring of Dehydration using RF Data Collected off the Chest and the Hand

We report a novel non-contact method for dehydration monitoring. We utilize a transmit software defined radio (SDR) that impinges a wideband radio frequency (RF) signal (of frequency 5.23 GHz) onto either the chest or the hand of a subject who sits nearby. Further, another SDR in the closed vicinity collects the RF signals reflected off the chest (or passed through the hand) of the subject. Note that the two SDRs exchange orthogonal frequency division multiplexing (OFDM) signal, whose individual subcarriers get modulated once it reflects off (passes through) the chest (the hand) of the subject. This way, the signal collected by the receive SDR consists of channel frequency response (CFR) that captures the variation in the blood osmolality due to dehydration. The received raw CFR data is then passed through a handful of machine learning (ML) classifiers which once trained, output the classification result (i.e., whether a subject is hydrated or dehydrated). For the purpose of training our ML classifiers, we have constructed our custom HCDDM-RF-5 dataset by collecting data from 5 Muslim subjects (before and after sunset) who were fasting during the month of Ramadan. Specifically, we have implemented and tested the following ML classifiers (and their variants): K-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), ensemble classifier, and neural network classifier. Among all the classifiers, the neural network classifier acheived the best classification accuracy, i.e., an accuracy of 93.8% for the proposed CBDM method, and an accuracy of 96.15% for the proposed HBDM method. Compared to prior work where the reported accuracy is 97.83%, our proposed non-contact method is slightly inferior (as we report a maximum accuracy of 96.15%); nevertheless, the advantages of our non-contact dehydration method speak for themselves.Comment: 8 pages, 9 figures, 2 table

Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks

تُعَدُّ أنظمة النعال الحسّاسة للحركة تقنية واعدة للعديد من التطبيقات في الرعاية الصحية والرياضة. حيث يمكن أن توفّر هذه الأنظمة معلومات قيّمة حول توزيع الضغط على القدم وأنماط المشي لأفراد مختلفين. ومع ذلك، فإن تصميم وتنفيذ مثل هذه الأنظمة يواجه العديد من التحديات، مثل اختيار الحسّاسات والمعايرة ومعالجة البيانات والتفسير. في هذه الدراسة، نقترح نظام نعل حساس باستخدام مقاومات استشعار القوى  لقياس الضغط المطبّق من القدم على مناطق مختلفة من النعل. يقوم هذا النظام بتصنيف أربعة أنواع من تشوهات القدم: طبيعي، مسطح، انحراف القدم إلى الداخل، وزيادة انحراف القدم إلى الخارج. تستخدم مرحلة التصنيف قيم الضغط الفرقية على نقاط الضغط كمدخلات لنموذج التغذية الأمامية للشبكات العصبية. تم جمع البيانات من 60 فرداً تم تشخيصهم بالحالات المدروسة. حقق تنفيذ التغذية الأمامية للشبكات العصبية دقة بنسبة 96.6٪ باستخدام 50٪ من المجموعة البيانية كبيانات تدريبية و 92.8٪ باستخدام 30٪ من البيانات التدريبية فقط. ويوضح المقارنة مع الأعمال ذات الصلة الأثر الإيجابي لاستخدام القيم الفرق لنقاط الضغط كمدخلات للشبكات العصبية مقارنة بالبيانات الأولية.Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data

Detecting change and dealing with uncertainty in imperfect evolutionary environments

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high

An evaluation of membrane properties and process characteristics of a scaled-up pressure retarded osmosis (PRO) process

YesThis work presents a systematic evaluation of the membrane and process characteristics of a scaled-up pressure retarded osmosis (PRO). In order to meet pre-defined membrane economic viability ( ≥ 5 W/m2), different operating conditions and design parameters are studied with respect to the increase of the process scale, including the initial flow rates of the draw and feed solution, operating pressure, membrane permeability-selectivity, structural parameter, and the efficiency of the high-pressure pump (HP), energy recovery device (ERD) and hydro-turbine (HT). The numerical results indicate that the performance of the scaled-up PRO process is significantly dependent on the dimensionless flow rate. Furthermore, with the increase of the specific membrane scale, the accumulated solute leakage becomes important. The membrane to achieve the optimal performance moves to the low permeability in order to mitigate the reverse solute permeation. Additionally, the counter-current flow scheme is capable to increase the process performance with a higher permeable and less selectable membrane compared to the co-current flow scheme. Finally, the inefficiencies of the process components move the optimal APD occurring at a higher dimensionless flow rate to reduce the energy losses in the pressurization and at a higher specific membrane scale to increase energy generation