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History assisted energy efficient spectrum sensing In cognitive radio networksThe ever-increasing wireless applications and services has generated a huge demand for the RF spectrum. The strict and rigid policy of spectrum management by the Federal Communications Commission (FCC) has rendered spectrum a valuable resource. The disproportion in the usage of spectrum between the licensed primary users (PUs) and the enormous unlicensed secondary users (SUs) in the band has created spectrum scarcity. This imbalance can be alleviated by the Dynamic Spectrum Access (DSA) based on Cognitive Radio Network (CRN) paradigm by significantly improving the efficiency of spectrum utilisation of the wireless networking systems. DSA enables unlicensed secondary users (SUs) also known as cognitive radios (CRs) to sense the spectral environment and access the licensed spectrum opportunistically without causing any interference to the licensed primary users (PUs). Spectrum sensing is the most prominent capability of CRs to effectively detect the presence or absence of licensed primary users (PUs) in the band. Sensing provides protection to primary users (PUs) from interference and creates opportunities of spectrum access to secondary users (SUs). However, scanning the spectrum continuously is critical and power intensive. The high-power consumption in battery operated CR devices reduces device lifetime thereby affecting the network performance. Research is being carried out to improve energy efficiency and offer viable solutions for extending lifetime for wireless devices. In this thesis, the work focuses on the energy efficient spectrum sensing of CR networks. The main aim is to reduce the percentage of energy consumption in the CR system in possible ways. Primarily, the conventional energy detection (ED) and the cyclostationary feature detection (CFD) spectrum sensing mechanisms were employed to sense the spectrum. Aiming on energy efficiency, a novel history assisted spectrum sensing scheme has been proposed which utilises an analytical engine database (AED). It generates a rich data set of spectrum usage history that can be used by CRs to make efficient sensing decisions modelled using Markov chain model. The usage of sensing history in decision making, results in decreasing the frequency of spectrum scanning by the CRs thereby reducing the processing cost and the sensing related energy consumption. It shows 17% improvement in energy saving compared to the conventional sensing scheme. The key performance parameters such as probability of miss detection (PMD), probability of false alarm (PF) and probability of detection (PD) were investigated using ROC curves. Extensive performance analysis is carried out by implementing two traditional sensing schemes ED and CFD in terms of computational cost and energy consumption and shows 50% improvement in effective energy saved by using history assisted spectrum sensing mechanism. Further, to address the high energy consumption during communication between CRs / stations (STAs) and the base station (BS), a novel energy efficient Group Control Slot allocation (GCSA) mac protocol has been proposed. Publish/Subscribe (PUB-SUB) and point-to-point messaging models have been implemented for data communication between BS, STAs and AED. The proposed mac protocol increases the number of STAs to enter in to sleep mode thereby conserving the energy consumed during idle state. Furthermore, cluster based co-operative spectrum sensing (CSS) is considered for reducing the energy utilised for data communication between CRs and BS by electing a cluster head (CH) using fuzzy logic-based clustering algorithm. The cluster head (CH) collects, aggregates data from cluster members and it is only the CHs that communicate to the BS. Thus, there is no communication between individual non-CH CRs and BS, thereby significantly reducing the energy consumption and improving the network lifetime of the CR system. Extensive simulations were performed in MATLAB and results are presented for all the proposed schemes.
The acute impact of breakfast consumption and omission on postprandial metabolic responses in adolescent girlsBreakfast consumption (BC) frequency declines from childhood to adolescence and is associated with poor metabolic health. This research aimed to analyse whether BC versus breakfast omission (BO) affects substrate oxidation during rest in adolescent girls. Secondly, it examined whether BC vs BO influences postprandial and 5 h glycaemia and insulineamia. Lastly, it evaluated the effects of BC vs BO on Fatmax, MFO, rate of perceived exertion and physical activity (PA) enjoyment during an exercise bout performed 2 h after lunch. Seventeen breakfast consuming girls (13.2 ± 0.7 years old) were recruited. Two experimental trials were completed in a randomised counterbalanced order: BC and BO. A standardised lunch was provided three hours after breakfast (BC) or after breakfast omission (BO). Finger prick blood samples for the analysis of plasma glucose and plasma insulin and expired gas samples for the analysis of substrate oxidation were taken throughout the trials. An incremental 7-stage cycling test was performed 2 h after lunch for the determination of maximum fat oxidation (MFO) and intensity at which MFO occurred (Fatmax). OMNI Scale was used to evaluate the perceived exertion at the end of each cycling stage. PA enjoyment was evaluated after the cool-down using Physical Activity Enjoyment Scale (PACES). There was a significant main effect of condition (BC vs BO) for fat (p= 0.008) and carbohydrate (p< 0.001) oxidation after lunch. Fat oxidation was significantly higher during BO compared to BC, while carbohydrate oxidation was significantly higher during BC compared to BO. The main effect of condition for glucose and insulin incremental area under the curve (iAUC) (p= 0.509; p= 0.603, respectively) and total area under the curve (tAUC) for glucose and insulin (p= 0.738; p= 0.665, respectively) throughout the whole day was not significant. However, post lunch glucose and insulin iAUC (p= 0.05; p= 0.001) and tAUC (p= 0.05; p= 0.001) were significantly higher during BO compared to BC. There was no significant difference in MFO (p= 0.104) or Fatmax (p= 0.945) between conditions. Physical activity enjoyment was higher during BC vs BO with an almost significant difference (p= 0.055). The main effect of condition for perceived exertion (p= 0.307) was not significantly different. In conclusion, BC resulted in lower fat oxidation and lower second meal glycaemic and insulineamic responses. Ultimately, the findings of this study will assist in understanding further the effects of BC vs BO on adolescents’ metabolism. This may have important implications in prevention of obesity and type 2 diabetes.
Hybrid energy-storage system for mobile RF energy harvesting wireless sensorsThis thesis discusses the impact of the supercapacitor size on the performance of the mobile battery-less RF energy harvesting system. The choice of supercapacitor is crucial in mobile systems. The small supercapacitor can charge quickly and activate the sensor in a few seconds in the low-energy area but cannot provide a significant amount of energy to the sensor to do heavy energy tasks such as programming or communication with the base station. On the other hand, large supercapacitors have a sensor node for heavy energy tasks in a high-energy zone but may not be able to activate in a low energy zone. The proposed hybrid energy-storage system contains two supercapacitors of different sizes and a switching circuit. An adaptive-learning switching algorithm controls the switching circuit. This algorithm predicts the available source energy and the period that the sensor node will remain in the high-energy area. The algorithm dynamically switches between the supercapacitors according to available ambient RF energy. Extensive simulation and experiments evaluated the proposed method. The proposed system showed 40% and 80% efficiency over single supercapacitor system in terms of the amount of harvested energy and sensor coverage.
Feature learning for EEG-based person identificationEvoked potentials recorded on a multielectrode EEG device are known to be a ected by volume conductance and functional connectivity while a task is performed by a person. Modelling functional connectivity represents neural interactions between electrodes which are distinguishable and genetically identical. However, the representations that are caused by volume conductance are not distinguishable because of unwanted correlations of the signal. Orthogonalisation using autoregressive modelling minimises the conductance component, and the connectivity features can be then extracted from the residuals. The proposed method shows it is possible to reduce the multidimensionality of the predicted AR model coe cients by modelling the residual from the EEG electrode channel baseline, which makes an important contribution to the functional connectivity. The results show that the required models can be learnt by Machine Learning techniques which are capable of providing the maximal performance in the case of multidimensional EEG data. The proposed method was able to learn accurate identification with few EEG recording channels, especially when the channel that is used has a functional connectivity with the interactive task. The study, which has been conducted on a EEG benchmark including 109 participants, shows a signi cant improvement of the identi cation accuracy.
Social work and poverty: an exploration of social workers’ attitudes and understandingThe context of this study is the dilemma that most service users of social interventions and practice are poor and yet poverty is marginalised within social work practice. The study therefore set out to explore social work practitioners’ understanding of poverty, attitudes towards poverty and social work and poverty relationship. A qualitative methodology was adopted, involving five focus group discussions and twenty-eight semi-structured interviews within three Local Authorities referred to as research sites. This was more than the originally anticipated sample. A narrative literature review undertaken concluded that social work definitions are contested and this, coupled with regulation of social work, limits social work effectiveness in addressing poverty as it is not one of its major remits. The review identified that poverty is a significant issue affecting most service users and associated with most social problems involved in social work interventions and practice. The review discovered that social work practice pathologizes poverty and generally attributes causation of poverty to service users’ lack of capacity to take advantage of opportunities within the market and provided by the state and a lack of motivation to overcome their problems at the expense of structural factors that either cause or exacerbate poverty. This resonates with individualistic social work frameworks which are risk-averse, reactive, punitive, authoritative, and ineffective given the scale and impact of poverty which seems to be increasing. This is aggravated by the neo-liberal socio-political environment and managerialist social work environment characterised by low morale, high caseloads, paucity of much needed resource for social work interventions. The literature review established that social workers’ attitudes towards poor service users are largely ambivalent and negative. The research data reveals that poverty is a significant and prevalent issue amongst most service users and associated with most social problems handled by social workers. Research participants expressed that there is no shared understanding of poverty, that definition of poverty is important in how it is understood and influencing how poverty is addressed. It emerged that poverty is marginalised in social work education and practice. Research data revealed that poverty is taken as background music and normal. It emerged that poverty is not viewed as a risk factor on its own. Participants revealed that social work lacks capacity, knowledge, and skills to address poverty. Social work education and training does not equip social workers with functional knowledge and skills to address poverty in practice. The research revealed social work professionals’ attitudes are generally negative, stereotypical, and judgemental towards service users. Government policies aggravate service users’ experiences and circumstances. The participants expressed an understanding that thresholds of social work interventions are high and therefore act as barriers. Participants expressed that poverty is an uncomfortable subject to discuss with service users given stigmatisation associated with being poor. This therefore results in service users hiding their financial struggles. It emerged that service users who are poor are discriminated against and punished to experiencing poverty and that this goes against main social work values. The findings echo findings undertaken by many academics and researchers in social work poverty and therefore add to the body of knowledge in social work and poverty The study recommends that that consideration should be made that poverty is taught as a main course in social work education and as a post-graduate course for social work in practice. It is also recommended that social work should promote poverty discourse at the policy level with a view to influencing structural change. It is also recommended that adequate funding be provided for poverty practice, family support and early intervention and prevention. It is also recommended that that government policies that impact negatively on service users be evaluated. Service users should play an integral role in all these recommendations.