Continuous property measurement techniques and physics based mathematical model for frost growth control

The development of a new set of frost property measurement techniques to be used in the control of frost growth and defrosting processes in refrigeration systems was investigated. Holographic interferometry and infrared thermometry were used to measure the temperature of the frost-air interface, while a beam element load sensor was used to obtain the weight of a deposited frost layer. The proposed measurement techniques were tested for the cases of natural and forced convection, and the characteristic charts were obtained for a set of operational conditions. ^ An improvement of existing frost growth mathematical models was also investigated. The early stage of frost nucleation was commonly not considered in these models and instead an initial value of layer thickness and porosity was regularly assumed. A nucleation model to obtain the droplet diameter and surface porosity at the end of the early frosting period was developed. The drop-wise early condensation in a cold flat plate under natural convection to a hot (room temperature) and humid air was modeled. A nucleation rate was found, and the relation of heat to mass transfer (Lewis number) was obtained. It was found that the Lewis number was much smaller than unity, which is the standard value usually assumed for most frosting numerical models. The nucleation model was validated against available experimental data for the early nucleation and full growth stages of the frosting process. ^ The combination of frost top temperature and weight variation signals can now be used to control the defrosting timing and the developed early nucleation model can now be used to simulate the entire process of frost growth in any surface material. ^

Orthogonal frequency division multiplexing based air interfaces and multiple input multiple output techniques in cooperative satellite communications for 4th generation mobile systems

Recently, wireless network technology has grown at such a pace that scientific research has become a practical reality in a very short time span. Mobile wireless communications have witnessed the adoption of several generations, each of them complementing and improving the former. One mobile system that features high data rates and open network architecture is 4G. Currently, the research community and industry, in the field of wireless networks, are working on possible choices for solutions in the 4G system. 4G is a collection of technologies and standards that will allow a range of ubiquitous computing and wireless communication architectures. The researcher considers one of the most important characteristics of future 4G mobile systems the ability to guarantee reliable communications from 100 Mbps, in high mobility links, to as high as 1 Gbps for low mobility users, in addition to high efficiency in the spectrum usage. On mobile wireless communications networks, one important factor is the coverage of large geographical areas. In 4G systems, a hybrid satellite/terrestrial network is crucial to providing users with coverage wherever needed. Subscribers thus require a reliable satellite link to access their services when they are in remote locations, where a terrestrial infrastructure is unavailable. Thus, they must rely upon satellite coverage. Good modulation and access technique are also required in order to transmit high data rates over satellite links to mobile users. This technique must adapt to the characteristics of the satellite channel and also be efficient in the use of allocated bandwidth. Satellite links are fading channels, when used by mobile users. Some measures designed to approach these fading environments make use of: (1) spatial diversity (two receive antenna configuration); (2) time diversity (channel interleaver/spreading techniques); and (3) upper layer FEC. The author proposes the use of OFDM (Orthogonal Frequency Multiple Access) for the satellite link by increasing the time diversity. This technique will allow for an increase of the data rate, as primarily required by multimedia applications, and will also optimally use the available bandwidth. In addition, this dissertation approaches the use of Cooperative Satellite Communications for hybrid satellite/terrestrial networks. By using this technique, the satellite coverage can be extended to areas where there is no direct link to the satellite. For this purpose, a good channel model is necessary.

Orthogonal Frequency Division Multiplexing Based Air Interfaces and Multiple Input Multiple Output Techniques in Cooperative Satellite Communications for 4th Generation Mobile Systems

Recently, wireless network technology has grown at such a pace that scientific research has become a practical reality in a very short time span. One mobile system that features high data rates and open network architecture is 4G. Currently, the research community and industry, in the field of wireless networks, are working on possible choices for solutions in the 4G system. The researcher considers one of the most important characteristics of future 4G mobile systems the ability to guarantee reliable communications at high data rates, in addition to high efficiency in the spectrum usage. On mobile wireless communication networks, one important factor is the coverage of large geographical areas. In 4G systems, a hybrid satellite/terrestrial network is crucial to providing users with coverage wherever needed. Subscribers thus require a reliable satellite link to access their services when they are in remote locations where a terrestrial infrastructure is unavailable. The results show that good modulation and access technique are also required in order to transmit high data rates over satellite links to mobile users. The dissertation proposes the use of OFDM (Orthogonal Frequency Multiple Access) for the satellite link by increasing the time diversity. This technique will allow for an increase of the data rate, as primarily required by multimedia applications, and will also optimally use the available bandwidth. In addition, this dissertation approaches the use of Cooperative Satellite Communications for hybrid satellite/terrestrial networks. By using this technique, the satellite coverage can be extended to areas where there is no direct link to the satellite. The issue of Cooperative Satellite Communications is solved through a new algorithm that forwards the received data from the fixed node to the mobile node. This algorithm is very efficient because it does not allow unnecessary transmissions and is based on signal to noise ratio (SNR) measures.

From theory-inspired to theory-based interventions : A protocol for developing and testing a methodology for linking behaviour change techniques to theoretical mechanisms of action

This research is funded by UK Medical Research Council grant number MR/L011115/1

From Theory-Inspired to Theory-Based Interventions : A Protocol for Developing and Testing a Methodology for Linking Behaviour Change Techniques to Theoretical Mechanisms of Action

This research is funded by UK Medical Research Council grant number MR/L011115/1. We would like to thank the 105 experts in behaviour change who have committed their time and offered their expertise for study 2 of this research. We are also very grateful to all those who sent us peer-reviewed behaviour change intervention descriptions for study 1. Finally, we would like thank Dr. Emma Beard and Dr. Dan Dediu for their statistical input and to all the researchers, particularly Holly Walton, who have assisted in the coding of papers for study 1.

Improved image analysis based corrosion assessment using preprocessing techniques

The importance of non-destructive techniques (NDT) in structural health monitoring programmes is being critically felt in the recent times. The quality of the measured data, often affected by various environmental conditions can be a guiding factor in terms usefulness and prediction efficiencies of the various detection and monitoring methods used in this regard. Often, a preprocessing of the acquired data in relation to the affecting environmental parameters can improve the information quality and lead towards a significantly more efficient and correct prediction process. The improvement can be directly related to the final decision making policy about a structure or a network of structures and is compatible with general probabilistic frameworks of such assessment and decision making programmes. This paper considers a preprocessing technique employed for an image analysis based structural health monitoring methodology to identify sub-marine pitting corrosion in the presence of variable luminosity, contrast and noise affecting the quality of images. A preprocessing of the gray-level threshold of the various images is observed to bring about a significant improvement in terms of damage detection as compared to an automatically computed gray-level threshold. The case dependent adjustments of the threshold enable to obtain the best possible information from an existing image. The corresponding improvements are observed in a qualitative manner in the present study.

Simplified Predictive Instrument to Rule Out Acute Coronary Syndromes in a High-Risk Population.

BACKGROUND: It is unclear whether diagnostic protocols based on cardiac markers to identify low-risk chest pain patients suitable for early release from the emergency department can be applied to patients older than 65 years or with traditional cardiac risk factors. METHODS AND RESULTS: In a single-center retrospective study of 231 consecutive patients with high-risk factor burden in which a first cardiac troponin (cTn) level was measured in the emergency department and a second cTn sample was drawn 4 to 14 hours later, we compared the performance of a modified 2-Hour Accelerated Diagnostic Protocol to Assess Patients with Chest Pain Using Contemporary Troponins as the Only Biomarker (ADAPT) rule to a new risk classification scheme that identifies patients as low risk if they have no known coronary artery disease, a nonischemic electrocardiogram, and 2 cTn levels below the assay's limit of detection. Demographic and outcome data were abstracted through chart review. The median age of our population was 64 years, and 75% had Thrombosis In Myocardial Infarction risk score ≥2. Using our risk classification rule, 53 (23%) patients were low risk with a negative predictive value for 30-day cardiac events of 98%. Applying a modified ADAPT rule to our cohort, 18 (8%) patients were identified as low risk with a negative predictive value of 100%. In a sensitivity analysis, the negative predictive value of our risk algorithm did not change when we relied only on undetectable baseline cTn and eliminated the second cTn assessment. CONCLUSIONS: If confirmed in prospective studies, this less-restrictive risk classification strategy could be used to safely identify chest pain patients with more traditional cardiac risk factors for early emergency department release.

Vibration based health monitoring of civil infrastructure using energy harvesting techniques

The use of structural health monitoring of civil structures is ever expanding and by assessing the dynamical condition of structures, informed maintenance management can be conducted at both individual and network levels. With the continued growth of information age technology, the potential arises for smart monitoring systems to be integrated with civil infrastructure to provide efficient information on the condition of a structure. The focus of this thesis is the integration of smart technology with civil infrastructure for the purposes of structural health monitoring. The technology considered in this regard are devices based on energy harvesting materials. While there has been considerable focus on the development and optimisation of such devices using steady state loading conditions, their applications for civil infrastructure are less known. Although research is still in initial stages, studies into the uses associated with such applications are very promising. Through the use of the dynamical response of structures to a variety of loading conditions, the energy harvesting outputs from such devices is established and the potential power output determined. Through a power variance output approach, damage detection of deteriorating structures using the energy harvesting devices is investigated. Further applications of the integration of energy harvesting devices with civil infrastructure investigated by this research includes the use of the power output as a indicator for control. Four approaches are undertaken to determine the potential applications arising from integrating smart technology with civil infrastructure, namely • Theoretical analysis to determine the applications of energy harvesting devices for vibration based health monitoring of civil infrastructure. • Laboratory experimentation to verify the performance of different energy harvesting configurations for civil infrastructure applications. • Scaled model testing as a method to experimentally validate the integration of the energy harvesting devices with civil infrastructure. • Full scale deployment of energy harvesting device with a bridge structure. These four approaches validate the application of energy harvesting technology with civil infrastructure from a theoretical, experimental and practical perspective.

Behaviour change techniques in home-based cardiac rehabilitation: a systematic review

BACKGROUND: Cardiac rehabilitation (CR) programmes offering secondary prevention for cardiovascular disease (CVD) advise healthy lifestyle behaviours, with the behaviour change techniques (BCTs) of goals and planning, feedback and monitoring, and social support recommended. More information is needed about BCT use in home-based CR to support these programmes in practice.

AIM: To identify and describe the use of BCTs in home-based CR programmes.

DESIGN AND SETTING: Randomised controlled trials of home-based CR between 2005 and 2015 were identified by searching MEDLINE(®), Embase, PsycINFO, Web of Science, and Cochrane Database.

METHOD: Reviewers independently screened titles and abstracts for eligibility. Relevant data, including BCTs, were extracted from included studies. A meta-analysis studied risk factor change in home-based and comparator programmes.

RESULTS: From 2448 studies identified, 11 of good methodological quality (10 on post-myocardial infarction, one on heart failure, 1907 patients) were included. These reported the use of 20 different BCTs. Social support (unspecified) was used in all studies and goal setting (behaviour) in 10. Of the 11 studies, 10 reported effectiveness in reducing CVD risk factors, but one study showed no improvement compared to usual care. This study differed from effective programmes in that it didn't include BCTs that had instructions on how to perform the behaviour and monitoring, or a credible source.

CONCLUSION: Social support and goal setting were frequently used BCTs in home-based CR programmes, with the BCTs related to monitoring, instruction on how to perform the behaviour, and credible source being included in effective programmes. Further robust trials are needed to determine the relative value of different BCTs within CR programmes.

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

Detection of mesoscale thermal fronts from 4km data using smoothing techniques: Gradient-based fronts classification and basin scale application

In order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 × 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel, and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km of resolution are compared. Fronts are divided in two intensity classes (“weak” and “strong”) according to their thermal gradient. A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels) and three detection window sizes (16 × 16, 24 × 24 and 32 × 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 × 16 pixel window size, and a 5 × 5 pixel kernel for strong fronts and a 7 × 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 × 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general strong fronts are observed in coastal areas whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction done by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method, using 4 km data is easily applicable to large regions or at the global scale with far less constraints of data manipulation and processing time relative to 1 km data.