Demodulator techniques in satellite communication systems for direct broadcast systems

This thesis is concerned with the FM demodulator techniques used in terrestrial TV receiver designs for Direct Broadcast Systems (DBS) from satellites. The various MAC/Packet schemes intended for DBS applications are described and the international standards that apply to them considered, with particular emphasis on the D2-MAC system. Noise in FM systems is discussed and a suitable threshold noise model is chosen for use in DBS TV demodulator systems. The characteristics of the various types of noise effects are considered in terms of their effect upon the TV picture. The threshold performance of a conventional FM demodulator for differing types of modulation is reviewed and it is shown how the threshold characteristic depends upon the nature of the modulation. The literature review carried out represents a significant component of the thesis and combines material from patent literature with more conventional source materials from professional journals, conferences, textbooks, etc. Some ten existing demodulator concepts that exhibit threshold extension characteristics are examined, and where relevant their potential performance in D2-MAC format systems is assessed. The demodulator characteristics that limit their performance in TV systems are identified. It is concluded that designing a threshold extension demodulator, with reliable operation, for all picture contents and for a wide range of input carrier-to-noise ratios, is a formidable task using existing design techniques. On the basis of this examination an adaptive threshold extension demodulator concept is proposed, that utilises information contained within the signal structure to achieve an improved performance over a wide range of input carrier-to-noise ratios and picture content. It is shown how the relevant signal structures may be derived from conventional (PAL, SECAM and NTSC), MAC format and all-digital television systems. Illustrations are given that show how the adaptive demodulator concept can be applied to certain existing threshold extension demodulators, enhancing their performance for television picture reception. Future trends in all-digital DBS TV systems intended ultimately for DBS applications are briefly discussed together with their demodlilation requirements

A space communication study Final report, 15 Sep. 1967 - 15 Sep. 1968

Transmitting and receiving analog and digital signals through noisy media - space communications stud

A Space Communications Study Final Report, Sep. 15, 1965 - Sep. 15, 1966

Reception of frequency modulated signals passed through deterministic and random time-varying channel

A space communications study Final report, 15 Sep. 1968 - 15 Sep. 1969

Analog and digital signal reception problems through noisy channels, and computerized digital TV system for space communication

A space communications study Final report, 15 Sep. 1966 - 15 Sep. 1967

Investigation of signal to noise ratios and signal transmission efficiency for space communication system

Click Fraud Detection in Online and In-app Advertisements: A Learning Based Approach

Click Fraud is the fraudulent act of clicking on pay-per-click advertisements to increase a site’s revenue, to drain revenue from the advertiser, or to inflate the popularity of content on social media platforms. In-app advertisements on mobile platforms are among the most common targets for click fraud, which makes companies hesitant to advertise their products. Fraudulent clicks are supposed to be caught by ad providers as part of their service to advertisers, which is commonly done using machine learning methods. However: (1) there is a lack of research in current literature addressing and evaluating the different techniques of click fraud detection and prevention, (2) threat models composed of active learning systems (smart attackers) can mislead the training process of the fraud detection model by polluting the training data, (3) current deep learning models have significant computational overhead, (4) training data is often in an imbalanced state, and balancing it still results in noisy data that can train the classifier incorrectly, and (5) datasets with high dimensionality cause increased computational overhead and decreased classifier correctness -- while existing feature selection techniques address this issue, they have their own performance limitations. By extending the state-of-the-art techniques in the field of machine learning, this dissertation provides the following solutions: (i) To address (1) and (2), we propose a hybrid deep-learning-based model which consists of an artificial neural network, auto-encoder and semi-supervised generative adversarial network. (ii) As a solution for (3), we present Cascaded Forest and Extreme Gradient Boosting with less hyperparameter tuning. (iii) To overcome (4), we propose a row-wise data reduction method, KSMOTE, which filters out noisy data samples both in the raw data and the synthetically generated samples. (iv) For (5), we propose different column-reduction methods such as multi-time-scale Time Series analysis for fraud forecasting, using binary labeled imbalanced datasets and hybrid filter-wrapper feature selection approaches

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

Communications and tracking relay experiment study program Final report

Communications and tracking relay experimen

Advanced analyses of physiological signals and their role in Neonatal Intensive Care

Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity