On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios

Novel channel sensing and access strategies in opportunistic spectrum access networks

textTraditionally radio spectrum was considered a commodity to be allocated in a fixed and centralized manner, but now the technical community and the regulators approach it as a shared resource that can be flexibly and intelligently shared between competing entities. In this thesis we focus on novel strategies to sense and access the radio spectrum within the framework of Opportunistic Spectrum Access via Cognitive Radio Networks (CRNs). In the first part we develop novel transmit opportunity detection methods that effectively exploit the gray space present in packet based networks. Our methods proactively detect the maximum safe transmit power that does not significantly affect the primary network nodes via an implicit feedback mechanism from the Primary network to the Secondary network. A novel use of packet interarrival duration is developed to robustly perform change detection in the primary network's Quality of Service. The methods are validated on real world IEEE 802.11 WLANs. In the second part we study the inferential use of Goodness-of-Fit tests for spectrum sensing applications. We provide the first comprehensive framework for decision fusion of an ensemble of goodness-of-fit tests through use of p-values. Also, we introduce a generalized Phi-divergence statistic to formulate goodness-of-fit tests that are tunable via a single parameter. We show that under uncertainty in the noise statistics or non-Gaussianity in the noise, the performance of such non-parametric tests is significantly superior to that of conventional spectrum sensing methods. Additionally, we describe a collaborative spatially separated version of the test for robust combining of tests in a distributed spectrum sensing setting. In the third part we develop the sequential energy detection problem for spectrum sensing and formulate a novel Sequential Energy Detector. Through extensive simulations we demonstrate that our doubly hierarchical sequential testing architecture delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection and False Alarm. We also demonstrate the throughput gains for a case study of sensing ATSC television signals in IEEE 802.22 systems.Electrical and Computer Engineerin

Development of a Web-Based Service to Transcribe Between Multiple Orthographies of the Iu Mien Language

Thesis (M.S.)--Indiana University South Bend, 2011.The goal of this study was to explore the use of machine learning techniques in the development of a web-based application that transcribes between multiple orthographies of the same language. To this end, source text files used in the publishing of the Iu Mien Bible translation in 4 scripts were merged into a single textbase that served as a text corpus for this study. All syllables in the corpus were combined into a list of parallel renderings which were subjected to ID3 and neural networks with the back propagation in an attempt to achieve machine learning of transcription between the different Iu Mien orthographies. The most effective set of neural net transcription rules were captured and incorporated into a web-based service where visitors could submit text in one writing system and receive a webpage containing the corresponding text rendered in the other writing systems of this language. Transcriptions that are in excess of 90% correct were achieved between a Roman script and another Roman script or between a non-Roman script and another non-Roman script. Transcriptions between a Roman script and a non-Roman yield output that were only 50% correct. This system is still being tested and improved by linguists and volunteers from various organizations associated with the target community within Thailand, Laos, Vietnam and the USA. This study demonstrates the potential of this approach for developing written materials in languages with multiple scripts. This study also provides useful insights on how this technology might be improved

Physiologically based pharmacokinetic (PBPK) modeling for dynamical liver function tests and CYP phenotyping

Die Phänotypisierung von Cytochrom P450 (CYP) und Leberfunktionstests sind wichtige Methoden in der Klinik. Die Methoden nutzen die Pharmakokinetik (PK) von Testsubstanzen und ihren Metaboliten, um Einblicke in die Stoffwechselkapazität der Leber und in die Aktivität von Enzymen und Transportern zu gewinnen. Die Leberfunktionstests werden nicht nur von zahlreichen Proband:innenmerkmalen, sondern auch von den Besonderheiten der Untersuchung beeinflusst. Eine zentrale Herausforderung besteht darin, die verschiedenen Faktoren, die das Ergebnis der Messungen beeinflussen, voneinander zu trennen, um ihren jeweiligen Einfluss auf das Messergebniss zu untersuchen. In dieser Arbeit wurde die Herausforderung durch Metaanalysen und physiologisch basierte Pharmakokinetik Modellierung (PBPK) angegangen. Es wurde eine offene Pharmakokinetik-Datenbank (PK-DB) entwickelt und PK-Daten für ein breites Spektrum von Testsubstanzen kuratiert. Meines Wissens enthält PK-DB derzeit den größten offenen PK-Datensatz zu Testsubstanzen. Der Datensatz ermöglichte die Identifizierung und Quantifizierung von demografischen und rassischen Bias (Geschlecht, ethnische Zugehörigkeit, Alter, Gesundheitszustand), Meldefehlern und Unstimmigkeiten in der Literatur. Auf der Grundlage der Daten wurde eine Metaanalyse der PK von Koffein im Hinblick auf verschiedene Faktoren bzgl. Leberfunktion und CYP1A2-Aktivität durchgeführt. Insbesondere wurde das vorhandene Wissen über die Auswirkungen des Rauchens, der Einnahme oraler Verhütungsmittel, verschiedener Krankheiten und Begleitmedikationen auf die PK von Koffein durch Metaanalysen und Datenintegration konsolidiert. Ebenso wurde die Messgenauigkeit der Koffeinkonzentration in Bezug auf den Messprotokol analysiert. Darüber hinaus wurde der Einfluss des CYP2D6-Polymorphismus untersucht. Hierzu wurde ein PBPK-Modell für Dextromethorphan und seine Metaboliten Dextrorphan und Dextrorphan O-Glucuronid entwickelt und mit den PK-Daten kalibriert und validiert.Cytochrome P450 (CYP) phenotyping and dynamic liver function testing are essential methods in clinical practice. These methods utilize the pharmacokinetics (PK) of test substances and their metabolites to gain insight into the liver's metabolic capacity and the activity of enzymes and transporters. Liver function tests are not only influenced by numerous characteristics of a studied subject but also by the specifics of individual study procedures. A key challenge is to disentangle the various factors which influence the outcome of the measurements from each other to study their influence on the dynamic liver function and CYP phenotype. In this work, the challenge was addressed through meta-analysis and physiologically based pharmacokinetic modeling. As a foundation, an open pharmacokinetics database was developed and pharmacokinetics data were curated for a wide range of test substances. To my knowledge, PK-DB currently contains the largest open pharmacokinetic dataset on substances used for phenotyping and dynamical liver function testing. The dataset allowed for identifying and quantifying demographic and racial bias (sex, ethnicity, age, health), reporting errors, and inconsistencies in pharmacokinetic literature. Based on the data, a caffeine pharmacokinetics meta-analysis was conducted concerning various factors affecting liver function and CYP1A2 activity. In particular, meta-analysis and data integration solidified existing knowledge on the effects of smoking, oral contraceptives, multiple diseases, and co-medications on caffeine pharmacokinetics. Similarly, the measurement accuracy of caffeine concentration was investigated with respect to various aspects of the measurement protocol. In addition, the impact of CYP2D6 polymorphism was investigated. Therefore, a PBPK model of dextromethorphan (DXM) and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) was developed, and calibrated, and validated with pharmacokinetics data

Computer Vision on Web Pages: A Study of Man-Made Images

This thesis is focused on the development of computer vision techniques for parsing web pages using an image of the rendered page as evidence, and on understanding this under-explored class of images from the perspective of computer vision. This project is divided into two tracks---applied and theoretical---which complement each other. Our practical motivation is the application of improved web page parsing to assistive technology, such as screenreaders for visually impaired users or the ability to declutter the presentation of a web page for those with cognitive deficit. From a more theoretical standpoint, images of rendered web pages have interesting properties from a computer vision perspective; in particular, low-level assumptions can be made in this domain, but the most important cues are often subtle and can be highly non-local. The parsing system developed in this thesis is a principled Bayesian segmentation-classification pipeline, using innovative techniques to produce valuable results in this challenging domain. The thesis includes both implementation and evaluation solutions. Segmentation of a web page is the problem of dividing it into semantically significant, visually coherent regions. We use a hierarchical segmentation method based on the detection of semantically significant lines (possibly broken lines) which divide regions. The Bayesian design allows sophisticated probability models to be applied to the segmentation process, and our method produces segmentation trees that achieve good performance on a variety of measures. Classification, for our purposes, is identifying the semantic role of regions in the segmentation tree of a page. We achieve promising results with a Bayesian classification algorithm based on the novel use of a hidden Markov tree model, in which the structure of the model is adapted to reflect the structure of the segmentation tree. This allows the algorithm to make effective use of the context in which regions appear as well as the features of each individual region. The methods used to evaluate our page parsing system include qualitative and quantitative evaluation of algorithm performance (using manually-prepared ground truth data) as well as a user study of an assistive interface based on our page segmentation algorithm. We also performed a separate user study to investigate users' perceptions of web page organization and to generate ground truth segmentations, leading to important insights about consistency. Taken as a whole, this thesis presents innovative work in computer vision which contributes both to addressing the problem of web accessibility and to the understanding of semantic cues in images

Practical interference management strategies in Gaussian networks

Increasing demand for bandwidth intensive activities on high-penetration wireless hand-held personal devices, combined with their processing power and advanced radio features, has necessitated a new look at the problems of resource provisioning and distributed management of coexistence in wireless networks. Information theory, as the science of studying the ultimate limits of communication e ciency, plays an important role in outlining guiding principles in the design and analysis of such communication schemes. Network information theory, the branch of information theory that investigates problems of multiuser and distributed nature in information transmission is ideally poised to answer questions about the design and analysis of multiuser communication systems. In the past few years, there have been major advances in network information theory, in particular in the generalized degrees of freedom framework for asymptotic analysis and interference alignment which have led to constant gap to capacity results for Gaussian interference channels. Unfortunately, practical adoption of these results has been slowed by their reliance on unrealistic assumptions like perfect channel state information at the transmitter and intricate constructions based on alignment over transcendental dimensions of real numbers. It is therefore necessary to devise transmission methods and coexistence schemes that fall under the umbrella of existing interference management and cognitive radio toolbox and deliver close to optimal performance. In this thesis we work on the theme of designing and characterizing the performance of conceptually simple transmission schemes that are robust and achieve performance that is close to optimal. In particular, our work is broadly divided into two parts. In the rst part, looking at cognitive radio networks, we seek to relax the assumption of non-causal knowledge of primary user's message at the secondary user's transmitter. We study a cognitive channel model based on Gaussian interference channel that does not assume anything about users other than primary user's priority over secondary user in reaching its desired quality of service. We characterize this quality of service requirement as a minimum rate that the primary user should be able to achieve. Studying the achievable performance of simple encoding and decoding schemes in this scenario, we propose a few di erent simple encoding schemes and explore di erent decoder designs. We show that surprisingly, all these schemes achieve the same rate region. Next, we study the problem of rate maximization faced by the secondary user subject to primary's QoS constraint. We show that this problem is not convex or smooth in general. We then use the symmetry properties of the problem to reduce its solution to a feasibly implementable line search. We also provide numerical results to demonstrate the performance of the scheme. Continuing on the theme of simple yet well-performing schemes for wireless networks, in the second part of the thesis, we direct our attention from two-user cognitive networks to the problem of smart interference management in large wireless networks. Here, we study the problem of interference-aware wireless link scheduling. Link scheduling is the problem of allocating a set of transmission requests into as small a set of time slots as possible such that all transmissions satisfy some condition of feasibility. The feasibility criterion has traditionally been lack of pair of links that interfere too much. This makes the problem amenable to solution using graph theoretical tools. Inspired by the recent results that the simple approach of treating interference as noise achieves maximal Generalized Degrees of Freedom (which is a measure that roughly captures how many equivalent single-user channels are contained in a given multi-user channel) and the generalization that it can attain rates within a constant gap of the capacity for a large class of Gaussian interference networks, we study the problem of scheduling links under a set Signal to Interference plus Noise Ratio (SINR) constraint. We show that for nodes distributed in a metric space and obeying path loss channel model, a re ned framework based on combining geometric and graph theoretic results can be devised to analyze the problem of nding the feasible sets of transmissions for a given level of desired SINR. We use this general framework to give a link scheduling algorithm that is provably within a logarithmic factor of the best possible schedule. Numerical simulations con rm that this approach outperforms other recently proposed SINR-based approaches. Finally, we conclude by identifying open problems and possible directions for extending these results

Computational Discovery and Analysis of rDNA Sequence Heterogeneity in Yeast

Ribosomal RNA genes, known as ribosomal DNA or rDNA, are commonly found in tandem arrays of hundreds of repeating units. The sequences of each unit in an array were thought to be near-identical but it is now known that frequent mutations may occur, causing heterogeneity amongst units. Opposing these divergent mutational processes, unit sequences are homogenised through concerted evolutionary processes such as unequal sister chromatid exchange (USCE) and gene conversion (GC). In this study Perl software has been used to uncover rDNA sequence variation in the yeast Saccharomyces paradoxus, using data derived from the Saccharomyces Genome Resequencing Project. This analysis, in conjunction with a reanalysis of the Saccharomyces cerevisiae data from the same project, has provided detailed information regarding rDNA sequence heterogeneity in two contrasting, yet closelyrelated yeast species. Additionally, the rDNA flanking sequences of four yeast strains have been characterised via an analysis of new next generation sequencing reads, adding to our knowledge of concerted evolutionary processes in these genomic regions. Partial Single Nucleotide Polymorphisms (pSNPs) within these datasets are shown to reflect genome mosaicism within a population, and to identify strains with signs of genome hybridisation undetectable by other means. This information provides further insights into the dynamics of the rDNA region in the two yeast species. In particular, examination of the percentage occupancies of pSNPs reveals U-shaped distributions which differ between the two species. Further investigations of rDNA evolutionary dynamics through the development of two Java simulation tools (SIMPLEX and CONCERTINA), which model USCE and GC events, follow the fate of both single and multiple pSNPs in one or more rDNA arrays. Initial simulations show the distribution of pSNPs varies depending upon the balance between mutations and concerted evolutionary events, and provide a framework to investigate the mechanisms involved in altered rDNA dynamics in various cellular processes

Channel parameter tuning in a hybrid Wi-Fi-Dynamic Spectrum Access Wireless Mesh Network

This work addresses Channel Assignment in a multi-radio multi-channel (MRMC) Wireless Mesh Network (WMN) using both Wi-Fi and Dynamic Spectrum Access (DSA) spectrum bands and standards. This scenario poses new challenges because nodes are spread out geographically so may have differing allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) avoid or minimise interference within the network and from external interference sources, and 2) maintain connectivity within the network. These two requirements must be met while staying within the link constraints and the radio interface constraints, such as only assigning as many channels to a node as it has radios. This work's original contribution to the field is a unified framework for channel optimisation and assignment in a WMN that uses both DSA and traditional Wi-Fi channels for interconnectivity. This contribution is realised by providing and analysing the performance of near-optimal Channel Assignment (CA) solutions using metaheuristic algorithms for the MRMC WMNs using DSA bands. We have created a simulation framework for evaluating the algorithms. The performance of Simulated Annealing, Genetic Algorithm, Differential Evolution, and Particle Swarm Optimisation algorithms have been analysed and compared for the CA optimisation problem. We introduce a novel algorithm, used alongside the metaheuristic optimisation algorithms, to generate feasible candidate CA solutions. Unlike previous studies, this sensing and CA work takes into account the requirement to use a Geolocation Spectrum Database (GLSD) to get the allowed channels, in addition to using spectrum sensing to identify and estimate the cumulative severity of both internal and external interference sources. External interference may be caused by other secondary users (SUs) in the vicinity or by primary transmitters of the DSA band whose emissions leak into adjacent channels, next-toadjacent, or even into further channels. We use signal-to-interference-plus-noise ratio (SINR) as the optimisation objective. This incorporates any possible source or type of interference and makes our method agnostic to the protocol or technology of the interfering devices while ensuring that the received signal level is high enough for connectivity to be maintained on as many links as possible. To support our assertion that SINR is a reasonable criterion on which to base the optimisation, we have carried out extensive outdoor measurements in both line-of-sight and wooded conditions in the television white space (TVWS) DSA band and the 5 GHz Wi-Fi band. These measurements show that SINR is useful as a performance measure, especially when the interference experienced on a link is high. Our statistical analysis shows that SINR effectively differentiates the performance of different channels and that SINR is well correlated with throughput and is thus a good predictor of end-user experience, despite varying conditions. We also identify and analyse the idle times created by Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) contention-based Medium Access Control (MAC) operations and propose the use of these idle times for spectrum sensing to measure the SINR on possible channels. This means we can perform spectrum sensing with zero spectrum sensing delay experienced by the end user. Unlike previous work, this spectrum sensing is transparent and can be performed without causing any disruption to the normal data transmission of the network. We conduct Markov chain analysis to find the expected length of time of a sensing window. We also derive an efficient minimum variance unbiased estimator of the interference plus noise and show how the SINR can be found using this estimate. Our estimation is more granular, accurate, and appropriate to the problem of Secondary User (SU)-SU coexistence than the binary hypothesis testing methods that are most common in the literature. Furthermore, we construct confidence intervals based on the probability density function derived for the observations. This leads to finding and showing the relationships between the number of sampling windows and sampling time, the interference power, and the achievable confidence interval width. While our results coincide with (and thus are confirmed by) some key previous recommendations, ours are more precise, granular, and accurate and allow for application to a wider range of operating conditions. Finally, we present alterations to the IEEE 802.11k protocol to enable the reporting of spectrum sensing results to the fusion or gateway node and algorithms for distributing the Channel Assignment once computed. We analyse the convergence rate of the proposed procedures and find that high network availability can be maintained despite the temporary loss of connectivity caused by the channel switching procedure. This dissertation consolidates the different activities required to improve the channel parameter settings of a multi-radio multi-channel DSA-WMN. The work facilitates the extension of Internet connectivity to the unconnected or unreliably connected in rural or peri-urban areas in a more cost-effective way, enabling more meaningful and affordable access technologies. It also empowers smaller players to construct better community networks for sharing local content. This technology can have knock-on effects of improved socio-economic conditions for the communities that use it