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In this thesis, an Interactive Video Data Service (IVDS) is developed. This service provides a mechanism for television viewers to interact with the program they are watching. Possible interactions include purchasing products from home shopping programs and requesting information from advertisers. Within the project, two areas were focused upon: channel simulation and the Repeater Unit. Additionally, the overall system was discussed along with its background. The purpose of channel simulation was to demonstrate the viability of the unique communication channel model proposed for the IVDS system. This channel was implemented using uni-directional transmissions, without acknowledgments. The Repeater Unit was designed to be a message processing system, intended to relay messages from system users to the home office. The design entailed both hardware and software. The hardware requirements were for a high level design, while the software required not only design, but implementation. / Master of Science

Message Sequence Charts are a visual representation of the system specification which shows how all the participating processes are interacting with each other. Message Sequence Graphs provide modularity by easily allowing combination of more than one Message Sequence Charts to show more complicated system behavior. Requirements modeled as Message Sequence Graphs give a global view of the system as interaction across all the participating processes can be viewed. Thus systems modeled as Message Sequence Graphs are like sequential composition of parallel process. This makes it very attractive during the requirements gathering and review phases as it needs inter-working between different stakeholders with varied domain knowledge and expertise – requirements engineers, system designers, end customers, test professionals etc. In this thesis we give a detailed construction of a finite-state transition system for a com-connected Message Sequence Graph. Though this result is fairly well-known in the literature there has been no precise description of such a transition system. Several analysis and verification problems concerning MSG specifications can be solved using this transition system. The transition system can be used to construct correct tools for problems like model-checking and detecting implied scenarios in MSG specifications. There are several contributions of this thesis. Firstly, we have provided a detailed construction of a transition system exactly implementing the message sequence graph. We have provided the detailed correctness arguments for this construction. Secondly, this construction works for general Message Sequence Graphs and not limited to com-connected graphs alone, although, we show that a finite model can be ensured only if the original graph is com-connected. Also, we show that the construction works for both synchronous and asynchronous messaging systems. Thirdly, we show how to find implied scenarios using the transition model we have generated. We also discuss some of the flaws in the existing approaches. Fourthly we provide a proof of undecidability argument for non com-connected MSG with synchronous messaging.

Master of Science / Department of Computing and Information Sciences / Gurdip Singh / TinyOS is an operating system designed for wireless embedded sensor network which supports the component based development language called Nesc. Wireless sensor network are becoming increasingly popular and are being used in various applications including surveillance applications related to object tracking. Wireless sensor devices called motes can generate an event in the network whenever there is some object moving in its vicinity. This project aims to develop an application which detects the path information of object moving in the sensor field by capturing the order of events occurs in the network. This application builds a logical topology called DAG (Directed acyclic graph) between the motes in the network which is similar to the tree topology where a child can have multiple parents which are in communication range and a level closer to the root. Using a DAG, motes can communicate efficiently to order the events occurring in the sensor field. The root of the DAG is the base station which receives all the events occurred in the network and orders them based on the information it has from previous events received. Every event occurring in the network is assigned a time stamp and is identified by a tuple (mote_id, timestamp) which describes that the mote with identity id has detected the object with the timestamp, and ordering all such events based on the timestamps we get the path information. There are two time stamping algorithms written in this project. In the first time stamping algorithm, whenever any event occurs, it updates the timestamp information of the entire neighboring mote in the field and when the object enters in the detection range of neighboring mote of previous detected mote, it assigns the new timestamp. The second time stamping algorithm just send the message to the parent and it passes on to its parent until the message is received at the base station, and base station itself assigns the timestamps based the event on first come first serve basis. The application is tested by displaying the path information received and ordered at the base station.

International Telemetering Conference Proceedings / October 22-25, 1984 / Riviera Hotel, Las Vegas, Nevada / For maritime applications of distress communications via geostationary satellites a special method of signal processing was developed, called superposition technique. The data frame containing the alert message is transmitted from a distress equipment repeatedly. After being relayed by the satellite the signal is detected and improved by superimposing the frames. Around 14 dB is the actual processing gain. Thus a distress buoy is able to transfer a message from all over the world with high reliability by only transmitting a power of 50 mW omnidirectionally over a slant range of about 40 000 km. The described system, called the Distress Radio Call System (DRCS) was tested in a Coordinated Trials Program (CTP) of 6 nations. Both in a simulation phase and in a field test under exactly the same environmental conditions the DRCS with its superposition technique was able to detect signals with lowest signal-to-noise-density ratio without error. In laboratory tests using GAUSS channel conditions, a system threshold of 13 dB-Hz could be demonstrated. In a real environment (North Cape) 15 dB-Hz was the lower limit for error-free reception. CCIR approved a recommendation in June, 1984 for a system operating through geostationary satellites at 1.6 GHz being a DRCS-type with very little modifications.

Recently developed next-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, thus enabling routine sequencing tasks and taking us one step closer to personalized medicine. To provide a blueprint of a target genome, next-generation sequencing systems typically employ the so called shotgun sequencing strategy and oversample the genome with a library of relatively short overlapping reads. The order of nucleotides in the short reads is determined by processing acquired noisy signals generated by the sequencing platforms, and the overlaps between the reads are exploited to assemble the target long genome. Next-generation sequencing utilizes massively parallel array-based technology to speed up the sequencing and reduce the cost. However, accuracy and lengths of the short reads are yet to surpass those provided by the conventional slower and costlier Sanger sequencing method. In this thesis, we first focus on Illumina's sequencing-by-synthesis platform which relies on reversible terminator chemistry and describe the acquired signal by a Hidden Markov Model. Relying on this model and sequential Monte Carlo methods, we develop a parameter estimation and base calling scheme called ParticleCall. ParticleCall is tested on an experimental data set obtained by sequencing phiX174 bacteriophage using Illumina's Genome Analyzer II. The results show that ParticleCall scheme is significantly more computationally efficient than the best performing unsupervised base calling method currently available, while achieving the same accuracy. Having addressed the problem of base calling of short reads, we turn our attention to genome assembly. Assembly of a genome from acquired short reads is a computationally daunting task even in the scenario where a reference genome exists. Errors and gaps in the reference, and perfect repeat regions in the target, further render the assembly challenging and cause inaccuracies. We formulate reference-guided assembly as the inference problem on a bipartite graph and solve it using a message-passing algorithm. The proposed algorithm can be interpreted as the classical belief propagation scheme under a certain prior. Unlike existing state-of-the-art methods, the proposed algorithm combines the information provided by the reads without needing to know reliability of the short reads (so-called quality scores). Relation of the message-passing algorithm to a provably convergent power iteration scheme is discussed. Results on both simulated and experimental data demonstrate that the proposed message-passing algorithm outperforms commonly used state-of-the-art tools, and it nearly achieves the performance of a genie-aided maximum a posteriori (MAP) scheme. We then consider the reference-free genome assembly problem, i.e., the de novo assembly. Various methods for de novo assembly have been proposed in literature, all of whom are very sensitive to errors in short reads. We develop a novel error-correction method that enables performance improvements of de novo assembly. The new method relies on a suffix array structure built on the short reads data. It incorporates a hypothesis testing procedure utilizing the sum of quality information as the test statistic to improve the accuracy of overlap detection. Finally, we consider an inference problem in gene regulatory networks. Gene regulatory networks are highly complex dynamical systems comprising biomolecular components which interact with each other and through those interactions determine gene expression levels, i.e., determine the rate of gene transcription. In this thesis, a particle filter with Markov Chain Monte Carlo move step is employed for the estimation of reaction rate constants in gene regulatory networks modeled by chemical Langevin equations. Simulation studies demonstrate that the proposed technique outperforms previously considered methods while being computationally more efficient. Dynamic behavior of gene regulatory networks averaged over a large number of cells can be modeled by ordinary differential equations. For this scenario, we compute an approximation to the Cramer-Rao lower bound on the mean-square error of estimating reaction rates and demonstrate that, when the number of unknown parameters is small, the proposed particle filter can be nearly optimal. In summary, this thesis presents a set of Bayesian inference methods for base-calling and sequence assembly in next-generation DNA sequencing. Experimental studies shows the advantage of proposed algorithms over traditional methods. / text

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