Abstract

The Business enterprises today are rapidly seeking ways to move towards automation of their business processes. With the fierce competition in the market, the enterprises are constantly looking to evaluate their business processes and reduce the operational costs. With the inclusion of information technology in all spheres of life now, much of this work is (semi) automated and is done through work-flows. Workflows are a collection of tasks organized to fulfill a business process goal. Each of these tasks are performed by a human or some computer application. The software systems that help define/specify the workflows and execute them are known as workflow management systems. Current workflow management systems need to be rewritten/extended to support complex business processes. With technology getting advanced and business processes getting complex by the day, it is likely that business enterprises will desire to change the specification and execution procedures for user workflows to ensure most tech- nological advanced and improved methods of specifying and executing their workflows. But, most of the workflow management systems specify or execute workflows according to a fixed set of rules coded in the workflow engine and do not provide for dynamic change in workflow specification and execution procedures.

Abstract

In this era of interconnected digital information society, increased risk of private data theft has led to the deployment of enhanced levels of security in the form of Biometric Authentication. Fingerprint based authentication is a reliable, well-established, trusted and continually employed trait of biometrics. In the recent past, Automated Fingerprint Identification Systems (AFIS) have been deployed in numerous commercial and civil applications for stronger authentication. In majority of these applications, the fingerprint capture mode is unsupervised and hence, the conditions in which the fingerprint is captured can deviate from ideal conditions. The quality of the digital version of fingerprint obtained is low with increased frequency. Various kinds of fingerprint noises are induced due to improper pressure, bad skin conditions, sensor noise and instantaneous capture of fingerprint scanner during fingerprint acquisition. Objective of a Fingerprint Enhancement algorithm is to restore the degraded, vandalized or smudged regions of fingerprint from the context and mask the regions where the contextual information does not serve as a reliable prior. Traditional enhancement is based on smoothening the ridges of the fingerprint image in their corresponding local orientation. Various models fail to incorporate the global smooth ridge and valley structure in the fingerprints. In this thesis, we propose a novel and robust approach that considers the fingerprint enhancement as a global fingerprint model restoration problem and model the Hierarchical Markov random fields to attribute to the ridge continuity in the coarser level and local orientation smoothness at a finer level. We attempt to deal with the problems faced by the traditional enhancement models during the noise elimination in the fingerprints. Issues we try to address include removing the artifacts and spurious structures introduced by the traditional filters in the enhancement stage of noisy fingerprints due to their basic assumption of fingerprint signal as a sinusoidal pattern and recovering the regions that contain sufficient neighbour information or finer level information to provide more information from the noisy fingerprints through our inference schema. We train our MRF with real world fingerprint patterns. The global ridge continuity modeling from local neighbourhood ensures that spurious structures in noisy fingerprints are suppressed to provide more reliable and genuine information which is alarmingly scarce in noisy fingerprints. Further, we propose a hybrid fingerprint enhancement algorithm that utilizes the parallel computation power of GPU. We divide the fingerprint into blocks and label them from good quality to very noisy regions and attempt to recover the noisy regions from the information provided by the good quality regions and the MRF inference from the neighbourhood regions. Finer level orientation information is vi vii used to index the candidate patterns for a region and belief propagation framework is used to infer the fingerprint structure. This model runs at higher speeds and switches from using the local parameters to global parameters wherever required. We perform our experiments on the highly noisy fingerprints obtained from the FVC databases and also a software called SFINGE which generates artificial fingerprints with controlled parameters. We adjust various parameters of noise to model highly noisy fingerprints. Parameters such as sensor noise, scratches, creases, noise due to improper contact etc. are varied from medium to high to generate various models of noisy fingerprints in various classes. Algorithms that restore the fingerprints at high noise levels are required to facilitate the robustness of an AFIS since noisy fingerprints contribute to more than 10% of the database. In this thesis, we try to address this issue and also try to reduce the gap between manual and automated fingerprint recognition in noisy images. The experimental results for various public and synthetic databases prove the efficiency of our approach.

Abstract

The semiconductor industry has witnessed vast improvements in the Integrated Circuits (IC) manufacturing process. Using modern fabrication process a single IC can accommodate billions of transistors in the same area on which there used to be few thousands of transistors not too far ago. However, as is the case with every other area of science, with the sophistication of the technology there is increased complexity involved in the manufacturing process of an IC. The modern IC manufacturing process contains hundreds of steps and there are bound to have errors in several if not all of the steps involved. These defects in manufacturing process translate into variations in the device parameters which in turn affect the performance of the circuits. This thesis addresses the issue of process variations and its effects on one such circuit namely Static Random Access Memory (SRAM). SRAMs are used extensively in cache memories in personal computers, multimedia computers, networking applications etc. SRAMs are known to be vulnerable against process variations and with the continuous scaling of the device sizes, the percentage of cells that do not have acceptable read and write characteristics is increasing. Therefore it is felt that there is a need to have an idea of the cells which do not meet the required characteristics because of process variations. In view of this, investigations have been carried out in this thesis on the read time and write time of SRAM cells with process variations. In the first instance, it is thought that the width, length, oxide thickness and threshold voltage of the transistors are the parameters that vary the most from device to device. Two possible models for these variations are assumed – The first one is Global variations i.e. all the device parameters in a cell vary almost by the same percentage and those variations are random from cell to cell. The second model (local variations) assumes that parameters of each device in a cell are affected independent of parameters of other devices. So the variation here is with respect to device to device. The effects of these variations on read and write time of SRAM cell has been evaluated through the simulations of SRAM cell using HSpice tool. The nominal device parameters are the ones corresponding to BSIM parameters of 65nm technology. It has been found that the variations in threshold voltage produces maximum variation in read time and write time of SRAM cells when only one of the parameters is varied keeping the other parameters constant. Nearly 20% of the cells have been found to be outside the range of +/- 10% of the median value. The effect of W, L and Tox does not seem to affect much pronounce if they are considered separately. When all the parameters are assumed to be varying simultaneously, nearly 25% of the cells have been found to be outside the tolerable limit. From the above it may be seen that the number of cells which have largely varying read time and write time due to process variations is quite high. Therefore, it is thought necessary to think of a simple method of reducing the number of unusable cells. Experiments have been conducted by varying address signal characteristics namely rise time. It has been found that with increase in rise time from 2ps to 0.2ns (200ps), it is found that the number of cells in which the writing and reading could be successfully carried out have gone up. The number of cells in which the writing and/or reading could not be carried out with address signals having 200ps rise time have dramatically reduced to 1.8% and 2% from 18% and 10% respectively. In doing so, however, the speed of the SRAM cell is found to be lower for higher rise time values of the address signal. Hence, for lower rise time, it is shown that the speed is good but the yield is low and for higher rise time, the speed is low but the yield is high. It is therefore thought necessary to have tradeoff between the yield and speed of operation and has been explained in the thesis. Thus the thesis first investigates the effect of process variations on the read ability and the write ability of SRAM. Thereafter it has come out with a simple technique of improving this feature of SRAM cells by modifying the characteristic of the address signal.

Abstract

In recent years, graph theory is being extensively used to study large scale and complex systems from diverse disciplines, viz. physical, biological, computer and social sciences. Various models viz. scale-free, small-world, etc. apart from random graph and regular graphs have been proposed. In this thesis, we present a detailed analysis of topological and structural properties of two air transportation networks, airport network of India (ANI) and world airport network (WAN) using graph theoretic approach. These air transportation networks have been constructed by considering airports as nodes, direct flight routes between them as edges and number of flights on each route as the weights. The heterogeneity in connectivity and long-range couplings observed in these networks suggest that certain nodes may play an important role in maintaining the stability and efficient flow through the network. Identification and analyzing the impact of targeted removal of such “critical” nodes on the network is the major focus of this thesis. This has been carried out by analyzing various graph centrality measures, viz., degree, strength, betweenness and closeness in the context of air-traffic flow. Such an analysis would not only enable us to improve the infrastructure and air connectivity and help in promoting tourism, but also help in identifying crucial airports and routes to regulate traffic in emergencies such as accidental failure of an airport, diverting traffic to avoid congestion and delays during unexpected climatic changes, etc. In the last few decades, we have observed the main cause of the epidemic turning into pandemic of an infectious disease is its transmission over the densely connected air transportation services. Using a simple SIR (Susceptible-Infected-Recovered) compartmental model for disease spread, our analysis of graph centrality measures suggests that by reducing flights on important routes, the spread of the disease can be curtailed. We observe that though these air transport networks exhibit small-world and scale free behaviour, the preferential growth Barabasi-Albert (BA) scale free model fails to explain the growth and certain topological properties of these networks. We carried out a comparative study of various scale-free models with the actual airport networks and propose a model which captures the evolution of these transportation networks.

Abstract

Hierarchical Ring Networks constitute an interesting class of networks, from the point of view of their range of applications and their design considered as a graph theoretic problem. They are widely used in logistics, communications sytems and the design of shared multiprocessors. In logistics, the distribution network comprising of manufacturing centres, warehouses and retail outlets can be modeled as Hierarchical Ring Networks (HRNs). In the context of processor design, shared memory multiprocessors face the problem of scaling due to the fact that the CPU-to-memory connection becomes a bottleneck, with multiple processors vying for the available RAM. The inherent structure of HRNs has been shown in literature to help scale these systems. In communications systems, they can be used to have equitable distribution of load in flat architectures that are prevalent in new generation communication systems. It is imperative that efficient design of these structures has to be carried out, to reduce costs of their installation and help increase their ease of management. In this thesis, we look at methods to design low cost HRNs. This np-hard problem is stated in the context of Euclideancosts of the links and the cost of the capacity to be installed on these links to help put up these networks. Extensive work has been done in literature for the design of 2-level HRNs only. Further, most approaches to a solution deal with the problem in a series of chronological steps, which will end up giving us sub-optimal solutions. We first present methods to design 2-level HRNs. Following this, we solve the generic, k-level HRN design problem. We provide formulations of the problems and present heuristics based on Simulated Annealing and Genetic Algorithm. Our algorithms provide a holistic approach to the problem at hand and we have used them to design networks containing upto 1000 nodes, where previous methods only solved for nearly 120 nodes. Our results show that our algorithms consistently perform better than CPLEX – which we have used for comparison. We then proceed to show one important application of HRNs in 4th generation Radio Access Networks. We try to understand the performance of a topology based on HRNs in the Evolved Universal Terrestrial Radio Access Network or EUTRAN – the radio access network of LTE systems.

Abstract

Domain Name System(DNS) is an inevitable component of the critical infrastructure of the Internet. It is a hierarchical distributed database system which provides a crucial service for the internet i.e. the mapping of human-friendly domain names to their respective machine-friendly IP addresses and vice versa. Almost all internet-based applications including http, ftp and email, need to resolve a given domain name to its respective IP address prior to establishing connections. DNS provides the mapping service which is fundamental not only to the health of the Internet but also to the protection and integrity of the data. In case, mapping of a domain name to an IP address in the system gets corrupted, the system would no longer be acceptable. Being probably the most valuable infrastructure in the Internet, its security is of utmost priority. The domain names in a DNS database are stored in the form a hierarchical tree structure which is known as domain name space. Each node in the tree contains zero or more resource records which hold information associated with a domain name. DNS cache these resource records for a specific time period, i.e., TTL (Time-To-Live). TTL too plays an important role in maintaining the consistency of the cached resource records. Short TTL reduces the likelihood of getting old information but increases the DNS utilization whereas long TTL decreases the DNS utilization but at the same time increases the chance of retrieving outdated information. DNS was not earlier designed to save itself against different kinds of attacks such as cache poisoning, rebinding attacks etc. This is the reason that DNS servers have been manipulated by attackers to launch phishing attacks, to commit click-frauds and to drive traffic to malicious websites. Among all the different kinds of attacks on DNS, DNS cache poisoning is the prominent one. DNS cache poisoning refers to the cases where the cache of a DNS server gets corrupted due to the injection of false mapping in the server which affects the accuracy of DNS lookups. Consequently, when queries arrive at the DNS server, inaccurate and probably malicious replies are sent as the response. False mapping can be injected into a DNS in many ways i.e. by dns spoofing, dns forgery etc. After one poisoned record is injected into the cache, it can spread to other parts of the cache or other servers through query/response between servers. DNS cache poisoning could be used by an attacker to redirect the querier to a non-existent IP address, thus causing Denial-of Service or the querier can be redirected to a malicious website which drops Malware/Spyware or s/he could even be redirected to attacker’s website, causing phishing attack. Till date, many different solutions have been proposed to overcome the problem of cache poisoning but none has been deployed successfully. Certain proposed solutions like DNSSEC, DNSCurve etc, found to be efficient against cache poisoning but they have not been successfully deployed primarily because of the complexity involved in key management. Certain other solutions were neglected because either they required changes in the DNS protocol or they introduced considerable latency in the system making them an undesirable solution. Hence, in spite of all the solutions proposed till date to mitigate cache poisoning attacks, the problem still persists. To mitigate cache poisoning attacks, we have proposed an approach - Domain ConsistencyManagement system (DCMS) which makes the use of response delays for a specific resource-record type between a DNS client and a DNS server. Our approach is similar to stimulus-response model in which a response is expected for a specific stimulus within a specific period of time. If the response is received out of the period then it could be the result of some flaw in the system. Similarly in our approach we expect the response of a DNS query for a specific resource-record type to be received within a certain period of time. In case the DNS response is received out of period we suspect an attack on the system and perform a check to ensure the consistency of the response received. The significant feature of our approach lies in its self-learning that in parallel it updates its database of response delays for a specific resource- record type between specific DNS client and DNS server, thereby helping the system not only in proper functioning, even it enhances its performance. DCMS makes cache poisoning attacks, almost infeasible, even for motivated and powerful attackers. The biggest advantage of our approach is that it does not require any change in the DNS protocol, hence it could be deployed on a large scale within a short period of time. It does not even require any changes at the server side as inWSEC-DNS to ensure the consistency of the responses. Since it does not involve any cryptography technique, hence any sort of key management is not re

Abstract

Biomolecules are the building blocks of life that regulate the biological processes within the organisms. The interplay of just three classes of biomolecules: DNA, RNA and proteins provide complexity to the biological world. Their structures, stabilities and dynamics are keys to understand the working of Nature. Advanced biophysical and biochemical techniques have proven useful to study the structure, dynamics and local flexibilities within the biomolecules. However, most of these sophisticated experimental techniques are complex and time consuming, apart from being expensive. Also, they do not always directly provide an atomistic picture of macromolecular dynamics in situ with sucient time resolution. In current scenario of enhanced computing power, computer simulations have emerged as a useful tool to not only better interpret experimental results; in several ways they complement experimental investigations. We have used simulation methods to probe some contemporary issues related to structural stability and functional dynamics of biomolecules, with focus on nucleic acids and proteins. This thesis describes investigations using simulation techniques as applied to three dierent systems, in order to address three types of issues related to biomolecular dynamics: functional behavior of purine (adenine, guanine) and lysine riboswitches; folding of small mitochondrial subunit binding protein and modeling the structure and possible functional dynamics of a drug target enzyme. The studies address a wide range of contemporary issues related to the compatibility between simulation methods used and scientific questions asked. Thus, wherever possible, the work involves benchmarking of simulation conditions and analysis methods and attempts at reconciliation of available experimental data with the interpretation of trajectory analysis results.

Abstract

Signal processing applications such as ones dealing with image/voice have an inherent capability to tolerate noise. These applications are primarily judged by human perception which can ignore certain inaccuracies in image/voice applications. Probabilistic computing is a form of computing that trades tolerable errors for large amounts of gains in terms of energy, area and speed in the underlying hardware for these applications. With the stringent requirements on energy consumption, size and processing speed through modern day computing devices, there is a need to evaluate what trade-offs can be made with respect to the quality of the application for gains in terms of energy, area and delay of the computing circuit. Motion estimation, computationally the most intensive portion of the video encoder, is a suitable candidate to be considered as an application for probabilistic computing. With a plethora of video applications that video compression caters to, motion estimation is also a widely used application necessitating research on energy, delay and area optimized circuit design for motion estimation. The focus of this research is mainly to optimize energy and processing speed of the motion estimation architecture using probabilistic computing. The term probabilistic computing encompasses various techniques out of which two have been considered in this thesis. These techniques are Probabilistic Complementary Metal Oxide Semiconductor (PCMOS) and probabilistic pruning. PCMOS is a model for CMOS emulating the erroneous nature of future technology nodes. CMOS design is continuously faced by the challenge of achieving low-energy computation, which is addressed by technology scaling accompanied by voltage scaling. Voltage scaling in turn affects the noise immunity of the device, which is predicted to result in erroneous nature of future CMOS technology nodes. The PCMOS model is used to build circuits for motion estimation algorithms. Motion estimation is established to have a high resilience towards PCMOS based computing and energy savings to the tune of 40% to 57% are seen. Furthermore, algorithmic modifications for motion estimation are suggested that further increase the achievable energy savings sizably (13% to 23% increase) at the target degradation allowed. Probabilistic pruning works towards removal of parts of the circuit that contribute least to the quality of the application, thereby, optimizing the circuit gains for tolerable amounts of error. The overall savings for motion estimation through probabilistic pruning are found to be 27% to 65% in energy, 14% to 30% in delay and 29% to 54% in area consumption of the motion estimation circuit. Probabilistic pruning is compared with bit-width reduction of the circuit for motion estimation proposed in past research. Probabilistic pruning is found to increase the processing speed via motion estimation circuit considerably (6% to 18%) for a minor increase in area consumption (3% to 5%) when compared with bit-width reduction at approximately same quality reduction and energy consumption through the motion estimation circuit.

Abstract

The amount of information available on the web is steeply increasing. We need efficient techniques to analyze such large data. Frequent itemset mining play an important role in analyzing such data. Frequent itemset mining, which extracts frequently occurring group of items, has many applications like Association rule mining, Correlations, Dimensionality Reduction, Clustering and Classification. In this thesis, we show how frequent itemsets handle the problems of evolutionary clustering and summarization of scientific articles. Evolutionary clustering is an emerging research area addressing the problem of clustering dynamic data. An evolutionary clustering should take care of two conflicting criteria: preserving the current cluster quality and not deviating too much from the recent history. We propose an algorithm for evolutionary clustering using frequent itemsets. A frequent itemset based approach for evolutionary clustering is natural and it automatically satisfies the two criteria of evolutionary clustering. We provide theoretical as well as experimental proofs to support our claims. We performed experiments on our approach using different datasets and the results show that our approach is comparable to most of the existing algorithms for evolutionary clustering. We also present a novel unsupervised approach to the problem of multi-document summarization of scientific articles, in which the document collection is a list of papers cited together within the same source article, otherwise known as a co-citation. At the heart of the approach is a Frequent Itemset based clustering which clusters fragments extracted from each co-cited article and relevance ranking using a query generated from the context surrounding the co-cited list of papers. This analysis enables the generation of an overview of common themes from the co-cited papers that relate to the context in which the co-citation was found. We present a system called SciSumm that embodies this approach and apply it on a large collection of scientific articles. We evaluate this summarization system for relevant content selection using gold standard summaries prepared on principle based guidelines. Evaluation with gold standard summaries demonstrates that our system performs better in content selection than an existing summarization system (MEAD). We present a detailed summary of our findings and discuss possible directions for future research.

Abstract

3-D medical images such as Computed Tomography(CT), Positron Emission Tomogra- phy(PET) and Magnetic Resonance Imaging(MRI) are commonly used for diagnosis. Tomo- graphic reconstruction is the underlying technique for all these images. It is often of interest to increase the resolution of these images. Some of the possible solutions for increasing the resolution are: increasing the number of detectors, changing the detector material/geometry, increasing the excitation energy level or scanning time. These solutions are limited in terms of practical implementation. Hence, nding solutions at the processing level (post data ac- quisition) is of interest. This thesis focuses on the problem of preserving details and quality of tomographic images after upsampling. We have explored two dierent approaches for generating higher resolution images. These are based on re-examining the lattice geometry for reconstruction. We oer two novel solutions, one which uses a hexagonal instead of the conventional square lattice and a second one based super resolution strategy which combines samples drawn from two lattices. In the rst solution, we reconstruct the image onto a hexagonal lattice. These samples are then interpolated to nd the samples on the desired higher resolution square lattice. In the super resolution (SR)- based solution we reconstruct the images onto 2 low resolution lattices. The lattices are rotated versions of each other which provide a 'dierent view' of an object/scene. For generating low resolution images both square and hexagonal sampling have been considered. The two solutions have been implemented and tested using a variety of test objects. These objects which are called phantoms were chosen to be of 2 types: analytical and real. Three analytically generated phantoms used were Concentric rings, dots and lines. Two real PET phantoms were Nema and Homan Brain phantoms. The results were benchmarked against upsampled images generated by: Direct upsampling and an existing super resolution technique which uses Union of Shifted Lattice (USL) samples. The evaluation results show a qualitative and quantitative improvement over results obtained by direct upsampling. The SR-based results are comparable to that obtained with USL in quality. However, the main strength of the proposed SR-based solution is the computational savings and a reduced number of required images for a given upsampling factor. i