Abstract

Biometrics has a long history, and is possibly as old as the human race itself. It is often used as an advanced security measure to safeguard important artifacts, buildings, information, etc. Biometrics is increasingly being used for secure authentication of individuals and is making its presence felt in our lives. With the fast increase in computational power, such systems can now be deployed on a very large scale. However, efficiency in large scale biometric matching is still a concern as the problem of deduplication (removal of duplicates) of a biometric database with N entries is O(N2), which can be extremely challenging for large databases. To make this problem tractable, many indexing methods have been proposed that would speed up the comparison process. However, the expectation of accuracy in such systems combined with the nature of biometric data makes the problem a very challenging one. Biometric identification often involves explicit comparison of a probe template against each template stored in a database. An effective approach to speed up the process is that of filtering, where a lightweight comparison is used to reduce the database to smaller set of candidates for explicit comparison. However, most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this work, we show that a cascade of simple linear projections on random lines can achieve significant levels of filtering. Each stage of filtering consists of projecting the probe onto a specific line and removal of database samples outside a window around the probe. The approach provides a way of automatic generation of filters and avoids the need of developing specific features for different biometric traits. The method also provides us with a variety of parameters such as the projection lines, the number and order of projections, and the window sizes to customize the filtering process to a specific application. The experiments are performed on the fingerprints, palmprints and iris. For both iris and palmprint datasets, the representation that we use (before projection) is the popularly used thresholded filter response from pre-defined regions of the image. Experimental results show that using an ensemble of projections reduce the search space by 60% without increasing the false negative identification rate in palmprint. However for stronger biometrics such as iris, the approach does not yield similar results. We further explore this problem to find a solution, specifically for the case of fingerprints. The fundamental approach here is to explore the effectiveness of weak features in a cascade for filtering fingerprint databases. We start with a set of potential indexing features computed from minutiae triplets and minutiae quadruplets. We show that by using a set of random lines and the proposed fitness function, one can achieve better results that optimized projection methods such as PCA or LDA. Exvi vii perimental results on fingerprint datasets show that using an ensemble of projections we can reduce the penetration to 26% at a hit rate of 99%. As each stage of the cascade is extremely fast, and filtering is progressive along the cascade, one can terminate the cascade at any point to achieve the desired performance. One can also combine this method with other indexing methods to improve the overall accuracy and speed. We present detailed experimental results on various aspects of the process on the FVC 2002 dataset. The proposed approach is scalable to large datasets due to the use of random linear projections and direcly lends to pipelined processing. The method also allows the use of multiple existing features without affecting the computation time.

Abstract

The aim of voice conversion is to transform an utterance spoken by an arbitrary (source) speaker to that of a specific (target) speaker. Text-to-speech (TTS), speech-to-speech translation, mimicry generation and human-machine interaction systems are among the numerous applications which can be greatly benefited by having a voice conversion module. Generally voice conversion systems require parallel data between source and target speakers. Parallel data is a set of utterances recorded by both source and target speakers. By having such data, one can build a mapping function at frame level to transform characteristics of source speaker to a specified target speaker using machine learning techniques (GMMs, ANNs, etc,.). These techniques are assumed to perform well as humans typically perceive transformed speech to sound more like the target speaker than the source speaker. But having parallel data is not always feasible, especially in cross-lingual voice conversion where the language of source and target speakers is dierent. In literature, voice conversion techniques have been proposed which do not require parallel data. But they require speech data apriori from source speaker. These techniques cannot be applied when an arbitrary source speaker wants to transform his/her voice to a target speaker without any apriori recording. In this dissertation, we propose a method to perform voice conversion without the need of training data from the source speaker. It alleviates the need for any speech data from source speaker apriori, and can be used for cross-lingual voice conversion system. In this method, we capture speaker-specific characteristics of target speaker. The problem of capturing speaker-specific characteristics can be viewed as modelling a noisy-channel model. The idea behind modelling a noisy-channel is as follows. Suppose, C is a canonical form of a speech signal (a generic and speaker-independent representation of the message in speech signal) passes through the speech production system of a target speaker to produce a surface form S . This surface form S carries the message as well as the identity of the speaker. One can interpret S as the output of a noisy-channel, for the input C. Here, the noisy-channel is the speech production system of the target speaker. We used an artificial neural network (ANN) to model the speech production system of a target speaker, which captures the essential speaker-characteristics of the target speaker. The choice of representation of C and S of a speech signal plays an important role in this method. We used articulatory features (AFs), which represents the characteristics of speech production process, as canonical form or speaker-independent representation of speech signal as they assumed to be speaker independent. But our analysis showed that AFs contain significant amount of speaker information in their trajectories. Thus, we propose suitable techniques to normalize the speaker-specific information in AF trajectories and the resultant AFs are used for voice conversion. We show that the proposed method could be used to alleviate the need for source speaker data, and in cross-lingual voice conversion. Subjective and objective evaluations reveal that the quality of the transformed speech using the proposed approach is intelligible and possess the characteristics of the target speaker. A set of transformed utterances corresponding to results discussed in this work are available for listening at http://researchweb.iiit.ac.in/~bajibabu.b/vc_evaluation.html

Abstract

In utility computing, users access services which are delivered in a manner similar to metered traditional utilities such as water, gas and electricity. This model is advantageous as it does not involve the initial cost to acquire computing resources because computation, storage, network and other services are available as metered services which can be provisioned as per the customers’ demands. These services can be broadly classified as: Infrastructure-as-a-Service (IaaS), Platformasa-Service (PaaS) and Software-as-a-Service (SaaS). We are thus approaching a model where everything shall be offered as a service - XaaS. These modern data- centers and clouds are distinguished by a utility pricing model where customers are charged based on their utilization of computational resources, storage and transfer of data. With the recent emergence of cloud computing based services on the Internet, MapReduce has emerged as the paradigm of choice for developing large scale data intensive applications which are distributed in nature. MapReduce works best for embarrassingly parallel workloads, in which little or no effort is required to separate the problem into a number of parallel tasks which can run independently on a cluster of machines. This is often the case when a bigger problem can be divided in a number of smaller problems that can run in parallel and there exists no dependency between those parallel tasks. MapReduce is used by more than a 100 organizations worldwide to perform tasks such as web crawling and indexing, social media monitoring, scientific data processing, data mining and machine learning. The Apache Hadoop framework is the leading open source implementation of the MapReduce model. It provides a distributed file system called HDFS or Hadoop Distributed File System that facilitates high throughput access to application data. The data stored on HDFS is divided into smaller chunks of configurable size anddistributed across the cluster. HDFS creates multiple replicas of data to ensure data availability at all times. Hadoop follows a rack aware data placement policy and ensures data availability under situations ranging from a single node failure to a complete rack disconnect. Hadoop is designed to work on cheap commodity hardware, is scalable and resilient against machine failures and works on clusters comprising a single node to thousands of nodes. It is also adapted by a number of educational institutes for performing research where the budgets are even tighter. The cost to support such an infrastructure is an important factor for consideration while setting up the cluster. Power consumption of datacenters has become a key factor contributing to the costs incurred by a service provider. This power related cost includes investment, operating expenses, cooling costs and environmental impacts. Also given the scale at which these applications are deployed, minimizing power consumption of these clusters can significantly cut down operational costs and reduce their carbon footprint - thereby increasing the utility from a provider’s point of view. For High Performance Computing systems, where the main focus is on improving the performance at any cost, these energy related costs have increased significantly to a point where they are able to surpass the actual hardware acquisition costs. The first problem that we address in this thesis is: Energy conservation for clusters of nodes that run MapReduce jobs. This problem becomes more important as there is no separate power controller in MapReduce frameworks such as Hadoop. We attempt to reduce the energy consumption of datacenters that run MapReduce jobs by reconfiguring the cluster. We propose an algorithm that dynamically re- configures the cluster based on the current workload and turns cluster nodes on or off when the average cluster utilization rises above or falls below administrator specified thresholds, respectively. Our implementation creates a channel between the cluster’s power controller module and the underlying distributed file system to dynamically scale the number of nodes and adapt to the current service demands on the cluster. We evaluate our algorithm on a variety of workloads and our results show that the proposed algorithm achieves substantial energy conservation, as compared to the default HDFS implementation. In our model, the amount of energy saved is proportional to the number of deactivated nodes. We implement the defaultrack aware replica placement policy followed by HDFS and incorporate the cluster reconfiguration decisions suggested by our algorithm to dynamically scale the number of nodes in the cluster. We demonstrate the scale up and scale down operations of our algorithm and their corresponding energy savings and observe that the cluster intelligently reconfigures itself based on the workload imposed, proving the effectiveness of our algorithm. As expected, the energy conserved in case of low workload

Abstract

A large portion of the data that resides in Enterprises, exists in the form of unstructured textual data. Unstructured data implies collection of natural language text in the form of word documents, HTML pages, plain text files, among others. Structured data on the other hand refers to relational databases, XML documents, etc; where there is a well articulated scheme to represent and store the data. Unstructured data is easy to produce and comprehend by humans, where as it is not trivial for the machines and electronic applications to extract information from it. As unstructured data lacks structure and patterns therefore, in order to target its specific portions for retrieving particular information, full text search has to be carried out. In between Internet and personal machines, we have the Intranets and data archives of various Enterprise establishments, which have their own set of guidelines for search. A typical enterprise search system would be benefited with the thoroughness of desktop search but would not want to suffer from the inherent slowness of typical on the fly, linear scans used in desktop search. An Internet search approach on the other hand may be too generic to produce precise results even though it would be advantageous for speedy indexing and retrieval. The difference and the uniqueness in the composition of enterprise data makes Enterprise search an entirely different beast. This thesis concentrates on enhancing the relevance of results in query based document retrieval in Enterprise Search (ES). Approaches to implement effective ES are different from standard document retrieval and web search. A number of such past and on-going works in ES research area are discussed in this thesis. Our approaches to ES work with two important open research areas in ES, namely, user context aware ES and resolving the issues of vocabulary bias and narrowness in ES. In user context aware ES, we concentrate on user context aware computerized work environment, to facilitate better understanding of user’s information requirements in ES systems. Issues of vocabulary bias and narrowness arise when there is a difference between vocabulary used for queries and that used in the content (though meaning could be the same). In any form of document retrieval, the result-set is strongly query dependent. As a query represents the information need of a user, a misrepresenting or an ambiguously formed query dilutes the result quality. We try to reduce the difference between the vocabulary used in constructing the query and that used in enterprise content for resolving this issue. We enhance ES by concentrating at two basic aspects of information retrieval, which are query expansion and re-ranking. User and work context aware ES is implemented by through a user role based query expansion using local & global analysis techniques and re-ranking result-set by a role based document classification. External lexicon and thesaurus repositories constructed from open source on-line encyclopedias such as Wikipedia are used as complementary knowledge bases for implementing the query expansion for countering the vocabulary bias issues in the enterprise. This is accomplished by extracting and using a Wikipedia concept thesaurus from the article link structure of Wikipeida for query expansion. The results are re-ranked step using large manually tagged sets rich in categories from all domains of Wikipedia. These tagged sets are used to train a classifies which classifies search results into various enterprise document classes. Documents belonging to the dominant classes in the result-sets are given higher preference in re-ranking. We also introduce broader vocabulary range into result-set with collection enrichment techniques usingWikipedia article text for pseudo relevance feedback. Both supervised and unsupervised classification techniques are used to determine good feedback documents from the pseudo-relevant set. We evaluate our approahes on two datasets, namely the IIIT-H corpus and the CERC dataset. Role based personalization in ES required a dataset with role based topics and relevance judgments. As no such dataset existed, we customized the IIIT-H intranet data for the same. Rest of the techniques were evaluated on standard ES evaluation platform, i.e. the CERC dataset provided by the TREC Enterprise track. Role based personalization shows improvements for both local and global analysis based query expansion technique as well as for the re- ranking compared to plain test retrieval. Wikipedia concept thesaurus based query expansion reveals gains in recall figures, without diluting the precision in search results. Wikipedia category network based re-ranking method shows moderate improvements in the precision figures for the results. Improvements are also observed in Wikipedia article text based pseudo relevance feedback in comparison to blind relevance feedback without enriching the pseudo relevant

Abstract

The problem of Secure Message Transmission (SMT) has been widely studied in the field of dis- tributed computing. The interplay between network connectivity and possibility/feasibility of SMT has been one of the most interesting topics in this area and has been studied by researchers for over three decades. The two main questions that are addressed in these studies are: Given a network which is not fully connected and contains some faulty nodes, (i) Is SMT possible in the network? and (ii) Is effi- cient SMT possible in the network? These questions have been answered for various network and fault models and for several variants of SMT. In our work, we study the case where the network is directed and the fault is active or Byzantine. In this model, we answer the first question for two variants of SMT, viz., (1, δ)-SMT and (ǫ, δ)-SMT. It has been a popular practice to model the network as a set of channels or wires between the sender node S and the receiver node R. Such a model treats all the intermediate nodes as simple message- forwarding routers and assumes that no local computations are required of them to enable SMT from S to R. But recent results have shown that, unlike in the case of undirected networks, such a modelling in directed networks can completely ignore nodes that play a crucial role in determining the possibility of SMT from S to R. Moreover, to actually affect the possibility of SMT in the network, these nodes must be capable of doing local computations. In other words, there exist directed networks in which SMT from a node S to a node R is possible only if the intermediate nodes (or routers) are given the power to compute locally. In this thesis, we focus on networks of this kind, i.e. directed networks with computational routers and find the minimal connectivity required in the network for the possibility of (1, δ)-SMT and (ǫ, δ)-SMT.

Abstract

The constant development of medical imaging technology and the dependence of medical professionals on medical images as an auxiliary data continues to increase the demand for more advanced and practical solutions that can be used to analyse the information in these images. Due to the wide variety of information present, its subtle nature, and the need for highest accuracy, analysis of medical images provide an excellent test case to many image analysis algorithms. Computer aided diagnostic (CAD) systems are developed considering these factors. Often, CAD systems comprise of multiple algorithms concatenated in a pipeline to perform different operations. The final output is aimed to be interpretable by a medical professional. In this thesis, three image processing techniques - i) enhancement, ii) segmentation and iii) shape analysis are studied. A CAD system is built using these three operations forming different blocks of pipeline. The intent of this system is to aid in diagnosis of a specific variant of dementia known as Alzheimer’s disease (AD) by detecting the stage of an object of interest, a sub-cortical structure called hippocampus. AD is a neurodegenerative disease with crippling effect on human mind and yet, there is no known cure to this disease. Hence, the detection of its early onset proves to be helpful to monitor and regulate its progress. Quantitative analysis performed on segmented volume of hippocampus has been used to classify it into one of the three stages of diagnosis - normal, mildly affected by the disease and severely affected by the disease. In order to study the structural changes in sub-hippocampal regions with respect to progression of the disease, the piecewise quantitative information has been used. Segmentation of hippocampus is performed by a physics-based model that operates on local and global image forces to evolve a surface. A coarse segmentation method is used to obtain an estimate of the surface to be used in evolution. Since the segmentation method used here is dependent on intensity-based features, image contrast enhancement is performed by an adaptive intensity windowing algorithm which forms the preprocessing stage to segmentation. Thus, each block operates on the output of the previous block, and the output of the final block is useful information to diagnosis. The evaluation of intensity windowing is done by comparing the quantitative measures of accuracy of segmentation algorithm on non-windowed and windowed images from one of the publicly available datasets. The evaluation of segmentation is done by conducting experiments on images from two publicly available datasets and by comparing its accuracy with those from other recent methods on the same datasets. Also, to demonstrate its general applicability, three other structures have been segmented using this segmentation method. Images and their corresponding diagnostic information, belonging to three different stages, have been used from another publicly available dataset for quantitative analysis of hippocampus. The segmentation is performed by the proposed method and its output is used for classification into different stages. Encouraging results have been observed at each of these stages. That is, the proposed method does not perform exceedingly better than some methods, but does so than some others. With further investigation to address the pitfall of the proposed method, one could expect to achieve results to match the state of the art, retaining the advantages of this method. The proposed segmentation method has been tested on 43 volumes drawn from two publicly available datasets and by measuring Dice coefficient with reference to hand segmented ground truth, hippocampus was segmented with an average accuracy of 77%, amygdala with an average accuracy of 68%, caudate with an average accuracy of 82% and putamen with an average accuracy of 80%. The classification scheme has been assessed on 150 volumes from the ADNI database and the normal hippocampus was differentiated from the abnormal cases with an overall accuracy of roughly 80%.

Abstract

The past decade has seen tremendous advancements in consumer electronics and web technology. The proliferation of digital cameras and popularity of content sharing sites have caused a rapid growth in creation and distribution of images and videos by home-users. Enhancement and manipulation of captured images is fairly popular among common users due to availability of numerous easy-to-use photo editing utilities like Instagram, Picasa, Photo Gallery, etc. In comparison, video manipulation is still less popular among common users due to the lack of easy-to-use yet powerful video editing platforms. Basic video editing platforms for home-users are simple and intuitive, but these tools provide limited functionality such as split and merge videos, add captions or audio etc. Professional video editing platforms are rich in functionality, but these tools demand high technical expertise for use. A novice user usually gets discouraged by complex interactions and cumbersome processing. Moreover, the traditional video editing interfaces model and represent videos as a collection of frames against a timeline. In a user’s perception, a video has more meaningful semantics such as objects, actions, events, interactions, etc. The gap between perception and representation makes object-centric manipulation of videos an unnatural and laborious task. In this thesis, we attempt to bridge the gap between the power and usability of video manipulation interfaces by using computer vision techniques. We propose a representation based on three high-level video semantics, scene mosaic, object motion, and camera motion to enable simple and meaningful interaction for object-centric navigation and manipulation of long shot videos. We build an extended field of view mosaic of the video scene and represent object motion in this scene mosaic using 3D space-time trajectories. We define novel object and camera manipulation operations using object trajectories as basic interaction elements. The use of object trajectories as basic video semantics replaces complex interface elements by interactive curve manipulation operations. The object operations allow the users to perform various temporal manipulations on the video objects by interactively manipulating the object trajectories. For example, users can delay or advance the video objects by dragging the trajectories along the timeline or replicate objects by creating multiple copies of the object trajectories. The camera operations model the camera as a movable and scalable aperture and allow the users to simulate camera pan, tilt, and zoom by creating new aperture trajectories. Object and camera operations, in combination allow users to perform a number of high-level video manipulations in a simple ‘click and drag’ fashion.

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.