Abstract

Computer aided diagnosis (CAD) using medical images is a vital outcome of application of computers in medicine. Over the years, several methods of CAD have been proposed and implemented for various pathologies and imaging modalities. In theory, CAD systems should achieve complete automation in detecting and diagnosing diseases, whereas in practice these systems aim at assisting the medical expert, for example a Radiologist, to perform effective diagnosis. CAD can also play a significant role in assisting the medical experts in public health enterprises like disease screening. Disease screening is performed by medical experts at specific community healthcare centers to detect diseases early in order to reduce the ensuing mortality and suffering. The two most prevalent applications of disease screening have been detecting retinopathy (retinal diseases) from color fundus images and breast cancer from digital mammograms. Application of CAD in such disease screening initiatives is known to produce high throughput in terms of reduction in effort, time spent and mistakes committed by the medical experts. Since screening is intended for prevention and early detection of diseases, a large number of patients arriving at the point of care turn out to be normal (no disease symptom). This poses a significant challenge for the medical expert using CAD as a large number of false alarms are detected due to the high sensitivity of disease detection in CAD systems. Therefore a significant time of the expert is spent on rejecting the false alarms during diagnosis. The large number of false alarms occurs essentially due to the difficulty in modeling various disease symptoms and normal background across images of normal and abnormal patients. This problem is further compounded due the disease centric approach of CAD where various manifestation of a disease is detected separately from the images resulting in accumulation of false alarms from each detection method. In order to address the challenges of existing CAD systems in disease screening, we propose to perform automated diagnosis in disease screening problems using a two-stage hierarchical approach. In the first stage, presence or absence of a disease is ascertained in the given image. We will refer to this stage as abnormality detection. Once the abnormality of an image is ascertained, further analysis of the disease symptom is performed for assessment of its severity, in the second stage. The abnormality detection task is treated as a normal learning problem, where only a single class corresponding to characteristics of normal medical cases are learnt. A medical case, not conforming to this normal behavior is termed as abnormal. The normal class is learnt using the global characteristics of normal medical images. The proposed approach is showcased on two disease screening problems, detection/assessment of lesions in color fundus images and classification of mammographic lesion as benign or malignant. Evaluation of our method for detection of lesions from color fundus images is performed on four publicly available image datasets. The detection performance has a sensitivity of 100 percent with specificity between 74 percent and 90 percent. Cases needing immediate referral are detected with a sensitivity of 100 percent and specificity of 97 percent. A 10-fold cross validation for lesion detection was also performed to evaluate the average detection performance. The Area Under the receiver operating characteristic Curve (AUC) ranges from 0.89 to 0.99 for various datasets. Assessment of retinal diseases into moderate and severe cases of retinopathy resulted in accuracy of 81 percent for the moderate case and 100 percent for severe cases. Lesion classification in mammograms was performed by treating benign lesions as belonging to the normal class while separating the malignant lesions by identifying them as abnormal. The sensitivity of detecting malignant lesions at 100 percent was achieved with a specificity of 97 percent. The best AUC for the lesion classification task was recorded as 0.98. The success in automated disease detection from color fundus images and mammograms establish the effectiveness of the proposed methodology.

Abstract

Recent technological advancements have led to the proliferation of uncertain data. Uncertainty is inherently present in many real world domains like images. Such databases are more complex because of the additional challenges of representing the probabilistic information. While analyzing such data, it should be handled cautiously. Traditional data-oriented techniques for analyses on uncertain data do not achieve best possible accuracy. There is a growing demand to develop efficient novel methods to handle such kind of data. Existing works on uncertain data mining are largely theoretical, where application areas are suggested but not experimented and are hypothetical. Whereas in this thesis, we present techniques and apply them in a full-fledged real-world application namely image classification and clustering. We utilize the concept of representing images in the form of a normalized model using interest points in images. This model is an uncertain-data-based adaptation of Bag of words (BoW), with each image not only represented by the visual words that it contains, but are also associated with a respective probability of occurrence in the clustered features of the image. We propose two novel algorithms for classification and clustering images which utilizes the normalized bag of words model for image representation. The uncertain associative classifier for object class identification in images (UACI) uses an Associative Classification approach to leverage latent frequent patterns in images for the identification of object classes. Unlike most image classifiers, which rely on positive and negative class sets (generally very vague) for training, UACI uses only positive class images for training. Negative class set is the set of all positive as well as negative class set. So, it is very vague and the choice of negative class set would affect the classifier. We empirically compare UACI with three other image classifiers. In our experimental evaluation, associative classification doesn’t compare well with more recent SVM based techniques like SVM-BoW-Baseline for uncertain image dataset but performed better than the BoW. Also, we propose a clustering algorithm to meaningfully group images into different categories. It uses an uncertain frequent itemset mining approach to extract the latent patterns in the form of frequent itemsets. These patterns eliminate much of the noise and represent the dominant trends which lead to a high degree of accuracy. To analyze the efficiency of this approach, we ran a set of experiments on two different datasets. This algorithm has good efficiency in generating quality clusters.

Abstract

With the daily expansion of computer network and internet applications, the risk of network related crimes are also increasing [1]. Most of the present day intrusion protection systems, security applications and antivirus software aim at detecting, and a few preventing, the damage done to end systems, at the premises where they are installed. In today’s world of rapidly paced networks, we need efficient techniques to identify the source of attacks so as to completely eliminate the malicious systems and the attacks caused by them which can benefit the entire network. Network Forensics includes the field of study which deals with techniques that aims at identifying the source of the attack. The most important part in the field of cybercrime investigation is the need to capture and store data for determining potential legal evidence and at the same time safeguard the privacy of the data plying through the network. Packet attribution systems have been used to perform trace back of the packet in the internet to its source. At the packet header level, determining the source machine from the source IP address is not an effective solution, as IP address can be altered. In order to perform trace back based on packet payload, payload attribution has to be performed at every network device through which packet traverses. This concept of payload attribution was introduced by Shan- mugasundaram et al. [2, 3]. From now throughout the remaining paper let us refer the content of packets to be queried for trace back as “excerpt”. During trace back, the excerpt has to be matched with the stored packet payload at every network device and determine the path taken by the packets. But storing the entire payload at every network device is not a practical solution due to storage and privacy concerns. Various efficient techniques based on bloom filter and its variations have been proposed until now [4]. Source Path Isolation Engine (SPIE) [5] is a payload attribution system which uses space efficient bloom filters for hashing and storing packet payload. SPIE performs repeated hashing of packets, including the invariant portion of the packet header and a part of packet payload and inserts them in Bloom Filters. These compact filters are then stored at the routers. When all routers implement SPIE, it is possible to trace back the packet to the source. The problem with SPIE is to store the packet with its non-mutable headers and a part of the payload and reconstruct the packets during trace back querying [2]. This issue is overcome by the concept of Block Bloom Filters (BBF), where the packet payload is split into blocks of certain size and hashes performed on these blocks are stored in the bloom filters. This technique has to consider all possible offset values and hence the false positive probability (FPP) increases [2]. The value of FPP can be reduced by performing attribution of hierarchies of blocks, a concept named as Hierarchical Bloom Filters (HBF) [2]. But the practical implementation of these concepts on all the routers installed in different networks and network levels increases burden on these routers in terms of processing and storage size. In this thesis, we propose a flexible environment for the implementation of these concepts and increase its practicality in the present day network devices. We put forward our analysis on certain parameters over which system administrators can analyze and take final decision on implementing these techniques on the network devices. To trace back the packet from the destination to the source, all routers in the path of the network have to implement these payload attribution mechanisms. Imposing the same implementation rules and constraints on all these routers is not practical owing to their difference in their capacity of processing and storage capabilities. Depending on the capabilities of the network device, parameters like block size, false positive probability can be adjusted to suite the situation. We have prototyped these con- cepts as software and provided experimental results for processing and storage requirements, by varying these parameters. In the later section of the thesis, we have proposed the use of attribution techniques with only the control packets that ply on the network. This information can be used to trace back vital network control information among routers like network access, session control, trafficking, etc. Our experimental results show that this approach is extremely efficient in terms of storage size and processing speeds. We then discuss our analysis on possible heterogeneous approaches which combine the idea of attributing control packets and data packets individually at every router with variable parameters to configure the bloom filter used for forensic analysis.

Abstract

The word Spoong has been defined differently by different people but the one that is used in this context is as follows: “Spoofing is a scenario in which a person or a program successfully impersonates another identity by falsifying the data and thereby gaining illegitimate advantage.” As the definition goes, spoofing is generally used by the malicious users to trick the victims into making inappropriate actions. These actions may or may not be profitable for the attackers but will surely affect the victims. The spoofing attacks are possible when the attackers are able to exploit the trust relationship between the entities. Hence, spoofing attacks are easy to implement in the scenarios where the authentication procedure between the communicating entities is weak. Though spoofing is not an attack by itself, it is an important step in many of the other dangerous attacks. Due to its effectiveness and ease of implementation, the usage of spoofing has been increasing. In the context of online world, many forms of spoofing have been observed. For an effective utilization of the available resources and in a secure fashion, the spoofing attacks need to be checked in the network. The present day Internet can be viewed as a huge network in which the users request services from the servers and servers provide them. Hence, the users and the servers are considered as the important actors of the Internet. Studies show that both these actors are susceptible to spoofing attacks. So, in this thesis we aim to focus on the following two most important spoofing techniques which are affecting these actors of the Internet: • IP Spoofing, which plays a major role in Denial of Service (DoS) attacks to bring down the servers and • Profile Spoofing in social networks, which enables privacy evasion of the social networking users. In order to mitigate IP spoofing attacks on the server, we have proposed a Hop-Count (HC) based Defensive Mechanism against IP Spoofing (DMIPS) scheme which can filter the spoofed packets effectively and also improves the overall network performance. The Quality of Service (QoS) also has been improved by reducing the number of false positives. At the user side, we focus on the online social networks where numerous attempts are being made by various people to extract private information from the users. This is done by deceiving the users by profile spoofing and then getting more information than they are allowed to get. To mitigate this attack, we have designed a challenge-response based scheme which assists the users in identifying their friends and differentiating them correctly which enables easy and efficient privacy management.

Abstract

Recent studies in classification have proposed ways of exploiting the association rule mining paradigm. These studies have performed extensive experiments to show their techniques to be both efficient and accurate. In this thesis, we propose Perturbed Frequent Itemset based Classification Techniques (PERFICT), a novel associative classification approach based on perturbed frequent itemsets. Most of the existing associative classifiers work well on transactional data where each record contains a set of boolean items. They are not very effective in general for relational data that typically contains real valued attributes. In PERFICT, we handle real valued attributes by treating items as (attribute,value) pairs, where the value is not the original one, but is perturbed by a small amount and is a range based value. We propose a pre-processing step based on perturbation as an alternative to the standard discretization step to convert real valued attributes into ranges. The PERFICT approaches are built on the Apriori frame-work for frequent itemsets generation. We also propose our own similarity measure which captures the nature of real valued attributes and provide effective weights to the frequent itemsets. This MJ similarity measure inherently prunes away the unnecessary frequent itemsets. The probabilistic contributions of different frequent itemsets is taken into considerations during classification. Some of the applications where such a technique is useful are in signal classification, medical diagnosis and handwriting recognition. Experiments conducted on the UCI Repository datasets show that variants of PERFICT are highly competitive in terms of accuracy in comparison with popular associative classification methods, decision trees and rule based classifiers. We developed PERICASA, PERturbed frequent Itemset based classification for Computational Auditory Scene Analysis(CASA), as an application to HistSimilar PERFICT. It provides a novel architecture for perception of sound waveforms. The purpose of this model is to develop a classifier which can correctly identify audio waveforms from noisy sound mixtures i.e. to solve the classical ‘Cocktail Party Problem’. The architecture is based on Gestalt principles of grouping like Pragnanz, Proximity, Common Fate and Similarity. The primary idea is that more the ease with which we can identify different associated feature values, easier it is to identify the sound waveform.

Abstract

General purpose computing on graphics processor units (GPGPU) has attained widespread acceptance in the high-performance computing community. This has largely been at- tributed to the rise of programming models and large peak performance to cost ratio of the GPU. The peak throughput of modern GPUs are typically 5 TFLOPS at a cost of 600 US $. These upper limits are found only for applications that have a regular and low frequency memory access. Many algorithms which have a computational model orthogo- nal to the GPU model, especially in the domain of graph theory, have been traditionally not known to be well suited for the GPU model. Graph theoretical algorithms are considered as one of the fundamental building blocks for Computer Science in general. They are considered impractical on many of the current high performance platforms because of the large dependence on irregular data movement, which in the GPU context is a major performance bottleneck. In this context, we focus on fundamental primitives with applications for graph theory, with a large number of immediate applications Typical primitives related to graph theory, can be divided into  Graph primitives: Graph primitives include traversal, connectivity algorithms, path based algorithms etc.. In this thesis, we have dealt with connectivity algorithms such as connected components, spanning tree and spanning forest.  Generic Primitives: Graph theory applications rely not just on domain specic primitives, but primitives from other domains play a signicant role especially in large graph operations on parallel machines. One such primitive is the Range Query Primitive (esp Minima and Maxima). We present a GPU specic implementation of range queries and use it to answer queries such as LCA, minimum/maximum element in a subtree etc.. All the primitives rely on an array based representation, and focus on reducing the dependence on the hierarchical structure of the graph. Problems such as minimum key in a subtree are converted into equivalent problems (Range Query) that have a solution more suited to the GPU model. Also, solutions presented here try to reduce data movement by exploiting data level as well as functional data movement redundancies. Thus the focus is on handling data movement judiciously. We show that the results we have obtained on data sizes tting the GPU are faster than results on any hardware currently present in literature.

Abstract

The exponential growth of the World-Wide Web has transformed it into an ecology of knowledge in which highly diverse information is linked in an extremely complex and arbitrary manner. At the same time, huge multilingual content is being added to it through various sources like blogs, news articles and several other forms. To facilitate the access of this enormous multilingual content, Cross Lingual Information Access systems (CLIA) are vital. CLIA systems take users’ information need and returns the required information to them. The information can be obtained from various sources and different languages, reducing the effort of user to search in different information sources. Query translation is the most important task in building an effective CLIA system as the accuracy of the results are dependent on the accuracy of the query translation. The user input, usually few words, is considered as the query. Given the differences in vocabulary across the languages, we have to capture the context of the query to increase the efficiency of the query translation. Query translation forms the vital component of any CLIA system as it captures the users need and translating queries effectively is required. We devised a language independent approach that captures the users intent from his query by mapping query words to Wikipedia topics. Wikipedia, the free on-line encyclopedia, is a hypertext document collection with a rich link structure. Unlike ordinary encyclopedias,Wikipedia is based on the wiki concept, which enables community to create or improve articles. Wikipedia articles cover many domain-specific and general topics, making it a rich knowledge resource. Due to its unique concept, Wikipedia is populated with articles in around 240 languages, making it a rich multi-lingual resource. We intend to leverage this multi lingual knowledge base as a resource to aid multi lingual query translation. In this work, we propose a query translation method that uses the rich link structure across the languages present in Wikipedia. The query translation approach we have adopted is language independent and focuses on minimizing the use of language specific resources, making our approach scalable. This approach is specifically useful for under-resourced languages (Indian, African etc) as these languages do not have sufficient resources and tools with good accuracies. For these under-resourced languages, a query translation approach is proposed using the resources mined from Wikipedia substituting the language-specific resources for query translation. Using the cross lingual links and the articles that are connected with these links, we build dictionaries that forms the resource for query translation. A language independent approach to identify parallel text is also proposed. A parallel text is a text placed alongside its translation or translations. Parallel text forms the major resource for many areas of linguistic research and their availability helps to identify many retrieval problems between the pair of languages. The query translation approach we have adopted takes the context of the query into consideration to perform the translation. We use Wikipedia concepts to mine the context of the user query and use them to form the translation. An evaluation method to evaluate query translation is also proposed using Wikipedia, by mining statistics from the articles and the cross lingual links. A separate evaluation method is required in-line with our intent to develop the system without using any language resources due to their non-availability. The coverage of the dictionaries is 78.1% while the accuracy of the query translation is 69.75%. In this work, we develop a language-independent approach to query translation that also considers the context of the query. Since no language resources were used, our approach is scalable and can be applied to any pair of languages present in Wikipedia. The performance of the system is encouraging and condone our intention of substituting Wikipedia for language resources. Further the resources mined and approaches proposed can be leveraged in various areas of linguistic research. Parallel sentence extraction approach can be used to solve the problem of cross lingual plagiarism detection. Extraction of context from a sentence can be extended to support Update Summarization task. Thus, in this work, we suggest a language independent query translation approach which can be a right direction towards building a CLIA system for less resourced languages.

Abstract

In view of the stringent requirements of measurement of capacitances associated with capacitive sensors, there is considerable work going on in the area of VLSI design. Typically MEM based capacitive sensors used in gyroscopes produce changes of atto Farads in nominal capacitance values of the order of pico Farads. The requirements of the measuring equipment are very stringent like the noise conditions, the temperature stability and frequency response etc. There are no readily available interface circuits to measure these order of capacitances. In view of this an attempt has been made to analyze the various issues that are associated with measuring the small capacitances. In the first instance the capacitance variations are converted to voltage variations. Even though there are several approaches for carrying out this operation it is found that a switched capacitor approach with DC source voltage is a suitable technique . In the first stage, amplification of the signal is carried out by minimizing the noise, temperature variations and offset variations. Keeping these in view a switched capacitor fully differential folded cascode amplifier with an open loop gain of 120dB has been designed and simulated using HSPICE simulator. The clock frequency chosen for this operation is much higher than the rate at which the capacitance changes. The high linearity of 0.05% in the maximum output of 0.6V could be achieved only through extensive simulations by varying circuit components. Finally it has been possible to arrive at a circuit with correlated double sampling, common mode feedback and optimal values of feedback capacitors. This circuit is designed as a low noise circuit to give the required resolution such that it can measure a small voltage of 10μV corresponding to 10aF. Since the circuit itself contributes to flicker noise the effect of flicker noise is countered by employing the correlated double sampling and designing the amplifier for low noise. The output of the amplifier is given to a sigma delta modulator so that the noise can be tackled and a digital output of high resolution is obtained. The sigma delta modulator is chosen to be a fourth order cascaded modulator which is realized using two second order modulators operating at same clock frequency as the main switched capacitor amplifier. The integrated blocks of switched capacitor amplifier and sigma delta modulator has been found to give a linearity of 0.05% with a maximum output value of 0.6V and minimum output value of 20μV. This thesis mainly concentrated on these two blocks and also suggests that these two blocks can be integrated in to single chip. Further processing of output of sigma delta modulator involves a process called decimation which converts the sigma delta modulator output into accurate digital output.

Abstract

The growth of Internet, like no other communication medium, has given a "Globalized dimension" to the world. It has become the Universal source of information for millions of people, at home, at school, and at work. Due to numerous contributors across the world, the amount of information available on the web is huge and is distributed across various languages. Hence, there is a need to develop applications to manage such huge amount of varied information. Multilingual Document Clustering (MDC) is one such technique which is highly useful for organizing huge amount of data present in different languages. One of its main applications is Cross-lingual Information Retrieval (CLIR), where search engine takes query in one language and retrieves results in different languages. Instead of providing results as a single long list, it would be very convenient for users if the search engine retrieves them as a list of clusters, where each cluster contains multilingual documents that are similar. It encourages users to opt for cluster based browsing which is very convenient for processing the results. Existing approaches in Multilingual document clustering uses various tools and resources such as Manually Annotated (MA) bilingual dictionaries, lemmatizers, Named Entity Recognizers (NERs), etc., for processing the data. However, many languages across the world do not have the luxury of such tools and resources. Hence, it is very difficult to process and organize the data of languages which do not have sufficient resources. In this thesis, we focus on clustering multilingual documents of resourcepoor languages. We propose three main approaches which do not make use of any language dependent resources or tools for multilingual document clustering. In the rst approach, we perform clustering of multilingual documents using Wikipedia as external knowledge. We avail various features of Wikipedia such as cross-lingual links, categories, outlinks, Infobox information, etc., to enrich the document representation. It helps in comparing two multilingual documents eciently for forming good multilingual clusters. The use of external knowledge improves the clustering performance because it considers the semantic association between the words. In the second approach, we propose a method to identify the Named Entities (NEs) present in resource-poor languages such as Hindi and Marathi. We use the Named Entities present in English, which is a resource-rich language for this purpose. The identied Named Entities help in detecting the topic of a given document, which is highly useful for comparing two documents efficiently for improving the performance of multilingual document clustering. In our final approach, we propose a method which combines our first and second approaches. In this approach, Named Entities and Wikipedia features are used together in enriching the document representation for forming good multilingual clusters. In all our approaches we use Bisecting k-means algorithm for forming multilingual clusters. We do not make use of any non-English linguistic tools or resources such as WordNet, Part-Of-Speech tagger etc., which makes our proposed work easy to extend to other languages. Experiments are conducted on English, Hindi and Marathi news datasets provided by FIRE1 for their 2010 Ad-hoc Cross-Lingual document retrieval task on Indian languages. All our approaches are evaluated using F-score, Purity and Normalized Mutual Information (NMI) measures and the results obtained are encouraging.

Abstract

There are many ways in which science is learnt by students: classroom lectures, real physical activities or computer generated interactions. Computer based simulation tools offer students a means to explore, experience and express themselves through computer generated interactions. Simulation tools are used for enhancing the learning by offering a different way of presenting the information via visualization. In simulation tools, the students can pose hypotheses about a science concept, conduct as many experiments as they want, observe and record data and draw conclusions. This might not always be possible in a real laboratory because of many reasons. The physical phenomenon could itself be complicated to observe or the experiment setup could be time consuming. Laboratory experiments are conducted in a more step-by-step manner following processes of examination and observation, which are very important to build necessary scientific skills in a student. Hence with a simulation tool students can miss out critical examination and observation skill required in a real laboratory. Therefore many researchers have suggested that the simulation can be very useful if used for experiencing before classroom instruction, and applying learned contents after classroom instruction. The classroom instruction and guidance by teachers are very important for making simulation tool an effective learning tool. Development of simulation tools for science education has been explored widely in literature. The results from these efforts indicate that the comfort level of a user with simulations is influenced by their prior experiences with other software environments. Hence for a teacher to be comfortable with a simulation tool, it needs to be customized to their preferences. Development and modification of simulations according to the specific need of teachers, while desirable, is a difficult and time-consuming process as it may require an iterative tailoring of the user interface to meet multiple constraints like their teaching style and students’ learning style. The developer must learn a software package, design the visual composition (menu, tools) and temporal behavior (stored program or task) of user interface to meet requirements. Hence, an authoring tool which allows a user to create various simulations and is easy to use will be an effective educational resource for content developers. An authoring tool, refers to a system that allows a user to create content, by defining the relationship between different media (i.e., text, video, images, etc.) and sequencing them in an appropriate order. In this thesis, we present such an authoring tool, ViPLab, to understand different components required in an authoring tool. We present the design details of the physics engine and user interface. This tool has been evaluated by science teachers and a number of undergraduate engineering students. Based on the preliminary feedback from teachers and students a set of guidelines are identified for authoring tool development. We propose an authoring tool framework that adheres to these guidelines by decoupling the user interface from the physics engine. This enables the integration of user interface with different physics engines. We present a Graphical interface construction Kit (GicK) which helps in creating user interfaces and combine it with a physics engine for creating interactive simulations. A 3D-ViPLab, an authoring tool, is developed as an exemplar of the proposed framework. We end by showing a way to convert simulations developed using 3D-ViPLab into simulation games.