Abstract

IoT (Internet of Things) is the foundation and infrastructure of any ‘Smart Solution’. It is the network of physical objects or ‘things’ embedded with electronics, software, sensors and connectivity. Things, in the IoT, can refer to a wide variety of devices such as your smartphone, tracking beacon, heart monitoring implants, biochip transponders on farm animals, electric clams in coastal waters, automobiles with built-in sensors, or field operation devices that assist fire-fighters in search and rescue. These devices are termed as smart devices. Internet of Things (IoT) solutions uses smart devices that have the properties of being programmable, sensor-based and connected. Today’s smart applications leverage IoT technologies that enable collaboration between different entities involved in the application. Further, smart applications provide local intelligence attached to each device/physical object. A smart solution constantly gets data and events at different levels of abstraction from underlying IoT devices, sensors, and environmental parameters to make decisions and perform actions. An IoT smart application interconnects various smart objects and interacts with the environment for a specific purpose. Smart solutions require fusion of heterogeneous sensors’ data from a variety of IoT applications and orchestration of data, events, control, and process flows arising from individual IoT applications. Data from IoT devices and sensors are an integral part of smart solutions. The data requirements at the application level are usually different from the data that is captured by the Sensors. IoT driven smart solutions typically use the raw sensor data that is aggregated, filtered and processed to drive the smart solution. Such data has different semantics in contrast to sensor data. There is a need for proper data logistics for the movement of data from the sensors producing data to smart solutions consuming it. Further, new smart devices appear and disappear on-the-fly during the smart solutions life-cycle. Smart solutions must be resilient and adapt to the changes in the environment seamlessly. Due to multiple data flows from Sensors to Smart Solutions, the modelling, and the enactment of IoT processes is a challenging task. In this work, we present IoT data logistics flows as workflows that are orchestrated by the events raised during the execution of a smart solution to create, update and deploy the workflow instances. The data and event handling through workflow events support resilient, smart solutions. As the execution of the smart solutions is mostly dependent on the context (such as location, time and events), there is a dependency between the context for the data generated and the data requirements for the smart solutions. Thus, there is a need for updating the workflows as well as adding new workflows as the execution of the smart solution is progressing for the effective vi vii processing of IoT data. Our approach uses Execution Workflows for the creation, modification, and the execution of IoT workflows on-the-fly. In this work, we also present an implementation framework of our approach and demonstrate the proposed approach for different scenarios in a smart office solution. Workflow management systems drive most of the complex business applications or processes. The workflow management system has a specification component, and an execution component along with other functional components for data flow and control flow, along with orchestration and management of tasks and worklists. Thus, the workflow management system becomes monolithic or composed of tightly coupled modules that enable the specification and execution of workflow instances. Therefore, any modification of execution components of a workflow system requires considerable effort and cost. We address this issue by using a meta execution workflow system that gives us control over specifying various workflow definitions to execute workflow instances as per our needs. The meta execution workflow system allows us to specify user workflows according to the smart application requirements and the workflow engine consists of modules required to execute the tasks of the workflows. The data, control and event flow in the smart solution is handled by the execution workflow system. We present a data logistics system that includes 1) A workflow model for a data logistics system 2) Architecture to support the movement of data from data producers to consumers. Our solution uses the meta execution workflows and user-specified workflows, to support the data fulfilment requirements for the enactment of smart solutions.

Abstract

Semantic Role Labelling (SRL) is a sub-task of Natural Language Understanding. It helps us understand the function of a particular token, with respect to its associated verb in a sentence. SRL identifies the event (verb) of the sentence and the associated participants in the sentence. SRL is used as an intermediate step in various tasks such as information extraction, question answering systems, machine translation, to name a few. In the past decade, social media has become increasingly popular. As a result, there is a huge amount of data available on forums like Facebook and Twitter which can’t be processed or analysed by traditional Natural Language Processing (NLP) tools. This data usually doesn’t follow grammatical rules, is spelt incorrectly and often, for multilingual users, contains a mixture of languages. In this thesis, we discuss our effort towards building a semantic role labeller for such ‘mixed language - hinglish’ data, i.e. Hindi-English code-mixed data. This is the first attempt at creating a SRL tool for Hindi-English code-mixed data, or for that matter, any code-mixed data, to the best of our knowledge. First, we created a dataset which is annotated with semantic roles. We released this dataset of 1460 Hindi-English code-mixed tweets consisting of 20,949 tokens, which is parsed and annotated with semantic roles (Proposition Bank labels). We also created required frame files to take into account mixed language complex predicate formations and certain special constructions which are unique to code-mixed data. We then built a rule-based baseline model for automated labelling of this data. Research shows that there is a strong relation between dependency labels and semantic roles and we leverage this information to build rules for our model. This model can further reduce annotation effort in future. The task is carried out in two steps. The first step is to identify the participants (arguments) of the event (verb) in the sentence. The second step is to classify these identified arguments according to their function, i.e. mark their semantic roles. We then did a thorough error analysis. Further, we worked on improving the accuracy scores obtained by our baseline model. For this we explored statistical methods used in the past for SRL in English and Indian Languages. Before that, we described our effort towards pre-processing and normalising the data to ensure consistency and uniformity of tokens across the corpus. Then, we formulated and extracted various features which work towards capturing semantic information from our data. These are a combination of features traditionally used for SRL, features used in Hindi SRL and features specific to the code-mixed nature of our data. We experimented with and compared the performance of our model by using Paninian dependency labels and Universal Dependency (UD) labels as well. We also trained our model on Hindi monolingual corpus and tested on code-mixed data. We compared and analysed the performance of our models for the various experiments.

Abstract

Internet and social media have fundamentally transformed the landscape of news consumption. Consumption of news from the social media is on increase and the consumption through the print media is on decline. The practice of digital journalism is on the rise, thanks to the low barrier of entry, almost zero distribution costs and availability of mobile computing. There has been a proliferation of news publishers and distributors online in the recent times. As more and more audience are consuming news from the internet, it is very important for the news publishers to engage the users on their websites to maximise the revenue, a majority of which is generated from the advertisements on the page. This also caused a raise in the concept of yellow journalism and tabloid journalism, that emphasizes sensational crime stories, gossip columns about celebrities and sports stars, extreme political views and opinions from one perspective, junk food news, and astrology. In fact, aggregating soft news has become a prospective and lucrative business compared to the business of aggregating only the hard news. Many of the publishers publish a mix of both soft news and hard news to maximise their revenue. Sensationalism is often the most common tactic used by the news editors to increase the engagement of the users is sensationalism. The topics and the news stories published are worded in a way to excite the readers and lure the user to click on the link of the story. This type of reporting by the editors can portray the news in a biased manner and may sometimes manipulate the truth of the story. A part of the sensationalism tactic is to report news on very insignificant matters and assigning undue importance to them. Reporting on controversial topics, deliberate omission of facts, using exaggerated words to seek attention, exaggerating trivial events out of proportion, reporting on the content which is insignificant and irrelevent to the audience are some of the tactics used to achieve higher page impressions in the online news media. News editors stoop to low journalistic reporting standards to maximize their engagement and the revenue, at the expense of the quality of the reporting. Such kind of reporting may lead to specific categories of news like Clickbait, Bizarre news, Fake news, Hoax news, Misleading news, Satire and Funny news and Hyperpartisan news. Mimics and clones of popular news outlets are also common. Some categories of the news like Bizarre News, Satire and Funny news are entertaining and harmless. However some categories like, fake news in specific, might cause problems ranging from financial loss, disturbances in society and may cause loss of human life in extreme cases. Clickbaits in the guise of providing entertainment may waste a lot of precious time of the readers and may ultimately cause disappointment to the readers. Fake health remedies disguised in the form of health tit-bits can harm the readers by compromising their health. There are both positive and the negative sides of the sensational softnews. Therefore, it is extremely important to automatically identify, evaluate and appraise the sensational content in the news media. In this thesis we consider the problem identifying sensational soft news content. In specific, we study two types of sensational content - bizarre news and clickbaits. We selected these two categories of the news as these news is usually sensationalized to increase the engagement of the readers. Identifying the sensational soft news by looking at the title is challenging because the titles are usually short text and they are often written in convoluted ways which requires requires high-order semantic understanding, usually with support of facts from some knowledge base. We do a thorough study of both of these types of content and build computational models to categorise the content as hard vs soft news category (like bizarre or clickbait) and also rank its intensity. We selected bizarre and clickbait as the broad categories as both of them have the characterstics of sensationalism in them. We begin the problem of bizarre news identification. We compile a dataset of bizarre news and normal news. We do a deep analysis of bizarre news and regular news and identify handcrafted features like semantic, lingustic and blah blah features for bizarre news detection. We also proceed to categorize the bizarre news into different categories. We then consider the problem of clickbaits. Clickbaits are ubuquitous. In this work, we explore multiple techniques to identify the clickbaits in online social media. We collect and annotate a huge dataset of clickbaits, which is many times larger than any of the existing datasets and use it in a semi supervised approach for doing clickbait classification. We explore various word and sentence embeddings for clickbait identification. We then consider the problem of clickbaits in Spanish. Specifically, we chose to build models to iden

Abstract

Ray Tracing is one of the two major approaches to 3D rendering. Images produced through ray tracing show a higher degree of visual realism, and are more accurate compared to other 3D rendering methods like rasterisation. It is primarily used to generate photo-realistic imagery, as it is based on tracing the path of light going into the scene space. Ray tracing was initially an offline algorithm, and was seen as more suitable for applications which required high quality rendering, where the render time constraints were not as strict. Still images, animated films and television series thus used Ray tracing for rendering visual effects like reflections, shadows, refractions etc. accurately. Computer Generated Imagery (CGI) was first used in the science fiction movie Westworld in 1973. In 1995, ’Toy Story’ became the first film to be fully developed using computer animation. RenderMan, a Ray tracing engine developed by Pixar Studios, made this possible. At that time, rendering enough frames to make a 80-minute film took more than 800,000 machine hours. With massive increases in computational power, and the invention of Graphics Processing Units (GPUs), things have considerably improved, leading to renewed interest in ray tracing research. GPUs gave a huge boost to Ray tracing because of it’s inherently parallel nature. Though GPUs accelerate almost all parts of the Ray trac- ing pipeline, it was still considered to be impractical for interactive, real-time rendering applications, like games. With the annoucement of specalised hardware units for Ray tracing in the Turing GPU microarchitecture by NVIDIA, real-time ray tracing is believed to be possible now. Camera technology has advanced a lot over the years. With standard optical cameras, it was impor- tant to focus accurately on the subject, as slight errors would cause a substantial loss of quality in the desired portions of the image. Digital cameras today have the capability to adjust focus and aperture on their own to accurately focus on a subject. While simulating a camera-esque setup using computer graphics, we don’t have the flexibility of a refocusing after rendering is completed. The only way is to render the setup again with the correct setting. The light field representation offers a viable solution to this problem. Light field is a 4D function that captures all the radiance information of a scene. Traditionally, the creation of light fields was done by image-based rendering mechanisms. These reconstruct the 4D space using pre-captured imagery from various views and employ refocusing algorithms to generate output images. Plenoptic cameras capture the information of all light coming towards the camera, and use that to generate images at any focus setting. Handheld plenoptic cameras, like the Lytro camera series also capture light fields using a microlens array between the sensor and the main lens, but physical constraints have limited their spatial and angular resolutions. The first part of this thesis presents a GPU-based synthetic light field rendering framework that is robust and physically accurate. It can create light fields of very high resolutions, which are orders of magnitude higher than currently available in commercial plenoptic cameras. We show how light field rendering is possible and viable in a synthetic rendering setup, and high-quality images at any desired focus and aperture setting can be generated in reasonable times with a very low memory footprint. We explain the theory behind synthetic light field creation, different representations possible for a 4D light field, and show results based on our implementation, as compared with state-of-the-art ray tracing frameworks like POVRay. In the second part of the thesis, we attempt to solve the problem of light field storage. The 4D light field is inherently rich in information, but is very bulky to store. We address this issue by separating the light field creation and output image generation passes of a synthetic light field framework. Our thesis presents a compact representation of the 4D light slabs using a video compression codec and demon- strate different quality-size combinations using this representation. We demonstrate the equivalence of the standard light field camera representation, called the sub-aperture representation, with light slab representation for synthetic light fields. We use this to exhibit the capability of our framework, not only to trace light fields of very high resolutions, but also to store them in memory. The required images can therefore be generated independently from the ray tracing pass with a very small cost

Abstract

Proteins are important building blocks of biological cells. They are not static entities, but they exhibit a wide spectrum of dynamics ranging from atomic vibrations, side chain motions to large scale domain motions. Their dynamics help them perform many biological functions in cells. Recent experimental studies on the binding of ligands to proteins have highlighted the importance of low-frequency vibrational dynamics in the kinetics and thermodynamics of ligand binding and in bringing about binding-induced structural changes in proteins. It is necessary to investigate the vibrational dynamics to comprehend on the functional aspects of proteins. In the present thesis, we have characterized the vibrational dynamics of proteins and how it is aected by perturbations such as ligand binding and surface water using normal mode analysis (NMA) and molecular dynamics (MD) simulation. Calmodulin, a calcium binding protein found in most eukaryotic cells, is used as a model system to study the changes in the vibrational dynamics after it binds to various peptides. We have also developed a web application with a GUI interface to perform NMA and to analyze vibrational properties of proteins. Chapter 1 presents an overview of the introduction to proteins and the importance of vibrational dynamics. A detailed overview of various computational methods used to characterize protein dynamics is presented in Chapter 2; specically, the fundamental theory behind NMA, MD and the methods to calculate the binding anity of protein-ligand complexes are provided in Chapter 2. The computational details of the development of the web application to perform NMA of proteins are presented in Chapter 3. The specics of GUI interface to our web application that enables calculation of vibrational properties such as the vibrational density of states (VDOS), vibrational entropy and participation ratios of proteins and that displays vibrational modes of interest are also reported in Chapter 3. Chapter 4 explores on a study of the vibrational dynamics of CaM in the presence and absence of ligands to understand ligandinduced changes in the vibrational motions of CaM. The correlation between binding-induced changes in vibrational dynamics of CaM and the binding anity of CaM-peptide complexes is also explored. The eect of solvent on the vibrational properties of CaM is also investigated by varying its hydration level and the corresponding results are also reported in Chapter 4. Chapter 5 concludes this thesis by presenting the concluding remarks of the work performed herein.

Abstract

Sports data is probably the most widely looked at and analysed data source in the world. Data captured during a sports match is of much interest to fans who use data to validate their hypotheses about player/team performances and to conduct an analysis of the happenings during the game. Data collection has largely been a manual process and thus the data was discrete in nature and outcome based. For example in the case of cricket, for a ball bowled the outcome was recorded as to how many runs were scored but the position the ball was pitched and where it passed the batsman can not be recorded with a reasonable level of consistency, if done manually. With the introduction of various technology based tracking solutions, such kinds of spatial data that is consistent, can now be complied. When you think of continuous sports such as football, hockey this kind of data is continuous and holds much more context than that the discrete, outcome based data used to present. The 2 methods used currently are either tracking through hardware tracking devices that need to be attached to each object of interest or by using computer vision techniques to analyze videos of the game that must be recorded by multiple specially installed single purpose cameras. Hence, as the data made available is becoming richer we see that sports players and teams are increasingly making use of data to devise strategies and to gain a competitive advantage. This is making such data very powerful as sports teams can use it for prescribing tactics, betting companies for better predictions, and sport broadcasters and fans for analysis. Spatial data is especially interesting in football because the dynamic positions of the players on the playing field are highly important to decide the outcome of the game. Football players are often advised by their managers precisely as to how they must position themselves during different passages of play. Managers are often thought to be playing chess with each other as they plan their moves and organize their troops in various tactical formations aimed at giving them a leg-up over their opponent. The importance of spatial data in football cannot be overstated. We have taken a different approach of using computer vision to gather data pertaining to player and ball location by working purely off a broadcast video for the sport of football. The benefits of this approach are that it is economically cheaper and can also be used to analyze any televised match from the past. The 2 other techniques discussed are highly intrusive and expensive to setup. Another drawback with these techniques is that they need specially installed hardware prior to the start of the game to gather data. Using our technique we can analyze data of any televised football game easily and also use it to analyze any televised game for the past. As data hungry machine learning models are being increasingly used to prescribe and predict, opening up the data from all the games in the past could be hugely significant and we hope our research proves a significant step in that direction

Abstract

Replacing overexposed or dull skies in outdoor photographs is a desirable photo manipulation. It is often necessary to color correct the foreground after replacement to make it consistent with the new sky. Methods have been proposed to automate the process of sky replacement and color correction. However, many times a color correction is unwanted by the artist or may produce unrealistic results. Style similarity is an important measure for many applications such as style transfer, fashion search, art exploration, etc. However, computational modeling of style is a difficult task owing to its vague and subjective nature. Most methods for style based retrieval use supervised training with pre-defined categorization of images according to style. While this paradigm is suitable for applications where style categories are well-defined and curating large datasets according to such a categorization is feasible, in several other cases such a categorization is either ill-defined or does not exist. In this thesis, we primarily study various image representations and their applications in understanding visual style and automatic background replacement. First, we propose a data-driven approach to sky-replacement that avoids color correction by finding a diverse set of skies that are consistent in color and natural illumination with the query image foreground. Our database consists of ∼1200 natural images spanning many outdoor categories. Given a query image, we retrieve the most consistent images from the database according to L2 similarity in feature space and produce candidate composites. The candidates are re-ranked based on realism and diversity. We used pre-trained CNN features and a rich set of hand-crafted features that encode color statistics, structural layout, and natural illumination statistics, but observed color statistics to be the most effective for this task. We share our findings on feature selection and show qualitative results and a user-study based evaluation to show the effectiveness of the proposed method. Next, we propose an unsupervised protocol for learning a neural embedding of visual style of images. Our protocol for learning style based representations does not leverage categorical labels but a proxy measure for forming triplets of anchor, similar, and dissimilar images. Using these triplets, we learn a compact style embedding that is useful for style-based search and retrieval. The learned embeddings outperform other unsupervised representations for style-based image retrieval task on six datasets that capture different meanings of style. We also show that by fine-tuning the learned features with datasetspecific style labels, we obtain best results for image style recognition task on five of the six datasets. To the best of our knowledge, ours is the first work that provides a comprehensive review and evaluation of style representations in an unsupervised setting. Our findings along with the curated outdoor scene database would be useful to the community for future research in the direction of sky-search and sky-replacement

Abstract

Climate change calls for a unanimous global initiative, and all nations have been requested to reduce their growing greenhouse gas emissions to tackle this issue. In the United Nations’ 21st Conference on Climate Change, India has pledged to reduce its emission intensity by 33-35% of its 2005 level by 2030. Several studies predicted that coal-based electricity generation in India could double by 2040. At present, the residential building sector alone accounts for 24% of total electricity consumption in India, representing second-highest compounded annual growth rate of 7.58% after the Industrial sector. The Alliance for an Energy Efficient Economy (AEEE) in India predicted that the electricity consumption growth rate in residences would further increase in the future, with increasing residential built-stock and growth in ownership of cooling appliances. However, the growth rate in consumption may not necessarily be linear with increasing residential built-stock and cooling appliances ownership per unit household. Climate Change impact studies conducted on residential buildings in cold and tropical climates mostly infer that cooling energy consumption would rise with the predicted change in the climate. This study aims to assess the potential impact of climate change on Indian residential building energy consumption in five climatic zones, using a transient building energy simulation approach. Future typical weather files are generated for the year 2050 for these climatic zones, using Climate Change World Weather File Generator developed by University of Southampton. The present conventional (PC) and present energy-efficient (PE) building cases are modelled to identify their energy performance for present and future weather conditions. Further, the study includes cases with future energy conservation measures for PC building – Present Conventional Buildings with Future Energy-efficiency retrofit Measures (PCFM) and PE building – Present Energy-efficient Buildings with Future Energy-efficient retrofit Measures (PEFM) in the scope. The PCFM and PEFM cases are simulated only for future climate to estimate energy savings through future energy conservation measures. A future energy-efficient (FE) building case is also modelled and simulated with features involving high insulated envelope and future energy-efficient appliances. Various global studies on residential energy and the impact of climate change have focused on measures for new buildings, and on revision of insulation code. These studies focus on insulation levels for building envelope and retrofit measures for cooling system towards energy conservation. This work contributes by incorporating future retrofit technologies such as electrochromic windows with solar PV glass, and simple home automation strategies for coupling ceiling fans with air conditioning systems for future energy conservation. The variable loads like ceilings fans and geysers are modelled and simulated through Energy Management System scripting in EnergyPlus. The Building Integrated Photovoltaic (BIPV) system is also modelled and simulated as part of future energy conservation retrofit measures Based on energy simulations, it is observed that the present-day built residential buildings energy performance would be influenced by climate change. The global warming would increase the cooling energy consumption of present-day residential buildings leading to increase in total energy consumption. The consumption rate can be reduced to the levels less than the current energy consumption by deploying future energy efficient retrofit strategies for present-day buildings. The present-day energy efficient buildings can reduce their consumption by half in future whereas conventional buildings with low-thermal envelope and energy in-efficient appliances could reduce by more than half by retrofitting in the future. The facades of high-rise buildings are widely exposed to solar radiation leading to large solar ingress. However, the BIPV system deployment would not only reduce the heat ingress from the envelope but also convert some part of the solar radiation into electricity.

Abstract

In this work, we try to advance knowledge about differences in neural processing of music between musicians and non-musicians, both through our findings and methodologically. Music is omnipresent in our daily lives. Music listening has been found to have many positive benefits on health, and musicbased interventions have been shown to be an effective form of alternative and complimentary therapy to aid treatment for various disorders. Importantly, it has been suggested that an individual’s musical background and musicality play a key role in modulating the successful acquisition of the benefits music has to offer. Musical training has been shown to engender structural and functional changes in the brain due to intense repetitive sensory-motor demands. In addition to engendering enhanced sensitivity in the processing, representation, and discrimination of sounds and music, musical training has also been associated with transfer effects such as enhanced cognitive function in speech and language processing, motor abilities, attention and memory, etc. Music is thus an ideal tool to study brain adaptation and plasticity, and musicians an ideal group to study brain changes driven by experience, especially when juxtaposed with a non-musicians group. Thus, along with understanding the neural underpinnings of music feature processing, it is also essential to look at how musical training modulates the functionality of the brain by investigating group level differences between musicians and non-musicians. Our work is a step in that direction. A distinctive aspect of the work presented is the use of the naturalistic paradigm wherein participants listen to entire pieces of music without performing any task while being scanned and renders itself an attractive and more ecologically valid approach to studying music perception as opposed to the hitherto controlled paradigms. In our work, we look at the differences between musicians and non-musicians from two different viewpoints - from a segregated viewpoint (which aims to identify local regions that are specialized for a particular task) in our first study, and from an integrated viewpoint (which allows for identification and characterization of interactions between brain regions that allow for integrated functioning) in our second study. Group differences were identified in both the studies thereby revealing differences in neural encoding as a function of musical expertise. Musical training and expertise is likely to enable top-down analytic processing in the musicians, especially of the more complex and higher-level aspects and features of music, leading to differences in listening and processing strategies caused predominantly by their varied backgrounds. This resulted in lesser homogeneity at the group level for the musicians.On the other hand, the non-musicians exhibited greater within group consistencies, indicating primarily sensory, bottom-up processing. In our first study which takes a segregated approach, we investigate the differences between musicians and non-musicians in the encoding of acoustic features encompassing musical timbre, rhythm, and tonality in a continuous music listening paradigm. 18 musicians and 18 non-musicians were scanned using functional magnetic resonance imaging (fMRI) while listening to three 8-minute long musical pieces representing different musical styles. Acoustic features corresponding to timbre, rhythm, and tonality were computationally extracted from the stimuli and correlated with brain responses. Overall, non-musicians exhibited broader regions of correlations, implying greater within group similarities in musical feature processing, especially in the auditory and default mode network-related regions. Musicians demonstrated significant correlations in regions possessing adaptations to music processing due to training, in addition to greater involvement of limbic and reward regions in response to rhythm, indicative of greater affective and analytic processing. However, as a group, they did not exhibit large regions of consistent correlation patterns, especially in processing high-level features, which could be attributed to differences in processing strategies arising out of their varied training and background. For our second study which takes an integrated approach, we note that studies from a segregated viewpoint and studies involving network analysis have indicated the presence of large-scale brain networks involved in the processing of music and have highlighted the differences between musicians and non-musicians. However, network analysis studies of functional connectivity in the brain during music listening have thus far focused solely on static network analysis. Dynamic Functional Connectivity (DFC) studies have lately been found useful in uncovering meaningful, time-varying functional connectivity information in both resting-state and task-based experimental settings. We examin

Abstract

Current mobile technology does not utilize the spectrum efficiently. This problem is going to increase in future because of increase in wireless devices. Cognitive Radio (CR) is one of the solutions to exploit the underutilized spectrum. It is key enabler for next generation mobile technology 5G with applications such as efficient spectrum utilization and interference management. Spectrum sensing is an important component of cognitive radio networks (CRNs) as it provides spectrum awareness required for cognitive processing. In this thesis, we first investigate a distributed and heterogeneous CRN, comprising of secondary users (SUs) employing either energy detector (ED) or autocorrelation detector (AD) to detect the pres- ence or absence of an orthogonal frequency division multiplexing (OFDM) based primary user (PU). For the considered heterogeneous cooperative spectrum sensing (CSS), the optimal soft combining rule is derived. The performance of this optimal fusion rule and different hard combining schemes such as OR, AND and MAJORITY is presented for the case when the noise variance is exactly known. Later, the effect of noise uncertainty is also presented. The proposed heterogeneous CSS is shown to com- bine the excellent performance of the EDs (when the noise variance is exactly known) and robustness of the ADs to the noise uncertainty. However, the proposed soft combining based fusion rule assumes independence between the observations which may not hold in practical scenarios. Incorporating dependence between observations in fusion rule for CSS scheme forced us to look for alternative concepts and theories beyond standard concepts used in the context of spectrum sensing. Copula theory provides a new dimension to the picture. It enables us to incorporate dependence in the fusion process. Using the concept of copula theory, we forged a novel copula-based fusion rule for spectrum sensing. We investigate a distributed detection model where SUs employ ADs for the detection of a PU. In the presence of a PU, it is assumed that the observations across different SUs and subsequently the decision statistics are dependent. For the fusion of these dependent statistics, different copulas such as t-copula, Gaussian, Clayton and Gumbel are employed. In the presence of dependence among decision statistics, significant improvement in detection performance is observed while using copula theory instead of the traditional assumption of independence. Simulation results are presented to show the superiority of copula-based spectrum sensing.