Abstract

The cellular users’ demand has increased drastically with the emergence of mobile internet technologies. In order to satisfy the demand, cellular service providers have improved each generation of cellular networks over its predecessors. However, the fifth generation (5G) cellular technologies would revolutionize the network architecture and would go beyond the trend of previous generations. Device-to-Device (D2D) communication is one such revolutionary technology in which the mobile users communicate with each other directly without traversing the base station. D2D communication is actively studied with special focus on public safety in 3GPP. D2D communication with network control has many other use-cases. Also, the 5G cellular technology includes millimeter wave (mmWave) for communication. Along with massive bandwidth availability at mmWave frequencies, the advantage of extensive frequency reuse and directive propagation is gained. The integration of device-to-device (D2D) and mmWave technology would improve the system performance and spectral efficiency to a great extent. Moreover, with the emerging trends in Internet-of-Things (IoT), massive device connectivity is required with high connection speed. Hence, D2D communication technology plays a vital role in maintaining Quality of Service (QoS) in IoT applications. The thesis studies D2D communication in inband mode where the resources of the licensed spectrum are used for communication. The work emphasizes on the system performance improvement because of the integration of mmWave technology and D2D communication. Underlay mode D2D communication, where the devices reuse the resources of a cellular user, is analyzed. The problems of resource allocation and interference avoidance are addressed. Then, the importance of fairness in resource allocation and the power allocation are studied. An approach for resource allocation and power allocation is proposed which is adaptive to traffic demand. Results show that the proposed scheme can significantly enhance the system performance in terms of fairness and spectral efficiency. Inband D2D communication with selective operation in underlay and overlay modes aiming to maximize QoS is explored. Spectrum underutilization is the key challenge in overlay mode which is addressed in this work. In areas with high call traffic density, D2D communication can operate in overlay mode because of its low complexity as resources are dedicated and the absence of interference with cellular users. In order to maximize spectrum utility, resources are dynamically assigned to overlay mode based on traffic demand. System performance in overlay mode is analyzed. A dynamic mode selection scenario is considered while addressing resource allocation, power control and interference avoidance. Thus, formulated problem is solved using a single heuristic algorithm for resource management in both the modes. Performance of the proposed algorithm is evaluated through simulations. The proposed scheme has optimal performance for a dense 5G network in controlled underlay and overlay operations and overcomes the problem of spectrum wastage in overlay mode by its selective operation.

Abstract

Abstract LiDAR has been emerging as one of the most sought after technologies with it’s novelty to perceive 3D environments accurately. One of the popular application of the lidar data has been object segmen- tation of different objects from a given scene. Lidar as a data format can record the 3D structure of the buildings in urban areas with precise object information. Such platforms to record lidar are mostly clas- sified into three types that is terrestrial, aerial and mobile based lidar systems. The data being recorded from respective platforms varies a lot in terms of range, coverage and point density. Our work presents new approaches to segment buildings from a given lidar scene with flexibilty to work with lidar data from multiple different platforms. The challenge of using LiDAR datasets is the very high resolution of datasets, with high computa- tional requirements in visualizing and processing the datasets. The current methods of object segmen- tation and extraction and classification of aerial LiDAR data are manual and tedious. Our work tries at modelling this problem into a generic pipeline which can be used to segment different varieties of lidar datasets. The pipeline constructed is a result of multiple geometric approaches taken to segment the buildings. The massiveness of data to process, the accuracy of groundline estimation, detection of connected roofplanes and occlusion and overlap of multiple objects in a given lidar scene poses a range of challenges while processing and segmenting such data. Our work tries to overcome some of the challenges and shortcomings of the existing works by proposing two new methods of segmenting objects. Objects in this thesis refer to only buildings. The justification of segmenting only buildings and not focusing on any other type of objects is primarily due to the uniformity in shape of the building and its adherance to geometric properties. In case of other objects, such advantages are not present making their segmentation more challenging and difficult to formulate. The scope of the thesis is limited to only aerial LiDAR because of it’s larger coverage and more processing friendly density compared to terrestrial scans. Terrestrial LiDAR can not capture the roof planes of the buildings while in case of aerial LiDAR we can capture the roof planes of the buildings, which are later used to segment buildings from the given scene. Existing methods of building segmentation from a LiDAR scene mostly involves the usage of image processing based techniques like morphological operations and fitting done in a plane through RANSAC algorithm. These methods often are highly dependent on the resolution of the input data being fed into it. Some methods also take the help of other data to aid in segmentation. Our work tries to tackle the shortcomings of the above works and present a new and novel method to segment buildings from a given scene. We propose two different approaches in segmenting buildings from a given scene. The commonality of the different approaches is that both of them focus on using the facade planes of the buildings to segment them. The algorithm has been tested in multiple datasets ranging from synthetic and real-world datasets. A bottom-up geometric rule-based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. Multiple lidar datasets were used to check the accuracy and performance of the algorithm. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real-world data with an accuracy of 68%. Our work tries to solve a part of the puzzle by segmenting only the buildings in a given lidar scene automatically with limited tuning, in an intuitive manner. The further advantage of the proposed work is its non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focusing on the roof. The focus on extracting the wall to reconstruct the buildings

Abstract

The thesis presents immensely pipelined parallel FPGA based architectures for Image segmentation by a graph-based partitioning approach and by state-of-the-art CNN’s approach. Efficient and real-time segmentation of color images has a variety of importance in many fields of computer vision such as image compression, medical imaging, mapping and autonomous navigation. Being one of the most computationally expensive operations, it is usually done through software implementation using high-performance processors. In robotic systems, however, with the constrained platform dimensions and the need for portability, low power consumption and simultaneously the need for real-time image segmentation, we envision hardware parallelism as the way forward to achieve higher acceleration. Field-programmable gate arrays (FPGAs) are attractive alternatives for this task because of their reconfigurability, high per-watt performance in a small physical area. They exceed the computing speed of various software-based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle operations by enabling hardware level parallelization at an architectural level. The first contribution proposes three novel architectures for a well known Efficient Graph-based Image Segmentation algorithm. These proposed implementations optimize time and power consumption when compared to software implementations. The hybrid design proposed, has notable furtherance of acceleration capabilities delivering at least 2X speed gain over other implementations, which henceforth allows real-time image segmentation that can be deployed on Mobile Robotic systems. The second contribution focuses on FPGA architectures designed for state-of-the-art approach CNN’s for image segmentation and classification. Convolutional Neural Networks (CNN’s) are rapidly gaining popularity in varied fields such as computer vision, information retrieval, and mobile robotics. Due to their increasingly deep structures, modern CNN’s are continuously becoming computationally and memory intensive. Although this enhances accuracy, it becomes difficult to deploy them on energy-constrained mobile devices such as drones. As a consequence, hardware accelerators such as FPGAs with their inherent hardware parallelism, have come up with an attractive alternative. The major bottleneck while implementing huge networks on FPGA is meeting high memory throughput requirement of CNN’s with limited on-chip memory. Hence, this work proposes a high-performance FPGA based architecture - Depth Concatenation and Inter-Layer Fusion based ConvNet accelerator-DeCoILFNet which exploits the intra-layer parallelism of CNN’s by flattening across the depth and combining it with the inter-layer fusion. DeCoILFNet perfectly pipelines the data flow across convolution layers which significantly reduces off-chip memory accesses and maximizes the throughput by using multiple line buffers. To validate our approach, we demonstrate results for first five convolution layers of the VGG-16 network implemented on Xilinx Virtex7 VC709 FPGA board in Verilog .

Abstract

Deep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks for news recommendation systems has received a relatively less amount of attention. Also, different recommendation scenarios have their own issues which creates the need for different approaches for recommendation. With news stories coming from a variety of sources, it is crucial for news aggregators to present interesting articles to the user to maximize their engagement. This creates the need to have a recommendation system which accounts for both the content of the articles and the user preferences. Methods such as Collaborative Filtering, which are well known for general recommendations, are not suitable for news because of the short life span of articles and because of the large amount of articles published each day. Typically, it is desirable that a news recommendation system be able to discriminate between and select articles from a pool to recommend to a user as soon as they are published. Apart from this, such methods do not harness the information present in the sequence in which the articles are read by the user and hence are unable to account for the various interests of the user which may keep changing with time. Alternatively, the other class of models based on Content Filtering, can handle cold start problems but in the long run tend to suffer from the problem of over specialization. In this thesis, we address these issues in a step-by-step manner. We start off with a problem that is commonly associated with deep learning based methods i.e lack of sufficient data. In order to tackle this issue we come up with an item-based collaborative filtering approach which utilizes Markov Decision Process (MDP). We also come up with a novel semantic similarity measure which we incorporate as a reward for the MDP. This helps us gain insights about the various interests that users may have by only having prior knowledge of a few articles read by them in the past. We then move on to the exploration of various deep learning based methods to tackle our problem. We give importance to utilizing the content of the articles, and taking into account the historical reading data of a user. We attempt to solve the cold-start problem, and make effective recommendations for users who have had little to no interaction with items. We design the model to keep learning parameters based on implicit feedback from the users. A description of how we go about it is described as follows. We first come up with a user profiling based approach and use it in combination with a Deep Semantic Structured Model (DSSM). We then later expand the model and utilize a recurrent neural network with an attention mechanism. Such a mechanism helps us discriminate between the various interests of the user. We then come up with the Recurrent Attentive Recommendation Engine (RARE). RARE consistsof two components and utilizes the distributed representations of news articles. The first component is used to model the user’s sequential behaviour of news reading in order to understand her general interests i.e to get a summary of her interests. The second component utilizes an article level attention mechanism to understand her particular preferences. We feed the information obtained from both the components to a Siamese Network in order to make predictions which pertain to the user’s generic as well as specific interests. We carry out extensive experiments to establish the effectiveness of our methods. We also perform experiments to prove the efficacy of our model on solving the item cold-start cases as well as making effective recommendations for users who have had very little interaction with articles. Finally, we experiment with a novel 3D Convolutional Neural Network based model in an attempt to solve similar problems as the ones we tried to address earlier. Applying 3D convolution helped us identify both spatial (features of a particular article) as well as temporal information (features present in the sequence of articles read by the user) which are pertinent to a user’s interest. The initial results of this experimentation are also presented in this thesis.

Abstract

Glaucoma is an eye disease characterized by weakening of nerve cells often resulting in a permanent loss of vision. Glaucoma progression can occur without any physical indication to patients. Hence, early diagnosis of Glaucoma is recommended for preventing the permanent damage to vision. Early Glaucoma is often characterized by thinning of the Retinal Nerve Fiber Layer which is commonly called as RNFL defect (RNFLD). Computer-aided diagnosis (CAD) of eye diseases is popular and is based on automated analysis of fundus images. CAD solutions for diabetic retinopathy have reached more maturity than for glaucoma as the latter is more difficult to detect from fundus images. This is due to the fact that nerve damage appears in the form of subtle change in the background around optic disc. SD-OCT (Spectral Domain - Optical Coherence Tomography), a recently introduced modality, helps to capture 3D information of retina. Hence, it is more reliable for detecting nerve damage in retina compared with fundus imaging. However, a wide usage of OCT is limited due to cost per scan, time and ease of acquisition. This thesis focuses on integrating information from multiple modalities (OCT and fundus) for improving retinal nerve fibre layer defect or detection of RNFLD from fundus images. We examine two key problems in the context of CAD development: i) spatial alignment or registration of two modalities of imaging, namely, 2D fundus and 3D OCT volume images. This can pave way to integrate information across the modalities. Multimodal registration is challenging because of the varied Field of View and noise levels across the modalities. We propose a computationally efficient registration algorithm which is capable of handling complementary nature of modalities. Extensive qualitative and quantitative evaluations are performed to show the robustness of proposed method. ii) Detection of RNFLD from fundus images with good accuracy. We propose a novel CAD solution which utilises information from the 2 modalities for learning a model and uses it to predict the presence of RNFLD from fundus images. The problem is posed as learning from 2 modalities (fundus, OCT images) and predicting from only one (fundus images) with the other (OCT) as missing data. Our solution consists of a deep neural network architecture which learn modality independent representations. In the final part of the thesis we explore the scope of a new imaging modality angiography-Optical Coherence Tomography (A-OCT) in diagnosing Glaucoma. Two case studies are reported which help in understanding the progression of Retinal Nerve Fiber Layer thickness, Capillary Density in normaland glaucoma effected patients. The experiments on new modality has shown potential for considering it as a reliable biomarker along with existing modalities.

Abstract

This study aims to investigate different aspects of sequencing. Sequencing and timing the elements of a sequence are fundamental to animal behaviour and in fact, even basic animal sustenance. Sequencing refers to a set of related events rolling out in time. Animals need to adopt mechanisms to execute several facets of sequencing in their day-to-day life, including the perception of, recognition of and response to sequences. In this thesis, we specifically explore the aspects of duration and order. To investigate duration estimation in humans, we conduct a behavioural study, using the paradigm of the temporal bisection task, and assess the influence of a neuromodulator, caffeine. The task requires candidates to form memory templates of two reference durations, and then, judge which standard a particular stimulus duration is closer to. When tested on a between-subject setting, caffeine administration was observed to lead to an accelerated perception of time. A given probe duration was perceived to be longer by subjects, under the influence of caffeine, than by participants in the control group. We argue that caffeine plausibly modulates memory representation of time, as well as, the internal pacemaker, on the basis of the scalar expectancy theory. We, additionally, conducted a computational investigation to explore the effect of caffeine on duration judgement. We constructed two models, one based on the Gaussian representation of time and one anchored in reinforcement learning. While one model gives us a quantitative account of the empirical data obtained above, the other offers a qualitative understanding of the circuitry involved in the perception of duration. In the second part of the thesis, we investigate the order aspect of timing using the paradigm of vocal learning in songbirds. Song is a sequence of syllables, i.e. motor commands. We attempt to model the HVc, i.e. an avian brain region involved in forming a representation of the conspecific song that the bird tries to imitate, using a biologically realistic adaptation of the pyramidical neurons found in the neocortex. In addition, electrophysiology experiments show that lesions to the basal ganglia homologue in birds, Area X, fatally affects vocal learning, while lesions made after crystallisation of song, has a negligible effect. Such experiments motivated us to set out to explore the circuitry underlying the learning and issuing of the motor commands responsible for song synthesis. We build a biologically realistic insightful model of vocal learning via the anterior frontal pathway, consisting of the Area X, and the eventual consolidation of this skill in the song motor pathway. The thesis concludes with a summary of the work, and a discussion on its limitations and future directions.

Abstract

Metal clusters with ultra-small size offer many novel properties such as molecule-like optical transitions, photoluminescence, quantized charging, chirality, and so on. Ligand-protected metal nanoclusters with precise compositions appear to be promising building units of various functional materials. Novel properties and promising applications make metal clusters a prolific target of fundamental and applied research. The major challenges of the nanocluster research are developing synthesis protocols for precise control of cluster size, achieving tunability of nanocluster properties, and imparting stability and desired functionality to the clusters particularly in aqueous and biological media. Cluster synthesis often involves multi-step, two-phase methods and results in mixed-sized products that require time-consuming post-synthesis size-separation and surface-modification steps. Therefore, we need to develop straightforward synthesis protocols that can directly produce stable, atomically precise metal nanoclusters in high yields. Further, we need to develop guiding principles for the rational design of desired nanoclusters and tailoring the properties of the nanoclusters. The thesis work attempts to address some of these challenges by taking the cases of two representative nanoclusters, Au18(SCH2CH2COOH)14 and Au25(SCH2CH2COOH)18 as examples. We have developed straightforward direct synthesis methods capable of producing atomically precise gold nanoclusters in high yield in an aqueous medium. We have synthesized two representative nanoclusters, Au18(SCH2CH2COOH)14 and Au25(SCH2CH2COOH)18. Among various gold nanoclusters, Au25L18 (where L= ligand) is a ubiquitous nanocluster because of its extraordinarily high stability, unlike Au18L14. Our synthesis of atomically-precise water-soluble gold nanoclusters involved a common reducing agent, NaBH4, and a short carbon chain bifunctional ligand, HSCH2CH2COOH (3-mercaptopropionic acid, MPA) under ambient conditions. We have explored the essential factors that influence the size focusing and stability of water-soluble gold clusters. The synthesis methods do not require maintenance of any special reaction conditions, such as pH, temperatures (such as high or low temperatures), special reagent, etc. By our choice of the bifunctional capping ligand, MPA, we were able to impart water solubility to the gold clusters. Further, we have demonstrated that this kind of capping ligand allows tailoring of nanocluster stability and properties such as photoluminescence, Raman scattering, catalytic properties, etc. over a wide range in aqueous media simply via the variation of the pH of the media. It also allows transfer of the clusters into an organic solvent such as toluene by using a phase transfer reagent such as tetraoctylammonium bromide (TOAB). We have demonstrated that relative quantities of capping agent to gold precursor ratio ([thiol/Au]) and the gold precursor to reducing agent ([Au]/[NaBH4]) ratios play decisive roles in obtaining size-focused atomically-precise gold nanoclusters. The work will open up new avenues for the rational synthesis of definite sized clusters and tuning of their properties in aqueous media.

Abstract

The integration density achievable in Complementary Metal-Oxide-Semiconductor (CMOS) technology is limited by the leakage power seen in nanoscale transistors. Efforts to overcome the bottlenecks of power dissipation set by classical Boltzmann physics, resulted in exploration of new transistor architectures having novel transport mechanisms in channel. Ferroelectric FETs use the negative capacitance phenomenon to achieve low subthreshold slopes and have good potential to replace the classical Metal Oxide Semiconductor Field Effect Transistors (MOSFETs). The transport mechanism of the channel remains unmodified and these novel transistors are fabricated by depositing a ferroelectric material on top of the gate oxide of a conventional transistors. The subthreshold slope (SS) in a ferroelectric device can go below the Boltzmann limit of 60 mV/dec, resulting in low leakage power in FeFET devices. Further, the current boosting observed in these transistors around the threshold region, aids in improving the speed of operation of these devices. In this thesis, we present hysteresis free operation conditions for Ferroelectric FETs, and derive the core compact models of Symmetric Double-Gate Ferroelectric Field Effect Transistors (SDG-FeFET) and Pillar Gate ferroelectric Field Effect Transistors (Pillar-FeFET). The quasi-static terminal-charge models proposed are shown to match very well with exact numerical terminal charge integrals, for all bias conditions and across various device parmeters. The proposed models are integrated into a professional SPICE simulators using the Verilog-A interface. Different combinational, sequential and SRAM circuits are built using SDG-FeFET and Pillar-FeFET transistors and improvement in their switching behavior is demonstrated through SPICE simulations.

Abstract

Sindhi is an Indo-Aryan language, which is spoken by about 53 million people in Pakistan and about 5.8 million people in India. Sindhi is also one of the 22 official languages in India. Despite all these statistics showing how widely spoken Sindhi is, it is still a computationally resource poor language. Development of natural language applications for any language is possible with the help of linguistic resources and computational tools for that language. In this work, we have developed some fundamental resources and tools that shall help natural language processing of Sindhi language. We have developed raw and part-of-speech (POS) annotated corpus for Sindhi Devanagari and subsequently created a Conditional Random Fields (CRF) based automatic POS Tagger that yields an accuracy of 91.78%. We have also built a paradigm based finite-state morphological analyser for Sindhi Perso-Arabic using Apertium’s lttoolbox. This morphological analyser currently has about 3500 entries and a coverage of more than 81% on Sindhi Wikipedia consisting of 341.5k tokens. We worked on Sindhi Perso-Arabic because the corpus of Sindhi Devanagari was very small and we needed very large corpus for good coverage of the vocabulary of the language, which was available in Perso-Arabic. To diminish the script barrier, we also worked on transliteration. We developed a rule-based transliteration system between Sindhi Devanagari and Sindhi Perso-Arabic which yields 91.33% accuracy. We have also conducted experiments to demonstrate resources leveraging and sharing among the scripts through transliteration. These include, generating more data for Sindhi Devanagari to bootsrap POS tagger and building POS tagger for Sindhi Perso-Arabic.

Abstract

Geovisualization or geographic visualization makes patterns become apparent which otherwise would not have been deciphered. It prompts users into thinking and developing new hypothesis which leads them to make better decisions based on the visualization. It can be 2D or 3D visualization generated using real or simulated data. Moreover, both static as well as dynamic processes can be visualized using geovisualization softwares and tools. The visualizations are graphical representations of properties and relationships between objects in space and time. Today, a large variety of geovisualization tools are available. Each of these have a different set of features. Study of current geovisualization tools was done to identify set of unique features. Based on this study, features of these tools were divided into data modelling, data analysis and data visualization. Data visualization helps us explore data, build and validate hypothesis. To gather further insights and identify patterns in this data, analytical functions are required. In some cases, you will have to model the data. Modelling is required to convert input data and develop a more sensible visualization. Different combinations of these features exist in various tools. Along with this, it is also important to understand other aspects such as whether the tool is map-based or graph-based, whether is open source or proprietary, what kind of data input format they have, whether it is interactive or not among others. This work involves the design and development of LSI-STAT, a web-based spatio-temporal interactive analytical platform. It is a visualization platform which captures both spatial and temporal trends in map along with neighborhood. It is a SOLAP platform which extends OLAP capabilities to Spatial. It provides user with some basic querying capabilities based on OLAP principle. The platform models on user-given data. It computes data aggregation over field names based on hierarchy. This hierarchy is user-defined. It enables users to do an in-depth visual analysis of data to discover hidden insights and patterns in data. With this platform, it is expected that users can visualize charts in combination with maps spatially distributed over the area of interest thus providing for a more enhanced integrated spatio-temporal experience. It allows user to configure various parameters and define hierarchy and time-intervals as required. A large number of visualizations can be generated by the user with the help of this platform using location, time and attribute combinations. The developed platform allows user to select multiple data attributes and generate chart for geovisualization. With the toggle basemap option, user can change the basemap used in geovisualization. As geo-spatial extent of the data can change the perspective of looking at the data at multiple geo-levels, the information is tagged to these zoom levels. Also, we anticipate that as the usage of this tool builds, there can be some pre-defined functions and some new toolsets may need to be incorporated. Hence, the platform has been entirely developed using a suite of open-source technologies. To demonstrate the utility and functionalities of the developed tool and to provide for a good understanding of the value that it provides, two case studies have been presented in this thesis. To cover the range, 2 extremely different levels of case studies have been taken - local and global level. Additionally, both of them have different number of parameters. One of them is a GeoBI case study which was done on a large online retail data of three products sold in different countries during January 2009. The combined view of the temporal trends over the regions helps provide insights into ’what sells more where’, ’is the growth patterns similar or not’, etc. It is apparent that this approach helps overcome the fragmented view that the separate views of space and time using the earlier paradigm provided. Another case study that was done using this tool was on Rajiv Aarogyasri health insurance data of Khammam district in Telangana for the years 2013 to 2015. Over the three year analysis which was done using this tool, it was seen that the map showed a lack of access to a large population. Results can help a policy maker to find places that are inaccessible, have limited access to healthcare facilities, lack certain healthcare services or have fewer number of healthcare facilities. Further, they can find the geographical extents of the population that are actually utilizing different healthcare facilities. By analyzing the geographical coverage and spatial distribution of existing healthcare facilities, they can take decisions for scaling up the existing healthcare facility network. It helps them to easily locate places to setup new healthcare facilities and identify services that need to be added to the healthcare facility. Also, capturing the tempora