Abstract

The Discrete Fourier transform (DFT) is one of the most prominent algorithms of signal processing. The algorithm for computing the DFT effectively is known as the Fast Fourier Transform (FFT) algorithm. FFT core is the key component in many of the digital signal processing, Audio, and video signal and telecommunication systems applications and most of the advanced communication systems need an FFT core to process the Orthogonal Frequency Division Multiplexing (OFDM) function. Cooley-Tukey radix-2 FFT algorithm is one of the most popular FFT algorithms which tries to reduce the computational complexity of DFT. In recent times, the use of FFT processors is even more significant and it is rated among the top 10 algorithms with greatest influence on the practice and development of engineering. Since an FFT core is the basic module in all of these applications, the performance and accuracy of the FFT core are of prime importance. Hence, we use the IEEE 754 half precision format to represent the floating point numbers. The increasing demands for speed and performance in some real-time DSP applications make sequential systems inadequate and led to the design of special-purpose parallel FFT processors that include several arithmetic units operating in parallel. In this work, we try to implement the new Modified Radix-2 decimation-in-time (DIT) FFT algorithm with reduced arithmetic complexity which is in the form of Tangent FFT algorithm. As implementation of the Tangent FFT forms is relatively unexplored even though these FFTs give a lot of advantage by reducing the number of computations. This algorithm is implemented using a design which is similar to that of a perfect shuffle architecture as they possess high computation rates by parallel processing and low latencies. Additionally, we proposed a new bit slicing scheme for the IEEE 754 half precision floating representation format to represent the twiddle factors in the proposed architecture to obtain a better implementation of the Modified Radix-2 DIT FFT algorithm. Further, we tried to implement the FFT algorithm using optimal Canonical Signed Digit (CSD) based complex constant multipliers in an attempt to reduce the area occupied by the complex multipliers and increase the precision/SNR of the output. The entire proposed system has been designed using Verilog Hardware description language. All the simulations are performed and verified using Xilinx Vivado tools and synthesis are done on Cadence Encounter RTL compiler with a UMC CMOS 180nm technology library

Abstract

Multiple access schemes play a crucial role in determining the performance of wireless networks. Already established access schemes which were part of the first generation (1G) to fourth generation (4G) cellular networks are time division multiple access (TDMA), frequency division multiple access (FDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA). Though these techniques provide good service with simple receivers, they lack the ability to tackle emerging challenges arising due to monumental rise in the number of devices accessing wireless channel. In this background, non-orthogonal multiple access (NOMA) is seen as a suitable candidate not only to handle the connectivity challenges but also to improve spectral efficiency significantly. NOMA has been in the research literature for long, but it has garnered a lot of interest recently and extensive research is underway. Major hurdles in making NOMA a reality is the complexity involved in realizing successive interference cancellation (SIC) and imperfections in the channel state information (CSI). Hence to make NOMA a reality, effective algorithms that addresses these issues are needed. In this thesis, we focus on system design with optimal resource allocation and relay precoder design in a cooperative relay network, which employs NOMA. The proposed optimal design aims at maximizing the weighted sum-rate. The design problems turn out to be a non-convex optimization problem. We employ minorization-maximization (MM) technique to tackle the non-convexity of the optimization problems. Then, we address the problem of maximization of weighted sum-rate (WSR) while making the system performance robust to errors in the CSI. To solve this problem, we establish a relationship between WSR and weighted mean square error (WMSE) and solve the latter problem which is simpler to obtain a solution for the former. Cognitive radio (CR) is another technology that can help in solving the spectrum scarcity problem. NOMA and CR in combination is expected to improve spectral efficiency significantly. But very few works have explored the two technologies together. So, we consider the design of secondary system working on principles of NOMA in a cognitive radio network by ensuring interference to a primary node is below the permissible threshold. Then, we modify the algorithm to take into account the errors in the CSI and thus ensuring the system performance is robust. Apart from the theoretical analyses, extensive numerical simulations have been performed in order to verify the effectiveness of our proposed designs in terms of sum-rate, weighted mean square error, convergence, etc.

Abstract

The construction in hilly regions is generally constrained by local topography resulting in vertical as well as horizontal irregularities in the buildings. These irregularities cause irregular distributions of mass and stiffness of the structures. As a result, the dynamic properties such as natural period and mode shape of such buildings vary significantly when compared to those constructed on the flat terrain. During past earthquakes like 2005 Kashmir earthquake, 2011 Sikkim earthquake, 2015 Nepal earthquake, severe damages were observed in such constructions on hill slopes. The damage attributed to these buildings was observed to be disproportionately higher when compared to those on a flat land, despite smaller intensity of shaking. This damage is attributed to several reasons viz.,a. Landslide issues, Natural period, Shear distribution in columns and Torsional effects. Therefore, there is an urgent need to understand the seismic behaviour of such buildings standing on hill slopes and include corresponding changes in codal provisions. In the current study, two important seismic parameters, Natural Period and Base Shear of the structure have been studied. Fundamental Natural Period of a building is one such important dynamic property which has its significant use especially during the design phase of the building i.e., while calculation of base shear. Hence, to understand the same the natural period study has been divided into two parts i.e., in first part, natural period of buildings constructed on hill slopes have been obtained and in the second part, parametric study was done to obtain the best fit curve. In the first part, Natural Period of nine buildings constructed on the hill slopes of Mussoorie were obtained using Ambient Vibration testing. From the recorded data, it has been observed that the existing code provisions for estimating the approximate natural period of a building are not efficient enough in the case of such buildings with a standard deviation of errors of 0.1. Since the available data is too less to derive any expression, in the second part, the data has been extrapolated by modelling 270 similar buildings in SAP 2000 by varying different parameters like angle of inclination (5o to 45o), base dimension (3x3 to 5x5) and height (3 to 7 stories). Regression Analysis has been performed on the above data to derive an Empirical Relation to estimate Fundamental Natural Period of buildings constructed on slopes. It has been observed that the standard deviation of errors of the proposed expression with respect to the recorded data has reduced to 0.05. Another major issue with the step back buildings constructed on hill slopes is the distribution of the total Base Shear acting on the structure among the respective columns. The irregular configurations of a step back building causes stiffness differences in the structure. This results in short column/uphill column attracting the maximum base shear causing severe damages to the corresponding short columns and hence the whole structure. For example, in a 3x3 bays building standing on 30o slope, it has been observed that all the short columns were attracting 118% of the total base shear acting on the structure whereas the largest ones were attracting only 1.5%. To understand and reduce these stiffness differences, different building configurations using strut elements were studied. And the configuration of struts which supports optimum distribution of stiffness differences has been identified. The short column of proposed model attracts only 38% of total base shear acting on the structure and hence reducing the design cost as well as damage vulnerability of the structure.

Abstract

Energy consumed in the building sector accounts for 20.1% of the total delivered energy consumed worldwide. In 2016, the residential and commercial sectors in the US accounted for around 40% of the total US energy consumption, which largely contributes to green house gas (GHG) emissions. Immediate action in the building sector is essential to avoid hazardous climate change and also to incorporate energy efficiency in the building design. The early design stage offers more opportunities for use of energy simulation in achieving energy efficiency because there are fewer constraints from other design decisions. Although there are a large number of building simulation tools available, these tools are mostly limited to the design development stage and not the early design stage. In the early design stage, there are a large number of variables involved, and it is difficult for design teams to consider all the solutions and take decisions because this might lead to missing some parameter combinations. In addition, in the early design stage, there are very few users of building energy simulation tools because: • Performing energy simulation requires sufficient knowledge of building physics and expertise • Simulating a large number of combinations takes significant time and computation power • After simulating a large number of combinations, extracting relevant information from output data can be tedious and difficult to understand To fill this gap, an early design methodology has been developed for energy efficient building design. The methodology includes: 1. Getting inputs from the user in a simple user interface 2. Generating input files for the simulations 3. Performing energy simulations efficiently by using parallel simulations and/or using machine learning techniques to speedup simulations 4. Identifying clusters in output data to communicate the strategies to users in the form of humanly describable rules. The contributions of this thesis are as follows: (i) Machine learning regression techniques to speed up the parametric simulations. There were two case studies performed for two different cities in which the use of two algorithms (kNN and SVR) were shown to increase speed of parametric energy simulations by approximately 40 times. (ii) Axis Aligned Hyper Rectangle (AAHR) clustering method for building energy simulation data was used to produce humanly describable or easy to understand rules for design strategies. An energy model was developed and simulated for five cities in different climates and findings from these are discussed in the thesis. (iii) Development of a tool called eDOT to demonstrate the working of early design methodology. eDOT considers five variable parameters, which are orientation, aspect ratio, window to wall ratio (WWR), overhang profile angle, and glass type. Once the user enters these parameters, backend simulations are performed in parallel on the cloud. The user is provided with interactive visualisation of output data. An algorithm is applied to find strategies in output data in humanly understandable rules. To use this tool, the user does not need not to be an expert at using any energy simulation tools. (iv) Development of the Surrogate City Finder (SCF) tool to find similar weather data based on location, latitude range, altitude range, temperature range and distance range. (v) An approach was developed using EnergyPlus simulations to calculate the equivalent SHGC for various fixed/dynamic shading configurations. For the fenestration, the prescriptive method provides maximum allowable U-value and Solar Heat Gain Coefficient (SHGC). Some modifications are allowed to SHGC if fixed horizontal overhangs and/or vertical fins are installed on the fenestration. The modified SHGC with shades is called the equivalent SHGC for code compliance requirements. In this approach, equivalent SHGC is calculated using energy simulations for a reference case (with fixed/dynamic shades) and other parametric cases with varying SHGC (without shades).

Abstract

Cameras and mobile phones have become integral part of our everyday lives as they become portable, powerful and cheaper. We capture and share hundreds of pictures and videos with our friends, family and social connections. Similarly, large volume of such visual content is generated in surveillance, entertainment, and biometrics applications. Without any doubt, people are the most important objects that dominate in these visual content. For instance, photos taken in a family event or movie videos focus around humans. It is utmost important to automatically detect, identify and analyze people appearing in images to obtain a better understanding of these content and make decisions around them. In this thesis, we consider the problem of person detection and recognition in images. This is a well explored topic in vision community with a vast literature focused on these problems. The current state-of-the-art recognition systems are able to identify people with high degree of accuracy in scenarios where images have high resolution, contain visible and near frontal faces, and recognition systems have access to sufficiently large training gallery. However, these systems need significant improvement in challenging real-world applications such as surveillance or entertainment videos where one needs to handle several practical issues such as non-visibility of faces, limitation of training samples, domain mismatch, etc in addition to other instance variations such as pose, illumination, and resolution. While there are plenty of challenges pertaining to person recognition, we are interested in some of the open challenges that are relevant from the deployment perspective in diverse recognition scenarios. We first consider people detection that is a pre-requisite for a recognition system. We detect people in images by detecting their faces through an exemplar based detector. Exemplar approach detects faces through hough voting using an exemplar training database indexed with bag-of-words method. We propose two key ideas referred as “Visual phrases” and “Contextual weighting” into the exemplar approach that improves its performance significantly. We show that visual phrases which encode dependencies between visual features are discriminative and propose a strategy to incorporate them into exemplar voting. We also introduce the notion of spatial consistency for a visual feature which weights each occurrence of a feature based on its global context. Our evaluation of popular in-the-wild face detection benchmarks demonstrate significant improvement obtained using these proposed ideas. We then focus on person recognition and consider several issues encountered in practical recognition systems. We initially address the common and important issue of “unavailability of sufficient training samples” during recognition. We propose a solution based on semi-supervised learning that can efficiently learn from a small amount of labeled data and a large amount of unlabeled data. We demonstrate how the similarities between labeled and unlabeled samples can be effectively exploited to improve the performance. We then consider the problem of “domain mismatch” between training gallery (source) and probe instances (target). We consider a recognition setup in which the objective is to identify people in a collection of probe images using a training gallery collected from different domain. We propose a novel two-stage solution that generates the labels of a few confident seed images from the target domain and propagate their labels to remaining images using a graph based framework. We evaluate our approach in several practical recognition scenarios such as movie videos and photo-albums. We then consider a different recognition scenario in which faces are not completely visible due to occlusion or people facing away from the camera. To deal with such occluded and partially or completely non-visible faces, we exploit information from other body regions such as head, upper body and body to improve the recognition. When considering different body regions, pose of different body regions pose a serious challenge. To handle the issues of unreliable facial region and pose variation, we propose a technique that learns multiple pose-specific representations from different body regions. Our approach involves training a separate deep convolutional network for each pose and then combining their predictions using adaptive weights determined by the pose of the person. Person recognition approaches based on multiple body regions however require training multiple deep convolution networks for different body regions resulting in large number of parameters with slower training and testing procedures. To overcome these, we develop an end-to-end person recognition approach based on pooling and aggregation of discriminative features from multiple body regions. Our end-to-end convolutional network pools features from several pre-determ

Abstract

Representation of words as dense vectors has recently gained traction following the introduction of word2vec model. Traditional word representation models are sparse, long and each dimension represents a word in vocabulary. Dense vectors in contrast are dense meaning most values are non zero, short meaning the dimension of vectors is very less compared to size of the vocabulary and also implicitly encode semantics within the dimensions as the dimensions no longer represent a word from the vocabulary. These advantages have made them the model of choice for various machine learning models. In this work we introduce a method to improve word embeddings and show the utility of word embeddings in tasks of controversy detection and item recommendation. In the first part we develop a method to improve word embeddings of a language using resources of some other language. Word embeddings learned from text corpus can be improved by injecting knowledge from external resources, while at the same time also specializing them for similarity or relatedness. These knowledge resources (like WordNet, Paraphrase Database) may not exist for all languages. We introduce a method to inject word embeddings of a language with knowledge resource of another language by leveraging bilingual embeddings. First we improve word embeddings of German, Italian, French and Spanish using resources of English and test them on variety of word similarity tasks. Then we demonstrate the utility of our method by creating improved embeddings for Urdu and Telugu languages using Hindi WordNet, beating the previously established baseline for Urdu. In second part we demonstrate a method to detect controversy on news issues. Detecting controversial news topics on web is a relevant problem today. It helps to identify the issues upon which people have divided opinion and is specially useful on topics such as a presidential election, government reforms, climate change etc. First we use word embeddings to find out similar news articles about a given topic. Then we perform an analysis of peoples reaction on social media to these news articles. We use sentiment analysis and word matching to accomplish this task. We show the application of our method for detecting controversial topics during the US Presidential elections 2016. The crucial step in controversy detection is to eliminate bias due to people from a particular political spectrum, so we carry out our analysis on a group of articles reporting an incident, rather than a single article. We use word embeddings to find articles reporting on a topic from different sources like left, right or mainstream. The quality of embeddings used has a direct effect on the performance of the system. In third part, we show another application of the word embedding literature where we borrow the concepts of word embeddings and retrofitting to recommend items in an aggregated marketplace.

Abstract

Units within a piece of text are not meant to be understood independently but understood by linking them with other units in the text. These units maybe clauses, sentences or even complete paragraphs. Establishing relations between units present in a text allows the text to be semantically well structured and understandable. Understanding the internal structure of text and the identification of discourse relations is called discourse analysis. A fully automated shallow discourse parser would greatly aid discourse analysis and improve performance of NLP applications such as question-answering and text summarization. Given a text, a shallow discourse parser would extract discourse relations, each between two spans of text. Discourse relations whose presence is explicitly marked by a discourse connective are called Explicit discourse relations and those which are not are called Implicit discourse relations. This thesis covers the experiments and research conducted to build a shallow discourse parser for explicit discourse relations in Hindi. The pipeline proposed achieves state of the art performance.

Abstract

Papillary renal cell carcinoma (PRCC) is the second most common subtype of renal cell carcinoma accounting for 10 − 15% cases. PRCC is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has led to the creation of multi-omics data of PRCC. In this thesis, multi-omics data involving gene expression and DNA methylation of PRCC is analyzed to identify biomarkers, develop predictive models of tumor (with Group Lasso) using both gene expression and DNA methylation features showed the overall best performance on test data with MCC and PR AUC of 0.77 and 0.82, respectively, across different rep-resentations of methylation data and feature sets. Our study not only generates insights into the gene regulation but also develops models that will have diagnostic applications. stage prediction and understand the relationship between them. We first developed a machine learning pipeline incorporating different feature selection algorithms and classification models for tumour stage prediction using RNA sequencing dataset (RNASeq). To get a reliable feature set, we extracted features from different partitions of the training dataset and aggregated them into feature sets for classification. We evaluated the performance of different algorithms on the basis of 10-fold cross validation (CV) and independent test dataset. 10-fold CV was also performed on a microarray dataset of PRCC. A random forest based feature selection yielded minimum number of features (104) and a best performance with area under Precision Recall curve (PR AUC) of 0.80, MCC (Matthews Correlation Coefficient) of 0.71 and accuracy of 88% with Shrunken Centroid classifier on a test dataset. We identified 80 genes that are consistently altered between stages by different feature selection algorithms. The extracted features are related to cellular components -centromere, kinetochore and spindle, and biological process mitotic cell cycle. The interactions between features also reveal the systems-level alteration driving the progression from early stage to late stage of PRCC. The potential mechanisms for an increase in chromosome instability in the late stage of PRCC has been proposed. We then investigated the methylation patterns of PRCC and its relationship with the gene expression using Illumina methylation 450K array. We found that both the promoter and body of tumor suppressor genes, microRNAs and gene clusters and families including cadherins, protocadherins, claudins and collagens are hypermethylated in PRCC. Hypomethylated genes in PRCC are associated with the immune function. The gene expression of several novel candidate genes including interleukin receptor IL17RE and immune checkpoint genes HHLA2, SIRPA and HAVCR2 shows significant correlation with the DNA methylation. A significant correlation within the tumor samples was also observed for genes RRM2, NCAPG and SLC7A11, which we found to be potential biomarkers for stage progression in the first part of our study. Further, we developed predictive models based on single- and multi-omics data to distinguish early and late stages of PRCC. A comparative study of predictive models, data in- tegration techniques and representations of methylation data was performed. Multiple kernel learning (with Group Lasso) using both gene expression and DNA methylation features showed the overall best performance on test data with MCC and PR AUC of 0.77 and 0.82, respectively, across different rep- resentations of methylation data and feature sets. Our study not only generates insights into the gene regulation but also develops models that will have diagnostic applications.

Abstract

In today’s world, users prefer getting a precise answer to their questions rather than a set of relevant documents provided by search engines. It has led to the immense popularity of Community Question Answering( CQA ) forums, where forum users respond directly to questions with targeted answers. Users ask question to other members of community and expect domain experts to answer questions. Questions asked on community Question answering are generally different in nature than traditional search queries due to specific user needs. A user may have a query about the general topic to a most specific one. Hence, user uses CQA platforms to express users query in more verbose form. Recently, Community Question Answering forums are gaining huge popularity in the health-care domain. The impact of the health-care industry on a day to day patient care and on biomedical research is immense. Owing to an exponential growth of online information seekers in the health domain, there is a particularly great and growing demand for QA systems that can effectively and efficiently aid consumers in their information search. However, in order to get better and quick responses to questions on health forums, it is important to categorize questions and direct them to appropriate experts based on question types. Due to huge popularity of CQA’s, people get noisy, non-relevant responses as well along with relevant one. In order to improve the quality of CQAs, it is important to filter out noisy answers and present only relevant answers. In this thesis, we propose a model to classify question-based on user intentions. We also introduce a model which predicts relevance of answers with respect to the question. After defining relevance of answers, our model also predicts polarity of answers with respect to question. In the first model, we propose a novel approach for classifying questions capturing a unique user intent posted on health forums. Our proposed model combines deep learning based features along with domain based knowledge and statistical features. We further added weak supervision to improve performance. We also proposed a unique model of weak supervision, called ”Self-training with Lookups”. Our best performing model for question classification provides an accuracy of 71.13%, which beats the state-of-the-art method with a margin of 3.13%. In the second model, we work towards finding the relevance and polarity of responses to questions. When questions get thousands of responses, it becomes difficult for the user to read every response and he or she might lose some important response. The dataset consists of questions and corresponding responses having some inherent implicit opinions. First, we devise a model to find if the answer is relevant to the question. In the next step, model categorizes relevant answers based on their polarity. Our Final model takes ensemble representation of TF-IDF vector and Doc2Vec vector as features. Traditional machine learning model like multinomial naive Bayes performed superior to other models in ensemble environment

Abstract

A smart grid is an efficient and sustainable energy system that integrates diverse generation entities,distributed storage capacity, and smart appliances and buildings. Smart grids also indirectly facilitate end-user involvement in grid stability. The brokers, supplying electricity to end-users, represent large population of customers in the electricity markets, and incentivization of such brokers to reduce their supply-demand imbalance can lead to utilization of sustainable but intermittent sources of energy like solar and wind energy. Overall, this will reduce grid operationalization costs by reducing peak demand, and reduce broker costs, and in-turn reduce energy costs for the end-users. Most of the electricity markets consist of Periodic Double Auctions (PDAs), where trillions of dollars worth electricity is traded. Any broker participating in these PDAs has to plan for bids in the current auction as well as for the future auctions, which highlights the necessity of good bidding strategies. Since the complexity and dimensionality of the actions taken by such brokers is huge, it opens avenues for deployment of autonomous energy brokers. Smart grids also bring new kind of participants in the energy markets, whose effect on the grid can only be determined through high fidelity simulations. Power TAC offers one such simulation platform using real-world weather data and complex state-of-the-art customer models. In Power TAC, autonomous energy brokers compete to make profits across tariff, wholesale and balancing markets while maintaining the stability of the grid. We make the following contributions to the areas of smart grid, autonomous agents, game theory and machine learning applications: (1) We do a Nash Equilibrium analysis of single unit single-shot double auctions with a certain clearing price and payment rule, which we refer to as ACPR, and find it intractable to analyze as number of participating agents increase. We further derive the best response for a bidder with complete information in a double auction with ACPR. (2) Leveraging the theory developed for single-shot double auction, we proceed by modeling the PDA of Power TAC wholesale market as a Markov Decision Process (MDP), and solve it using dynamic programming. Based on that, we propose a novel bidding strategy, namely MDPLCPBS. We empirically show that MDPLCPBS follows the equilibrium strategy for double auctions that we previously analyze. In addition, we benchmark our strategy against the baseline and the state-of-the art bidding strategies for the Power TAC wholesale market PDAs, and show that MDPLCPBS outperforms most of them consistently. (3) We design a electricity usage predictor, which uses customer usage patterns and weather data, using neural networks. This is the first usage predictor which utilizes weather data in the Power TAC scenario. (4) We use an MDP based formulation for the tariff market, and solve it using Q-learning to generate tariffs. The novelty lies in defining the reward functions for the MDP, solving the MDP, and the transformation of the solution into a tariff in the tariff market. We also propose a few heuristics which convert nearoptimal fixed tariffs to time-of-use tariffs aimed at mitigating transmission capacity fees. (5) We discuss the architecture of our autonomous energy broker VidyutVanika, which was the runner-up in Power TAC 2018 Finals. VidyutVanika implements our learning strategies for the tariff and wholesale market, along with a few heuristic ideas. We have also released VidyutVanika’s binary for offline testing and research purposes. (6) We do an empirical analysis of VidyutVanika’s performance during Power TAC 2018 Finals, using Power TAC 2018 tournament data. We also illustrate the efficacy of its sub-modules and strategies using controlled experiments.