Abstract

Semiconductor industry has long struggled to develop systems that operate at higher speeds, thereby reaching giga-hertz or even tera-hertz in the recent times. However, newly emerging applications like biomedical, environmental monitoring, surveillance etc., place contrast requirements to these high speed systems. These applications do not require higher speeds, however they do not have access to wall power (in most cases) and must resort to energy harvesting, miniaturized batteries, thereby placing power consumption as the bottleneck. Additionally, systems powered through energy harvesting must be able to work with lower supply voltages, while systems powered by miniaturized batteries must be able to work for wide supply voltages precluding the need of voltage regulator. For biomedical applications, these systems should also occupy lower form factor, so that they could lead to less invasive surgeries. Temperature is one of most important sensing modality for any IoT system, and hence temperature sensor must be designed keeping the above constraints in mind. Consequently, we propose 47nW temperature sensor in TSMC 180nm technology node and 419pW temperature sensor in UMC 55nm technology node. Both the sensors are based on completely different architectures. In TSMC 180nm technology, temperature is transduced to delay using temperature characteristics of resistance and capacitance. The architecture is digital friendly, since delay can be easily converted to digital code. Since temperature to delay conversion depends mainly on passive elements, the architecture is highly resilient to supply variations. To further push the power consumption from nano-watt to sub nano-watt regime and reduce the area further, we exploit the temperature dependency of gate-leakage in lower technology nodes. A process and supply-invariant proportional-to-absolute-temperature voltage (VP T AT ) is generated by using BJTs and gate leakage characteristics in UMC 55nm technology. The obtained VP T AT is highly resilient to process and supply variations and occupies lower form factor. Biasing circuits being common to any analog circuits, must also be designed for the above constraints. Consequently, we propose 50nA current reference in TSMC 180nm technology node and 60pA current reference, 600mV voltage reference in UMC 55nm technology node. Both the current references are based on completely different architectures. In TSMC 180nm technology, we design the 50nA current reference by cancelling the temperature coefficient of voltage and temperature characteristics of resistance. A voltage bias circuit is designed in a way that the obtained current reference temperature coefficient is independent of process variations, necessitating only single point trim. However, this architecture cannot be used for designing sub-nW currents, as it requires impracticably high resistances. To push the power consumption of current reference for sub-nW regime without compromising on area, we exploit the temperature characteristics of gate leakage in lower technology nodes. The proposed 60pA current reference is the first sub-nW current reference that solves the problem of exponential power consumption with temperature and process. With a slight modification to this architecture, we develop a 600mV voltage reference for sub-nW regime. Both these sub-nW voltage and current references require only single-point trim. All the above mentioned voltage and current references work for wide temperature range and show high supply resilience over wide supply ranges. Sleep mode timers are essential to any duty cycled IoT nodes for synchronizing data transmission and data reception. Although active power consumption is reduced by duty cycling, sleep mode power consumption of timers becomes a bottleneck in scaling the overall system power consumption. These timers are also expected to be robust to supply and temperature variations, as error in frequency necessitates additional guard band time, thereby increasing the overall system power consumption. RC oscillators are preferred due to their excellent temperature and supply stability, However scaling power consumption to sub-10nW, requires impracticably high resistances in the order of hundreds of mega ohms/giga ohms. Although gate leakage in thin oxide transistors serve as effective replacements for large resistance in lower technology nodes, TSMC 180nm does not have gate leakage and requires large physical resistances. Consequently, we propose a resistance amplifier, a CMOS circuit that realizes the characteristics of large resistance using small resistance, thereby saving area and hence cost. This resistance amplifier is substituted in the conventional offset compensated RC oscillator architecture, and the overall functionality of the system is evaluated.

Abstract

In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. It is an essential component in the autonomous driving pipeline as the forecasted trajectories are used as inputs in the planning and control modules of the autonomous vehicles. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (e.g., image sequences). As a result, such methods do not generalize to datasets they have not been trained on. On the other end of the representation spectrum, some approaches represent the vehicle of interest in a scene in terms of geometric properties such as 3D location w.r.t ego vehicle, rotational parameters, and velocity. These approaches suffer from a lack of semantics / contextual information and cannot produce the most accurate forecasts. In this thesis, we propose intermediate representations that are particularly well-suited for trajectory forecasting. We show that these intermediate representations, generated using off-the-shelf computer vision methods for object detection and semantic segmentation, provide the right balance between competing features of expressiveness and generalizability. As opposed to using texture (color) information from images, we condition on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles). We demonstrate that semantics provide a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize across different sensing modalities (stereo imagery, LiDAR, a combination of both), and also across completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs. right-handed driving). We also conduct detailed ablation studies to understand which semantics generalize across datasets and demonstrate their importance in the generalizability of the model. Additionally, we demonstrate an application of our approach in multi-object tracking (data association).

Abstract

Few resource-rich languages like English and French have been extensively analyzed for Natural Language Processing (NLP) tasks. Domain expertise is essential requirement for studying the properties of a language to build linguistic resources, prepare annotated training data, select appropriate features, configure parameters and set exceptions in the systems for building models for solving NLP tasks of a language. And to build data driven models like, deep learning models, huge amount of annotated or untagged training data is required. So, handling each of the remaining 6500+ “resource-poor” languages would require the same amount of intensive effort, expertise, expenses, time and large training datasets. Hence, it is required that we build domain independent and language independent data driven systems which can work reasonably and effectively with less amount of training data without requiring domain expertise. For this purpose, in this thesis, we propose generic concepts and data driven methods which can be used to build systems for solving the NLP tasks of resource-poor languages. We propose a generic associative classification approach called associative context classification which we have developed using our proposed context based list concept that groups items of some specific context and other proposed parameters and concepts. In our research, we have demonstrated the application of this proposed approach in developing solutions to a few representative Natural Language Processing tasks. We have focused on developing semi-supervised methods using small sized annotated data. Our methods perform well even with less amount of training data without using domain knowledge explicitly and hence, are especially suitable for resource-poor languages which lack domain resources. Our proposed approach is based on associative classification and on the one sense per collocation hypothesis which states that the sense of a word in a document is effectively determined by its context. Hence, our proposed approach can be applied for NLP tasks which depend on collocation property. We have validated the utility of our proposed approach for NLP tasks of resource-poor languages by successfully applying it for developing generic methods for Part-of-Speech tagging and Word Sense Disambiguation tasks. Part-of-speech (POS) tagging is a NLP classification task that assigns a POS tag or other lexical class marker to an item or to each item in the sentence. Here, we use the term “item” to represent all the words and tokens of a language. All the available POS taggers including the state-of-the-art taggers require training data and linguistic resources like dictionaries in large quantities. These taggers do not perform well for resource-poor languages. So, there is a need to develop generic semi-supervised tagging methods which use untagged corpus and require less or no lexical resources. Most of the existing vi vii semi-supervised techniques require large untagged corpus, while for many resource-poor languages, even obtaining a small untagged corpus is hard. Word sense disambiguation (WSD) is a classification task which involves determining the correct meaning of each word in a sentence or phrase based on the neighboring context items. Automated WSD methods use knowledge structures like, WordNet and dictionaries and hand crafted features and rules crafted by domain and linguistic experts from the training data. This is a costly and time taking process and requires extensive amount of domain resources and linguistic expertise. These requirements make it difficult to design a WSD algorithm for resource-poor languages and hence domain independent methods are needed to be developed for this task also. For our experiments, we have cleaned and prepared resource-rich English, resource-moderate Hindi and resource-poor Bengali, Marathi, Tamil, Telugu and Urdu language datasets for POS tagging experiments and English, Hindi and Marathi language datasets for WSD experiments. As part of our research work, we have developed: • Two semi-supervised POS tagging methods using proposed associative context classification approach. • Various ensemble POS tagging methods using proposed associative context classification approach, Support Vector Machine, Conditional Random Field, Decision Tree and Semi-supervised Condensed Nearest Neighbor method. • One semi-supervised associative WSD model using proposed associative context classification approach. • One ensemble WSD model using proposed associative context classification approach and Support Vector Machine. • One negation rule finding algorithm for the POS tagged data to find annotation errors from the tagged data.

Abstract

Analytic reasoning differences, as gauged from intelligence metrics, in students engaged in streams requiring a predominantly divergent (arts) or convergent thinking (science and engineering) is a topic of interest. In this thesis an attempt is made to examine the differential application of solution analysis in standard non-verbal intelligence tests by fine arts and engineering students, two cohorts with distinct academic coursework, skill training and attributed differences in their thinking. As understood from previous research, intelligence being a multi-dimensional entity, can be broadly categorized into two divisions (Cattel,1963) - a) Fluid Intelligence (Gf) b) Crystallized intelligence (Gc). The former refers to the ability of humans to solve novel problems and is measured using tests requiring induction, deduction and reasoning. The latter defines the skill of humans in applying existing knowledge and experience, mostly evaluated by verbal comprehension and working memory tests. There are debates about fluid intelligence being an innate ability or genetic factor but measures of Gf have been observed to be affected by various factors such as socio-economic differences, gender, skill training etc. Keeping in context the larger debate on the role of each factor and the correlations, the aim of my study is to find whether skill training creates a difference in performance in a Gf test. Ravens Progressive Matrices (RPM) - a standard measure of fluid intelligence was used to examine not just the score-wise performance but the analytical processes applied and problem solving technique. The premise for considering fine arts students for a fluid intelligence test with shapes, patterns and constrast, is their artistic talent to create identified visual patterns, manipulate geometrical shapes and sharper contrast elimination. Other reasoning applied for the selection of this participant cohort for comparison is that they differ in their thinking style based on Hudson’s theory (1966), that this variation in thinking style is reflected in choice of profession/education. That is, this theory proposed that convergent thinkers display a rule-based thinking and engage in science, mathematics while divergent thinkers are defined as engaging in freethinking and excel in arts and/or humanities. Various arguments have been made on the accuracy of this distinction, fine arts such as music also have certain set of rules (mathematical) while fields such as engineering encourage divergent thinking in design and applications. Hence, in my work I extend the research on this theory by exploring the approach towards analytical problem solving and make inferences on the thinking styles as per the above-proposed theory, with two distinct cohorts – computer science and fine art students. To examine further the role of academic skill training, the engineering students were grouped into art-trained and non art-trained sets A difference in performance just based on numerical data such as scores would be insufficient. Insights of the approach to solution can be extracted from the differences in visual strategies applied with eye-tracking technique as valid method. This technique has been established in literature as efficient for analyzing attention patterns, which is said to reveal the underlying cognitive processes applied. Hence in this study, visual search strategy (scan-path) was inspected to extract the approaches towards a problem solution for each item of the RPM test. Additional measurements included analysis of fixation duration and area of interest in the matrix elements and options spaces. The selection of RPM for the current study is based on established concepts : RPM being a patternbased test, studies have indicated that visual information processing or visual strategies might be crucial to understand and solve these questions. To further identify the underlying differential visual strategies applied, the Navon stimuli and Embedded Figures Task (EFT) were also administered. Navon stimuli allows for insights to the variations in visual information processing technique, principally global vs. local processing and the Embedded Figures Test is utilized to analyze field independence and its association to thinking style. The results show that engineering students performed better than art students in terms of score (T-test: p <0.0001) while no significant difference was seen among art-trained and non art-trained engineering students (p >0.05). Analysis of eye-tracking data showed considerable differences in the various performance metrics such as time and fixations. Scan-paths generated from fixation mapping were compared within-subject and for each question. First, three major scan-path directions that indicated pattern identification variations were observed - Horizontal, Vertical and Defocused [elements of matrix scanned in no particular direction]. On an average, artists pref

Abstract

About 2% [1] of the dam failures are said to be due to seismic activity. Dams are extremely important lifeline structures and predicting possible damage in the face of an event is essential. Failure of dams due to earthquakes is extensively studied because while the earthquake cannot be terminated, the loss associated to the damage of the dam can be highly mitigated. The loss caused due to earthquake ground motions an be reduced by studying the damage to the dam design under various earthquake loading scenarios and iteratively modifying the design to be resistant to the highest earthquake loading possible in the area of construction. Several concrete gravity dams were severely shaken by earthquakes in the past[2]. Thus the study of response of dams against the seismic ground motions is very necessary. This work addresses the seismic analysis of concrete dams. For this purpose, the Koyna Dam has been cho- sen. Firstly, the performance of the Koyna dam subjected to the Koyna earthquake is evaluated through 2 numerical models : Linear dynamic analysis and Non-linear dynamic analysis. The results from the linear analysis and the non-linear analysis are compared. Another earthquake of similar intensity of the Koyna earthquake, but different parameters has been applied and the dam performance has been anal- ysed. It has been found that the earthquake with lesser peak ground acceleration (PGA) caused greater damage to the dam as compared to the earthquake ground motion with higher PGA. The IS Code con- siders the effects of peak ground acceleration predominantly and the aforementioned result proves that there is a need to study the response of the dam under other ground motions with varying ground motion characteristics of Amplitude (PGA), Frequency(Kramer Frequency F P ) and Duration (significant time duration T sig5%−95% ). To quantify the damage in Koyna dam, an incremental dynamic analysis has been conducted to study the impact of varying ground motion characteristics on the performance of the dam. Non-linear analysis is performed on the dam cross-section subjected to the ground motions. Engineering Demand Param- eters are obtained from the analysis. We aim to understand the damage through 3 damage parameters and classify the damage .The damage parameters are based on the frequently used Engineering Demand Parameters (EDPs). A relation between the damage parameters and damage states is established. Fur- ther, the damage is classified using Decision Directed Acyclic Graph Support Vector Machine (DDAG- SVM) to study the relation between ground motion parameters (GMPs) and the Damage Parameters for the separate classes. Finally, a shallow Artificial Neural Network is modelled to understand the correlation between GMPs and the Damage Parameters. The high correlation coefficient and low root mean squared error show that the correlation between ground motion data and system response can be predicted very precisely, proving that, performance based earthquake design can be extremely efficient and economical. A sensitivity analysis has been carried out to determine the relative importance of the 3 main ground motion parameters. Further, the seismic hazard assessment of the Koyna region using ground seismotonic research by previous researchers has been conducted. The possible threat to the dam and the return periods of the earthquakes have been calibrated. Based on the ground motion data available in the Koyna region, po- tentially damaging earthquakes, selected based on results obtained from ANN model, have been selected and scaled to the peak ground acceleration found from the seismic hazard assessment. Taking the extent of tensile damage to increase linearly with the increase in PGA for a ground motion (Established by the analysis of Koyna dam under koyna earthqauke scaled to 10 PGAs) , the damage possible between 2 scaled PGAs of a ground motion can be linearly interpolated. Based on this the demand of koyna dam in the region has been assessed probabilistically. The range of damage capable on the body of the dam, from minimum to maximum, has been obtained by subjecting the dam to only 4 ground motions (scaled to 3 PGAs) selected based on the ground motion parameter study conducted in this work. A computationally cheaper, Incremental dynamic analysis has been proposed.

Abstract

Curriculum Learning (CL) strategies have been proposed for text classification tasks. These strategies propose “difficulty” score for each training sample. Curriculum Learning hypothesize that within this ordering, training on the easier samples first followed by the difficult, leads to an increment in performance. The idea is derivative of cognitive science’s theory of how human and animal brains learn, and that learning a difficult task can be made easier by phrasing it as a sequence of easy to difficult tasks. Application of curriculum learning for sentiment analysis is a rather new topic and hence requires through experiments and analysis. We experiment the effectiveness of popular language model BERT coupled with curriculum learning. Previous works have approached Curriculum Learning especially for text classification tasks such as sentiment analysis in two major pacing methods - Baby Steps and One Pass. Pacing methods decide how the model observes the curriculum divided data irrespective of the architecture and the curriculum strategy. Baby Steps proposes increasing the training data for the classification model in a cumulative fashion. This implies that model is trained on an easy set and then harder data is added to the previous set. This makes the training to be time consuming. As opposed to Baby Steps, One Pass proposes making distinct, mutually exclusive sets of the training data and trains on each one of these sets one after the other. However, in practise, One Pass while being faster, always was shown under-performing Baby Steps or even no curriculum. We analyze the reasons behind the consistent failure of One Pass. Our experiments show that One Pass suffers from Catastrophic forgetting every time its trained on the next set leading to model forgetting the previous set. We further derive an effective curriculum strategy based on SentiWordNet for sentiment analysis. Unlike sentence length strategy which proposes training network on samples ordered by sentence length, proposed strategy trains the network based on a difficulty ordering according to the task of sentiment analysis. Our results across multiple deep learning architectures demonstrate that this is an important prerequisite for an effective curriculum strategy. We then analyze curriculum learning’s relation to difficulty of a task by experiments with BERT. Previous works have suggested that curriculum learning shows higher improvement for harder tasks and low improvement for easier tasks. We call this Task Difficulty Hypothesis. We experiment on multiple synthetic datasets to prove this hypothesis more strongly. Finally, we visualize curriculum learning through a novel attention movement visualization methodology. These visualizations build a qualitative study to aid in understanding how curriculum learning works. We see that complicated tasks are broken down into easier sub-tasks which are addressed in a step by step manner by curriculum learning.

Abstract

As more robots continue to pervade our workplaces, homes, and lives, developing accurate and robust algorithms for their effective and safe operation is of paramount importance. We see more and more of such robots being used for various applications and we certainly have a new future ahead of us filled with smart robots navigating autonomously, be it a vacuum cleaner robot or logistics drone or an autonomous car. There is a plethora of ongoing research work trying to improve upon various algorithms for autonomous navigation of robots. Object detection is one such research area which has gained significant momentum. The 6 degree of freedom object pose estimation enables the robot to localize the object in the surroundings and perform its task accordingly. We see that there have been some very interesting work done for 6DOF pose estimation from monocular images. In this thesis we propose a new method for 6DOF object pose estimation using a single monocular camera with the assumption of known camera height. We detect the objects in the RGB images using deep learning and use a renderer called neural renderer to optimize for the 6DOF pose of the object. Furthermore we also explore how object detection can aid monocular SLAM. Here we propose the first most principled formulation of it’s kind which incorporates the object optimization into the bundle adjustment of SLAM, we call it the joint bundle adjustment. This joint formulation allows us to optimize the object shape, pose, and also the 3D semantic points and camera localization in a joint fashion.

Abstract

Medical imaging has been aiding diagnosis and treatment of diseases by creating visual representations of the interior of the human body. Experts hand-mark these images for abnormalities and diagnosis. Supplementing experts with these rich visualization has enabled detailed clinical analysis and rapid medical intervention. However, deep learning-based methods rely on abundantly large volumes of data for training. Procuring data for medical imaging applications is especially difficult because abnormal cases by definition are rare and the data, in general, requires experts for labelling. With Deep learning algorithms, data with high class imbalance or of insufficient variability leads to poor classification performance. Thus, alternate approaches like using generative modelling to artificially generate more data have been of interest. Most of these methods are GAN [11] based approaches. While they can be helpful with data imbalance they still require a lot of data to be able to generate realistic images. Additionally, a lot of these methods have been shown to work on natural images where the images are relatively noise-free and smaller artifacts aren’t as damaging. Thus, this thesis aims at providing synthesis methods which overcome the limitations of smaller datasets and noisy profile. We do this for two different modalities, Fundus imaging and Optical Coherence Tomography (OCT). Firstly, we present a fundus image synthesis method aimed at providing paired Optic Cup and Image data for Optic Cup (OC) Segmentation. The synthesis method works well on small datasets by minimising the information to be learnt by leveraging domain-specific knowledge and by providing most of the structural information to the network. We demonstrate this method’s advantages over a more direct synthesis method. We show how leveraging domain-specific knowledge can provide higher quality images and annotations. Inclusion of these generated images and their annotations in training of an OC segmentation model showed a significant improvement in performance, thus showing their reliability. Secondly, we present a novel unpaired image to image translation method which can introduce abnormality (Drusen) to OCT images while avoiding artifacts and preserving the noise profile. Comparison with other state-of-the-art images to image translation methods shows that our method is significantly better at preserving the noise profile and is better at generating morphologically accurate structures.

Abstract

High-utility itemset mining (HUIM) has emerged as an important research topic since the quantity and profit factors employed to mine the high-utility itemsets (HUIs). In HUIM, utility measure used to extract high-utility itemsets from the given transactional database. An itemset called a high-utility itemset if it satisfies a user-specified minimum utility (minU til) constraint. Another distinguishing characteristic of HUIM is identifying interestingness about the periodic behavior of high-utility itemsets in the given transactional database. Finding such itemsets have many real-world applications. As an example, such knowledge can identify recurring customer purchase behavior in retail databases. The popular adoption and successful industrial application of the HUIM model suffer from the issue of computational expensiveness. Furthermore, the existing approach to extract periodic behavior of highutility itemsets fails to discover high-utility itemsets that have exhibited partial periodic behavior in the database. In this thesis, we have proposed two improved HUIM approaches. First, to improve the performance of HUIM, we have proposed a generic high-utility frequent itemset model to find all itemsets in the database that satisfy user-specified minimum support and minimum utility constraints. Two new pruning measures, named cutoff utility and suffix utility, are proposed to reduce the computational cost of finding the desired itemsets. A single phase fast algorithm, called High-Utility Frequent Itemset Miner (HUFIMi), is introduced to efficiently discover the itemsets. Experimental results demonstrate that the proposed algorithm is efficient. Second, to enable the extraction of knowledge about the partial periodic behavior of customers, we have proposed a flexible model called Partial Periodic High-Utility Itemset Mining (PPHUIM). The proposed model discovers only those interesting high-utility itemsets that occur at regular intervals in a given temporal transactional database. The proposed model allows the variation of external utilities and exploits the notion of periodic support. An efficient depth-first search algorithm, called PPHUI-Miner, has been proposed to enumerate all partial periodic high-utility itemsets in a given temporal transactional database. We have demonstrated the efficiency of the proposed approach through extensive experimental results. Overall, we have proposed improved approaches to mine high-utility frequent itemsets and partial periodic high-utility itemsets from transactional databases and demonstrated the efficiency through extensive experimental results.

Abstract

Dependency Parsing is extremely useful in several downstream NLP tasks. However dependency parsers are not available for several Indian Languages. The primary reason for that is the unavailability of annotated treebanks. Telugu is a resource poor language and the existing Telugu treebank is very small in size. In this work, we strive to extend and improve the existing Telugu Dependency treebank annotated with Paninian dependencies released as part of the ICON 2009 shared task on Indian Language Parsing. The existing treebank consists of around 1600 sentences and we extend this treebank by another 987 sentences and clean up the existing treebank. The final extended treebank consists of 2436 sentences. The original treebank is only annotated with inter-chunk dependencies. We automatically annotate the intra-chunk dependency labels for the extended treebank using a Shift-Reduce parser based on Context Free Grammar rules written for Telugu. Annotating the intra-chunk dependencies finally provides a complete parse tree for every sentence. We also convert the treebank from Anncorra POS schema to the latest BIS POS schema. In the last decade, there has been an increased focus on multilingual NLP. The Universal Dependencies (UD) project has been created to promote multilingual research on syntax and parsing and to facilitate cross-lingual learning experiments. In this work, we automatically convert the existing Telugu Paninian dependency treebank to UD. We also try to build an easily usable and robust dependency parser for Telugu. Previous work on Telugu dependency parsing was primarily focused on either rule-based approaches or data-driven statistical approaches using Malt Parser. These approaches typically made use of intermediate tools like POS tagger, morph-analyzer, shallow parser etc which are expensive to build. Therefore, in this work we propose to bypass the pipeline approach and develop an end-to-end dependency parser for Telugu based on the recent developments in the field of NLP such as contextual vector representations. We train a BERT model on the Telugu Wikipedia data and use vector representations from this model to train the parser. Each sentence token is associated with a vector representing the token in the context of that sentence and the feature vectors are constructed by concatenating two token representations from the stack and one from the buffer. We put the feature representations through a feed forward network and train with a greedy transition based system. The resulting parser has a very simple architecture with minimal feature engineering and achieves state-of-the-art results for Telugu. We report the parser results on the Telugu Paninian dependency treebank and UD treebank.