Abstract

Imparting machines the capability to understand documents like humans do is an AI-complete problem since it involves multiple sub-tasks such as reading unstructured and structured text, understanding graphics and natural images, interpreting visual elements such as tables and plots, and parsing the layout and logical structure of the whole document. Except for a small percentage of documents in structured electronic formats, a majority of the documents used today, such as documents in physical mediums, born-digital documents in image formats, and electronic documents like PDFs, are not readily machine readable. A paper-based document can easily be converted into a bitmap image using a flatbed scanner or a digital camera. Consequently, machine understanding of documents in practice requires algorithms and systems that can process document images—a digital image of a document. Successful application of deep learning-based methods and use of large-scale datasets significantly improved the performance of various sub-tasks that constitute the larger problem of machine understanding of document images. Deep-learning based techniques have successfully been applied to the detection, and recognition of text and detection and recognition of various document sub-structures such as forms and tables. However, owing to the diversity of documents in terms of language, modality of text present (typewritten, printed, handwritten or born-digital), images and graphics (photographs, computer graphics, tables, visualizations, and pictograms), layout and other visual cues, building generic-solutions to the problem of machine understanding of document images is a challenging task. In this thesis, we address some of the challenges in this space, such as text recognition in low-resource languages, information extraction from historic/handwritten collections and multimodal modeling of complex document images. Additionally, we introduce new tasks that call for a top-down perspective— to understand a document image as a whole, not in parts—of document image understanding, different from the mainstream trend where the focus has been on solving various bottom-up tasks. Most of the existing tasks in Document Image Analysis (DIA) deal with independent bottom-up tasks that aim to get a machine-readable description of certain, pre-defined document elements at various abstractions such as text tokens or tables. This thesis motivates a purpose-driven DIA wherein a document image is analyzed dynamically, subject to a specific requirement set by a human user or an intelligent agent. We first consider the problem of making document images printed in low-resource languages machine-readable using an OCR and thereby making these documents AI-ready. To this end, we propose to use an end-to-end neural network model that can directly transcribe a word or line image from a document to corresponding Unicode transcription. We analyze how the proposed setup overcomes many challenges to text recognition of Indic languages. Results of our synthetic to real transfer learning experiments for text recognition demonstrate that models pre-trained on synthetic data and further fine-tuned on a portion of the real data perform as well as models trained purely on real data. For 10+ languages for which there have not been public datasets for printed text recognition, we introduce a new dataset that has more than one million word images in total. We further conduct an empirical study to compare different end-to-end neural network architectures for word and line recognition of printed text. Another significant contribution of this thesis is the introduction of new tasks that require a holistic understanding of document images. Different from existing tasks in Document Image Analysis (DIA) that attempt to solve independent bottom-up tasks, we motivate a top-down perspective of DIA that requires a holistic understanding of the image and purpose-driven information extraction. To this end, we propose two tasks—DocVQA and InfographicVQA— fashioned along Visual Question Answering (VQA) in computer vision. For DocVQA, we show results using multiple strong baselines that are adapted from existing models for existing VQA and QA problems. For InfographicVQA, we propose a transformer-based, BERT-like model that jointly models multimodal—vision, language, and layout—input. We conduct open challenges for both tasks, attracting hundreds of submissions so far. Next, we work on the problem of information extraction from a document image collection. Recognizing text from historical and/or handwritten manuscripts is a major challenge to information extraction from such collections. Similar to open-domain QA in NLP, we propose a new task in the context of document images that seek to get answers for natural language questions asked on collections of manuscripts. We propose a two-stage retrieval-based approach for the problem that

Abstract

Individual differences, encompassing a wide array of physical, cognitive, and emotional characteristics such as age, gender, personality, musical expertise, and empathy, play a pivotal role in shaping our interactions with the world around us. These differences, stable over time and across situations, not only influence our end goals but also the processes through which we perceive and interact with them. Recent research in neuroscience has brought to light that each individual possesses unique and fundamentally stable functional brain connections. These connections remain consistent, irrespective of the task at hand, suggesting that functional brain networks could potentially be employed as a measure of stable individual traits. Such an approach could revolutionize personalized medicine, offering tailored therapeutic interventions based on an individual’s unique brain signature. In the context of music, distinct patterns in functional brain networks related to individual differences become crucial. Music, as a continuous task, offers a naturalistic paradigm to explore these patterns. This study aims to bridge the gap in the existing literature by examining how brain responses to music may vary based on individual characteristics, such as gender and musical expertise. Furthermore, previous studies have predominantly focused on Pearson correlation, which captures linear relationships but may not encapsulate the full complexity of functional brain networks. Therefore, our study explores various functional connectivity (FC) measures. By comparing these measures, we aim to understand which ones are most suitable for a given study, ensuring that the chosen measures capture the intricacies of FC effectively. The study utilized data from the ”Tunteet” project, which involved multiple fMRI scans and behavioral tests. A total of 36 participants underwent fMRI scanning while listening to three distinct 8-minute-long musical pieces representing different musical styles. Their responses were analyzed using various temporal and spectral FC measures. The aim was to compare these FC measures to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, a binary Support Vector Machine (SVM) was employed to classify distinct population groupings. Our results align with previous research, suggesting that musical preferences can indeed be considered as a distinct personality trait. This was particularly evident in the differences in liking ratings specific to gender and musical expertise. We also found that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, when classifying individuals based on gender and musical expertise, a composite measure, which combined all measures obtained by concatenation followed by a feature selection procedure, outperformed any single measure. This composite measure consistently achieved better results, suggesting that each FC measure captures different aspects of the relationship between brain regions. In summary, our research offers a fresh perspective on the interplay between musical preferences, gender, musical expertise, and brain responses. By leveraging diverse functional connectivity measures, we’ve shed light on the complexities of functional brain networks, paving the way for future research in this domain.

Abstract

As the number of applications of Internet-of-things (IoT) is increasing daily, there is a growing challenge of providing real-time tracking data to users with a larger coverage area. The satellite-based network can provide global coverage to IoT devices. Due to the low power and low complexity of IoT devices using slotted-ALOHA protocol, it is desired to have visible satellites at a lower altitude than that altitude of commonly used GEO satellite constellations. Recently, sizeable Low-earth-orbit (LEO) satellite constellations such as Starlink-SpaceX and OneWeb launched in orbit can address the coverage issues at much lower power and lower latency. In addition to this, there is minimal coordination among IoT devices. Thus, the packets from multiple devices reaching the gateway may cause collisions and thus reduce the throughput at the gateway. This thesis considers a LEO satellite-based topology for an IoT network, where multiple IoT devices broadcast the information to all the visible satellites over a shared channel using slotted ALOHA. The satellites selectively Decode-and-forward (DF) the data from the IoT devices over orthogonal channels to the Ground station (GS), which does Maximal Ratio Combining (MRC). Capture and Successive interference cancellation (SIC) schemes are considered for decoding at the satellites to mitigate the interference at the satellites in the uplink. For the considered topology, the closed-form expressions are derived for the end-to-end Outage probability (OP) for an arbitrary number of IoT devices and satellites in the capture model and the two-device, two-satellite case in the case of the SIC model. The expressions are derived for both independent and non-identically distributed (inid) and independent and identically distributed (iid) uplink channels. The OP is analyzed as a function of the parameters like the number of satellites, devices, and the desired data rate. The results demonstrate that the proposed approach leverages the benefits of mega-LEO satellites to make the topology feasible and attractive for low-powered and low-complexity IoT networks.

Abstract

Water resources sources have deteriorated in recent years due to rapid population growth, chemical industry effluents, agricultural practices, and climate change. The primary cause of contamination of water-bodies is due to disposal of untreated sewage water pollution. Hence effective sewage treatment is necessary to safeguard water-bodies from contamination. Many cutting-edge techniques have been developed in recent years to increase the effectiveness of the removal of organic matter and nutrients by Waste Water Treatment Plants (WWTPs). To comprehend the effectiveness of wastewater treatment, the current study considered a Sewage Treatment Plant (STP) located in Amberpet, Hyderabad. The capacity of the STP at Amberpet is 339 MLD (Million Liters per Day) and is evaluated by collecting 156 samples for 12 months (January 2018 – December 2018). The data collected was based on grab and composite collection. In STPs, grab sampling and composite sampling are two frequently used techniques for gathering wastewater samples. Grab sampling is the process of taking one wastewater sample at a specific time. Contrarily, composite sampling involves collecting multiple effluent samples over a predetermined period. An STP aims to minimize or remove organic debris, sediments, disease-causing organisms, and other pollutants in sewage water before disposing into streams and other water bodies. The current study observed the removal efficiencies of the constituents such as Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and several other parameters. This investigation assesses whether the effluents emitted into the river body are within the National River Conservative Directorate (NRCD) set limitations as the treated sewage water is discharged into the Musi River (a tributary of Krishna Basin). This study investigated the seasonal variation of WWTP efficiencies for different water quality parameters for the year 2018. The study was conducted over four seasons: winter (December, January, February), pre-monsoon (March, April, May), monsoon (June, July, August, September), and post-monsoon (October, November). The study analyzed the influents and effluents of each available water quality parameter, using both grab and composite sampling and the performance efficiency of STP. For each water quality parameter, the findings revealed considerable seasonal fluctuations in treatment plant efficiencies. For example, if Dissolved Oxygen (DO) level is too low, the microorganisms may become stressed or die, leading to reduced treatment efficiency. On the other hand, if DO level is too high, it can lead to the growth of aerobic bacteria that consume DO, reducing the available oxygen for the treatment process. Therefore, it is important to monitor the DO levels in the STP regularly and maintain them within the recommended range to ensure efficient and effective treatment of the wastewater. The type of treatment technique used in STP determines the DO saturation level necessary for a sewage treatment facility. For an activated sludge process, the DO saturation level should typically be kept between 2 and 3 mg/L, and between 1 and 2 mg/L for a trickling filter process. Water temperature is a prominent variable for water quality and aquatic habitat affecting saturation dissolved oxygen concentrations, algal metabolism, fish growth, and production in aquatic systems. Water temperature also signifies the health of the river water body and regulates many physical and chemical parameters related to river water quality parameters, which speculatively depend on many factors. The study provided valuable insights into the influence of water temperature on saturation oxygen levels in the water, highlighting the importance of considering temperature as a crucial environmental factor in assessing water quality. So far, most River Water Temperature (RWT) models are either physical or datadriven models requiring large amounts of hydrological and meteorological observations. Many climate change studies have been conducted with increasing stream water temperatures, but how it affects saturated DO levels have not been addressed. To address these, the present work aims to work with a hybrid model - Air2Stream as a function of air temperature and discharge and also demonstrates how the Air2Stream method can be used to generate accurate RWT predictions and subsequent DO concentrations in river water quality modeling. RWT, which combines the ideas of the heat budget equation that generalizes physical processes and infers relationships between input and output data, can be predicted using the Air2Stream model. With two river gauging stations at Mantralayam, Shimoga in the Krishna River Basin, the efficiency of the suggested modeling framework is demonstrated. By comparing simulated RWT time series with matched observations and calculating the Nash Sutcliffe Efficiency (NSE),

Abstract

Sentiment Analysis is an important task for analysing online content across languages for tasks such as content moderation and opinion mining. However, state-of-the-art NLP modelling techniques often require a large amount of training data to achieve their results. Unfortunately, high-quality annotated data is often a rare commodity for many languages other than English, including most Indian languages. We attempt to tackle this data scarcity in this thesis in two ways - by creating additional resources, and by exploring more data-efficient modelling techniques. Over the past few years, while some significant resources for Sentiment Analysis have been developed in several Indian languages, there do not exist any large-scale, open-access corpora for Gujarati. In this thesis, we present and describe the Gujarati Sentiment Analysis Corpus (GSAC), which has been sourced from Twitter and manually annotated by native speakers of the language. We describe in detail our collection and annotation processes and conduct extensive experiments on our corpus to provide reliable baselines for future work using our dataset. We then explore modelling techniques that work well in a low-resource setting by experimenting with AfriSenti, a collection of sentiment analysis datasets in 12 African languages. We propose an XGBoost-based ensemble model trained on emoticon frequency-based features and the predictions of several statistical models such as SVMs, Logistic Regression, Random Forests, and BERT-based pretrained language models such as AfriBERTa and AfroXLMR. We also report results from additional experiments not in the system and conduct an ablation study to observe the effects of different types of models and features on the final ensemble.

Abstract

The human brain is a highly complex network of interconnected neurons exhibiting emergent phenomena. The theory of the brain functioning at a critical state - near a second-order phase transition where its capabilities for memory, learning, and information processing - has gained widespread popularity in recent years. This criticality has often been likened to the critical state observed in the Ising model, which is influenced by a temperature parameter. Neural dysfunction due to neurodegenerative conditions such as Alzheimer’s Disease influences the proximity to this critical state. We study the disrupted proximity to criticality of the dysfunctional Alzheimer’s brain using a spin-lattice pairwise maximum entropy model where connectivity is inferred from resting-state fMRI data. The closeness to criticality is quantified through the critical temperature, which is derived by analysing the susceptibility of the system. We find an insignificant difference between the two classes, which we attribute to the dependence of susceptibility on spatial dynamics, with the hypothesis that the existence of a spin-glass-like criticality could hinder the sensitivity of this metric. To characterise the nature of brain criticality, we explore the spatial characteristics and temporal correlations of criticality for both normal and Alzheimer’s brains. We find a weak distinction in the spatial dynamics between the two classes - the sizes and number of spatial domains formed, as well as the self-averaging behaviour of the system, were not found to vary significantly. However, we notice a stretched exponential form in the temporal relaxation times, which is characteristic of spin-glasses. We find a significant difference between the relaxation behaviour of Alzheimer’s and Cognitively Normal subjects. In spin-glass systems, a third phase exists near the phase transition boundary - the spin-glass phase. This phase is characterised by frustration and competing interactions. It allows for the emergence of complex properties such as long-term memory, which are features not found in simple Ising-like criticality. Despite the difference in closeness to criticality, the lack of a significant distinction in the spatial correlation lengths can be attributed to local frustrations, which might not allow the system to reach equilibrium domain distributions. Based on the differences observed between the spatial and temporal correlations, we conclude that the criticality found in the brain is more akin to a spin-glass criticality, which is more sensitive to temporal rather than spatial measures.

Abstract

Education plays a vital role in shaping up one’s life. Education in early childhood includes learning from different activities where counting and other concepts of mathematics act as major building blocks. Mathematics is not just a subject to be taught in schools, but also it has myriad applications in our daily lives such as counting the total number of stationery items, calculating their individual prices, and adding them up for a purchase made in a grocery shop. This kind of real world situations are posited in mathematical word problems which are an essential part of a child’s learning that requires natural language understanding (NLU) as well as knowledge of mathematical operations. Mathematical Word Problem Solvers can assist both students and teachers. It is a challenging field and this thesis attempts to provide NLP solutions for math word problems in English and Indian languages. Our approach for developing word problem solvers follows a pipeline. For a word problem, first, we identify the relevant operands and required operations. In the second step, we form an equation from these identified components. The first two stages can be combined to generate equations at once using neural network based approaches. At the last stage, the equation is solved by a mathematical solver to get the final solution. We focus primarily on the first two stages of this pipeline. We developed solvers using three kinds of approaches: frame based, composition of classifiers based, and end-to-end neural based. We also shed light on the limitations of the current automatic solvers with respect to the data. We designed different data augmentation techniques to overcome the data scarcity problem. As a part of resource building, we developed word problem datasets and solvers for Indian Languages. In addition to this, we compared different models related to our developed approaches. We empirically show the difference in generation of various equation notation types. For this study, we present the results of equations in infix, postfix, and prefix notations. We also show two natural language processing tasks where components of word problem solvers can be utilized. In the first task, we studied the impact of simple number based pre-processing on the performance of machine translation systems. In the second task, we analyze speech transcript texts to extract equation spans. This kind of text is present mainly in transcripts from mathematical domain. For achieving this, we develop an equation identifier and convertor involving math notations for transcripts. This can make the transcripts easily readable for transcripts which have wide usage of mathematical terms.

Abstract

Recent advancements in the field of advanced driver assistance systems (ADAS) have led to increased demands for mmWave (millimeter-wave) radars operating within the 24 GHz and 77 GHz frequency bands. Among the various radar techniques, the Frequency Modulated Continuous Wave (FMCW) method has gained considerable popularity among mmWave radars. This approach relies on utilizing frequency synthesizers with low phase noise and high bandwidth to attain higher precision and accuracy in radar applications. Of particular interest in recent years is the growing demand for FMCW radars that operate within the 76-81 GHz frequency band due to their exceptional precision and accuracy, thanks to their wide bandwidth. These radars are well-suited for a range of applications, including autopilot systems and ADAS. The primary focus of this thesis is to present research efforts aimed at enhancing the performance of FMCW radars at the circuit level, with a strong emphasis on the design, optimization, and simulation of Voltage-Controlled Oscillators (VCOs). VCOs play a critical role in radar systems, and two crucial design considerations are minimizing phase noise and reducing power consumption. These parameters directly impact the sensitivity and energy efficiency of the radar system. Various VCO topologies are explored, primarily based on LC oscillators, and comprehensive optimization endeavors are done to simultaneously minimize phase noise and power consumption. The VCO designs are implemented using TSMC 65 nm CMOS technology, allowing us to investigate innovative techniques for mitigating phase noise in VCOs. In summary, this thesis encompasses recent developments in FMCW radar technology, specifically addressing improvements at the circuit level by focusing on the design and optimization of VCOs.

Abstract

Damage caused by an earthquake depends not just on the intensity of an earthquake but on the engineering and construction practices of the region. Past earthquakes in Asian countries have highlighted inadequate construction practices and caused huge property and life loss, indicating the severe need to retrofit existing structures. For this, an understanding and quantification of damage are required to assess the amount of damage. Many researchers have proposed many indices; out of those, energy-based indices are chosen for this study. In literature, energy-based damage indices have progressed well but with a gap in mapping of local-to-global indices though they can be computed at local and global levels. This study bridges the gap between global and local energy-based indices using weighting factors, which further proposes sequence of strengthening. Strengthening activities shall be done first at the higher weighted members to increase the capacity of the building immediately and then to the other members. The proposed strengthening sequence is proposed to make it more economical and efficient. This study is conducted on existing buildings, mainly on gravity load-designed buildings (Type G) and precode buildings of IS 1893 designed as per the 2002 version (Type P). These buildings represent a significant stock of RCC MRF buildings on site that should be strengthened. The members contributing significant damage are identified in existing buildings, different weights are allotted to members as per their contribution to global energy dissipation. Nonlinear static analysis is conducted to quantify the damage. The methodology adopted to compute the contribution of local damage to the exterior and interior columns is verified with nonlinear time history analysis with eleven earthquakes. In gravity, load-designed buildings, higher weight for exterior columns is observed in the energy dissipation capacity of the building than interior columns. However, in precode buildings of IS1893, the significance of exterior and interior columns is similar. Storey-wise weights are computed using energy dissipated by each hinge in that storey. Therefore, the highest weighted stories are identified for each type of building. Damage is distributed within stories as a parabolic curve. In type G buildings, as the height of the building increases, parabolic distribution changes from convex to concave, and the maxima location of the parabola shifts from bottom to middle stories. In type P buildings, parabolic damage distribution remains convex or like a straight line. As the height of the building increases, damage shifts to upper stories in a convex parabolic shape. An increase in the storey height of a building does not change the damage distribution pattern and the quantity of damage. The sequence for strengthening activities is proposed as per the computed weighting factors. Strengthening activities shall be done first at the higher-weighted members to increase the capacity of the building immediately. Further, a strengthening sequence is proposed as per the weighting factor in descending order for regular RCC buildings. Therefore, proposals made in the study would increase the efficacy of strengthening activities.

Abstract

Electrical load forecasting is a prime element for power system planning, scheduling, economic dispatch, unit commitment, maintenance, and electricity market. In practice, the electrical load is having nonlinear behavior and electrical load demand depends upon various factors like the type of day (day of the week etc.), weather variables like temperature, humidity, and the load on the previous day etc. Electrical load forecasting can become more accurate by considering these dominant factors. Soft computing techniques have been successfully incorporated into a lot of scientific and engineering problems during recent years. Soft computing methods are capable of dealing with nonlinearity and these techniques are suitable candidates for electrical load forecasting problems. In this thesis work, soft computing techniques like fuzzy logic and bio-inspired computation techniques are chosen for similar day based electrical short term load forecasting. The fuzzy logic is able to model uncertain and ambiguous data. The fuzzy logic system gives better forms of rule expressions, reasoning logic like human thought. This work presents fuzzy logic based Short Term Load Forecasting (STLF) by selecting similar days, which are found from previous data by using Mahalanobis distance norm. The fuzzy logic is chosen in the correction of similar days of the forecast day. The average of the corrected hourly loads of the similar days achieved through the Mahalanobis distance norm of the forecast day is then considered as the hourly load of the forecast day. The parameter limits of fuzzy membership functions are determined and optimized through different optimization techniques like Heuristic Approach, Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). In the first approach, Fuzzy membership parameter values are tuned in a heuristic way. Another approach for the optimization of fuzzy parameter values is to use PSO. It simulates the bird flocking behavior where each particle fitness value can be evaluated in a search space by using the fitness function. The particle’s direction of flying can be decided by its velocity and position. PSO function updates the latest particle position by using an optimization equation based on the global best of the previous iteration provided that it is better than the previous one. NPSO technique is also attempted for the optimization of fuzzy parameter values. Each particle tries to leave its previous worst position and the previous worst position of its group. NPSO function modifies the latest particle position using the optimization equations based on the global worst of the previous iteration provided that it is better than the previous one. The inclusion of the evolution process in PSO is providing the Evolutionary Particle Swarm Optimization (EPSO). EPSO has the properties of Replication, Mutation, Reproduction, Evaluation and Selection. In this thesis, EPSO is also used for the optimization of fuzzy parameter values. These optimized fuzzy parameter values are incorporating in Fuzzy Inference System (FIS) and used for forecasting the load of forecast month. It is difficult to forecast a load of anomalous days like holidays, religious days and special days because their load profile is different when compared to normal days. Load forecasting for anomalous days is also a challenging job because of small number of data availability compared to the availability of data for normal weekdays and weekends. Hence, various methodologies are developed for anomalous day load forecasting and presented in the present research work. The short term load forecasting algorithms are developed for each optimization technique and also forecasting methodologies for various case studies of anomalous day load forecasting and these are tested by doing simulation studies on real-time data and results obtained are found good, which have been presented in this thesis.