Abstract

Biometric technology has long relied on fingerprint recognition as a trusted means of identity verification. While fingerprint enhancement shares similarities with general image denoising tasks, the unique properties of biometric data make it crucial to treat fingerprints differently from typical realworld images. Deep learning methods have the capacity to capture and improve complex patterns and subtle details in fingerprint data, offering a comprehensive solution. This thesis proposes a deep learning-based method, specifically leveraging the U-Net architecture to achieve superior fingerprint enhancement. At its core, the approach employs a multi-task deep network with domain knowledge from minutia and orientation fields for the enhancement of rolled and plain fingerprint images. We investigate different configurations of the U-Net architecture, examine the effects of various architectural elements, and present extensive experimental evidence to support the effectiveness of our proposed approach. Subsequently, we explore a self-supervised learning paradigm with fingerprint biometrics for robust feature representation learning. In this direction, we introduce a pre-training technique by utilizing the learned information from the enhancement task. We adopt the enhancement pre-trained encoder for learning fixed-length fingerprint embeddings. We evaluate the performance of the learned embeddings in the verification task compared to standard self-supervised techniques without the explicit need for enhanced fingerprints. By merging the capabilities of deep learning with the specificity of fingerprint features, the proposed paradigm offers significant improvements in the robustness and accuracy of fingerprint verification. Experimental evaluations on standard benchmarks such as the NIST and FVC datasets demonstrate the efficacy of this approach. We present compelling evidence that our methodology not only improves the quality of fingerprint images but also facilitates a more effective embedding extraction, ultimately leading to enhanced recognition performance. This work highlights the promising role that deep learning, tailored to biometrics, can play in advancing the field of fingerprint recognition. In addition, it opens up avenues for future research in biometrics, particularly in optimizing computational efficiency and in tackling challenges related to partial and distorted fingerprints.

Abstract

Advancements in 3D sensing, driven by the adoption of LiDAR technology, have reignited innovation in autonomous navigation. LiDAR sensors offer real-time, large-scale point clouds, surpassing traditional vision solutions. Datasets like nuScenes and KITTI empower researchers in tasks such as Localization, Place Recognition, and Obstacle Trajectory Prediction. This thesis contributes by exploring the modeling and prediction of large-scale point cloud sequences. Additionally, it showcases a practical application, representing point-cloud sequences as occupancy grid maps and generating trajectories for autonomous navigation. This dual focus enhances our understanding of autonomous navigation complexities, providing valuable insights into real-world implementation challenges. The first study introduces ATPPNet, an innovative architecture tailored to predict future point cloud sequences using Conv-LSTM, channel-wise and spatial attention, and a 3D-CNN branch. Extensive experiments conducted on publicly available datasets demonstrate the model’s impressive performance, outperforming existing methods. The thesis includes a comprehensive ablative study of ATPPNet and an application study showcasing its potential for tasks such as odometry estimation. In the second study, the thesis proposes NeuroSMPC, a novel integration of data-driven frameworks with sampling-based optimal control for real-time applications, particularly onroad autonomous driving. The 3D-CNN layers in NeuroSMPC predicts optimal mean control without iterative resampling, reducing computation time. The approach proves effective in generating diverse control samples around the predicted optimal mean, facilitating real-time trajectory rollout in the presence of dynamic obstacles. The 3D-CNN architecture implicitly learns future trajectories of dynamic agents, ensuring collision-free navigation without explicit future trajectory predictions. Performance gains are demonstrated over multiple baselines in on-road scenes through closed-loop simulations in CARLA. Real-world applicability is showcased by deploying the system on a custom Autonomous Driving Platform (AutoDP). These studies collectively advance the understanding and implementation of autonomous navigation systems in dynamic environments.

Abstract

A Parkinson’s Detection System refers to a set of tools designed to assist in the early diagnosis of Parkinson’s Disease, a progressive neuro-degenerative disease that affects movement control. In literature about Parkinson’s Disease (PD) detection systems, various speech analysis tools were used which included features derived from the voice harmonics, jitter and shimmer, speech rate, etc. While these features describe the changes in the voice patterns of the patient, we found that statistical data of the cepstral coefficients are a more effective classification criteria. This resulted in our use of cepstral coefficients derived from two different methods in our system and the results are compared to a baseline model which uses MFCCs. The first method is focused on the transformation of the speech signal through the time frequency treatment of a wavelet transform to make use of the multi resolution property of the wavelet transform. It is followed by a cepstral analysis in order to extract the cepstral coefficients. The resultant feature dimensions are classified using Support Vector Machine (SVM) with the help of different kernels. This model has been applied to PC-GITA database of diadokinetic word /pa-ta-ka/. The results showed us a performance of 65%. The second method is the Zero-Time Windowing of the speech signal and it allows us a higher spectro-temporal resolution. The cepstral coefficients derived from this signal are used as as the basis for training the model for detection of Parkinson’s Disease. The dataset and classifiers are same as the first method and resulted in 70% when the derivatives of the cepstral features are also used. Both the proposed systems compared favourably to the baseline used which is MFCC based detection system.

Abstract

Creativity is an interesting and exciting topic of research due its mysterious and complex nature. There is an ocean of literature present on the topic and our work is a drop in the ocean. To understand creativity, it is essential to understand the underlying cognitive strategies. Our thesis aims to understand how inducing different types of constraints impacts creative reasoning. To address this question, we conducted a study that examines the role of constraints in welldefined problem-solving in ill-defined problem-solving. We chose variants of Raven’s advanced progressive matrices(APM) for well-defined problem-solving and creative reasoning tasks (CRT) for ill-defined problem-solving. Using traditional APM, we created a novel version of APM with comparatively lesser constraints available to solve the puzzle, called creative APM(cAPM). The cAPM task was designed to induce divergent thinking along with convergent thinking. It is assumed that the difference in constraints changes the nature of the problem space in solving APM and cAPM and may differently affect the following creative reasoning task. We randomly assigned 50 participants to perform APM or cAPM, followed by the CRT, in a fixed order. We observed a significant effect of constraints available to solve well-defined problems on ill-defined problem-solving. The current result showed higher CRT scores when CRT preceded cAPM (Median = 79.25) than APM (Median = 53.00). The result suggests that the flexibility in constraints to solve a well-defined problem induces more divergent thinking alongside convergent thinking and facilitates creative thinking required in ill-defined problem-solving. Following this study, we conducted another study to understand whether the constraints in the well-defined problem solving task impacts the ill-defined problem solving task of a different knowledge domain. The well-defined problem solving tasks remain same as the previous study. For the ill-defined problem solving task we have chosen creative reasoning tasks (CRT) which is of same knowledge domain and alternate uses task[46] of a different knowledge domain. We have collected a sample of 44 participants. We observed a significant effect of the variant of APM on CRT performance. Higher number of rules in the CRT was observed when it was preceded by cAPM (Median = 2) compared to the classic APM (Median = 1). Further, we observed a higher CRT Relationship Score under cAPM (Median = 78.5) compared to the classic APM (Median = 50.5) puzzle conditions. However, the variant of APM did not show a significant effect on AUT, fluency score(p = 0.8 for independent t-test) and elaboration score (p = 0.48 for Mann-Whitney U test). The current results fail to show domain general than specific nature of creative thinking, especially when it is induced varying the constraints of abstract reasoning.

Abstract

Among the many 3D representations, Coordinate-based implicit neural networks or neural fields gained much appreciation in recent times for their ability to represent shape and appearance with very high fidelity and accuracy in 3D computer vision. Despite the advances, however, it remained challenging to build generalizable neural fields for the category of the objects without datasets like shapenet that provide “canonicalized” object instances that are consistently aligned for their 3D position and orientation (pose). Aligning the objects in 3D helps in many tasks for better generalization on 3d scene understanding, classification, and segmentation. 3D pose estimation can also be obtained by aligning the objects in the 3D. There are methods that align 3d objects represented as point clouds/meshes. Now that we have a new promising 3d implicit representation, there is a need to develop a method that helps to align the neural-fields so that we can enjoy the same benefits we had in the space of point clouds/meshes. Unlike point clouds/meshes neural-fields are parametrized by deep neural networks which is very hard to interpret. In this thesis, we present Canonical Field Network (CaFi-Net), a self-supervised method to canonicalize the 3D pose of instances from an object category represented as neural fields, specifically neural radiance fields (NeRFs). Neural-fields, specifically NeRfs describe the 3D scene as a function of density and viewdependent color. Aligning the objects of a category depends on the geometry rather than the color. That’s why CaFi-Net uses density alone to align the objects within the category. Canonicalization is tightly coupled with equivariant networks. In this work, we draw inspiration from 3D Equivariant networks and construct a CaFi-Net as an Equivariant network for rotations. This network directly learns from continuous and noisy density fields by employing a Siamese network architecture. Previous work has done this for points, but handling fields, specifically vector fields, require us to consider rotation equivariance in both the position and orientation of the field. So, to incorporate the rotation equivariance in the fields, we chose the gradient of a scalar field density, which is a vector field, as the signal for building the rotation equivariance in the CaFi-Net. We used spherical harmonics as a basic building block for the equivariant convolution kernels for CaFi-Net. To handle the noisy signal, we weighted the features with the density value at the point. We employed density-based clustering for the segregation of the background and foreground parts, which is utilized in the calculation of the losses. As there is no publicly available dataset, in order to train the CaFi-Net, we created a simulator that renders 54 camera omnidirectional views for 1300 Nerf instances across 13 shapenet object categories. During inference, our method takes pre-trained neural radiance fields of novel object instances at arbitrary 3D pose and estimates a canonical field with consistent 3D pose across the entire category. As there are no metrics available for canonicalization for neural fields, we used the same metrics used for the point clouds to evaluate the CaFi-Net Performance. Along with the above metrics we have introduced a new metric Ground Truth Equivariance Consistency(GEC) which measures the canonical performance of CaFi-Net to manual labels. Extensive experiments on the above dataset of 1300 NeRF models show that our method matches or exceeds the performance of 3D point cloud-based methods. We conducted ablation studies, which included exploring the choice of the signal, weighing the equivariant features with the density value, assessing the need for the Siamese network, and finally justifying the design choice of the CaFi-Net. In the results section we showed renderings of the Neural-Fields of the object from the canonical pose that are consistent across the category.

Abstract

In the field of robotics, the accurate modeling of uncertainty in the robot’s motion and perception is crucial for effective collision avoidance. Many commodity sensors exhibit nonGaussian noise characteristics, yet existing approaches often assume Gaussian uncertainty to ensure computational tractability. This thesis addresses the gap between non-Gaussian uncertainty and collision avoidance by developing a framework that leverages the distributional characteristics of motion and perception noise. We propose a novel approach that interprets reactive collision avoidance as a distribution matching problem between collision constraint violations and the Dirac Delta distribution. To ensure fast reactivity, we embed each distribution in a Reproducing Kernel Hilbert Space and reformulate the distribution matching as the minimization of the Maximum Mean Discrepancy (MMD). By exploiting the insight that evaluating the MMD reduces to matrix-matrix products, we develop a simple control sampling approach for reactive collision avoidance with dynamic and uncertain obstacles. Furthermore, this thesis advances the state-of-the-art in two key aspects. Firstly, we conduct an extensive empirical study to demonstrate that our planner can effectively infer distributional bias from sample-level information. This insight enables the planner to guide the robot towards good homotopy, utilizing the distributional characteristics of motion and perception noise. In contrast, we highlight how a Gaussian approximation of uncertainty can lead to loss of bias estimation and guide the robot towards unfavorable states with high collision probabilities. Secondly, we compare our proposed distribution matching approach with previous non-parametric and Gaussian approximated methods of reactive collision avoidance. Through tangible comparative advantages, we showcase the superior performance of the distribution matching approach. In summary, this thesis presents a comprehensive framework that addresses the challenge of non-Gaussian uncertainty in collision avoidance. By leveraging the distributional characteristics of motion and perception noise, our approach provides a more accurate and effective method for reactive collision avoidance with dynamic and uncertain obstacles. The empirical study and comparative evaluations demonstrate the advantages of the proposed distribution matching approach over previous methods. This research contributes to advancing the understanding and applicability of collision avoidance strategies, particularly in the context of non-holonomic motion and non-Gaussian uncertainty.

Abstract

From last two decades, India is witnessing rapid urbanisation. Many tall buildings are coming up in tier I and tier II cities of India. To accommodate the functional needs of an occupant, such as car parking, within the same building, two different floor plans are needed. The requirement for unobstructed space for car parking and residential units is fulfilled by discontinuing the closely spaced vertical elements, such as structural walls and columns at certain floor levels. Elements supporting such discontinued elements are known as transfer elements, and buildings with such features are becoming popular in India. Transfer elements introduce stiffness irregularity in the structure, which is undesirable from the design code point of view. The recently introduced tall building code IS 16700 does not have detailed guidelines for analysing and designing such structures. Weather these structures are safe is the question that needs to be answered. With that question in mind, a detailed literature review is carried out to know the global practice of analysing and designing tall buildings with transfer elements located in lower seismic zones of respective countries. Overall, the literature reviewed supports the idea of constructing buildings with transfer elements in the lower seismic zone. However, these recommendations are supported by comprehensive experimental and numerical studies tailored to local seismic hazard and construction practices. Therefore, the motivation of this study is to contribute to the understanding of the global response of RC tall buildings with transfer beams subjected to seismic activity. Particular attention is given to the residential building with RC transfer beam (TB) located in seismic zone II. Along with this, the study also attempts to evaluate the advantages and disadvantages of attaching a podium to the buildings under focus. For this purpose, residential buildings inspired by current building stock are analysed and designed using relevant IS codes. And later, the seismic performance of these buildings is evaluated using Linear Time History Analysis (LTHA) method. Three global parameters, viz., base shear, displacement and inter-storey drift ratio (IDR), and two local parameters of PMM capacity ratio and shear demand to capacity ratio, are used as performance indicators. Assessment of these buildings revealed that the base shear values due to LTHA were observed to increase by 19-32% compared to the design base shear of buildings. However, roof displacements for all the buildings are found to be very lower. Also, displacement variation due to podium configurations were found to be negligible. The maximum IDR value did not exceed 1/5th of code limiting IDR values. Furthermore, despite the stiffness and mass irregularities near the TB level, the maximum IDR demand was found to be at the upper storeys. The PMM capacity ratio of all the columns of different buildings does not exceed 0.30, which indicates that the brittle failure of transfer columns will not be present. Similarly, the transfer column shear demand to capacity ratio reached only 0.50. The study found that the building performance was satisfactory under LTHA, even after qualifying for several irregularities, thus eliminating the need to revise the code limits for inter storey drift ratio. Additionally, it was found that residential buildings with RC transfer beams can perform well in seismic zone II when all analysis and design criteria are properly followed. The impact of podium configurations on the seismic resistance of buildings was found to be negligible. This implies that architects and structural engineers can choose whether or not to include a separation joint based solely on functional and execution requirements. Hence, the transfer beam feature can be allowed in seismic zone II and for zone III, IV and V further investigation is needed before it is recommended

Abstract

Deep learning frameworks have consistently pushed the state-of-the-art limit across various problem domains such as computer vision, and natural language processing applications. Such performance improvements have only been made possible by the increasing availability of labeled data and computational resources, which makes applying such systems to low data regimes extremely challenging. Computationally simple systems which are effective with limited data are essential to the continued proliferation of DNNs to more problem spaces. In addition, generalizing from limited data is a crucial step toward more human-like machine intelligence. Reducing the label requirement is an active and worthwhile area of research since getting large amounts of high-quality annotated data is labor intensive and often impossible, depending on the domain. There are various approaches to this: artificial generation of extra labeled data, using existing information (other than labels) as supervisory signals for training, and designing pipeline that specifically learn using only a few samples. We focus our efforts on that last class of channels which aims to learn from limited labeled data, also known as Few Shot Learning. Few-Shot learning systems aim to generalize to novel classes given very few novel examples, usually one to five. Conventional few-shot pipelines use labeled data from the training set to guide training, then aim to generalize to the novel classes which have limited samples. However, such approaches only shift the label requirement from the novel to the training dataset. In low data regimes, where there is a dearth of labeled data, it may not be possible to get enough training samples. Our work aims to alleviate this label requirement by using no labels during training. We examine how much performance is achievable using extremely simple pipelines overall. Our contributions are hence twofold. (i) We present a more challenging label-free few-shot learning setup and examine how much performance can be squeezed out of a system without labels. (ii) We propose a computationally and conceptually simple pipeline to tackle this setting. We tackle both the compute and data requirements by leveraging self-supervision for training and image similarity for testing

Abstract

Advancements in electronics have revolutionized technology by making devices more portable and powerful. Integrated circuit technology has allowed for packing more functionality into compact spaces, leading to the development of portable gadgets that offer convenience and advanced features. Industries like healthcare, aerospace, and automotive have also benefited from miniaturized electronics, enabling remote patient monitoring, enhancing aircraft performance, and advancing smart vehicle technology. Additionally, miniaturization has improved energy efficiency, resulting in wireless and battery-powered devices for remote and off-grid applications. In this thesis, various circuit design techniques have been presented and prototype systems implemented for portable sensing applications. Magnetometers form a key component of sensing systems used in fields such as geophysical and oceanic exploration and aerospace. Recently, diamond colour defect-based quantum sensing applications such as nitrogen-vacancy (NV) centre magnetometry have emerged in CMOS technology, which uses optically detected magnetic resonance (ODMR) for sensing magnetic field strengths (|B˜|) from different environmental physical quantities. For ODMR based sensing, CMOS quantum sensors seek an on-chip 2.87 GHz microwave (MW) signal generator. Moreover, in order to sense smaller |B˜|, these CMOS quantum sensors also require that MW signal should be swept with a sufficiently small step size near 2.87 GHz. It is also required that the PLLs should have low noise and low jitter for high stability and fast settling time. These requirements seek low phase noise voltage-controlled oscillator (VCO) with a small variation in its gain (KV CO) within the desired tuning range. In this thesis, a fractional-N synthesizer based 2.87 GHz MW-generator (MWG) is presented with an extremely small programmable sweep step-size for improved sensitivity of |B˜| measurements in CMOS NV magnetometry along with a technique for designing a low-phase noise VCO with low KV CO and small KV CO variation is also presented. Respiratory diseases contribute to a majority of deaths worldwide every year. Diseases such as asthma, bronchitis and pneumonia also adversely impact a person’s social and economic conditions. They can seriously threaten their health if left undiagnosed and untreated. Techniques such as auscultation are used in the diagnosis of most respiratory diseases. However, using such techniques requires an experienced physician and the diagnosis is subjective. To overcome these challenges, in this thesis, a portable handheld system has also been proposed and a proof of concept implemented to detect and classify respiratory diseases automatically through the use of convolutional neural networks (CNN) running on mobile platforms. Recent developments in advanced driver assistance systems (ADAS) used in the automotive industry have raised the demands of mmWave radars in 24 GHz and 77 GHz bands. For higher accuracy and precision, frequency modulated continuous wave (FMCW) technique has become very popular for mmWave radars, which requires low phase noise and high bandwidth chirp frequency synthesizers. Such high-frequency band radars require programmable dividers with large divide ratios and fine frequency resolution to obtain high-frequency chirps with sufficiently large bandwidth. In this thesis, the implementation of a low phase noise, transformer tank based mmWave voltage controlled oscillator (VCO) near 20 GHz for multiplier based 77 GHz FMCW chirp synthesizer is presented along with a low-power multi-modulus programmable frequency divider for a frequency synthesizer operating in the 19.25-20.25 GHz frequency band from a reference frequency of 40 MHz. The applications of such radars in road safety and driver assistance is further explored by presenting a novel proof-of-concept that can efficiently classify targets in real-time under multiple classes. Hardware realisation, using a prototype Frequency Modulated Continuous Wave (FMCW) radar system, of the same has also been demonstrated.

Abstract

Remote labs allow students from anywhere in the world to access and conduct experiments without the need to physically be present in a lab at anytime. This is extremely important for students with little to no access to proper science labs because of a lack of infrastructure or a pandemic. A major open problem statement is adding scalability to existing Remote Labs, for several reasons, including the ability to handle growing demand while reducing costs and increasing flexibility. There is a potential for scaling up the process so that queue delay can be removed and people are able to access dedicated experiment setups. The work presented in this thesis is aimed at scalability of Remote Labs by using miniaturization and partial streams. Miniaturization involves making the experiment apparatus simple, smaller and portable. Partial streams involves using a single camera for creating point-of-view video streams for multiple units of the same experiment apparatus. Both aspects operate in tandem in order to be able to scale up the in-house Remote Labs system. The proof of concept demonstration of the methodology put forward is conducted using two miniaturized setups of the school-level experiment “Vanishing Rod”. These experiments are set up in our Remote Labs at IIIT Hyderabad, India (IIIT-H). The miniaturized setups are 3D printed in the lab. To demonstrate the effectiveness of the proposed approach, a performance comparison in terms of cost, size, and energy consumption is carried out for the proposed architecture (miniaturized + partial streaming) compared to the traditional setup (lab-scale + single-streaming). In both cases, the software framework for the dashboard is developed and implemented at IIIT-H. With the above approach, power cost is reduced to one-sixth and actuation component cost is reduced to one-fifth. In addition, the miniature setups require less space as compared to the lab-scale setups, making it easier to install.