http://repository.iiitd.edu.in/xmlui/handle/123456789/502 Fri, 10 Apr 2026 10:30:11 GMT 2026-04-10T10:30:11Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1825 Inverse synthetic aperture radar imaging of automotive targets Pandey, Neeraj; Ram, Shobha Sundar (Advisor) Advanced driver assistance systems (ADAS) aim to improve road safety, reduce fatalities, and enable autonomous driving. Modern vehicles rely on multiple sensors such as lidar, cameras, thermal detectors, and radar to detect and classify road users. Among these, millimeter-wave automotive radars offer robust range and velocity estimation, all-weather operation, and unobtrusive bumper integration. High-resolution two-dimensional radar images, and in particular inverse synthetic aperture radar (ISAR) images, can provide detailed information on target size, shape, and motion. However, existing ISAR studies of ground vehicles at automotive radar frequencies have produced only limited datasets for a few targets under controlled conditions, and these datasets are not publicly available. As a result, there is a lack of realistic, large-scale ISAR data suitable for modern machine learning (ML) algorithms for automotive applications. This thesis develops a framework for simulating high-fidelity ISAR images of automotive targets at millimeter-wave (mm-wave) frequencies. The simulation model incorporates vehicle kinematics, radar scattering phenomenology, range–Doppler clutter, and receiver noise for a 77 GHz automotive radar. Static and dynamic mm-wave clutter for automotive scenarios is modelled using measurements acquired on Indian roads, taking into account different surface types and roughness conditions. The resulting clutter statistics are used to parameterise phenomenological clutter models in the simulator. The framework is validated qualitatively and quantitatively against measurement data gathered from real automotive radars. The simulated ISAR images are then used as inputs to traditional ML classifiers and deep neural networks for the classification of automotive targets; the results show that ISAR radar images are excellent features for accurately classifying different road vehicles. The thesis further investigates the reliability and interpretability of these classification decisions. It is shown that misclassifications can occur even when noise and clutter are relatively low. To analyse such cases, a method based on counterfactual explanations is proposed, using generative adversarial networks (GANs) to perturb ISAR images of a query class until they are classified as a distractor class, while enforcing that the perturbations remain realistic and consistent with the original class distribution. The resulting counterfactual images provide physics-based insight into which target regions and micro-Doppler features drive the classifier’s decisions. Finally, two application-oriented studies using ISAR images are presented: (i) an automated parking test framework in which an externally mounted radar generates high-resolution images of a car parking in a designated slot and a polynomial trajectory fit is used to assess parking performance; and (ii) an around-the-corner radar (ACR) proof-of-concept at 77 GHz for non-line-of-sight collision avoidance, where sparsity-based dictionary learning is used to separate overlapping range–Doppler returns and reconstruct the signatures of multiple dynamic targets in an urban NLOS configuration Sun, 01 Feb 2026 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1825 2026-02-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1818 Accelerated reconstruction of HARDI data and fiber orientation estimation Vaish, Ashutosh; Gupta, Anubha (Advisor); Rajwade, Ajit (Advisor) The human brain has a complex network of nerve fibers, particularly in the white matter regions. This nerve fiber network forms the basis of connectivity within brain regions and to the other parts of the body. Traditional imaging techniques like magnetic resonance imaging (MRI) provide an accurate anatomical picture of the brain but offer limited insights into neural connectivity. This limitation is critical, as some neurological disorders can only be diagnosed by examining the alterations in nerve fiber bundles. To address this challenge, diffusion MRI (dMRI) has emerged as a powerful imaging modality capable of accurately characterizing the orientation of axonal fiber bundles. Diffusion MRI is acquired as a collection of MR volumes (referred to as q-samples). Among dMRI techniques, High Angular Resolution Diffusion Imaging (HARDI) is known to produce better fiber orientation representations than Diffusion Tensor Imaging (DTI) and has more practical computational requirements compared to Diffusion Spectrum Imaging (DSI). Despite its potential, HARDI faces challenges in acquisition and post-processing, requiring innovative solutions. For instance, to achieve the better resolution that HARDI offers over DTI, a large number of q-samples are required, which makes the scanning process slow and prone to motion artifacts. Long scanning times can also be inconvenient for patients. On the other hand, reducing the number of q-samples can reduce reconstruction accuracy. To solve this issue, compressive sensing of HARDI data in k-space and/or q-space presents a potential solution to accelerate the scanning process, where the signal is reconstructed later by exploiting the inherent regularity of the signal. There are two prominent sampling schemes in k-space: ‘Cartesian’ and ‘Radial’, with their pros and cons. The Cartesian sampling scheme is more immune to hardware-based distortions, while radial sampling patterns are more immune to motion-induced artifacts. We propose two methods to reconstruct the compressively acquired measurements from the scanner: MSR-HARDI and TL-HARDI. Both methods primarily focus on acquisition through Cartesian sampling schemes in k-space. The first method, MSR-HARDI, utilizes Multiple Sparsity Regularizers in joint (k − q)-space, allowing higher subsampling ratios that are not feasible with only k-space or only q-space subsampling. Additionally, combining regularizers has been shown to yield improved reconstructions compared to individual regularizers. Building upon this work using fixed sparsifying dictionaries, our second method, TL-HARDI, further explores the application of adaptively learned transforms for the accelerated reconstruction of HARDI. This transform is learned using compressively sensed measurements, eliminating the overhead of selecting data-specific fixed sparsifying dictionaries. Further, since the transform is learned on overlapping patches, it captures local image structure effectively, providing an additional denoising effect to the framework. We also recognize that the radial sampling patterns have less pronounced aliasing due to undersampling compared to Cartesian sampling. These advantages can be leveraged particularly in the acquisition of multidimensional signals like HARDI, with a tremendous scope of acceleration. However, despite these benefits, some hardware issues may lead to k-space samples being acquired along deviated radial trajectories, severely degrading the image reconstruction quality. To address this problem, we have proposed a method called CSR-PERT. In CSR-PERT, we investigated a realistic model of gradient delays, which leads to the measurements being acquired from unknown miscentered radial trajectories. This method proposes a joint framework where these perturbed radial trajectories are estimated and used for reconstructing images from the compressively sensed measurements of MRI and HARDI data. After addressing the acquisition aspect, we also explored another important avenue, i.e., the estimation of fiber orientations from HARDI data. Accurate estimation of the local white matter fiber orientations is required for a reliable neural connectivity analysis. In the absence of ground truth, most previous methods relied on assumptions about the physical models of the diffusion signal. This leads to overly simplistic mathematical models, such as DTI, which fail in regions with multiple fiber crossings or excessively complex models that may fail to converge in real time using conventional optimization techniques. Additionally, previous deep learning (DL) methods also attempted to estimate fiber orientations using orientation distribution functions (ODFs), with possible orientations on a predefined set of directions on the sphere, leading to unavoidable discretization errors. To solve these issues, we proposed two methods: FOREST and DL-MuTE. In FOREST, we estimate the peak fiber orientations directly from the diffusion signal using a branched multi-layer perceptron (MLP). In DL-MuTE, we utilize a multi-tensor model to analyze regions with complex tissue structures, such as crossing fibers. This method presents a DL pipeline that uses a branched architecture to estimate individual tensors of a multi-tensor model, which are then used to infer the underlying fiber orientations within an imaging voxel. The models are trained on synthetic data and evaluated on phantoms with known ground truth. The approach is validated on signals with varying noise levels. Wed, 31 Dec 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1818 2025-12-31T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1791 Modelling and analysis of UAV-Assisted cellular networks R R, Neetu; Ghatak, Gourab (Advisor); Bohara, Vivek Ashok (Advisor); Srivastava, Anand (Advisor) Emerging as a transformative solution in next-generation wireless networks, unmanned-aerial vehicles (UAVs) provide unprecedented flexibility, rapid deployment, and enhanced connectivity. Their integration into conventional cellular networks presents numerous opportunities, such as dynamic coverage expansion, disaster relief and emergency response, and military and surveillance applications. However, it also brings challenges, including energy constraints, fronthaul and backhaul limitations, and mobility and handover management. This thesis explores three critical aspects of UAV-enabled networks: spectrum management in integrated access and backhaul (IAB) networks, mobility management for seamless handovers, and joint UAV activation control and power optimization in UAV enabled cell-free massive multiple input multiple output (mMIMO) networks under fronthaul capacity limitations. In the first part, we investigate spectrum management in UAV-enabled IAB networks, where the UAVs act as access points and relay data to the core network. We address the challenge of optimally allocating limited spectrum resources between access and backhaul links to maximize network efficiency. Our analysis includes disaster recovery scenarios, where optimal UAV positioning and resource partitioning are derived to sustain user connectivity and maximize throughput. Additionally, in urban environments, we introduce cache-enabled UAVs that reduce reliance on backhaul links, improving content delivery performance. Key metrics such as signal to interference noise ratio (SINR) coverage probability and successful content delivery probability are evaluated. The second focus is on mobility management in UAV-enabled networks with mobile users. Frequent handovers (HOs) due to user mobility present significant challenges to network performance. To address this, we propose a caching-based handover management scheme that reduces handover occurrences by utilizing device caching, thereby enhancing quality of service (QoS). Using spatio-temporal analysis, we assess the scheme’s effectiveness in minimizing handover frequency and ensuring seamless connectivity. Additionally, we examine network reliability by analyzing the conditional success probability (CSP) experienced by users in the presence of blockages. Furthermore, we derive the meta distribution (MD) of SINR and mean local delay (MLD), offering deeper insights into network reliability. The third aspect focuses on the joint optimization of UAV activation and power consumption in UAV-based cell-free mMIMO networks. These networks promise uniform service quality over large areas but are constrained by the limited capacity of wireless fronthaul links connecting UAVs to the central processing unit (CPU). We incorporate functional split options, specifically Options 8 and 7.2, to balance fronthaul capacity, computational complexity, and latency. By formulating and solving a joint placement and power optimization problem, we ensure efficient resource utilization while maintaining fair SINR coverage across users. This thesis provides a structured framework for integrating UAVs into cellular networks, addressing key challenges in spectrum management, mobility, and resource optimization, paving the way for more reliable and efficient UAV-based wireless communication systems. Mon, 01 Dec 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1791 2025-12-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1781 Investigating probabilistic computing: devices, circuits, and systems Haroon, Amina; Saurabh, Sneh (Advisor) The semiconductor industry has been driven by digital computation using binary digits, following Moore’s law for over half a century. However, the demands of emerging applications like artificial intelligence (AI), cloud computing, exascale computing, and the Internet of Things (IoT) require scalable computational resources. Implementing these applications sometimes requires solving a certain class of problems, such as optimization problem, which requires stochasticity to arrive at the solution. It should be noted that the optimization problems can be implemented using digital computation that includes a pseudo-random number generator. However, the area and power overhead to implement a pseudo-random number generator is significant. One way to reduce the area- and power-overhead is to choose the computational paradigm where the stochasticity is inherently present in the fundamental building block. A recently proposed computational paradigm, probabilistic computing, has shown promising results as an energy- and area-efficient alternative to digital computation. The fundamental building block of probabilistic computing is a probabilistic bit or a p-bit. A p-bit is a classical entity similar to bits with logic levels 0 and 1, however, unlike bits, a p-bit fluctuates between the two logic levels. The p-bits can be realized using different semiconductor devices such as metal-oxide-semiconductor field-effect transistor (MOSFET), diodes, low-barrier magnets (LBM), etc. Among these alternatives, the LBM-based implementation have shown promising results in reducing the area and power overhead. This work provides a comprehensive overview of probabilistic computing, from material physics to the system level, to optimize the entire stack from algorithms to device design and address challenges in hardware implementation to enhance the performance and robustness of applications using probabilistic computing. In this thesis, the design of an LBM for integration in the one transistor-one magnetic tunnel junction spin-transfer torque magnetic random access memory (1T-1MTJSTT-MRAM) structure is investigated. A method is proposed for the selection of material parameters in the LBM-based p-bit implementation to improve flips per second (fps), a critical system-level metric. The significance of specific material properties in the LBM design is highlighted. It is demonstrated that, beyond material selection, several design parameters are significantly influenced by process-induced variations and are, therefore, critical to the development of a robust p-bit-based computational system. Subsequently, the work systematically investigates the impact of non-idealities, such as process variations, environmental factors, and ageing, on the performance of p-bit networks. An analytical model is proposed to incorporate these non-idealities, and the model’s predictions are validated using numerical and SPICE simulations. For demonstration, the image completion problem for digits (0 to 9) using non-ideal p-bits is implemented. The analytical model closely aligns with the behavioral model, revealing that non-idealities in p-bits significantly affect the performance of probabilistic computing. Furthermore, the impact of these non-idealities in p-bit-based implementations is demonstrated in circuit simulations using SPICE models. This work highlights the importance of considering process-induced variations when designing p-bit networks. By incorporating these considerations, the performance and robustness of p-bit networks can be enhanced, paving the way for their real-world application. In line with efforts to enhance the robustness of p-bit networks, this work investigates the impact of faults arising from fabrication defects, ageing, and variability in the p-bits that lead to stuck-at faults, which can degrade system functionality. The effect of such faults is examined using the Modified National Institute of Standards and Technology (MNIST) dataset, and a mutual information-based criticality score (CS) is proposed to guide fault-tolerance strategies. To further improve fault resilience, testable, isolatable, and fault-tolerant p-bit architectures are proposed and validated through Simulation Program with Integrated Circuit Emphasis (SPICE) simulations using 14nm Fin Field-Effect Transistor (FinFET) technology. Testable p-bits integrate conventional p-bits with scan cells, introducing controllability and observability into the network. Isolatable p-bits enable the disconnection of faulty p-bits, while fault-tolerant p-bits restore network functionality by activating faulty p-bits with redundant counterparts. By selectively replacing only the most critical p-bits, accuracy degradation is minimized with limited overhead, thus demonstrating an effective framework for fault-tolerant p-bit systems. Next, the exploration of probabilistic computing from an algorithmic perspective is discussed. The effectiveness of a p-bit system in tasks such as image completion, where the system uses partially clamped inputs, such as images of digits (0 to 9), to generate a complete output, is demonstrated. Additionally, a method is proposed to sparsify a probabilistic computing network by leveraging mutual information, a concept from information theory. The findings show that the proposed method is computationally efficient and can produce a sparse network with only 42% of the original connections while delivering accuracy comparable to the fully connected network. In summary, this work comprehensively investigates probabilistic computing, spanning device materials, circuit-level implementations, and system-level design considerations, focusing on understanding non-idealities and enabling fault tolerance for practical applications. Mon, 01 Sep 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1781 2025-09-01T00:00:00Z