http://localhost:8081/jspui/handle/123456789/9 2026-11-05T12:22:34Z http://localhost:8081/jspui/handle/123456789/21763 Title: A CIRCUIT DESIGN FOR ROBUST SRAM COMPUTE-IN-MEMORY ARCHITECTURE Authors: Kushwaha, Dinesh Abstract: Artificial intelligence (AI) and machine learning (ML) are revolutionized technologies widely adopted to solve real-time problems such as speech recognition, facial recognition, cancer detection, and image classifications. Deep convolutional neural networks (CNNs) have succeeded exceptionally in AI/ML tasks. The CNN models have intensive multiplication and accumulation (MAC) computation and require ample memory space to store the pre-trained models. Therefore, they consume large amounts of power on traditional computing systems, restricting their implementations to resourceconstrained edge devices. The conventional computing system has limited memory bandwidth due to slow memory access time and consumes more power during the data movement between the processor and memory. This is known as the “Von-Neumann bottleneck.” Thus, there is a need to build domainspecific hardware primitives for energy-efficient and high-speed ML processing at the edge. Computein- memory (CiM) is an alternate hardware solution to overcome the limitations of traditional computing systems. The CiM enables the memory to perform the computations inside the memory array, where the data is locally stored. Therefore, CiM improves the energy-efficiency of the computing system by reducing the data movement between the computation unit and the memory array. The CiM also allows parallel computing, an inherent characteristic of the CNN. This thesis aims to explore the SRAM-based compute-in-memory architectures because SRAM technology is more mature and easily integrates with modern System On Chips (SoCs). First, in this thesis work, robust SRAM bit-cells have been developed and designed to support compute-in-memory 1-bit XNOR and accumulation (XAC) operation for binary neural networks (BNNs) and multi-bit MAC operation for CNNs. The proposed 10T1C SRAM bit-cell is robust and provides full-swing output in computation mode. The 10T1C XNOR SRAM bit-cell performs the inmemory 1-bit XAC operation without pre-charging the larger bitline capacitances and significantly reducing energy consumption per XAC operation. As a proof of concept and to show the application of the proposed 10T1C SRAM bit-cell, a small BNN has been implemented using 10T1C SRAM bitcell in compute-in-memory XNOR mode. The functional verification of BNN implementation was done through simulations using 65 nm CMOS technology. The proposed XNOR and accumulation (XAC) scheme is energy-efficient and achieves a high compute signal-to-noise ratio (CSNR). The achieved 1ns of latency and 2.36 fJ of energy consumption per XAC operation are (7.2×, 7.2×) and (2×, 1.31×) lower than state-of-the-art digital and analog compute in-memory (CIM) XAC schemes respectively. The proposed XAC design achieves an 8.6× improvement in figure-of-merit (FoM) over the prior stateof- the-art. Moreover, the (σ/μ) average of 0.2% from Monte Carlo simulations shows that the proposed XAC scheme is robust against systematic mismatch and process variations. 2024-09-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21751 Title: DEVELOPMENT OF SIGNAL PROCESSING TECHNIQUES FOR SPATIAL MODULATION Authors: Singh, Shekhar Pratap Abstract: Spatial modulation (SM) is a transmission scheme that involves a larger set of radiating elements than the number of transmit/receive electronics. This facilitates high-rate MIMO implementation to have reduced signal processing and circuitry complexity, as well as an improved energy efficiency (EE). SM combined with Non-Orthogonal Multiple Access (SM-NOMA) is a technique that combines the advantages of NOMA’s power-domain multiplexing and SM’s antenna-based indexing to enhance SE and EE in wireless communication systems. SM-NOMA combines the spatial diversity provided by SM and the spectrum sharing capability of NOMA, resulting in a system with enhanced user capacity, reduced hardware complexity, and improved SE. NOMA has been combined with different variants of SM such as quadrature spatial modulation (QSM) and generalized spatial modulation (GSM), and are referred as QSM-NOMA and GSM-NOMA. 2025-01-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21750 Title: Vision-based Deep-learning Models for Gesture Synthesis and Recognition Authors: Mallika Abstract: Over the past few decades, the progress in gesture and sign language recognition systems has made human-computer interaction more intuitive and user-friendly. By enabling computers to understand and interpret human gestures and sign language, we have made significant strides toward making technology more accessible to a broader range of individuals. Moreover, gesture and sign language recognition systems have improved communication and accessibility for individuals with hearing or speech impairments. These systems can convert sign language into text or speech, enabling effective communication between individuals who use sign language and those who are not familiar with it. This breakthrough has expanded opportunities for social interaction, education, employment, and various aspects of daily life for people with hearing or speech impairments. Environmental factors and variations, such as self-occlusion, differences in hand size and shape, background noise, and changes in illumination, can greatly impact the accuracy and reliability of the recognition process. Self-occlusion occurs when parts of the hand or body obstruct each other, making it difficult to determine the boundaries of the gesture accurately. This can lead to errors in recognition and misinterpretation of the intended gesture. Some methods aim to minimize the impact of these noises and improve recognition accuracy. Despite the difficulties posed by environmental noises, ongoing research in gesture recognition continues to explore novel algorithms, machine learning models, and sensor technologies to improve the robustness and accuracy of the recognition process. In this thesis, we take these challenges into account for designing various hand gesture algorithms such as static and dynamic hand gesture recognition systems, multiview gesture generation systems, and gesture-to-gesture systems. Many multiview recognition systems are available in the literature that deals with the problem of occlusion, but it requires the same gesture from different views. Collecting multiview data is a challenging task, as it requires the use of multiple cameras positioned at various angles or repeated performance of the actions to capture sufficient variability. To overcome this difficulty, we focus on generating different views of the input gesture using conditional multiview gesture synthesis. Static gestures are generated using paired data, with the generation algorithm designed to prioritize the perceptual and structural quality of the generated images as a primary objective. The aim is to generate realistic and visually appealing gestures that closely resemble the intended gestures. Once the views are generated, they are given as input into the proposed gesture recognition system for classification. This system employs a multiview approach for gesture recognition, utilizing decision-level fusion for improved accuracy. 2025-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21627 Title: Social Media Analysis to Identify Friend or Foe Authors: Aseeja, Ashish Abstract: The Data generated online is increasing at a breakneck pace. With over 315 million monthly active users and over 502 million tweets per day, Social Media platforms have become a source of significant data generation that, if analyzed, can be used to prevent cyber-attacks. Social media platforms are used by enemy agencies and banned organi zations for attacking defense personnel. Identifying these personnels on social media will help safeguard assets and avoid information lapses. Using manual searches, this identification of profiles is laborious, manpower-consuming, and inefficient. Identifying friend or foe on social media can be complex due to the inherent nature of online interactions, where individuals may hide their true identities and motivations. However, some certain strategies and indicators can help users distinguish between gen uine friends and potential adversaries. One area where social media, particularly Twit ter, has made a significant impact is sentiment analysis. Sentiment analysis involves the process of gauging the emotions, opinions, and attitudes expressed in text, in this case, tweets. The aim of this research is to automate the process of identifying friend or foe from the tweets extracted using Twitter.Identifying Friend or Foe is based on identifying excessive positive or excessive negativity and aggression in online interactions. In this research, it is shown that the users can be clustered into friend or foe based on the contents of the tweets and the same can be used for any trending topic in general. These users can then be monitored for their activities on social media, which can help prevent social media crimes. 2023-05-01T00:00:00Z