http://localhost:8081/jspui/handle/123456789/29 2026-05-12T05:50:39Z 2026-05-12T05:50:39Z Seema http://localhost:8081/jspui/handle/123456789/20490 2026-04-24T06:31:10Z 2024-03-01T00:00:00Z

Title: Analysis of multibit spin devices, memory and neural network Authors: Seema Abstract: In recent decades, the semiconductor industry has undergone remarkable growth, catalyzing a transformative impact on daily life through the evolution of electronic devices such as mobile phones and computers. This surge in technological advancements has concurrently intensified the demand for higher storage density. The emergence of spintronics has boosted storage capabilities by utilizing the effect of Tunnel Magnetoresistance. Magnetic Tunnel Junctions (MTJs), fundamental storage element in spintronics devices, have garnered interest due to their endurance, non-volatility, scalability, and compatibility with CMOS technologies. Different methods have been employed to write information into MTJs such as field-induced magnetic switching, spin transfer torque (STT), spin orbit torque (SOT), and voltage controlled magnetic anisotropy (VCMA). Single MTJ stores one bit per cell, hence, significant efforts have been made by the researchers to achieve multiple bits per cells by stacking multiple MTJs utilizing hybrid switching techniques such as STT, SOT and VCMA and a storage capacity up to 3 bits per cell has been achieved till now. Stacking of multiple MTJs is challenging because it requires additional writing steps that leads to additional footprint area, power, and latency overhead. Therefore, there is need to explore more novel structures or devices to achieve high storage density that overcomes these overheads. Magnetic Domain Wall (DW) devices have emerged as promising alternatives, offering high storage density, low driving current requirements, and improved cascading compared to existing technologies. DW memory has evolved from field-induced to current driven motion, resulting in advancements such as decreased bit size, improved thermal stability, and enhanced DW speed. However, scalability of DW devices is constrained by pinning defects along the nanowire. Therefore, it is a difficult task to develop an artificial pinning potential for storing magnetic bits at specific positions precisely. Various geometrical and non-geometrical techniques have been demonstrated to control and achieve the pinning of DWs. With increasing efforts, there is a growing need to explore innovative approaches to create, propagate, and detect the magnetic DWs in order to propel advancements in research and development. Despite their potential, applying DW devices at the circuit and system level remains challenging due to the gap between device physics and hardware circuit design. Existing dynamic-based compact models do not include all the factors responsible for the motion of the DW, hence, there is a need for a more accurate and robust model that considers all factors influencing DW motion.

2024-03-01T00:00:00Z Thakur, Aditya Singh http://localhost:8081/jspui/handle/123456789/20488 2026-04-24T06:29:18Z 2024-06-01T00:00:00Z

Title: INVESTIGATION ON METMAMTERIAL BASED HIGH POWER MICROWAVE DEVICES Authors: Thakur, Aditya Singh Abstract: High-power microwave (HPM) devices are advanced vacuum electron devices known for their exceptional efficiency and energy output. These devices have diverse applica tions across numerous sectors, including military and defense, communications, high energy research, industrial processes, and healthcare. In the advancement of HPM de vices, the assistance of metamaterial (MTM) structure significantly improved the device performance by increasing their efficiency and power generation. The MTM-loaded inter action structures are compact in size, highly resonant, and provide a dispersive negative refractive medium for wave propagation. Therefore, the MTM-inspired vacuum electron devices (VEDs) are designed and investigated for traveling wave tubes (TWTs) and back ward wave (BW) oscillators (BWOs) applications. In this dissertation, metallic MTM loaded slow-wave structures (SWSs) are designed for high efficiency and HPM genera tion. Moreover, the double-negative MTM (DNM)-assisted helical SWS is investigated using theoretical and simulation approaches for VED applications. An MTM-loaded all-metallic SWS is designed. The structure consists of periodically arranged split-ring resonator (SRR) pairs within a cylindrical guide, with azimuthal repe tition at every 120◦. The frequency range of 1.85-2.60GHz defined the DNM regime for the designed structure. The structure exhibits MTM properties such as negative refrac tive index, extremely small and negative group velocity, below-cutoff BW propagation, among others. The hot-test simulation analysis of the MTM-SWS determined the power generation of 140MW at the operating frequency of 2.1GHz within the pulse duration of 18-20ns. Furthermore, the structure demonstrates an efficiency of 41% during this operational phase. A novel multibeam MTM-BWO is designed using a four-beam all-metallic MTM SWS comprising several broadside-coupled SRR (BC-SRR) pairs, arranged periodically in axial direction and repeated azimuthally. This MTM-BWO is investigated with the ob jectives of DNMoptimization, dispersion and interaction characterization, and S-parameter validation. The designed multibeam MTM-BWO oscillates at 2.17GHz operating fre quency and radiates TE21-like electromagnetic (EM) waves. It generates an average out put power of 175MW within 22-24ns with an efficiency of 43%.

2024-06-01T00:00:00Z Kukunuri, Anjana Naga Jyothi http://localhost:8081/jspui/handle/123456789/20487 2026-04-24T06:29:04Z 2024-09-01T00:00:00Z

Title: Multi Sensor Data Application with Machine Learning for Classification and Soil Moisture Retrieval Authors: Kukunuri, Anjana Naga Jyothi Abstract: The spectral reflectance and/or backscattering characteristics of certain land cover classes or targets can be similar, and are termed as mixed classes. Examples of these include agricultural crops with similar canopy characteristics, non-oriented urban structures, tall vegetation classes, crops at early growth stages, in-field crop variations etc. Classifying these complex scenarios using single-sensor data is challenging due to limited information. Multi-sensor data, capturing at different parts of the electromagnetic spectrum, provides unique insights into various complex scenarios. Synthetic aperture radar (SAR) sensors offer ideal capabilities for modelling complex land cover scenarios, surpassing weather-dependent optical sensors due to their all-day, all weather imaging and sensitivity to various structural and electrical characteristics of the target. The sensitivity of SAR backscatter to different structural components of the vegetation relative to the wavelength and polarization of the incoming signal, SAR data obtained at different wavelengths and polarizations provide valuable complementary information for distinguishing mixed classes by capturing differences in scattering mechanisms. While full polarimetric data improves crop classification accuracy, it is often limited and expensive. Dual polarization SAR data, though less detailed, offers advantages like larger swath coverage and more frequent revisit periods. Other complex land cover scenarios, such as intra-field classification and early-stage crop classification are challenging using coarse resolution satellite data. On the other hand, unmanned aerial vehicles (UAVs) or drones provide high-resolution data at the field level. Hence, there is a need to fuse drone and satellite data for precision agriculture information at a large scale. Traditional parametric-based classifiers often struggle with complex scenarios, highlighting the need for advanced machine learning (ML) and deep learning (DL) techniques combined with multi-sensor data for complex land cover classification. Further, integrating land cover classifications with large-scale agricultural drought assessments can enhance monitoring systems, helping the decision makers make informed decisions at both field and regional levels. Soil moisture (SM) retrieval is an important surface parameter, particularly under vegetation where multiple scattering effects complicate accurate estimation. Several theoretical, empirical, and semi-empirical methods exist for SM retrieval using SAR data, but their application is limited by model complexity. ML models offer an alternative, requiring less a priori information but needing extensive training data. Synthetic SAR data generation provides a cost-effective solution for generating diverse training datasets. Microwave Modelling of different crops enhances SM retrieval by accurately characterizing vegetation parameters.

2024-09-01T00:00:00Z Nayan, Navneet http://localhost:8081/jspui/handle/123456789/20464 2026-04-20T10:34:47Z 2024-07-01T00:00:00Z

Title: Multimodal Approach to Hand Gesture Recognition and Synthesis Authors: Nayan, Navneet Abstract: Hand gesture recognition and synthesis have an important place in the field of computer vi sion. There is significant involvement of humans and computers in hand gesture recognition and synthesis. Due to this, they have a prominent position and play a crucial role in Human Computer Interaction (HCI). Addition of multi-modality offers researchers to explore new ways and open doors for making noteworthy improvements in their performance. However, hand gesture recognition and synthesis suffers from many issues. In this thesis, we focus on finding efficient solution to some of these issues in hand gesture recognition. Also, we explore multi-modality and observe its effect on hand gesture recognition performance. We also develop an animation-based Indian Sign Language (ISL) e-learning app for the educa tion of hearing and impaired community in India. In this thesis, we take into account both isolated and continuous hand gesture recogni tion having static as well as dynamic signs. There are several challenges in hand gesture recognition like Movement Epenthesis (ME), co-articulation, occlusion, misclassification in case of similar type of gestures and signer dependent gesture recognition. To some extent every issue detriments the recognition performance. But, the major issue among these is the presence of ME segments in continuous sign language videos. ME segments pose serious challenge to sign language recognition accuracy and also increase the computational load. So, in this work, we first focus on the following two research problems. 1. Developing an algorithm for detection and removal of ME segments in continuous gesture. 2. Developing a multimodal framework for isolated and continuous hand gesture recog nition utilizing ME removal. In ME detection and removal, we have proposed a generic approach that detects ME in continuous sign gesture videos. For this, we extract features via calculating the singular value decomposition of the difference frame obtained after computing the absolute differ ence between every current video frame and a designated reference video frame in the input gesture video. The set of all these feature values so obtained for all the frames in the gesture video are clustered into two groups. This helps in separating the frames of gesture video into insignificant movement epenthesis frames and meaningful valid sign frames. To cluster the gesture video frames, we use three clustering algorithms, namely K-means, Gaussian Mix ture Models using Expectation Maximization and Spectral clustering. Among these three, K-means clustering proved to be the best suited clustering algorithm giving superior perfor mance than the other two. The proposed approach was tested on continuous gesture videos of ISL fingerspelling, ISL words and ChaLearn LAP ConGD datasets. The gesture recogni tion accuracy and computational load in terms of number of frames processed during gesture recognition was compared before and after removal of movement epenthesis frames. It is found that there is a significant improvement is gesture recognition accuracy and a notewor thy reduction in the number of frames processed during recognition stage due to removal of MEframes. Theefficiency of a handgesture recognition framework primarily depends on two factors. One is how cleanly the gestures are being fed to the framework or in other words, how well the constituent gestures are segmented from one another. The second factor is related to the extraction, selection and type of features. In case of gesture videos, spatial and temporal features play crucial roles. Several 2D and 3D deep neural networks are being used to extract these features. Adding to these, multi-modality has also shown its important participation in several computer vision applications, hand gesture recognition being one of them. In our proposed research on isolated and continuous hand gesture recognition, we have employed multi-modality, utilized ME detection and removal and proposed a novel modal ity based on the temporal difference that extracts hand regions, removes gesture irrelevant factors and provides temporal information contained in the hand gesture videos. We have also utilized different modalities available in various publicly available large scale isolated and continuous hand gesture datasets. Several combinations of these modalities, in addition to our proposed modality, have also been used while employing multimodal framework. For feature extraction from these modalities, Googlenet CAFFE model have been used. A set of discriminative features is derived by fusing the acquired features from these modalities to form a feature vector representing the query sign or hand gesture. A Bidirectional Long Short-Term Memory Network (Bi-LSTM) is designed for the classification purpose. The proposed multimodal framework is tested on various publicly available continuous and iso lated hand gesture datasets like ChaLearn LAP IsoGD, ChaLearn LAP ConGD, IPN Hand, and NVGesture. We find in our experiments that the proposed framework performs ex ceptionally well on individual modalities as well as on combination of modalities of these datasets. Also, the combined effect of the proposed modality and ME frames removal leads to a significant amount of improvement in gesture recognition accuracy and substantial re duction in computational burden. The obtained results support that our proposed multimodal framework performs at par with the state-of-art methods. Hand gesture synthesis and animation is another facet of communication between a nor mal hearing person and a person with hearing and speech impairment. It is equally important for providing education to the hearing and speech community. It can also be used to train normal hearing person with sign language so as to make them capable of communicating with the community suffering from hearing and speech impairment. It is noticed that ani mated videos in many context are better than live-action videos. Animated videos are more creative and consistent in terms of visuals and design. Due to flexibility in editing and design, animated videos are easy and cost-effective in making changes compared to the live-action videos. When it comes to present or convey a message to the differently-abled community, compared to live-action videos, animated videos provide more inclusiveness and leaves long lasting impression due to its creativity and design. Animated videos are also cost effective than a live-action video. To the best of our knowledge, an animation based ISL fingerspelling e-learning app is still missing in the literature reported till now. So, in our work on hand gesture animation, an animation-based ISL e-learning app for the education of hearing and speech impaired com munity has been developed. This app can play animation of ISL fingerspelling (that contains alphabets, digits, or combination of these) and some ISL words as per the given input. For this, tasks like hand region extraction, key frame identification, hand parameters extraction and hand gesture synthesis and animation using image metamorphosis are performed se quentially. With the help of MATLAB App Designer, a standalone MATLAB application for ISL fingerspelling (including digits, alphabets and combination of these two) and some ISL words of daily usage is developed. This app can be used by the community suffering with hearing and speech impairment for learning Indian Sign Language even while seated at home, or for training signers and teachers at special schools. Synthesis and generation of sentence level sign language is a bit challenging task than synthesizing alphabets, digits and words. This challenge is posed due to the requirement of synthesis of smooth transition segments between two consecutive signs in the gesture se quence. In this work, we propose to use multimodal approach for the synthesis of smooth transition segments thereby producing an effective sentence level sign language synthesis system. Our proposed system learns from the edge features and trajectory features to synthe size smooth transition segments thereby synthesizing sentence level sign language system.

2024-07-01T00:00:00Z