NUST Institutions Library Catalogue Search for 'an:"119733"'

Immersive VR based Digital Twinning for Mobile Robotic Platforms /

By Malik, Muhammad Faiq. . 47p. 30cm.

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Intelligent Environment Monitoring and Control /

By Faiz, Muhammad Faizan . . 73p. 30cm.

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Comparison of Quantitative Proxemics to measure trust in HRI /

By Ahmad, Fatima . . 45p. ; 30cm..

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Socio- Technical System for Effective Classroom Learning /

By Kainat.. . 61p. ; 30cm..

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Human Robot Interaction- Personality Prediction of a Human Using Humanoid Robot /

By Jaffar,Anum . . 67p. ; 30cm..

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Ball Interception in Humanoid Robot Soccer /

By Khan, Saman . . 70p. , Humanoid Robots are widely used in various real-world applications which contributed in raising the interest of researchers to enhance Robot development and deployment in dynamic environments. Robocup Soccer league is a platform for qualitative development in the fields of Robot Kinematics and Dynamics, Motion Planning and Control, Navigation, Computer Vision and Machine Learning. New challenges are introduced each year for teams that aim to improve robot behaviors in real world scenarios. One of the most popular leagues in Robocup is Standard Platform League that majorly focuses on improving individual robot capabilities, team capabilities and coordinated strategies in dynamic soccer matches played by teams of Aldebaran Nao humanoid robots. The robots with capability to quickly gain ball possession from opponent by intercepting the moving ball path will contribute to a better defense strategy. The aim of this study is to improve individual robot and team skills during game scenarios where ball is moving using motion primitives and geometric parameters. The proposed strategy is used to compute the interception point using geometric equations given the ball position and velocity information. The robot response is dependent on factors including initial position and velocities of robot and ball. Furthermore, to demonstrate the feasibility of proposed approach, different scenarios with single and multi-agents are simulated using SimRobot. Simulations validate the effectiveness of proposed algorithm in moving ball interception scenario. 30cm.

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An Unreal Engine Based Human Robot Interaction Framework /

By Asif, Muhammad Hassaan . . 64p. , Existing frameworks lack the support for testing Human Robot Interaction (HRI) research which in turn often has to be tested practically making it time consuming and expensive. To overcome this issue, a HRI framework based on Unreal Engine is proposed which consists of a virtual Nao or Pepper robot along with virtual humans with verbal and non-verbal behaviours in an environment. Machine Learning (ML) algorithms along with the behaviour of the virtual robot in response to the interaction with the virtual humans and the environment can be programmed using a Python API which communicates with Unreal Engine C++ in real time. Several experiments related to multiple aspects of HRI: (1) Verbal Interaction; (2) Non-Verbal Interaction; (3) Emotional Interaction were conducted in both the virtual and real world environments and the results were compared to validate the feasibility of the framework. A Reinforcement Learning (RL) algorithm was also tested to further indicate the usefulness of the framework. Through the use of a Virtual Reality (VR) headset, a human can be immersed in the framework to interact with the robot in real time 30cm,.

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Perception of Emotion in Human-Robot Interaction /

By Zia, Muhammad Faisal. . 59p. , Perception of emotion is an intuitive replication of a person’s internal state without the need for verbal communication. Visual emotion recognition has been broadly studied and several end-toend deep neural networks (DNNs)-based and Machine learning-based models have been proposed but they lack the ability to be implemented in low-specification devices like robots, and vehicles. The drawbacks of conventional handcrafted feature-based Facial Emotion Recognition (FER) methods are eliminated by DNNs-based FER approaches. In spite of that, Deep Neural Network based FER techniques suffer from high processing costs and exorbitant memory requirements, their application is constrained in fields like Human-Robot Interaction (HRI) and HumanComputer Interaction (HCI) and relies on hardware requirements. In aforementioned study, we presented a computationally inexpensive and robust FER system for the perception of six basic emotions (i.e., disgust, surprise, fear, anger, happy, and sad) that is capable of running on embedded devices with constrained specifications. In the first step after pre-processing input images, geometric features are extracted from detected facial landmarks, considering the facial spatial position among influential landmarks. The extracted features are given as input to trainthe SVM classifier. Our proposed FER system was trained and evaluated experimentally using two databases, Karolinska Directed Emotional Faces (KDEF) and Extended Cohn-Kanade (CK+) database. Fusion of KDEF and CK+ datasets at the training level were also employed in order to generalize the FER system’s response to the variations of ethnicity, race, national and provincial backgrounds. The results show that our proposed FER system is optimized for real-time embedded applications with constrained specifications and yields an accuracy of 96.8%, 86.7% and 86.4% for CK+, KDEF and fusion of CK+ and KDEF databases respectively. As a part of our future research objectives, the developed system will make a robotic agent capable of perceiving emotion and interacting naturally without the need for additional hardware during HRI. 30cm.

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Examining the Effectiveness of Virtual Reality in Stress Management /

By Tahir, Sheza . . 64p. , Virtual Reality (VR) has emerged as a promising tool in healthcare management, with recent studies exploring its effectiveness in addressing various psychological and physiological disorders. Stress is prevalent in modern society, necessitating effective strategies for its management. While sports and extended reality (XR) gaming have shown promising effects on mental health, this study aims to investigate the effectiveness of VR in reducing stress by comparing conventional and VR-based relaxation techniques using HRV parameters and EEG responses. A total of 40 participants (28 males, 12 females) with a mean age of 25 ± 3.21 years participated in the study. Baseline recordings were obtained, followed by a stress phase induced by a timed IQ quiz. Participants were then randomly assigned to either VR-based relaxation or conventional relaxation techniques. Both relaxation methods significantly improved heart rate variability (HRV) and decreased sympathetic dominance, indicating enhanced adaptability to stress and activation of the parasympathetic nervous system (PNS).However, VR-based relaxation resulted in a more pronounced decrease in heart rate and a significant reduction in the LF/HF ratio compared to conventional relaxation, suggesting a deeper state ofxv relaxation. Furthermore, VR-based relaxation led to a significant increase in the alpha-to-beta ratio, indicating a calmer mental state compared to non-VR relaxation. Notable changes were also recorded in Alpha Power in the frontal channels and Beta Power across all channels, suggesting greater effectiveness in inducing PNS activation and recovery. 30cm.

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Towards Automatic Weather Classification Using DCNNs /

By Mattia Tun Nabi. . 94p. , The utilization of remote sensing (RS) technology has resulted in the extensive accessibility of a significant amount of satellite image data. In order to ensure the successful execution of the RS in real-life scenarios, it is imperative to create effective and adaptable solutions that can be utilized across different transdisciplinary domains. Deep Convolutional Neural Networks (CNNs) are frequently used to accomplish the goal of fast analysis and precise categorization in RS imaging. This study introduces a unique residual network known as ResNet101. The network comprises FC-1024 fully connected layers, dropout layers, a thick layer, and data augmentation algorithms. To resolve the issue of similarity between different classes, architectural enhancements are implemented. On the other hand, imbalanced classes are dealt with by employing data augmentation techniques. The ResNet101 model use the rigorous Large-Scale Cloud pictures Dataset for Meteorology Research (LSCIDMR), which has 10 classes and a multitude of highresolution photos. The goal of the model is to precisely classify these photos into their respective categories. The model we have created outperforms numerous previously published deep learning algorithms in terms of Precision, Accuracy, and F1 scores. The accuracy reaches up to 99% and approximately 92%, respectively. 30cm.

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Detection and Recognition of Medicine Packaging and Information Using Deep Learning and Computer Vision /

By Rukhsar, Muhammad Khurram . . 101p. ; 30cm..

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Breast Cancer Detection through the Introduction of Computer-Aided Diagnosis (CAD) /

By Saleem ,Nada. . 97p ; 30cm..

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Novel Hybrid Neural Network Architecture For Multi-modal Brain Tumor mpMRI Segmentation /

By Faizan, Muhammad . . 73p. , Medical image segmentation is a critical step in clinical decision-making, enabling precise localization of anatomical structures and lesions. While Convolutional Neural Networks, particularly U-shaped architectures like U-Net, have been popular in this domain, their limited receptive fields hinder the accurate delineation of anomalies with irregular shapes and sizes. Hybrid approaches integrating convolution and vision transformers Vision Transformers (ViTs) have demonstrated improved performance due to their ability to capture dependencies over an extended length. However, ViTs are computationally expensive, particularly for volumetric image segmentation, such as MRI, making them challenging to deploy on hardware with limited resources. To address these challenges, recent studies have revisited convolutional architectures, leveraging large kernel (LK) depth-wise convolution to emulate the hierarchical transformer’s behavior. Building on this direction, we propose 3D SegUXNet, a novel U-shaped encoder-decoder architecture for volumetric biomedical image segmentation. Our model introduces the SegUX block, which combines large kernel depth-wise and point-wise convolutions to enhance the receptive field while maintaining computational efficiency. The addition of a residual block further refines features, improving model robustness and generalization. Empirical results demonstrate that 3D SegUX-Net consistently outperforms state-of-the-art CNN and transformer methods on multiple benchmarks, including BraTS 2019, BraTS 2020, BraTS 2023, and organ segmentation of BTCV dataset. The proposed architecture establishes new SOTA performance in volumetric medical semantic segmentation, combining simplicity, efficiency, and scalability. 30cm..

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Automated Karyotyping: Segmentation and Classification /

By Umbreen, Neelam. . 130p. , Karyotyping continues to be the bedrock of cytogenetic diagnosis, providing key information on chromosomal abnormalities causative of a broad range of genetic disorders, developmental abnormalities, and cancers. But standard karyotyping is time-consuming, requires extensive specialist interpretation, and is vulnerable to human mistake and inefficiency, especially in high-volume clinical settings. Despite advances in medical image analysis, the automation of karyotyping faces persistent challenges, including the lack of large-scale annotated datasets, difficulties in segmenting overlapping chromosomes, and variability in chromosome morphology and staining. These challenges define a significant research gap in developing scalable, accurate, and clinically deployable deep learning models for automated chromosome analysis. In order to overcome this gap, we first present a large-scale, clinically annotated cytogenetic database, built from 1,311 patients and consisting of 10,057 karyograms with 514,949 manually annotated chromosome singlets. Furthermore, 3,935 metaphase images are annotated at the instance level in COCO format. This data set reflects true-world diversity, ranging from normal and abnormal karyotypes to different Giemsa (G-banding) staining intensities, structural abnormalities, and overlapping difficult cases, and thus presents a solid basis for the creation and testing of deep learning models within automated cytogenetics. Based on this work, we create two primary methodologies aimed at the fundamental tasks of karyotyping. For segmentation of chromosomes, we introduce a variant Mask R-CNN model involving an Attention-based Feature Pyramid Network (AttFPN), spatial attention, and a LastLevelMaxPool component to improve multi-scale feature representation and contextual perception. It enhances performance in difficult situations, including overlapping chromosomes and weak banding patterns, and gains considerable improvements in mean Average Precision (mAP) compared to standard baselines. For chromosome classification, we present the Dual Attention Multiscale Pyramid Network (DAMP), a specifically designed model that combines channel and spatial attention mechanisms to concentrate on discriminative features, as well as a multiscale pyramid architecture to cope with size, orientation, and quality variation in chromosomes. DAMP's highest classification accuracy is 96.76% on both public and commercial datasets, performingxv better than state-of-the-art models like ResNet-50, Vision Transformers, and Siamese Networks. Overall, this thesis provides interpretable and scalable deep learning models for automating chromosome classification and segmentation. Through the closure of key gaps in dataset quality, model resilience, and clinical utility, the work facilitates the insertion of clever decision-support systems into cytogenetic pipelines, ultimately leading to improved diagnostic reliability and efficiency in the face of chromosomal disorders. 30cm.

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