NUST Institutions Library Catalogue Search for 'an:"119961"'

Human Tracking by Mobile Robot using Stereo Camera

By Badar Ali. Islambad : SMME NUST, 2014 . viii, 42 p. ; ill. : , Hardcover. 30cm.

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BALL DETECTION ROBOT DETECTION SELF LOCALIZATION AND PROBABILISTIC TRACKING OF A ROBOTS IN ROBOCUP STANDARD PLATFORM LEAGUE /

By ASHFAQ, ASBAH . . 41p. ; 33cm.

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ANOMALY DETECTION FOR RADIOGRAPHY IMAGES & TIME SERIES DATA /

By Hasan , Momena . . 101p. ; 30cm..

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Smart Control and Automation of Mannequin /

By Ali, Muhammad Tayab. . 40p. ; 30cm..

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Development of Semi-Autonomous RISE wheelchair with Multiple user interfaces using Robot Operating System (ROS) /

By Nawaz ,Hassan. . 41p. ; 30cm..

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Design and Development of a Motorized Mannequin /

By Kayani , Behram . . 43p. ; 30cm..

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Design Optimization of Coanda Effect Based UAV using CFD Simulations /

By LIAQAT, SAAD ALI. . 44p. ; 30cm..

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IRIS Orientation Based Mobile Robot Steering Using Regional Division of Human Eye /

By REHMAN, FAZAL UR . . 49p. ; 30cm..

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Interfacing Niha With Nao (Nina) By Implementing SensoryMotor And Perceptual Memory Units /

By Ahmed ,Syed Kamran. . 63p. ; 30cm..

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Analyzing Human Behavior and Memory Activities using Deep Learning for BMI /

By Khalil ,Khurram . . 85p. ; 30cm..

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Maritime Accident Analysis and AI based HCD IoOT Larch for Operator Vigilance and Cognition /

By Johar ,Ahmed Husnain. . 82p. ; 30cm..

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Rapidly Re-planning RRT* A Novel Re-Planning Algorithm /

By KHAN ,USAMA TARIQ. . 64p. ; 30cm..

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RL based Differential Drive Primitive Policy for Transfer Learning /

By Shahid, Mahrukh . . 52p. , To ensure the steady navigation for robot stable controls are the basic unit and control values selection is highly environment dependent. Adding Generalization to system is the key to reusability of control parameters to ensure adaptability in robots to perform with sophistication, in the environments about which they have no prior knowledge, for this Reinforcement Leaning (RL) based control systems are promising. However, tuning appropriate parameters to train RL algorithm is a challenge. Therefore, we designed a continuous reward function to minimizing the sparsity and stabilizes the policy convergence, to attain control generalization for differential drive robot. We Implemented Twin Delayed Deep Deterministic Policy Gradient-TD3 on Open-AI Gym Race Car. System was trained to achieve smart primitive control policy, moving forward in the direction of goal by maintaining an appropriate distance from walls to avoid collisions. Resulting policy was tested on unseen environments and observed precisely performing results. Upon comparative analysis of TD3 with DDPG, TD3 policy outperformed the DDPG policy in both training and testing phase, proving TD3 to be resource efficient and stable. 30cm.

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Multi-Gesture Decoding using Hybrid EMG-IMU for Rehabilitation Applications/

By Shahzad Waseem . . 135,p; 30,cm..

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Multi-Robot Interactive Therapy for Children with Autism Spectrum Disorder/

By Ali Sara . . 113,p; 30,cm..

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Multi-Robot Interactive Therapy for Children with Autism Spectrum Disorder /

By Ali ,Sara . . 139p. , Robot-Assisted Therapies (RAT) is an emerging field and has shown promising results. Recently, one of the prominent applications of robots is assistive therapy is for Autism Spectrum Disorder (ASD). Autism Spectrum Disorder is a set of neurodevelopment disorder affecting 7.5 million people around the world. Children with ASD lack social and communication skills which affects their ability in schools as well as in community. Recently, humanoid robots are used for the treatment of children with autism spectrum disorder to improve the development of communicational, behavioral, motor movements, joint attention, and physical behaviors. These interactive interventions that use robots for children with ASD, is one of the favorable tools for improving the behavior of children. In particular, the area of robotics is helping a lot in the treatment of ASD as the robot acts as a mediator as well as measures the response of an autistic child. However, the research aiming that the treatment of children with autism is limited, these therapies introduced by robots are successful in establishing basic communication skills. This research has proposed a novel mathematical model for an adaptive therapy of children with Autism Spectrum Disorder called Multi-robot-mediated Intervention System (MRIS). Three different therapies related to improvement in joint attention and imitation skills, effective human-human interaction and comparison of effective stimulus are introduced under this mathematical model. This research aims to introduce multi-sensory data that provides the quantitative support for improvement in social skills of children with autism, replacing the current techniques of measuring the improvement from physically observing the ASD child and with video analysis. Besides ensuring the accuracy in results, this method also introduces consistency as robots are immune to fatigue, unlike humans. The effectiveness of the model has been validated using cognitive brain state of the children with Electroencephalogram (EEG) neuroheadsets. Moreover, the effectiveness of the results has been validated using statistical analysis and the Childhood Autism Rating Scale (CARS). 30cm.

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Brain-Computer Interface for Mental State Detection of Drivers /

By Arif, Saad . . 169p. , Background: Each year millions of vehicles suffer crashes on the roads globally due to deteriorated mental state of the drivers during driving tasks which result in higher casualties. Drowsy driving is the leading cause of high fatality rate which is instigated due to sleep deprivation, fatigue, and anxiety, etc. Vehicular, and driver’s behavioral data-dependent systems detect the drowsiness after its onset when an accident is more likely, and they are also subject to false identification. The drowsy mental state of drivers must be detected earlier for in-time warning to avoid fatal losses, and also the system must be less intrusive, and adaptable for normal driving tasks. Detection systems using physiological signals from human organs are comparatively underexplored in which bodily states of the subject can be identified earlier and with more reliability. It is postulated that all the bodily states are primarily originated from the human brain which could be a promising region to detect drowsiness at an earlier stage. Among many physiological signals, electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) are chosen for this study because they are proved to be more portable, easy to use, non-invasive, and reliable brain modalities for human mental state detection. Aim and Objectives: This study aimed to design a passive brain-computer interface (pBCI) scheme for the earlier detection of driver drowsiness with minimum intrusion into the driving task. Design objectives for the pBCI scheme were to select the channel of interest (COI), online detection with a shorter time window, minimum recalibration and setup time, and development of a widely applicable standard as an inter-subject transfer framework (iSTF) to cater to the inter-subject variability. All these objectives lead to such a pBCI system that is readily available for any subject, anytime, easy to use with minimal design, and yet detecting the drowsiness correctly at an earlier stage to avoid life losses. Methodology: Multichannel EEG and fNIRS brain signals from anterior, posterior, and lateral brain regions of sleep-deprived drowsy subjects were acquired during the simulated driving task for post hoc analysis. Initial pBCIschemes used labeled EEG data acquired from prefrontal (PFC), frontal, and occipital cortices for extracting the eight spectral, and eight temporal xvi features of EEG signals. Seven supervised machine learning classifiers were used to do the cross-validated binary classification of drowsy, and alert brain states. Initial design only achieved a few objectives and generated the need to use different modalities to meet all the requirements. The final pBCI scheme used labeled fNIRS data acquired from PFC and dorsolateral prefrontal cortices (DLPFC) for extracting the six cerebral oxygen regulation (CORE) and three hemodynamic signal features. CORE states of wakefulness and non-rapid eye movement (NREM) sleep stages were used to design a novel standard framework for wide applicability, and sleep stage classification using the vector phase analysis (VPA) approach. VPA was used for classifying microsleep/lapse, and drowsiness detection was done using the proposed brain hemodynamic patterns. Results: 𝛿, 𝜃, 𝛼, 𝛽 band powers as EEG spectral features achieved 82% accuracy in 10 s detection window. Signal skewness, variance, mean, peak as EEG temporal features achieved 87.2% accuracy in 1 s detection window. Ensemble classifier declared F8 as COI for earlier drowsiness detection using both the EEG pBCI schemes. Only the objectives of the short detection window and COI were achieved with the initial designs. In the final design with fNIRS, the novel VPA features: CORE vector gradients, achieved 94.1% accuracy in 5 s detection window for NREM sleep stage classification using ensemble classifier with the least computation time of 44 ms. Precise spatial localization of fNIRS declared AF8 position in right DLPFC as COI. The novel sleep stages-based threshold criteria along with VPA were crossvalidated as a standard iSTF for online microsleep detection with the least recalibration and setup time. Feature selection and achieved results were validated with various statistical significance tests. All the design objectives were attained with the fNIRS-based pBCI scheme. Conclusion: The aim to detect the driver's drowsiness earlier with minimum intrusion into the driving task, is accomplished. The presented fNIRS-based adaptive pBCI scheme is readily available for any subject, anytime, easy to wear with minimal ergonomic design, capable of real-time, correct, and early detection of the driver drowsiness to lessen the life losses in vehicular driving scenarios. The recommended research directions will surely justify, improve, and broaden the application horizons of the presented design 30cm..

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DETECTION, ESTIMATION AND FORECAST FOR NONLINEAR SYSTEMS /

By Zaheer Ud Din, Asim . . 142p. , This thesis presents and implements detection, estimation, and forecast algorithms in context of smart infrastructure system for smart grid. A novel application of radio frequency wireless mesh network and general packet radio service technologies in a telemetry solution has been proposed. The telemetry solution measures power flow in the energy network of an electricity distribution company. The solution utilizes some selected circuits of grid stations, and calculates total power consumed, total power imported and total power exported by the distribution company. The selection of circuits for sensors installation is the key for reducing solution cost as compare to the case when sensors are installed on all the power output points. The framework involves installation of specially developed energy sensors (smart energy meters) and data concentrator units at the selected grid stations. The approach has been tested on two electricity distribution companies of Pakistan: Islamabad Electric Supply Company and Peshawar Electric Supply Company. Also in this work, result of over-load detection based on generalized likelihood ratio test for an industrial feeder of Islamabad Electric Supply Company is included. Detection probability of 0.96 with a false alarm probability of 0.04 has been achieved for 30 minutes data interval. Further, 4 years power data obtained from above mentioned system is utilized in a multivariate multi-step-ahead short-term forecasting formulation. The formulation operates on multiple inputs from multiple variables, and provides multi-step-ahead forecasts by generating multiple outputs for multiple variables. The presented framework is effective for large forecasting horizons since it forecasts for temporally dependent sub- xiii intervals called runs from large horizon. Thus the framework forecasts are less biased and suffer low variance, as compared with direct method and iterated method estimators respectively. Feedward neural network and Long-short-term-memory network models have been evaluated in presented framework. The proposed framework has demonstrated forecasts of power import and power export with a horizon value of 48 for Peshawar Electric Supply Company (PESCO), Pakistan. The averaged mean absolute percentage error of two forecasted time series is 12.76 %, whereas, 24 hours ahead power consumption of PESCO total consumers has been forecasted with mean absolute percentage error of 8.6%. Furthermore, exploiting 24 hours ahead power consumption forecasts has resulted in better power dispatch for PESCO grid stations by reducing mean absolute error by 11.52 times between PESCO power allocated and PESCO power consumed. Next the thesis presents an Euler approximate discrete-time Sliding Mode observer (SMO) which simultaneously estimates states and combined effect of unmodeled system dynamics and disturbances. Emulation Design procedure is employed in designing of discrete feedback linearization controller. Computer simulations demonstrate performance of presented output feedback scheme for tracking applications of magnetic levitation and DC motor systems. Results illustrate that reducing sampling period more adversely affects Euler approximate discrete observer performance for faster changing system dynamics than for slower changing dynamics. The proposed scheme also exhibits good performance in presence of disturbances and parameters perturbation. Furthermore, it is demonstrated via simulations that robust tracking control is achived on using estimator (e.g Kalman filter, SMO, SSRLS filter) in sampled-data output feedback configuration, as compared to performing tracking using sampled-data state feedback scheme. Simulation results show that SMO based output feedback tracking is most robust, followed by CKF and EKF based output feedback scheme. UKF based output feedback scheme is robust against external disturbance; but for case of system parameter perturbation, UKF tracking error takes longer time to converge. State-Space Recursive Least Squares (SSRLS) based scheme behaves poorly in presence of external disturbance. This is because SSRLS estimation is based on constant velocity model and not on actual nonlinear system model. xiv Finally, output feedback control scheme for case of unknown system parameters has been presented. The scheme employs dual UKF estimation algorithm and Emulation Design based discrete feedback linearization controller. Implementation results exhibit that presented output feedback control scheme demonstrates better tracking performance and parameter estimation error when parameter estimate is initialized with a value (in dual estimation algorithm) which is closer to actual system parameter value. 30cm..

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TRAJECTORY TRACKING FOR AGRICULTURAL DYNAMIC MULTI COPTER AERIAL ROBOT /

By Nashit Arshad , Syed . . 161p. , Unmanned aerial vehicles (UAVs) have become a popular choice for spraying pesticide in agricultural use due to their versatility and maneuverability. Quadcopters carrying suspended water containers are widely used for firefighting services. The efficient transportation of liquids by UAVs is of utmost importance in various autonomous missions, including agriculture field spraying. A lot of research is being carried out on the control of these UAVs subject to the constraints of unwanted forces created by the sloshing liquid. However, the complex dynamics of this system can result in the degradation of flight safety due to the linkage among the UAV maneuver, container swing, and liquid sloshing. Liquid sloshing in a container is a well-known and longstanding challenge within the field of engineering. In this study, the word liquid sloshing refers to the variable wave surface elevation of the fluid in a container. Nevertheless, liquid sloshing can lead to undesirable effects such as instability, unwanted forces, position error, and increased control effort resulting in inefficient power utilization and payload constraints. To ensure the effective implementation of the control system for an agricultural spraying drone, it is essential to estimate the pesticide slosh model. The objective of this study is to ascertain sloshing parameters by employing an innovative technique that leverages a costeffective sensor. The proposed experimental setup employed during this investigation comprises a rectangular beaker positioned on a conveyor belt. A Kalman estimator based ultrasonic sensor, mounted atop the liquid-filled container whose slosh parameters necessitate identification, is employed. System identification techniques were employed to derive the system model. Comparative analysis involving calculation of the Root Mean Square Error (RMSE) were conducted to evaluate accuracy and error. Following numerous tests conducted at various slosh levels, the acquired data was subjected to analysis. The results obtained substantiate the feasibility of our concept in measuring slosh under dynamic conditions. To mitigate the effects of liquid sloshing, an approach based on Lagrangian is utilized that enables the development of dynamic model of UAV and resulting nonlinear coupled dynamics of liquid carrying quadrotor. This developed hybrid model, incorporating both slosh and drone dynamics, is thoroughly examined. It enables the application of different control strategies to attain satisfactory performance and meet energy requirements based xii on actuator control efforts. The study delves into two specific control methods: Linear Quadratic Regulator (LQR) and Proportional-Integral-Derivative (PID), extensively presenting, investigating, validating, and comparing their effectiveness in achieving stability and calculating energy demands for a hovering liquid-carrying quadcopter. The utilization of LQR and PID controllers offers notable enhancements in the overall quadcopter performance, accompanied by reduced operational expenses. Simulations based on Coppelia V-rep are also presented to investigate the real-time application of the suggested system. The results demonstrate a decrease in liquid slosh amplitude and, consequently, a reduction in the control effort of the controller. These findings have significant implications for improving the quality of quadcopter control in various real-world applications. 30cm..

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Human Reliability Analysis using AI /

By Abdullah ,Muhammad. . 87p. ; 30cm..

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Multi-Task Learning using Brain Computer Interface /

By Fazal, Mariyam . . 102p. , Brain-computer interfaces (BCIs) can decode not only what users are thinking but also the intensity of their cognitive effort. However, BCIs have traditionally been constrained to single-task applications. Multitask learning (MTL) offers a promising solution by enabling BCIs to handle multiple related tasks simultaneously, enhancing both performance and usability.This study applied MTL to EEG data from N-back working memory tasks (0-back, 2-back, and 3-back) using openaccess data from 26 participants at Technische Universität Berlin. We developed a novel hybrid CNN-LSTM-tAPEformer architecture that integrates Convolutional Neural Networks for spatial feature extraction, Long Short-Term Memory networks for temporal sequence modeling, and Transformer blocks with specialized attention mechanisms for capturing long-range temporal dependencies. The proposed model performs dual functions by classifying accurate behavioral responses while simultaneously measuring cognitive workload across varying task complexity levels. Notable innovations include the development of Time Absolute Position Encoding (tAPE) that enhances temporal processing by integrating sinusoidal positional encoding with adaptive channel-specific encoding to preserve temporal relationships in EEG data. The system incorporates regional and temporal self-attention mechanisms along with global attention pooling to achieve enhanced neural pattern detection. Through leave-one-subject-out cross-validation methodology, the model was trained using data from all participants except one, then evaluated on the excluded individual to assess cross-subject generalization performance. Findings validate the hybrid CNNLSTM-tAPEformer model's efficacy for practical multi-task learning implementations, establishing its utility for BCI applications that demand concurrent cognitive state identification and workload assessment. 30cm.

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Assesment of Cognitive Load in Augmented Reality Based Mobile Robot Navigation /

By Memon, Muhammad Uzair . . 90p. , Mobile robots are increasingly becoming integral to human-centered environments, ranging from industrial automation to assistive applications. As their role expands, ensuring effective and intuitive interaction with robots is essential, particularly for tasks in volving navigation in dynamic environments. This requires the development of advanced control mechanisms capable of controlling robots with high precision while simultaneously avoiding obstacles. Traditionally, teleoperation via remote control devices such as joysticks has served as the standard interface for navigation. However, these systems demand continuous mental focus, offer limited situational awareness due to indirect visual feedback, which contributes to a higher cognitive load. To address these challenges, Augmented Reality (AR) provides spatially immersive interfaces that enhance situational awareness by providing direct, intuitive visual understanding of the environment. In this research, we evaluate the cognitive load of AR integrated with Motion planning algorithm using the NASA Task Load Index (TLX) score. Experiments were conducted with participants from diverse technical and demographic backgrounds, comparing AR-based navigation with traditional teleoperation. The results demonstrated a 30.09% reduction in cognitive load when using AR compared to remote control. These findings suggest that AR-based systems improve ease of use and accessibility, making them valuable for assistive robotic applications. 30cm.

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Intelligent Ball Throw Using Humanoid Robot /

By Tariq, Muhammad Affan . . 67p. , Humanoid Robots has numerous applications among which throwing is one of them. Most of the research focuses on wheeled robots and their throwing applications. Using humanoid robot to throw the ball at a target, keeping itself stable, located at a distance is a novel challenging task that requires architecture for throw. This work focuses on comprehensive control framework for enabling stable, autonomous throwing behaviour in humanoid robots by integrating vision-based targeting with stability. The task is framed as a targeted throwing leveraging all motors of humanoid robot. The system is evaluated on the NAO humanoid robot within Webots simulation. The most important contribution is demonstrating realistic humanoid throwing in majority of which balance limitations form the direct design parameters both the synergism of trajectory planning and implementation, the integration of perception, ballistics, and stability-dependent movement of Nao. The approach generalises to target in 3D space. The results show that unlike prior work, adaptive throwing locates and throws at the targets in range keeping robot stable. 30cm.

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