NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Robotics.")'

By Gibilisco Stan. New York : McGraw-Hill, 2003 . xiii, 365 p. : , Includes index. http://www.amazon.com/Concise-Encyclopedia-Robotics-Stan-Gibilisco/dp/0071410104/ref=sr_1_1?s=books&ie=UTF8&qid=1413886507&sr=1-1&keywords=0071410104 https://www.worldcat.org/title/concise-encyclopedia-of-robotics/oclc/53042710&referer=brief_results 23 cm.. 0071410104

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Principles of robot motion : theory, algorithms, and implementation /

By Choset, Howie. Cambridge, Mass. : MIT Press, 2005 . xix, 603 p. : , "A Bradford book." 24 cm.. 0262033275 (alk. paper)

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Automated manufacturing systems : actuators, controls, sensors, and robotics /

By Morriss, S. Brian.. New York : Glencoe, 1995 . xiii, 301 p. : , Includes index. 25 cm.. 0028023315

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Robotics, Vision and Control : Fundamental Algorithms In MATLAB® Second, Completely Revised, Extended And Updated Edition /

By Corke, Peter.. . 1 online resource (XXIX, 693 pages 402 illustrations, 335 illustrations in color.) 9783319544137

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Development Of An Fnirs Equipment For Monitoring Human Brain Activity In The Pre-Frontal Region

By Ehtisham Ul Hasan. Islamabad : SMME - NUST, 2016 . 38 p. ; 30cm..

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Artificial Intelligence for Robotics/ Bring a new degree of interconnectivity to your world by building your own intelligent robots

By Govers, Francis X. Packt UK 2018 . v, 331: pages 978-1-78883-544-2

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Mathematical Control Theory : An Interoduction/

. 260p. 9780817647322

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Enhancing Tumor Segmentation in 3D MRI Scans with YOLOv8 And Volumetric segmentation: A Computational Efficient Approach /

By Abdullah. . 71p. ; 30cm..

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Multimodal 3D-MRI Brain Tumor Segmentation via Modified 3D U-Net with Transformer /

By Khan, Hussain Nasir . . 61p. 30cm.

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Diagnosing and localizing Covid-19 in High resolution CT(HRCT) scans using Deep learning /

By Munir, Zonaira. . 62p. ; 30cm..

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Comparison of Quantitative Proxemics to measure trust in HRI /

By Ahmad, Fatima . . 45p. ; 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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Automatic Detection and Recognition of Citrus Fruits Diseases Using Deep Learning Model /

By Faisal, Shah . . 59p. , In a country's economy, agriculture plays a very vital role. Agriculture's yield and production are reduced by plant diseases, resulting in significant economic losses and instability in the food market. In plants, the citrus fruit crop is one of the most important agricultural products in the world, produced and grown in around 140 countries. It has a lot of nutrients, such as vitamin C. However, due to pests and diseases, citrus cultivation is widely affected and suffers significant losses in both yield and quality. The majority of plant diseases exhibit visible symptoms, and the accepted method used today is for a skilled plant pathologist to detect the diseases by examining affected plant leaves under a microscope, which is a costly and time-consuming method. During the last decade, computer vision and machine learning have been widely adopted to detect and classify plant diseases, providing opportunities for early disease detection and bringing improvements to agricultural production. The early detection and accurate diagnosis of plant diseases are essential for reducing their spread and damage to crops. In this work, we presented an automatic system for early detection and recognition of citrus plant diseases based on a deep learning (DL) model to improve accuracy and reduce computational complexity. The most recent transfer learning-based models were applied to our dataset in order to increase classification accuracy. In this work, we successfully proposed a CNN-based pre-trained model (EfficientNetB3, ResNet50, MobiNetV2, (InceptionV3) for the identification and classification of citrus plant diseases using transfer learning. In order to assess the performance of the model, we found that the transfer of an EfficientNetb3 model led to the highest training, validating, and testing accuracies, which were 99.43%, 99.48%, and 99.58%, respectively. The proposed CNN model exceeds other cutting-edge CNN network architectures developed in earlier literature in the identification and categorization of citrus plant diseases. 30cm.

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Conducting Research in Developmental Psychology: A Topical Guide for Research Methods Utilized Across the Lifespan /

New York: Routledge, 2020 . xiii, 213 p. 9780367340223(pbk)

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Estimation of cognitive state improvement using EEG brain imaging after tDCS stimulation therapy /

By AYUB ,MUDASSAR. . 88p. ; 30cm..

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Development of High-Resolution Imaging System for Blood Activity Monitoring /

By Qasim, Syed Muhammad Ali . . 46p. , fNIRS is a non-invasive, portable & easy to use brain imaging modality. It can estimate the hemodynamic response of the brain by measuring the absorption of IR light with respect to time. The standard channel separation between source & detector in fNIRS is 3cm but this distance has a disadvantage of higher channel noise. To resolve the issue, we have presented a new fNIRS design with small channel separation to minimize the channel noise. In this research, we have designed the fNIRS device using 2 sources & 14 detectors in a circular configuration. The detectors are placed in two circles each circle having 7 LEDs with a radius of 1.5cm & 2.25cm respectively. After the software design, we implemented it on a hardware & tested the device using occlusion. Once the device got tested through occlusion, we acquired the brain signals by placing it on the left frontal cortex. After placing the device on the frontal cortex, we reverse counted for 200sec with rest & count intervals. The whole experimental design is described in the sections below. We applied Modified Beer Lambert Law (MBLL) to deduce results. The results proved to be promising as the channel noise reduced & we got better signals. The results can be improved further by using the high-power IR LEDs having better penetrating ability 30cm.

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Stream Classification & Brand Recognition for Media Analysis /

By Ijaz, M Adeel . . 55p. 30cm.

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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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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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ENHANCED NOVEL VIEW SYTHESIS VIA DEEP LEARNING-BASED 3D GAUSSIAN SPLATTING /

By Ullah Noor, Zabeeh . . 83p. ; , 3D Gaussian Splatting (3DGS) has emerged as a breakthrough in explicit radiance fields and computer graphics which has enabled precise scene representation, real time rendering, and efficient novel view synthesis. This paper explores the evolution of 3D rendering and recent advancements in 3DGS, with a particular focus on different techniques for synthesizing novel views with the incorporation of deep learning architectures especially transformers. To enhance scene quality, this research investigates the integration of monocular depth information during rendering and refines the loss function to improve reconstruction accuracy. By incorporating depth information our method enhances geometric details by capturing intricate details and reduction of artifacts. The findings contribute to the reconstruction of 3D scene with high fidelity, offering insights to optimize Gaussian Splatting technique for more efficient and realistic 3D rendering applications. 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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Long-Term Temperature Forecasting using Deep Learning and Extreme Indices Computation /

By Munir, Safdar . . 105p. , Climate change has intensified the demand for accurate long-term temperature forecasting, particularly in highly vulnerable regions such as Pakistan, where rising temperatures and extreme events threaten agriculture, water security, public health, and infrastructure. Conventional climate models, including General Circulation Models (GCMs) and Regional Climate Models (RCMs), provide essential large-scale projections but face constraints in spatial resolution, computational cost, and applicability in data-scarce contexts. Recent advances in deep learning offer powerful alternatives, capable of capturing complex nonlinear relationships and producing high-resolution forecasts that can also inform the computation of extreme climate indices. This study integrates DL based forecasting with extreme temperature index computation to generate actionable insights for adaptation planning in Major Cities of Pakistan. Daily temperature data from the EC-EARTH3-VEG climate model (converted to °C) were bias-corrected using quantile mapping under two Shared Socioeconomic Pathways (SSP4.8 and SSP5.85). After data preprocessing multiple deep learning models LSTM, CNN-LSTM, Transformer, Transformer XL and Deep Ensemble with Monte Carlo (MC) Dropout, were trained on 1980–2040 data and evaluated for 2040–2050 and then forecasted till 2100. Model was assessed using MAE, RMSE, R² with MC dropout. Deep Ensemble outperformed all the single model (MAE = 0.4588; RMSE = 0.751; R² = 0.992). Projections indicate a Tmax rise of ~2–4 °C and Tmin rise of ~1.5–3 °C by 2100. According to extreme climates indices calculation there continue to be increases in warming, especially with regards to more heat waves that last longer in addition to increases in the number of hot days and warm nights as well as fewer frost days. The warming of nights more than the warming of days, closing the diurnal temperature range. There is also more total precipitation which is distributed in a growing number of days, and longer periods of rain which means that heavier rains are becoming less common, which indicates the beginnings of a warmer, more humid, but drier and stressed climate. The study highlights that DL Ensemble frameworks can deliver robust, long term temperature projections and calculation of extreme indices for 25 major cities of Pakistan and for sake of brevity only data of Lahore, Karachi, Quetta and Gilgit city shown in paper reset are in appendix. 30cm.

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Improving Frequency-Aware Image Restoration Techniques for Real-World Degradations /

By Ayub, Nooh . . 74p. , Image quality often deteriorates under challenging weather conditions such as haze and snow, which create significant barriers for vision-driven intelligent systems, including autonomous vehicles, real-time surveillance, and remote sensing applications. To address this pressing challenge, we propose a comprehensive deep learning framework capable of simultaneously handling both image dehazing and desnowing in a unified manner. At the core of our design lies the NAFXT block—an advanced extension of NAFNet—that integrates Dynamic Tanh (DyT) normalization in place of the traditional layer normalization. Notably, DyT normalization is introduced for the first time in this context as a more effective alternative, offering superior adaptability compared to standard layer normalization within convolutional neural network (CNN) architectures, particularly the NAFNet framework. While layer normalization primarily ensures stable training and faster convergence, DyT normalization enhances the model’s ability to capture richer and more expressive feature representations. As a result, the proposed framework achieves better restoration quality while requiring fewer training epochs than several cutting-edge methods. In addition, we incorporate the Multi-Branch Dynamic Selective Frequency (MDSF) and Multi-Branch Compact Selective Frequency (MCSF) modules, originally introduced in FSNet, to promote frequency-aware global feature learning across multiple scales. The network further adopts a multi-input and multi-output encoder–decoder structure, where both NAFXT and residual blocks are systematically integrated at every stage to progressively refine the restored outputs. Extensive experiments performed on three widely recognized benchmark datasets confirm that our method consistently surpasses state-of-the-art approaches, achieving higher quantitative scores and delivering visually sharper, clearer, and more natural results in both dehazing and desnowing tasks. 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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Omics technologies and bio-engineering : towards improving quality of life /

. xvii, 625 pages : 25 cm. 9780128046593 | 0128046597

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Omics technologies and bio-engineering : towards improving quality of life /

. xvii, 625 pages : 25 cm. 9780128046593 | 0128046597

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