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

The art and science of ultrawideband antennas

By Schantz, Hans.. . xxi, 563 pages : 24 cm.. 1608079554 | 9781608079551

Place Hold on The art and science of ultrawideband antennas

Electric power systems : a first course

By Mohan, Ned.. Hoboken, N.J. : John Wiley & Sons, 2012 . xii, 243 p. : 27 cm.. 9781118074794 (acid free paper)

Place Hold on Electric power systems : a first course

Practical transformer design handbook /

By Lowdon, Eric.. Blue Ridge Summit, PA : TAB Professional and Reference Books, 1989 . ix, 389 p. : , Includes index. http://www.amazon.com/Practical-Transformer-Design-Handbook-Lowdon/dp/0830632123/ref=sr_1_1?s=books&ie=UTF8&qid=1413275358&sr=1-1&keywords=0830632123 25 cm.. 0830632123

Place Hold on Practical transformer design handbook /

Electrical control for machines /

By Rexford, Kenneth B.. Albany, NY : Delmar Publishers, 1992 . xviii, 455 p. : 24 cm.. 0827348681 (textbook)

Place Hold on Electrical control for machines /

High-voltage engineering /

By Razevig D.V. Delhi Khanna Pub 1985 . vii, 726p. : 22 cm.. 008024212X (pbk.) : | 0080242138 :

Place Hold on High-voltage engineering /

Electronic communication /

By Shrader, Robert L.. New York : Gregg Division, McGraw-Hill, 1980 . xiv, 801 p. : 25 cm.. 0070571503

Place Hold on Electronic communication /

Telephony vol. I

By Atkinson, J.. London Sir Isaac Pitman 1961 . 514 p.

Place Hold on Telephony vol. I

Photovoltaic design & installation for dummies /

By Mayfield, Ryan.. Hoboken, NJ : Wiley, 2010 . xviii, 366 p. : , Includes index. 24 cm.. 9780470598931 | 047059893X

Place Hold on Photovoltaic design & installation for dummies /

Electrical energy efficiency technologies and applications /

Chichester [England] ; | Hoboken, N.J. : Wiley, 2012 9781119990055 (ebook)

Place Hold on Electrical energy efficiency

Handbook of switchgears /

McGraw Hill Education New Dellhi 2005 . xxxiii. 0070532389

Place Hold on Handbook of switchgears /

Learning deep learning : theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow /

By Ekman, Magnus,. Boston : Addison-Wesley; 2022 . 688p 9780137470358

Place Hold on Learning deep learning :

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..

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

Algorithms in advanced artificial intelligence /

New Yprk CRC Press 2024 . xxiii,520 Pages.; 9781032867984

Place Hold on Algorithms in advanced artificial intelligence /

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..

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

Comparison of Different Machine Learning Models for Quality Control in Biscuit Manufacturing Industry of Pakistan /

By Awais, Muhammad . . 113p. , Ensuring consistent product quality is a critical challenge in the biscuit manufacturing industry, particularly in developing economies like Pakistan, where manual inspection remains the norm. This approach, however, is prone to human error, fatigue, and inconsistency, leading to variable quality control outcomes. This research proposes an intelligent, machine learning–based automated quality inspection system tailored for real-world biscuit production lines. A comprehensive dataset of 40,000 high-resolution images was curated, representing four popular biscuit varieties Rio, Sooper, Candy, and Marie captured under actual factory lighting conditions. The dataset includes both defective and non-defective samples, covering a wide range of defect types such as breakage, charring, deformation, and missing pieces. To ensure high-quality annotations with scalable efficiency, a semi-automated iterative annotation framework was developed, combining initial manual labeling with model-assisted annotation and human-in-the-loop refinement across multiple cycles. Multiple state-ofthe-art machine learning models were implemented, fine-tuned, and rigorously evaluated, including YOLOv8, YOLOv11, YOLOv12, Faster R-CNN (via Detectron2), and Vision Transformer (ViT-B/16). Models were assessed using key performance metrics such as mAP@0.5:0.95, precision, recall, F1-score, inference speed (FPS), and computational efficiency. The results demonstrate that modern deep learning models, particularly YOLOv12 and Vision Transformers, achieve high detection accuracy (mAP > 90%) while maintaining feasibility for real-time deployment when optimized. This study provides a comparative analysis of accuracy-speed trade-offs, offering actionable insights for manufacturers seeking cost-effective, scalable solutions. The research concludes with practical recommendations for integrating AI-driven inspection systems into existing production infrastructure in resource-constrained environments, balancing performance, hardware requirements, and long-term maintainability. 30cm.

Place Hold on Comparison of Different Machine Learning Models for Quality Control in Biscuit Manufacturing Industry of Pakistan /

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.

Place Hold on Automated Karyotyping: Segmentation and Classification /