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

By Huber, Alexander Joseph.. Boston : Artech House, 2002 . xvii, 438 p. : , http://www.amazon.com/Mobile-Computing-Alexander-Joseph-Huber/dp/1580532640/ref=sr_1_1?s=books&ie=UTF8&qid=1410599115&sr=1-1&keywords=1580532640 24 cm.. 1580532640

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The first information war : the story of communications, computers, and intelligence systems in the Persian Gulf War /

Fairfax, Va. : AFCEA International Press, 1992 . xxi, 195 p. : 24 cm.. 0916159248

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Lecture Notes in Computer Science /

By Jung.H, Albrecht .A. Berlin Springer-Verlag 1987 . 205p .: 3540180990

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Computer architecture and parallel processing /

By Hwang, Kai.. New York : McGraw-Hill, 1984 . xviii, 846 p. : 25 cm.. 0070315566 :

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Super distribution : objects as property on the electronic frontier /

By Cox, Brad J.,. Reading, Mass. : Addison-Wesley, 1996 . xvi, 205 p. : 24 cm.. 0201502089

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Real-Time Design Patterns :robust scalable architecture for Real-time systems -(E-BOOK)

By Douglass, Bruce Powel.. Boston, MA : Addison-Wesley, 2003 . xx, 500 p. : 24 cm. +. 0201699567 (alk. paper)

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Systems management for information technology and software engineering /

By Sage, Andrew P.. [S.l.] : Wiley-Interscience, 1995 . 624 p. ; , Hardcover. 25 cm.. 0471015830 | 9780471015833

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Internet measurement : infrastructure, traffic and applications /

By Crovella, Mark.. [S.l.] : Wiley, 2006 . 520 p. ; , Hardcover. 24 cm.. 047001461X | 9780470014615

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Introduction to Volume Rendering

By Barthold Lichtenbelt, Randy Crane, . [S.l.] : Prentice Hall, PTR 1998 . 236 p. ; 24 cm.. 0138616833 (hardcover) | 9780138616830 (hardcover)

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Elegance /

London : Wiley ; 2007 . 128 p. : 28 cm.. 0470029684 (paperback) | 9780470029688 (paperback)

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Object-Oriented Analysis, Design and Implementation An Integrated Approach

By Dathan, Brahma.. New York Springer 2015 9783319242804 | 9783319242781 (print)

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The art of memory forensics : detecting malware and threats in Windows, Linux, and Mac memory /

By Ligh, Michael Hale.. . xxiii, 886 pages : 24 cm. 9781118825099 (pbk.) | 1118825098 (pbk.) | 9781118825044 (ebk.) | 1118825047 (ebk.) | 9781118824993 (ebk.) | 1118824997 (ebk.)

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Historic construction and conservation : materials, systems and damage /

By Roca, Pere,. . 1 online resource 9780429052767 | 9780429628054

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It encyclopaedia.com,vol-4 computer networks and e-commerce

By Diwan, Parag Suri, R.K Kaushik, Sanjay. NEW DELHI PENTAGON PRESS 2001 . 598P 81-86830-18-9

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Inside money : Brown Brothers Harriman and the American way of power

By Karabell, Zachary,. New York : Penguin Press, 2021 . 438p 9780698197961

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The Pre-Fabrication of Building Facades /

. 95p.: 9783319226941

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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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Satellite communications systems : systems, techniques and technology /

By Maral, Gérard,. . 1 online resource 9781119382126 | 9781119382072

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Building construction illustrated /

By Ching, Francis D. K.,. . 1 online resource 9781119583080

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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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A Web of Things Architecture Focusing on Trust /

By Javaid, Sabeen . Rawalpindi, MCS (NUST), 2019 . xiii, 135 p

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Development of Decision Support System for Non-Destructive Fruit Quality Estimation /

By Zeb, Ayesha. Rawalpindi, MCS (NUST), 2020 . xii, 143 p

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