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

Handbook of ambient intelligence and smart environments \

By Nakashima, Hideyuki.. New York : Springer, 2010 9780387938073 (hbk.)

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Unmanned Aerial Vehicles /

By Valavanis, Kimon P.. 2011 9789400711099

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UMTS and mobile computing /

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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Biomedical Engineering (E-Book)

By Carlos Alexandre Barros de Mello. India In-Teh 2009 . x,658 P;

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Recent Advances in Biomedical Engineering (E-Book)

By Dr Ganesh R Naik. India In-Teh 2009 . xii,660 P;

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Application of Machine Learning (E-Book)

By Yagang Zhang. India In-Teh 2010 . viii,280p;

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Producing Open Source Software(E BOOK) How to Run a Successful Free Software Project

CreativeCommons Attribution 2009 . 200p

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A companion to urban economics /

Malden, MA ; | Oxford : Blackwell Pub., 2006 . xxvii, 574 p. : 26 cm.. 1405106298 (hardcover : alk. paper) | 9781405106290 (hardcover : alk. paper)

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Hot and cold water supply /

By Garrett, R. H.. Chichester, West Sussex, U.K. ; | Ames, Iowa : Wiley-Blackwell, 2008 . x, 411 p. : , Includes index 25 cm.. 9781405130028 (pbk. : alk. paper) | 1405130024 (pbk. : alk. paper)

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Wealth building strategies in energy, metals and other markets /

By Waltzek, Chris.. Hoboken, N.J. : Wiley, 2010 . xvii, 265 p. : , Includes index. 24 cm.. 9780470638309 (hardback) | 0470638303 (hardback)

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Energy and process optimization for the process industries /

By Zhu, Frank Xin X.. . xvii, 513 pages ; 25 cm. 9781118101162 (hardback)

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Foundation engineering /

By Hansbo, Sven,. Amsterdam ; | New York : Elsevier, 1994 . xviii, 519 p. : 25 cm.. 0444885498

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Aircraft and rotorcraft system identification : engineering methods with flight-test examples /

By Tischler, Mark B.. Reston, VA : American Institute of Aeronautics and Astronautics, 2006 . xxxv, 523 p. : 24 cm.. 1563478374 | 9781563478376

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Smart Logistics System (SLS) /

By Dua, Fakhrunissa . . 125 p

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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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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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Algorithms in advanced artificial intelligence /

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

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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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An Intelligent Insider Threat Detection using ML Techniques /

By Nawaz Janjua, Muhammad Faisal . Rawalpindi, MCS (NUST), 2021 . xviii, 128 p

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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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