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

Multihop wireless networks : opportunistic routing opportunistic routing

By Zeng, Kai.. New York Wiley, 2011 . xix, 285 p. : 24 cm.. 9780470666173 (hardback)

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Semiconductors : integrated circuit design for manufacturability integrated circuit design for manufacturability

By Balasinki, Artur.. Boca Raton, FL : CRC Press, 2012 . xv, 232 p. : 25 cm.. 9781439817148 (hbk. : alk. paper)

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Damage and Fracture Mechanics Failure Analysis of Engineering Materials and Structures /

Dordrecht, The Netherlands: Springer Verlag, 2009 9789048126682 (alk. paper)

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A first course in quality engineering : integrating statistical and management methods of quality

By Krishnamoorthi, K. S.. Boca Raton, FL : CRC Press, 2012 . xxi, 612 p. : 25 cm.. 9781439840344 (hardback)

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Handbook of human factors and ergonomics /

By Salvendy Gavriel. Hoboken, NJ : Wiley, 2012 . xx, 1732 p. : , Includes index. http://www.amazon.com/Handbook-Factors-Ergonomics-Gavriel-Salvendy/dp/0470528389/ref=sr_1_1?s=books&ie=UTF8&qid=1405925902&sr=1-1&keywords=9780470528389 25 cm.. 9780470528389 (hardback)

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ARTIFICIAL INTELLIGENCE and SOFTWARE ENGINEERING(EBOOK)

By Partridge, Derek. New Delhi Glenlake Publishing 1998 . xi,274p 0814404413

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Robust design methodology for reliability (E-BOOK) exploring the effects of variation and uncertainty /

Chichester, U.K. : Wiley, 2009 . 1 online resource (xxi, 191 p.) : 9780470748794

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Micro-turbine generators /

[S.l.] : Wiley, 2005 . 126 p. ; 24 cm.. 1860583911 | 9781860583919

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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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Water resources engineering /

By Wurbs, Ralph Allen.. Upper Saddle River, NJ : Prentice Hall, 2002 . x, 828 p. : 25 cm.. 0130812935

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Structural reliability analysis of superstructure of highway bridges on cpec /

By Abdur Rashid. Islamabad: SCEE - NUST, 2017 . 104 p. : 30cm..

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Pavement design.

Washington, D.C. : Transportation Research Board, National Research Council, 1987 . vi, 132 p. : 28 cm.. 0309045169 (pbk.) :

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Introduction to Dynamics of Structures and Earthquake Engineering /

By Cimellaro, Gian Paolo,. . 1 online resource (XXIII, 598 pages 552 illustrations, 191 illustrations in color.) 9783319725413

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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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Impact localization on Composite Panel using Fiber-Bragg Gratings optical sensors /

By Arhaam Bin Touqeer, . . 70p. , The advancing use of composites in high stake environments, particularly aerospace industry demands strict monitoring of composite’s health over the entire period of life. Health of composite structures could easily be affected by internal damages like debonding or delamination that are invisible to naked eyes. These damages are normally caused by unexpected impact incidences mid-flight or during ground handling. If these damages are not identified timely, could lead to catastrophic failure. So, there is a need of a system that could timely identify and locate these damages in real-time. FiberBragg Grating sensors have gained confidence with their capability of measuring strains accurately along with their versatile and low-cost integration, particularly in composites. In this research work, FBG sensors have been used to identify and locate impact incidences on a composite laminate. Capability of FBG sensors of measuring strain wave initiated by impact incidence is being investigated. In order to extract quantitative information from strain response for impact localization, novel concepts like Empirical Mode Decomposition (EMD) and Hilbert Huan Spectrum have been used. Lastly, normalized values from spectrum are utilized and Artificial Neural Network has been applied to train the system to differentiate between different damage states. On the bases of predictions made by optimized ANN, comments on FBG sensors reliability have been presented and prospects for future research work discussed. 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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