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

Physics of optoelectronics

By Parker, Michael A.. Boca Raton, FL : Taylor & Francis, 2005 . xvi, 742 p. : 26 cm.. 0824753852

Place Hold on Physics of optoelectronics

Physics of optoelectronics

By Parker, Michael A.. Boca Raton, FL : Taylor & Francis, 2005 . xvi, 742 p. : 26 cm.. 0824-53852

Place Hold on Physics of optoelectronics

Gene cloning and DNA analysis : an introduction /

By Brown, T. A.. Oxford ; | Hoboken, NJ : Wiley-Blackwell, 2010 . xvi, 320 p. : 26 cm.. 9781405181730 | 1405181737 | 9781444334074 (hbk.) | 1444334077 (hbk.)

Place Hold on Gene cloning and DNA analysis :

GIS : a visual approach /

By Davis, Bruce Ellsworth.. Albany, N.Y. : Delmar Thomson Learning, 2001 . x, 438 p. : 24 cm.. 076682764X (alk. paper)

Place Hold on GIS :

Advanced techniques in RF power amplifier design-(E-BOOK)

By Cripps, Steve C.. Boston : Artech House, 2002 . xv, 320 p. : 24 cm.. 1580532829 (alk. paper)

Place Hold on Advanced techniques in RF power amplifier design-(E-BOOK)

Image processing for remote sensing-(E-BOOK)

Boca Raton, FL : CRC Press/Taylor & Francis Group, 2008 . 380 s. 1-4200-6664-1 | 978-1-4200-6664-7

Place Hold on Image processing for remote sensing-(E-BOOK)

Application of Machine Learning (E-Book)

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

Place Hold on Application of Machine Learning (E-Book)

Handbook of mathematical formulas and integrals /

By Jeffrey, Alan.. [S.l.] : Academic Press, 1995 . 448 p. ; , Paperback. 23 cm.. 0123825806 | 9780123825803

Place Hold on Handbook of mathematical formulas and integrals /

Time series analysis /

By Hamilton, James Douglas.. [S.l.] : Princeton University Press, 1994 . 820 p. ; , Hardcover. 26 cm.. 0691042896 | 9780691042893

Place Hold on Time series analysis /

Tensor Algebra and Tensor Analysis for Engineers : With Applications to Continuum Mechanics /

By Itskov, Mikhail.. . 1 online resource (XIX, 300 pages 20 illustrations, 13 illustrations in color.) 9783319988061

Place Hold on Tensor Algebra and Tensor Analysis for Engineers :

Mathematics for machine learning

By Deisenroth, Marc Peter,. UK Cambridge University Press 2020 . 371p 9781108679930

Place Hold on Mathematics for machine learning

Microwave power amplifier design with MMIC modules /

By Hausman, Howard,. Boston Artech House 2018 . xxii, 366 pages ; , Minimal Level Cataloging Plus. 27 cm.. 9781630813468

Place Hold on Microwave power amplifier design with MMIC modules /

An introduction to microwave measurements

By Basu, Ananjan.. Boca Raton CRC Press 2015 . xv, 303 pages : 24 cm. 9781138749610

Place Hold on An introduction to microwave measurements

Decoding of Hand Motion Using State of Art Time Domain, Frequency Domain And Feature Extraction /

By Munawar, Sulaiman . . 69p. , Exoskeletons that are activated by the muscles and brain have been suggested to train the motor skills of stroke victims. Training can incorporate task variety since an exoskeleton allows for the execution of various movement types.Differentiating between movement types at the same time from brain activity is challenging, but it might be accessible from residual muscular activity that many patients retain regain.This study examines whether forearm EMG from five stroke patients can be used to decode seven distinct motion classes of the hand and forearm. This study evaluates classifiers like Support vector machine (SVM), Lineardiscriminant analysis (LDA) and K nearest neighbor (KNN). It investigated the relation of motor impairment with classification accuracy by the classifiers. During the following motion classes: Supination, Pronation, Hand Close, Hand Open, Wrist Extension, Wrist Flexion, and Pich, five surface EMG channels were recorded.Every motion was performed by patients three times repetition over the course of eight weeks.Support vector machines, k nearest neighbor, and linear discriminant analysis were used to classify decoding of hand moments for stroke patients. On average,73.69 ± 6.39%SVM,71.6 ± 5.09% KNNand 50±4.56 LDA of the movements were correctly classified.Seven motion classes were demonstrated to be decoded from residual EMG, and SVM proved to be the most effective classification method when compared to the other three classifiers for decoding of hand motion for stroke patients.The results of this study may have implications for the development of exoskeletons, suits, or gadgets, that are powered by EMG signals. These devices might be utilized in the comfort of the patient's home to assist stroke sufferers with their training activities. Therefore, the findings of this study may assist in improving the effectiveness and accessibility of these useful tools for stroke survivors. 30cm.

Place Hold on Decoding of Hand Motion Using State of Art Time Domain, Frequency Domain And Feature Extraction /

Python data science handbook : essential tools for working with data /

By Vanderplas, Jacob T.,. Sebastopol, CA : O'Reilly Media, 2022 . xxiv, 563 pages : , Previous edition: 2016. 24 cm. 9781098121228 | 1098121228

Place Hold on Python data science handbook :

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..

Place Hold on Brain-Computer Interface for Mental State Detection of Drivers /

Development of New Content Based Image Retrieval Techniques /

By Sukhia, Komal Nain . Rawalpindi, MCS (NUST), 2020 . xv, 118 p

Place Hold on Development of New Content Based Image Retrieval Techniques /