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

The art of action : how leaders close the gaps between plans, actions, and results /

By Bungay, Stephen.. Boston, MA : Nicholas Brealey Pub., 2011 . p. cm. 9781857885590

Linear control system analysis and design with MATLAB /

By Houpis, Constantine H.. . xxiii, 729 p 26 cm. 9781466504264 (hardback)

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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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The Oxford handbook of human resource management /

By Boxall, Peter. Oxford ; | New York : Oxford University Press, 2007 . xv, 658 p. : , Includes Index http://www.amazon.com/Oxford-Handbook-Resource-Management-Handbooks/dp/019928251X/ref=sr_1_1?s=books&ie=UTF8&qid=1413016118&sr=1-1&keywords=019928251X 26 cm.. 019928251X | 0199547025 | 9780199282517 (alk. paper)

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Magnetic and electrical methods of non-destructive testing; report prepared for the Magnetic and Electrical Methods Sub-committee of the British Iron and Steel Research Association;

By Lewis, D. M.. 1951 -

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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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Deformable Image Registration for Neurosurgical Procedures /

By Ahmad, Sahar. Rawalpindi, MCS (NUST), 2016 . xv, 111 p

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The zealot and the emancipator : John Brown, Abraham Lincoln and the struggle for American freedom

By Brands, H. W.,. New York : Doubleday, 2020 . 445 pages : , Map on endsheets. 25 cm. 9780385544009

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Low-Cost Development Full Duplex Communication System using ADALM-PLUTO SDRs /

By Ali, Syed Shujaat. . x, 18 p

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Comparison of Quantitative Proxemics to measure trust in HRI /

By Ahmad, Fatima . . 45p. ; 30cm..

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Through Wall Image Enhancement /

By Riaz, Muhammad Mohsin. Rawalpindi, MCS (NUST), 2013 . xxii,100 -p,

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Identifying Neurophysiological Correlates of Frontotemporal Dementia: Resting State EEG and Phase Synchronization Analysis /

By Ali, Salwa . . 123p. , The need to develop more efficient neuropsychological biomarkers is paramount in the identification of neurodegenerative diseases, tracking the efficiency of treatment and in an effort to avoid the huge financial cost required. While previous research utilizing neuroimaging techniques has pinpointed changes in functional connectivity (FC) as promising biomarkers for frontotemporal dementia (FTD), the constraints of cost and availability of neuroimaging equipment underscore the necessity for accessible alternatives. Electroencephalography (EEG) has emerged as a viable option due to its increasing robustness, wider usage, and affordability. To this end, the research focuses on a resting-state EEG data created from AD, FTD, and HC groups. Here ground data were obtained from nineteen leads using a clinical EEG device when the subjects were in a resting state and their eyes were closed. Another challenge was to follow strict standards for data quality and quality management for data quality to enhance consistency. It is a cross-sectional study, including data from MiniMental State Examination conducted on each participant, and tapes recorded from 20 AD patients, 20 FTD patients, and 20 HC. The Neuroimaging Data Structure (BIDS) format was utilized to present both preprocessed and raw EEG data. The foremost aim was to determine the Feasibility, Sensitivity, and Specificity of the preprocessed, feature extracted, time-efficient, and artifact reduced EEG-derived FC patterns as markers in FTD. Phase-lock values (PLVs) were computed among nineteen pairs of electrodes across five frequency bands using MATLAB and the Hilbert transform. Significant variations in brain connectivity were identified through statistical analyses. The study revealed significant differences in alpha and beta frequency patterns among the control, Alzheimer's, and FTD groups, particularly in frontal and temporal regions. These differences suggest alterations in neural activity associated with cognitive processing, potentially serving as biomarkers for distinguishing between the three groups. Alterations in beta frequency PLV were noted across various EEG pairs, indicating disruptions in neural communication and coordination. These alterations suggest xvi compensatory mechanisms or hyperactivity in frontal and prefrontal regions, alongside potential cognitive and motor deficits due to decreased PLV in central and temporal regions. While no statistically significant differences were observed in delta and theta frequency synchronization between groups, trends suggest potential regions of interest for further research, aligning with existing literature exploring neural oscillations in neurodegenerative diseases. Similarly, no significant differences were observed in gamma frequency synchronization between groups, indicating relatively preserved neural synchronization in this frequency range across control, Alzheimer's, and FTD patients. In summary, both Alzheimer's and FTD demonstrate significant reductions in alpha and beta frequency values, particularly in frontal and temporal regions, compared to healthy controls. These findings underscore the altered functional network topology in AD and FTD, offering valuable insights into the neural mechanisms underlying these conditions. The study's results contribute to the development of electrophysiological markers, potentially enhancing the clinical diagnosis and understanding of AD and FTD. The specificity and sensitivity of EEG-derived FC patterns highlight their potential as costeffective, accessible biomarkers for neurodegenerative disease. 30cm.

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Fixed-Value MBLL based Cognitive Hemodynamic response assessment using P-fNIRS system: Applications to Deep Learning Brain Machine Interface (BMI) /

By Asgher,Umer . . 172p. , Humans in the modern systems not only interact with other humans but also have to interact with intelligent machines, robots in form of cyber physical systems to collaborate in order to carry out different tasks in real working environment. The modern Industrial system comprises of humans, machines, and cyber systems with a collective aim of optimized industrial manufacturing objectives, human factors, and ergonomics goals. Different macrohuman factors are considered while designing and formulating human work safety of such systems and one of the important neuroergonomic factors in is the Cognitive and Mental Workload (C-MWL). The mental workload (MWL) in the human’s brain is measured with difference non-invasive neuroimaging techniques. Most of the cognitive load measuring methods either require massive system protocols like fMRI (functional magnetic resonance imagining), positron-emission tomography (PET) or strict human anatomical movements restrictions like electroencephalogram (EEG) and magnetoencephalography (MEG). To address these limitations, fNIRS (functional Near infrared Spectroscopy) is used in this research to measure the hemodynamic changes in the human brain’s tissues as a measure of the brain activity. The brain’s hemodynamic signals are measured using a light weight portable fNIRS system (P-fNIRSSyst) that is designed to measure relative change in concentration of chromophores (oxy and deoxy hemoglobin) in brain tissues. In this study a novel variant of MBLL (Modified Beer-lambert Law) is designed by keeping the previous intensity value as a reference by taking the average from initial four seconds activity stimuli in optical density. The four second stimuli average in novel and important in calculation the changes in concentration of chromophores. This novel variant of MBLL is Fixed Value - Modified Beer-lambert Law (FV-MBLL). In this research, MWL is measured and classified in different real time working environments. The two-state cognitive load is measured with fNIRS system and classified using FV-MBLL using machine learning techniques like k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN). The classification accuracies of FV-MBLL are better than MBLL. The research further explores the classification capabilities of deep neural networks (DNN) such as convolutional neural network s (CNN) and Long short-term memory (LSTM) for the first time in assessment and classification of four- iv state MWL. The classification accuracies of LSTM outperform not only ML algorithms (SVM, KNN and ANN) but CNN as well in classification of multi-state MWL. The research experimental validation is performed using the accuracies with MWL that are further utilized in neurorehabilitation as neurofeedback to operate bionic systems using Brain Machine Interface (BMI). 30cm.

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Multi-Robot Interactive Therapy for Children with Autism Spectrum Disorder /

By Ali ,Sara . . 139p. , Robot-Assisted Therapies (RAT) is an emerging field and has shown promising results. Recently, one of the prominent applications of robots is assistive therapy is for Autism Spectrum Disorder (ASD). Autism Spectrum Disorder is a set of neurodevelopment disorder affecting 7.5 million people around the world. Children with ASD lack social and communication skills which affects their ability in schools as well as in community. Recently, humanoid robots are used for the treatment of children with autism spectrum disorder to improve the development of communicational, behavioral, motor movements, joint attention, and physical behaviors. These interactive interventions that use robots for children with ASD, is one of the favorable tools for improving the behavior of children. In particular, the area of robotics is helping a lot in the treatment of ASD as the robot acts as a mediator as well as measures the response of an autistic child. However, the research aiming that the treatment of children with autism is limited, these therapies introduced by robots are successful in establishing basic communication skills. This research has proposed a novel mathematical model for an adaptive therapy of children with Autism Spectrum Disorder called Multi-robot-mediated Intervention System (MRIS). Three different therapies related to improvement in joint attention and imitation skills, effective human-human interaction and comparison of effective stimulus are introduced under this mathematical model. This research aims to introduce multi-sensory data that provides the quantitative support for improvement in social skills of children with autism, replacing the current techniques of measuring the improvement from physically observing the ASD child and with video analysis. Besides ensuring the accuracy in results, this method also introduces consistency as robots are immune to fatigue, unlike humans. The effectiveness of the model has been validated using cognitive brain state of the children with Electroencephalogram (EEG) neuroheadsets. Moreover, the effectiveness of the results has been validated using statistical analysis and the Childhood Autism Rating Scale (CARS). 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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