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

Introduction to biomedical equipment technology

By Joseph J.Carr. New Delhi : Pearson Education Pte. Ltd 2001 . xv, 743 p. : , Includes bibliographical references and index 25 cm.. 8178083272

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Advanced Biomaterials : Fundamentals, Processing, and Applications /

Hoboken, N.J. : | [Westerville, Ohio] : John Wiley & Sons ; | The American Ceramic Society, 2009 . xxii, 746 p., [8] p. of plates : , This volume is an outcome of the International Conference on Design of Biomaterials, organised at Indian Institute of Technology Kanpur, India during 8-11th December, 2006. 25 cm.. 9780470193402 (cloth) | 0470193409 (cloth)

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Neuroprosthetics Theory and Practice /

River Edge, N.J. : World Scientific, 2004 . xxv, 1261 p. : 24 cm..

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Laser Material Processing

By Steen, William M.. 9781849960618

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A practitioner's guide to resampling for data analysis, data mining, and modeling /

By Good, Phillip I.. Boca Raton, FL : CRC Press, 2012 . x, 214 p. : 25 cm.. 9781439855508

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Handbook of Biomedical Instrumentation

By Khandpur. New York : | Maidenhead : McGraw-Hill Professional Publishing | McGraw-Hill Education [Distributor] 2015 9789339205430 | 933920543X (Trade Paper)

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Introduction to biomedical engineering

By Enderle, John. San Diego : Academic Press, 2000 . xvii, 1062 p. : , Includes Index http://www.amazon.com/Introduction-Biomedical-Engineering-John-Enderle/dp/0122386604/ref=sr_1_1?s=books&ie=UTF8&qid=1409388550&sr=1-1&keywords=0122386604 24 cm.. 0122386604

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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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Energy Systems Engineering

By Pistikopoulos, Efstratios. Hoboken : Wiley-VCH [Imprint] | John Wiley & Sons, Incorporated 2008 9783527316946 | 3527316949 (Trade Cloth)

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Individual Differences in Producing Movement Related Potentials & Online Multiclass Brain-Computer Interface for Detection and Classification of Movement-Related Cortical Potentials Associated with Task Force and Speed /

By Navid, Muhammad Samran. Islamabad : SMME - NUST, 2015 . xvi, 75 p. : ill. ; , Hardcover. 30cm..

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Biomedical applications of nanotechnology /LABHASETWAR, VINOD, LESLIE, PELECKY DIANDRA L

By Labhasetwar, Vinod. Leslie-Pelecky, Diandra L.. Hoboken, N.J. : Wiley-Interscience, 2007 . x, 249 p. : 25 cm.. 9780471722427 (cloth) | 0471722421 (cloth)

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Early Detection & Stage Classification of Parkinson’s Disease using Deep Learning /

By Zeeshan, Muhammad Muzzamil . . 38p. ; 30cm..

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Comparison And Correlation of Hamstring Reinforcing Exercise With Sprint In terms of Muscles Activity And Force Production /

By Habib, Adnan . . 44p. 30cm.

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Oral Administration of Fluoxetine Incorporated Liposomal Nanoparticles coated with PEG in Treatment of Chronic Mild stress (CMS) /

By Sadiq, Nadia . . 55p. , Depression is one of the most increasing psychological mood disorders, depression does not have some specific physical symptoms, but it negatively affects the person mood, way of living and thinking chronic mild stress (CMS) is one of its type, depression is affecting more than 300million people in the world. Shortness of breath, headache, being miserable, stomach disturbance and physical tensions are some of the common symptoms of depression that is reported according to National Alliance of Mental Health, Anti-depressant are used for altering the neurotransmitters those are serotonin, dopamine and norepinephrine that are primarily involved in regulating and mood alleviating mood. Despite the availability of large number of drugs, many of patients are resistant to the current mode of treatment that are available now adays. The obstacle that is significant for the transportation of beneficial therapeutic entities to the nervous system is BBB that is the blood brain barrier. There are junctions that are present in the endothelial cells of the blood brain barrier that stop the drugs passage. Now nanoparticles are receiving significant limelight owing to their small size and efficient brain targeting activity, making them likely to cross the blood brain barrier while carrying the intact drug molecule otherwise incapable of permeation. In this study the fluoxetine loaded liposomal nanoparticle coated with PEG were developed to transport the drug across the BBB to the central nervous system having much greater efficiency. For testing its delivery, the animal that is mice model of depression is used with the specific name that is designed in order to induce depression like symptoms in mice. Before and After treatment great differences between the physical behaviors like Elevated maze, open field test, force swim test etc and sucrose consumption test were identified and plotted, as liposomes that having capabilities to carry the anti-depressant drug molecule that is administered through the Oral route of administration or the better and improved method of treatment. 30cm.

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Automated Brain Tumor Segmentation using Multimodal MRI Scans /

By Ehsan ,Fatima . . 73p. ; 30cm..

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FNIRS DATA CLASSIFICATION FOR BRAIN COMPUTER INTERFACE USING DEEP LEARNING /

By SUBHANI , AHMAD . . 67p. ; 30cm..

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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.

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Finite Element Analysis of Intracranial Pressure under the influence of Brain Tumor /

By Ahmed ,Ali . . 87p. ; 30cm..

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Study of various gas concentrations in exhaled breath of hemodialysis patients /

By Ahmad ,Osama . . 79p. ; 30cm..

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Investigation of Ablation Techniques for Different Types of Brain Tumors /

By BINTE IRFAN ,MANAHIL . . 41p. ; 30cm..

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3D Neural Network for Detection of ACL Injury in Knee MRI Scans /

By Kamran, Abdullah . . 43p. , Computer aided diagnosis is widely used in medical imaging for the diagnosis of many diseases such as cardiomegaly, brain and kidney tumor, lung cancer, COVID-19 and may more. For the past few decades, computer aided diagnosis has significantly improved due to the development of better architecture used for the diagnosis. Knee injury diagnosis using deep learning techniques is highly popular due its high detection rate and is highly localized. Many state-of-the-art-deep learning models have been used for the detection of abnormalities, meniscus tear and ACL tears in Knee MRI scans. These models include RESNET, Google-Net, VGG19 and VGG16, Alex-Net and many other, all giving significant results. In this study we used a custom 3D CNN model which is light in weight. For training we are using two datasets, one provided by Stanford ML group and the other form Hospital in Croatia. We combined the two dataset and split it into 80-20 ration (80% of the data used for training and remaining for testing purposes). Both the dataset has extreme class imbalance, so we used data augmentation and class weights to rectify its effect on the training process. Further the voxel intensities for the two datasets were different (one dataset was in 8-bit format and the second was in 12-bit format), so we normalized the intensity values using mathematical formulas. For contrast, we performed adaptive histogram equalization Average accuracy and AUC achieved by our model on training set is 97.6 and 99.3 respectively, during 5-fold cross validation. 30cm.

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Study of compounds in exhaled breath for detection of obstructive lung disease /

By Shahzad ,Adil Ahmad . . 58p. ; 30cm..

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Observation of restricted diffusion pattern & extent on DWI/ADC sequences and mechanism of acute ischemic stroke on MRI brain involving different vascular territories /

By Majeed ,Sana . . 71p. ; 30cm..

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Assessment of Stress Biomarkers in the Saliva of Smokers and Nonsmokers via UV Photospectrometry and POMS /

By Fahim, Maria . . 99p. , Smoking is one of the major health catastrophes. Smoking is believed to be the major cause of chronic diseases like Cardiovascular complications, stroke, pregnancy issues, respiratory failure, etc. There are three important transdiagnostic emotional factors that make the population vulnerable to initiation of smoking i.e. anhedonia anxiety sensitivity, distress tolerance. Research studies for the past five decades have proven the adverse effect of stress on brain physiology and functioning. The human body responds to trauma (physical or non-physical stress) in a definite manner. This response of the body can be qualitatively and quantitatively monitored through several chemicals in the bloodstream, saliva, or urine; responding to the stress, called stress biomarkers i.e. Brain-derived neurotrophic factor, cortisol, cytokines etc. Saliva delivers an efficient specimen for various diagnostic procedures due to the presence of different biological products and secondary metabolites of xenobiotics and helps in determining the disease progressions as well as therapy outcomes depending on the variations in the markers/triggers. The nature of mindset and mood states are evaluated by a scales designed to rate the behavior of an individual towards the environmental stimuli that may be physical or psychological in nature. This psychological rating scale is known as the profile of mood state (POMS). This scale was initially originated by McNair, Droppleman, and Lorr in 1971. This scale is presented in the form a questionnaire including different questions regarding the mode and feelings of a subject.This research work aims to further elucidate the utilization of UV Photospectrometry for quantitatively assessing POMS and its relation to the stress biomarker. The samples were collected form the vicinity of the university campus H-12 Islamabad. The samples were processed and stored at the biomedical laboratory of School of Mechanical and Manufacturing Engineering (SMME), NUST. A total of twenty-four (24) male subjects were analyzed. A total of two groups were considered. Group 1 included the non-smoking participants, while group 2 included smoking participants. Simple spitting technique was used for the collection of unstimulated saliva. About 4 ml unstimulated saliva was collected in the sterile falcon tube. Saliva was temporarily stored in cool boxes at 4°C and immediately II shifted to the facility. Centrifugation of the salivary sample was done at 4°C for 5 minutes and 10,000 rpm. Saliva sample was frozen at -80°C until sample collection span was completed. The mood state of the participants was also evaluated using the profile of mood state technique used initially by McNair, Lorr, and Droppleman in 1971. The total mood disturbance (TMD) score was calculated that ranges from -32 to 200. The questionnaire was accessed from ―Mackenzie, B. (2001) Profile of Mood States (POMS) [WWW] Available from: https://www.brianmac.co.uk/poms.htm [Accessed 26/6/2022]‖. Simulated neural networking (SNN) was applied to the collected data from smokers and non-smokers for accuracy scoring. The required statistical analysis was performed and the data was statistically analyzed through a software ―GraphPad Prism 8.0‖ and the respective graphs were plotted. UV spectrophotometry studies provided peak plasma concentration peaks at the lower UV range of 190 to 210 nm, but with no significant difference, representing the presence of biological stress markers. The profile of mood state evaluation studies concluded that the smoking participants were presented with a significantly higher level of individual mood profile scores i.e. anger (****, P<0.0001), confusion (**, P<0.0014), fatigue (*, P<0.0354), tension (*, P<0.0422) and stress as compared to nonsmoking participants. The vigorous score was significantly high in the nonsmoking individuals (****, P<0.0001). Similarly, total mood disturbance score was also significantly high in the smoking participants. The application of artificial neural networking through artificial machine learning scored the accuracy of the results 84% which is a reliable outcome. The current research work concludes that different stress stimuli including physiological stress and psychological stress tends to initiate/increase the smoking behavior among the community. Likewise, it is also concluded that smoking initiation may not be always triggered in response to stress. Numerous factors i.e. lack of education, negative inspiration, or behavior to impress are also involved. Furthermore, the adaptation of smoking behavior as a result of stressful stimuli is not a valid approach to reduce the noxious/stressful stimuli. The stress may further be exaggerated by smoking. 30cm.

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Adaptive Hemodynamic Signal Estimation Using Kalman Estimator /

By Quddusi, Basiq Warrad . . 80p. , Functional Near Infrared Spectroscopy (fNIRS) is a technology that measures changes in the oxygenation level of blood present in brain whenever an activity is performed by human being. It is a non-invasive technique and uses near infrared light to detect changes in the concentration of two chromophores i.e., oxygenated and deoxygenated haemoglobin. During the recording, information related to neural activity in fNIRS signals gets compromised. This is due to the interference of noises from the environment outside as well as inside the human body. External noises can be light in the room and powerline noise. Internal noises are physiological noises such as cardiac, respiratory and mayer waves. Therefore, during analysis, it is required to remove these noises first and then extract main activity signal i.e., hemodynamic signal. Many techniques and methods have been proposed and practiced up to this date. Among them the most popular technique is General Linear Modelling (GLM). GLM models the signal by breaking it down into sum of all components present in the signal along with an error term. Previous studies and research that have used GLM for the reconstruction of activity signal used single frequency value for each noise but in reality, the frequency for each noise varies with the level of activity performed by the subject. This can lead to less accurate reconstruction of activity signal. In this study, this problem is kept under consideration and a method is developed to keep account for all the values of frequency that can corrupt fNIRS signal. Ranges of frequencies are considered instead of single values. These frequency ranges are first extracted using Continuous Wavelet Transform (CWT) and their possible magnitudes are estimated using Kalman filter. Similarly, activity signal is extracted from fNIRS signal using Discrete Wavelet Transform (DWT) and then its magnitude is estimated using Kalman filter. Output of these two steps is fed to GLM for reconstruction of possible hemodynamic signal. Results from this method are compared with the results of conventional GLM and significant improvement is observed both visually and statistically. 30cm,.

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Effects of Broadband Noise on Sleep Quality /

By Zulfiqar, Zanib . . 78p. , Quality sleep is related to multiple valued performances in human life, including maintaining health and improving outcomes at work. Disturbance of sleep can increase the morbidity rate and can create multiple psychological and physiological issues. Broadband noises were hypothesized to mask disruptive noises and improve sleep quality. However, the efficacy of noise in improving sleep measures remained unclear due to the smaller sample size, uncontrolled noise environment, and sleep duration. The main aim of the study was to quantify sleep quality, sleep fragmentation, sleep latency, and relevant sleep factors by providing broadband noises and to assess whether white noise could be a non-pharmacological treatment for better sleep quality. For that purpose, sleep monitoring devices i.e., Fitbit Charge 4 and 5 as well as multiple questionnaires including PSQI, rMEQ, AASP, and St Mary questionnaire were used for data collection. Both hardware and questionnaire data were used to evaluate the effect of white noise on all the sleep factors i.e., sleep stages, sleep latency, total sleep duration, and overall sleep score. Also, environmental noise was measured by using decibel meters. Mostly healthy participants having the age of 25.07+/-4.66 for the questionnaire-based study and 24.25+/- 2.57 for hardware-based data were selected for data collection. Multiple statistical tests were performed on the collected data. ANOVA tests were performed on Fitbit’s data along with the post-hoc tests. Also, chi-square tests were conducted on questionnaire data. The p-value>0.0.5 in all tests suggested that no significant effect of white noise on sleep quality. The results concluded that white noise does not play a significant role in improving sleep quality. However, it can be used as a placebo effect for better sleep for specific persons. The results are based on both sleep monitoring devices and questionnaires with proper statistical analysis, having greater sample size and under controlled environmental noise. However, this work can be extended by changing the population of the experiment i.e., infants and ICU patients to check the effects of white noise. 30cm.

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Introduction to Biomedical Engineering

By Enderle John. New Dehli: Elsevier, 2005 9788131200025

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Introduction to Biomedical Engineering

By Enderle John. New Dehli: Elsevier, 2005 9788131200025

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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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Analysis of variations in brain states and impact of TES during behavioral task /

By Arshad, Sidra . . 88p. , This research targeted to investigate the effects of low-gamma High Definition transcranial alternating current (HD-tACS) at the left DLPFC and primary motor cortex in healthy individuals performing continuous attention task. We selected an openly accessible dataset from openneuro.org. Dataset includes within participant implementation of High-Definition tACS (HD-tACS), stimulating two cephalic regions (frontal & motor) with biphasic stimulation waveform (30 Hz) with a total 600 stimulation trials in 30 sessions. The physiological data i.e. EOG, ECG, along with EEG and behavioral data were being recorded over the course of two 70 and 70.5 minutes’ sessions. The demographic data were acquired before and after each session together with the wellness questionnaires. The participants were given two stimulation doses separately, with 20 stimulation trials per session. The within-subject results showed significant differences between the pre- and post-stimulation data (p-value <0.05) in the F30, in each frequency band. While in M30 session, there was a significant increase in alpha and beta oscillations (p-value < .05). The gamma oscillations were not altered by low-gamma tACS at M1, whereas the theta oscillations showed a significant decrease. The phase-locking values (PLV) of frontal channels decreased in theta, alpha, beta & gamma bands, suggesting a drop in the attention of participants with the onset of stimulation. Hence, the results indicate, that lowgamma HD-tACS over left DLPFC has the potential to inhibit attention and information processing. And the low-gamma HD-tACS can improve motor function over the left primary motor cortex. 30cm.

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Real-Time Target Acquisition Test for Rehabilitation Using EMG /

By Ain ,Qurat Ul . . 109p. ; , This research investigates the use of electromyography (EMG) signals for real-time control in rehabilitation applications. Utilizing the Myo armband, we captured EMG signals corresponding to 12 distinct hand and finger movements. We compared the performance of two machine learning classifiers, Long Short-Term Memory (LSTM) networks and Vanilla Neural Networks (VNN), in accurately classifying these movements. LSTM networks demonstrated superior performance, achieving higher accuracy and robustness in signal classification compared to VNN. To address adaptability and reduce training time for new users, we employed transfer learning techniques. Our research also incorporated transfer learning techniques to enhance model performance, leveraging both a broad dataset collected from multiple subjects and a focused dataset from a single individual over an extended period. Our results show that transfer learning significantly improves the adaptability of the system, allowing for quicker and more efficient integration of new subjects into the model. The study further includes statistical analysis to validate the performance improvements, with paired t-tests and ANOVA confirming the significance of our findings. This work highlights the potential of LSTM networks and transfer learning in enhancing the usability and effectiveness of EMG-based control systems for rehabilitation, paving the way for more responsive and adaptable prosthetic devices. The integration of advanced machine learning techniques into EMG signal processing presents a promising avenue for future research and clinical applications. 30cm..

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Effect of Tactile Stimulation on Visual Memory Performance /

By Latif, Asma . . 80p. , The human eye continuously perceives information about surroundings to be processed and stored in memory so that it can be retrieved. Environmental factors may have positive and negative effects on memory performance and human cognitive processing. Many studies have addressed the effect of auditory circumstances on spatial tasks and visual memory performance. However, only a few studies highlighted the cross-modal interaction between vision for visual cue and touch for training of same visual pattern in tactile pattern. In addition, very little research has been conducted on the effect of tactile stimulation towards memorizing visual tasks. The main objective of this study is to investigate the effect of visuo-tactile stimulation on adult memory. Sixty-two subjects participated in this behavioral study having normal and corrected to normal vision. Participants are divided into two groups and each subject goes thorough Mini mental state examination and Edinburg handedness inventory. The visual assessment task consists of different shapes along with three-digit numbers. During the memorization period, visual assessment task was displayed on computer screen and tactile stimulation was delivered on index finger of the dominant hand of the participant. The participant was provided with an evaluation sheet containing shapes only. If the shape is paired with its corresponding number, then it was be considered correct. The p-value < 0.05 in visual assessment test showed a significant effect of tactile stimulation on visual memory performance. The findings of this study concluded that participants memorized the object number pair task better in the presence of tactile stimulation as compared to control/no stimulation. One of the conclusions of our work is that combing vision and touch sense may improve cognitive ability and may be provided to people during learning and remembering visual tasks. For future recommendations, heterogenous sample along with brain response can be studied. 30cm.

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Novel Hybrid Neural Network Architecture For Multi-modal Brain Tumor mpMRI Segmentation /

By Faizan, Muhammad . . 73p. , Medical image segmentation is a critical step in clinical decision-making, enabling precise localization of anatomical structures and lesions. While Convolutional Neural Networks, particularly U-shaped architectures like U-Net, have been popular in this domain, their limited receptive fields hinder the accurate delineation of anomalies with irregular shapes and sizes. Hybrid approaches integrating convolution and vision transformers Vision Transformers (ViTs) have demonstrated improved performance due to their ability to capture dependencies over an extended length. However, ViTs are computationally expensive, particularly for volumetric image segmentation, such as MRI, making them challenging to deploy on hardware with limited resources. To address these challenges, recent studies have revisited convolutional architectures, leveraging large kernel (LK) depth-wise convolution to emulate the hierarchical transformer’s behavior. Building on this direction, we propose 3D SegUXNet, a novel U-shaped encoder-decoder architecture for volumetric biomedical image segmentation. Our model introduces the SegUX block, which combines large kernel depth-wise and point-wise convolutions to enhance the receptive field while maintaining computational efficiency. The addition of a residual block further refines features, improving model robustness and generalization. Empirical results demonstrate that 3D SegUX-Net consistently outperforms state-of-the-art CNN and transformer methods on multiple benchmarks, including BraTS 2019, BraTS 2020, BraTS 2023, and organ segmentation of BTCV dataset. The proposed architecture establishes new SOTA performance in volumetric medical semantic segmentation, combining simplicity, efficiency, and scalability. 30cm..

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Design Optimization and Surface Modification of Biodegradable Magnesium Alloy AZ91 for Biomedical Implants Using Electrical Discharge Machining /

By Shafiq, Danyal . . 105p. , Magnesium alloy AZ91, known for its lightweight nature, biocompatibility, and controlled biodegradability, is a promising material for orthopedic implants. However, faster corrosion in a physiological environment remains a challenge. This work investigates Electrical Discharge Machining (EDM) with copper and brass electrodes in a kerosene dielectric to optimize the surface roughness, hardness, and corrosion resistance of AZ91. Electrochemical deposition of copper and brass coatings was also performed to modulate the degradation rate. Surface assessment through Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) indicated that optimizing EDM parameters greatly improved the surface quality, which was characterized by less roughness and higher hardness. Of the coatings, copper exhibited better corrosion resistance, which slowed down the degradation of AZ91 in simulated body fluid (SBF). Therefore, this combination of EDM with electrochemical deposition opens up the possibility of developing patient-specific implants with controlled degradation rates, which ensures mechanical support during healing and avoids follow-up surgeries. This work paves the way for next-generation bioresorbable implants, effectively providing a bespoke solution to orthopedic applications integrating precision machining and surface engineering techniques. 30cm.

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Predicting Healthy and Pathological EEG Patterns with Machine Learning Algorithms /

By Abbas, Ghulam . . 85p. , Neurological disorders pose major global health challenge, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is important for the diagnosis and management of these disorders. However, manual interpretation of EEG recordings is not only time-consuming but also requires expertise. This problem is compounded by the scarcity of trained neurologists in the healthcare sector, especially in low- and middleincome countries. These limitations emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, have sparked significant interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of distinct path, Hybrid-CNNTransformer, for the automatic detection of abnormal raw EEG data. Through multiple ablation studies, we demonstrated the effectiveness of all parts of proposed model. The performance of our proposed model was evaluated using NMT Scalp EEG Dataset and achieved a high classification accuracy of 87.77%, which outperforms the original baseline model and other research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUH abnormal EEG Corpus V.2.0.0. (TUAB), without any hyperparameter tuning or adjustment. Furthermore, a Explainable AI (XAI) analysis confirmed that the model's decision-making process is not only transparent but also neurologically plausible. 30cm.

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High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain /

By Shabbir, Nida . . 94p. , Low back pain (LBP) is one of the most prevalent musculoskeletal disorders worldwide, with diagnosis often relying on subjective evaluation rather than objective physiological measures. This thesis introduces a data-driven framework for quantitative assessment of LBP using high-density surface electromyography (HD-sEMG). As a non-invasive technique, HD-sEMG provides insight into spinal neuromuscular behavior, enabling spatial and temporal characterization of muscle activity patterns associated with dysfunction. The study recruited 39 participants, divided into three groups that are healthy, sub-clinical, and LBP based on chiropractic evaluation. In the first study, machine learning classifiers including Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were trained on time and frequencydomain features to discriminate between groups. The SVM model achieved the highest accuracy, effectively distinguishing subtle neuromuscular differences between healthy and dysfunctional subjects. In the second study, a regression-based framework was developed to predict vertebral joint dysfunction scores (C1-Sacral) derived from chiropractic assessment. ANN and Convolutional Neural Network (CNN) models were trained under a CORAL (Consistent Rank Logits) ordinal regression framework, preserving the ordinal nature of dysfunction severity. The ANN model demonstrated superior predictive performance, capturing non-linear relationships between HD-sEMG activity and graded dysfunction levels. Overall, this research bridges the gap between clinical assessment and computational diagnostics, showing that HD-sEMG signatures can objectively quantify spinal dysfunction and support data-driven LBP diagnosis. The proposed framework establishes a foundation for personalized rehabilitation, automated dysfunction mapping, and AI-assisted musculoskeletal diagnostics, advancing the integration of biomedical signal processing with clinical neurophysiology. 30cm.

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Customization of 3D-Printed Knee Implants: Design Optimization and Lattice Structure Integration fo Enhanced Performance /

By Ahmad, Fatima Ajaz . . 93p. , Total Knee Replacement (TKR) surgeries are becoming increasingly common globally as an effective measure to counter knee arthritis. Total knee replacement implants are very advantageous in a sense that they offer 99% success rate to patients. This thesis presents the design, simulation and additive manufacturing of a patient specific, Functionally Graded Lattice Structure (FGLS) knee implant in Ti 6Al-4V alloy with specific reference to the healthcare situation in Pakistan. This was to explore the local manufacturing facilities of Pakistan as all knee implants are imported from abroad. The strategy involved a high degree of workflow consisting of Computer-Aided Design (CAD), finite element analysis (FEA), and topology optimization using nTopology to create Gyroid-based lattice work. The structures were to resemble the trabecular bone structure to ensure that stiffness discrepancies were minimized. This helped counter only one drawback of solid knee implants, stress shielding. The simulations of the physiological loading conditions (static and cyclic) demonstrated a Von Mises peak of 620.45 Mpa and safety factor of 12.66 on the average and unlimited predicted life of fatigue of over 10^7 cycles. The use of FGLS was effective in making the weight of 490 g to 292, leading to a 40 percent weight reduction, with no structural integrity lost. Selective Laser Melding (SLM) was used to fabricate the implant and the heat treatment allowed stress relieving of the additively manufactured implant. Compressive testing was also mechanically vindicated to be on an of average 95.02 kN with little variation and Micro-CT scanning confirmed high dimensional fidelity and showed internal lattice geometries without defects. According to this research, SLM produced FGLS implants usage has proven to be an option to traditional prosthetics, which is mechanically stable, biologically desirable, and cost-effective, and has a bright future of being a locally manufactured orthopedics product. 30cm.

Place Hold on Customization of 3D-Printed Knee Implants: Design Optimization and Lattice Structure Integration fo Enhanced Performance /

Omics technologies and bio-engineering : towards improving quality of life /

. xvii, 625 pages : 25 cm. 9780128046593 | 0128046597

Place Hold on Omics technologies and bio-engineering :

Omics technologies and bio-engineering : towards improving quality of life /

. xvii, 625 pages : 25 cm. 9780128046593 | 0128046597

Place Hold on Omics technologies and bio-engineering :

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 /

Social Media Analytics for Mental Health Assessment /

By Liaquat, Umna . . 94p. , Social media has emerged as a tool for exploring the human psyche, offering exceptional access to real-time behavioral signals that are transforming the landscape of computational psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility, requiring robust modeling of emotion, behavior, and timing. Prior studies have largely focused on text-based sentiment analysis and linguistic features to classify mental health conditions; however, these approaches often neglect non-verbal markers such as circadian rhythms and affective variability. Existing models primarily depend on static textual cues, limiting their ability to capture the dynamic, multimodal nature of psychiatric expression. This study addresses these limitations by integrating temporal rhythms, emotional dynamics, and behavioral signals extracted from Reddit user histories to develop predictive models of bipolar disorder and high-risk psychological states. We propose a series of interpretable multimodal architectures employing classical machine learning (Logistic regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal posting features, emotional entropy, and community-level interaction structures. Compared to benchmark studies, our models demonstrate significant improvements in both classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader implications for public health by offering a foundation for real-time, ethically deployable digital mental health tools. 30cm.

Place Hold on Social Media Analytics for Mental Health Assessment /