NUST Institutions Library Catalogue Search for 'an:"119666"'

Development of Long Range Flight Control System and Airdrop Mechanism for Unmanned Disaster Relief Helicopter /

By Umair Iqbal. Islamabad : SMME - NUST, 2015 . ill, xxiii, 125 p. ; , Hardcover.

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Machine Vision Based Automatic Quality Inspection System for Connecting-rod /

By Bajwa, Nadia Riaz. . 74p. 30cm.

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Thrust Embedded 6DOF Modeling and Experimentation for Handling Fixed Wing Micro Aerial Vehicle Flight Instabilities/

By Shams, Taimur Ali. . ill: 30,cm..

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Modeling and Monitoring of Performance Limiting Factors for Ball-screw Linear Motion Systems/

By Riaz Naveed . . 122,p; 2022.

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Development and testing of rotary unmanned aerial vehicle/

By Ali, Imran Nusrat. . iv. 85p. : , Hardcover 30cm..

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A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis /

. 114p. , Automated vertebrae analysis from medical images plays an important role in computer aided diagnosis (CAD). It provides an initial and early identification of various vertebral abnormalities to doctors and radiologists. Vertebrae segmentation and classification are important but difficult tasks in medical imaging due to low contrasts in image, noise and high topological shape variations in radiological scans. It becomes even more challenging when dealing with various deformities and pathologies present in the vertebral scans like osteoporotic vertebral fractures. In this work, we want to address the challenging problem of vertebral image analysis for vertebra segmentation and classification. In the past, various traditional imagery techniques were employed to address these problems. Recently, deep learning techniques have been introduced in biomedical image processing for segmentation and characterization of several abnormalities. These techniques are becoming popular in solving various medical image analysis problems due to their robustness and accuracy. In this research, we present a solution of vertebrae segmentation and classification problem with the help of deep learning approach. We present a novel combination of traditional region based level-set with deep learning framework in order to extract the shape of vertebral bones accurately; which would be able to handle the deformities in the vertebral bones precisely and efficiently. After vertebrae segmentation, we further extend the work to abnormal vertebrae classification with the help of deep learning approach. This novel framework would be able to help the medical doctors and radiologists with better visualization of vertebral bones and providing the initial automated classification of vertebrae to be normal or abnormal. The proposed method of vertebrae segmentation was successfully tested on different datasets with various fields of views. The first dataset comprises of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae in sagittal view, collected from a local hospital. The second dataset comprises 73 CT scans of cervical vertebrae in sagittal view, also collected from a local hospital. The third dataset comprises 20 CT scans of thoracolumbar (thoracic and lumbar) vertebrae in sagittal view collected from spine segmentation challenge CSI 2014. The forth dataset comprises 25 CT scans of lumbar vertebrae in sagittal view collected from spine segmentation challenge CSI 2016. Lastly, we have utilized the same locally collected set of 173 iii CT scans of thoracolumbar (thoracic and lumbar) vertebrae and extracted in axial view to perform the segmentation task. For classification purpose, we have utilized the locally collected set of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae as these include osteoporotic vertebral fractures in it. The details of these datasets have been presented in respective sections. We have achieved promising results on our proposed techniques. The evaluation of the segmentation performance on the datasets with various machines and field of views helped us to ensure the robustness of our proposed method. On validation sets of these datasets, we have achieved an average dice score of around 95% for vertebrae segmentation; and accuracy of above 80% for the vertebrae classification. The detailed results have been presented in the results section. These results reveal that our proposed techniques are competitive over the other state of the arts in terms of accuracy, efficiency, flexibility and time 30cm.

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Modeling and Monitoring of Performance Limiting Factors for Ball-screw Linear Motion Systems /

By Riaz, Naveed . . 143p. , Reliability of high precision linear motion systems is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective Ball Screw (BS) linear drives. A fault in these members severely affects positioning accuracy and safe working of overall system. BS linear drives perform flight / application critical job and are responsible to provide precise linear motion while carrying thrust loading. BS drives are specifically designed on the basis of desired operational parameters like power rating, drive torque, slew rate, efficiency, friction, and mechanical backlash etc. These operating parameters significantly affect the functional performance of linear electro-mechanical systems. At present, few techniques are available to monitor BS drives for aerospace and industrial systems. This research works to improve reliability of ball screw drive linear systems by modeling and monitoring the performance factors through analytical redundancy and intelligent deep learning. In the past, some traditional techniques have been employed to address these problems; however these techniques show limitations like insufficient data acquisition, requirement of dedicated model developer and poor domain adaptation. Recently, deep learning techniques have been introduced and are becoming more popular to detect and characterize various fault signal analysis problems due to their robustness and accuracy. The aim of this research is to provide solution of mechanical faults identification and classification problems for BS linear drives. A fault diagnostic algorithm is designed based on dynamic mathematical model and a remnant filter is implemented to detect signal errors. The remnant filter generates residual signal proportional to the error induced. Fault detection thresholds are set and decision logic is established based on position measurement corrections to compare residual signal with the lower and upper pre-defined threshold constants. Accuracy in faults identification is highly dependent on improved features extraction. For this purpose, a novel Residual Twin CNN (ResT-CNN) is proposed that uses combination of 1-D and 2-D CNN in parallel learning which improves features extraction performance; followed by knowledge base-Remnant-PCA (Kb-Rem-PCA) architecture in combination with multi-class support vector machine (Mc-SVM). Current and Position signal data was collected under different load domains. This novel hybrid combination proved very effective in accurate faults identification and classification. The performance of proposed intelligent technique was successfully tested and validated on different datasets including IMS-UC (Intelligent Maintenance Systems – University of Cincinnati) publically published bearing dataset, Paderborn published multi-stage bearing dataset, Current signal dataset for multiple fault modes of BS drive and Position measurement data for multi faults cases for BS linear drive. The actual Signal and Model Fit Simulated Data for BSD system was compared. The testing results proved the effectiveness and superiority of proposed model against different state of the art techniques. The proposed novel framework was also tested for system's stability under different load domains. The results reveal highly competitive values greater than 95% in terms of accuracy and precision for different faults cases 30cm..

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Novel Design Methodology and Development of Composite Landing Gear Struts for an Unmanned Aircraft

By Ahmad, Muhammad Ayaz. . 213, p. 35 cm..

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