http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/198 2026-04-07T07:14:34Z 2026-04-07T07:14:34Z Rabhowmik, Neem Chand http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4665 2025-05-27T04:42:42Z 2025-05-27T00:00:00Z

Optical and thermal performance evaluation of seasonally intermittently tracked linear solar concentrators Rabhowmik, Neem Chand This thesis is submitted for the degree of Doctor of Philosophy.

2025-05-27T00:00:00Z hussain, Kazi md. Amjad http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4664 2025-05-27T04:41:31Z 2025-05-27T00:00:00Z

Study of thin film semiconductor materials for the Fabrication of solar cells hussain, Kazi md. Amjad This thesis is submitted for the degree of Doctor of Philosophy.

2025-05-27T00:00:00Z Sultana, Afroza http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4078 2025-04-13T03:54:02Z 2025-04-13T00:00:00Z

Processing and Multi-scale Analysis for the Classification of Surface Electromyographic Signal and Finding out its Correlation with Different Motor Neuron Activities Sultana, Afroza Surface Electromyographic (sEMG) signals have become prevalent in a variety of applications, such as human-computer interface, rehabilitation, and medical diagnostics. To improve the classification of sEMG signals and clarify their link with various motor neuron activities, this study explores new directions in signal processing and multi-scale analysis. The study aims to explore and understand the intricate dynamics of neuromuscular control through electromyographic (EMG) signal processing and multiscale analysis, elucidating fundamental mechanisms underlying movement execution and coordination. By analyzing EMG signals using multiscale analysis, researchers unveil intricate patterns of muscle activation, offering insights into single motor unit firings and coordinated movements involving multiple muscle groups. Through meticulous examination, the research unveils the correlation between surface electromyographic (sEMG) signals and motor neuron functions, highlighting potential applications in medical diagnostics and rehabilitation robotics. The study begins with a systematic literature review (SLR) that provides a comparative overview of recent research on sEMG-based hand gesture recognition systems, identifying gaps and evaluating data collection, processing, and classification algorithms. The research presents a simple approach to decomposing sEMG signals, crucial for understanding muscle activation patterns and improving prosthetics and ergonomic interfaces. Utilizing Maximal Overlapping Discrete Wavelet Transform (MODWT) for signal decomposition, the research achieves up to 94% accuracy in identifying neural activity. Correlation analysis reveals discriminative features for differentiating signals, enhancing classification accuracy for finger movements. The insights gleaned from the correlation analysis pave the way for future investigations into the complexities of neuromuscular function and motor control mechanisms. A proposed algorithm for classifying finger gestures demonstrates the effectiveness of processing raw sEMG signals and extracting dominant features using machine learning classifiers. With an ii average classification accuracy of 94.15% from the observed dominating channels, the study emphasizes the importance of an effective model for myoelectric pattern recognition systems in controlling prosthetic limbs. As the research advances, its implications hold promise for enhancing the precision and efficacy of various neurorehabilitation strategies and augmenting our understanding of human motor control mechanisms. The benefits and drawbacks of the algorithms and techniques used to identify, process, and quantify certain patterns and properties of myoelectric signals were covered. These techniques pave the way for more effective clinical diagnoses and rehabilitation interventions, advancing our understanding of human movement and enhancing motor function restoration. This thesis is submitted for the degree of Doctor of Philosophy.

2025-04-13T00:00:00Z Hossain, Md. Nahid http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4069 2025-04-10T08:03:06Z 2025-04-10T00:00:00Z

Data-driven motion estimation in Single Photon Emission Computed Tomography (SPECT) Hossain, Md. Nahid Single Photon Emission Computed Tomography (SPECT) is one of the most important imaging modalities in nuclear medicine used for imaging different body organs for diagnostic purposes. It is well known that patient movement during the acquisition time do introduce artifacts in the reconstructed SPECT images. The projection frames become out of alignment due to the patient's movements, degrading the reconstructed image and do introducing artifacts. The accuracy of the diagnosis could be considerably impacted by these motion artifacts. Getting consistent projection data from the acquisition requires motion correction. Therefore, the motion correction of patients in tomographic images is very essential for more accurate diagnosis of diseases. The aim of this project is to develop and evaluate a data driven approach to motion estimation and correction of SPECT data without necessity for motion tracking system. The objectives can be summarized as the different groups of experiment were carried out on several set of data to validate the motion estimation and correction procedure. A fully three-dimensional (3-D) algorithm is proposed that estimates the patient motion based on the projection of motion-corrupted data and updated the image and finally correct the motion. At first, initial studies have been performed using a digital version of the Hoffman brain phantom. Movement was simulated by constructing a mixed set of projections in discrete positions of the phantom. Several number of motion induced projections set were generated from the physical phantom. The single or multiple movement have been applied to the physical phantom and the data were acquired. The translational movement, angular location and rotational motion (all 6 dof parameters) were analyzed. Motion estimation and correction algorithms were applied to the acquired data as well as simulated motion induced physical phantom data. Clinical data with/without motion were collected and motion correction algorithms were applied to estimate motion and reconstructed image were improved. The proposed method iteratively estimate and compensate the motion during image reconstruction. In every iteration, the rigid motion was assessed view-by-view and then used to update the system matrix. A first rough motion estimate can be produced using the initial 1 reconstructed image, which is motion contaminated. In order to provide a motion-corrected image at the initial iteration, this motion is taken into consideration during the reconstruction process. To increase the likelihood, the motion estimate and the motion-corrected image are then updated alternately. The iterations are stopped when it appears that the updated motion has converged. There are two steps in the algorithm: (1) combined image and motion estimation, and (2) final reconstruction (motion compensation). Each iteration consists of 2 steps: a motion update and an image update. The OSEM algorithm is run through several iterations to update the image. With the last motion estimate, a final iterative reconstruction was carried out. The method was evaluated on physical phantom study, simulations phantom study and patient scans. In the physical phantom studies, the motion correction algorithms were applied to the phantom’s both stationary data and motion induced data. Most of motion blurring in the reconstructed images disappeared after the compensation on the motion induced data. Various simulated motion-induced data sets were produced from the simulation phantom data and calculated the Mean Square Difference (MSD) value. The average MSD values between two different data sets were found 933.25 and 1247.5. With the simulated motion-induced data, motion correction algorithms were applied. The quality of the reconstructed images was improved significantly after the compensation. In the physical phantom studies, the motion estimation for translational movements were found 7.6 mm in X axis, 7.1 mm in Y axis and 8.2 mm in Z axis. And it was also found that the heighest rotational motions were 12.3⁰ in X axis, 16.6⁰ in Y axis and -14.1⁰ in Z axis. The motion correction algorithms were applied to the clinical patient’s data also. And it was clearly seen that the motion corrected reconstructed images were significantly improved after the compensation. The proposed method effectively eliminated motion-induced artifacts in mentioned three types of SPECT data. The proposed method can be used retroactively on SPECT scans affected by motion because only measured raw data are required for motion estimate and compensation in this method. 2 The contributions of this research are summarized as the development of an innovative technique that can correct the six DOF motion in SPECT using the acquired data only. The methodology for six DOF data-driven motion estimation and correction in SPECT was developed and validated for using the digital and physical phantom studies as well as in clinical applications including human subjects. The developed technique was made automated and did not require any external equipment. It was not necessary to have any prior knowledge of motion. It can be readily adjusted to various types of collimation and multiple detector geometries. This thesis is submitted for the degree of Doctor of Philosophy.

2025-04-10T00:00:00Z