Search

Your search keyword '"HUSSAIN, MUHAMMAD A."' showing total 6,760 results
Start Over Author "HUSSAIN, MUHAMMAD A."
6,760 results on '"HUSSAIN, MUHAMMAD A."'

108. Wearable sensors for monitoring marine environments and their inhabitants

Author

Kaidarova, Altynay, Geraldi, Nathan R., Wilson, Rory P., Kosel, Jürgen, Meekan, Mark G., Eguíluz, Víctor M., Hussain, Muhammad Mustafa, Shamim, Atif, Liao, Hanguang, Srivastava, Mani, Saha, Swapnil Sayan, Strano, Michael S., Zhang, Xiangliang, Ooi, Boon S., Holton, Mark, Hopkins, Lloyd W., Jin, Xiaojia, Gong, Xun, Quintana, Flavio, Tovasarov, Adylkhan, Tasmagambetova, Assel, and Duarte, Carlos M.

Published
2023
Full Text
View/download PDF

110. The Impact of Sandbars on Bank Protection Structures in Low-Land Reaches: Case of Ganges and Brahmaputra-Jamuna

Author

Shampa, Hussain Muhammad Muktadir, Israt Jahan Nejhum, A. K. M. Saiful Islam, Md. Munsur Rahman, and G. M. Tarekul Islam

Subjects
sandbar, bank protection structure, scouring, Ganges, Brahmaputra-Jamuna, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500
Abstract

Sandbars are an integral part of the alluvial river’s geophysical system due to these rivers’ wide sediment availability and varied transport capacity. The sandbars’ evolution and translation considerably influence the stability of the riverbank. However, while designing the riverbank protection structures (RBPS), the impact of such sandbars is often overlooked, as the evolution of such bars is quite uncertain in terms of location, amplitude, and translation. This study evaluates the localized impact of sandbars on bank protection structures in two types of alluvial rivers: meandering (Ganges) and braided (Brahmaputra-Jamuna), utilizing time series satellite images, hydraulic characteristics, and numerical modeling. We found that sandbar development initiates width adjustment in both meandering and braided rivers when the ratio of width to depth surpasses 90. In the case of meandering rivers, riverbank erosion mostly occurs as a result of the presence of alternate bars or point bars. Sandbars in a meandering river (Ganges) can lead to an approximate 18% increase in flow depth. The depth-averaged velocity is anticipated to rise by approximately 29%, and the tractive force may increase by a factor of 1.6. On the other hand, the braided river (the Brahmaputra-Jamuna) underwent significant bank erosion due to the presence of both free unit and hybrid types of bars. In such rivers, the depth of the flow may experience a notable increase of 18%, while the depth-averaged velocity undergoes an approximate increase of 50%, and the tractive force has the potential to grow by a factor of 5.3. Consequently, we recommend allowing the natural evolution of sandbars while preserving the riverbank (where needed only) through RBPS, considering these additional loads.

Published
2024
Full Text
View/download PDF

115. Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis

Author

Saeed, Fahman, Hussain, Muhammad, Aboalsamh, Hatim A, Adel, Fadwa Al, and Owaifeer, Adi Mohammed Al

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

The prevalence of diabetic retinopathy (DR) has reached 34.6% worldwide and is a major cause of blindness among middle-aged diabetic patients. Regular DR screening using fundus photography helps detect its complications and prevent its progression to advanced levels. As manual screening is time-consuming and subjective, machine learning (ML) and deep learning (DL) have been employed to aid graders. However, the existing CNN-based methods use either pre-trained CNN models or a brute force approach to design new CNN models, which are not customized to the complexity of fundus images. To overcome this issue, we introduce an approach for custom-design of CNN models, whose architectures are adapted to the structural patterns of fundus images and better represent the DR-relevant features. It takes the leverage of k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to automatically determine the depth and width of a CNN model. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging benchmark datasets from Kaggle: EyePACS and APTOS2019. The custom-designed models outperform the famous pre-trained CNN models like ResNet152, Densnet121, and ResNeSt50 with a significant decrease in the number of parameters and compete well with the state-of-the-art CNN-based DR screening methods. The proposed approach is helpful for DR screening under diverse clinical settings and referring the patients who may need further assessment and treatment to expert ophthalmologists., Comment: 20 pages, 6 figures

Published
2021

116. A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images

Author

Asiri, Norah, Hussain, Muhammad, Adel, Fadwa Al, and Aboalsamh, Hatim

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Red-lesions, microaneurysms (MAs) and hemorrhages (HMs), are the early signs of diabetic retinopathy (DR). The automatic detection of MAs and HMs on retinal fundus images is a challenging task. Most of the existing methods detect either only MAs or only HMs because of the difference in their texture, sizes, and morphology. Though some methods detect both MAs and HMs, they suffer from the curse of dimensionality of shape and colors features and fail to detect all shape variations of HMs such as flame-shaped. Leveraging the progress in deep learning, we proposed a two-stream red lesions detection system dealing simultaneously with small and large red lesions. For this system, we introduced a new ROIs candidates generation method for large red lesions on fundus images; it is based on blood vessel segmentation and morphological operations, and reduces the computational complexity, and enhances the detection accuracy by generating a small number of potential candidates. For detection, we proposed a framework with two streams. We used pretrained VGGNet as a backbone model and carried out several extensive experiments to tune it for vessels segmentation and candidates generation, and finally learning the appropriate mapping, which yields better detection of the red lesions comparing with the state-of-the-art methods. The experimental results validated the effectiveness of the system in the detection of both MAs and HMs; it yields higher performance for per lesion detection; its sensitivity equals 0.8589 and good FROC score under 8 FPIs on DiaretDB1-MA reports FROC=0.7518, and with SN=0.7552 and good FROC score under 2,4and 8 FPIs on DiaretDB1-HM, and SN=0.8157 on e-ophtha with overall FROC=0.4537 and on ROCh dataset with FROC=0.3461 which is higher than the state-of-the art methods. For DR screening, the system performs well with good AUC on DiaretDB1-MA, DiaretDB1-HM, and e-ophtha datasets.

Published
2021

124. Biallelic loss-of-function variants of ZFTRAF1 cause neurodevelopmental disorder with microcephaly and hypotonia

Author

Asif, Maria, Khayyat, Arwa Ishaq A., Alawbathani, Salem, Abdullah, Uzma, Sanner, Anne, Georgomanolis, Theodoros, Haasters, Judith, Becker, Kerstin, Budde, Birgit, Becker, Christian, Thiele, Holger, Baig, Shahid M., Isidoro-García, María, Winter, Dominic, Pogoda, Hans-Martin, Muhammad, Sajjad, Hammerschmidt, Matthias, Kraft, Florian, Kurth, Ingo, Martin, Hilario Gomez, Wagner, Matias, Nürnberg, Peter, and Hussain, Muhammad Sajid

Published
2024
Full Text
View/download PDF

140. Micro-crack detection in electroluminescence-processed photovoltaic cell surfaces via a gradient guided filter tuning framework coupled with filter fused representations

Author

Hussain, Muhammad, Chen, Tianhua, and Mather, Peter

Subjects
Photovoltaic Installations, Energy, Cell surfaces
Abstract

Photovoltaic (PV) installations as an alternative energy source are actively encouraged by governments around the world. However, the surge in deployment of PV systems has exposed energy conversion issues at a larger scale due to damaged PV cells, the cause of which, in many cases can be traced back to the manufacturing process. To address this issue clients commission quality assurance organisations to act as the intermediary quality certifier between the solar manufacturer and the end client. These companies monitor the process of cell manufacturing to guarantee cells leaving the manufacturing complex are defect-free. One of the key tools used for this screening process is Electroluminescence (EL) Imaging, allowing inspectors to essentially 'X-ray' PV cells followed by a manual inspection of each cell to unearth and remove defective cells from production. However, the manual inspection of thousands of EL images, brings many limitations such as human error, bias, time consumption and high cost of expert personnel. Succinctly, this research presents a novel framework, Gradient Guided Filter Tuning, for the development of a CNN architecture tasked with the detection of Micro-cracks within PV manufacturing facilities. The resultant architecture performs highly in a wide variety of performance indicators including the conventional accuracy metric but also computational, architectural and post deployment metrics. Inspired by the output filter maps as a result of the proposed framework, the internal framework logic is manipulated and extended to address the issue of domain-specific data scarcity, by generating representative augmentations from within the architectural convolutional blocks, introduced as Filter Fused Data Scaling. Taking note of the advancements in Convolutional Neural Networks (CNN), the project devised a custom CNN architecture, aimed at the PV manufacturing industry, for automated defect detection in PV cells. Contrary to the convention of requiring a humongous dataset for training CNN's, the project proposed to work with a small dataset, as a manifestation of accounting for the difficulty in acquiring EL processed PV cell images from the production floor. Focus was directed towards comprehension of variance present within the internal classes of the data, enabling the formation of assumptions to dictate the type of transformations and filter configurations within the proposed architecture. The objective of creating a light-weight architecture, enabled subscription to an empirical approach for filter selection, by propagating network gradients back onto the input image, prior to any optimisation of the weights. This provided a mechanism of interpreting the capacity of the filters with respect to the data. After ensuring the required capacity had been developed, various regularisation techniques were utilized for improving model performance. Our proposed Filter Fused Data Scaling process outperformed generic augmentation strategies enabling the developed architecture to learn from domain-specific, well-representative training dataset outputting an F1-score of 98%. The trained and tuned architecture was compared to various state-of-the-art models in the field of image classification, benchmarked against a wide scope of metrics. In order to gain the fundamental understanding of PV systems, the research was initiated by focusing on the use of numerical input variables extracted from a deployed PV installation at the University of Huddersfield with a generation capacity of 2.2kW. The aim was to address the issue of data extraction and the associated cost and time by implementing an Artificial Neural Network requiring only the solar irradiance and output power for determining the fault type. As a result of utilizing only two input parameters, we were able to introduce the concept of irradiance-power 'mapping' to enhance the training process in a progressive manner. The trained network was validated on a separate PV system maintaining an accuracy of above 95% when trained on a different installation with a significantly higher generation capacity of 4.16kW.

Published
2022

142. Biologically Inspired Model for Timed Motion in Robotic Systems

Author

Doliwa, Sebastian, Hussain, Muhammad Ayaz, Sziburis, Tim, and Iossifidis, Ioannis

Subjects
Computer Science - Robotics, Mathematics - Dynamical Systems
Abstract

The goal of this work is the development of a motion model for sequentially timed movement actions in robotic systems under specific consideration of temporal stabilization, that is maintaining an approximately constant overall movement time (isochronous behavior). This is demonstrated both in simulation and on a physical robotic system for the task of intercepting a moving target in three-dimensional space. Motivated from humanoid motion, timing plays a vital role to generate a naturalistic behavior in interaction with the dynamic environment as well as adaptively planning and executing action sequences on-line. In biological systems, many of the physiological and anatomical functions follow a particular level of periodicity and stabilization, which exhibit a certain extent of resilience against external disturbances. A main aspect thereof is stabilizing movement timing against limited perturbations. Especially human arm movement, namely when it is tasked to reach a certain goal point, pose or configuration, shows a stabilizing behavior. This work incorporates the utilization of an extended Kalman filter (EKF) which was implemented to predict the target position while coping with non-linear system dynamics. The periodicity and temporal stabilization in biological systems was artificially generated by a Hopf oscillator, yielding a sinusoidal velocity profile for smooth and repeatable motion.

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results