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Browsing by Author "Rizkalla, Maher"
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Item A Multi-head Attention Approach with Complementary Multimodal Fusion for Vehicle Detection(2024-05) Tabassum, Nujhat; El-Sharkawy, Mohamed; King, Brian; Rizkalla, MaherThe advancement of autonomous vehicle technologies has taken a significant leap with the development of an improved version of the Multimodal Vehicle Detection Network (MVDNet), distinguished by the integration of a multi-head attention layer. This key enhancement significantly refines the network's capability to process and integrate multimodal sensor data, an aspect that becomes crucial in the face of challenging weather conditions. The effectiveness of this upgraded Multi-Head MVDNet is rigorously verified through an extensive dataset acquired from the Oxford Radar Robotcar, demonstrating its enhanced performance capabilities. Notably, in complex environmental conditions, the Multi-Head MVDNet shows a marked superiority in terms of Average Precision (AP) compared to existing models, underscoring its advanced detection capabilities. The transition from the traditional MVDNet to the enhanced Multi-Head Vehicle Detection Network signifies a notable breakthrough in the arena of vehicle detection technologies, with a special emphasis on operation under severe meteorological conditions, such as the obscuring presence of dense fog or the complexities introduced by heavy snowfall. This significant enhancement capitalizes on the foundational principles of the original MVDNet, which skillfully amalgamates the individual strengths of lidar and radar sensors. This is achieved through an intricate and refined process of feature tensor fusion, creating a more robust and comprehensive sensory data interpretation framework. A major innovation introduced in this updated model is the implementation of a multi-head attention layer. This layer serves as a sophisticated replacement for the previously employed self-attention mechanism. Segmenting the attention mechanism into several distinct partitions enhances the network's efficiency and accuracy in processing and interpreting vast arrays of sensor data. An exhaustive series of experimental analyses was undertaken to determine the optimal configuration of this multi-head attention mechanism. These experiments explored various combinations and settings, ultimately identifying a configuration consisting of seven distinct attention heads as the most effective. This setup was found to optimize the balance between computational efficiency and detection accuracy. When tested using the rich radar and lidar datasets from the ORR project, this advanced Multi-Head MVDNet configuration consistently demonstrated its superiority. It not only surpassed the performance of the original MVDNet but also showed marked improvements over models that relied solely on lidar data or the DEF models, especially in terms of vehicular detection accuracy. This enhancement in the MVDNet model, with its focus on multi-head attention, not only represents a significant leap in the field of autonomous vehicle detection but also lays a foundation for future research. It opens new pathways for exploring various attention mechanisms and their potential applicability in scenarios requiring real-time vehicle detection. Furthermore, it accentuates the importance of sophisticated sensor fusion techniques as vital tools in overcoming the challenges posed by adverse environmental conditions, thus paving the way for more resilient and reliable autonomous vehicular technologies.Item Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images(Springer Nature, 2023-01-27) Huang, Zhi; Shao, Wei; Han, Zhi; Alkashash, Ahmad Mahmoud; De la Sancha, Carlo; Parwani, Anil V.; Nitta, Hiroaki; Hou, Yanjun; Wang, Tongxin; Salama, Paul; Rizkalla, Maher; Zhang, Jie; Huang, Kun; Li, Zaibo; Electrical and Computer Engineering, School of Engineering and TechnologyAdvances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.Item Asset allocation in frequency and in 3 spatial dimensions for electronic warfare application(2016-04) Crespo, Jonah Greenfield; Christopher, Lauren Ann; Dos Santos, Euzeli Cipriano, Jr.; Rizkalla, Maher; Li, Lingxi; King, BrianThis paper describes two research areas applied to Particle Swarm Optimization (PSO) in an electronic warfare asset scenario. First, a three spatial dimension solution utilizing topographical data is implemented and tested against a two dimensional solution. A three dimensional (3D) optimization increases solution space for optimization of asset location. Topography from NASA's Digital Elevation Model is also added to the solution to provide a realistic scenario. The optimization is tested for run time, average distances between receivers, average distance between receivers and paired transmitters, and transmission power. Due to load times of maps and increased iterations, the average run times were increased from 123ms to 178ms, which remains below the 1 second target for convergence speeds. The spread distance between receivers was able to increase from 86km to 89km. The distance between receiver and its paired transmitters as well as the total received power did not change signi cannily. In the second research contribution, a user input is created and placed into an unconstrained 2D active swarm. This \human in the swarm" scenario allows a user to change keep-away boundaries during optimization. The blended human and swarm solution successfully implemented human input into a running optimization with a time delay. The results of this research show that a electronic warfare solutions with real 3D topography can be simulated with minimal computational costs over two dimensional solutions and that electronic warfare solutions can successfully optimize using human input data.Item Bilateral and adaptive loop filter implementations in 3D-high efficiency video coding standard(2015-09) Amiri, Delaram; El-Sharkawy, Mohamed; King, Brian; Salama, Paul; Rizkalla, MaherIn this thesis, we describe a different implementation for in loop filtering method for 3D-HEVC. First we propose the use of adaptive loop filtering (ALF) technique for 3D-HEVC standard in-loop filtering. This filter uses Wiener–based method to minimize the Mean Squared Error between filtered pixel and original pixels. The performance of adaptive loop filter in picture based level is evaluated. Results show up to of 0.2 dB PSNR improvement in Luminance component for the texture and 2.1 dB for the depth. In addition, we obtain up to 0.1 dB improvement in Chrominance component for the texture view after applying this filter in picture based filtering. Moreover, a design of an in-loop filtering with Fast Bilateral Filter for 3D-HEVC standard is proposed. Bilateral filter is a filter that smoothes an image while preserving strong edges and it can remove the artifacts in an image. Performance of the bilateral filter in picture based level for 3D-HEVC is evaluated. Test model HTM- 6.2 is used to demonstrate the results. Results show up to of 20 percent of reduction in processing time of 3D-HEVC with less than affecting PSNR of the encoded 3D video using Fast Bilateral Filter.Item Building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans using landmark-free surface registration(IEEE, 2016-10) Cong, Shan; Rizkalla, Maher; Salama, Paul; Electrical and Computer Engineering, School of Engineering and TechnologyThe hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools. But this method had some limitations, including the analysis of T1-weighted MRI scans without detailed subfield information and hippocampal registration without using important subfield information. To bridge these gaps, in this work, we propose a new framework for building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans by integrating state-of-the-art methods for automated segmentation of hippocampal subfields and landmark-free, subfield-aware registration of hippocampal surfaces. Our experimental results have shown the promise of the new framework.Item Building a Surface Atlas of Hippocampal Subfields from MRI Scans using FreeSurfer, FIRST and SPHARM(Institute of Electrical and Electronics Engineers, 2014-08) Cong, Shan; Rizkalla, Maher; Du, Eliza Y.; West, John; Risacher, Shannon; Saykin, Andrew J.; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Department of Medicine, IU School of MedicineThe hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for brain disorders such as Alzheimer's disease and epilepsy. However, its complex folding anatomy often presents analytical challenges. In particular, the critical hippocampal subfield information is usually ignored by hippocampal registration in detailed morphometric studies. Such an approach is thus inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To bridge this gap, we present our initial effort towards building a computational framework for subfield-guided hippocampal morphometry. This initial effort is focused on surface-based morphometry and aims to build a surface atlas of hippocampal subfields. Using the FreeSurfer software package, we obtain valuable hippocampal subfield information. Using the FIRST software package, we extract reliable hippocampal surface information. Using SPHARM, we develop an approach to create an atlas by mapping interpolated subfield information onto an average surface. The empirical result using ADNI data demonstrates the promise and good reproducibility of the proposed method.Item Compressed convolutional neural network for autonomous systems(2018-12) Pathak, Durvesh; El-Sharkawy, Mohamed; Rizkalla, Maher; King, BrianThe word “Perception” seems to be intuitive and maybe the most straightforward problem for the human brain because as a child we have been trained to classify images, detect objects, but for computers, it can be a daunting task. Giving intuition and reasoning to a computer which has mere capabilities to accept commands and process those commands is a big challenge. However, recent leaps in hardware development, sophisticated software frameworks, and mathematical techniques have made it a little less daunting if not easy. There are various applications built around to the concept of “Perception”. These applications require substantial computational resources, expensive hardware, and some sophisticated software frameworks. Building an application for perception for the embedded system is an entirely different ballgame. Embedded system is a culmination of hardware, software and peripherals developed for specific tasks with imposed constraints on memory and power. Therefore, the applications developed should keep in mind the memory and power constraints imposed due to the nature of these systems. Before 2012, the problems related to “Perception” such as classification, object detection were solved using algorithms with manually engineered features. However, in recent years, instead of manually engineering the features, these features are learned through learning algorithms. The game-changing architecture of Convolution Neural Networks proposed in 2012 by Alex K [1], provided a tremendous momentum in the direction of pushing Neural networks for perception. This thesis is an attempt to develop a convolution neural network architecture for embedded systems, i.e. an architecture that has a small model size and competitive accuracy. Recreate state-of-the-art architectures using fire module’s concept to reduce the model size of the architecture. The proposed compact models are feasible for deployment on embedded devices such as the Bluebox 2.0. Furthermore, attempts are made to integrate the compact Convolution Neural Network with object detection pipelines.Item Compressed MobileNet V3: An efficient CNN for resource constrained platforms(2021-05) Prasad, S. P. Kavyashree; El-Sharkawy, Mohamed; King, Brian; Rizkalla, MaherComputer Vision is a mathematical tool formulated to extend human vision to machines. This tool can perform various tasks such as object classification, object tracking, motion estimation, and image segmentation. These tasks find their use in many applications, namely robotics, self-driving cars, augmented reality, and mobile applications. However, opposed to the traditional technique of incorporating handcrafted features to understand images, convolution neural networks are being used to perform the same function. Computer vision applications widely use CNNs due to their stellar performance in interpreting images. Over the years, there have been numerous advancements in machine learning, particularly to CNNs.However, the need to improve their accuracy, model size and complexity increased, making their deployment in restricted environments a challenge. Many researchers proposed techniques to reduce the size of CNN while still retaining its accuracy. Few of these include network quantization, pruning, low rank, and sparse decomposition and knowledge distillation. Some methods developed efficient models from scratch. This thesis achieves a similar goal using design space exploration techniques on the latest variant of MobileNets, MobileNet V3. Using DPD blocks, escalation in the number of expansion filters in some layers and mish activation function MobileNet V3 is reduced to 84.96% in size and made 0.2% more accurate. Furthermore, it is deployed in NXP i.MX RT1060 for image classification on CIFAR-10 dataset.Item Correction to: Multidimensional insights into the repeated electromagnetic field stimulation and biosystems interaction in aging and age-related diseases(BMC, 2022-09-09) Perez, Felipe P.; Bandeira, Joseph P.; Perez Chumbiauca, Cristina N.; Lahiri, Debomoy K.; Morisaki, Jorge; Rizkalla, Maher; Medicine, School of MedicineCorrection to: Journal of Biomedical Science (2022) 29:39 https://doi.org/10.1186/s12929-022-00825-yItem DC-DC power converters with multiple outputs(2016-08) Sabbarapu, Bharath Kumar; Li, Lingxi; Dos Santos, Euzeli Cipriano, Jr.; Rizkalla, MaherThis study presents a novel converter configuration that is related to the area DC-DC power converters. To begin with, a brief introduction is given by stating the importance of power electronics. Different types of converters, their operating principles and several new topologies that are being proposed over the years, to suit a particular application with specific advantages are listed in detail. In addition, pro- cedure for performing small signal analysis, which is one among the several averaging techniques is summarized in the first chapter. In the second chapter, small signal modeling is carried out on the single input dual output DC-DC buck converter. This analysis is performed to get a clear un- derstanding on the dynamics of this novel configuration. Routh stability criterion is also applied on this converter topology to determine the limiting conditions for operating the converter in its stability. Third chapter proposes the single input multiple output DC-DC synchronous buck converter. It’s operation, implementation and design are studied in detail. In further, small signal analysis is performed on this topology to determine the transfer function. In the following chapter, results obtained on comparison of a losses between the conventional and traditional topologies are presented in detail. In addition, results achieved during the analysis performed in the previous chapter are displayed. In the end, advantages and its highlights of this novel configuration proposed in this study is summarized. Future course of actions to be done, in bringing this configuration in to practice are discussed as well.