Spatial multiplexing for photon-counting MIMO-FSO communication systems by Abou-Rjeily, Chadi

“…In this paper, we consider the problem of multiple-input multiple-output (MIMO) free-space optical communications under the Poisson photon-counting detection model. Aiming for high bit rate objectives, we consider the spatial-multiplexing (SMux) solution with M-ary pulse-position modulation, where we propose appropriate optimal and suboptimal decoders and evaluate their complexities. …”
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Optical spatial modulation for FSO IM/DD communications with photon counting receivers by Abou-Rjeily, Chadi

“…A performance analysis is carried out over gamma-gamma channels with the exact Poisson photon-counting detection model. Exact Symbol Error Probability (SEP) expressions, simple upper bounds and the achievable transmit diversity orders are derived for both the open-loop and closed-loop scenarios. …”
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Precoding‐Aided Spatial Modulation for the Wiretap Channel with Relay Selection and Cooperative Jamming by Zied Bouida (18711545)

“…<p dir="ltr">We propose in this paper a physical‐layer security (PLS) scheme for dual‐hop cooperative networks in an effort to enhance the communications secrecy. The underlying model comprises a transmitting node (Alice), a legitimate node (Bob), and an eavesdropper (Eve). …”

Precoding-Aided Spatial Modulation for the Wiretap Channel with Relay Selection and Cooperative Jamming by Zied Bouida (18711545)

“…<p dir="ltr">We propose in this paper a physical-layer security (PLS) scheme for dual-hop cooperative networks in an effort to enhance the communications secrecy. The underlying model comprises a transmitting node (Alice), a legitimate node (Bob), and an eavesdropper (Eve). …”

H.264/AVC Motion Vector Concealment Solutions Using Online and Offline Polynomial Regression by Shanableh, Tamer

“…Both solutions make use of the spatially and temporally neighboring motion vectors for building the regression models. …”
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MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

“…The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (<i>η</i>). …”

Daytime Variation of Urban Heat Islands: The Case Study of Doha, Qatar by Yasuyo Makido (18808108)

“…We validated the predictions of the statistical models by computing the Root Mean Square Error (RMSE) and discovered that temporal variations in urban heat are mediated by different factors throughout the day. …”

Partially Lagging One-Equation Turbulence Model by Elkhoury, Michel

“…The amount of relaxation is based on the von Kármán length scale. The present model does not assume equal diffusion coefficients of the [Math Processing Error] and [Math Processing Error] equations; therefore, third-order velocity gradients emerge. …”
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Improving MRI Resolution: A Cycle Consistent Generative Adversarial Network-Based Approach for 3T to 7T Translation by Zakaria Shams Siam (22048001)

Optimizing the Energy Production for a Photovoltaic Thermal (PVT) Water System by Alsayegh, Khader M.J.

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Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better? by Ahmed Soliman (4591621)

“…While conventional manual techniques are time-consuming and prone to errors, advanced deep learning models have shown promising results in medical image segmentation. …”

Effect of Pilot-Points Location on Model Calibration: Application to the Northern Karst Aquifer of Qatar by Husam Baalousha (18140608)

“…<div><p>In hydrogeological modelling, two approaches are commonly used for model calibration: zonation and the pilot-points method. …”

Performance comparison of variable-stepsize IMEX SBDF methods on advection-diffusion-reaction models by Raed Ali Mara'Beh (17337892)

“…<p>Advection-diffusion-reaction (ADR) models describe transport mechanisms in fluid or solid media. …”

Performance comparison of variable-stepsize IMEX SBDF methods on advection-diffusion-reaction models by Raed Ali Ayesh Marabeh (21142247)

“…<p dir="ltr">Advection-diffusion-reaction (ADR) models describe transport mechanisms in fluid or solid media. …”

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model by Md Abrar Jahin (20108252)

“…Comparative benchmarking against seven other deep-learning models validates MCDFN’s efficacy, showing it outperforms its counterparts across key metrics with a mean squared error (MSE) of 23.5738, root mean squared error (RMSE) of 4.8553, mean absolute error (MAE) of 3.9991, and mean absolute percentage error (MAPE) of 20.1575%. …”

Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends by Abdellatif M. Sadeq (16931841)

“…For model 2, the maximum absolute error percentage was 6.58%. …”

Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil (11513178)

Large-eddy simulation of the flow in Z-Shape duct by Mohammed Karbon (20351925)

“…Some slight over-predictions and under-predictions were found at certain separation distances. These numerical errors are due to the limited modeling approach to predict small eddies structures with the current SGS model. …”

Arabic Dialect Speech-Text Recognition Using Deep Learning by RAEIALBOOM, OMAR SALEH DARWISH

“…To address these limitations, a hybrid approach is proposed, combining the TestRCNN and CNN architectures. This hybrid model leverages the CNN’s ability to extract detailed spatial features and the TestRCNN’s proficiency in capturing long-term dependencies. …”
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Receiver design for MIMO FSO communication systems with photon-counting detection. (c2019) by Mawla, Rami Issam El

“…Unlike the existing literature that considers the simplistic additive white Gaussian noise (AWGN) model, this work revolves around the more accurate Poisson noise model where the number of detected photons at the receiver follows the Poisson distribution for shot-noise limited receivers. …”
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