Optimized VLSI Architectures for Power-Efficient Deep Neural Networks in Edge-AI Enabled Robotics by Bostani, Ali

“…The work introduces a software–hardware codesign of VLSI/SoC DNN accelerators combining a technology-constrained processing element array, an on-chip energy-optimized memory hierarchy with traffic-constraining tiling and compression, and a hardware-constrained adaptable quantization policy with accuracy and latency guardrails. …”
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Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System by Alaa Altawallbeh (22565837)

“…We further propose a hybrid Particle Swarm Optimization (PSO) algorithm with a multi-objective cost function to optimize VSG controller gains. …”

Meta-Heuristic Procedures for the Multi-Resource Leveling Problem with Activity Splitting by AlSayegh, Hadeel Yacoub

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Low-Complexity Machine Learning-based Behavioral Modeling of Power Amplifiers by Aziz, Mohammad Rabih

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Rate Adaptation in Dynamic Adaptive Video Streaming Over HTTP by Abdelhafez, Nada

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UAV-Aided Projection-Based Compressive Data Gathering in Wireless Sensor Networks by Ebrahimi, Dariush

“…Our problem definition aims at clustering the sensors, constructing an optimized forwarding tree per cluster, and gathering the data from selected cluster head nodes based on projection-based CDG with minimized UAV trajectory distance. …”
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Hybrid deep learning based threat intelligence framework for Industrial IoT systems by Jahanzaib Malik (23718816)

“…The proposed approach was also compared against several contemporary deep learning-based architectures and existing benchmark algorithms. …”

On the Performance Analysis of WPT-Based Dual-Hop AF Relaying Networks in α - μ Fading. by Galymzhan Nauryzbayev (16855473)

Peak Loads Shaving in a Team of Cooperating Smart Buildings Powered Solar PV-Based Microgrids by Ahmed Ouammi (14151039)

“…The main objective is to formulate a constrained optimization problem embedded in a model predictive control (MPC) scheme to optimally control the operation of each microgrid to reduce/shave the peak load in case of occurrence, optimizing the power flows exchanges and energy storages, while ensuring a high quality of service to the EVs owners in each microgrid. …”

Multi-agent reinforcement learning for privacy-aware distributed CNN in heterogeneous IoT surveillance systems by Emna Baccour (16896366)

“…A distributed solution scheme is also developed based on the Lagrangian dual problem. Next, to relax the optimization, we shape our approach as a cooperative and competitive Multi-Agent Reinforcement Learning (MARL) that supports heterogeneous/independent agents. …”

A new multiobjective evolutionary algorithm forenvironmental/economic power dispatch by Abido, A.A.

“…In this paper, a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) optimization problem is presented. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. …”
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Environmental/economic power dispatch using multiobjective evolutionary algorithms by Abido, M.A.

“…The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new strength Pareto evolutionary algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable objectives. …”
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

“…Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

“…Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
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An evolutionary algorithm for network topology design by Youssef, H.

“…The topology design of campus networks is a hard constrained combinatorial optimization problem, dictated by physical and technological constraints and must optimize several objectives. …”
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A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS by Youssef, H.

“…ABSTRACT The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes), and the links. …”
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2022 IEEE Congress on Evolutionary Computation (CEC) by El-Abd, Mohammed

“…RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. …”
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Fuzzy simulated evolution algorithm for topology design of campusnetworks by Youssef, H.

“…The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes) and links. …”
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Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem by Haraty, Ramzi A.

“…Genetic algorithms were successfully useful to solve many optimization problems including the university Timetable Problem. …”
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Uplink Noma in UAV-Assisted IoT Networks by Mrad, Ali

“…Given the complexity of the problem and the incomplete knowledge about the environment, the problem is divided into two subproblems: the first models the UAV trajectory and the selection of the first device in the NOMA cluster at each time slot as a Markov Decision Process, and uses Proximal Policy Optimization to solve it. The second device is then selected using a heuristic algorithm based on prioritizing devices with higher bit rate requirements and strict deadlines. …”
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