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Found 8 papers, 1 papers with code
Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks
Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units.
Communication-Efficient Distributed Kalman Filtering using ADMM
The DKF problem is formulated within a distributed optimization framework, where coupling constraints require the exchange of local state and covariance updates between neighboring nodes to achieve consensus.
Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks
This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA).
Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data
This work aims to create a synthetic dataset and evaluate its effectiveness to improve the prediction accuracy of object detection systems.
CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher
Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models.
Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab-Column Connections
The study is complemented with FEA of slab for validating the experimental results and machine learning predictions. In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data.
Adaptive Three Layer Hybrid Reconfigurable Intelligent Surface for 6G Wireless Communication: Trade-offs and Performance
So, there might be a tradeoff between the additional energy consumption for the RIS technologies and the overall gain acquired by deploying this potential advancement.
Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor
To address this issue, we propose a point pair descriptor that is robust to noise and occlusion and achieves high retrieval accuracy.
