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Found 14 papers, 0 papers with code
Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models
The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4.
The Effect of Dust and Sand on the 5G Terrestrial Links
A recent study investigates the effect of rain and snow on the 5G communication channel to reduce the challenge of using high millimeter-wave frequencies.
Bayesian Parameter Estimations for Grey System Models in Online Traffic Speed Predictions
This paper presents Bayesian parameter estimation for first order Grey system models' parameters (or sometimes referred to as hyperparameters).
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles
Change point models, can be used for real-time anomaly detection caused by the false information attack.
Hybrid Quantum-Classical Neural Network for Incident Detection
The incident detection performance of the hybrid model is evaluated against baseline classical ML models.
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack
To make classification models resilient against adversarial attacks, we used a hybrid deep-learning model with both the quantum and classical layers.
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels
Dynamic behavior of traffic adversely affect the performance of the prediction models in intelligent transportation applications.
Improved Grey System Models for Predicting Traffic Parameters
To evaluate the performance of the proposed models, they are compared against a set of benchmark models: GM(1, 1) model, Grey Verhulst models with and without Fourier error corrections, linear time series model, and nonlinear time series model.
Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks
This study presents a methodology to quantify vulnerability of cyber attacks and their impacts based on probabilistic graphical models for intelligent transportation systems under connected and autonomous vehicles framework.
Cycle-to-Cycle Queue Length Estimation from Connected Vehicles with Filtering on Primary Parameters
The results show that with Kalman and Particle filters, parameter estimators are able to find the true values within 15 minutes and meet and surpass the accuracy of known parameter scenarios especially for low market penetration rates.
Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment
Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure, and traffic management centers.
Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control
Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS using four variations of Grey systems: (i) the first order single variable Grey model (GM(1, 1)); (ii) GM(1, 1) with Fourier error corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections.
Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections
The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds).
Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment
Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure and traffic management centers.
Cryptography and Security
