Mary Ahmadi

Mohammad Javad Ahmadi, Rafael F. Schaefer, H. Vincent Poor

We propose a task-oriented multiuser wireless communication framework for distributed classification based on a MAP-driven system design under wireless channel impairments. By deriving a tractable class-mean separation objective, the proposed approach enables low-complexity design of both learning-based feature extraction and precoding strategies. Unlike existing covariance-based and reconstruction-oriented methods, the proposed formulation avoids repeated covariance inversions and eigen-decomposition operations while directly improving class separability after channel distortion. Simulation results demonstrate that the proposed method achieves higher classification accuracy than existing schemes, while simultaneously reducing computational complexity.

Masood Tehrani, Mary Ahmadi

In this study, an Artificial Neural Network (ANN) approach is utilized to perform a parametric study on the process of extraction of lubricants from heavy petroleum cuts. To train the model, we used field data collected from an industrial plant. Operational conditions of feed and solvent flow rate, Temperature of streams and mixing rate were considered as the input to the model, whereas the flow rate of the main product was considered as the output of the ANN model. A feed-forward Multi-Layer Perceptron Neural Network was successfully applied to capture the relationship between inputs and output parameters.