摘要：Embedded systems used in real-time applications require low power, less area and high computation speed. For digital signal processing, image processing and communication applications, data are often received at a continuously high rate. The type of necessary arithmetic functions and matrix operations may vary greatly among different applications. The RTL-based design and verification of one or more of these functions could be time-consuming. Some High Level Synthesis tools reduce this design and verification time but may not be optimal or suitable for low power applications. The design tool proposed in this paper can improve the design time and reduce the verification process. The design tool offers a fast design and verification platform for important matrix operations. These operations range from simple addition to more complex matrix operations such as LU and QR factorizations. The proposed platform can improve design time by reducing verification cycle. This tool generates Verilog code and its testbench that can be realized in FPGA and VLSI systems. The designed system uses MATLAB-based verification and reporting.

摘要：In recent years,extensive research has focused on applying machine learning(ML)techniques to predict the properties of engineered cementitious composites(ECCs).ECCs exhibit crucial characteristics such as compressive strength(CS),tensile strength(TS),and tensile strain(TSt).Accurate forecasting of these critical properties can reduce material waste,lower construction expenses,and expedite project timelines for engineers and *** study investigates mixture design components and corresponding strengths of ECCs based on only polyethylene fiber drawing from existing *** neural network(ANN)models are developed to predict CS,TS,and TSt using a dataset of 339 experimental results with twelve input *** ANN models,implemented in MATLAB,consider various hidden layers and neurons to optimize accuracy and validation metrics demonstrate the model’s high *** analysis explores individual parameter *** inspiration from this study,it would be advantageous to enhance the predictive modeling toolkit by leveraging the progress made in existing technologies,thereby driving the green and low-carbon development of civil *** approach not only improves the efficiency and sustainability of construction practices but also aligns with global environmental goals by reducing the carbon footprint associated with civil engineering projects.

摘要：This study integrates previous experimental data and employs machine learning(ML)methods,including Random Forest(RF),Support Vector Machine(SVM),Artificial Neural Network(ANN),and eXtreme Gradient Boosting(XGBoost),to predict the compressive strength(CS)and tensile strength(TS)of engineered cementitious composites(ECC).XGBoost emerged as the superior model among the four ML models,providing an interpretable and highly accurate predictive *** optimize the model performance,hyperparameter tuning using a fivefold cross-validation approach with the data divided into 80%training and 20%testing *** Shapley Additive Explanations(SHAP)algorithm was also employed to reveal the impact of important features,such as the water/binder ratio,fly ash content,and water reducer dosage,on the model’s predictions and their *** XGBoost demonstrates the most exemplary performance,as reflected in the R^(2)values of 0.92 and 0.97 for CS and TS testing,*** SHAP analysis provided insights into the impact of individual features on CS and TS,shedding light on how specific characteristics influence the predictive accuracy of these *** highly accurate prediction model uncovers insights into correlated features,aids in creating new mix designs of ECC,and supports global efforts toward a low-carbon future in the construction industry by reducing carbon emissions.