| Titre : | Python-Driven Deep Learning Approaches for Microstrip Filter Design |
| Auteurs : | Amrane Mohamed Belkebir, Auteur ; Chetioui Mohamed, Directeur de thèse |
| Type de document : | texte imprimé |
| Editeur : | [S.l.] : [S.l.] : [S.l.] : Université Saïda – Dr. Tahar Moulay – Faculté des Mathématiques, de l’Informatique et de Télécommunications, 2023/2024 |
| Format : | 94 p |
| Accompagnement : | CD |
| Langues: | Anglais |
| Catégories : | |
| Mots-clés: | Microstrip ; Deep learning ; Filters ; Convolutional neural network ; Python ; Hfss ; Design. |
| Résumé : |
Python-Driven Deep Learning Approaches for Waveguide Filter Design
(Microstrip) is a study that focuses on using Python programming and deep learning techniques to design waveguide filters. Waveguide filters are critical components in many communication and signal processing systems. The approach involves leveraging deep learning algorithms to optimize the design and performance of these filters. Python serves as the primary programming language, providing a versatile and powerful platform for implementing deep learning models. Deep learning algorithms, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), are employed to analyze and optimize waveguide filter designs. This research aims to streamline the design process, improve the efficiency of waveguide filters, and potentially discover novel filter designs that may not be apparent through traditional methods. By using Python and deep Learning. |
Exemplaires
| Code-barres | Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|---|
| aucun exemplaire |
Documents numériques (1)
Python-Driven Deep Learning Approaches for Microstrip Filter Design Adobe Acrobat PDF |
