News & Press Releases

KabuMerikanus "The Sum of US: Exploring The Cabo Verde" United States Musical Connection and Legacy NEW BEDFORD WHALING MUSEUM 18 Johnny Cake Hill New Bedford, MA 02747 Call: 508-997-0043

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Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for FAFSA Help Labs in LARTS 203 on Tuesdays from 10am-11am for help filing your FAFSA and learning more about financial aid. Contact Mark Yanni myanni@umassd.edu

Liberal Arts Building 203

FREE Tour of the Historical Schooner Ernestina @ Pier 3, New Bedford, MA 02747 Contact Diane Gomes at dgomes@umassd.edu for information.

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The parade steps off from Buttonwood Park at 11:00 AM The parade route is Union Street, Acushnet Avenue, Grinnell Street, Purchase Street ending at the Cape Verdean Veteran's Memorial Hall. The Parade Grand Marshal For 2024 will be Viola Pina. The Parade will be followed by the Kultura Festa Held at the Serenity Gardens Field, 11:00 AM - 6:00 PM, between Bedford Street, South Sixth Street, Wing Street & Pleasant Street with live music, cultural food, vendors and much more. Contact Diane Gomes at dgomes@umassd.edu for information.

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Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for FAFSA Help Labs in LARTS 203 on Tuesdays from 10am-11am for help filing your FAFSA and learning more about financial aid. Contact Mark Yanni myanni@umassd.edu

Liberal Arts Building 203

Topic: Machine Learning Optimization for Dynamic Spectrum Awareness Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Link: https://umassd.zoom.us/j/99705551586 Meeting ID: 997 0555 1586 Passcode: 604747 Abstract: This dissertation proposes a machine learning-based approach focused on improving dynamic spectrum awareness in wireless communications. The approach is comprised of four main components: network optimization with genetic algorithm convolutional neural networks (GACNN), which focuses on optimizing neural network architectures for specific tasks to reduce the complexity and cost of the network optimization process; unsupervised prototype learning, which uses hierarchical clustering and a prototype-based learning objective to estimate signal-to-noise ratio (SNR) regions and perform modulation classification to improve the identification of new spectrum users; deep neural network explainable AI (DNN XAI), which will develop methods to increase the transparency and interpretability of machine learning models in deep neural networks to ensure compliance with spectrum regulatory standards; and lastly, incremental learning class representation drift, which will evaluate the performance of incremental learning methods in baseband modulation classification to establish a continuous learning process that adapts to dynamic environments. By addressing gaps in current spectrum management techniques, these components aim to improve spectrum utilization, increase machine learning-based communication interpretability, and provide an informed model for future spectrum management strategies. Advisor(s): Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Alfa Heryudono, Associate Professor, Department of Mathematics, UMASS Dartmouth; Dr. Eugene Chabot, Engineer, Naval Undersea Warfare Center (NUWC) NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Ruolin Zhou via email at ruolin.zhou@umassd.edu.

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