Abstract: Multibaseline (MB) phase unwrapping (PU), as the core step in MB InSAR, breaks the limitation of phase continuity assumption. However, it still suffers from insufficient noise robustness and ...

Abstract: The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through ...

This group project explores the use of neural networks to model avalanche hazard forecasts using a 15-year dataset from the Scottish Avalanche Information Service (SAIS). Our group has been assigned ...

Over the past decade, deep learning (DL) techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have played a pivotal role in advancing the field of ...

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...

Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...

This project presents the design and implementation of a Low-Power BFloat16 Pipelined MAC (Multiply-Accumulate) Unit for deep neural network (DNN) applications. The MAC unit is optimized for low power ...

ABSTRACT: The storage layer within the Moxizhuang Oilfield in the Junggar Basin develops various types of interlayer barriers with significant differences in morphology and scale of development. In ...