Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks
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Hardware accelerators for Deep Neural Networks (DNNs) that use reduced precision parameters are more energy efficient than the equivalent full precision networks. While many studies have focused on reduced precision training methods for supervised networks ...
Objective quality assessment of compressed images is very useful in many applications. In this paper we present an objective quality metric that is better tuned to evaluate the quality of images distorted by compression artifacts. A deep convolutional neur ...
Deep neural networks have recently achieved tremen-dous success in image classification. Recent studies havehowever shown that they are easily misled into incorrectclassification decisions by adversarial examples. Adver-saries can even craft attacks by que ...
Despite the recent success of deep neural network-based approaches in sound source localization, these approaches suffer the limitations that the required annotation process is costly, and the mismatch between the training and test conditions undermines th ...
Recently, the interest in haptic feedback is growing thanks to its ability to enhance the interaction with Human Machine Interfaces (HMIs). This research project is exploring the potential of machine learning combined with piezoelectric actuators to genera ...
We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled porovis ...
Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...
Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...
Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey sum ...
Optical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look ...