The role of image quality assessment in tasks such as (i) pan sharpening (PS) (i.e. merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image) and (ii) super-resolution (SR) has not been researched extensively from the natural scene statistics (NSS) perspective. For instance, even though there are several well-known measures that quantify the quality of PS and SR images, there has been little work done on analyzing the statistics of PS and SR images and associated distortions.
Many modern neural networks are trained in an over-parameterized regime where the parameters of the model exceed the size of the training dataset. Due to their over-parameterized nature these models in principle have the capacity to (over)fit any set of labels including pure noise. Despite this high fitting capacity, somewhat paradoxically, models trained via first-order methods (often with early stopping) continue to predict well on yet unseen test data.
Soft electronic devices that can acquire vital signs from the human body represent an important trend for healthcare. Combined strategies of materials design and advanced microfabrication allow the integration of a variety of components and devices on a stretchable platform, resulting in functional systems with minimal constraints on the human body. In this presentation, I will demonstrate a wearable multichannel patch that can sense a collection of signals from the human skin in a wireless mode.
The great progress achieved by communications in the last twenty years can be attested by the amount of audio-visual multimedia services available nowadays, such as digital television and IP-based video transmission. The success of these kind of services relies on their trustworthiness and the delivered quality of experience. Therefore, the development of efficient real-time quality monitoring tools that can quantify the audio-visual experience (as perceived by the end user) is key to the success of any multimedia service or application.
The following problem arose from the work of Fonio et al, a group of ecologists and computer scientists, who tried to understand the behaviour of longhorn crazy ants (Paratrechina longicomis) in navigating back to their nest after gathering food. Single ants were demonstrated to be laying pheromone ‘pointers’ to be followed by groups of ants carrying large loads. Sometimes the pointers are wrong. This leads to an optimization problem on networks with a destination node (the nest). A GPS or other system selects a direction (pointer) to the nest at every node.