Deep generative adversarial residual convolutional networks for real-world super-resolution

Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data. They usually take as assumption that the LR image is a bicubic down-sampled version of the HR image. However, such degradation process is not available in real-world settings i.e. inherent sensor noise, stochastic noise, compression artifacts, possible mismatch between image degradation process and camera device. It reduces significantly the performance of current SISR methods due to real-world image corruptions. To address these problems, we propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network (SRResCGAN1) to follow the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart. The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques. We demonstrate our proposed approach in quantitative and qualitative experiments that generalize robustly to real input and it is easy to deploy for other downscaling operators and mobile/embedded devices

Acoustic target tracking through a cluster of mobile agents

This paper discusses the problem of tracking a moving target by means of a cluster of mobile agents that is able to sense the acoustic emissions of the target, with the aim of improving the target localization and tracking performance with respect to conventional fixed-array acoustic localization. We handle the acoustic part of the problem by modeling the cluster as a sensor network, and we propose a centralized control strategy for the agents that exploits the spatial sensitivity pattern of the sensor network to estimate the best possible cluster configuration with respect to the expected target position. In order to take into account the position estimation delay due to the frame-based nature of the processing, the possible positions of the acoustic target in a given future time interval are represented in terms of a compatible set, that is, the set of all possible future positions of the target, given its dynamics and its present state. A frame-by-frame cluster reconfiguration algorithm is presented, which adapts the position of each sensing agent with the goal of pursuing the maximum overlap between the region of high acoustic sensitivity of the entire cluster and the compatible set of the sound-emitting target. The tracking scheme iterates, at each observation frame, the computation of the target compatible set, the reconfiguration of the cluster, and the target acoustic localization. The reconfiguration step makes use of an opportune cost function proportional to the difference of the compatibility set and the acoustic sensitivity spatial pattern determined by the mobile agent positions. Simulations under different geometric configurations and positioning constraints demonstrate the ability of the proposed approach to effectively localize and track a moving target based on its acoustic emission. The Doppler effect related to moving sources and sensors is taken into account, and its impact on performance is analyzed. We compare the localization results with conventional static-array localization and positioning of acoustic sensors through genetic algorithm optimization, and results demonstrate the sensible improvements in terms of localization and tracking performance. Although the method is discussed here with respect to acoustic target tracking, it can be effectively adapted to video-based localization and tracking, or to multimodal information settings (e.g., audio and video)

Acoustic target tracking through a cluster of mobile agents

This paper discusses the problem of tracking a moving target by means of a cluster of mobile agents that is able to sense the acoustic emissions of the target, with the aim of improving the target localization and tracking performance with respect to conventional fixed-array acoustic localization. We handle the acoustic part of the problem by modeling the cluster as a sensor network, and we propose a centralized control strategy for the agents that exploits the spatial sensitivity pattern of the sensor network to estimate the best possible cluster configuration with respect to the expected target position. In order to take into account the position estimation delay due to the frame-based nature of the processing, the possible positions of the acoustic target in a given future time interval are represented in terms of a compatible set, that is, the set of all possible future positions of the target, given its dynamics and its present state. A frame-by-frame cluster reconfiguration algorithm is presented, which adapts the position of each sensing agent with the goal of pursuing the maximum overlap between the region of high acoustic sensitivity of the entire cluster and the compatible set of the sound-emitting target. The tracking scheme iterates, at each observation frame, the computation of the target compatible set, the reconfiguration of the cluster, and the target acoustic localization. The reconfiguration step makes use of an opportune cost function proportional to the difference of the compatibility set and the acoustic sensitivity spatial pattern determined by the mobile agent positions. Simulations under different geometric configurations and positioning constraints demonstrate the ability of the proposed approach to effectively localize and track a moving target based on its acoustic emission. The Doppler effect related to moving sources and sensors is taken into account, and its impact on performance is analyzed. We compare the localization results with conventional static-array localization and positioning of acoustic sensors through genetic algorithm optimization, and results demonstrate the sensible improvements in terms of localization and tracking performance. Although the method is discussed here with respect to acoustic target tracking, it can be effectively adapted to video-based localization and tracking, or to multimodal information settings (e.g., audio and video).Accepted Author ManuscriptTeam Tamas Keviczk

Self-Organizing Functional Materials via Ionic Self Assembly: Porphyrins H- and J-Aggregates on Synthetic Chrysotile Nanotubes

Positively charged, synthetic chrysotile nanotubes act as inorganic tectons supporting H- and J-type aggregates of anionic porphyrins with markedly different optical properties, making these nanohybrid materials of interest for application in nanotechnology

Phase composition of CuxS thin films: spectroscopic evidence of covellite formation

Transition metal sulphides, grown in alternate layers by Electrochemical Atomic Layer Deposition (ECALD) to form thin films, are relevant candidates as photovoltaic materials for solar-cell production. However, the knowledge of the phase composition of the deposited sulphide, which is a fundamental information to assign and predict its effective semiconducting properties, is often hindered by the limited amount of material and by its morphological constraints. In the present study, an X-ray Photoelectron Spectroscopy (XPS) and Atomic Force Microscopy (AFM) investigation of a copper sulphide thin film, synthesised according to the ECALD method, is proposed as an alternative and efficient way to identify the deposited semiconductor. The detailed XPS analysis of the Cu and S contents in the film points to a 1:1 stoichiometric ratio, corresponding to the CuS compound. Moreover, Cu is observed only as monovalent, and the possible co-existence of sulphide ions and of disulphide units is inferred. Accordingly, the attribution of the structure of the thin film to covellite, CuS, is proposed, as being the only one able to account for all experimental evidences. We suggest that the covellite structure can be set up by spontaneous rearrangement starting from a wurtzite-type CuS deposit, driven by the negative Gibbs energy involved in the formation of the compound from the elements. Finally the morphological characterisation of the film confirms its spatial homogeneity, thus making the covellite film a potential candidate for solar-cell applications