101,969 research outputs found
Analisi di un componente di involucro a isolamento dinamico (Dynamic Insulation) in un edificio residenziale
No full textConvolutional Autoencoder Algorithm for On-Board Image Compression
No full textThe growing amount of data currently collected by earth observation satellites requires new processing procedures able to manage huge quantity of information. Among these, data reduction techniques represent a viable solution. In particular, data reduction on-board is significant because allows to save on-board storage space and bandwidth for data transmission to the ground. However, the algorithm used for compression must be able to preserve the key information contained in the acquired data, so that the applicability of the collected information is still guaranteed in the different fields of work. Artificial intelligence, and in particular deep learning, are well suited for this purpose because of their ability to extract valuable information from complex data.This work proposes a lossy image compression procedure based on a Convolutional Autoencoder (CAE) that can be performed on-board the satellite. The images acquired by the sensor can be compressed through the algorithm, stored, and sent to the ground where they are reconstructed, saving space and bandwidth for data transmission. The performance of the compression algorithm will be evaluated in terms of original-reconstructed image similarity and also with regard to the applicability of the reconstructed images to common applicative cases.The algorithm here proposed has been idealized and is currently in development in the context of the European Space Agency's (ESA) PhiSat-2 mission, that aims at demonstrating the advantages of the Artificial Intelligence (AI) on-board for Earth observation applications
On-Board Image Compression using Convolutional Autoencoder: Performance Analysis and Application Scenarios
No full textThe amount of raw data generated by instruments on board Earth Observation (EO) satellites is quite often more than what can be transmitted to the ground, so new advanced onboard processing procedures are required. Artificial Intelligence (AI) and Deep Learning (DL) can provide advanced information from EO data and thanks to specific hardware platforms these algorithms can be used also in space. We present here the Convolutional AutoEncoder (CAE)-based algorithm developed for on-board lossy image compression of the European Space Agency (ESA) Phi-Sat-2 mission. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative onboard environment. Therefore, besides analyzing the results for the local hardware environment, we investigate the performance variation for the on-board setting. Moreover, we introduced an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks
Artificial Intelligence Based On-Board Image Compression for the Φ-Sat-2 Mission
No full textThe growing amount of data collected by Earth Observation (EO) satellites requires new processing procedures able to manage huge quantity of information. Artificial intelligence (AI) and deep learning (DL) can provide advanced information also because of their ability to extract valuable information from complex data. Thanks to specific hardware platforms, these algorithms can be used also in space, opening the possibility for new procedures for intelligent data processing. The European Space Agency Φ-Sat-2 mission was designed with the purpose of demonstrating the benefits of using AI in space by running AI-based applications on-board a CubeSat. We present here the convolutional autoencoder-based algorithm developed for on-board lossy image compression of the Φ-Sat-2 mission and provide a first benchmark addressing a real space mission and a new image compression end-to-end architecture based on AI. Image compression is a crucial application that allows to save transmission bandwidth and storage. In fact, images acquired by the sensor can be compressed on-board and sent to the ground where they are reconstructed. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative on-board environment. Therefore, besides analyzing the results for the local hardware environment, this article investigates the performance variation for the on-board setting. An additional piece of innovation is the introduction of an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks. Such metric completes those more traditional based on the original-reconstructed image similarity
Satellite On-Board Change Detection via Auto-Associative Neural Networks
No full textThe increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety of applications for Earth observation and monitoring. At the same time, it requires new techniques that are able to manage the amount of data stored and transmitted to the ground. Advanced techniques for on-board data processing answer this problem, offering the possibility to select only the data of interest for a specific application or to extract specific information from data. However, the computational resources that exist on-board are limited compared to the ground segment availability. Alternatively, in applications such as change detection, only images containing changes are useful and worth being stored and sent to the ground. In this paper, we propose a change detection scheme that could be run on-board. It relies on a feature-based representation of the acquired images which is obtained by means of an auto-associative neural network (AANN). Once the AANN is trained, the dissimilarity between two images is evaluated in terms of the extracted features. This information can be subsequently turned into a change detection result. This study, which presents one of the first techniques for on-board change detection, yielded encouraging results on a set of Sentinel-2 images, even in light of comparison with a benchmark technique
On-Board Multispectral Image Compression with an Artificial Intelligence Based Algorithm
No full textRemote Sensing (RS) is applied for a variety of purposes, thanks to the large availability of heterogeneous data. Furthermore, the growing number of CubeSat missions is encouraging increasingly advanced, flexible, and configurable RS missions, that also include the use of Artificial Intelligence (AI) on-board. Indeed, specific hardware allows advanced processing on-board the satellites even if the computational capability is not the same as on the ground. In the context of on-board processing, the compression of acquired images is crucial because permits to save bandwidth for data transmission. We propose an AI-based lossy image compression algorithm for multispectral images that can be executed on-board a CubeSat. The algorithm is based on a Convolutional AutoEncoder (CAE) Neural Network (NN). In lossy compression part of the information stored in the original image is lost. Therefore, the results evaluation includes the assessment of the usability of the decompressed images for common applications
Diffusion Tensor Imaging through orthogonal tensor invariants and generalized differential operators
No full textDiffusion Tensor Imaging through orthogonal tensor invariants and generalized differential operators
No full text[Technical principles of solid tridimensional modeling with computerized tomography for the study of maxillofacial diseases].
No full text3DCT allows the solid modeling of body structures from contiguous slices. The 3D images are free to rotate on the x, y, and z axes. It is possible to evidentiate structures with various densities by means of threshold operators, which allow a 3D model of both soft tissues and bones to be obtained. Shading operations allow image quality to be improved by varying the elementary units to surface units ratio. Implemented 3D rendering reduces the spatial edges by means of anti-aliasing functions. The 3D images allow the study of the complexity of maxillofacial bony structures, and they are especially useful in both surgical planning and in postoperative follow-up. We studied 58 patients with maxillofacial diseases (34 traumatic, 14 malformations, 4 dysplastic, and 6 neoplastic). In most cases (96.5%), we obtained high-quality images, which allowed both the site and the extension of the lesions to be evaluated, together with their relationships to adjacent structures. In 65% of traumatic cases, the 4 basic views thoroughly demonstrated lesion spread, while in the extant 35% of cases cutting operations and rotatory translations were necessary. In all malformation cases a clear visualization of somatic asymmetries was obtained. In both dysplastic and neoplastic cases, the best lesion evidence was obtained in superficial lesions with cortical bone involvement. This technique was always easy and quick to perform, with no need for supplemental dose exposure to the patient
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