Spatio-temporal resolution enhancement for cloudy thermal sequences

Many applications require remotely sensed brightness temperature (BT) data acquired with high temporal and spatial resolutions. In this regard, a viable strategy to overtake the physical limitations of space-borne sensors to achieve these data relies on fusing low temporal but high spatial resolution (HSR) data with high temporal but low spatial resolution data. The most promising methods rely on the fusion of spatially interpolated high temporal resolution data with temporally interpolated HSR data. However, the unavoidable presence of cloud masses in the acquired image sequences is often neglected, compromising the functionality and/or the effectiveness of the most of these fusion algorithms. To overcome this problem, a framework combining techniques of temporal smoothing and spatial enhancement is proposed to estimate surface BTs with high spatial and high temporal resolutions even when cloud masses corrupt the scene. Numerical results using real thermal data acquired by the SEVIRI sensor show the ability of the proposed approach to reach better performance than techniques based on either only interpolation or only spatial sharpening, even dealing with missing data due to the presence of cloud masses

Fusion of MultiSpectral and Panchromatic Images Based on Morphological Operators

International audienceNonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradients operators and demonstrate the suitability of this algorithm through the comparison with state of the art approaches. Four datasets acquired by the Pleiades, Worldview-2, Ikonos and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor

Multi-resolution analysis techniques and nonlinear PCA for hybrid pansharpening applications

International audienceHyperspectral images have a higher spectral resolution (i.e., a larger number of bands covering the electromagnetic spectrum), but a lower spatial resolution with respect to multispectral or panchromatic acquisitions. For increasing the capabilities of the data in terms of utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by combining the hyperspectral image with a high spatial resolution panchromatic image. These techniques are generally known as pansharpening and can be divided into component substitution (CS) and multi-resolution analysis (MRA) based methods. In general, the CS methods result in fused images having high spatial quality but the fused images suffer from spectral distortions. On the other hand, images obtained using MRA techniques are not as sharp as CS methods but they are spectrally consistent. Both substitution and filtering approaches are considered adequate when applied to multispectral and PAN images, but have many drawbacks when the low-resolution image is a hyperspectral image. Thus, one of the main challenges in hyperspectral pansharpening is to improve the spatial resolution while preserving as much as possible of the original spectral information. An effective solution to these problems has been found in the use of hybrid approaches, combining the better spatial information of CS and the more accurate spectral information of MRA techniques. In general, in a hybrid approach a CS technique is used to project the original data into a low dimensionality space. Thus, the PAN image is fused with one or more features by means of MRA approach. Finally the inverse projection is used to obtain the enhanced image in the original data space. These methods, permit to effectively enhance the spatial resolution of the hyperspectral image without relevant spectral distortions and on the same time to reduce the computational load of the entire process. In particular, in this paper we focus our attention on the use of Non-linear Principal Component Analysis (NLPCA) for the projection of the image into a low dimensionality feature space. However, if on one hand the NLPCA has been proved to better represent the intrinsic information of hyperspectral images in the feature space, on the other hand, an analysis of the impact of different fusion techniques applied to the nonlinear principal components in order to define the optimal framework for the hybrid pansharpening has not been carried out yet. More in particular, in this paper we analyze the overall impact of several widely used MRA pansharpening algorithms applied in the nonlinear feature space. The results obtained on both synthetic and real data demonstrate that, an accurate selection of the pansharpening method can lead to an effective improvement of the enhanced hyperspectral image in terms of spectral quality and spatial consistency, as well as a strong reduction in the computational time

Resolution Enhancement of Hyperspectral Data Exploiting Real Multi-Platform Data

Multi-platform data introduce new possibilities in the context of data fusion, as they allow to exploit several remotely sensed images acquired by different combinations of sensors. This scenario is particularly interesting for the sharpening of hyperspectral (HS) images, due to the limited availability of high-resolution (HR) sensors mounted onboard of the same platform as that of the HS device. However, the differences in the acquisition geometry and the nonsimultaneity of this kind of observations introduce further difficulties whose effects have to be taken into account in the design of data fusion algorithms. In this study, we present the most widespread HS image sharpening techniques and assess their performances by testing them over real acquisitions taken by the Earth Observing-1 (EO-1) and the WorldView-3 (WV3) satellites. We also highlight the difficulties arising from the use of multi-platform data and, at the same time, the benefits achievable through this approach

A Novel Approach for Multiple Material Extrusion in Arthroscopic Knee Surgery

Articular cartilage defects and degenerative diseases are pathological conditions that cause pain and the progressive loss of joint functionalities. The most severe cases are treated through partial or complete joint replacement with prostheses, even if the interest in cartilage regeneration and re-growth methods is steadily increasing. These methods consist of the targeted deposition of biomaterials. Only a few tools have been developed so far for performing these procedures in a minimally invasive way. This work presents an innovative device for the direct deposition of multiple biomaterials in an arthroscopic scenario. The tool is easily handleable and allows the extrusion of three different materials simultaneously. It is also equipped with a flexible tip to reach remote areas of the damaged cartilage. Three channels are arranged coaxially and a spring-based dip-coating approach allows the fabrication and assembly of a bendable polymeric tip. Experimental tests were performed to characterize the tip, showing the ability to bend it up to 90 degrees (using a force of similar to 1.5 N) and to extrude three coaxial biomaterials at the same time with both tip straight and tip fully bent. Rheometric analysis and fluid-dynamic computational simulations were performed to analyze the fluids' behavior; the maximum shear stresses were observed in correspondence to the distal tip and the channel convergence chamber, but with values up to similar to 1.2 kPa, compatible with a safe extrusion of biomaterials, even laden with cells. The cells viability was assessed after the extrusion with Live/Dead assay, confirming the safety of the extrusion procedures. Finally, the tool was tested arthroscopically in a cadaveric knee, demonstrating its ability to deliver the biomaterial in different areas, even ones that are typically hard-to-reach with traditional tools

How future surgery will benefit from SARS-COV-2-related measures: a SPIGC survey conveying the perspective of Italian surgeons

COVID-19 negatively affected surgical activity, but the potential benefits resulting from adopted measures remain unclear. The aim of this study was to evaluate the change in surgical activity and potential benefit from COVID-19 measures in perspective of Italian surgeons on behalf of SPIGC. A nationwide online survey on surgical practice before, during, and after COVID-19 pandemic was conducted in March-April 2022 (NCT:05323851). Effects of COVID-19 hospital-related measures on surgical patients' management and personal professional development across surgical specialties were explored. Data on demographics, pre-operative/peri-operative/post-operative management, and professional development were collected. Outcomes were matched with the corresponding volume. Four hundred and seventy-three respondents were included in final analysis across 14 surgical specialties. Since SARS-CoV-2 pandemic, application of telematic consultations (4.1% vs. 21.6%; p < 0.0001) and diagnostic evaluations (16.4% vs. 42.2%; p < 0.0001) increased. Elective surgical activities significantly reduced and surgeons opted more frequently for conservative management with a possible indication for elective (26.3% vs. 35.7%; p < 0.0001) or urgent (20.4% vs. 38.5%; p < 0.0001) surgery. All new COVID-related measures are perceived to be maintained in the future. Surgeons' personal education online increased from 12.6% (pre-COVID) to 86.6% (post-COVID; p < 0.0001). Online educational activities are considered a beneficial effect from COVID pandemic (56.4%). COVID-19 had a great impact on surgical specialties, with significant reduction of operation volume. However, some forced changes turned out to be benefits. Isolation measures pushed the use of telemedicine and telemetric devices for outpatient practice and favored communication for educational purposes and surgeon-patient/family communication. From the Italian surgeons' perspective, COVID-related measures will continue to influence future surgical clinical practice

Progettazione e sviluppo di un sistema ergonomico per l'attivazione magnetica del dispositivo medico "RELIEF"

Relief srl sta sviluppando uno sfintere endo-uretrale magnetico innovativo contro l’incontinenza urinaria. Lo sfintere, impiantato a livello del collo vescicale, può essere aperto e chiuso avvicinando un magnete esterno in zona perianale. Lo scopo della tesi è progettare e sviluppare un sistema ergonomico per quest’ultimo. In particolare, l’obiettivo è realizzare un prodotto funzionante, di facile utilizzo e portabilità, che garantisca la sicurezza dell’utilizzatore. L'aspetto della sicurezza è stato affrontato analizzando i problemi creati dal magnete. La soluzione proposta consiste in una scatola in materiale schermante la cui efficacia è stata valutata con modelli FEM successivamente validati sperimentalmente. Sono state stimate la quantità di materiale schermante e le dimensioni della scatola tali da definire la soluzione sicura e portabile. Lo studio dell'integrazione dei principi ergonomici nello sviluppo del prodotto ha permesso di individuare i principali metodi di integrazione e gli obiettivi da perseguire per ottimizzare il processo produttivo. Dall’analisi degli “user needs” sono state definite le caratteristiche delle soluzioni realizzative ed è stata svolta un’analisi preliminare, sono stati poi disegnati e prodotti i prototipi. È stato realizzato un setup per la valutazione delle performance dei dispositivi. Tale valutazione ha permesso di individuare la soluzione ottimale tra quelle proposte e che rappresenta un miglioramento rispetto alla condizione attuale

Comparing Particle Filter and Extended Kalman Filter for Battery State-Of-Charge Estimation

The battery State-Of-Charge (SOC) and parameters estimation is one of the crucial points to be addressed in the development of innovative electric/hybrid electric vehicles. Extended Kalman Filter (EKF) and Particle Filters (PF) are two possible approaches to the problem. While EKF is attractive for its computational efficiency, it may not be accurate for the non-linearity and for the uncertainties involved in the battery modelling. PF is a promising alternative, even if it is computationally more demanding. In this paper, we compare the EKF and PF performance in the dual Bayesian estimation of battery state and parameters, with particular reference to lithium batteries, showing that PF is attractive, especially in the presence of inaccurate battery models

Rao-Blackwellised Particle Filter for Battery State-Of-Charge and Parameters Estimation

State-of-charge and parameters online estimation is one of the key features of battery management systems for hybrid-electric vehicles applications. Using model-based approaches, simultaneous sequential Bayesian estimation of battery state and parameters has been shown to be a very powerful tool for the tracking, even in the presence of non-perfectly known models. Monte Carlo implementations are very suited to strongly nonlinear and unreliable dynamics, such those of batteries. In this framework, current paper proposes the use of a Rao-Blackwellized Particle Filter (RBPF) for the joint estimation of battery state and parameters. The results are compared with the existing approaches, highlighting the appealing features of RBPF, both in terms of performances and robustness