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2019 Other Open Access OPEN

Conoscenze Popolazione Radiazioni
Martinelli M., Bastiani L., Paolicchi F.
Sito web per il progetto "Conoscenze della popolazione sui rischi delle procedure radiologiche" dedicato alla raccolta e alla elaborazione dei dati per la valutazione delle conoscenze della popolazione in merito ai rischi delle procedure radiologiche e alla comprensione delle corrette modalità con cui comunicare tali rischi ai pazienti.

See at: ISTI Repository Open Access | radiazioni.isti.cnr.it | CNR People


2019 Article Open Access OPEN

Generalized bayesian model selection for speckle on remote sensing images
Karakus O., Kuruoglu E. E., Altinkaya M. A.
Synthetic aperture radar (SAR) and ultrasound (US) are two important active imaging techniques for remote sensing, both of which are subject to speckle noise caused by coherent summation of back-scattered waves and subsequent nonlinear envelope transformations. Estimating the characteristics of this multiplicative noise is crucial to develop denoising methods and to improve statistical inference from remote sensing images. In this paper, reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with a wider interpretation and a recently proposed RJMCMC-based Bayesian approach, trans-space RJMCMC, has been utilized. The proposed method provides an automatic model class selection mechanism for remote sensing images of SAR and US where the model class space consists of popular envelope distribution families. The proposed method estimates the correct distribution family, as well as the shape and the scale parameters, avoiding performing an exhaustive search. For the experimental analysis, different SAR images of urban, forest and agricultural scenes, and two different US images of a human heart have been used. Simulation results show the efficiency of the proposed method in finding statistical models for speckle.Source: IEEE transactions on image processing 28 (2019): 1748–1758. doi:10.1109/TIP.2018.2878322
DOI: 10.1109/TIP.2018.2878322

See at: Explore Bristol Research Open Access | Explore Bristol Research Open Access | Explore Bristol Research Open Access | Explore Bristol Research Open Access | Explore Bristol Research Open Access | DOI Resolver | ieeexplore.ieee.org | CNR People


2019 Report Open Access OPEN

Developing a Tele-Visit system in ACTIVAGE project
Carboni A.
The work described in this technical note is part of the technological development activities, for the year 2018, related to the H2020 project ACTIVAGE, a European Multi Centric Large Scale Pilot on Smart Living Environments.Project(s): ACTIVAGE via OpenAIRE

See at: ISTI Repository Open Access | CNR People


2019 Report Unknown

Deep learning in precision agriculture
Martinelli M., Benassi A., Pardini F., Righi M., Salvetti O., Moroni D.
The work described in this research report is part of the activities carried out within the Scientific Collaboration between the Laboratory of Signals and Images at CNR-ISTI and CNR-IBIMET.

See at: CNR People


2019 Other Unknown

Innovazione in telemedicina
Martinelli M.
Seminario sulle tecnologie innovative in telemedicina.

See at: CNR People


2019 Other Unknown

Le Tecnologie Web - PCTO
Martinelli M.
Una introduzione a HTML e CSS con esercizi da svolgere - Materiale didattico per gli studenti del Liceo Scientifico Ulisse Dini di Pisa in ambito PCTO (ex alternanza scuola-lavoro).

See at: CNR People | www1.isti.cnr.it


2019 Conference object Unknown

Sequential Monte Carlo for studying effective connectivity in fMRI
Costagli M., Kuruoglu E., Ambrosi P., Buonincontri G., Biagi L., Tosetti M.
The signal fluctuations in Functional Magnetic Resonance Imaging (fMRI) have been proved suitable for investigating brain connectivity. Sequential Monte Carlo methods ("particle filters") aim at estimating internal states in dynamic systems when only partial and noisy observations are available. Differently from the majority of techniques commonly used for investigating brain connectivity, which assume stationarity, particle filters are designed for stochastic time-varying systems. We present a particle filtering algorithm tailored to fMRI data, whose purpose is to help assessing the presence of causal influences that certain brain areas may exert over others. The algorithm has been validated on a simulated network and applied to real fMRI data.Source: International Society for Magnetic Resonance in Medicine 27th Annual Meeting and Exhibition, Montreal, Canada, 11-15/5/2019

See at: CNR People


2019 Other Unknown

Un salto nel futuro, oggi nel presente: la Telemedicina
Pratali L., Martinelli M.
Presentazione sulla telemedicina al Corso di Medicina di Montagna - Rifugio Casati (SO).

See at: CNR People


2019 Article Open Access OPEN

Remote sensing for maritime prompt monitoring
Reggiannini M., Righi M., Tampucci M., Lo Duca A., Bacciu C., Bedini L., D'Errico A., Di Paola C., Marchetti A., Martinelli M., Mercurio C., Salerno E., Zizi B.
The main purpose of this paper is to describe a software platform dedicated to sea surveillance, capable of detecting and identifying illegal maritime traffic. This platform results from the cascade pipeline of several image processing algorithms that input Radar or Optical imagery captured by satellite-borne sensors and try to identify vessel targets in the scene and provide quantitative descriptors about their shape and motion. This platform is innovative since it integrates in its architecture heterogeneous data and data processing solutions with the goal of identifying navigating vessels in a unique and completely automatic processing streamline. More in detail, the processing chain consists of: (i) the detection of target vessels in an input map; (ii) the estimation of each vessel's most descriptive geometrical and scatterometric (for radar images) features; (iii) the estimation of the kinematics of each vessel; (iv) the prediction of each vessel's forthcoming route; and (v) the visualization of the results in a dedicated webGIS interface. The resulting platform represents a novel tool to counteract unauthorized fishing and tackle irregular migration and the related smuggling activities.Source: Journal of marine science and engineering 7 (2019). doi:10.3390/jmse7070202
DOI: 10.3390/jmse7070202

See at: ISTI Repository Open Access | DOI Resolver | CNR People | www.mdpi.com


2019 Article Open Access OPEN

Estimation of the spatial chromatin structure based on a multiresolution bead-chain model
Caudai C., Salerno E., Zoppe M., Tonazzini A.
We present a method to infer 3D chromatin configurations from Chromosome Conformation Capture data. Quite a few methods have been proposed to estimate the structure of the nuclear DNA in homogeneous populations of cells from this kind of data. Many of them transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid inconsistencies, our method is based on a score function that does not require any frequency-to-distance translation. We propose a multiscale chromatin model where the chromatin fibre is suitably partitioned at each scale. The partial structures are estimated independently, and connected to rebuild the whole fibre. Our score function consists in a data-fit part and a penalty part, balanced automatically at each scale and each subchain. The penalty part enforces "soft" geometric constraints. As many different structures can fit the data, our sampling strategy produces a set of solutions with similar scores. The procedure contains a few parameters, independent of both the scale and the genomic segment treated. The partition of the fibre, along with intrinsically parallel parts, make this method computationally efficient. Results from human genome data support the biological plausibility of our solutions.Source: IEEE/ACM transactions on computational biology and bioinformatics (Print) 16 (2019): 550–559. doi:10.1109/TCBB.2018.2791439
DOI: 10.1109/TCBB.2018.2791439

See at: ISTI Repository Open Access | DOI Resolver | ieeexplore.ieee.org | CNR People


2019 Report Unknown

Deep Learning in Precision Agriculture: prototype 2
Martinelli M., Berton A., Moroni D.
The work described in this research report is part of the activities carried out within the Scientific Collaboration between the Laboratory of Signals and Images at CNR-ISTI and the Institute of Biometeorology (CNR-IBIMET).

See at: CNR People


2019 Article Unknown

A new infrared true-color approach for visible-infrared multispectral image analysis
Grifoni E., Campanella B., Legnaioli S., Lorenzetti G., Marras L., Pagnotta S., Palleschi V., Poggialini F., Salerno E., Tonazzini A.
In this article, we present a newmethod for the analysis of visible/Infraredmultispectral sets producing chromatically faithful false-color images, whichmaintain a good readability of the information contained in the non-visible Infrared band. Examples of the application of this technique are given on the multispectral images acquired on the Pietà of Santa Croce of Agnolo Bronzino (1569, Florence) and on the analysis and visualization of the multispectral data obtained on Etruscanmural paintings (Tomb of the Monkey, Siena, Italy, V century B.C.). The fidelity of the chromatic appearance of the resulting images, coupled to the effective visualization of the information contained in the Infrared band, opens interesting perspectives for the use of the method for visualization and presentation of the results of multispectral analysis in Cultural Heritage diffusion, research, and diagnostics.Source: ACM journal on computing and cultural heritage (Print) 12 (2019): 8:1–8:11. doi:10.1145/3241065
DOI: 10.1145/3241065

See at: dl.acm.org | DOI Resolver | CNR People


2019 Report Unknown

SCIADRO Algorithms for real-time object detection and recognition
Martinelli M., Benassi A., Salvetti O., Moroni D.
The purpose of this research report is to describe the final prototype implementing algorithms for real-time object detection and recognition.

See at: CNR People


2019 Other Unknown

La telemedicina sulle montagne italiane il progetto e-Rés@mont
Martinelli M., Pratali L., Giardini G., La Monica D., Bastiani L., Fosson J. P., Bonin S., Cugnetto S., Fiorini A., Pernechele N., Ranfone M., Caligiana L., De La Pierre F., Stella M., Salvetti O., Moroni D.
Presentazione della Serata dedicata al Mal di Montagna e al progetto Europeo Interreg-Alcotra e-Rés@mont, organizzata dal Club Alpino Italiano sezione di Pisa presso il salone storico della Leopolda di Pisa

See at: CNR People


2019 Article Open Access OPEN

Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review
Tonazzini A., Salerno E., Abdel-salam Z. A., Harith M. A., Marras L., Botto A., Campanella B., Legnaioli S., Pagnotta S., Poggialini F., Palleschi V.
In this work, a critical review of the current nondestructive probing and image analysis approaches is presented, to revealing otherwise invisible or hardly discernible details in manuscripts and paintings relevant to cultural heritage and archaeology. Multispectral imaging, X-ray fluorescence, Laser-Induced Breakdown Spectroscopy, Raman spectroscopy and Thermography are considered, as techniques for acquiring images and spectral image sets; statistical methods for the analysis of these images are then discussed, including blind separation and false colour techniques. Several case studies are presented, with particular attention dedicated to the approaches that appear most promising for future applications. Some of the techniques described herein are likely to replace, in the near future, classical digital photography in the study of ancient manuscripts and paintings. (C) 2019 The Authors. Published by Elsevier B.V.Source: Journal of Advanced Research (Print) (Print) 17 (2019): 31–42. doi:10.1016/j.jare.2019.01.003
DOI: 10.1016/j.jare.2019.01.003

See at: ISTI Repository Open Access | DOI Resolver | CNR People


2019 Article Open Access OPEN

From human mesenchymal stromal cells to osteosarcoma cells classification by deep learning
D'Acunto M., Martinelli M., Moroni D.
Early diagnosis of cancer often allows for a more vast choice of therapy opportunities. After a cancer diagnosis, staging provides essential information about the extent of disease in the body and the expected response to a particular treatment. The leading importance of classifying cancer patients at the early stage into high or low-risk groups has led many research teams, both from the biomedical and bioinformatics field, to study the application of Deep Learning (DL) methods. The ability of DL to detect critical features from complex datasets is a significant achievement in early diagnosis and cell cancer progression. In this paper, we focus the attention on osteosarcoma. Osteosarcoma is one of the primary malignant bone tumors which usually afflicts people in adolescence. Our contribution to classification of osteosarcoma cells is made as follows: a DL approach is applied to discriminate human Mesenchymal Stromal Cells (MSCs) from osteosarcoma cells and to classify the different cell populations under investigation. Glass slides of different cell populations were cultured including MSCs, differentiated in healthy bone cells (osteoblasts) and osteosarcoma cells, both single cell populations or mixed. Images of such samples of isolated cells (single-type of mixed) are recorded with traditional optical microscopy. DL is then applied to identify and classify single cells. Proper data augmentation techniques and cross-fold validation are used to appreciate the capabilities of a convolutional neural network to address the cell detection and classification problem. Based on the results obtained on individual cells, and to the versatility and scalability of our DL approach, the next step will be its application to discriminate and classify healthy or cancer tissues to advance digital pathology.Source: Journal of intelligent & fuzzy systems (2019).

See at: ISTI Repository Open Access | CNR People


2019 Other Unknown

Lifestyle of mountain-goers
Martinelli M., Pratali L., Moroni D.
Tool to assess the characteristics of the lifestyle and present individual risk factors related to cardiovascular disease in the subjects attending the mountain. This is aimed at those who, not only for reasons closely associated with sports but simply for tourism, hiking, mountaineering and work, have the opportunity to stay in the mountains.

See at: altamontagna.isti.cnr.it | CNR People


2019 Book Open Access OPEN

Special Issue "Ocean Big Data Application - Engineering"
Pieri G., Reggiannini M.
Ocean observation represents a crucial task for human communities. Above sea level, this entails the implementation of maritime surveillance platforms, typically addressing security and safety issues (vessel traffic monitoring, search and rescue) as well as environmental sustainability aspects (fishery, pollution). On the other hand, the submerged ocean environment poses equally hard challenges for what concerns oil and gas exploitation or biology and cultural heritage safeguard. Performing the mentioned activities operationally requires the collection of a huge amount of multi-source and multi-sensor data, typically including optical images, videos, sonograms, radar/synthetic aperture radar maps, hydrocarbon concentration measurements, and so on. Publications in this Special Issue will aim at composing a comprehensive overview of the several aspects that emerge in the implementation of ocean observation platforms through big data processing. With these issues in mind, among the various subjects the authors are invited to discuss theoretical issues in big data processing for ocean observation, as well as methods for big data processing through high performance computing (such as cloud computing infrastructures, big data fusion, etc.), not excluding application case studies exploiting big data issues. To this purpose, authors are invited to submit contributions that take into considerations the following topics: Ocean data representation, analysis and learning; Deep learning applied to big data for ocean observation; Techniques for data processing applied to ocean observation and cultural heritage safeguard; Target detection, classification and identification in ocean data; Vessel traffic monitoring; Marine pollution monitoring along with sea environment monitoring issues. Dr. Gabriele Pieri Dr. Marco Reggiannini Guest EditorsSource: Basilea: Multidisciplinary Digital Publishing Institute (MDPI), 2019

See at: ISTI Repository Open Access | CNR People | www.mdpi.com


2019 Other Unknown

Hypertension
Martinelli M., Pratali L., Moroni D., Parati G.
Tool to assess the presence of individual risk factors related to cardiovascular disease in the subjects frequenting the mountain. This is aimed at those who, not only for reasons closely associated with sports activities but simply for tourism, hiking, mountaineering and work, have the opportunity to stay in the mountains

See at: altamontagna.isti.cnr.it | CNR People


2019 Article Open Access OPEN

Environmental decision support systems for monitoring small scale oil spills: existing solutions, best practices and current challenges
Moroni D., Pieri G., Tampucci M.
In recent years, large oil spills have received widespread media attention, while small and micro oil spills are usually only acknowledged by the authorities and local citizens who are directly or indirectly affected by these pollution events. However, small oil spills represent the vast majority of oil pollution events. In this paper, multiple oil spill typologies are introduced, and existing frameworks and methods used as best practices for facing them are reviewed and discussed. Specific tools based on information and communication technologies are then presented, considering in particular those which can be used as integrated frameworks for the specific challenges of the environmental monitoring of smaller oil spills. Finally, a prototype case study actually designed and implemented for the management of existing monitoring resources is reported. This case study helps improve the discussion over the actual challenges of early detection and support to the responsible parties and stakeholders in charge of intervention and remediation operations.Source: Journal of marine science and engineering 7 (2019). doi:10.3390/jmse7010019
DOI: 10.3390/jmse7010019
Project(s): ARGOMARINE via OpenAIRE

See at: Journal of Marine Science and Engineering Open Access | Hyper Article en Ligne Open Access | Hyper Article en Ligne Open Access | Hyper Article en Ligne Open Access | ISTI Repository Open Access | Journal of Marine Science and Engineering Open Access | ZENODO Open Access | DOI Resolver | CNR People | www.mdpi.com