2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2025
Other
Open Access
Basics of image processing and analysis
Moroni D., Salvetti O.
Syllabus – Imaging, computer vision and UAVs in the Agritech Domain Lesson 1: Basics of Image Processing and Analysis Date: 18 April Time: 14:00 -20:00 Topics: • Geometric Transformations: Resizing, rotation, translation, and their applications in image pre-processing. • Filtering: Basics of convolution, edge detection, smoothing, and sharpening filters. • Image Enhancement: Task definition; Histogram equalization, contrast adjustment, and noise reduction techniques. • Image Registration: Task definition; Aligning images from different sources or points of view using keypoint detection and matching. • Image Segmentation: Task definition; Introduction to thresholding, region growing, and clustering for image segmentation. Methodology: • Lecture: Introduction to each concept with examples. • Hands-on Tutorials: Use open-source software (e.g., OpenCV, Scikit-Image) for practical exercises.
Moroni D., Salvetti O.
Syllabus – Imaging, computer vision and UAVs in the Agritech Domain Lesson 1: Basics of Image Processing and Analysis Date: 18 April Time: 14:00 -20:00 Topics: • Geometric Transformations: Resizing, rotation, translation, and their applications in image pre-processing. • Filtering: Basics of convolution, edge detection, smoothing, and sharpening filters. • Image Enhancement: Task definition; Histogram equalization, contrast adjustment, and noise reduction techniques. • Image Registration: Task definition; Aligning images from different sources or points of view using keypoint detection and matching. • Image Segmentation: Task definition; Introduction to thresholding, region growing, and clustering for image segmentation. Methodology: • Lecture: Introduction to each concept with examples. • Hands-on Tutorials: Use open-source software (e.g., OpenCV, Scikit-Image) for practical exercises.
2025
Conference article
Open Access
Raman Spectroscopy Diagnosis of Melanoma
Lazzini Gianmarco, Massi Daniela, Moroni Davide, Salvetti Ovidio, Viacava Paolo, Laurino Marco, D'Acunto Mario
Cutaneous melanoma is an aggressive form of skin cancer and a leading cause of cancer-related mortality. In this sense, Raman Spectroscopy (RS) could represent a fast and effective method for melanoma-related diagnosis. We therefore introduced a new method based on RS to distinguish Compound Naevi (CN) from Primary Cutaneous Melanoma (PCM) from ex vivo solid biopsies. To this aim, integrating Confocal Raman Micro-Spectroscopy (CRM) with four Machine Learning (ML) algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Random Forest Classifier (RFC). We focused our attention on the comparison between traditional pre-processing operations with Continuous Wavelet Transform (CWT). In particular, CWT led to the maximum classification accuracy, which was ∼89.0%, which highlighted the method as promising in view of future implementations in devices for everyday use.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129010
Metrics:
Lazzini Gianmarco, Massi Daniela, Moroni Davide, Salvetti Ovidio, Viacava Paolo, Laurino Marco, D'Acunto Mario
Cutaneous melanoma is an aggressive form of skin cancer and a leading cause of cancer-related mortality. In this sense, Raman Spectroscopy (RS) could represent a fast and effective method for melanoma-related diagnosis. We therefore introduced a new method based on RS to distinguish Compound Naevi (CN) from Primary Cutaneous Melanoma (PCM) from ex vivo solid biopsies. To this aim, integrating Confocal Raman Micro-Spectroscopy (CRM) with four Machine Learning (ML) algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Random Forest Classifier (RFC). We focused our attention on the comparison between traditional pre-processing operations with Continuous Wavelet Transform (CWT). In particular, CWT led to the maximum classification accuracy, which was ∼89.0%, which highlighted the method as promising in view of future implementations in devices for everyday use.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129010
Metrics:
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| www.mdpi.com | CNR IRIS 
2024
Other
Restricted
Barilla Agrosat+ Aggiornamento 1/24
Martinelli M., Salvetti O., Moroni D.
Aggiornamento Modelli - Prossimi stepProject(s): Agrosat+
Martinelli M., Salvetti O., Moroni D.
Aggiornamento Modelli - Prossimi stepProject(s): Agrosat+
2023
Journal article
Open Access
Efficient lung ultrasound classification
Bruno A, Ignesti G, Salvetti O, Moroni D, Martinelli M
A machine learning method for classifying Lung UltraSound is here proposed to provide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.Source: BIOENGINEERING, vol. 10 (issue 5)
DOI: 10.3390/bioengineering10050555
Metrics:
Bruno A, Ignesti G, Salvetti O, Moroni D, Martinelli M
A machine learning method for classifying Lung UltraSound is here proposed to provide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.Source: BIOENGINEERING, vol. 10 (issue 5)
DOI: 10.3390/bioengineering10050555
Metrics:
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2023
Other
Restricted
Artificial Intelligence in TiAssisto: first results
Ignesti G, Bruno A, Deri C, D'Angelo G, Salvetti O, Moroni D, Pratali L, Martinelli M
Artificial Intelligence (AI) is integrated into medical applications since its beginning. The advent of deep learning algorithms, powerful computation power and large datasets has made possible the development of numerous new medical applications. A significant part of these applications is focused on the classification or on the segmentation of medical images. In this paper we present an innovative solution for clinical images classification.
Ignesti G, Bruno A, Deri C, D'Angelo G, Salvetti O, Moroni D, Pratali L, Martinelli M
Artificial Intelligence (AI) is integrated into medical applications since its beginning. The advent of deep learning algorithms, powerful computation power and large datasets has made possible the development of numerous new medical applications. A significant part of these applications is focused on the classification or on the segmentation of medical images. In this paper we present an innovative solution for clinical images classification.
2023
Conference article
Open Access
Deep learning methods for point-of-care ultrasound examination
Ignesti G, Deri C, D'Angelo G, Pratali L, Bruno A, Benassi A, Salvetti O, Moroni D, Martinelli M
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way.The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion.The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.DOI: 10.1109/sitis61268.2023.00078
Metrics:
Ignesti G, Deri C, D'Angelo G, Pratali L, Bruno A, Benassi A, Salvetti O, Moroni D, Martinelli M
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way.The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion.The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.DOI: 10.1109/sitis61268.2023.00078
Metrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2022
Conference article
Open Access
An intelligent platform of services based on multimedia understanding and telehealth for supporting the management of SARS-CoV-2 multi-pathological patients
Ignesti G., Bruno A., Deri C., D'Angelo G., Bastiani L., Pratali L., Memmini S., Cicalini D., Dini A., Galesi G., Pardini F., Tampucci M., Benassi A., Salvetti O., Moroni D., Martinelli M.
The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involvedin our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images.DOI: 10.1109/sitis57111.2022.00089
Metrics:
Ignesti G., Bruno A., Deri C., D'Angelo G., Bastiani L., Pratali L., Memmini S., Cicalini D., Dini A., Galesi G., Pardini F., Tampucci M., Benassi A., Salvetti O., Moroni D., Martinelli M.
The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involvedin our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images.DOI: 10.1109/sitis57111.2022.00089
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2022
Other
Open Access
SI-Lab annual research report 2021
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/10.32079/isti-ar-2022/003
Metrics:
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/10.32079/isti-ar-2022/003
Metrics:
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CNR IRIS | ISTI Repository | CNR IRIS
2021
Journal article
Open Access
Signals and Images in Sea Technologies
Moroni D, Salvetti O
Source: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 41 (issue 9)
DOI: 10.3390/jmse9010041
DOI: 10.3390/books978-3-0365-1355-3
Metrics:
Moroni D, Salvetti O
Source: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 41 (issue 9)
DOI: 10.3390/jmse9010041
DOI: 10.3390/books978-3-0365-1355-3
Metrics:
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Journal of Marine Science and Engineering | hal.archives-ouvertes.fr | CNR IRIS | ISTI Repository | Journal of Marine Science and Engineering | Journal of Marine Science and Engineering | ZENODO | doi.org | Hyper Article en Ligne | Hyper Article en Ligne | CNR IRIS
2021
Contribution to conference
Restricted
Mediterranean diet mitigates acute mountain sickness
Agazzi Gc, Valoti P, Bastiani L, Denoth F, Pratali Ll, D'Angelo G, Carrara B, Parigi Gb, Malanninom, Spinelli A, Calderoli A, Orizio L, Giardini G, Salvetti O, Moroni D, Martinelli M, Mrakic Sposta S
A pilot study was conducted in the framework of the Save the Mountains initiative, an education and sustainability project, promoted by Italian Alpine Club of Bergamo, Bergamo section of the National Alpine Association, Province of Bergamo, Observatory for the Bergamasque Mountains and Alpine and Speleological Rescue. As a part of this study, an anonymous online questionnaire was designed and prepared, collecting lifestyle information (eating habits, alcohol, tobacco, sleep, exercise) of the mountaineers in order to recommend specific measures useful for staying in mountain areas and for preventing individual risk factors related to lifestyle and Acute Mountain Sickness (AMS): http://altamontagna.isti.cnr.it:8080/Stiledivita/. The study will continue the collection of questionnaire responses until at least the end of Summer 2021; at the time of writing (February 2021), 804 questionnaire responses were already collected and analyzed. The initial sample refers to the people who attended mountain huts in the Orobie Alps; then the online questionnaire form was publicly extended to other regions. About 99% of the interviewed people are Italian; the rest are Swiss, Polish, British and French people. Mean age is 48 years(+/-15), 62% males and 38% females. Only 8.8% of them answered they suffered from altitude sickness, but self-reported Lake Louise Score (LLS) classified the 21.3% of people with Acute Mountain Sickness (AMS), light AMS 15,4% and severe AMS 5,8% (To assess AMS the original LLS questionnaire was used: AMS is classified as severe when a headache is present and the LLS is greater than 5, it is instead light when there is a headache and the LLS is between 3 and 5, else is normal). The Mediterranean Diet's adherence, collected as the frequency of food items consumption, was assessed by the MEDI-LITE score, a validated questionnaire, ranging from 0 to 13. In this sample, a median score of 8 was found, while the 25th percentile corresponds to a score lower than 6 and the 75th percentile to a score greater than 9. The 14% of the sample resulted in being not adherent to the Mediterranean diet, the 51% was in the mean, the 35% was adherent. This study confirms that the predisposing factor most associated with the AMS is "having had the same episode in the past" (OR 2.50, CI 1.88/3.13), having sleep disturbs (OR 1.29, CI 1.03 /1.55), age (OR -0.03, CI -0.35/-0.02). Moreover, it underlines that lifestyle is important with respect to risk to develop the AMS: actually, despite the structural limitations of surveys, this study pointed out that lifestyle contributes to mitigating the risk of developing the AMS (Mediterranean diet score OR - 0.34, CI -0.64 -> - 0.55). Gender, smoke and high physical activity are instead not significant. Future studies should investigate more deeply how lifestyle can change the impact on high altitude diseases.
Agazzi Gc, Valoti P, Bastiani L, Denoth F, Pratali Ll, D'Angelo G, Carrara B, Parigi Gb, Malanninom, Spinelli A, Calderoli A, Orizio L, Giardini G, Salvetti O, Moroni D, Martinelli M, Mrakic Sposta S
A pilot study was conducted in the framework of the Save the Mountains initiative, an education and sustainability project, promoted by Italian Alpine Club of Bergamo, Bergamo section of the National Alpine Association, Province of Bergamo, Observatory for the Bergamasque Mountains and Alpine and Speleological Rescue. As a part of this study, an anonymous online questionnaire was designed and prepared, collecting lifestyle information (eating habits, alcohol, tobacco, sleep, exercise) of the mountaineers in order to recommend specific measures useful for staying in mountain areas and for preventing individual risk factors related to lifestyle and Acute Mountain Sickness (AMS): http://altamontagna.isti.cnr.it:8080/Stiledivita/. The study will continue the collection of questionnaire responses until at least the end of Summer 2021; at the time of writing (February 2021), 804 questionnaire responses were already collected and analyzed. The initial sample refers to the people who attended mountain huts in the Orobie Alps; then the online questionnaire form was publicly extended to other regions. About 99% of the interviewed people are Italian; the rest are Swiss, Polish, British and French people. Mean age is 48 years(+/-15), 62% males and 38% females. Only 8.8% of them answered they suffered from altitude sickness, but self-reported Lake Louise Score (LLS) classified the 21.3% of people with Acute Mountain Sickness (AMS), light AMS 15,4% and severe AMS 5,8% (To assess AMS the original LLS questionnaire was used: AMS is classified as severe when a headache is present and the LLS is greater than 5, it is instead light when there is a headache and the LLS is between 3 and 5, else is normal). The Mediterranean Diet's adherence, collected as the frequency of food items consumption, was assessed by the MEDI-LITE score, a validated questionnaire, ranging from 0 to 13. In this sample, a median score of 8 was found, while the 25th percentile corresponds to a score lower than 6 and the 75th percentile to a score greater than 9. The 14% of the sample resulted in being not adherent to the Mediterranean diet, the 51% was in the mean, the 35% was adherent. This study confirms that the predisposing factor most associated with the AMS is "having had the same episode in the past" (OR 2.50, CI 1.88/3.13), having sleep disturbs (OR 1.29, CI 1.03 /1.55), age (OR -0.03, CI -0.35/-0.02). Moreover, it underlines that lifestyle is important with respect to risk to develop the AMS: actually, despite the structural limitations of surveys, this study pointed out that lifestyle contributes to mitigating the risk of developing the AMS (Mediterranean diet score OR - 0.34, CI -0.64 -> - 0.55). Gender, smoke and high physical activity are instead not significant. Future studies should investigate more deeply how lifestyle can change the impact on high altitude diseases.
2021
Journal article
Open Access
Acupuncture effects on cerebral blood flow during normoxia and normobaric hypoxia: results from a prospective crossover pilot study
Pecchio O, Martinelli M, Lupi G, Giardini G, Caligiana L, Bonin S, Scalese M, Salvetti O, Moroni D, Bastiani L
Cerebral blood flow (CBF) is significantly influenced by exposure to hypoxia, both hypobaric and normobaric. Alterations in cerebral blood flow can play a crucial role in the pathogenesis of acute mountain sickness (AMS) and its symptoms, especially headache, dizziness and nausea. Acupuncture has been proven to be effective in treating some cerebrovascular disorders and PC6 Nei Guan stimulation seems to enhance cerebral blood flow. Therefore we have hypothesized that PC6 Nei Guan stimulation could affect CBF in acute hypoxia and it could be used to contrast AMS symptoms. We evaluated blood flow in middle cerebral artery (MCA) in normoxia and after 15 minutes in normobaric hypoxia Fraction of Inspired Oxygen (FIO2) 14% corresponding to 3,600 m.a.s.l.) in basal conditions and after PC6 Nei Guan stimulation, both by needle and by pressure. No comparisons with other acupuncture points and sham acupuncture were done. PC6 stimulation seems to counteract the effect of acute normobaric hypoxia on end-diastolic velocity (EDV) in MCA, especially after acupuncture and reduces significantly systolic and diastolic blood pressure. A re-balance of CBF could control some AMS symptoms, but further studies are necessary.Source: TECHNOLOGIES, vol. 9 (issue 4)
DOI: 10.3390/technologies9040102
Metrics:
Pecchio O, Martinelli M, Lupi G, Giardini G, Caligiana L, Bonin S, Scalese M, Salvetti O, Moroni D, Bastiani L
Cerebral blood flow (CBF) is significantly influenced by exposure to hypoxia, both hypobaric and normobaric. Alterations in cerebral blood flow can play a crucial role in the pathogenesis of acute mountain sickness (AMS) and its symptoms, especially headache, dizziness and nausea. Acupuncture has been proven to be effective in treating some cerebrovascular disorders and PC6 Nei Guan stimulation seems to enhance cerebral blood flow. Therefore we have hypothesized that PC6 Nei Guan stimulation could affect CBF in acute hypoxia and it could be used to contrast AMS symptoms. We evaluated blood flow in middle cerebral artery (MCA) in normoxia and after 15 minutes in normobaric hypoxia Fraction of Inspired Oxygen (FIO2) 14% corresponding to 3,600 m.a.s.l.) in basal conditions and after PC6 Nei Guan stimulation, both by needle and by pressure. No comparisons with other acupuncture points and sham acupuncture were done. PC6 stimulation seems to counteract the effect of acute normobaric hypoxia on end-diastolic velocity (EDV) in MCA, especially after acupuncture and reduces significantly systolic and diastolic blood pressure. A re-balance of CBF could control some AMS symptoms, but further studies are necessary.Source: TECHNOLOGIES, vol. 9 (issue 4)
DOI: 10.3390/technologies9040102
Metrics:
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CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS | CNR IRIS
2021
Book
Open Access
Signals and images in sea technologies
Moroni D, Salvetti O
Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas and marine resources for sustainable development. It is not difficult to argue that Signals and Image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Indeed, besides increasing general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructures to the discovery, documentation and protection of the sunken cultural heritage. The main scope of this Special Issue has been to investigate the techniques and ICT approaches, and in particular the study and application of signal- and image-based methods and, in turn, to explore the advantages of their application to the main areas mentioned above.DOI: 10.3390/books978-3-0365-1355-3
Metrics:
Moroni D, Salvetti O
Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas and marine resources for sustainable development. It is not difficult to argue that Signals and Image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Indeed, besides increasing general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructures to the discovery, documentation and protection of the sunken cultural heritage. The main scope of this Special Issue has been to investigate the techniques and ICT approaches, and in particular the study and application of signal- and image-based methods and, in turn, to explore the advantages of their application to the main areas mentioned above.DOI: 10.3390/books978-3-0365-1355-3
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CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2021
Other
Open Access
SI-Lab Annual Research Report 2020
Leone Gr, Righi M, Carboni A, Caudai C, Colantonio S, Kuruoglu Ee, Leporini B, Magrini M, Paradisi P, Pascali Ma, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Buongiorno R, Bruno A, Germanese D, Matarese F, Coscetti S, Coltelli P, Jalil B, Benassi A, Bertini G, Salvetti O, Moroni D
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.DOI: https://doi.org/10.32079/isti-ar-2021/001
DOI: 10.32079/isti-ar-2021/001
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Leone Gr, Righi M, Carboni A, Caudai C, Colantonio S, Kuruoglu Ee, Leporini B, Magrini M, Paradisi P, Pascali Ma, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Buongiorno R, Bruno A, Germanese D, Matarese F, Coscetti S, Coltelli P, Jalil B, Benassi A, Bertini G, Salvetti O, Moroni D
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.DOI: https://doi.org/10.32079/isti-ar-2021/001
DOI: 10.32079/isti-ar-2021/001
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CNR IRIS | ISTI Repository | CNR IRIS
2020
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Open Access
Augmented reality and intelligent systems in Industry 4.0
Benassi A, Carboni A, Colantonio S, Coscetti S, Germanese D, Jalil B, Leone R, Magnavacca J, Magrini M, Martinelli M, Matarese F, Moroni D, Paradisi P, Pardini F, Pascali M, Pieri G, Reggiannini M, Righi M, Salvetti O, Tampucci M
Augmented reality and intelligent systems in Industry 4.0 - Presentazione ARTESDOI: 10.5281/zenodo.4277713
DOI: https://dx.doi.org/10.5281/zenodo.4277713
DOI: 10.5281/zenodo.4277712
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Benassi A, Carboni A, Colantonio S, Coscetti S, Germanese D, Jalil B, Leone R, Magnavacca J, Magrini M, Martinelli M, Matarese F, Moroni D, Paradisi P, Pardini F, Pascali M, Pieri G, Reggiannini M, Righi M, Salvetti O, Tampucci M
Augmented reality and intelligent systems in Industry 4.0 - Presentazione ARTESDOI: 10.5281/zenodo.4277713
DOI: https://dx.doi.org/10.5281/zenodo.4277713
DOI: 10.5281/zenodo.4277712
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CNR IRIS | ISTI Repository | CNR IRIS
2020
Journal article
Open Access
Optimized dislocation of mobile sensor networks on large marine environments using voronoi partitions
D'Acunto M, Moroni D, Puntoni A, Salvetti O
The real-time environmental surveillance of large areas requires the ability to dislocate sensor networks. Generally, the probability of the occurrence of a pollution event depends on the burden of possible sources operating in the areas to be monitored. This implies a challenge for devising optimal real-time dislocation of wireless sensor networks. This challenge involves both hardware solutions and algorithms optimizing the displacements of mobile sensor networks in large areas with a vast number of sources of pollutant factors based mainly on diffusion mechanisms. In this paper, we present theoretical and simulated results inherent to a Voronoi partition approach for the optimized dislocation of a set of mobile wireless sensors with circular (radial) sensing power on large areas. The optimal deployment was found to be a variation of the generalized centroidal Voronoi configuration, where the Voronoi configuration is event-driven, and the centroid set of the corresponding generalized Voronoi cells changes as a function of the pollution event. The initial localization of the pollution events is simulated with a Poisson distribution. Our results could improve the possibility of reducing the costs for real-time surveillance of large areas, and other environmental monitoring when wireless sensor networks are involved.Source: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 8 (issue 2)
DOI: 10.3390/jmse8020132
Project(s): ARGOMARINE
Metrics:
D'Acunto M, Moroni D, Puntoni A, Salvetti O
The real-time environmental surveillance of large areas requires the ability to dislocate sensor networks. Generally, the probability of the occurrence of a pollution event depends on the burden of possible sources operating in the areas to be monitored. This implies a challenge for devising optimal real-time dislocation of wireless sensor networks. This challenge involves both hardware solutions and algorithms optimizing the displacements of mobile sensor networks in large areas with a vast number of sources of pollutant factors based mainly on diffusion mechanisms. In this paper, we present theoretical and simulated results inherent to a Voronoi partition approach for the optimized dislocation of a set of mobile wireless sensors with circular (radial) sensing power on large areas. The optimal deployment was found to be a variation of the generalized centroidal Voronoi configuration, where the Voronoi configuration is event-driven, and the centroid set of the corresponding generalized Voronoi cells changes as a function of the pollution event. The initial localization of the pollution events is simulated with a Poisson distribution. Our results could improve the possibility of reducing the costs for real-time surveillance of large areas, and other environmental monitoring when wireless sensor networks are involved.Source: JOURNAL OF MARINE SCIENCE AND ENGINEERING, vol. 8 (issue 2)
DOI: 10.3390/jmse8020132
Project(s): ARGOMARINE
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Journal of Marine Science and Engineering | CNR IRIS | ISTI Repository | Journal of Marine Science and Engineering | Journal of Marine Science and Engineering | ZENODO | Hyper Article en Ligne | CNR IRIS
2020
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Forecasting industrial components life cycle: Futura Prototype 1
Martinelli M, Moroni D, Pardini F, Benassi A, Salvetti O
The purpose of this research report is to describe the first working prototype able to forecast the life cycle of an industrial component by Futura S.p.A.
Martinelli M, Moroni D, Pardini F, Benassi A, Salvetti O
The purpose of this research report is to describe the first working prototype able to forecast the life cycle of an industrial component by Futura S.p.A.
2020
Book
Open Access
Radiazioni Ionizzanti e Popolazione Generale - RadIoPoGe
Caramella D, Paolicchi F, Dore A, Feriani G, Aringhieri G, Pozzessere C, Di Coscio L, Marcheschi A, Grattadauria S, Bastiani L, Trivellini G, Serasini L, Banti D, Martinelli M, Benassi A, Galesi G, Pardini F, Salvetti O, Chiappino D, Micaela P, Rinaldi R, Della Latta D, Martini C, Curlo I, Rossi G, Cornacchione P, Giardina M, Carnevali F, Iacovone S, Pertoldi D, Favat M, Contato E, Pelati C, Baccarin F, Negro D, Pizzi M, Gelmi C, Carlevaris P, Rossato C, Ribaudo K, Ceccarelli M, Saba L, Muntoni E, Caoci D, Busonera C, Spano A, Tronci A, Mura M, Giannoni D, Tamburrino P, Leggieri V, Rizzo V, Farese R, Pastore S, Rossetti F, Nuzzi G, Calligari D, Cioce P, Di Fuccia G, Liparulo M, Petriccione G, Romano S, Stringile M, Travaglione G, Negri J, Marinelli E, Angelini G, Gerasia R, Lo Sardo C
Report Finale progetto RadIoPoGe.
Caramella D, Paolicchi F, Dore A, Feriani G, Aringhieri G, Pozzessere C, Di Coscio L, Marcheschi A, Grattadauria S, Bastiani L, Trivellini G, Serasini L, Banti D, Martinelli M, Benassi A, Galesi G, Pardini F, Salvetti O, Chiappino D, Micaela P, Rinaldi R, Della Latta D, Martini C, Curlo I, Rossi G, Cornacchione P, Giardina M, Carnevali F, Iacovone S, Pertoldi D, Favat M, Contato E, Pelati C, Baccarin F, Negro D, Pizzi M, Gelmi C, Carlevaris P, Rossato C, Ribaudo K, Ceccarelli M, Saba L, Muntoni E, Caoci D, Busonera C, Spano A, Tronci A, Mura M, Giannoni D, Tamburrino P, Leggieri V, Rizzo V, Farese R, Pastore S, Rossetti F, Nuzzi G, Calligari D, Cioce P, Di Fuccia G, Liparulo M, Petriccione G, Romano S, Stringile M, Travaglione G, Negri J, Marinelli E, Angelini G, Gerasia R, Lo Sardo C
Report Finale progetto RadIoPoGe.
2019
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Conoscenze Popolazione Radiazioni - Sito del Progetto RadioPoGe
Martinelli M, Bastiani L, Paolicchi F, Salvetti O, Caramella D
Aggiornamento del 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. Versione 1.3_4 del 2019-12-20
Martinelli M, Bastiani L, Paolicchi F, Salvetti O, Caramella D
Aggiornamento del 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. Versione 1.3_4 del 2019-12-20
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CNR IRIS | CNR IRIS | radiazioni.isti.cnr.it
2019
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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.
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.