2025
Conference article
Restricted
Reducing training data for indoor positioning through physics-informed neural networks
Lombardi G., Crivello A., Barsocchi P., Chessa S., Furfari F.
In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.DOI: 10.1109/ipin66788.2025.11213454
Project(s): A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society
Metrics:
Lombardi G., Crivello A., Barsocchi P., Chessa S., Furfari F.
In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.DOI: 10.1109/ipin66788.2025.11213454
Project(s): A novel public-private alliance to generate socioeconomic, biomedical and technological solutions for an inclusive Italian ageing society
Metrics:
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CNR IRIS 
| ieeexplore.ieee.org | CNR IRIS
2025
Journal article
Open Access
Comprehensive assessment of open science practices in indoor positioning: open data, code, and material
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 3, pp. 175-194
DOI: 10.1109/jispin.2025.3570258
DOI: 10.5281/zenodo.14931103
DOI: 10.5281/zenodo.14931104
Metrics:
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 3, pp. 175-194
DOI: 10.1109/jispin.2025.3570258
DOI: 10.5281/zenodo.14931103
DOI: 10.5281/zenodo.14931104
Metrics:
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IEEE Journal of Indoor and Seamless Positioning and Navigation 
| CNR IRIS | ieeexplore.ieee.org | ZENODO | ZENODO | ZENODO | CNR IRIS
2025
Journal article
Open Access
ORDIP: Principle, practice and guidelines for open research data in indoor positioning
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research dissemination. Despite recent efforts to openly share data and code, accompanying research results with Open Research Data (ORD) is far from being the de facto standard option for publications in the indoor positioning field. The lack of recognized public benchmarks and the rather slow adoption of ORD, set a great volume of astute contributions in the field to remain irreproducible. Performance comparisons may often be made on experiments performed in different settings, hindering their consistency, and eventually slowing down progress and the evolution of knowledge in the field. In this work, we systematically review the landscape of Open Research Data in Indoor Positioning, enlisting, presenting, and analyzing the characteristic features of the relevant available open datasets of the field. As a result of our systematic review, the statistical analysis of the 119 identified open datasets, highlights the tendencies and the missing elements, such as underrepresented technologies (such as Ultra-Wideband) and measurement types (such as Angle of Arrival, Time Difference of Arrival). A result that stands out is the frequency of crucial metadata information that remains undefined, such as the size of the area of collection (50% of the datasets), the ground truth collection protocol (21%), or the environment type (13%). As a fruit of the systematic analysis, we discuss potential shortcomings, and we share lessons learned and observed good practices regarding the provision of a new ORD and the reuse of existing ones. A significant practical contribution of this work is a list of guidelines that researchers aiming to collect and share a new ORD can follow as a simple checklist. In a broader context, we consider that ORDIP can help measure the future progress of the Indoor Positioning field in the ORD front through the snapshot of the current landscape that it provides. The Open provision of our full systematic analysis of the ORDs (Anagnostopoulos et al., 2024) can serve as a look-up table for easy access to the ORDs containing the most relevant features for each interested researcher, while our guidelines aim to support the community and spark the discussion towards a consensus-based standard for ORD of the field.Source: INTERNET OF THINGS
DOI: 10.1016/j.iot.2024.101485
Project(s): European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan, Investment Partenariato Esteso PE8 “Conseguenze e sfide dell’invecchiamento”, Project Age-IT, CUP: B83C22004880006
Metrics:
Anagnostopoulos G. G., Barsocchi P., Crivello A., Pendão C., Silva I., Torres-Sospedra J.
The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research dissemination. Despite recent efforts to openly share data and code, accompanying research results with Open Research Data (ORD) is far from being the de facto standard option for publications in the indoor positioning field. The lack of recognized public benchmarks and the rather slow adoption of ORD, set a great volume of astute contributions in the field to remain irreproducible. Performance comparisons may often be made on experiments performed in different settings, hindering their consistency, and eventually slowing down progress and the evolution of knowledge in the field. In this work, we systematically review the landscape of Open Research Data in Indoor Positioning, enlisting, presenting, and analyzing the characteristic features of the relevant available open datasets of the field. As a result of our systematic review, the statistical analysis of the 119 identified open datasets, highlights the tendencies and the missing elements, such as underrepresented technologies (such as Ultra-Wideband) and measurement types (such as Angle of Arrival, Time Difference of Arrival). A result that stands out is the frequency of crucial metadata information that remains undefined, such as the size of the area of collection (50% of the datasets), the ground truth collection protocol (21%), or the environment type (13%). As a fruit of the systematic analysis, we discuss potential shortcomings, and we share lessons learned and observed good practices regarding the provision of a new ORD and the reuse of existing ones. A significant practical contribution of this work is a list of guidelines that researchers aiming to collect and share a new ORD can follow as a simple checklist. In a broader context, we consider that ORDIP can help measure the future progress of the Indoor Positioning field in the ORD front through the snapshot of the current landscape that it provides. The Open provision of our full systematic analysis of the ORDs (Anagnostopoulos et al., 2024) can serve as a look-up table for easy access to the ORDs containing the most relevant features for each interested researcher, while our guidelines aim to support the community and spark the discussion towards a consensus-based standard for ORD of the field.Source: INTERNET OF THINGS
DOI: 10.1016/j.iot.2024.101485
Project(s): European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan, Investment Partenariato Esteso PE8 “Conseguenze e sfide dell’invecchiamento”, Project Age-IT, CUP: B83C22004880006
Metrics:
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CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Conference article
Open Access
AI-Empowered IoT data collection via UAV in rural areas
Vo P. T., Giambene G., Barsocchi P., Crivello A.
Soil monitoring is essential for smart agriculture in remote rural areas with limited connectivity. It helps forecast regional runoff, soil erosion, and weather impacts while promoting more efficient irrigation. Current artificial intelligence (AI) methods often struggle to adapt to heterogeneous environments and limited connectivity. This study presents a vertical federated architecture called multi-head split learning (MHSL), utilizing AI-powered devices onboard Unmanned Aerial Vehicles (UAVs) mission that is designed to increase awareness of in-situ soil moisture collected data to forecast environmental trends for enhanced monitoring in rural areas. Our architecture connects the local convolutional neural network (CNN) head model of multiple worker UAVs to the long-short-term memory (LSTM) tail model of a central master UAV, creating a global model. This is made possible by adopting GPUs onboard and WiFi connectivity among UAVs. To validate our approach, we have used the real datasets of the TERENO-Wüstebach network. The numerical results show that our CNN-LSTM approach can forecast the SSM data for the next days with sufficient accuracy measured in terms of mean square error (MSE). The good performance of CNN-LSTM has been supported by comparisons with other schemes in the literature.DOI: 10.1109/infocomwkshps65812.2025.11152890
Metrics:
Vo P. T., Giambene G., Barsocchi P., Crivello A.
Soil monitoring is essential for smart agriculture in remote rural areas with limited connectivity. It helps forecast regional runoff, soil erosion, and weather impacts while promoting more efficient irrigation. Current artificial intelligence (AI) methods often struggle to adapt to heterogeneous environments and limited connectivity. This study presents a vertical federated architecture called multi-head split learning (MHSL), utilizing AI-powered devices onboard Unmanned Aerial Vehicles (UAVs) mission that is designed to increase awareness of in-situ soil moisture collected data to forecast environmental trends for enhanced monitoring in rural areas. Our architecture connects the local convolutional neural network (CNN) head model of multiple worker UAVs to the long-short-term memory (LSTM) tail model of a central master UAV, creating a global model. This is made possible by adopting GPUs onboard and WiFi connectivity among UAVs. To validate our approach, we have used the real datasets of the TERENO-Wüstebach network. The numerical results show that our CNN-LSTM approach can forecast the SSM data for the next days with sufficient accuracy measured in terms of mean square error (MSE). The good performance of CNN-LSTM has been supported by comparisons with other schemes in the literature.DOI: 10.1109/infocomwkshps65812.2025.11152890
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Journal article
Open Access
MCSim: A multi-access edge computing mobile crowdsensing simulator
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
Belli D., Barsocchi P., Crivello A., La Rosa D., Girolami M.
This paper introduces MCSim, a modular and extensible simulator designed to support the planning and evaluation of Mobile CrowdSensing (MCS) campaigns in urban environments. MCSim integrates a useful approximation of urban mobility patterns based on real-world street networks, as well as the simulation of task execution effectiveness within configurable data transmission ranges. Unlike other simulators, MCSim is built to accommodate future extensions, such as edge/fog computing architectures. The current version of the software offers a user-friendly interface, customizable configuration options, and robust output analysis. By combining realistic mobility modeling, configurable task logic, and architectural flexibility, MCSim provides researchers and practitioners with a powerful tool for optimizing MCS strategies while minimizing deployment costs and risks.Source: SOFTWAREX, vol. 31 (issue 102229)
DOI: 10.1016/j.softx.2025.102229
Project(s): Cyber and Human Intelligence for Physical Systems
Metrics:
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SoftwareX | SoftwareX | CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
SegAN for recognition of caries from 2D-panoramic x-ray images
Naga Srinivasu P., Aruna Kumari G. L., Kumari D. J., Barsocchi P., Kumar Bhoi A. K.
Accurate recognition and segmentation of dental caries in 2D panoramic X-ray images are crucial for timely diagnosis and strategic treatment planning. The current study uses a Generative Adversarial Network (GAN) model named SegAN to segment the X-ray samples to recognize the caries. The SegAN model works in an adversarial architecture in which the generator focuses on creating precise segmentation maps of caries from 2D panoramic X-ray images. On the other hand, the discriminator ensures that the output matches realistic segmentation patterns. The SegAN model efficiently handles the local and global contextual information for precise segmentation by considering Pixel-Wise and structural loss measures that assist in better segmentation of complex structures. Moreover, the SegAN model efficiently deals with noisy data and effectively handles class imbalances. Data augmentation, like histogram equalization and affine transforms, is performed on the input images for precise segmentation of the samples. The model was evaluated on both raw and preprocessed dental X-ray images using standard quantitative metrics. SegAN demonstrated superior performance compared to traditional segmentation approaches, achieving an accuracy of 98.5%, and a dice coefficient of 0.936 in caries detection.Source: IEEE ACCESS, vol. 13, pp. 100419-100432
DOI: 10.1109/access.2025.3576914
Metrics:
Naga Srinivasu P., Aruna Kumari G. L., Kumari D. J., Barsocchi P., Kumar Bhoi A. K.
Accurate recognition and segmentation of dental caries in 2D panoramic X-ray images are crucial for timely diagnosis and strategic treatment planning. The current study uses a Generative Adversarial Network (GAN) model named SegAN to segment the X-ray samples to recognize the caries. The SegAN model works in an adversarial architecture in which the generator focuses on creating precise segmentation maps of caries from 2D panoramic X-ray images. On the other hand, the discriminator ensures that the output matches realistic segmentation patterns. The SegAN model efficiently handles the local and global contextual information for precise segmentation by considering Pixel-Wise and structural loss measures that assist in better segmentation of complex structures. Moreover, the SegAN model efficiently deals with noisy data and effectively handles class imbalances. Data augmentation, like histogram equalization and affine transforms, is performed on the input images for precise segmentation of the samples. The model was evaluated on both raw and preprocessed dental X-ray images using standard quantitative metrics. SegAN demonstrated superior performance compared to traditional segmentation approaches, achieving an accuracy of 98.5%, and a dice coefficient of 0.936 in caries detection.Source: IEEE ACCESS, vol. 13, pp. 100419-100432
DOI: 10.1109/access.2025.3576914
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Journal article
Open Access
Climatic vulnerability and adaptation strategies for vegetable production in the Northern Himalayan region
Singh P., Vaidya M. K., Guleria A., Adhale P., Bhoi P. B., Bhoi A. K., Barsocchi P.
Vegetable production in the low and mid hills is highly vulnerable to climatic vulnerability. The study evaluated the Agricultural Climatic Vulnerability Index (ACVI) for 51 blocks in the regions using the IPCC AR4 conceptual framework. The developmental blocks were categorized into three groups (Low, Moderate and Highly Vulnerable) to collect the primary data. A multistage stratified random sampling technique was employed, using a pre-tested questionnaire. The ACVI findings reveal that the Balh Valley is the most climate-vulnerable block, while Paonta Sahib is the least. Vulnerability is primarily driven by the temperature variations in the Kharif and Rabi seasons of exposure dimension. The farm income analysis shows a decline in crop feasibility from low to high-vulnerability groups. Maximum temperature significantly reduced net crop returns, except in the case of cauliflower. Rainfall negatively impacted the profitability of crops such as tomatoes, capsicum and peas. However, an increase in the minimum temperature significantly boosted vegetable crop profitability in vulnerable groups. A balanced use of fertilizer and pesticide application, crop diversification and increased irrigation coverage significantly mitigated climate change impacts across all vulnerability groups and improved crop profitability. Among the crops studied, tomato exhibited the highest carbon sequestration potential, followed by capsicum, pea, French beans and cauliflower. A significant variation was observed in the carbon sequestration level across vulnerability groups. Farmers in these regions have adopted various adaptation strategies, including crop diversification (76.11%), nutrient management (71.11 %), varietal changes (65.56 %), and water conservation (65.56 %). To enhance resilience, the study emphasizes the importance of improved technical knowledge, capacity building, adoption of better agronomic practices, increased financial support, and comprehensive stakeholder consultation within the agricultural and allied sectors.Source: SCIENCE OF THE TOTAL ENVIRONMENT, vol. 969 (issue 178343)
DOI: 10.1016/j.scitotenv.2024.178343
Metrics:
Singh P., Vaidya M. K., Guleria A., Adhale P., Bhoi P. B., Bhoi A. K., Barsocchi P.
Vegetable production in the low and mid hills is highly vulnerable to climatic vulnerability. The study evaluated the Agricultural Climatic Vulnerability Index (ACVI) for 51 blocks in the regions using the IPCC AR4 conceptual framework. The developmental blocks were categorized into three groups (Low, Moderate and Highly Vulnerable) to collect the primary data. A multistage stratified random sampling technique was employed, using a pre-tested questionnaire. The ACVI findings reveal that the Balh Valley is the most climate-vulnerable block, while Paonta Sahib is the least. Vulnerability is primarily driven by the temperature variations in the Kharif and Rabi seasons of exposure dimension. The farm income analysis shows a decline in crop feasibility from low to high-vulnerability groups. Maximum temperature significantly reduced net crop returns, except in the case of cauliflower. Rainfall negatively impacted the profitability of crops such as tomatoes, capsicum and peas. However, an increase in the minimum temperature significantly boosted vegetable crop profitability in vulnerable groups. A balanced use of fertilizer and pesticide application, crop diversification and increased irrigation coverage significantly mitigated climate change impacts across all vulnerability groups and improved crop profitability. Among the crops studied, tomato exhibited the highest carbon sequestration potential, followed by capsicum, pea, French beans and cauliflower. A significant variation was observed in the carbon sequestration level across vulnerability groups. Farmers in these regions have adopted various adaptation strategies, including crop diversification (76.11%), nutrient management (71.11 %), varietal changes (65.56 %), and water conservation (65.56 %). To enhance resilience, the study emphasizes the importance of improved technical knowledge, capacity building, adoption of better agronomic practices, increased financial support, and comprehensive stakeholder consultation within the agricultural and allied sectors.Source: SCIENCE OF THE TOTAL ENVIRONMENT, vol. 969 (issue 178343)
DOI: 10.1016/j.scitotenv.2024.178343
Metrics:
See at:
The Science of The Total Environment | CNR IRIS | www.sciencedirect.com | CNR IRIS | pubmed.ncbi.nlm.nih.gov
2024
Journal article
Open Access
What are data spaces? Systematic survey and future outlook
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
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Data in Brief | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Conference article
Open Access
A novel architectural schema for constant monitoring and assessment of older adults’ health status at Home
Barsocchi P., Belli D., Gabrielli E., La Rosa D., Miori V., Palumbo F., Russo D., Tolomei G.
In recent years the demand for health care among older adults, along with requests for hospitalization and related costs, has increased at an unprecedented rate. In the coming decades, this trend is likely to worsen. This detrimental tendency can be mitigated by addressing the problem with a proactive approach. The goal is to ensure continuous monitoring of the older’s health status to promptly detect worsening and disease onsets. The paper extends the mid-term results of the Project ChAALenge, by detailing the sensors and the framework underlying the high-level predictive techniques, as well as by reporting qualitative results in terms of physiological measurements from a 4-month data collection campaign in a nursing home.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, vol. 572, pp. 501-511. Malmö, Sweden, 27-29/11/2023
DOI: 10.1007/978-3-031-59717-6_33
Metrics:
Barsocchi P., Belli D., Gabrielli E., La Rosa D., Miori V., Palumbo F., Russo D., Tolomei G.
In recent years the demand for health care among older adults, along with requests for hospitalization and related costs, has increased at an unprecedented rate. In the coming decades, this trend is likely to worsen. This detrimental tendency can be mitigated by addressing the problem with a proactive approach. The goal is to ensure continuous monitoring of the older’s health status to promptly detect worsening and disease onsets. The paper extends the mid-term results of the Project ChAALenge, by detailing the sensors and the framework underlying the high-level predictive techniques, as well as by reporting qualitative results in terms of physiological measurements from a 4-month data collection campaign in a nursing home.Source: LECTURE NOTES OF THE INSTITUTE FOR COMPUTER SCIENCES, SOCIAL INFORMATICS AND TELECOMMUNICATIONS ENGINEERING, vol. 572, pp. 501-511. Malmö, Sweden, 27-29/11/2023
DOI: 10.1007/978-3-031-59717-6_33
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | Archivio della ricerca- Università di Roma La Sapienza | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Optimized forest framework with a binary multineighborhood artificial bee colony for enhanced diabetes mellitus detection
Pradhan G., Thapa G., Pradhan R., Khandelwal B., Panigrahi R., Bhoi A. K., Barsocchi P.
Diabetes mellitus (DM) is a common chronic condition that mainly affects older adults. It's important to identify it early to prevent complications. Machine learning is essential for early detection of DM. This article introduces a new method for detecting DM using a random forest ensemble within an optimized framework. The optimized forest framework depends on finding the best DM features, which are identified using the binary multineighborhood artificial bee colony (BMNABC) technique. During preprocessing, the BMNABC algorithm efficiently identifies important features and then inputs them into the random forest within the optimized forest framework for accurate classification. Five modern DM datasets were used to validate the suggested model. The comparison of the proposed model with other leading models revealed significant insights. The BMNABC + ODF(RFE) model demonstrated exceptional proficiency in detecting diabetes mellitus (DM) across various datasets. It achieved an accuracy of 96.36% and a sensitivity of 99.95% on the merged dataset (130 US and PIMA images). The Iranian Ministry of Health dataset showed an accuracy of 97.28% and a sensitivity of 97.12%. In the Sylhet Diabetes Hospital dataset, the accuracy and sensitivity were 96.81% and 98.07% respectively. However, on the PIMA dataset, the model displayed a nuanced performance, with an accuracy of 77.21% and a sensitivity of 68.83%. Lastly, on the questionnaire dataset, the BMNABC + ODF(RFE) model achieved an accuracy of 96.43% and a sensitivity of 97.15%. These findings emphasize the model's ability to adapt and perform effectively in different clinical environments, outperforming other models in terms of accuracy and sensitivity in detecting DM.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 119 (article number))
DOI: 10.1007/s44196-024-00598-2
Project(s): Smart and high-efficient technologies for fruits and vegetable production in greenhouse and plant factory
Metrics:
Pradhan G., Thapa G., Pradhan R., Khandelwal B., Panigrahi R., Bhoi A. K., Barsocchi P.
Diabetes mellitus (DM) is a common chronic condition that mainly affects older adults. It's important to identify it early to prevent complications. Machine learning is essential for early detection of DM. This article introduces a new method for detecting DM using a random forest ensemble within an optimized framework. The optimized forest framework depends on finding the best DM features, which are identified using the binary multineighborhood artificial bee colony (BMNABC) technique. During preprocessing, the BMNABC algorithm efficiently identifies important features and then inputs them into the random forest within the optimized forest framework for accurate classification. Five modern DM datasets were used to validate the suggested model. The comparison of the proposed model with other leading models revealed significant insights. The BMNABC + ODF(RFE) model demonstrated exceptional proficiency in detecting diabetes mellitus (DM) across various datasets. It achieved an accuracy of 96.36% and a sensitivity of 99.95% on the merged dataset (130 US and PIMA images). The Iranian Ministry of Health dataset showed an accuracy of 97.28% and a sensitivity of 97.12%. In the Sylhet Diabetes Hospital dataset, the accuracy and sensitivity were 96.81% and 98.07% respectively. However, on the PIMA dataset, the model displayed a nuanced performance, with an accuracy of 77.21% and a sensitivity of 68.83%. Lastly, on the questionnaire dataset, the BMNABC + ODF(RFE) model achieved an accuracy of 96.43% and a sensitivity of 97.15%. These findings emphasize the model's ability to adapt and perform effectively in different clinical environments, outperforming other models in terms of accuracy and sensitivity in detecting DM.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 119 (article number))
DOI: 10.1007/s44196-024-00598-2
Project(s): Smart and high-efficient technologies for fruits and vegetable production in greenhouse and plant factory
Metrics:
See at:
bmcmedinformdecismak.biomedcentral.com | International Journal of Computational Intelligence Systems | International Journal of Computational Intelligence Systems | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Machine learning-empowered sleep staging classification using multi-modality signals
Satapathy S. K., Brahma B., Panda B., Barsocchi P., Bhoi A. K.
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.Source: BMC MEDICAL INFORMATICS AND DECISION MAKING, vol. 24 (issue 1)
DOI: 10.1186/s12911-024-02522-2
Project(s): Scotland's Net Zero Infrastructure (SNZI)
Metrics:
Satapathy S. K., Brahma B., Panda B., Barsocchi P., Bhoi A. K.
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.Source: BMC MEDICAL INFORMATICS AND DECISION MAKING, vol. 24 (issue 1)
DOI: 10.1186/s12911-024-02522-2
Project(s): Scotland's Net Zero Infrastructure (SNZI)
Metrics:
See at:
bmcmedinformdecismak.biomedcentral.com | BMC Medical Informatics and Decision Making | PubMed Central | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Agricultural Data Space: the METRIQA platform and a case study in the CODECS project
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
See at:
annals-csis.org | Annals of computer science and information systems | CNR IRIS | CNR IRIS
2024
Contribution to book
Restricted
A review of automated sleep stage scoring using machine learning techniques based on physiological signals
Satapathy S. K., Agrawal P., Shah N., Panigrahi R., Khandelwal B., Barsocchi P., Bhoi A. K.
The background and goal of this research are to address the importance of sleep in our lifestyle and health. To analyze sleep problems, legitimate scoring of sleep stages is fundamental, and this is usually finished through a tedious visual survey of, for the time being, polysomnograms by a human expert. However, this process can be improved with artificial intelligence algorithms. To accurately interpret the physiological signals associated with sleep disorders, it is essential to understand how changes in sleep stages are reflected in the signal waveform. With this knowledge, automated sleep stage scoring systems can be developed, making the diagnosis of sleep disorders more efficient and providing insight into the amount of information about sleep stages that can be gleaned from a particular physiological signal. The review study thoroughly examines automated sleep stage rating systems developed since 2000. These systems were created to analyze electrocardiograms (ECGs), electroencephalograms (EEGs), electDOI: 10.1002/9781394227990.ch5
Metrics:
Satapathy S. K., Agrawal P., Shah N., Panigrahi R., Khandelwal B., Barsocchi P., Bhoi A. K.
The background and goal of this research are to address the importance of sleep in our lifestyle and health. To analyze sleep problems, legitimate scoring of sleep stages is fundamental, and this is usually finished through a tedious visual survey of, for the time being, polysomnograms by a human expert. However, this process can be improved with artificial intelligence algorithms. To accurately interpret the physiological signals associated with sleep disorders, it is essential to understand how changes in sleep stages are reflected in the signal waveform. With this knowledge, automated sleep stage scoring systems can be developed, making the diagnosis of sleep disorders more efficient and providing insight into the amount of information about sleep stages that can be gleaned from a particular physiological signal. The review study thoroughly examines automated sleep stage rating systems developed since 2000. These systems were created to analyze electrocardiograms (ECGs), electroencephalograms (EEGs), electDOI: 10.1002/9781394227990.ch5
Metrics:
See at:
CNR IRIS | CNR IRIS | CNR IRIS | onlinelibrary.wiley.com
2024
Conference article
Open Access
Inclusive navigation systems: perspectives and challenges for the visually-impaired
Belli D., Barsocchi P., Crivello A., Furfari F., Leporini B., Paratore M. T.
Despite significant advances in technology, the area of mobility and orientation for visually impaired persons continues to present significant challenges. Digital maps have become essential for navigation, but their usability is often compromised for users who rely on assistive technologies, especially when accessed on small touch screens. This calls for innovative approaches to making digital maps more accessible and usable, as these tools are crucial for creating mental maps of navigational spaces. This paper explores the need for inclusive localization and positioning systems that accommodate a wide range of users, including those with visual impairments. It highlights the critical role of user context, such as device experience and positional awareness, in improving the usability of these systems. The integration of haptic and audio feedback may offer promising new interaction methods, although further development is needed. In addition, user interface design and system characteristics such as security, robustness and usability need to be aligned with user acceptance, with a focus on low cost and simplicity. Our analysis identifies key requirements for the design of inclusive systems and proposes steps for the scientific community to take to advance the field, with the aim of bridging the gap between technological capabilities and practical usability, and promoting inclusive design principles for future innovation.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3919. Hong Kong, China, 14-15/102024
Belli D., Barsocchi P., Crivello A., Furfari F., Leporini B., Paratore M. T.
Despite significant advances in technology, the area of mobility and orientation for visually impaired persons continues to present significant challenges. Digital maps have become essential for navigation, but their usability is often compromised for users who rely on assistive technologies, especially when accessed on small touch screens. This calls for innovative approaches to making digital maps more accessible and usable, as these tools are crucial for creating mental maps of navigational spaces. This paper explores the need for inclusive localization and positioning systems that accommodate a wide range of users, including those with visual impairments. It highlights the critical role of user context, such as device experience and positional awareness, in improving the usability of these systems. The integration of haptic and audio feedback may offer promising new interaction methods, although further development is needed. In addition, user interface design and system characteristics such as security, robustness and usability need to be aligned with user acceptance, with a focus on low cost and simplicity. Our analysis identifies key requirements for the design of inclusive systems and proposes steps for the scientific community to take to advance the field, with the aim of bridging the gap between technological capabilities and practical usability, and promoting inclusive design principles for future innovation.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3919. Hong Kong, China, 14-15/102024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
SSO-CCNN: a correlation-based optimized deep CNN for brain tumor classification using sampled PGGAN
Sahoo S., Mishra S., Brahma B., Barsocchi P., Bhoi A. K.
Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 179 (n. articolo))
DOI: 10.1007/s44196-024-00574-w
Metrics:
Sahoo S., Mishra S., Brahma B., Barsocchi P., Bhoi A. K.
Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 17 (issue 179 (n. articolo))
DOI: 10.1007/s44196-024-00574-w
Metrics:
See at:
International Journal of Computational Intelligence Systems | International Journal of Computational Intelligence Systems | CNR IRIS | link.springer.com | CNR IRIS
2024
Journal article
Open Access
Integrating indoor localization systems through a handoff protocol
Furfari F., Girolami M., Barsocchi P.
The increasing adoption of location-based services drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs) require managing the transition from one ILS to another in order to continue localizing the user's device even when moving indoor or outdoor. In this article, we focus on the handoff procedure, whose goal is to enable a device to trigger the transition between ILSs when specific conditions are verified. We distinguish between the triggering and managing operations, each requiring specific actions. We describe the activation of the handoff procedure by considering three types of ILSs design, each with increasing complexity. Moreover, we define five handoff algorithms-based RSSI signal analysis and we test them in a realistic environment with two nearby ILSs. We establish a set of evaluation metrics to measure the performance of the handoff procedure.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 2, pp. 130-142
DOI: 10.1109/jispin.2024.3377146
Project(s): Investment Partenariato Esteso PE8 “Conseguenze e sfide dell'invecchiamento”
Metrics:
Furfari F., Girolami M., Barsocchi P.
The increasing adoption of location-based services drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs) require managing the transition from one ILS to another in order to continue localizing the user's device even when moving indoor or outdoor. In this article, we focus on the handoff procedure, whose goal is to enable a device to trigger the transition between ILSs when specific conditions are verified. We distinguish between the triggering and managing operations, each requiring specific actions. We describe the activation of the handoff procedure by considering three types of ILSs design, each with increasing complexity. Moreover, we define five handoff algorithms-based RSSI signal analysis and we test them in a realistic environment with two nearby ILSs. We establish a set of evaluation metrics to measure the performance of the handoff procedure.Source: IEEE JOURNAL OF INDOOR AND SEAMLESS POSITIONING AND NAVIGATION, vol. 2, pp. 130-142
DOI: 10.1109/jispin.2024.3377146
Project(s): Investment Partenariato Esteso PE8 “Conseguenze e sfide dell'invecchiamento”
Metrics:
2024
Journal article
Open Access
A review of the electric vehicle charging technology, impact on grid integration, policy consequences, challenges and future trends
Sarda J., Patel N., Patel H., Vaghela R., Brahma B., Bhoi A. K., Barsocchi P.
The effectiveness of electric vehicles (EVs) in mitigating petrol emissions and diminishing reliance on oil for transportation is well recognized. The increasing popularity of EVs has resulted in a proportional increase in the number of charging stations, so significantly affecting the energy grid. In order to promote the general implementation of EVs worldwide, it is crucial to develop a strong charging infrastructure that can satisfy rural and urban areas, especially those that have an inconsistent or non-existent electrical grid. However, the incorporation of EVs into the power grid has posed several challenges in terms of power grid management, network planning, and safety. These issues stem from the rising demand for electricity, negative impacts on the quality of power, and higher power losses. This article offers a comprehensive analysis of the infrastructure of EV charging stations, emphasizing the advantages and consequences associated with it. Moreover, it provides a review of the impact of EVs on the integration of power grids, as well as a thorough study of the standards and rules that regulate the operation of EV charging stations.Source: ENERGY REPORTS, vol. 12, pp. 5671-5692
DOI: 10.1016/j.egyr.2024.11.047
Metrics:
Sarda J., Patel N., Patel H., Vaghela R., Brahma B., Bhoi A. K., Barsocchi P.
The effectiveness of electric vehicles (EVs) in mitigating petrol emissions and diminishing reliance on oil for transportation is well recognized. The increasing popularity of EVs has resulted in a proportional increase in the number of charging stations, so significantly affecting the energy grid. In order to promote the general implementation of EVs worldwide, it is crucial to develop a strong charging infrastructure that can satisfy rural and urban areas, especially those that have an inconsistent or non-existent electrical grid. However, the incorporation of EVs into the power grid has posed several challenges in terms of power grid management, network planning, and safety. These issues stem from the rising demand for electricity, negative impacts on the quality of power, and higher power losses. This article offers a comprehensive analysis of the infrastructure of EV charging stations, emphasizing the advantages and consequences associated with it. Moreover, it provides a review of the impact of EVs on the integration of power grids, as well as a thorough study of the standards and rules that regulate the operation of EV charging stations.Source: ENERGY REPORTS, vol. 12, pp. 5671-5692
DOI: 10.1016/j.egyr.2024.11.047
Metrics:
See at:
Energy Reports | CNR IRIS | www.sciencedirect.com | CNR IRIS
2024
Conference article
Open Access
Design of postural analysis and indoor localization services in AAL scenarios
Barsocchi P., Girolami M., Palumbo F.
Advancements in Ambient Assisted Living (AAL) technology have enabled innovative solutions to enhance the quality of life for older adults. The AA@THE project focuses on personalized coaching and monitoring systems to prevent risky conditions and promote healthy ageing. Two crucial domains, sedentariness and stability, are addressed through advanced technologies such as proximity detection and postural analysis services. By analyzing these specific health and behavioural aspects, personalized feedback is provided to improve overall well-being.Source: LECTURE NOTES IN BIOENGINEERING, pp. 157-160. Bari, Italy, 14-16/06/2023
DOI: 10.1007/978-3-031-63913-5_14
Project(s): Tuscany Health Ecosystem
Metrics:
Barsocchi P., Girolami M., Palumbo F.
Advancements in Ambient Assisted Living (AAL) technology have enabled innovative solutions to enhance the quality of life for older adults. The AA@THE project focuses on personalized coaching and monitoring systems to prevent risky conditions and promote healthy ageing. Two crucial domains, sedentariness and stability, are addressed through advanced technologies such as proximity detection and postural analysis services. By analyzing these specific health and behavioural aspects, personalized feedback is provided to improve overall well-being.Source: LECTURE NOTES IN BIOENGINEERING, pp. 157-160. Bari, Italy, 14-16/06/2023
DOI: 10.1007/978-3-031-63913-5_14
Project(s): Tuscany Health Ecosystem
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Multi-scale based approach for denoising real-world noisy image using curvelet thresholding: scope and beyond
Panigrahi S. K., Tripathy S. K., Bhowmick A., Satapathy S. K., Barsocchi P., Bhoi A. K.
Naive simulated additive white Gaussian noise (AWGN) may not fully characterize the complexity of real world noisy images. Owing to optimal sparsity in image representation, we propose a curvelet based model for denoising real-world RGB images. Initially, the image is decomposed in three curvelet scales, namely: the approximation scale (that retains low-frequency information), the coarser scale and the finest scale (that preserves high-frequency components). Coefficients in the approximation and finest scale are estimated using NLM filter, while a scale dependent threshold is adopted for signal estimation in the coarser scale. The reconstructed image in spatial domain is further processed using Guided Image Filter (GIF) to suppress the ringing artifacts due to curvelet thresholding. The proposed approach known as CTuNLM method is extended for color image denoising using uncorrelated YUV color space. Extensive experiments on multi-channel real noisy images are conducted in comparison with eight sate-ofthe-art methods. With four encouraging qualitative and quantitative measures including PSNR and SSIM, we found that CTuNLM method achieves better denoising performance in terms of noise reduction and detail preservation. We further examined the potential of proposed approach by focusing only on the Finest scale curvelet Coefficients (FC). Features like small details, edges and textures always add up to improve the overall denoising performance, while minimizing spurious details. We studied ''The Curious Case of the Finest Scale'' and constructed ''Deep Curvelet-Net'': an encoder-decoder-based CNN architecture, as a pilot work. The encoder uses multiscale spatial characteristics from noisy FC, while the decoder processes de-noised FC under the supervision of encoder's multiscale spatial attention map. The ''Deep CurveletNet'' links encoder multiscale feature modeling with decoder spatial attention supervision to learn the most essential features for denoising. The CNN-based architecture only estimates FC, while all other CTuNLM stages are left unchanged to produce the denoised output. Results presented in this article validated the design of proposed CNN architecture in curvelet domain and motivated us to search beyond classical thresholding and/or filtering approaches.Source: IEEE ACCESS, vol. 12, pp. 25090-25105
DOI: 10.1109/access.2024.3364397
Metrics:
Panigrahi S. K., Tripathy S. K., Bhowmick A., Satapathy S. K., Barsocchi P., Bhoi A. K.
Naive simulated additive white Gaussian noise (AWGN) may not fully characterize the complexity of real world noisy images. Owing to optimal sparsity in image representation, we propose a curvelet based model for denoising real-world RGB images. Initially, the image is decomposed in three curvelet scales, namely: the approximation scale (that retains low-frequency information), the coarser scale and the finest scale (that preserves high-frequency components). Coefficients in the approximation and finest scale are estimated using NLM filter, while a scale dependent threshold is adopted for signal estimation in the coarser scale. The reconstructed image in spatial domain is further processed using Guided Image Filter (GIF) to suppress the ringing artifacts due to curvelet thresholding. The proposed approach known as CTuNLM method is extended for color image denoising using uncorrelated YUV color space. Extensive experiments on multi-channel real noisy images are conducted in comparison with eight sate-ofthe-art methods. With four encouraging qualitative and quantitative measures including PSNR and SSIM, we found that CTuNLM method achieves better denoising performance in terms of noise reduction and detail preservation. We further examined the potential of proposed approach by focusing only on the Finest scale curvelet Coefficients (FC). Features like small details, edges and textures always add up to improve the overall denoising performance, while minimizing spurious details. We studied ''The Curious Case of the Finest Scale'' and constructed ''Deep Curvelet-Net'': an encoder-decoder-based CNN architecture, as a pilot work. The encoder uses multiscale spatial characteristics from noisy FC, while the decoder processes de-noised FC under the supervision of encoder's multiscale spatial attention map. The ''Deep CurveletNet'' links encoder multiscale feature modeling with decoder spatial attention supervision to learn the most essential features for denoising. The CNN-based architecture only estimates FC, while all other CTuNLM stages are left unchanged to produce the denoised output. Results presented in this article validated the design of proposed CNN architecture in curvelet domain and motivated us to search beyond classical thresholding and/or filtering approaches.Source: IEEE ACCESS, vol. 12, pp. 25090-25105
DOI: 10.1109/access.2024.3364397
Metrics:
See at:
IEEE Access | IEEE Access | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2024
Conference article
Open Access
An IoT Platform for smart hydroponics: building blocks and open challenges
Sportelli M., Crivello A., La Rosa D., Bacco M., Incrocci L., Barsocchi P.
Hydroponics addresses inefficiencies in traditional soil-based farming by optimizing water, nutrients, and pesticide use. This method has the potential to boost crop yields and significantly reduce water consumption, tackling issues of inefficient irrigation and fertilization. This study presents the development of an IoT-based platform designed to optimize the management of smart hydroponic systems. The proposed platform facilitates real-time monitoring of key environmental parameters for hydroponic farming as well as automatic regulation of greenhouse conditions, such as temperature, humidity and nutrient levels. The platform leverages middleware software to ensure seamless communication and data management, enabling efficient decision-making processes. The primary aim of this work is to enhance the productivity and efficiency of hydroponic systems through a scalable and user-friendly solution by integrating different sensors and actuators that could be accessible through both web and mobile applications. The platform's open and flexible architecture supports the integration of advanced sensing technologies and artificial intelligence, contributing to the digitalization of agriculture and promoting environmental sustainability.DOI: 10.1109/metroagrifor63043.2024.10948780
Project(s): CODECS
Metrics:
Sportelli M., Crivello A., La Rosa D., Bacco M., Incrocci L., Barsocchi P.
Hydroponics addresses inefficiencies in traditional soil-based farming by optimizing water, nutrients, and pesticide use. This method has the potential to boost crop yields and significantly reduce water consumption, tackling issues of inefficient irrigation and fertilization. This study presents the development of an IoT-based platform designed to optimize the management of smart hydroponic systems. The proposed platform facilitates real-time monitoring of key environmental parameters for hydroponic farming as well as automatic regulation of greenhouse conditions, such as temperature, humidity and nutrient levels. The platform leverages middleware software to ensure seamless communication and data management, enabling efficient decision-making processes. The primary aim of this work is to enhance the productivity and efficiency of hydroponic systems through a scalable and user-friendly solution by integrating different sensors and actuators that could be accessible through both web and mobile applications. The platform's open and flexible architecture supports the integration of advanced sensing technologies and artificial intelligence, contributing to the digitalization of agriculture and promoting environmental sustainability.DOI: 10.1109/metroagrifor63043.2024.10948780
Project(s): CODECS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS