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Dr. Paolo Barsocchi
ISTI-CNR

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0 Cyber-Physical Systems
0 Signal Processing
0 Wireless Applications
0 AAL
0 indoor localization

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AAL

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Journal article
Published: 06 July 2021 in Biomedical Signal Processing and Control
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Sleep staging is an important part of diagnosing the different types of sleep-related disorders because any discrepancies in the sleep scoring process may cause serious health problems such as misinterpretations of sleep patterns, medication errors, and improper diagnosis. The best way of analyzing sleep staging is visual interpretations of the polysomnography (PSG) signals recordings from the patients, which is a quite tedious task, requires more domain experts, and time-consuming process. This proposed study aims to develop a new automated sleep staging system using the brain EEG signals. Based on a new automated sleep staging system based on an ensemble learning stacking model that integrates Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting). Additionally, this proposed methodology considers the subjects’ age, which helps analyze the S1 sleep stage properly. In this study, both linear (time and frequency) and non-linear features are extracted from the pre-processed signals. The most relevant features are selected using the ReliefF weight algorithm. Finally, the selected features are classified through the proposed two-layer stacking model. The proposed methodology performance is evaluated using the two most popular datasets, such as the Sleep-EDF dataset (S-EDF) and Sleep Expanded-EDF database (SE-EDF) under the Rechtschaffen & Kales (R&K) sleep scoring rules. The performance of the proposed method is also compared with the existing published sleep staging methods. The comparison results signify that the proposed sleep staging system has an excellent improvement in classification accuracy for the six-two sleep states classification. In the S-EDF dataset, the overall accuracy and Cohen’s kappa coefficient score obtained by the proposed model is (91.10%, 0.87) and (90.68%, 0.86) with inclusion and exclusion of age feature using the Fpz-Cz channel, respectively. Similarly, the Pz-Oz channel’s performance is (90.56%, 0.86) with age feature and (90.11%, 0.86) without age feature. The performed results with the SE-EDF dataset using Fpz-Cz channel is (81.32%, 0.77) and (81.06%, 0.76), using Pz-Oz channel with the inclusion and exclusion of the age feature, respectively. Similarly the model achieved an overall accuracy of 96.67% (CT-6), 96.60% (CT-5), 96.28% (CT-4),96.30% (CT-3) and 97.30% (CT-2) for with 16 selected features using S-EDF database. Similarly the model reported an overall accuracy of 85.85%, 84.98%, 85.51%, 85.37% and 87.40% for CT-6 to CT-2 with 18 selected features using SE-EDF database.

ACS Style

Santosh Kumar Satapathy; Akash Kumar Bhoi; D. Loganathan; Bidita Khandelwal; Paolo Barsocchi. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal. Biomedical Signal Processing and Control 2021, 69, 102898 .

AMA Style

Santosh Kumar Satapathy, Akash Kumar Bhoi, D. Loganathan, Bidita Khandelwal, Paolo Barsocchi. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal. Biomedical Signal Processing and Control. 2021; 69 ():102898.

Chicago/Turabian Style

Santosh Kumar Satapathy; Akash Kumar Bhoi; D. Loganathan; Bidita Khandelwal; Paolo Barsocchi. 2021. "Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal." Biomedical Signal Processing and Control 69, no. : 102898.

Review
Published: 29 June 2021 in Energies
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Energy consumption is a crucial domain in energy system management. Recently, it was observed that there has been a rapid rise in the consumption of energy throughout the world. Thus, almost every nation devises its strategies and models to limit energy usage in various areas, ranging from large buildings to industrial firms and vehicles. With technological advancements, computational intelligence models have been successfully contributing to the prediction of the consumption of energy. Machine learning and deep learning-based models enhance the precision and robustness compared to traditional approaches, making it more reliable. This article performs a review analysis of the various computational intelligence approaches currently being utilized to predict energy consumption. An extensive survey procedure is conducted and presented in this study, and relevant works are discussed. Different criteria are considered during the aggregation of the relevant studies relating to the work. The author’s perspective, future trends and various novel approaches are also presented as a part of the discussion. This article thereby lays a foundation stone for further research works to be undertaken for energy prediction.

ACS Style

Sunil Mohapatra; Sushruta Mishra; Hrudaya Tripathy; Akash Bhoi; Paolo Barsocchi. A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches. Energies 2021, 14, 3900 .

AMA Style

Sunil Mohapatra, Sushruta Mishra, Hrudaya Tripathy, Akash Bhoi, Paolo Barsocchi. A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches. Energies. 2021; 14 (13):3900.

Chicago/Turabian Style

Sunil Mohapatra; Sushruta Mishra; Hrudaya Tripathy; Akash Bhoi; Paolo Barsocchi. 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches." Energies 14, no. 13: 3900.

Review
Published: 09 May 2021 in Sustainability
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Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.

ACS Style

Lewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability 2021, 13, 5279 .

AMA Style

Lewlisa Saha, Hrudaya Tripathy, Soumya Nayak, Akash Bhoi, Paolo Barsocchi. Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review. Sustainability. 2021; 13 (9):5279.

Chicago/Turabian Style

Lewlisa Saha; Hrudaya Tripathy; Soumya Nayak; Akash Bhoi; Paolo Barsocchi. 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review." Sustainability 13, no. 9: 5279.

Journal article
Published: 23 January 2021 in Array
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The way people access services in indoor environments has dramatically changed in the last year. The countermeasures to the COVID-19 pandemic imposed a disruptive requirement, namely preserving social distance among people in indoor environments. We explore in this work the possibility of adopting the indoor localization technologies to measure the distance among users in indoor environments. We discuss how information about people’s contacts collected can be exploited during three stages: before, during, and after people access a service. We present a reference architecture for an Indoor Localization System (ILS), and we illustrate three representative use-cases. We derive some architectural requirements, and we discuss some issues that concretely cope with the real installation of an ILS in real-world settings. In particular, we explore the privacy and trust reputation of an ILS, the discovery phase, and the deployment of the ILS in real-world settings. We finally present an evaluation framework for assessing the performance of the architecture proposed.

ACS Style

Paolo Barsocchi; Antonello Calabrò; Antonino Crivello; Said Daoudagh; Francesco Furfari; Michele Girolami; Eda Marchetti. COVID-19 & privacy: Enhancing of indoor localization architectures towards effective social distancing. Array 2021, 9, 100051 .

AMA Style

Paolo Barsocchi, Antonello Calabrò, Antonino Crivello, Said Daoudagh, Francesco Furfari, Michele Girolami, Eda Marchetti. COVID-19 & privacy: Enhancing of indoor localization architectures towards effective social distancing. Array. 2021; 9 ():100051.

Chicago/Turabian Style

Paolo Barsocchi; Antonello Calabrò; Antonino Crivello; Said Daoudagh; Francesco Furfari; Michele Girolami; Eda Marchetti. 2021. "COVID-19 & privacy: Enhancing of indoor localization architectures towards effective social distancing." Array 9, no. : 100051.

Journal article
Published: 08 January 2021 in Applied Sciences
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Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson’s detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson’s Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson’s and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson’s disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%.

ACS Style

Moumita Pramanik; Ratika Pradhan; Parvati Nandy; Akash Kumar Bhoi; Paolo Barsocchi. Machine Learning Methods with Decision Forests for Parkinson’s Detection. Applied Sciences 2021, 11, 581 .

AMA Style

Moumita Pramanik, Ratika Pradhan, Parvati Nandy, Akash Kumar Bhoi, Paolo Barsocchi. Machine Learning Methods with Decision Forests for Parkinson’s Detection. Applied Sciences. 2021; 11 (2):581.

Chicago/Turabian Style

Moumita Pramanik; Ratika Pradhan; Parvati Nandy; Akash Kumar Bhoi; Paolo Barsocchi. 2021. "Machine Learning Methods with Decision Forests for Parkinson’s Detection." Applied Sciences 11, no. 2: 581.

Conference paper
Published: 01 December 2020 in GLOBECOM 2020 - 2020 IEEE Global Communications Conference
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The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key-metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

ACS Style

Paolo Barsocchi; Stefano Chessa; Luca Foschini; Dimitri Belli; Michele Girolami. Impact of Evolutionary Community Detection Algorithms for Edge Selection Strategies. GLOBECOM 2020 - 2020 IEEE Global Communications Conference 2020, 1 -6.

AMA Style

Paolo Barsocchi, Stefano Chessa, Luca Foschini, Dimitri Belli, Michele Girolami. Impact of Evolutionary Community Detection Algorithms for Edge Selection Strategies. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. 2020; ():1-6.

Chicago/Turabian Style

Paolo Barsocchi; Stefano Chessa; Luca Foschini; Dimitri Belli; Michele Girolami. 2020. "Impact of Evolutionary Community Detection Algorithms for Edge Selection Strategies." GLOBECOM 2020 - 2020 IEEE Global Communications Conference , no. : 1-6.

Journal article
Published: 10 November 2020 in IEEE Access
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IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

ACS Style

Francesco Potorti; SangJoon Park; Antonino Crivello; Filippo Palumbo; Michele Girolami; Paolo Barsocchi; Soyeon Lee; Joaquin Torres-Sospedra; Antonio Ramon Jimenez Ruiz; Antoni Perez-Navarro; German Martin Mendoza-Silva; Fernando Seco; Miguel Ortiz; Johan Perul; Valerie Renaudin; Hyunwoong Kang; Soyoung Park; Jae Hong Lee; Chan Gook Park; Jisu Ha; Jaeseung Han; Changjun Park; Keunhye Kim; Yonghyun Lee; Seunghun Gye; Keumryeol Lee; Eunjee Kim; Jeong-Sik Choi; Yang-Seok Choi; Shilpa Talwar; Seong Yun Cho; Boaz Ben-Moshe; Alex Scherbakov; Leonid Antsfeld; Emilio Sansano-Sansano; Boris Chidlovskii; Nikolai Kronenwett; Silvia Prophet; Yael Landay; Revital Marbel; Lingxiang Zheng; Ao Peng; Zhichao Lin; Bang Wu; Chengqi Ma; Stefan Poslad; David R. Selviah; Wei Wu; Zixiang Ma; Wenchao Zhang; Dongyan Wei; Hong Yuan; Jun-Bang Jiang; Shao-Yung Huang; Jing-Wen Liu; Kuan-Wu Su; Jenq-Shiou Leu; Kazuki Nishiguchi; Walid Bousselham; Hideaki Uchiyama; Diego Thomas; Atsushi Shimada; Rin-Ichiro Taniguchi; Vicente Cortes Puschel; Tomas Lungenstrass Poulsen; Imran Ashraf; Chanseok Lee; Muhammad Usman Ali; Yeongjun Im; Gunzung Kim; Jeongsook Eom; Soojung Hur; Yongwan Park; Miroslav Opiela; Adriano Moreira; Maria Joao Nicolau; Cristiano Pendao; Ivo Silva; Filipe Meneses; Antonio Costa; Jens Trogh; David Plets; Ying-Ren Chien; Tzu-Yu Chang; Shih-Hau Fang; Yu Tsao. The IPIN 2019 Indoor Localisation Competition—Description and Results. IEEE Access 2020, 8, 206674 -206718.

AMA Style

Francesco Potorti, SangJoon Park, Antonino Crivello, Filippo Palumbo, Michele Girolami, Paolo Barsocchi, Soyeon Lee, Joaquin Torres-Sospedra, Antonio Ramon Jimenez Ruiz, Antoni Perez-Navarro, German Martin Mendoza-Silva, Fernando Seco, Miguel Ortiz, Johan Perul, Valerie Renaudin, Hyunwoong Kang, Soyoung Park, Jae Hong Lee, Chan Gook Park, Jisu Ha, Jaeseung Han, Changjun Park, Keunhye Kim, Yonghyun Lee, Seunghun Gye, Keumryeol Lee, Eunjee Kim, Jeong-Sik Choi, Yang-Seok Choi, Shilpa Talwar, Seong Yun Cho, Boaz Ben-Moshe, Alex Scherbakov, Leonid Antsfeld, Emilio Sansano-Sansano, Boris Chidlovskii, Nikolai Kronenwett, Silvia Prophet, Yael Landay, Revital Marbel, Lingxiang Zheng, Ao Peng, Zhichao Lin, Bang Wu, Chengqi Ma, Stefan Poslad, David R. Selviah, Wei Wu, Zixiang Ma, Wenchao Zhang, Dongyan Wei, Hong Yuan, Jun-Bang Jiang, Shao-Yung Huang, Jing-Wen Liu, Kuan-Wu Su, Jenq-Shiou Leu, Kazuki Nishiguchi, Walid Bousselham, Hideaki Uchiyama, Diego Thomas, Atsushi Shimada, Rin-Ichiro Taniguchi, Vicente Cortes Puschel, Tomas Lungenstrass Poulsen, Imran Ashraf, Chanseok Lee, Muhammad Usman Ali, Yeongjun Im, Gunzung Kim, Jeongsook Eom, Soojung Hur, Yongwan Park, Miroslav Opiela, Adriano Moreira, Maria Joao Nicolau, Cristiano Pendao, Ivo Silva, Filipe Meneses, Antonio Costa, Jens Trogh, David Plets, Ying-Ren Chien, Tzu-Yu Chang, Shih-Hau Fang, Yu Tsao. The IPIN 2019 Indoor Localisation Competition—Description and Results. IEEE Access. 2020; 8 ():206674-206718.

Chicago/Turabian Style

Francesco Potorti; SangJoon Park; Antonino Crivello; Filippo Palumbo; Michele Girolami; Paolo Barsocchi; Soyeon Lee; Joaquin Torres-Sospedra; Antonio Ramon Jimenez Ruiz; Antoni Perez-Navarro; German Martin Mendoza-Silva; Fernando Seco; Miguel Ortiz; Johan Perul; Valerie Renaudin; Hyunwoong Kang; Soyoung Park; Jae Hong Lee; Chan Gook Park; Jisu Ha; Jaeseung Han; Changjun Park; Keunhye Kim; Yonghyun Lee; Seunghun Gye; Keumryeol Lee; Eunjee Kim; Jeong-Sik Choi; Yang-Seok Choi; Shilpa Talwar; Seong Yun Cho; Boaz Ben-Moshe; Alex Scherbakov; Leonid Antsfeld; Emilio Sansano-Sansano; Boris Chidlovskii; Nikolai Kronenwett; Silvia Prophet; Yael Landay; Revital Marbel; Lingxiang Zheng; Ao Peng; Zhichao Lin; Bang Wu; Chengqi Ma; Stefan Poslad; David R. Selviah; Wei Wu; Zixiang Ma; Wenchao Zhang; Dongyan Wei; Hong Yuan; Jun-Bang Jiang; Shao-Yung Huang; Jing-Wen Liu; Kuan-Wu Su; Jenq-Shiou Leu; Kazuki Nishiguchi; Walid Bousselham; Hideaki Uchiyama; Diego Thomas; Atsushi Shimada; Rin-Ichiro Taniguchi; Vicente Cortes Puschel; Tomas Lungenstrass Poulsen; Imran Ashraf; Chanseok Lee; Muhammad Usman Ali; Yeongjun Im; Gunzung Kim; Jeongsook Eom; Soojung Hur; Yongwan Park; Miroslav Opiela; Adriano Moreira; Maria Joao Nicolau; Cristiano Pendao; Ivo Silva; Filipe Meneses; Antonio Costa; Jens Trogh; David Plets; Ying-Ren Chien; Tzu-Yu Chang; Shih-Hau Fang; Yu Tsao. 2020. "The IPIN 2019 Indoor Localisation Competition—Description and Results." IEEE Access 8, no. : 206674-206718.

Conference paper
Published: 31 August 2020 in Communications in Computer and Information Science
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The availability of mobile devices has led to an arising development of indoor location services collecting a large amount of sensitive information. However, without accurate and verified management, such information could become severe back-doors for security and privacy issues. We propose in this paper a novel Location-Based Service (LBS) architecture in line with the GDPR’s provisions. For feasibility purposes and considering a representative use-case, a reference implementation, based on the popular Telegram app, is also presented.

ACS Style

Paolo Barsocchi; Antonello Calabrò; Antonino Crivello; Said Daoudagh; Francesco Furfari; Michele Girolami; Eda Marchetti. A Privacy-By-Design Architecture for Indoor Localization Systems. Communications in Computer and Information Science 2020, 358 -366.

AMA Style

Paolo Barsocchi, Antonello Calabrò, Antonino Crivello, Said Daoudagh, Francesco Furfari, Michele Girolami, Eda Marchetti. A Privacy-By-Design Architecture for Indoor Localization Systems. Communications in Computer and Information Science. 2020; ():358-366.

Chicago/Turabian Style

Paolo Barsocchi; Antonello Calabrò; Antonino Crivello; Said Daoudagh; Francesco Furfari; Michele Girolami; Eda Marchetti. 2020. "A Privacy-By-Design Architecture for Indoor Localization Systems." Communications in Computer and Information Science , no. : 358-366.

Journal article
Published: 23 August 2020 in Electronics
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In this manuscript, an antenna on textile (jeans) substrate is presented for the WLAN, C band and X/Ku band. This is a wearable textile antenna, which was formed on jeans fabric substrate to reduce surface-wave losses. The proposed antenna design consists of a patch and a defected ground. To energize the wearable textile antenna, a microstrip line feed technique is used in the design. The impedance band width of 23.37% (3.4–4.3 GHz), 56.48% (4.7–8.4 GHz) and 31.14% (10.3–14.1 GHz) frequency bands are observed, respectively. The axial ratio bandwidth (ARBW) of 10.10% (4.7–5.2 GHz), 4.95% (5.9–6.2 GHz) and 10.44% (11.8–13.1 GHz) frequency bands are observed, respectively. A peak gain of 4.85 dBi is analyzed at 4.1-GHz frequency during the measurement. The SAR value was calculated to observe the radiation effect and it was found that its utmost SAR value is 1.8418 W/kg and 1.919 W/kg at 5.2/5.5-GHz frequencies, which is less than 2 W/kg of 10 gm tissue. The parametric study is performed for the validation of the proper functioning of the antenna.

ACS Style

Ashok Yadav; Vinod Kumar Singh; Pranay Yadav; Amit Kumar Beliya; Akash Kumar Bhoi; Paolo Barsocchi. Design of Circularly Polarized Triple-Band Wearable Textile Antenna with Safe Low SAR for Human Health. Electronics 2020, 9, 1366 .

AMA Style

Ashok Yadav, Vinod Kumar Singh, Pranay Yadav, Amit Kumar Beliya, Akash Kumar Bhoi, Paolo Barsocchi. Design of Circularly Polarized Triple-Band Wearable Textile Antenna with Safe Low SAR for Human Health. Electronics. 2020; 9 (9):1366.

Chicago/Turabian Style

Ashok Yadav; Vinod Kumar Singh; Pranay Yadav; Amit Kumar Beliya; Akash Kumar Bhoi; Paolo Barsocchi. 2020. "Design of Circularly Polarized Triple-Band Wearable Textile Antenna with Safe Low SAR for Human Health." Electronics 9, no. 9: 1366.

Journal article
Published: 20 July 2020 in Sensors
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Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diabetes is forecasted. EAGA model is further used with Multilayer Perceptron (MLP) to determine the presence or absence of type 2 diabetes in patients based on the symptoms detected. The proposed classification approach was named as Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP). It is also implemented on seven different disease datasets to assess its impact and effectiveness. Performance of the proposed model was validated against some vital performance metrics. The results show a maximum accuracy rate of 97.76% and 1.12 s of execution time. Furthermore, the proposed model presents an F-Score value of 86.8% and a precision of 80.2%. The method is compared with many existing studies and it was observed that the classification accuracy of the proposed Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP) model clearly outperformed all other previous classification models. Its performance was also tested with seven other disease datasets. The mean accuracy, precision, recall and f-score obtained was 94.7%, 91%, 89.8% and 90.4%, respectively. Thus, the proposed model can assist medical experts in accurately determining risk factors of type 2 diabetes and thereby help in accurately classifying the presence of type 2 diabetes in patients. Consequently, it can be used to support healthcare experts in the diagnosis of patients affected by diabetes.

ACS Style

Sushruta Mishra; Hrudaya Kumar Tripathy; Pradeep Kumar Mallick; Akash Kumar Bhoi; Paolo Barsocchi. EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors 2020, 20, 4036 .

AMA Style

Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Akash Kumar Bhoi, Paolo Barsocchi. EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors. 2020; 20 (14):4036.

Chicago/Turabian Style

Sushruta Mishra; Hrudaya Kumar Tripathy; Pradeep Kumar Mallick; Akash Kumar Bhoi; Paolo Barsocchi. 2020. "EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis." Sensors 20, no. 14: 4036.

Articles
Published: 17 February 2020 in International Journal of Architectural Heritage
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The recent developments of micro-electro-mechanical systems and wireless sensor networks allow today the use of low-cost and small-size sensors for continuous monitoring of civil structures. Both these features are very important for the low impact of the sensor grid in heritage structures, ensuring a low-cost and sustainable dynamic monitoring system. Over the last 20 years the use of sensor networks for continuous monitoring has received a growing interest. Anyway, still numerous questions remain opened about the sensitivity of measurement devices, the optimization of number and positioning of sensors, the energy efficiency of the network, and the development of algorithms for real-time data analysis. This paper, based on the aforementioned motivations, discusses about a monitoring system made of micro-electro-mechanical sensors connected through a wireless network. The architecture of the wireless sensor network and the automatized procedure proposed for the continuous processing of the recorded signals are discussed and described with reference to an explicative masonry tower case study. It is believed that the proposed technologies can provide an economical and relatively non-invasive tool for real-time structural monitoring and that, moreover, the availability of large amounts of data from actual measurements can give effective information on the structural behaviour of historic constructions.

ACS Style

Paolo Barsocchi; Gianni Bartoli; Michele Betti; Maria Girardi; Stefano Mammolito; Daniele Pellegrini; Giacomo Zini. Wireless Sensor Networks for Continuous Structural Health Monitoring of Historic Masonry Towers. International Journal of Architectural Heritage 2020, 15, 22 -44.

AMA Style

Paolo Barsocchi, Gianni Bartoli, Michele Betti, Maria Girardi, Stefano Mammolito, Daniele Pellegrini, Giacomo Zini. Wireless Sensor Networks for Continuous Structural Health Monitoring of Historic Masonry Towers. International Journal of Architectural Heritage. 2020; 15 (1):22-44.

Chicago/Turabian Style

Paolo Barsocchi; Gianni Bartoli; Michele Betti; Maria Girardi; Stefano Mammolito; Daniele Pellegrini; Giacomo Zini. 2020. "Wireless Sensor Networks for Continuous Structural Health Monitoring of Historic Masonry Towers." International Journal of Architectural Heritage 15, no. 1: 22-44.

Journal article
Published: 15 November 2019 in Sensors
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A cruise ship is a concentrate of technologies aimed at providing passengers with the best leisure experience. As tourism in the cruise sector increases, ship owners turned their attention towards novel Internet of things solutions able, from one hand, to provide passengers with personalized and comfortable new services and, from the other hand, to enable energy saving behaviors and a smart management of the vessel equipment. This paper introduces the E-Cabin system, a software architecture that leverages sensor networks and reasoning techniques and allows a customized cabin indoor comfort. The E-Cabin architecture is scalable and easily extendible; sensor networks can be added or removed, rules can be added to/changed in the reasoner software, and new services can be supported based on the analysis of the collected data, without altering the system architecture. The system also allows the ship manager to monitor each cabin status though a simple and intuitive dashboard, thus providing useful insights enabling a smart scheduling of maintenance activities, energy saving, and security issues detection. This work delves into the E-Cabin’s system architecture and provides some usability tests to measure the dashboard’s efficacy.

ACS Style

Paolo Barsocchi; Erina Ferro; Davide La Rosa; Atieh Mahroo; Daniele Spoladore. E-Cabin: A Software Architecture for Passenger Comfort and Cruise Ship Management. Sensors 2019, 19, 4978 .

AMA Style

Paolo Barsocchi, Erina Ferro, Davide La Rosa, Atieh Mahroo, Daniele Spoladore. E-Cabin: A Software Architecture for Passenger Comfort and Cruise Ship Management. Sensors. 2019; 19 (22):4978.

Chicago/Turabian Style

Paolo Barsocchi; Erina Ferro; Davide La Rosa; Atieh Mahroo; Daniele Spoladore. 2019. "E-Cabin: A Software Architecture for Passenger Comfort and Cruise Ship Management." Sensors 19, no. 22: 4978.

Journal article
Published: 01 September 2019 in Array
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The impulse towards a larger introduction of Information and Communication Technology (ICT) in the agricultural field is currently experiencing its momentum, as digitisation has large potentialities to provide benefits for both producers and consumers; on the other hand, pushing technological solutions into a rural context encounters several challenges. In this work, we provide a survey of the most recent research activities, in the form of both research projects and scientific literature, with the objective of showing the already achieved results, the current investigations, and the still open challenges, both technical and non technical. We mainly focus on the EU territory, identifying threats and concerns, and then looking at existing and upcoming solutions to overcome those barriers.

ACS Style

Manlio Bacco; Paolo Barsocchi; Erina Ferro; Alberto Gotta; Massimiliano Ruggeri. The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming. Array 2019, 3-4, 100009 .

AMA Style

Manlio Bacco, Paolo Barsocchi, Erina Ferro, Alberto Gotta, Massimiliano Ruggeri. The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming. Array. 2019; 3-4 ():100009.

Chicago/Turabian Style

Manlio Bacco; Paolo Barsocchi; Erina Ferro; Alberto Gotta; Massimiliano Ruggeri. 2019. "The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming." Array 3-4, no. : 100009.

Journal article
Published: 23 July 2019 in Sensors
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This paper introduces technical solutions devised to support the Deployment Site - Regione Emilia Romagna (DS-RER) of the ACTIVAGE project. The ACTIVAGE project aims at promoting IoT (Internet of Things)-based solutions for Active and Healthy ageing. DS-RER focuses on improving continuity of care for older adults (65+) suffering from aftereffects of a stroke event. A Wireless Sensor Kit based on Wi-Fi connectivity was suitably engineered and realized to monitor behavioral aspects, possibly relevant to health and wellbeing assessment. This includes bed/rests patterns, toilet usage, room presence and many others. Besides hardware design and validation, cloud-based analytics services are introduced, suitable for automatic extraction of relevant information (trends and anomalies) from raw sensor data streams. The approach is general and applicable to a wider range of use cases; however, for readability's sake, two simple cases are analyzed, related to bed and toilet usage patterns. In particular, a regression framework is introduced, suitable for detecting trends (long and short-term) and labeling anomalies. A methodology for assessing multi-modal daily behavioral profiles is introduced, based on unsupervised clustering techniques. The proposed framework has been successfully deployed at several real-users' homes, allowing for its functional validation. Clinical effectiveness will be assessed instead through a Randomized Control Trial study, currently being carried out.

ACS Style

Niccolò Mora; Ferdinando Grossi; Dario Russo; Paolo Barsocchi; Rui Hu; Thomas Brunschwiler; Bruno Michel; Francesca Cocchi; Enrico Montanari; Stefano Nunziata; Guido Matrella; Paolo Ciampolini. IoT-Based Home Monitoring: Supporting Practitioners' Assessment by Behavioral Analysis. Sensors 2019, 19, 3238 .

AMA Style

Niccolò Mora, Ferdinando Grossi, Dario Russo, Paolo Barsocchi, Rui Hu, Thomas Brunschwiler, Bruno Michel, Francesca Cocchi, Enrico Montanari, Stefano Nunziata, Guido Matrella, Paolo Ciampolini. IoT-Based Home Monitoring: Supporting Practitioners' Assessment by Behavioral Analysis. Sensors. 2019; 19 (14):3238.

Chicago/Turabian Style

Niccolò Mora; Ferdinando Grossi; Dario Russo; Paolo Barsocchi; Rui Hu; Thomas Brunschwiler; Bruno Michel; Francesca Cocchi; Enrico Montanari; Stefano Nunziata; Guido Matrella; Paolo Ciampolini. 2019. "IoT-Based Home Monitoring: Supporting Practitioners' Assessment by Behavioral Analysis." Sensors 19, no. 14: 3238.

Journal article
Published: 17 December 2018 in Sensors
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Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.

ACS Style

Paolo Baronti; Paolo Barsocchi; Stefano Chessa; Fabio Mavilia; Filippo Palumbo. Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction. Sensors 2018, 18, 4462 .

AMA Style

Paolo Baronti, Paolo Barsocchi, Stefano Chessa, Fabio Mavilia, Filippo Palumbo. Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction. Sensors. 2018; 18 (12):4462.

Chicago/Turabian Style

Paolo Baronti; Paolo Barsocchi; Stefano Chessa; Fabio Mavilia; Filippo Palumbo. 2018. "Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction." Sensors 18, no. 12: 4462.

Conference paper
Published: 01 September 2018 in 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
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Indoor localisation systems have been studied in the literature for more than ten years and are starting to approach the market. The absence of standard evaluation methods is one of the obstacles to their adoption outside of customised environments. Specifically, the definition of benchmarking methodologies, common evaluation criteria, standardised methodologies useful to developers, testers, and end users is an open challenge. The need for common benchmarks has been tackled by some initiatives in recent years: EvAAL, EVARILOS, the Microsoft competition and the IPIN competition. The first formal attempt at defining a standard methodology to evaluate indoor localisation systems is the ISO/IEC 18305:2016 International Standard, which defines a complete framework for performing Test&Evaluation of localisation and tracking systems. This work is a first critical reading of the standard, intended to be a key contribution to the activities of the International Standards Committee of IPIN.

ACS Style

Francesco Potorti; Antonino Crivello; Paolo Barsocchi; Filippo Palumbo. Evaluation of Indoor Localisation Systems: Comments on the ISO/IEC 18305 Standard. 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2018, 1 -7.

AMA Style

Francesco Potorti, Antonino Crivello, Paolo Barsocchi, Filippo Palumbo. Evaluation of Indoor Localisation Systems: Comments on the ISO/IEC 18305 Standard. 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN). 2018; ():1-7.

Chicago/Turabian Style

Francesco Potorti; Antonino Crivello; Paolo Barsocchi; Filippo Palumbo. 2018. "Evaluation of Indoor Localisation Systems: Comments on the ISO/IEC 18305 Standard." 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) , no. : 1-7.

Chapter
Published: 26 August 2018 in Topics in Intelligent Engineering and Informatics
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Long-term sleep quality assessment is essential to diagnose sleep disorders and to continuously monitor the health status. However, traditional polysomnography techniques are not suitable for long-term monitoring, whereas, methods able to continuously monitor the sleep pattern in an unobtrusive way are needed. In this paper, we present a general purpose sleep monitoring system that can be used for the pressure ulcer risk assessment, to monitor bed exits, and to observe the influence of medication on the sleep behavior. Moreover, we compare several supervised learning algorithms in order to determine the most suitable in this context. Experimental results obtained by comparing the selected supervised algorithms show that we can accurately infer sleep duration, sleep positions, and routines with a completely unobtrusive approach.

ACS Style

Antonino Crivello; Filippo Palumbo; Paolo Barsocchi; Davide La Rosa; Franco Scarselli; Monica Bianchini. Understanding Human Sleep Behaviour by Machine Learning. Topics in Intelligent Engineering and Informatics 2018, 227 -252.

AMA Style

Antonino Crivello, Filippo Palumbo, Paolo Barsocchi, Davide La Rosa, Franco Scarselli, Monica Bianchini. Understanding Human Sleep Behaviour by Machine Learning. Topics in Intelligent Engineering and Informatics. 2018; ():227-252.

Chicago/Turabian Style

Antonino Crivello; Filippo Palumbo; Paolo Barsocchi; Davide La Rosa; Franco Scarselli; Monica Bianchini. 2018. "Understanding Human Sleep Behaviour by Machine Learning." Topics in Intelligent Engineering and Informatics , no. : 227-252.

Research article
Published: 19 June 2018 in IET Wireless Sensor Systems
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Device-free indoor localisation based on received signal strength (RSS) is unobtrusive and cheap. In a world where most environments are rich in ubiquitous small radio transmitters, it has the potential of being used in a ‘parasitic’ way, by exploiting the transmissions for localisation purposes without any need for additional hardware installation. Starting from state of the art, several steps are needed to reach this aim, the first of which are tackled in this study. The most promising algorithms from the literature are used to experiment in a real-world environment and with a rigorous measurement and analysis framework. Their positioning error performance is analysed versus number and position of devices. The original results obtained show that the currently available RSS-based device-free indoor localisation methods may be well suited to serve as a basis for providing a cheap localisation service in smart environments rich in Internet of things radio devices.

ACS Style

Francesco Potortì; Pietro Cassarà; Paolo Barsocchi. Device‐free indoor localisation with small numbers of anchors. IET Wireless Sensor Systems 2018, 8, 152 -161.

AMA Style

Francesco Potortì, Pietro Cassarà, Paolo Barsocchi. Device‐free indoor localisation with small numbers of anchors. IET Wireless Sensor Systems. 2018; 8 (4):152-161.

Chicago/Turabian Style

Francesco Potortì; Pietro Cassarà; Paolo Barsocchi. 2018. "Device‐free indoor localisation with small numbers of anchors." IET Wireless Sensor Systems 8, no. 4: 152-161.

Journal article
Published: 08 June 2018 in Sensors
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Smart Home has gained widespread attention due to its flexible integration into everyday life. Pervasive sensing technologies are used to recognize and track the activities that people perform during the day, and to allow communication and cooperation of physical objects. Usually, the available infrastructures and applications leveraging these smart environments have a critical impact on the overall cost of the Smart Home construction, require to be preferably installed during the home construction and are still not user-centric. In this paper, we propose a low cost, easy to install, user-friendly, dynamic and flexible infrastructure able to perform runtime resources management by decoupling the different levels of control rules. The basic idea relies on the usage of off-the-shelf sensors and technologies to guarantee the regular exchange of critical information, without the necessity from the user to develop accurate models for managing resources or regulating their access/usage. This allows us to simplify the continuous updating and improvement, to reduce the maintenance effort and to improve residents’ living and security. A first validation of the proposed infrastructure on a case study is also presented.

ACS Style

Paolo Barsocchi; Antonello Calabrò; Erina Ferro; Claudio Gennaro; Eda Marchetti; Claudio Vairo. Boosting a Low-Cost Smart Home Environment with Usage and Access Control Rules. Sensors 2018, 18, 1886 .

AMA Style

Paolo Barsocchi, Antonello Calabrò, Erina Ferro, Claudio Gennaro, Eda Marchetti, Claudio Vairo. Boosting a Low-Cost Smart Home Environment with Usage and Access Control Rules. Sensors. 2018; 18 (6):1886.

Chicago/Turabian Style

Paolo Barsocchi; Antonello Calabrò; Erina Ferro; Claudio Gennaro; Eda Marchetti; Claudio Vairo. 2018. "Boosting a Low-Cost Smart Home Environment with Usage and Access Control Rules." Sensors 18, no. 6: 1886.

Journal article
Published: 01 June 2018 in Ad Hoc Networks
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ACS Style

Francesco Potortì; Antonino Crivello; Michele Girolami; Paolo Barsocchi; Emilia Traficante. Localising crowds through Wi-Fi probes. Ad Hoc Networks 2018, 75-76, 87 -97.

AMA Style

Francesco Potortì, Antonino Crivello, Michele Girolami, Paolo Barsocchi, Emilia Traficante. Localising crowds through Wi-Fi probes. Ad Hoc Networks. 2018; 75-76 ():87-97.

Chicago/Turabian Style

Francesco Potortì; Antonino Crivello; Michele Girolami; Paolo Barsocchi; Emilia Traficante. 2018. "Localising crowds through Wi-Fi probes." Ad Hoc Networks 75-76, no. : 87-97.