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In this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models.
Nikolaos Bakalos; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Kassiani Papasotiriou; Matthaios Bimpas. Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 12618, 77 -86.
AMA StyleNikolaos Bakalos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Kassiani Papasotiriou, Matthaios Bimpas. Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; 12618 ():77-86.
Chicago/Turabian StyleNikolaos Bakalos; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Kassiani Papasotiriou; Matthaios Bimpas. 2021. "Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM." Transactions on Petri Nets and Other Models of Concurrency XV 12618, no. : 77-86.
The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.
Nikolaos Bakalos; Nikolaos Papadakis; Antonios Litke. Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data. Logistics 2020, 4, 1 .
AMA StyleNikolaos Bakalos, Nikolaos Papadakis, Antonios Litke. Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data. Logistics. 2020; 4 (2):1.
Chicago/Turabian StyleNikolaos Bakalos; Nikolaos Papadakis; Antonios Litke. 2020. "Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data." Logistics 4, no. 2: 1.
In this paper, we present WaterSpy, a project developing an innovative, compact, cost-effective photonic device for pervasive water quality sensing, operating in the mid-IR spectral range. The approach combines the use of advanced Quantum Cascade Lasers (QCLs) employing the Vernier effect, used as light source, with novel, fibre-coupled, fast and sensitive Higher Operation Temperature (HOT) photodetectors, used as sensors. These will be complemented by optimised laser driving and detector electronics, laser modulation and signal conditioning technologies. The paper presents the WaterSpy concept, the requirements elicited, the preliminary architecture design of the device, the use cases in which it will be validated, while highlighting the innovative technologies that contribute to the advancement of the current state of the art.
Nikolaos Doulamis; Athanasios Voulodimos; Anastasios Doulamis; Matthaios Bimpas; Aikaterini Angeli; Nikolaos Bakalos; Alessandro Giusti; Panayiotis Philimis; Antonio Varriale; Alessio Ausili; Sabato D’Auria; George Lampropoulos; Matthias Baer; Bernhard Schmauss; Stephan Freitag; Bernhard Lendl; Krzysztof Młynarczyk; Aleksandra Sosna-Głębska; Artur Trajnerowicz; Jarosław Pawluczyk; Mateusz Żbik; Jacek Kułakowski; Panagiotis Georgiadis; Stéphane Blaser; Nicola Bazzurro. WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis. Sensors 2018, 19, 33 .
AMA StyleNikolaos Doulamis, Athanasios Voulodimos, Anastasios Doulamis, Matthaios Bimpas, Aikaterini Angeli, Nikolaos Bakalos, Alessandro Giusti, Panayiotis Philimis, Antonio Varriale, Alessio Ausili, Sabato D’Auria, George Lampropoulos, Matthias Baer, Bernhard Schmauss, Stephan Freitag, Bernhard Lendl, Krzysztof Młynarczyk, Aleksandra Sosna-Głębska, Artur Trajnerowicz, Jarosław Pawluczyk, Mateusz Żbik, Jacek Kułakowski, Panagiotis Georgiadis, Stéphane Blaser, Nicola Bazzurro. WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis. Sensors. 2018; 19 (1):33.
Chicago/Turabian StyleNikolaos Doulamis; Athanasios Voulodimos; Anastasios Doulamis; Matthaios Bimpas; Aikaterini Angeli; Nikolaos Bakalos; Alessandro Giusti; Panayiotis Philimis; Antonio Varriale; Alessio Ausili; Sabato D’Auria; George Lampropoulos; Matthias Baer; Bernhard Schmauss; Stephan Freitag; Bernhard Lendl; Krzysztof Młynarczyk; Aleksandra Sosna-Głębska; Artur Trajnerowicz; Jarosław Pawluczyk; Mateusz Żbik; Jacek Kułakowski; Panagiotis Georgiadis; Stéphane Blaser; Nicola Bazzurro. 2018. "WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis." Sensors 19, no. 1: 33.