Fire Early Warning Using Fire Sensors, Microcontroller and SMS Gateway

A fire disaster that does not save can certainly cause losses, both in the form of objects and casualties. This occurs for several reasons: late information obtained from the fire department or the owner's ignorance at the time of a fire. In this study, a fire early detection system was built using smoke, heat, and gas sensors based on an SMS gateway and an alarm. This system is used to provide information about fire detection as early as possible to protect against fire disasters. With this system, the potential and risk of fire can be reduced. This system is used to identify potential fires that occur in housing. Several experiments were carried out with fire simulations to get the reaction from the sensors used. Covers smoke testing, temperature testing, gas testing, and SMS message responses from various providers. This research produces a fire early warning system that provides SMS and alarm alerts

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

[EN] This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as "objects" to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein.Suarez-Paez, J.; Salcedo-Gonzalez, M.; Climente, A.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information. 10(12):1-19. https://doi.org/10.3390/info10120365S1191012Wang, L., Rodriguez, R. M., & Wang, Y.-M. (2018). A dynamic multi-attribute group emergency decision making method considering expertsr hesitation. International Journal of Computational Intelligence Systems, 11(1), 163. doi:10.2991/ijcis.11.1.13Esteve, M., Perez-Llopis, I., & Palau, C. E. (2013). Friendly Force Tracking COTS solution. IEEE Aerospace and Electronic Systems Magazine, 28(1), 14-21. doi:10.1109/maes.2013.6470440Senst, T., Eiselein, V., Kuhn, A., & Sikora, T. (2017). Crowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation. IEEE Transactions on Information Forensics and Security, 12(12), 2945-2956. doi:10.1109/tifs.2017.2725820Shi, Y., Tian, Y., Wang, Y., & Huang, T. (2017). Sequential Deep Trajectory Descriptor for Action Recognition With Three-Stream CNN. IEEE Transactions on Multimedia, 19(7), 1510-1520. doi:10.1109/tmm.2017.2666540Arunnehru, J., Chamundeeswari, G., & Bharathi, S. P. (2018). Human Action Recognition using 3D Convolutional Neural Networks with 3D Motion Cuboids in Surveillance Videos. Procedia Computer Science, 133, 471-477. doi:10.1016/j.procs.2018.07.059Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., & Feng, D. D. (2019). Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(9), 1806-1819. doi:10.1109/tsmc.2018.2850149Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2018). Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs. IEEE Transactions on Image Processing, 27(5), 2326-2339. doi:10.1109/tip.2018.2791180Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016). Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 142-158. doi:10.1109/tpami.2015.2437384Suarez-Paez, J., Salcedo-Gonzalez, M., Esteve, M., Gómez, J. A., Palau, C., & Pérez-Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems, 12(1), 123. doi:10.2991/ijcis.2018.25905186Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. doi:10.1109/tpami.2016.2577031Hao, S., Wang, P., & Hu, Y. (2019). Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction. Information, 10(2), 81. doi:10.3390/info10020081Peng, M., Wang, C., Chen, T., & Liu, G. (2016). NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification. Information, 7(4), 61. doi:10.3390/info7040061NVIDIA CUDA® Deep Neural Network library (cuDNN)https://developer.nvidia.com/cuda-downloadsWu, X., Lu, X., & Leung, H. (2018). A Video Based Fire Smoke Detection Using Robust AdaBoost. Sensors, 18(11), 3780. doi:10.3390/s18113780Park, J. H., Lee, S., Yun, S., Kim, H., & Kim, W.-T. (2019). Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework. Sensors, 19(9), 2025. doi:10.3390/s19092025García-Retuerta, D., Bartolomé, Á., Chamoso, P., & Corchado, J. M. (2019). Counter-Terrorism Video Analysis Using Hash-Based Algorithms. Algorithms, 12(5), 110. doi:10.3390/a12050110Zhao, B., Zhao, B., Tang, L., Han, Y., & Wang, W. (2018). Deep Spatial-Temporal Joint Feature Representation for Video Object Detection. Sensors, 18(3), 774. doi:10.3390/s18030774He, Z., & He, H. (2018). Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks. Symmetry, 10(9), 375. doi:10.3390/sym10090375Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., & Baik, S. W. (2018). Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption. IEEE Transactions on Industrial Informatics, 14(8), 3679-3689. doi:10.1109/tii.2018.2791944Barthélemy, J., Verstaevel, N., Forehead, H., & Perez, P. (2019). Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors, 19(9), 2048. doi:10.3390/s19092048Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., & Altowaijri, S. M. (2019). Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors, 19(9), 2206. doi:10.3390/s19092206Xu, S., Zou, S., Han, Y., & Qu, Y. (2018). Study on the Availability of 4T-APS as a Video Monitor and Radiation Detector in Nuclear Accidents. Sustainability, 10(7), 2172. doi:10.3390/su10072172Plageras, A. P., Psannis, K. E., Stergiou, C., Wang, H., & Gupta, B. B. (2018). 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DESIGN AND IMPLEMENTATION OF AN AUTONOMOUS VEHICLE FOR WASTE MATERIAL COLLECTION AND FIRE DETECTION

Autonomous vehicles are becoming increasingly popular in a variety of applications, including waste collection and fire detection. In this work, we present the design and implementation of an autonomous vehicle for these tasks in urban environments. The vehicle is equipped with sensors and control algorithms to navigate, detect and collect plastic bottle wastes, and detect fires in real-time. The system uses an off-the-shelf, small-sized, battery-operated vehicle, a simple conveyor belt, and a vision-based, computerized system. Machine learning (ML-) based vision tasks are implemented to direct the vehicle to waste locations and initiate the waste removal process. A fire detection and alarm system are also incorporated, using a camera and machine learning algorithms to detect flames automatically. The vehicle was tested in a simulated urban environment, and the results demonstrate its effectiveness in waste material collection and fire detection. The proposed system has the potential to improve the efficiency and safety of such tasks in urban areas

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Phase change materials for life science applications

Phase change materials (PCMs) are a class of thermo-responsive materials that can be utilized to trigger a phase transition which gives them thermal energy storage capacity. Any material with a high heat of fusion is referred to as a PCM that is able to provide cutting-edge thermal storage. PCMs are commercially used in many applications like textile industry, coating, and cold storage typically for heat control. These intriguing substances have recently been rediscovered and employed in a broad range of life science applications, including biological, human body, biomedical, pharmaceutical, food, and agricultural applications. Benefiting from the changes in physicochemical properties during the phase transition makes PCMs also functional for barcoding, detection, and storage. Paraffin wax and polyethylene glycol are the most commonly studied PCMs due to their low toxicity, biocompatibility, high thermal stability, high latent enthalpy, relatively wide transition temperature range, and ease of chemical modification. Current challenges in employing PCMs for life science applications include biosafety and/or engineering difficulties. The focus of this review article is on the life science applications, evaluation, and safety aspects of PCMs. Herein, the advances and the potential of employing PCMs as a versatile platform for various types of life science applications are highlighted.Peer reviewe

A Systematic Review on Social Robots in Public Spaces: Threat Landscape and Attack Surface

There is a growing interest in using social robots in public spaces for indoor and outdoor applications. The threat landscape is an important research area being investigated and debated by various stakeholders. Objectives: This study aims to identify and synthesize empirical research on the complete threat landscape of social robots in public spaces. Specifically, this paper identifies the potential threat actors, their motives for attacks, vulnerabilities, attack vectors, potential impacts of attacks, possible attack scenarios, and mitigations to these threats. Methods: This systematic literature review follows the guidelines by Kitchenham and Charters. The search was conducted in five digital databases, and 1469 studies were retrieved. This study analyzed 21 studies that satisfied the selection criteria. Results: Main findings reveal four threat categories: cybersecurity, social, physical, and public space. Conclusion: This study completely grasped the complexity of the transdisciplinary problem of social robot security and privacy while accommodating the diversity of stakeholders’ perspectives. Findings give researchers and other stakeholders a comprehensive view by highlighting current developments and new research directions in this field. This study also proposed a taxonomy for threat actors and the threat landscape of social robots in public spaces.publishedVersio

Advanced Topics in Systems Safety and Security

This book presents valuable research results in the challenging field of systems (cyber)security. It is a reprint of the Information (MDPI, Basel) - Special Issue (SI) on Advanced Topics in Systems Safety and Security. The competitive review process of MDPI journals guarantees the quality of the presented concepts and results. The SI comprises high-quality papers focused on cutting-edge research topics in cybersecurity of computer networks and industrial control systems. The contributions presented in this book are mainly the extended versions of selected papers presented at the 7th and the 8th editions of the International Workshop on Systems Safety and Security—IWSSS. These two editions took place in Romania in 2019 and respectively in 2020. In addition to the selected papers from IWSSS, the special issue includes other valuable and relevant contributions. The papers included in this reprint discuss various subjects ranging from cyberattack or criminal activities detection, evaluation of the attacker skills, modeling of the cyber-attacks, and mobile application security evaluation. Given this diversity of topics and the scientific level of papers, we consider this book a valuable reference for researchers in the security and safety of systems