Multisensor network system for wildfire detection using infrared image processing

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires.This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations.The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system.The systemperformance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA).The necessity of including decision fusion is thereby demonstrated.This work has been supported by Generalitat Valenciana under Grant PROMETEO 2010-040 and Spanish Administration and European Union FEDER Programme under Grant TEC2011-23403 01/01/2012.Bosch Roig, I.; Serrano Cartagena, A.; Vergara Domínguez, L. (2013). Multisensor network system for wildfire detection using infrared image processing. The Scientific World Journal. https://doi.org/10.1155/2013/402196SRauste, Y., Herland, E., Frelander, H., Soini, K., Kuoremaki, T., & Ruokari, A. (1997). Satellite-based forest fire detection for fire control in boreal forests. International Journal of Remote Sensing, 18(12), 2641-2656. doi:10.1080/014311697217512Giglio, L., Descloitres, J., Justice, C. O., & Kaufman, Y. J. (2003). An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sensing of Environment, 87(2-3), 273-282. doi:10.1016/s0034-4257(03)00184-6Carlotto, M. J. (1997). Detection and analysis of change in remotely sensed imagery with application to wide area surveillance. IEEE Transactions on Image Processing, 6(1), 189-202. doi:10.1109/83.552106Arrue, B. C., Ollero, A., & Matinez de Dios, J. R. (2000). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems, 15(3), 64-73. doi:10.1109/5254.846287Vicente, J., & Guillemant, P. (2002). An image processing technique for automatically detecting forest fire. International Journal of Thermal Sciences, 41(12), 1113-1120. doi:10.1016/s1290-0729(02)01397-2Briz, S. (2003). Reduction of false alarm rate in automatic forest fire infrared surveillance systems. Remote Sensing of Environment, 86(1), 19-29. doi:10.1016/s0034-4257(03)00064-6Martinez-de Dios, J. R., Arrue, B. C., Ollero, A., Merino, L., & Gómez-Rodríguez, F. (2008). Computer vision techniques for forest fire perception. Image and Vision Computing, 26(4), 550-562. doi:10.1016/j.imavis.2007.07.002Töreyin, B. U. (2007). Fire detection in infrared video using wavelet analysis. Optical Engineering, 46(6), 067204. doi:10.1117/1.2748752Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors, 9(11), 8722-8747. doi:10.3390/s91108722Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. Sensors, 11(6), 6165-6196. doi:10.3390/s110606165Ho, C.-C. (2009). Machine vision-based real-time early flame and smoke detection. Measurement Science and Technology, 20(4), 045502. doi:10.1088/0957-0233/20/4/045502Günay, O., Taşdemir, K., Uğur Töreyin, B., & Enis Çetin, A. (2009). Video based wildfire detection at night. Fire Safety Journal, 44(6), 860-868. doi:10.1016/j.firesaf.2009.04.003Pastor, E. (2003). Mathematical models and calculation systems for the study of wildland fire behaviour. Progress in Energy and Combustion Science, 29(2), 139-153. doi:10.1016/s0360-1285(03)00017-0Vergara, L., & Bernabeu, P. (2000). Automatic signal detection applied to fire control by infrared digital signal processing. Signal Processing, 80(4), 659-669. doi:10.1016/s0165-1684(99)00159-0Vergara, L., & Bernabeu, P. (2001). Simple approach to nonlinear prediction. Electronics Letters, 37(14), 926. doi:10.1049/el:20010616Bernabeu, P., Vergara, L., Bosh, I., & Igual, J. (2004). A prediction/detection scheme for automatic forest fire surveillance. Digital Signal Processing, 14(5), 481-507. doi:10.1016/j.dsp.2004.06.003Bosch, I., Gómez, S., & Vergara, L. (2011). A ground system for early forest fire detection based on infrared signal processing. International Journal of Remote Sensing, 32(17), 4857-4870. doi:10.1080/01431161.2010.49024

Improving detection of acoustic signals by means of a time and frequency multiple energy detectors

Standard energy detectors (ED) are optimum to detect unknown signals in presence of uncorrelated Gaussian noise. However, in real applications the signal duration and bandwidth are unpredictable and this fact can considerably degrade the detection performance if the appropriate observation vector length is not correctly selected. Therefore, a multiple energy detector (MED) structure is applied in the time as well as in the frequency domain and it is evaluated in real acoustic scenarios. The results obtained demonstrate the robustness of the MED structure and a performance improvement in comparison to the standard ED. © 2011 IEEE.Manuscript received March 01, 2011; revised May 26, 2011; accepted May 29, 2011. Date of publication June 07, 2011; date of current version June 17, 2011. This work was supported by the Spanish Administration and the FEDER Programme of the EC under Grant TEC 2008-02975, and by the Generalitat Valenciana under Grant PROMETEO/2010/040. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Saeed Sanei.Moragues Escrivá, J.; Serrano Cartagena, A.; Vergara Domínguez, L.; Gosálbez Castillo, J. (2011). Improving detection of acoustic signals by means of a time and frequency multiple energy detectors. IEEE Signal Processing Letters. 18(8):458-461. https://doi.org/10.1109/LSP.2011.2158644S45846118

Detection of acoustic events with application to environment monitoring

The goal of this work is to present different detection techniques and its feasibility for detecting unknown acoustic signals with general applicability to different noise conditions. These conditions replicate those commonly found in real-world acoustic scenarios where information about the noise and signal characteristics is frequently lacking. For this purpose, different extensions of the energy detector and even new structures for improving the robustness in detection are considered and explained. Furthermore, three different research lines of application are presented in which the energy detector and its extensions are used to improve the localization accuracy and the classification rates of acoustic sounds.Moragues Escrivá, J.; Serrano Cartagena, A.; Lara Martínez, G.; Gosálbez Castillo, J.; Vergara Domínguez, L. (2012). Detection of acoustic events with application to environment monitoring. Waves. 4:25-33. http://hdl.handle.net/10251/56161S2533

Clasificación y predicción basada en mezclas de analizadores de componentes independientes

[ES] El procesado estadístico de señal clásico se basa en explotar información de segundo orden. Desde la perspectiva de optimización (detección y estimación óptima), los estadísticos de segundo orden son estadísticos suficientes cuando se presenta Gaussianidad, pero conducen a soluciones sub-óptimas cuando se trata con modelos de densidad de probabilidad generales. En este trabajo se presentan diversas aplicaciones que hacen uso de una estructura de procesamiento no lineal que explota los estadísticos de orden superior. Las aplicaciones son: control de calidad mediante impacto-eco, segmentación y similaridad de objetos en procesamiento de imágenes y detección de estilos de aprendizajes en e-learning. Por último también se desarrolla un nuevo algoritmo de predicción basado en mezclas de analizadores de componentes independientes.[EN] Classical statistical signal processing is based on exploiting second-order statistics. Spectral analysis and adaptive lineal filtering are probably the most representative examples. From an optimization point of view (detection and optimal estimation), second-order statistics are enough when Gaussianity is present, but they are sub-optimal when dealing with general probability densities. In this work several applications that use a nonlinear processing machine for exploiting higher-order statistics are presented. The applications are: material quality control using impact-echo testing, segmentation and similarity in image processing, and detection of learning styles in e-learning. Also a new algorithm of prediction based on mixtures of independent component analyzers have been developedSerrano Cartagena, A. (2009). Clasificación y predicción basada en mezclas de analizadores de componentes independientes. http://hdl.handle.net/10251/11782Archivo delegad