Method, system and device useful with conventional personal computers respond automatically to an identified alarm sound by sending a notification signal via the Internet only when a special sound monitoring program is active. Additionally, bedside detection of acoustic alarms is combined with enhanced waking devices to insure the waking of a child or hearing impaired person in response to an emergency. Home safety and security are provided using a bedside unit to monitor audible safety and security alarms and send notification signals to the appropriate communication site. A health monitoring method and system utilizes the bedside device to monitor breathing patterns and other health measuring signals and communicate these patterns and signals to a medical monitoring station.

Enhanced fire, safety, security and health monitoring and alarm response method, system and device

A fast accumulative motion orientation model based on integral image for video smoke detection

http://yoksis.bilkent.edu.tr/pdf/files/10.1016-j.dsp.2013.07.003.pdf

Video fire detection - Review

Smoke detection in video using wavelets and support vector machines

Video-based smoke detection with histogram sequence of LBP and LBPV pyramids

Advanced fire detection using multi-signature alarm algorithms *

The Navy program, Damage Control Automation for Reduced Manning (DC-ARM), is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and, more importantly, increase the reliability of the detection system through improved nuisance alarm immunity. Improved reliability is needed such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria-based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to nuisance alarm sources. A multi-signature early warning fire detection system is being developed to provide reliable warning of actual fire conditions in less time with fewer nuisance alarms than can be achieved with commercially available smoke detection systems. In this study, a large database consisting of the responses of 20 sensors to several different types of fires and nuisance sources was generated and analyzed using a variety of multivariate methods. Three data matrices were developed at discrete times corresponding to the different alarm levels of a conventional photoelectric smoke detector. The alarm times represent 0.82%, 1.63% and 11% obscurations per meter. The datasets were organized into three classes representing the sensor responses for baseline (nonfire), fires and nuisance sources. A robust data analysis strategy for use with a sensor array consisting of four to five sensors for early fire detection and nuisance source rejection was developed using a probabilistic neural network (PNN) that was developed at the Naval Research Laboratory for chemical sensor arrays. The analysis algorithms described in this paper evaluate discrete samples and develop classification models that examine individual chemical signatures at discrete points.

Multi-criteria fire detection systems using a probabilistic neural network

The development and mitigation of backdraft: a real-scale shipboard study

A multi-criteria event detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of an event and providing an output indicating the same A processor for receiving each output of the plurality of sensors is also employed The processor includes a probabilistic neural network for processing the sensor outputs The probabilistic neural network comprises a nonlinear, nor-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter The plurality of data sets includes a baseline, non-event, first data set; a second, event data set; and a third, nuisance data set The algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discrimination between said data sets, and whether the outputs suffice to substantially indicate the presence of an event, as opposed to a non-event or nuisance situation

Probabilistic neural network for multi-criteria event detector

The Navy program, Damage Control-Automation for Reduced Manning is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and,more importantly increase the reliability of the detection system through improved nuisance alarm immunity. Improved reliability is needed, such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires,and reduced susceptibility to nuisance alarm sources. A multi-criteria early warning fire detection system, has been developed to provide reliable warning of actual fire conditions, in less time, with fewer nuisance alarms,than can be achieved with commercially available smoke detection systems. In this study a four-sensor array and a Probabilistic Neural Network have been used to produce an early warning fire detection system. A prototype early warning fire detector was built and tested in a shipboard environment. The current alarm algorithm resulted in better overall performance than the commercial smoke detectors, by providing both improved nuisance source immunity with generally equivalent or faster response times.

Daniel T. Gottuk

Papers

Advanced fire detection using multi-signature alarm algorithms *

Multi-criteria fire detection systems using a probabilistic neural network

The development and mitigation of backdraft: a real-scale shipboard study

Probabilistic neural network for multi-criteria event detector

Early Warning Fire Detection System Using a Probabilistic Neural Network