Industrial and petrochemical facilities present unique challenges for fire protection and safety. Typical scenarios include detection of an unintended fire in a scene, wherein the scene also includes a flare stack in the background. Maintaining a high level of process and plant safety is a critical concern. In this paper, we present a failsafe industrial flame detector which has significant performance benefits compared to current flame detectors. The design involves use of microbolometer in the MWIR and LWIR spectrum and a dual band filter. This novel flame detector can help industrial facilities to meet their plant safety and critical infrastructure protection requirements while ensuring operational and business readiness at project start-up.
Iris-based biometric identification is increasingly used for facility access and other security applications. Like all methods that exploit visual information, however, iris systems are limited by the quality of captured images. Optical defocus due to a small depth of field (DOF) is one such challenge, as is the acquisition of sharply-focused iris images from subjects in motion. This manuscript describes the application of computational motion-deblurring cameras to the problem of moving iris capture, from the underlying theory to system considerations and performance data.