In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fourier domain and discard similar filters using the cosine similarity measure in the frequency domain. We test the performance of the neural network with a variety of wildfire video clips and the pruned system performs as good as the regular network in daytime wild fire detection, and it also works well on some night wild fire video clips.

Physically unclonable functions (PUFs) are a class of hardware-specific security primitives based on secret keys extracted from integrated circuits, which can protect important information against cyberattacks and reverse engineering. Here, we put forward an emerging type of PUF in the electromagnetic domain by virtue of the self-dual absorber-emitter singularity that uniquely exists in the non-Hermitian parity-time ( PT )–symmetric structures. At this self-dual singular point, the reconfigurable emissive and absorptive properties with order-of-magnitude differences in scattered power can respond sensitively to admittance or phase perturbations caused by, for example, manufacturing imperfectness. Consequently, the entropy sourced from inevitable manufacturing variations can be amplified, yielding excellent PUF security metrics in terms of randomness and uniqueness. We show that this electromagnetic PUF can be robust against machine learning–assisted attacks based on the Fourier regression and generative adversarial network. Moreover, the proposed PUF concept is wavelength-scalable in radio frequency, terahertz, infrared, and optical systems, paving a promising avenue toward applications of cryptography and encryption.

In this article, we present a computationally efficient algorithm to detect Volatile Organic Compounds (VOC) leaking out of components used in chemical processes in petrochemical refineries and chemical plants. A leaking VOC plume from a damaged component appears as a dynamic dark cloud in infrared videos. We describe a two-stage deep neural network structure, taking advantage of both spatial and temporal structure of the dynamic texture regions created by the leaking VOC plume. We first detect moving pixels which are darker then their neighboring pixels. We extract one-dimensional (1-D) signals representing the temporal history of such pixels from video and feed the 1-D signals to a 1-D convolutional neural network. If those pixels are near the edge of a VOC plume, their 1-D temporal signals exhibit high-frequency behavior. The neural network generates high probability estimates for such pixels. If 1-D neural network generates high confidence values, final decision is reached using a deep convolutional neural network (CNN) which processes image frames. The overall structure is computationally efficient because the spatio-temporal CNN does not process all of the image frames of the captured video. Experimental results are presented.