Graph convolutional networks (GCNs) as the emerging neural have shown great success in Prognostics and Health Management because they can not only extract node features but also mine relationship between nodes graph data. However, most existing GCNs-based methods are still limited by quality, variable working conditions, data, making them difficult to obtain remarkable performance. Therefore, it is proposed this paper a two stage importance-aware subgraph network based on multi-source sensors named I

This paper proposes a novel method to detect flames in video by processing the data generated an ordinary camera monitoring scene. In addition motion and color clues, flame flicker process is also detected using hidden Markov model. models representing colored moving objects are used distinguish from of objects. Spatial variations evaluated same models, as well. These clues combined reach final decision. False alarms due greatly reduced when compared existing based fire detection systems.

This paper proposes a novel method to detect smoke in video. It is assumed the camera monitoring scene stationary. The semi-transparent at early stages of fire. Therefore edges present image frames start loosing their sharpness and this leads decrease high frequency content image. background estimated energy monitored using spatial wavelet transforms current images. Edges produce local extrema domain these an important indicator viewing range camera. Moreover, becomes grayish when there...

In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. framework, it assumed that the compound algorithm consists of several subalgorithms, each which yields its own as a real number centered around zero, representing confidence level particular subalgorithm. Decision values are linearly combined with weights updated according to active method based on performing entropic projections onto...

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

We aim to apply deep learning achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. propose an innovative custom-designed Convolutional Neural Network (CNN) with a built-in set novel directional filters that highlight edges in X-ray images.

The paper proposes a novel method to detect fire and/or flame by processing the video data generated an ordinary camera monitoring scene. In addition motion and color clues, flicker are detected analyzing in wavelet domain. Periodic behavior boundaries is performing temporal transform. Color variations computing spatial transform of moving fire-colored regions. Other clues used detection algorithm include irregularity boundary region growth such regions time. All above combined reach final decision.

This paper describes an online learning based method to detect flames in video by processing the data generated ordinary camera monitoring a scene. Our fire detection consists of weak classifiers on temporal and spatial modeling flames. Markov models representing flame colored moving objects are used distinguish flicker process from motion objects. Boundary represented wavelet domain high frequency nature boundaries regions is also as clue model spatially. Results irregularity region updated...

Early detection of wildfire smoke in real-time is essentially important forest surveillance and monitoring systems. We propose a vision-based method to detect using Deep Convolutional Generative Adversarial Neural Networks (DC-GANs). Many existing supervised learning approaches convolutional neural networks require substantial amount labeled data. In order have robust representation sequences with without smoke, we two-stage training DCGAN. Our framework includes, the regular DCGAN real...

A study of supervised automated classification the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. parallel structured convolutional neural (CNN) with a pre-processing layer that takes X-ray images and age as input proposed.A total 1018 cephalometric radiographs were labelled classified according to CVM stages. The separated gender for better model-fitting. cropped extract automatically an object detector. resulting inputs used train proposed DL...

Abstract Introduction Obstructive sleep apnea (OSA) is associated with hypertension due to intermittent hypoxia and fragmentation. Due the complex pathogenesis of hypertension, it difficult predict incident OSA. A Machine Learning (ML) model identified up five years after diagnosis OSA by polysomnography developed. Methods Polysomnography provides time-series data on multiple physiological signals. We used heart health study (SHHS) cohort, where 4,797 participants had After excluding...

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 PUF in the electromagnetic domain by virtue self-dual absorber-emitter singularity that uniquely exists non-Hermitian parity-time (PT)-symmetric structures. At this singular point, reconfigurable emissive absorptive...

Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method classifying third molar using OPGs. Initially, our data consisted 3422 OPG images, each classified curated by expert evaluators. The dataset includes images both Q3 (lower jaw left side) Q4 right regions extracted panoramic resulting in a total 6624 analysis. Following...