ASSESSMENT OF THE UTILITY OF THE ADVANCED HIMAWARI IMAGER TO DETECT ACTIVE FIRE OVER AUSTRALIA

Wildfire detection and attribution is an issue of importance due to the socio-economic impact of fires in Australia. Early detection of fires allows emergency response agencies to make informed decisions in order to minimise loss of life and protect strategic resources in threatened areas. Until recently, the ability of land management authorities to accurately assess fire through satellite observations of Australia was limited to those made by polar orbiting satellites. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard, in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia. The near real-time availability of images, at a ten minute frequency, may also provide contextual information (background temperature) leading to improvements in the assessment of fire characteristics. This paper investigates the application of the high frequency observation data supplied by this sensor for fire detection and attribution. As AHI-8 is a new sensor we have performed an analysis of the noise characteristics of the two spectral bands used for fire attribution across various land use types which occur in Australia. Using this information we have adapted existing algorithms, based upon least squares error minimisation and Kalman filtering, which utilise high frequency observations of surface temperature to detect and attribute fire. The fire detection and attribution information provided by these algorithms is then compared to existing satellite based fire products as well as in-situ information provided by land management agencies. These comparisons were made Australia-wide for an entire fire season - including many significant fire events (wildfires and prescribed burns). Preliminary detection results suggest that these methods for fire detection perform comparably to existing fire products and fire incident reporting from relevant fire authorities but with the advantage of being near-real time. Issues remain for detection due to cloud and smoke obscuration, along with validation of the attribution of fire characteristics using these algorithms.

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The Noise Characteristics Research in Subway Cars

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1051.431 ◽

2014 ◽

Vol 1051 ◽

pp. 431-434

Author(s):

Jian Hui Tian ◽

Lian Jian ◽

Bing Li

Keyword(s):

Real Time ◽

High Frequency ◽

Noise Spectrum ◽

Frequency Range ◽

Noise Characteristics ◽

Spectrum Distribution ◽

Spectrum Characteristics ◽

Relationship Of ◽

The Relationship

The noise of different points in four sections of Shenzhen subway was tested by this paper. Real-time SPL and noise spectrum distribution curves at bogie were given, while the equivalent SPL curve was also shown. The relationship of subway speed and noise SPL was investigated. The regular of noise spectrum characteristics and SPL was obtained. The results show that the noise SPL increases with the increase of the speed. Looking from the frequency range, the frequency corresponding to larger noise SPL amplitude mainly concentrates in the lower frequency while the subway stopping, the frequency corresponding to larger noise SPL amplitude mainly concentrates in high frequency while the subway running.

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Real-Time Update of Two H.F. (High Frequency) Channel Evaluation Models by Oblique Sounding

10.21236/ada137556 ◽

1984 ◽

Cited By ~ 1

Author(s):

D. R. Uffelman ◽

P. A. Hoover

Keyword(s):

Real Time ◽

High Frequency ◽

Frequency Channel ◽

Evaluation Models ◽

Time Update

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Design and implementation of decoupled periodic control scheme for a laboratory-based quadruple-tank process

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/09596518211022897 ◽

2021 ◽

pp. 095965182110228

Author(s):

Jatin K Pradhan ◽

Arun Ghosh

Keyword(s):

Real Time ◽

High Frequency ◽

Linear Time ◽

Disturbance Attenuation ◽

Minimum Phase ◽

Periodic Control ◽

Linear Time Invariant ◽

Control Scheme ◽

Gain Margin ◽

Time Invariant

It is well known that linear time-invariant controllers fail to provide desired robustness margins (e.g. gain margin, phase margin) for plants with non-minimum phase zeros. Attempts have been made in literature to alleviate this problem using high-frequency periodic controllers. But because of high frequency in nature, real-time implementation of these controllers is very challenging. In fact, no practical applications of such controllers for multivariable plants have been reported in literature till date. This article considers a laboratory-based, two-input–two-output, quadruple-tank process with a non-minimum phase zero for real-time implementation of the above periodic controller. To design the controller, first, a minimal pre-compensator is used to decouple the plant in open loop. Then the resulting single-input–single-output units are compensated using periodic controllers. It is shown through simulations and real-time experiments that owing to arbitrary loop-zero placement capability of periodic controllers, the above decoupled periodic control scheme provides much improved robustness against multi-channel output gain variations as compared to its linear time-invariant counterpart. It is also shown that in spite of this improved robustness, the nominal performances such as tracking and disturbance attenuation remain almost the same. A comparison with [Formula: see text]-linear time-invariant controllers is also carried out to show superiority of the proposed scheme.

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Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) ◽

10.1109/icmla51294.2020.00030 ◽

2020 ◽

Author(s):

William Thomson ◽

Neelanjan Bhowmik ◽

Toby P. Breckon

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Fire Detection ◽

Network Architectures ◽

Neural Network Architectures

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RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽

10.3390/s21123955 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3955

Author(s):

Jung-Cheng Yang ◽

Chun-Jung Lin ◽

Bing-Yuan You ◽

Yin-Long Yan ◽

Teng-Hu Cheng

Keyword(s):

Feedback Control ◽

Real Time ◽

High Frequency ◽

Indoor Environment ◽

Indoor Localization ◽

Sampling Rate ◽

Outdoor Environment ◽

Position Accuracy ◽

Graph Optimization ◽

Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

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Spectrum Aggregation Dual-Band Real-Time RF/Microwave Analog Signal Processing From Microstrip Line High-Frequency Hilbert Transformer

IEEE Transactions on Microwave Theory and Techniques ◽

10.1109/tmtt.2021.3085877 ◽

2021 ◽

pp. 1-1

Author(s):

Rakibul Islam ◽

Md Hedayatullah Maktoomi ◽

Han Ren ◽

Bayaner Arigong

Keyword(s):

Signal Processing ◽

Real Time ◽

High Frequency ◽

Microstrip Line ◽

Analog Signal Processing ◽

Analog Signal ◽

Dual Band ◽

Hilbert Transformer ◽

Spectrum Aggregation ◽

Rf Microwave

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Offline Algorithms in Low-Frequency Trading

Queue ◽

10.1145/3442632.3448307 ◽

2020 ◽

Vol 18 (6) ◽

pp. 37-51

Author(s):

Terence Kelly

Keyword(s):

Real Time ◽

Real World ◽

High Frequency ◽

Low Frequency ◽

Combinatorial Auctions ◽

Hard Problem ◽

Np Hard ◽

High Stakes ◽

Drill Bits ◽

Np Hard Problem

Expectations run high for software that makes real-world decisions, particularly when money hangs in the balance. This third episode of the Drill Bits column shows how well-designed software can effectively create wealth by optimizing gains from trade in combinatorial auctions. We'll unveil a deep connection between auctions and a classic textbook problem, we'll see that clearing an auction resembles a high-stakes mutant Tetris, we'll learn to stop worrying and love an NP-hard problem that's far from intractable in practice, and we'll contrast the deliberative business of combinatorial auctions with the near-real-time hustle of high-frequency trading. The example software that accompanies this installment of Drill Bits implements two algorithms that clear combinatorial auctions.

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Long-Term Observations of Beach Variability at Hasaki, Japan

Journal of Marine Science and Engineering ◽

10.3390/jmse8110871 ◽

2020 ◽

Vol 8 (11) ◽

pp. 871

Author(s):

Masayuki Banno ◽

Satoshi Nakamura ◽

Taichi Kosako ◽

Yasuyuki Nakagawa ◽

Shin-ichi Yanagishima ◽

...

Keyword(s):

Morphological Change ◽

High Frequency ◽

Climate Changes ◽

Wave Climate ◽

Time Interval ◽

Observation Data ◽

Beach Profile ◽

Water Level Variations ◽

Monthly Variations

Long-term beach observation data for several decades are essential to validate beach morphodynamic models that are used to predict coastal responses to sea-level rise and wave climate changes. At the Hasaki coast, Japan, the beach profile has been measured for 34 years at a daily to weekly time interval. This beach morphological dataset is one of the longest and most high-frequency measurements of the beach morphological change worldwide. The profile data, with more than 6800 records, reflect short- to long-term beach morphological change, showing coastal dune development, foreshore morphological change and longshore bar movement. We investigated the temporal beach variability from the decadal and monthly variations in elevation. Extremely high waves and tidal anomalies from an extratropical cyclone caused a significant change in the long-term bar behavior and foreshore slope. The berm and bar variability were also affected by seasonal wave and water level variations. The variabilities identified here from the long-term observations contribute to our understanding of various coastal phenomena.

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