scholarly journals TECHNICAL MEANS OF AIRINTELLIGENCE TO ENSURE THE PHYSICAL SECURITY OF INFORMATION ACTIVITIES

2021 ◽  
Vol 12 (4) ◽  
pp. 143-150
Author(s):  
Artem Platonenko ◽  
Volodymyr Sokolov ◽  
Pavlo Skladannyi ◽  
Heorhii Oleksiienko
Keyword(s):  
Thermal Imaging ◽  
Critical Infrastructure ◽  
Weather Conditions ◽  
Unmanned Vehicles ◽  
License Plate ◽  
Thermal Imager ◽  
Camera Model ◽  
Thermal Imagers ◽  
Private And Public

This article is devoted to highlighting the real practical capabilities of UAV thermal imaging cameras, which allow you to effectively and safely identify potentially dangerous objects that may threaten the object of information activities, or the safety of citizens or critical infrastructure of Ukraine. Based on many years of flight experience and training of specialists for private and public institutions, it was decided to compare the quality characteristics and capabilities of detection, recognition and identification of objects using modern unmanned vehicles. To ensure public safety and control of the territory, there are models with multiple optical zoom, which from a distance of 500 m allow to recognize the license plate of the car, or versions with thermal imager, which in night can help see the car, the temperature difference against other cars, and the fact that a person comes out of it. Test flights were performed at altitudes from 15 to 100 m, in the open, without the presence of bushes, trees or obstacles. Depending on the camera model and weather conditions, the figures obtained may differ significantly. The main advantages and differences in the quality of thermal imaging cameras for UAVs are described. The quality of the obtained image is demonstrated on real examples and under the same conditions. A number of requirements have been developed for shooting a quadcopter with thermal imagers of objects such as a car and a person from different heights, according to Johnson's criteria, and a work plan has been developed for further research to prepare and provide effective recommendations for pilots using this technique territories of objects of information activity and during performance of service in air reconnaissance units of law enforcement agencies of Ukraine.

scholarly journals Thermal Imager Range: Predictions, Expectations, and Reality

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3313 ◽  
Author(s):  
Dragana Perić ◽  
Branko Livada ◽  
Miroslav Perić ◽  
Saša Vujić
Keyword(s):  
Long Range ◽  
Imaging System ◽  
Weather Conditions ◽  
Theoretical Models ◽  
Field Conditions ◽  
Design Parameters ◽  
Surveillance Systems ◽  
Thermal Imager ◽  
Adverse Weather ◽  
Thermal Imagers

Imaging system range defines the maximal distance at which a selected object can be seen and perceived following surveillance task perception criteria. Thermal imagers play a key role in long-range surveillance systems due to the ability to form images during the day or night and in adverse weather conditions. The thermal imager range depends on imager design parameters, scene and transmission path properties. Imager range prediction is supported by theoretical models that provide the ability to check range performance, compare range performances for different systems, extend range prediction in field conditions, and support laboratory measurements related to range. A condensed review of the theoretical model’s genesis and capabilities is presented. We applied model-based performance calculation for several thermal imagers used in our long-range surveillance systems and compared the results with laboratory performance measurement results with the intention of providing the range prediction in selected field conditions. The key objective of the paper is to provide users with reliable data regarding expectations during a field mission.

Estimation of Effect of Emissivity on Target Detection through Thermal Imaging Systems

2017 ◽  
Vol 67 (2) ◽  
pp. 177 ◽  
Author(s):  
Amit Kumar Sharma ◽  
Sanjay Kumar Sharma ◽  
Prashant Vasistha ◽  
Jagdish Prasad Mangalhara
Keyword(s):  
Thermal Imaging ◽  
Background Radiation ◽  
Thermodynamic Temperature ◽  
Night Vision ◽  
Thermal Imager ◽  
Thermal Contrast ◽  
Detection Range ◽  
Thermal Imagers ◽  
Long Wave Infrared ◽  
Ir Bands

<p>The effects of target emissivity on apparent thermal contrast as well as on detection range capabilities of thermal imagers in long wave infrared and middle wave infrared bands were evaluated. The apparent thermal contrast (to be seen by the thermal imager at standoff distance), considering only the emission from target and background, was first computed in both the IR bands in terms of target emissivity and secondly the apparent thermal contrast, considering the background radiation reflected off the target, was also computed. A graphical user interface simulation in MATLAB was prepared for the estimation of total apparent thermal contrast taking into account both the emission and reflection. This total apparent thermal contrast was finally used in night vision thermal and image processing model for predicting the detection range performance of thermal imagers. Results of the analysis show that the effect of target emissivity on thermal contrast estimates is more pronounced in LWIR. The lower thermodynamic temperature difference between target and background at lower values of target emissivity leads to negative thermal contrast which in-turn leads to higher detection ranges.</p>

scholarly journals Thermal imaging as a viable tool for monitoring plant stress

OENO One ◽  
2007 ◽  
Vol 41 (2) ◽  
pp. 77 ◽  
Author(s):  
Manfred Stoll ◽  
Hamlyn G. Jones
Keyword(s):  
Infrared Thermography ◽  
Thermal Imaging ◽  
Plant Stress ◽  
Leaf Temperature ◽  
Thermal Imager ◽  
First Line ◽  
Frequency Distributions ◽  
Long Wave ◽  
Thermal Data ◽  
Thermal Imagers

<p style="text-align: justify;"><strong>Aims</strong>: The objective of this paper was to describe an approach to the use of thermal data for shaded leaves rather than areas fully exposed to the sun. Secondly to make use of infrared thermography as a powerful tool to measure effects of solar radiation on berry temperature.</p><p style="text-align: justify;"><strong>Methods and results</strong>: Thermal images were obtained with a long-wave thermal imager. There is often less variability within an image for a shaded portion of the canopy than for a sunlit canopy. The temperature frequency distributions of sunlit leaves displayed a far wider range of temperature variation compared to shaded leaves.</p><p style="text-align: justify;"><strong>Conclusion</strong>: With thermal imagers it is feasible to select precisely the leaves for investigation. The remote sensing approach using infrared thermography combined with techniques available for image analysis open up a number of opportunities for comparative studies such as screening activities.</p><p style="text-align: justify;"><strong>Significance and impact of study</strong>: Infrared thermography can be implemented as a first line of detection to determine the onset of plant stress due to changes in stomatal aperture. This approach can give reliable and sensitive indications of leaf temperature and hence to calculate stomatal conductance.</p>

Water quality of Tampa Bay, Florida : wet-weather conditions, October 1971

1975 ◽  
Author(s):  
Carl R. Goodwin ◽  
Joseph S. Rosenshein ◽  
D.M. Michaelis
Keyword(s):  
Water Quality ◽  
Weather Conditions ◽  
Tampa Bay ◽  
Wet Weather

Urban Transport

2018 ◽  
Author(s):  
José van
Keyword(s):  
Urban Areas ◽  
Urban Transport ◽  
Transport Sector ◽  
Public And Private ◽  
Transport Services ◽  
Ride Sharing ◽  
The World ◽  
Private And Public ◽  
Selection Mechanisms

Platformization affects the entire urban transport sector, effectively blurring the division between private and public transport modalities; existing public–private arrangements have started to shift as a result. This chapter analyzes and discusses the emergence of a platform ecology for urban transport, focusing on two central public values: the quality of urban transport and the organization of labor and workers’ rights. Using the prism of platform mechanisms, it analyzes how the sector of urban transport is changing societal organization in various urban areas across the world. Datafication has allowed numerous new actors to offer their bike-, car-, or ride-sharing services online; selection mechanisms help match old and new complementors with passengers. Similarly, new connective platforms are emerging, most prominently transport network companies such as Uber and Lyft that offer public and private transport options, as well as new platforms offering integrated transport services, often referred to as “mobility as a service.”

Robust Environmental Sensing Using UAVs

2021 ◽  
Vol 2 (4) ◽  
pp. 1-20
Author(s):  
Ahmed Boubrima ◽  
Edward W. Knightly
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
High Performance ◽  
Weather Conditions ◽  
Ambient Air ◽  
Mission Planning ◽  
Pollution Monitoring ◽  
Compound Pollution ◽  
Planning Algorithm

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

scholarly journals Economic Analysis of a Transport Company in the Aspect of Car Vehicle Operation

2021 ◽  
Vol 13 (1) ◽  
pp. 427
Author(s):  
Magdalena Rykała ◽  
Łukasz Rykała
Keyword(s):  
Weather Conditions ◽  
Neural Model ◽  
Motor Vehicles ◽  
Bulk Materials ◽  
The Sustainable Development ◽  
Average Temperature ◽  
Transport Services ◽  
Vehicle Operation ◽  
The Impact

The article describes the issues of transport of bulk materials. The knowledge of this process has a key impact on the rational planning of transport tasks. It is necessary to have knowledge about the transport services market and the competition that exists in it. In order to achieve a competitive advantage on the market, enterprises should analyze data on the implementation of transport tasks on an ongoing basis. It is also important that the costs incurred from the conducted activity are minimized, while increasing the quality of services and taking into account the sustainable development of the enterprise. The study analyzes data from a few selected motor vehicles in the period of 3 years of operation, coming from an enterprise specializing in the transport of bulk materials. Moreover, a global sensitivity analysis was performed based on a neural model describing the impact of the analyzed factors on the company’s profit. The results show that the most important factors influencing the company’s profit are the fuel consumption of individual vehicles, the driver (driving style) and the month (average temperature, weather conditions).

scholarly journals Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3240
Author(s):  
Tehreem Syed ◽  
Vijay Kakani ◽  
Xuenan Cui ◽  
Hakil Kim
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Spiking Neural Networks ◽  
License Plate ◽  
Training Techniques ◽  
Neuromorphic Hardware ◽  
Private And Public ◽  
Embedded Platforms ◽  
Public Datasets ◽  
Event Based

In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training spiking models is still considered as a tedious task. Until recently, various ANN to SNN conversion methods in the literature have been proposed to train deep SNN models. Nevertheless, these methods require hundreds to thousands of time-steps for training and still cannot attain good SNN performance. This work proposes a customized model (VGG, ResNet) architecture to train deep convolutional spiking neural networks. In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks. Moreover, this work also proposes fewer time-steps for training SNNs with surrogate gradient descent. During the training with surrogate gradient descent backpropagation, overfitting problems have been encountered. To overcome these problems, this work refines the SNN based dropout technique with surrogate gradient descent. The proposed customized SNN models achieve good classification results on both private and public datasets. In this work, several experiments have been carried out on an embedded platform (NVIDIA JETSON TX2 board), where the deployment of customized SNN models has been extensively conducted. Performance validations have been carried out in terms of processing time and inference accuracy between PC and embedded platforms, showing that the proposed customized models and training techniques are feasible for achieving a better performance on various datasets such as CIFAR-10, MNIST, SVHN, and private KITTI and Korean License plate dataset.

CLASSIFICATION OF HUMAN FACIAL AND AURAL TEMPERATURE USING NEURAL NETWORKS AND IR FEVER SCANNER: A RESPONSIBLE SECOND LOOK

2005 ◽  
Vol 05 (01) ◽  
pp. 165-190 ◽  
Author(s):  
E. Y. K. NG ◽  
COLIN CHONG ◽  
G. J. L. KAW
Keyword(s):  
Neural Network ◽  
Body Temperature ◽  
False Negative ◽  
The Body ◽  
Body Core Temperature ◽  
Thermal Imager ◽  
Facial Skin ◽  
Thermal Imagers ◽  
Cardinal Symptom ◽  
Elevated Body Temperature

Severe Acute Respiratory Syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect potential SARS infected subject with elevated body temperature plays an important role in preventing the spread of SARS. The use of infrared (IR) thermal imaging cameras has thus been proposed as a non-invasive, speedy, cost-effective and fairly accurate means for mass blind screening of potential SARS infected persons. Infrared thermography provides a digital image showing temperature patterns. This has been previously utilized in the detection of inflammation and nerve dysfunctions. It is believed that IR cameras may potentially be used to detect subjects with fever, the cardinal symptom of SARS and avian influenza. The accuracy of the infrared system can, however, be affected by human, environmental, and equipment variables. It is also limited by the fact that the thermal imager measures the skin temperature and not the body core temperature. Thus, the use of IR thermal systems at various checkpoints for mass screening of febrile persons is scientifically unjustified such as what is the false negative rate and most importantly not to create false sense of security. This paper aims to study the effectiveness of infrared systems for its application in mass blind screening to detect subjects with elevated body temperature. For this application, it is critical for thermal imagers to be able to identify febrile from normal subjects accurately. Minimizing the number of false positive and false negative cases improves the efficiency of the screening stations. False negative results should be avoided at all costs, as letting a SARS infected person through the screening process may result in potentially catastrophic results. Hitherto, there is lack of empirical data in correlating facial skin with body temperature. The current work evaluates the correlations (and classification) between the facial skin temperatures to the aural temperature using the artificial neural network approach to confirm the suitability of the thermal imagers for human temperature screening. We show that the Train Back Propagation and Kohonen self-organizing map (SOM) can form an opinion about the type of network that is better to complement thermogram technology in fever diagnosis to drive a better parameters for reducing the size of the neural network classifier while maintaining good classification accuracy.

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