Devising information technology for determining the redundant information content of a digital image

The information technology that implements evaluation of redundant information using the methods of preprocessing and segmentation of digital images has been devised. The metrics for estimating redundant information containing a photo image using the approach based on texture variability were proposed. Using the example of aerial photography data, practical testing and research into the proposed assessment were carried out. Digital images, formed by various optoelectronic facilities, are distorted under the influence of obstacles of various nature. These obstacles complicate both the visual analysis of images by a human and their automatic processing. A solution to the problem can be obtained through preprocessing, which will lead to an increase in the informativeness of digital image data at a general decrease in content. An experimental study of the dependence of image informativeness on the results of overlaying previous filters for processing digital images, depending on the values of parameters of methods, was carried out. It was established that the use of algorithms sliding window analysis can significantly increase the resolution of analysis in the time area while maintaining a fairly high ability in the frequency area. The introduced metrics can be used in problems of computer vision, machine and deep learning, in devising information technologies for image recognition. The prospect is the task of increasing the efficiency of processing the monitoring results by automating the processing of the received data in order to identify informative areas. This will reduce the time of visual data analysis. The introduced metrics can be used in the development of automated systems of air surveillance data recognition.

Exploring Integrated Environmental Viral Surveillance of Indoor Environments: A comparison of surface and bioaerosol environmental sampling in hospital rooms with COVID-19 patients

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has dramatically transformed policies and practices surrounding public health. One such shift is the expanded emphasis on environmental surveillance for pathogens. Environmental surveillance methods have primarily relied upon wastewater and indoor surface testing, and despite substantial evidence that SARS-CoV-2 commonly travels through space in aerosols, there has been limited indoor air surveillance. This study investigated the effectiveness of integrated surveillance including an active air sampler, surface swabs and passive settling plates to detect SARS-CoV-2 in hospital rooms with COVID-19 patients and compared detection efficacy among sampling methods. The AerosolSense active air sampler was found to detect SARS-CoV-2 in 53.8% of all samples collected compared to 12.1% detection by passive air sampling and 14.8% detection by surface swabs. Approximately 69% of sampled rooms (22/32) returned a positive environmental sample of any type. Among positive rooms, ~32% had only active air samples that returned positive, while ~27% and ~9% had only one or more surface swabs or passive settling plates that returned a positive respectively, and ~32% had more than one sample type that returned a positive result. This study demonstrates the potential for the AerosolSense to detect SARS-CoV-2 RNA in real-world healthcare environments and suggests that integrated sampling that includes active air sampling is an important addition to environmental pathogen surveillance in support of public health.

A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications

Voice communication is the main channel to exchange information between pilots and Air-Traffic Controllers (ATCos). Recently, several projects have explored the employment of speech recognition technology to automatically extract spoken key information such as call signs, commands, and values, which can be used to reduce ATCos’ workload and increase performance and safety in Air-Traffic Control (ATC)-related activities. Nevertheless, the collection of ATC speech data is very demanding, expensive, and limited to the intrinsic speakers’ characteristics. As a solution, this paper presents ATCO2, a project that aims to develop a unique platform to collect, organize, and pre-process ATC data collected from air space. Initially, the data are gathered directly through publicly accessible radio frequency channels with VHF receivers and LiveATC, which can be considered as an “unlimited-source” of low-quality data. The ATCO2 project explores employing context information such as radar and air surveillance data (collected with ADS-B and Mode S) from the OpenSky Network (OSN) to correlate call signs automatically extracted from voice communication with those available from ADS-B channels, to eventually increase the overall call sign detection rates. More specifically, the timestamp and location of the spoken command (issued by the ATCo by voice) are extracted, and a query is sent to the OSN server to retrieve the call sign tags in ICAO format for the airplanes corresponding to the given area. Then, a word sequence provided by an automatic speech recognition system is fed into a Natural Language Processing (NLP) based module together with the set of call signs available from the ADS-B channels. The NLP module extracts the call sign, command, and command arguments from the spoken utterance.

Weighted Kernel Filter Based Anti-Air Object Tracking for Thermal Infrared Systems

Visual object tracking is an important component of surveillance systems and many high-performance methods have been developed. However, these tracking methods tend to be optimized for the Red/Green/Blue (RGB) domain and are thus not suitable for use with the infrared (IR) domain. To overcome this disadvantage, many researchers have constructed datasets for IR analysis, including those developed for The Thermal Infrared Visual Object Tracking (VOT-TIR) challenges. As a consequence, many state-of-the-art trackers for the IR domain have been proposed, but there remains a need for reliable IR-based trackers for anti-air surveillance systems, including the construction of a new IR dataset for this purpose. In this paper, we collect various anti-air thermal-wave IR (TIR) images from an electro-optical surveillance system to create a new dataset. We also present a framework based on an end-to-end convolutional neural network that learns object tracking in the IR domain for anti-air targets such as unmanned aerial vehicles (UAVs) and drones. More specifically, we adopt a Siamese network for feature extraction and three region proposal networks for the classification and regression branches. In the inference phase, the proposed network is formulated as a detection-by-tracking method, and kernel filters for the template branch that are continuously updated for every frame are introduced. The proposed network is able to learn robust structural information for the targets during offline training, and the kernel filters can robustly track the targets, demonstrating enhanced performance. Experimental results from the new IR dataset reveal that the proposed method achieves outstanding performance, with a real-time processing speed of 40 frames per second.

EVOLUTION OF THE AERIAL DEFENCE OF AIR BASES. CONCEPTS AND LESSONS LEARNED

The lessons learned during wars and armed conflicts indicate that the main factor influencing the aerial defence of air bases were directly related to the rapid development of the combat capabilities of aerial threats. Air bases have been lucrative targets for enemy air strikes since the first documented attack by a British aircraft on a German airfield in 1914 and have remained so for contemporary military air operations. The article discusses the evolution of concepts and lessons learned in the field of aerial defence of air bases that resulted from armed conflicts and local wars. The analysis includes armed conflicts, which, according to the author, have reflected the changes in the organisation of the aerial defence of air bases, including the repulsion of air strikes against aviation on the ground. Attention was paid to the conditions related to the aerial defence of aviation on the ground during the First World War. A more thorough analysis was made of the Second World War period, focused on the Western Front and the defence of Poland. Particular attention was paid to the Battle of Britain, noting the importance of the organisation of the radar air surveillance system in the context of the effectiveness of air defence. The focus of the analysis then shifts to the aerial defence of air bases during armed conflicts after the Second World War: the Vietnam War (1965-1973), the Yom Kippur War (1973), the defence of air bases in the Yom Kippur War (1973), and NATO operations from the air against air bases during the Deny Flight / Deliberate Force (1992-1995) and Allied Force (1999) operations. The article also makes a preliminary assessment of the aerial defence of air bases during the ongoing conflict in Syria.