Methodological Selection of Optimal Features for Object Classification Based on Stereovision System.

With the expansion of green energy, more and more data show that wind turbines can pose a significant threat to some endangered bird species. The birds of prey are more frequently exposed to collision risk with the wind turbine blades due to their unique flight path patterns. This paper shows how data from a stereovision system can be used for an efficient classification of detected objects. A method for distinguishing endangered birds from common birds and other flying objects has been developed and tested. The research focused on the selection of a suitable feature extraction methodology. Both motion and visual features are extracted from the Bioseco BPS system and retested using a correlation-based and a wrapper-type approach with genetic algorithms (GAs). With optimal features and fine-tuned classifiers, birds can be distinguished from aeroplanes with a 98.6% recall and 97% accuracy, whereas endangered birds are delimited from common ones with 93.5% recall and 77.2% accuracy.

Application of Radar Solutions for the Purpose of Bird Tracking Systems Based on Video Observation.

Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds' activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study.

A Runway Safety System Based on Vertically Oriented Stereovision.

In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup's flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system's high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty.

Comprehensive Bird Preservation at Wind Farms.

Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird's detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system's desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.

A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography.

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.

The influence of electromagnetic pollution on living organisms: historical trends and forecasting changes.

Current technologies have become a source of omnipresent electromagnetic pollution from generated electromagnetic fields and resulting electromagnetic radiation. In many cases this pollution is much stronger than any natural sources of electromagnetic fields or radiation. The harm caused by this pollution is still open to question since there is no clear and definitive evidence of its negative influence on humans. This is despite the fact that extremely low frequency electromagnetic fields were classified as potentially carcinogenic. For these reasons, in recent decades a significant growth can be observed in scientific research in order to understand the influence of electromagnetic radiation on living organisms. However, for this type of research the appropriate selection of relevant model organisms is of great importance. It should be noted here that the great majority of scientific research papers published in this field concerned various tests performed on mammals, practically neglecting lower organisms. In that context the objective of this paper is to systematise our knowledge in this area, in which the influence of electromagnetic radiation on lower organisms was investigated, including bacteria, E. coli and B. subtilis, nematode, Caenorhabditis elegans, land snail, Helix pomatia, common fruit fly, Drosophila melanogaster, and clawed frog, Xenopus laevis.

A system for heart sounds classification.

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases - one of the major causes of death around the globe - a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.

Wavelet-based denoising method for real phonocardiography signal recorded by mobile devices in noisy environment.

The main obstacle in development of intelligent autodiagnosis medical systems based on the analysis of phonocardiography (PCG) signals is noise. The noise can be caused by digestive and respiration sounds, movements or even signals from the surrounding environment and it is characterized by wide frequency and intensity spectrum. This spectrum overlaps the heart tones spectrum, which makes the problem of PCG signal filtrating complex. The most common method for filtering such signals are wavelet denoising algorithms. In previous studies, in order to determine the optimum wavelet denoising parameters the disturbances were simulated by Gaussian white noise. However, this paper shows that this noise has a variable character. Therefore, the purpose of this paper is adaptation of a wavelet denoising algorithm for the filtration of real PCG signal disturbances from signals recorded by a mobile devices in a noisy environment. The best results were obtained for Coif 5 wavelet at the 10th decomposition level with the use of a minimaxi threshold selection algorithm and mln rescaling function. The performance of the algorithm was tested on four pathological heart sounds: early systolic murmur, ejection click, late systolic murmur and pansystolic murmur.