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This article was motivated by the doctors' demand to make a technical support in pathologies of gastrointestinal tract research [10], which would be based on machine vision tools. Proposed solution should be less expensive alternative to already existing RF (radio frequency) methods. The objective of whole experiment was to evaluate the amount of animal motion dependent on degree of pathology (gastric ulcer). In the theoretical part of the article, several methods of animal trajectory tracking are presented: two differential methods based on background subtraction, the thresholding methods based on global and local threshold and the last method used for animal tracking was the color matching with a chosen template containing a searched spectrum of colors. The methods were tested offline on five video samples. Each sample contained situation with moving guinea pig locked in a cage under various lighting conditions., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

This paper presents a detailed investigation into the application of transient Fourier analysis in select electrical engineering contexts. Two novel approaches for addressing transient analysis are introduced. The first approach combines the Fourier series with the Laplace–Carson ( L - C ) transform in the complex domain, utilizing complex time vectors to streamline the computation of the original function. The inverse transformation back into the time domain is achieved using the Cauchy-Heaviside ( C - H ) method. The second approach applies the Fourier transform ( F - Τ ) for the transient analysis of a power converter circuit with both passive and active loads. The method of complex conjugate amplitudes is employed for steady-state analysis. Both contributions represent innovative approaches within this study. The process begins with Fourier series expansions and the computation of Fourier coefficients, followed by solving the system's steady-state and transient responses. The transient states are then confirmed using the Fourier transform. To validate these findings, the analytical results are verified through simulations conducted in the Matlab/Simulink R2023b environment. [ABSTRACT FROM AUTHOR]

Automatic detection of railroad infrastructure using Mobile Laser Scanning systems is a key technology for both advanced rail driver assistance and intelligent track maintenance. The recent research into railway facility extraction or condition monitoring usually relies on high-density point cloud dataset with known sensor parameters but ignores the processing performance in actual deployment and has high requirements for the acquisition device. To address these limitations, a novel rail extraction algorithm is proposed for processing Mobile Laser Scanning data in real time during acquisition, which could extract rail features by a hierarchical coarse-to-fine method with basic structural parameters. Using the geometric and global statistical characteristics of rail in raw data, a new rail descriptor is defined based on the quantitative statistics of points satisfying height difference in generalized local neighborhood. The approach is evaluated experimentally by a simulated real-time acquisition data and compared with a reference method. The experimental results show that the proposed algorithm has a finer extraction effect and good real-time performance. [ABSTRACT FROM AUTHOR]

Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, the rat can move only in the middle room. The rat's head pose is the first parameter needed. Secondly, the rodent could walk in all three compartments. The entry number in each area and visit duration are the other indicators used in the final evaluation. The second application is related to a neuroscience experiment. Besides the electroencephalographic (EEG) signals yielded by a radio frequency link from a headset mounted on a monkey, the head placement is a useful source of information for reliable analysis, as well as its orientation. Finally, a fusion method to construct the displacement of a panda bear in a cage and the corresponding motion analysis to recognize its stress states are shown. The arena is a zoological garden that imitates the native environment of a panda bear. This surrounding is monitored by means of four video cameras. We have applied the following stages: (a) panda detection for every video camera; (b) panda path construction from all routes; and (c) panda way filtering and analysis. [ABSTRACT FROM AUTHOR]

The paper deals with the issue of calibration of an analogue force sensor with a voltage output. For measurement, the force sensor uses a deformation member with a tensometric bridge and a measuring amplifier. This measuring chain must be used for force measurement, but the measurement uncertainty of this measuring chain is not known. Force sensors are planned for use in intelligent traumatological external fixation systems. [ABSTRACT FROM AUTHOR]

The article deals with the design of a PLC control system for controlling cascade tanks in the technological process of liquid mixing. The mixing process is carried out automatically according to information from individual sensors. The system also includes elements for manual control of the technological system. A human machine interface system was designed to operate the system. The simulation showed the correctness of the designed system. [ABSTRACT FROM AUTHOR]

The article deals with the design of a concept for a mobile robotic platform for transporting equipment or people. For disabled people, this system can represent an alternative to a wheelchair with better mobility in an urbanized environment. The proposed solution is unique in that it maintains a stabilized vertical position of the base. [ABSTRACT FROM AUTHOR]

Image recording is now ubiquitous in the fields of endangered-animal conservation and GIS. However, endangered animals are rarely seen, and, thus, only a few samples of images of them are available. In particular, the study of endangered-animal detection has a vital spatial component. We propose an adaptive, few-shot learning approach to endangered-animal detection through data augmentation by applying constraints on the mixture of foreground and background images based on species distributions. First, the pre-trained, salient network U2-Net segments the foregrounds and backgrounds of images of endangered animals. Then, the pre-trained image completion network CR-Fill is used to repair the incomplete environment. Furthermore, our approach identifies a foreground–background mixture of different images to produce multiple new image examples, using the relation network to permit a more realistic mixture of foreground and background images. It does not require further supervision, and it is easy to embed into existing networks, which learn to compensate for the uncertainties and nonstationarities of few-shot learning. Our experimental results are in excellent agreement with theoretical predictions by different evaluation metrics, and they unveil the future potential of video surveillance to address endangered-animal detection in studies of their behavior and conservation. [ABSTRACT FROM AUTHOR]

This research mainly focuses on the humanoid robot's recognition model for the multiple targets and complex color. The purpose of this research is to apply visual detection technology on a humanoid robot and propose a real time image extraction method to enhance the robot's visual application like a human. The multi-target objects and complex color experiments for various geometric shapes were conducted using humanoid robot NAO. The main procedure of the humanoid robot performing the operation of the multiple targets and complex color is as follows: firstly, the image is acquired by the vision system mounted on the camera of the humanoid robot; secondly, the pixel position of the object point is obtained through the image processing; furthermore, the outer shape position is set for the humanoid robot and then the improvement is performed to let it become more efficiencies. These experimental results demonstrate it can successfully identify the target objects and their outer contour, and also through the learning function in the Choregraphe platform, it can learn more complex definitions on the outer contour such as bottle and book and their recognition. The superiority of the proposed work is that it can be for various complex conditions. In the three-dimensional environment, the visual system of the NAO robot was firstly used to perceive its surrounding environment, and then the image processing technology was used to identify the chess position. The first general remark is that this work can be conceived as a good technological/engineering achievement. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS. [ABSTRACT FROM AUTHOR]

In this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal images requires smart algorithms for automatic processing of millions of thermal frames. Instance image segmentation allows to extract each animal from a frame and track its activity and thermal patterns. In this work, we adopted two instance segmentation algorithms, i.e., Mask R-CNN and TensorMask. Both methods in different configurations were applied to a set of thermal sequences, and both achieved high results. The best results were obtained for the TensorMask model, initially pre-trained on visible light images and finally trained on thermal images of rodents. The achieved mean average precision was above 90 percent, which proves that model pre-training on visible images can improve results of thermal image segmentation. [ABSTRACT FROM AUTHOR]

The paper deals with robot, which locomotes inside chimney for cleaning and inspection purposes. Carbon particles are in sediments on inner chimney wall. Chimney has to be periodically cleaned and inspected because of fire risk. Paper introduces concept of service robot for this application. Tracked locomotion has been selected as principle of locomotion inside chimney. [ABSTRACT FROM AUTHOR]

The paper deals with snake-like robots. There are several types of snake-like locomotions. Biological example - snake always select the best type of locomotion in accordance with terrain. Big manoeuvrability leads many teams to develop snake-like robots. These structures have many degrees of freedom and it is complicated to control them. Articulated bodies of these snake-like robots consist of segments. The paper deals with segment with one and two degree of freedom. [ABSTRACT FROM AUTHOR]

Copyright of Czecho-Slovak Pediatrics / Česko-Slovenská Pediatrie is the property of Czech Medical Association of JE Purkyne and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Primary ciliopathies are a group of disorders associated with abnormal formation and function of primary cilia. Many cilia-associated proteins found in primary cilia are also present in motile cilia. Such proteins are important for the ciliary base, such as the transition zone or basal bodies, and the intraflagellar transport. Their exact role in the respiratory motile cilia is unsettled. In this prospective clinical single-center study, we investigated the hypothesis that these proteins regulate the function of motile cilia. We addressed the issue by defining the motile cilia beat frequency in the respiratory tract of patients with primary ciliopathies accompanied by chronic kidney disease and comparing it in those without kidney involvement. Ciliary beat frequency in the nasal mucosa samples was evaluated by the ciliary analysis software LabVIEW. Both children and their parents with primary ciliopathies and kidney involvement had significantly lower median airway ciliary beat frequencies than those without kidney involvement who have normal ciliary motility. Further, the ciliary beat frequency is inversely associated with the serum creatinine level. These findings strongly suggest that kidney involvement in patients with primary ciliopathy may underlie the development of motile cilia dysfunction in the respiratory tract, potentially increasing respiratory morbidity., (© 2021. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Human–robot collaboration is becoming ever more widespread in industry because of its adaptability. Conventional safety elements are used when converting a workplace into a collaborative one, although new technologies are becoming more widespread. This work proposes a safe robotic workplace that can adapt its operation and speed depending on the surrounding stimuli. The benefit lies in its use of promising technologies that combine safety and collaboration. Using a depth camera operating on the passive stereo principle, safety zones are created around the robotic workplace, while objects moving around the workplace are identified, including their distance from the robotic system. Passive stereo employs two colour streams that enable distance computation based on pixel shift. The colour stream is also used in the human identification process. Human identification is achieved using the Histogram of Oriented Gradients, pre-learned precisely for this purpose. The workplace also features autonomous trolleys for material supply. Unequivocal trolley identification is achieved using a real-time location system through tags placed on each trolley. The robotic workplace's speed and the halting of its work depend on the positions of objects within safety zones. The entry of a trolley with an exception to a safety zone does not affect the workplace speed. This work simulates individual scenarios that may occur at a robotic workplace with an emphasis on compliance with safety measures. The novelty lies in the integration of a real-time location system into a vision-based safety system, which are not new technologies by themselves, but their interconnection to achieve exception handling in order to reduce downtimes in the collaborative robotic system is innovative. [ABSTRACT FROM AUTHOR]

The objective of this article is to propose data processing from laser range finder that will provide simple, fast, and reliable object recognition including moving objects. The whole method is based on four steps: segmentation, simplification, correspondence between consequent measurements, and object classification. Segmentation uses raw data from laser range finder and it is significant in logical connection of related segments. The most important step is simplification which provides data reduction and acceleration of object classification. The output of simplification is an object represented by significant points. Correspondence between consequent measurements is based on kd-tree nearest neighborhood search. The object is then classified by its spatial changes. These changes are evaluated by position of correspondent significant points. Input of proposed procedure is a probabilistic model of laser range finder. In this article, versatile probabilistic model of Hokuyo URM-30 LX was used. The method was verified by simulations and by tests in real environment. The results show that proposed method is reliable and with small modifications (of parameters), it is usable with any other planar laser range finder. [ABSTRACT FROM AUTHOR]