In this paper, we continue a topic of modeling measuring processes by perceiving them as a kind of signal sampling. And, in this respect, note that an ideal model was developed in a previous work. Whereas here, we present its nonideal version. This extended model takes into account an effect, which is called averaging of a measured signal. And, we show here that it is similar to smearing of signal samples arising in nonideal signal sampling. Furthermore, we demonstrate in this paper that signal averaging and signal smearing mean principally the same, under the conditions given. So, they can be modeled in the same way. A thorough analysis of errors related to the signal averaging in a measuring process is given and illustrated with equivalent schemes of the relationships derived. Furthermore, the results obtained are compared with the corresponding ones that were achieved analyzing amplitude quantization effects of sampled signals used in digital techniques. Also, we show here that modeling of errors related to signal averaging through the so-called quantization noise, assumed to be a uniform distributed random signal, is rather a bad choice. In this paper, an upper bound for the above error is derived. Moreover, conditions for occurrence of hidden aliasing effects in a measured signal are given.

There is a consensus in signal processing that the Gaussian kernel and its partial derivatives enable the development of robust algorithms for feature detection. Fourier analysis and convolution theory have a central role in such development. In this paper, we collect theoretical elements to follow this avenue but using the q-Gaussian kernel that is a nonextensive generalization of the Gaussian one. Firstly, we review the one-dimensional q-Gaussian and its Fourier transform. Then, we consider the two-dimensional q-Gaussian and we highlight the issues behind its analytical Fourier transform computation. In the computational experiments, we analyze the q-Gaussian kernel in the space and Fourier domains using the concepts of space window, cut-o frequency, and the Heisenberg inequality.

Horns, teeth, claws, beaks… Given this mighty arsenal it’s a wonder there isn’t more physical conflict in the animal world, such as among birds.

This paper presents a new modification of the least-squares Prony’s method with reduced sampling, which allows for a significant reduction in the number of the analysed signal samples collected per unit time. The specific combination of non-uniform sampling with Prony’s method enables sampling of the analysed signals at virtually any average frequency, regardless of the Nyquist frequency, maintaining high accuracy in parameter estimation of sinusoidal signal components. This property allows using the method in measuring devices, such as for electric power quality testing equipped with low power signal processors, which in turn contributes to reducing complexity of these devices. This paper presents research on a method for selecting a sampling frequency and an analysis window length for the presented method, which provide maximum estimation accuracy for Prony’s model component parameters. This paper presents simulation tests performed in terms of the proposed method application for analysis of harmonics and interharmonics in electric power signals. Furthermore, the paper provides sensitivity analysis of the method, in terms of common interferences occurring in the actual measurement systems.

In many physical experiments, linear frequency modulated (LFM) signals are widely used to probe objects in different environments, from outer-space to underwater. These signals allow a significant improvement in measurement resolution, even when the observation distance is great. For example, using LFM probe signals in underwater investigations enables discovery of even small objects covered by bottom sediments.

Recognition of LFM (chirp) signals depends on their compression based on matched filtering. This work presents two simple solutions to improve the resolution of the short chirp signals recognition. These methods are effective only if synchronization between the signal and matched filter (MF) is obtained. This work describes both the aforementioned methods and a method of minimizing the effects of the lack of synchronization.

The proposed matched filtering method, with the use of n parallel MFs and other techniques, allows only one sample to be obtained in the main lobe and to accurately locate its position in the appropriate sampling period Ts with accuracy Ts/n. These approaches are appropriate for use in probe signal processing.

Noise-like binary sequences combined with signals with linear frequency modulation might be successfully used to increase the reliability of the recognition of both probe and communication signals in the presence of natural and artificial interference. To identify such formed sequences the usage of the two-step matched filtering was suggested and the probabilistic model of the recognition of noise-like code sequences transferred by LFM signals was developed.

A computer measurement system, designed and built by authors, dedicated to location and description of partial discharges (PD) in oil power transformers examined by means of the acoustic emission (AE) method is presented. The measurement system is equipped with 8 measurement channels and ensures: monitoring of signals, registration of data in real time within a band of 25–1000 kHz in laboratory and real conditions, basic and advanced analysis of recorded signals. The basic analysis carried out in the time, frequency and time-frequency domains deals with general properties of the AE signals coming from PDs. The advanced analysis, performed in the discrimination threshold domain, results in identification of signals coming from different acoustic sources as well as location of these sources in the examined transformers in terms of defined by authors descriptors and maps of these descriptors on the side walls of the tested transformer tank. Examples of typical results of laboratory tests carried out with the use of the built-in measurement system are presented.

In this article is revealed the systems of a good delivery witch implement unmanned aerial vehicles during providing the service. the one channel systems of a goods delivery are a goal of this research work. the close analysing of their functional features, the classification, the types and parameters of different systems from this band are presented. in addition, the modelling of the different types of the one channel systems of goods delivery are has done.

In this paper, we show that signal sampling operation can be considered as a kind of all-pass filtering in the time domain, when the Nyquist frequency is larger or equal to the maximal frequency in the spectrum of a signal sampled. We demonstrate that this seemingly obvious observation has wideranging implications. They are discussed here in detail. Furthermore, we discuss also signal shaping effects that occur in the case of signal under-sampling. That is, when the Nyquist frequency is smaller than the maximal frequency in the spectrum of a signal sampled. Further, we explain the mechanism of a specific signal distortion that arises under these circumstances. We call it the signal shaping, not the signal aliasing, because of many reasons discussed throughout this paper. Mainly however because of the fact that the operation behind it, called also the signal shaping here, is not a filtering in a usual sense. And, it is shown that this kind of shaping depends upon the sampling phase. Furthermore, formulated in other words, this operation can be viewed as a one which shapes the signal and performs the low-pass filtering of it at the same time. Also, an interesting relation connecting the Fourier transform of a signal filtered with the use of an ideal low-pass filter having the cut frequency lying in the region of under-sampling with the Fourier transforms of its two under-sampled versions is derived. This relation is presented in the time domain, too.

In industrial processes electrical motors are serviced after a specific number of hours, even if there is a need for service. This led to the development of early fault diagnostic methods. Paper presents early fault diagnostic method of synchronous motor. This method uses acoustic signals generated by synchronous motor. Plan of study of acoustic signal of synchronous motor was proposed. Two conditions of synchronous motor were analyzed. Studies were carried out for methods of data processing: Line Spectral Frequencies and K-Nearest Neighbor classifier with Minkowski distance. Condition monitoring is useful to protect electric motors and mining equipment. In the future, these studies can be used in other electrical devices.

Although the emotions and learning based on emotional reaction are individual-specific, the main features are consistent among all people. Depending on the emotional states of the persons, various physical and physiological changes can be observed in pulse and breathing, blood flow velocity, hormonal balance, sound properties, face expression and hand movements. The diversity, size and grade of these changes are shaped by different emotional states. Acoustic analysis, which is an objective evaluation method, is used to determine the emotional state of people’s voice characteristics. In this study, the reflection of anxiety disorder in people’s voices was investigated through acoustic parameters. The study is a case-control study in cross-sectional quality. Voice recordings were obtained from healthy people and patients. With acoustic analysis, 122 acoustic parameters were obtained from these voice recordings. The relation of these parameters to anxious state was investigated statistically. According to the results obtained, 42 acoustic parameters are variable in the anxious state. In the anxious state, the subglottic pressure increases and the vocalization of the vowels decreases. The MFCC parameter, which changes in the anxious state, indicates that people can perceive this situation while listening to the speech. It has also been shown that text reading is also effective in triggering the emotions. These findings show that there is a change in the voice in the anxious state and that the acoustic parameters are influenced by the anxious state. For this reason, acoustic analysis can be used as an expert decision support system for the diagnosis of anxiety.