Stealth in military sonars applications may be ensured through the use of low power signals making them difficult to intercept by the enemy. In recent years, silent sonar design has been investigated by the Department of Marine Electronic Systems of the Gdansk University of Technology. This article provides an analysis of how an intercept sonar operated by the enemy can detect silent sonar signals. To that end a theoretical intercept sonar model was developed with formulas that can numerically determine the intercept ranges of silent sonar sounding signals. This was tested for a variety of applications and water salinities. Because they are also presented in charts, the results can be used to compare the intercept ranges of silent sonar and traditional pulse sonar.
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.
Perception takes into account the costs and benefits of possible interpretations of incoming sensory data. This should be especially pertinent for threat recognition, where minimising the costs associated with missing a real threat is of primary importance. We tested whether recognition of threats has special characteristics that adapt this process to the task it fulfils. Participants were presented with images of threats and visually matched neutral stimuli, distorted by varying levels of noise. We found threat superiority effect and liberal response bias. Moreover, increasing the level of noise degraded the recognition of the neutral images to higher extent than the threatening images. To summarise, recognising threats is special, in that it is more resistant to noise and decline in stimulus quality, suggesting that threat recognition is a fast ‘all or nothing’ process, in which threat presence is either confirmed or negated.
Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch’s technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.
This paper focuses on testing the monitoring system of the Direct Current motor. This system gives the possibility of diagnosing various types of failures by means of analysis of acoustic signals. The applied method is based on a study of acoustic signals generated by the DC motor. A study plan of the DC motor’s acoustic signal was proposed. Studies were conducted for a faultless DC motor and Direct Current motor with 3 shorted rotor coils. Coiflet wavelet transform and K-Nnearest neighbor classifier with Euclidean distance were used to identify the incipient fault. This approach keeps the motor operating in acceptable condition for a long time and is also inexpensive.
The paper presented the wavelet transform method for de-noising and singularity detection to soil compressive stress signal. The study results show that the reconstruction signals by the wavelet de-noising keeps the low frequency component at [0, 31.25 Hz] of the original signal and improves the high frequency property at other frequency bands. The impaction time from the start time to resonance time of the stress signals is varies with the depth of the soil. With the increase of times of compaction, the impaction time of the stress is decreasing in every layer. But the speed of reaching compacted status in each layer is different.
Stealth is a frequent requirement in military applications and involves the use of devices whose signals are difficult to intercept or identify by the enemy. The silent sonar concept was studied and developed at the Department of Marine Electronic Systems of the Gdansk University of Technology. The work included a detailed theoretical analysis, computer simulations and some experimental research. The results of the theoretical analysis and computer simulation suggested that target detection and positioning accuracy deteriorate as the speed of the target increases, a consequence of the Doppler effect. As a result, more research and measurements had to be conducted to verify the initial findings. To ensure that the results can be compared with those from the experimental silent sonar model, the target's actual position and speed had to be precisely controlled. The article presents the measurement results of a silent sonar model looking at its detection, range resolution and problems of incorrect positioning of moving targets as a consequence of the Doppler effect. The results were compared with those from the theoretical studies and computer simulations.
This article discusses a system of recognition of acoustic signals of loaded synchronous motor. This software can recognize various types of incipient failures by means of analysis of the acoustic signals. Proposed approach uses the acoustic signals generated by loaded synchronous motor. A plan of study of the acoustic signals of loaded synchronous motor is proposed. Studies include following states: healthy loaded synchronous motor, loaded synchronous motor with shorted stator coil, loaded synchronous motor with shorted stator coil and broken coil, loaded synchronous motor with shorted stator coil and two broken coils. The methods such as FFT, method of selection of amplitudes of frequencies (MSAF-5), Linear Support Vector Machine were used to identify specific state of the motor. The proposed approach can keep high recognition rate and reduce the maintenance cost of synchronous motors.
Reliable monitoring for detection of damage in epicyclic gearboxes is a serious concern for all industries in which these gearboxes operate in a harsh environment and in variable operational conditions. In this paper, autonomous multidimensional novelty detection algorithms are used to estimate the gearbox’ health state based on vectors of features calculated from the vibration signal. The authors examine various feature vectors, various sources of data and many different damage scenarios in order to compare novel detection algorithms based on three different principles of operation: a distance in the feature space, a probability distribution, and an ANN (artificial neural network)-based model reconstruction approach. In order to compensate for non-deterministic results of training of neural networks, which may lead to different network performance, the ensemble technique is used to combine responses from several networks. The methods are tested in a series of practical experiments involving implanting a damage in industrial epicyclic gearboxes, and acquisition of data at variable speed conditions.
This paper describes the theoretical background of electromagnetic induction from metal objects modelling. The response function of a specific case of object shape - a homogenous sphere from ferromagnetic and non-ferromagnetic material is introduced. Experimental data measured by a metal detector excited with a linearly frequency-swept signal are presented. As a testing target various spheres from different materials and sizes were used. These results should lead to better identification of the buried object.
Electroencephalogram (EEG) is one of biomedical signals measured during all-night polysomnography to diagnose sleep disorders, including sleep apnoea. Usually two central EEG channels (C3-A2 and C4- A1) are recorded, but typically only one of them are used. The purpose of this work was to compare discriminative features characterizing normal breathing, as well as obstructive and central sleep apnoeas derived from these central EEG channels. The same methodology of feature extraction and selection was applied separately for the both synchronous signals. The features were extracted by combined discrete wavelet and Hilbert transforms. Afterwards, the statistical indexes were calculated and the features were selected using the analysis of variance and multivariate regression. According to the obtained results, there is a partial difference in information contained in the EEG signals carried by C3-A2 and C4-A1 EEG channels, so data from the both channels should be preferably used together for automatic sleep apnoea detection and differentiation.