In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.

In this study, it was achieved by using the method of impulse noise to detect internal or surface cracks that can occur in the production of ceramic plates. Ceramic materials are often used in the industry, especially as kitchenware and in areas such as the construction sector. Many different methods are used in the quality assurance processes of ceramic materials. In this study, the impact noise method was examined. This method is a test technique that was not used in applications. The method is presented as an examination technique based on whether there is a deformation on the material according to the sound coming from it as a result of a plastic bit hammer impact on the ceramic material. The application of the study was performed on plates made of ceramic materials. Here, it was made with the same type of model plates manufactured from the same material. The noise that would occur as a result of the impact applied on a point determined on the materials to be tested has been examined by the method of time-frequency analysis. The method applied gives pretty good results for distinguishing ceramic plates in good condition from those which are cracked.

The impulse noise is agent harmful to health not only in the case of shots from firearms and the explosions of explosive materials. This kind of noise is also present in many workplaces in the industry. The paper presents the results of noise parameters measurements in workplaces where four different die forging hammers were used. The measured values of the C-weighted peak sound pressure level, the A-weighted maximum sound pressure level and A-weighted noise exposure level normalized to an 8 h working day (daily noise exposure level) exceeded the exposure limit values. For example, the highest measured value of the C-weighted peak sound pressure level was 148.9 dB. In this study possibility of the protection of hearing with the use of earplugs or earmuffs was assessed. The measurement method for the measurements of noise parameters under hearing protection devices using an acoustical test fixture instead of testing with the participation of subjects was used. The results of these measurements allows for assessment which of two tested earplugs and two tested earmuffs sufficiently protect hearing of workers in workplaces where forging hammers are used.

The tests reported in this paper were carried out to evaluate the exposure of soldiers to noise at operator and control positions during military field exercises. The tests were conducted during firing from a T-72 tank, a BWP-1 Infantry Fighting Vehicle, antitank guided missiles, a ZU-23-2K anti-aircraft gun, and a 2S1 GOZDZIK howitzer. The evaluation of noise exposure showed that the limit values of sound pressure level, referred to by both Polish occupational noise protection standards and the Pfander and Dancer hearing damage risk criteria developed for military applications, were repeatedly exceeded at the tested positions. Despite of the use of tank crew headgear, the exposure limit values of sound pressure level were exceeded for the crew members of the T-72 tank, the BWP-1 infantry fighting vehicle, and the 2S1 GOZDZIK howitzer. The results show that exposure of soldiers to noise during military field exercises is a potentially high hearing risk factor.

Noise induced hearing loss (NIHL) is a serious occupational related health problem worldwide. The A-wave impulse noise could cause severe hearing loss, and characteristics of such kind of impulse noise in the joint time-frequency (T-F) domain are critical for evaluation of auditory hazard level. This study focuses on the analysis of A-wave impulse noise in the T-F domain using continual wavelet transforms. Three different wavelets, referring to Morlet, Mexican hat, and Meyer wavelets, were investigated and compared based on theoretical analysis and applications to experimental generated A-wave impulse noise signals. The underlying theory of continuous wavelet transform was given and the temporal and spectral resolutions were theoretically analyzed. The main results showed that the Mexican hat wavelet demonstrated significant advantages over the Morlet and Meyer wavelets for the characterization and analysis of the A-wave impulse noise. The results of this study provide useful information for applying wavelet transform on signal processing of the A-wave impulse noise.

The article describes and compares two OFDM based communications schemes for reducing the effects of the combination of Narrowband Interference (NBI) and Impulsive Noise (IN), which are noise types typical in Power Line Communication (PLC). The two schemes are Modified BPSK-OFDM (called MBPSK, for brevity) and QFSK-OFDM (called QFSK, for brevity), which are non-conventional OFDM schemes. We give a description of the two schemes, showing how they are derived and also show their similarities and eventually compare their performances. Performance simulation results, in terms of bit error rate, are given to compare the systems under the effect of IN and NBI. The popular Middleton Class A model is used for modelling IN. The results show that MBPSK scheme outperforms the QFSK scheme in terms of minimum distance, and hence in terms of bit error probability when no preprocessing is performed. However, under clipping/nulling, both schemes eventually reach the bit error rate floor.

To overcome the detrimental influence of α impulse noise in power line communication and the trap of scarce prior information in traditional noise suppression schemes , a power iteration based fast independent component analysis (PowerICA) based noise suppression scheme is designed in this paper. Firstly, the pseudo-observation signal is constructed by weighted processing so that single-channel blind separation model is transformed into the multi-channel observed model. Then the proposed blind separation algorithm is used to separate noise and source signals. Finally, the effectiveness of the proposed algorithm is verified by experiment simulation. Experiment results show that the proposed algorithm has better separation effect, more stable separation and less implementation time than that of FastICA algorithm, which also improves the real-time performance of communication signal processing.