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Underwater signal processing includes several application areas like military application, disaster detection, finding natural resources, etc. When signal transmits through water, it may get overlap due to some inherent mechanisms like ship noise, wind noise, marine mammals noise, noise generated during all stages of oil production, and due to other industrial sources. In this paper, the main focus...
In underwater signal processing, acoustic signals are used to transmit and receive information for communication. But the acoustic signals get affected due to spreading, reverberation, attenuation and absorption which produce ambient noise. Ambient noise is the background noise created due to natural sources as well as anthropogenic (man-made) sources. Ambient noise can be characterized as Gaussian,...
Counting of semen straw is challenging task due to a smaller size of straws, non-uniform bunch, and presence of liquid nitrogen. Straws are placed in the compact bunch with liquid nitrogen surrounding for preservation. Straw diameter is tiny (0.2 mm). Therefore, manual counting is tedious and produces inaccurate counting results. In such condition, automatic counting can be achieved by using image...
Increasing ambient noise levels in the oceans has been a cause of concern due to its impact on sonar performance deployed for military as well as non-military applications as they present sub-optimal behavior in poor SNR conditions. Classical noise filtering techniques offer limited utility in tropical littoral waters because of the random fluctuation of the ambient noise. The repeated surface and...
The performance predictions for sonar assume a background of Gaussian noise, however, deviation from this assumption may generate false alarms. This paper attempts to find out whether the ambient noise in tropical waters of the western coast of Indian continental is Gaussian. Gaussianity is tested using graphical, numerical and verified using formal normality tests like Kolmogorov Smirnov (KS) test...
This paper investigates better time frequency representations of littoral water ambient noise recorded at 30 meter of depth in Indian tropical water. Linear time frequency representations (TFR) like spectrogram and Continuous Wavelet Transform are compared by combining natural and anthropogenic ambient noise sources from the tropical waters. Underwater ambient noise is represented by appropriate wavelet...
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