Degradation of whitethroat vocalisations: implications for song flight and communication network activities

Behaviour ◽  
2003 ◽  
Vol 140 (6) ◽  
pp. 695-719 ◽  
Author(s):  
Thorsten Balsby ◽  
Simon Boel Pedersen ◽  
Torben Dabelsteen

AbstractTransmission of acoustic signals through the habitat modifies the signals and may thus influence their use in communication. We investigated the transmission of five different types of whitethroat (Sylvia communis) vocalisations, three types of song and two calls. Typical examples were broadcast and re-recorded in a whitethroat habitat with hedgerows and open meadow. We used a complete factorial design with speaker and microphone placed in different natural sender and receiver positions including high perches and song flights. Sound degradation was quantified in terms of signal-to-noise ratio, excess attenuation, tail-to-signal ratio and blur ratio. The results suggest that sound degradation generally increased with distance along a hedgerow, which means that birds here potentially may use degradation in assessing the distance to a vocalising individual. This is unlike the open meadow where the change in degradation with distance was negligible. Surprisingly, song flight relative to perched singing seems not to facilitate transmission of own vocalisations or perception of vocalisations from other individuals, and song flight vocalisations do not transmit differently from other types of vocalisations during song flights. One purpose of song flights might therefore be visual location by potential receivers and surveillance by the territory owner. Source level and degradation differed between the five types of vocalisations in accordance with their functions. Motif song and song flight songs used in attraction of females and/or deterrence of males could transmit through neighbouring territories, whereas the calls and the courtship diving song where a specific individual within or near the territory is addressed had relatively short communication ranges.

2021 ◽  
Author(s):  
Mathiruban Tharmalingam

There has been a growing interest in the different types of dictionaries that can be used in image processing applications. We propose a hybrid dictionary composed of transform based atoms and additional nonlinear atoms generated using the polynomial, rectangular and exponential functions. The additional nonlinear atoms improve signal reconstruction quality for both transient and smooth signals. To further improve signal reconstruction quality, we optimize the hybrid dictionary using training samples from the signal. We also propose a signal coding algorithm that generates additional atoms by performing a circular shift on the provided dictionary prior to coding the signal. We have evaluated the proposed methods against existing predefined dictionaries by visually examining the reconstructed images as well as evaluating the peak signal to noise ratio of the reconstructed signal. All methods proposed in this thesis improved signal reconstruction quality however; we require an in-depth cost analysis study to evaluate its limitations.


2021 ◽  
Vol 39 (1B) ◽  
pp. 53-66
Author(s):  
Tareq Z. Hammood ◽  
Matheel E. Abdulmunim

Motion Estimation (ME) is a very important operation in video coding. In order to reduce complexity of computations involved in ME and to increase quality of this process, many Block Matching Motion Estimation (BMME) Algorithms are proposed. The aim of this paper is to compare between these algorithms and find the best one. Seven BMME algorithms are used in this paper. The performance of each algorithm is evaluated for different types of motion to determine the best one of these algorithms. The evaluation is based on search points, and Peak Signal to Noise Ratio (PSNR). The simulation shows that Hexagonal Search is faster than all other Block Matching (BM) algorithms used in this paper regardless the type of video because it requires less number of search points to evaluate motion vectors for the video sequence. It requires 11.2424 average search point (SP) for small motions and 13.9708 for fast motions. It also gives a good quality that is close enough to the quality given by Full Search


1999 ◽  
Vol 09 (01) ◽  
pp. 267-272 ◽  
Author(s):  
FRANÇOIS CHAPEAU-BLONDEAU

Stochastic resonance (SR) is a nonlinear effect whereby a system is able to improve, via noise addition, the detectability of a signal in noise. SR has been demonstrated with different types of systems and signals where in each case, an appropriate detectability measure is shown improvable at the output of the stochastic resonator when noise is added at its input. A complementary issue, important for practical applications of SR, is the possibility of making the signal detectability at the ouput exceed that at the input when noise is added. We demonstrate this possibility, for both periodic and aperiodic SR, with a simple nonlinear system that we show exactly tractable analytically.


2021 ◽  
pp. 198-206
Author(s):  
Sami Hasan ◽  
Shereen S. Jumaa

The main targets for using the edge detection techniques in image processing are to reduce the number of features and find the edge of image based-contents. In this paper, comparisons have been demonstrated between classical methods (Canny, Sobel, Roberts, and Prewitt) and Fuzzy Logic Technique to detect the edges of different samples of image's contents and patterns. These methods are tested to detect edges of images that are corrupted with different types of noise such as (Gaussian, and Salt and pepper). The performance indices are mean square error and peak signal to noise ratio (MSE and PSNR). Finally, experimental results show that the proposed Fuzzy rules and membership function provide better results for both noisy and noise-free images.


The explosion of numerous medical images lead to the development of many different techniques to provide an accurate result. Although the signal to noise ratio (SNR), resolution and speed of magnetic resonance imaging technology have increased, still magnetic resonance images are affected by noise, contrast, and artifacts. To provide the image content or features relevant to diagnosis, contrast enhancement and reduction of noise with preservation of actual content should be carried out. The purpose of this paper is to present a critical review of different types of noises with an overview of diverse techniques for denoising and contrast enhancement for magnetic resonance images and discuss the advantages and limitations of these techniques with broad ideology


Author(s):  
Fadhil Sahib Hasan

Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.


2021 ◽  
Vol 11 (21) ◽  
pp. 10190
Author(s):  
Borislav Stoyanov ◽  
Tsvetelina Ivanova

In this paper, we present an algorithm for encrypting audio files based on the Ikeda map, a mathematical function of chaos theory. Detailed experimental, security and theoretical analysis is provided on the proposed algorithm using histogram analysis, using different measurements including the signal-to-noise ratio, the peak signal-to-noise ratio, the number of samples change rate and the correlation coefficient. The provided results show a highly secure and strong algorithm against different types of attacks.


2021 ◽  
Vol 4 (2) ◽  
pp. 78
Author(s):  
Agus Prabowo ◽  
Gusti Muhammad Lucky Junursyah ◽  
Wahyu Hidayat

Magnetotelurik atau dikenal dengan MT merupakan metode geofisika pasif yang mengukur variasi medan elektromagnetik alami bumi untuk menyelediki struktur bawah permukaan bumi pada kedalaman 10 meter sampai 10 kilometer berdasarkan sifat resistivitas bawah permukaan. Kualitas data merupakan suatu kunci untuk mendapatkan hasil interpretasi yang baik. Permasalahan utama dari data magnetotelurik adalah pengaruh noise reguler yang dapat mempengaruhi signal ratio. Noise koheren umumnya dijumpai di daerah pengukuran yang dekat dengan sumber noise seperti instalasi listrik rumah tangga atau Saluran Udara Tegangan Ekstra Tinggi (SUTET). Untuk mengatasinya perlu dilakukan kajian analisis data berdasarkan parameter koherensi. Penelitian ini memanfaatkan data pengukuran di daerah padat penduduk di Kota Bandung Jawa Barat dengan jumlah 25 titik pengukuran. Untuk meningkatkan S/N ratio (Signal to Noise ratio) dilakukan beberapa treatment data yaitu dengan menggunakan robust, time series, dan edit XPR. Hasil dari pengolahan tersebut terbukti dapat menaikan nilai koherensi pada setiap titik pengukuran dengan rata-rata nilai koherensi dari 65.32% menjadi rata-rata nilai 83.85% atau mengalami kenaikan sebesar 18.54%. Penelitian ini membuktikan bahwa metode MT dapat dilakukan pada daerah perkotaan yang biasanya mempunyai banyak noise.


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