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Detecting chaos in heavy-noise environments is an important issue in many fields of science and engineering. In this paper, first, a new criterion is proposed to recognize chaos from noise based on the distribution of energy. Then, a new method based on stationary wavelet transform (SWT) is presented for chaos detection that is recommended for data that contain more than 60% noise. This method is dependent on the distribution of signal’s energy in different frequency bands based on SWT for chaos detection which is robust to noisy environments. In this method, the effect of white noise and colored noise on the chaotic system is considered. As a case study, the proposed method is applied to detect chaos in two different oscillators based on memristor and memcapacitor. The simulation results are used to display the main points of the paper.

Most of the documents use fingerprint impression for authentication. Property related documents, bank checks, application forms, etc., are the examples of such documents. Fingerprint-based document image retrieval system aims to provide a solution for searching and browsing of such digitized documents. The major challenges in implementing fingerprint-based document image retrieval are an efficient method for fingerprint detection and an effective feature extraction method. In this work, we propose a method for automatic detection of a fingerprint from given query document image employing Discrete Wavelet Transform (DWT)-based features and SVM classifier. In this paper, we also propose and investigate two feature extraction schemes, DWT and Stationary Wavelet Transform (SWT)-based Local Binary Pattern (LBP) features for fingerprint-based document image retrieval. The standardized Euclidean distance is employed for matching and ranking of the documents. Proposed method is tested on a database of 1200 document images and is also compared with current state-of-art. The proposed scheme provided 98.87% of detection accuracy and 73.08% of Mean Average Precision (MAP) for document image retrieval.

In this paper, we propose a new image denoising technique which consists in applying a stationary wavelet transform (SWT)-based image denoising technique, in the domain of 2D dual-tree discrete wavelet transform (DWT). In fact, this proposed technique consists first of applying the 2D dual-tree DWT to the noisy image. Then, the noisy wavelet coefficients obtained from this application are denoised by applying to each of them, a SWT-based image denoising technique. Finally, the denoised image is reconstructed by applying the inverse of the 2D dual-tree DWT to the obtained denoised wavelet coefficients. For applying this SWT-based image denoising technique, we use the soft thresholding, the Daubechies 4 as the mother wavelet, and the decomposition level is equal to 5. The performance of this image denoising technique proposed in this work is proven by its comparison to three other denoising techniques existing in the literature. These three techniques are the denoising technique based on the soft thresholding in the SWT domain, the image denoising technique based on soft thresholding in the domain of 2D dual-tree DWT and the image denoising approach using deep neural network. All the previously mentioned techniques, including our proposed denoising approach, are applied to a number of noisy images, and the obtained results are in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Those results show that this proposed denoising technique outperforms the other three denoising techniques used in this evaluation.

When our proposed neurosurgical robot is applied, the neurosurgeon usually wants to sense the force on the remote site to operate on patients. The force signal analysis is of critical importance for neurosurgeons to perform stable, reliable, and safe operations. In this paper, based on the stationary wavelet transform (SWT), force information analysis and process is designed. Since force sampled by the JR3 sensor contains noise from the sensor and mechanical vibration when drilling, to smooth the force signal sent to the operator, SWT-based force information de-noising is proposed to reduce the noise significantly, especially for the force along the x and y axes. Simulations and experiments further verified the proposed research.