Autor: Muhammad, Ali - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Probing 3D chaotic Thomas’ cyclically attractor with multimedia encryption and electronic circuitry
Autorzy:: Khan, NajeebAlam
Qureshi, Muhammad Ali
Akbar, Saeed
Ara, Asmat
Powiązania:: https://bibliotekanauki.pl/articles/27312000.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: Thomas’ cyclically attractor
fractional calculus
chaos
encryption
Opis:: This study investigates Thomas’ cyclically symmetric attractor dynamics with mathematical and electronic simulations using a proportional fractional derivative to comprehend the dynamics of a given chaotic system. The three-dimensional chaotic flow was examined in detail with Riemann-Liouville derivative for different values of the fractional index to highlight the sensitivity of chaotic systems with initial conditions. Thus, the dynamics of the fractional index system were investigated with Eigenvalues, Kaplan-Yorke dimension, Lyapunov exponent, and NIST testing, and their corresponding trajectories were visualized with phase portraits, 2D density plot, and Poincaré maps. After obtaining the results, we found that the integer index dynamics are more complex than the fractional index dynamics. Furthermore, the chaotic system circuit is simulated with operational amplifiers for different fractional indices to generate analog signals of the symmetric attractor, making it an important aspect of engineering. The qualitative application of our nonlinear chaotic system is then applied to encrypt different data types such as voice, image, and video, to ensure that the developed nonlinear chaotic system can widely applied in the field of cyber security.
Źródło:: Archives of Control Sciences; 2023, 33, 1; 239--271
1230-2384
Pojawia się w:: Archives of Control Sciences
Dostawca treści:: Biblioteka Nauki

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Tytuł:: Deep Neural Network for Supervised Single-Channel Speech Enhancement
Autorzy:: Saleem, Nasir
Irfan Khattak, Muhammad
Ali, Muhammad Yousaf
Shafi, Muhammad
Powiązania:: https://bibliotekanauki.pl/articles/177497.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: deep neural network
intelligibility
speech enhancement
speech quality
supervised learning
Wiener filtering
Opis:: Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.
Źródło:: Archives of Acoustics; 2019, 44, 1; 3-12
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

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