Robust Image Compression Algorithm using Discrete Fractional Cosine Transform

The discrete fractional Fourier transform become paradigm in signal processing. This transform process the signal in joint time-frequency domain. The attractive and very important feature of DFrCT is an availability of extra degree of one free parameter that is provided by fractional orders and due to which optimization is possible. Less execution time and easy implementation are main advantages of proposed algorithm. The merit of effectiveness of proposed technique over existing technique is superior due to application of discrete fractional cosine transform by which higher compression ratio and PSNR are obtained without any artifacts in compressed images. The novelty of the proposed algorithm is no artifacts in compressed image along with good CR and PSNR. Compression ratio (CR) and peak signal to noise ratio (PSNR) are quality parameters for image compression with optimum fractional order.

Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems

Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals’ autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics.

Three Term Weighted Type Fractional Fourier Transform Based Generalized Hybrid Carrier and Its Application into PAPR Suppression

To suppress the peak to average power ratio (PAPR) of wireless communication based upon multi-carrier system. We, in this paper, proposed the three term weighted type fractional Fourier transform (3-WFRFT) based generalized hybrid carrier (GHC) system. We first provide the definition of 3-WFRFT. Moreover, some useful properties of 3-WFRFT have been presented, in this paper, which will helpful to comprehend the novel 3-WFRFT transform. Furthermore, we take PAPR of the proposed algorithm, in comparison with orthogonal frequency division multiplexing (OFDM) system and single carrier modulation (SC) system under typical complementary cumulative density function (CCDF) level. It would be demonstrated that, from some numerical simulations, the proposed 3-WFRFT based GHC performs better than OFDM system and will be useful to reduce the PAPR level.

Method for time-of-flight estimation of low frequency acoustic signals in reverberant and noisy environment with sparse impulse response

For acoustic procedures which rely on the speed of sound to derive process parameters, the determination of the acoustic time of flight is essential. In this work, a method for the determination of the time of flight (TOF) is presented. It is intended for reverberant and noisy environments and can be applied in the gas holdup determination in bubbly liquids via acoustic transmission tomography (GHATT) for example. The method includes the selection and design of the transmitted signal to optimize the disambiguate of the autocorrelation, the narrowing of the time window based on the Fractional Fourier Transform (FrFT) to accelerate the TOF estimation. Furthermore, it includes the consideration of the system-induced signal distortion through prior quasi-anechoic measurements and the sparse reconstruction of the spatial impulse response for TOF estimation using non-negative sparse deconvolution algorithms. The method is tested analytically on numerically generated signals and various sparse deconvolution algorithms are investigated with respect to their applicability and limitations.

The impact of financial repression on manufacturing upgrade based on fractional Fourier transform and probability

This paper proposes a method combining fractional Fourier transform (FRFT) and a high peak-to-average power ratio suppression algorithm. Calculations show that as the order of the FRFT decreases, the peak-to-average power ratio of the signal gradually decreases; combined with the suppression algorithm can further reduce the peak-to-average power ratio of the system and solve the problem of the suppression algorithm affecting system performance. At the same time, this paper uses the 2012 World Bank survey data of Chinese manufacturing enterprises to study the influence of financial repression on the selection of financing channels of manufacturing enterprises. The research results show that financial repression has a significant impact on the choice of financing channels for manufacturing enterprises, which greatly increases the proportion of informal financial financing in the operating capital of enterprises and reduces the proportion of formal financial financing. Financial repression has led to an increase in the cost of formal financial financing, making enterprises choose informal financial channels for financing. Among them, large enterprises tend to choose commercial credit in informal finance, while small and medium-sized enterprises choose private credit It is possible to choose two financing channels, which reflects the ‘scale discrimination’ characteristics of financial repression.

Partial Fractional Fourier Transform (PFrFT)-MIMO-OFDM for Known Underwater Acoustic Communication Channels

Communication over doubly selective channels (both time and frequency selective) suffers from significant intercarrier interference (ICI). This problem is severe in underwater acoustic communications. In this paper, a novel partial fractional (PFrFT)-MIMO-OFDM system is proposed and implemented to further mitigate ICI. A new iterative band minimum mean square error (BMMSE) weight combining based on LDLH factorization is used in a scenario of perfect knowledge of channel information. The proposed method is extended from SISO-OFDM configuration to MIMO-OFDM. Simulation results demonstrate that the proposed PFrFT-LDLH outperforms the other methods in the SISO-OFDM scenario and that its performance can be improved in MIMO-OFDM scenarios.

Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults

Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode functions by the signal decomposition method, and the amplitude of the component is modulated by the weighted average method to enhance the fault impulse component. Then, the fractional Fourier transform is used to filter the reconstructed signal. Regarding classification issues, the eigenclass classifier is optimized by the IDE method that can be used for feature dimensionality reduction. Finally, the optimal features are selected and input into the IDE-EigenClass model. The experimental results show that the bearing fault diagnosis method proposed in this paper has higher accuracy and stability than the traditional PNN, SVM, BP, and other methods.

Multiweighted-Type Fractional Fourier Transform: Unitarity

The definition of the discrete fractional Fourier transform (DFRFT) varies, and the multiweighted-type fractional Fourier transform (M-WFRFT) is its extended definition. It is not easy to prove its unitarity. We use the weighted-type fractional Fourier transform, fractional-order matrix and eigendecomposition-type fractional Fourier transform as basic functions to prove and discuss the unitarity. Thanks to the growing body of research, we found that the effective weighting term of the M-WFRFT is only four terms, none of which are extended to M terms, as described in the definition. Furthermore, the program code is analyzed, and the result shows that the previous work (Digit Signal Process 2020: 104: 18) based on MATLAB for unitary verification is inaccurate.