Explore the vast range of titles available.

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

A coal mine roadway is a long and narrow confined space filled with high-power magnetic interference from electrical equipment, which may be accompanied by explosive substances such as gas, dust, etc. Thus, coal mine roadway wireless transmission is confronted with strong noises, severe multipath fading, limited transmission power, and many other challenges. Time-domain matching compression (TDMC) characteristics of linear frequency modulation (LFM) signals allow for signal detection via energy focalization, while the distribution of signal power during transmission is low and meets mine explosion-proof intrinsic safety requirements, making LFM-TDMC-binary orthogonal keying (BOK) one of the ideal schemes for wireless transmission for coal mine roadways. However, it is difficult to eliminate strong electromagnetic interference in the time-domain, which increases the bit error rate (BER) of this transmission scheme. Previous research has shown that an LFM signal through fractional Fourier transform (FrFT) can obtain similar energy focalization characteristics while avoiding noises and interference in the time-domain. Thus, compared to TDMC-based transmission, FrFT-based transmission has superior transmission performance by time-frequency transformation. In this study, based on the comparison and analysis of the energy aggregation properties of TDMC and FrFT, an LFM-FrFT-BOK transmission model is proposed. Analysis and simulation results show that, within acceptable algorithm complexity, the BER performance of LFM-FrFT-BOK is better than LFM-TDMC-BOK.

The fractional Fourier transform (FRFT) serves as an effective tool for linear frequency modulated (LFM) signal parameter estimation, whose performance depends on the search efficiency for the optimal transform order. To address the issues of fixed inertia weight in the standard particle swarm optimization (PSO) algorithm, which tends to fall into local optima and suffers from insufficient convergence accuracy, this paper introduces a proportional-integral-derivative (PID) control strategy and proposes a PID-PSO-FRFT-based LFM signal parameter estimation method. This approach introduces a PID controller, which takes the deviation between the particle’s current position and the global best position as input and dynamically adjusts the inertia weight through proportional, integral, and derivative regulation, thereby achieving an adaptive balance between global exploration and local exploitation capabilities of the particles. Simulation results demonstrate that, compared with the basic PSO-FRFT algorithm, the proposed method significantly improves the estimation accuracy of the center frequency and chirp rate of LFM signals under SNR conditions ranging from −9 dB to −7 dB, while considerably reducing computation time, exhibiting superior noise resistance, and exhibiting superior robustness.

Due to the problem that the existing Doppler frequency rate estimation method is limited by the estimation accuracy, a novel estimation method of Doppler frequency rate is proposed. The present method searches the frequency rate according to the characteristic of the chirp signal in the FrFT domain. Firstly, dechirp is performed on several strong scattering points extracted from the data domain after pulse compression, and a frequency domain focused image is obtained after FFT. Then the maximum point of each distance unit is extracted. The energy of the maximum point is selected by using the window processing. After that, IFFT is performed and the dechirp conjugate reference function is multiplied by using the selected points. FrFT is performed according to the preset orders. The entropy is used to evaluate whether the order of FRFT is optimal or not. The Doppler frequency rate is calculated by using the optimal order. The simulation and real data are processed and analyzed. The present method can estimate the Doppler frequency rate accurately. A well-focused SAR image is obtained after azimuth matching filtering. 针对现有多普勒调频率估计方法估计精度有限的问题，提出一种新的估计方法，该方法根据线性调频信号在FrFT域具有明显聚焦的特性实现对调频率的搜索。对从脉压后的数据域提取的若干强散射单元分别进行dechirp处理，进行FFT得到频域聚焦图像。提取各距离单元最大值点并进行加窗处理，然后经IFFT变换到慢时间域与dechirp共轭参考函数相乘，根据设定的阶数进行FrFT处理。通过评估FrFT变换后信号的熵值确定阶数是否达到最优，利用搜索的最优阶数计算出多普勒调频率。在实验数据分析部分，分别利用仿真数据以及实测数据对所提方法进行验证，最终结果分析表明该方法具有很高的估计精度，经过方位匹配滤波后可以得到聚焦良好的SAR图像。

The precise separation of multicomponent signals encounters numerous challenges due to the complexity of signals and widespread interference. Synchrosqueezing Transform (SST) is one of the important technologies for improving the accurate separation of multicomponent signals, but it faces challenges in terms of the difficulty and effectiveness of squeezing. This paper introduces a multicomponent signal separation method based on innovative Fractional Synchrosqueezing Transform (FrSST). FrSST rearranges along the fractional frequency axis, improving the accuracy of time–frequency ridges and, consequently, enhancing the precision of multicomponent signal separation. In the signal reconstruction process, chirp multiplication and energy rearrangement compensate for chirp bases’ effects, boosting energy concentration and reconstruction potential. Utilizing improved ridges from FrSST ensures effective signal reconstruction. Simulation comparisons demonstrate that, with varying SNRs from − 5 to 15 dB, the reconstructed components based on FrSST exhibit favorable approximation to the original signal components. Furthermore, as the sample size increases, the proposed algorithm shows satisfactory computational efficiency.

The micro-Doppler signal generated by the rotors of an Unmanned Aerial Vehicle (UAV) contains the structural features and motion information of the target, which can be used for detection and classification of the target, however, the standard STFT has the problems such as the lower time-frequency resolution and larger error in rotor parameter estimation, an FRFT (Fractional Fourier Transform)-FSST (STFT based synchrosqueezing)-based method for micro-Doppler signal detection and parameter estimation is proposed in this paper. Firstly, the FRFT is used in the proposed method to eliminate the influence of the velocity and acceleration of the target on the time-frequency features of the echo signal from the rotors. Secondly, the higher time-frequency resolution of FSST is used to extract the time-frequency features of micro-Doppler signals. Moreover, the specific solution methodologies for the selection of window length in STFT and the estimation of rotor parameters are given in the proposed method. Finally, the effectiveness and accuracy of the proposed method for target detection and rotor parameter estimation are verified through simulation and measured data.

An electrocardiogram (ECG) is world’s most recognized, widely accepted and essential primitive diagnostic tool to assess health status of heart of a subject (patient) by analyzing its constituent P, QRS and T waves. QRS wave is further consists of three waves namely; Q-wave, R-wave, and S-wave, where R-wave has highest amplitude (about 1 mVolt known as R-peaks). Despite of higher amplitudes, their detection by visual inspection is still challenging due to physiological variability and presence of various types of noise/distortion in acquired ECG signal. Pre-processing of raw ECG datasets can help in tackling these two problems to some extent but that incurs an appreciable amount of computational effort. Therefore in this paper, the need of pre-processing is made redundant by using fractional Fourier transform (FrFT) for extracting features i.e. directly using the raw ECG datasets alongwith using well-known principal component analysis (PCA) for detecting R-peaks effectively in the presence of varying morphologies of ECG signal and various types of noise/distortions. Obviating the need of pre-processing altogether results in faster computations and use of PCA results in higher detection accuracies. The proposed technique has been evaluated on the basis of sensitivity (Se), positive predictive value (PPV), & accuracy (Acc) with 99.93% of Se, 99.95% of PPV, & 99.88% of Acc on MIT-BIH Arrhythmia database (M/B Ar DB). The proposed methodology will a long way in assisting the cardiologists in efficient, effective and timely computer-aided diagnosis of irregularities in heart rhythms of a subject (patient).

This paper presents a novel feature extraction scheme for automatic classification of musical instruments using Fractional Fourier Transform (FrFT)-based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using Counter Propagation Neural Network (CPNN). The discriminating capability of the proposed features have been maximized for between-class instruments and minimized for within-class instruments compared to other conventional features. Also, the proposed features show significant improvement in classification accuracy and robustness against Additive White Gaussian Noise (AWGN) compared to other conventional features. McGill University Master Sample (MUMS) sound database has been used to test the performance of the system.

Biometric systems are widely used now for security applications. Two major problems are encountered in biometric systems: the security problem and the dependence on a single biometric for verification. The security problem arises from the utilization of the original biometrics in databases. So, if these databases are attacked, the biometrics are lost forever. Hence, there is a need to secure original biometrics by keeping them away from utilization in biometric databases. Cancelable biometrics is an emerging security trend in the field of biometric authentication. Cancelable biometric systems depend on the transformation of biometric features into new formats so that users can replace their biometric templates in the same or different systems. In this paper, we present a proposed cancelable face and fingerprint recognition algorithm based on the 3D jigsaw transform and optical encryption. The algorithm adopts the Fractional Fourier Transform (FRFT) in the optical encryption scheme with a single random phase mask. This structure can be implemented all optically with a single lens. The proposed cancelable biometric recognition algorithm employs an optical image encryption scheme that depends on two cascaded stages of 2D-FRFT with separable kernels in both dimensions. The two stages are separated with a random phase mask. A preceding bit plane permutation process is performed on the obtained biometrics prior to the FRFT operation to achieve a high level of security. To validate the proposed algorithm for cancelable biometric recognition, different sets of face and fingerprint images are used. A comparative study is presented between the proposed algorithm and the optical Double Random Phase Encoding (DRPE) algorithm. The simulations results obtained for performance evaluation show that the proposed algorithm is safe, reliable, and feasible. It has good encryption and cancelability that reveal good performance.

The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)–least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion.