Frequency response functions (FRFs) are important for assessing the behavior of stochastic linear dynamic systems. For large systems, their evaluations are time-consuming even for a single ...simulation. In such cases, uncertainty quantification by crude Monte-Carlo simulation is not feasible. In this paper, we propose the use of sparse adaptive polynomial chaos expansions (PCE) as a surrogate of the full model. To overcome known limitations of PCE when applied to FRF simulation, we propose a frequency transformation strategy that maximizes the similarity between FRFs prior to the calculation of the PCE surrogate. This strategy results in lower-order PCEs for each frequency. Principal component analysis is then employed to reduce the number of random outputs. The proposed approach is applied to two case studies: a simple 2-DOF system and a 6-DOF system with 16 random inputs. The accuracy assessment of the results indicates that the proposed approach can predict single FRFs accurately. Besides, it is shown that the first two moments of the FRFs obtained by the PCE converge to the reference results faster than with the Monte-Carlo (MC) methods.
This paper presents an analysis method to design quad‐band bandpass filters (BPFs) based on the mixed frequency transformation method. The frequency mapping technique is employed to obtain the design ...parameters by defining the frequency ranges of each passband. By using the proposed approach, the resonant angular frequencies and susceptance slope parameters can be obtained analytically. To validate our theory, a third‐order Chebyshev BPF and a fourth‐order elliptic BPF with different bandwidths are successfully synthesized. Moreover, a second‐order test BPF with a Chebyshev approximation and 20 dB passband return loss is synthesized, designed, fabricated, and measured. The measured insertion losses are 1.82, 2.01, 3.12, and 2.51 dB for each passband and measured return losses are better than 15.2 dB for all passbands. The measured results verify the designed BPF and also confirm the theoretical prediction.
In this study, we highlighted the growing need for automated electrocardiogram (ECG) signal classification using deep learning to overcome the limitations of traditional ECG interpretation algorithms ...that can lead to misdiagnosis and inefficiency. Convolutional neural networks (CNN) application to ECG signals is gaining significant attention owing to their exceptional image-classification capabilities. However, we addressed the lack of standardized methods for converting 1D ECG signals into 2D-CNN-compatible input images by using time-frequency methods and selecting hyperparameters associated with these methods, particularly the choice of function. Furthermore, we investigated the effects of fine-tuned training, a technique where pre-trained weights are adapted to a specific dataset, on 2D-CNNs for ECG classification. We conducted the experiments using the MIT-BIH Arrhythmia Database, focusing on classifying premature ventricular contractions (PVCs) and abnormal heartbeats originating from ventricles. We employed several CNN architectures pre-trained on ImageNet and fine-tuned using the proposed ECG datasets. We found that using the Ricker Wavelet function outperformed other feature extraction methods with an accuracy of 96.17%. We provided crucial insights into CNNs for ECG classification, underscoring the significance of fine-tuning and hyperparameter selection in image transformation methods. The findings provide valuable guidance for researchers and practitioners, improving the accuracy and efficiency of ECG analysis using 2D-CNNs. Future research avenues may include advanced visualization techniques and extending CNNs to multiclass classification, expanding their utility in medical diagnosis.
Extracting fault frequencies from noisy vibration signal is a challenging task for bearing fault diagnosis. The state-of-the-art sparse representation (SR)-based methods usually consist of two steps: ...1) fault impulse recovery in the time domain and 2) frequency transformation of the estimated signal envelope. However, any inaccurate time-domain signal recovery can cause an error accumulation problem for the following frequency transformation, and the frequency transformation itself encounters a low-resolution shortcoming especially for short-time sampling data. To handle these shortcomings, in this article, we propose a novel sparse Bayesian learning (SBL) framework to evade the time-domain signal recovery and extract the fault frequencies directly from the frequency domain. We first present a new formulation for the sparse frequency recovery problem using the sparsity structure of the envelope spectrum, and then introduce a truncated off-grid model into the SBL framework to speed up the proposed method. Moreover, an improved grid refinement is developed to jointly combat the off-grid frequency mismatch and exploit the arithmetic sparsity structure of fault frequencies. Both the simulation and experimental results indicate the effectiveness of our proposed method.
The presence of background noise or competing talkers is one of the main communication challenges for cochlear implant (CI) users in speech understanding in naturalistic spaces. These external ...factors distort the time-frequency (T-F) content including magnitude spectrum and phase of speech signals. While most existing speech enhancement (SE) solutions focus solely on enhancing the magnitude response, recent research highlights the importance of phase in perceptual speech quality. Motivated by multi-task machine learning, this study proposes a deep complex convolution transformer network (DCCTN) for complex spectral mapping, which simultaneously enhances the magnitude and phase responses of speech. The proposed network leverages a complex-valued U-Net structure with a transformer within the bottleneck layer to capture sufficient low-level detail of contextual information in the T-F domain. To capture the harmonic correlation in speech, DCCTN incorporates a frequency transformation block in the encoder structure of the U-Net architecture. The DCCTN learns a complex transformation matrix to accurately recover speech in the T-F domain from a noisy input spectrogram. Experimental results demonstrate that the proposed DCCTN outperforms existing model solutions such as the convolutional recurrent network (CRN), deep complex convolutional recurrent network (DCCRN), and gated convolutional recurrent network (GCRN) in terms of objective speech intelligibility and quality, both for seen and unseen noise conditions. To evaluate the effectiveness of the proposed SE solution, a formal listener evaluation involving four CI recipients was conducted. Results indicate a significant improvement in speech intelligibility performance for CI recipients in noisy environments. Additionally, DCCTN demonstrates the capability to suppress highly non-stationary noise without introducing musical artifacts commonly observed in conventional SE methods.
This paper presents a novel approach to the design and implementation of a distributed transmission line negative group delay filter (NGDF) with a predefined negative group delay (NGD) time. The ...newly proposed filter is based on a simple frequency transformation from a low-pass filter to a bandstop filter. The NGD time can be purely controlled by the resistors inserted into the resonators. The performance degradation of the NGD time and signal attenuation (SA) of the proposed NGDF according to the temperature dependent resistance variation is also analyzed. From this analysis, it is shown that the NGD time and SA variations are less sensitive to the resistance variation compared to those of the conventional NGD circuit. For an experimental validation of the proposed NGDF, a two-stage distributed microstrip line NGDF is designed, simulated, and measured at an operating center frequency of 1.962 GHz. These results show a group delay time of -7.3 ns with an SA of 22.65 dB at the center frequency and have good agreement with the simulations. The cascaded response of two NGDFs operating at different center frequencies is also presented in order to obtain broader NGD bandwidth. NGDFs with good reflection characteristics at the operating frequencies are also designed and experimentally verified.
In this article, a novel synthesis method for lumped‐element dual‐band bandpass filters (DBPFs) with independently controllable bandwidth is proposed. The proposed lumped‐element DBPF exhibits the ...advantages including two passbands with independently controllable bandwidth, arbitrary frequency ratio, availability for different lowpass prototypes, and compact size below 6 GHz. This design theory is based on the equivalents of dual‐band J‐inverters and LC resonators which combines generalized dual‐band resonators theory with independently controllable bandwidth. A dual‐band bandpass frequency transformation and four circuit conversions are derived. For demonstration, three cases of DBPF with different bandwidths, frequency ratios, and orders are designed, fabricated, and measured. The simulated and measured results have a good agreement, validating the proposed synthesis theory.
In this paper, a new multiband frequency mapping function is proposed to design multiband filter. The presented mapping function is a generalized form of the sequential low pass to band pass (LPtoBP) ...transformation. The multiband filter design is based on the application of the frequency mapping function on a LP prototype. The synthesis of the resulting multiband filter is obtained by lumped element resonators. Several examples are presented to validate the proposed design approach. A triple band filter implementation and measurement results are presented.
In this research, the authors extract features from intermediate frequency band radar signals in the time–frequency domain for classification. The extracted features are classified via support vector ...machine and K-nearest neighbour classifiers. They show the accuracy of classification is above 99% for different classes of radar signals except for frequency shift keying signal with accuracy 83% in negative signal-to-noise ratio (SNR). To identify the radars with the same class, the classification accuracy is 91% for SNR between 5 to 15 dB and 64% in the worst case for SNR between −1 to 10 dB. The proposed method is compared with some methods based on the empirical mode decomposition (EMD), cumulant and Zhao Atlas Mark Distribution (ZAMD). The results show that the classification error in the proposed method is less than that of EMD method 55% in the best case and 9% in the worst case. The performance of the cumulant-based method is weaker than that of the proposed method in common designed scenarios becoming almost similar only in one scenario. The ZAMD-based method could only distinguish the signals with different modulations in high SNR while it is unable to classify the signals with the same modulation but different parameters.
This letter presents a method for the design of highly selective dual-band filters. The frequency-variant triplets (FVTs) are applied to dual-band filter design. In addition to different passbands ...with different orders, bandwidths, or return losses, this method can generate <inline-formula> <tex-math notation="LaTeX">{N} </tex-math></inline-formula>-1 transmission zeros (TZs) for the specified <inline-formula> <tex-math notation="LaTeX">{N} </tex-math></inline-formula>th-order filter, significantly improving the frequency selectivity of dual-band filters. Moreover, the limitation of TZs achieved by the frequency-variant couplings (FVCs) structure widely used in coaxial cavity filter design is discussed for the first time. Furthermore, to facilitate physical implementation, a strategy is proposed to guide the selection of the production sequence of TZs in dual-band filters formed by inline FVCs and FVTs. Finally, a dual-band filter with four TZs is fabricated, whose synthesis, simulation, and test results perform the desired responses and are well-matched with each other.
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