High-frequency vibration of motion platform leads to paired echo for synthetic aperture radar (SAR) imaging, especially in terahertz band due to its shorter wave length. Different from most existing parameters estimation methods only considering single component high-frequency vibration, in this paper a novel method considering multi-components vibration model is proposed based on fractional Fourier transform (FrFT) with combination of quasi-maximum likelihood (QML) and random sample consensus (RANSAC). Based on the model establishment of high-frequency vibration error in the echo, its instantaneous chirp rate (ICR) is firstly estimated by FrFT in sliding sub-aperture, followed which the vibration parameters are coarsely obtained through spectrum analysis and least square (LS) regression. To further refine the parameters estimates, QML is developed for compensating the deviation both caused by the frequency spectrum leakage and the error propagation effects by one-dimensional search over the vibration frequency. Meanwhile, RANSAC is adopted for avoiding the outlier of the ICR estimates in LS regression, especially at low signal-to-noise ratio (SNR). Thus, the refinement strategy based on the combination of QML and RANSAC is developed, whose utilization improves the estimation accuracy of vibration parameters. Finally, the paired echo in terahertz SAR (THz-SAR) imaging is effectively suppressed by the proposed method, and the high-quality THz-SAR imaging results are achieved. Both simulations of single component and multi-components high-frequency vibration are used to verify the validity of the proposed method. The simulation results show that the proposed method has higher estimation accuracy even at low SNR.

Compared with microwave synthetic aperture radar (SAR), terahertz SAR (THz-SAR) is easier to achieve ultrahigh-resolution image due to its higher frequency and shorter wavelength. However, higher carrier frequency makes THz-SAR image quality very sensitive to high-frequency vibration error of motion platform. Therefore, this paper proposes a novel high-frequency vibration error estimation and compensation algorithm for THz-SAR imaging based on local fractional Fourier transform (LFrFT). Firstly, the high-frequency vibration error of the motion platform is modeled as a simple harmonic motion and THz-SAR echo signal received in each range pixel can be considered as a sinusoidal frequency modulation (SFM) signal. A novel algorithm for the parameter estimation of the SFM signal based on LFrFT is proposed. The instantaneous chirp rate of the SFM signal is estimated by determining the matched order of LFrFT in a sliding small-time window and the vibration acceleration is obtained. Hence, the vibration frequency can be estimated by the spectrum analysis of estimated vibration acceleration. With the estimated vibration acceleration and vibration frequency, the SFM signal is reconstructed. Then, the corresponding THz-SAR imaging algorithm is proposed to estimate and compensate the phase error caused by the high-frequency vibration error of the motion platform and realize high-frequency vibration error estimation and compensation for THz-SAR imaging. Finally, the effectiveness of the novel algorithm proposed in this paper is demonstrated by simulation results.

Deaf and hearing-impaired people always face communication barriers. Non-invasive surface electromyography (sEMG) sensor-based sign language recognition (SLR) technology can help them to better integrate into social life. Since the traditional tandem convolutional neural network (CNN) structure used in most CNN-based studies inadequately captures the features of the input data, we propose a novel inception architecture with a residual module and dilated convolution (IRDC-net) to enlarge the receptive fields and enrich the feature maps, applying it to SLR tasks for the first time. This work first transformed the time domain signal into a time–frequency domain using discrete Fourier transformation. Second, an IRDC-net was constructed to recognize ten Chinese sign language signs. Third, the tandem CNN networks VGG-net and ResNet-18 were compared with our proposed parallel structure network, IRDC-net. Finally, the public dataset Ninapro DB1 was utilized to verify the generalization performance of the IRDC-net. The results showed that after transforming the time domain sEMG signal into the time–frequency domain, the classification accuracy (acc) increased from 84.29% to 91.70% when using the IRDC-net on our sign language dataset. Furthermore, for the time–frequency information of the public dataset Ninapro DB1, the classification accuracy reached 89.82%; this value is higher than that achieved in other recent studies. As such, our findings contribute to research into SLR tasks and to improving deaf and hearing-impaired people’s daily lives.

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.

This article proposes a novel dual-band filter topology by integrating two cascaded quadruplet (CQ) units and a dual-composite bandpass bandstop resonator (CBPBSR) unit. The CQ units generate out-of-band and in-band transmission zeros (TZs) to improve stopband roll-off, while the dual-CBPBSR unit introduces two TZs at the main passband center frequency $f_{0}$ to enhance inter passband rejection. This approach enables flexible control over the distribution of TZs and achieves excellent frequency selectivity. Based on this topology, a high-temperature superconducting (HTS) dual-band ultra-narrowband bandpass filter (BPF) is designed and fabricated using folded compact double-spiral microstrip resonators. The filter operates at 2533.25 ( $f_{01}$ ) and 2560.85 MHz ( $f_{02}$ ), both with a relative bandwidth of 0.44% at $f_{0}$ . Six TZs are realized, with a maximum out-of-band roll-off rate of 23.0 dB/MHz, insertion loss below 0.2 dB, and inter passband rejection of 60 dB. At 77 K, the measured results agree well with simulations, validating the proposed topology for high-performance ultra-narrowband dual-band filter applications.

Gait, fundamental to human movement, becomes compromised in cerebral palsy (CP), a childhood‐onset central nervous system motor disorder. Precise assessment of patients’ gait is crucial for tailored rehabilitation interventions. Currently, clinical scales assessing CP gait dysfunction mostly, while valuable, rely on subjective clinician observations. To enhance objectivity and efficiency in CP diagnosis and rehabilitation, there is a need for more objective assessment procedures. This study introduces a multi‐modal and multi‐scale feature fusion (MMFF) framework, a new framework for automating gait dysfunction assessment in children with CP. By utilizing surface electromyography and acceleration signals recorded during children's walking, MMFF generates a feature vector enriched with adaptively refined feature maps, cross‐mode correlations, and both local and global information. Validation of MMFF's effectiveness is evident through an accomplished classification accuracy of 99.13%. The mean values for precision, recall, and F1‐score in Gross Motor Function Classification System (GMFCS)‐1, GMFCS‐2, and GMFCS‐3, reaching 99.00%, 99.00%, and 98.33%, respectively, further reflect the accuracy of functional assessments at each level. This study underscores MMFF's potential as an objective, streamlined tool for clinicians, promising improved gait assessment and well‐informed rehabilitation strategies for children with CP.