Search Results (425)

This study comprehensively compares the performance of two non-destructive testing (NDT) techniques—active thermography (AT) and digital shearography (DS)—for identifying various damage types in composite structures. Three distinct composite specimens were inspected: a carbon-fiber-reinforced polymer (CFRP) plate with flat-bottom holes, an aluminum honeycomb core sandwich plate with a circular skin-core disbond, and a CFRP plate with two low-energy impacts damage. The research highlights the significant role of post-processing methods in enhancing damage detectability. For AT, algorithms such as fast Fourier transform (FFT) for temperature phase extraction and principal component thermography (PCT) for identifying significant temperature components were employed, generally making anomalies brighter and easier to locate and size. For DS, a novel band-pass filtering approach applied to phase maps, followed by summing the filtered maps, remarkably improved the visualization and precision of damage-induced anomalies by suppressing background noise. Qualitative image-based comparisons revealed that DS consistently demonstrated superior performance. The sum of DS filtered phase maps provided more detailed and precise information regarding damage location and size compared to both pulsed thermography (PT) and lock-in thermography (LT) temperature phase and amplitude. Notably, DS effectively identified shallow flat-bottom holes and subtle imperfections that AT struggled to clearly resolve, and it provided a more comprehensive representation of the impacts damage location and extent. This enhanced capability of DS is attributed to the novel phase map filtering approach, which significantly improves damage identification compared to the thermogram post-processing methods used for AT. Full article

Background: Biomechanical and neuromuscular differences between falls and recoveries have been well-studied; however, the cortical correlations remain unclear. Using mobile brain imaging via electroencephalography (EEG), we examined differences in sensorimotor beta frequencies between falls and recoveries during an unpredicted slip in walking. Methods: We recruited 22 young adults (15 female; 18–35 years) who experienced a slip (65 cm) during walking. Raw EEG signals were band-pass filtered, and independent component analysis was performed to remove non-neural sources, eventually three participants were excluded due to excessive artifacts. Peak beta power was extracted from three time-bins: 400 milliseconds pre-, 0–150 milliseconds post and 150–300 milliseconds post-perturbation from the midline (Cz) electrode. A 2 × 3 Analysis of Covariance assessed the interaction between time-bins and group on beta power, followed by Independent and Paired t-tests for between and within-group post hoc comparisons. Results: All participants (n = 19) experienced a balance loss, seven experienced a fall. There was a time × group interaction on beta power (p < 0.05). With no group differences pre-perturbation, participants who experienced a fall exhibited higher beta power during 0–150 milliseconds post-perturbation than those who recovered (p < 0.001). However, there were no group differences in beta power during 150–300 milliseconds post-perturbation. Conclusions: Young adults exhibiting a greater increase in beta power during the early post-perturbation period experienced a fall, suggesting a higher cortical error detection due to a larger mismatch in the expected and ongoing postural state and greater cortical dependence for sensorimotor processing. Our study results provide an overview of the possible cortical governance to modulate slip-fall/recovery outcomes. Full article

Quadcolor cameras with conventional RGB channels were studied. The fourth channel was designed to improve the estimation of the spectral reflectance and chromaticity from the camera signals. The RGB channels of the quadcolor cameras considered were assumed to be the same as those of the Nikon D5100 camera. The fourth channel was assumed to be a silicon sensor with an optical filter (band-pass filter or notch filter). The optical filter was optimized to minimize a cost function consisting of the spectral reflectance error and the weighted chromaticity error, where the weighting factor controls the contribution of the chromaticity error. The study found that using a notch filter is more effective than a band-pass filter in reducing both the mean reflectance error and the chromaticity error. The reason is that the notch filter (1) improves the fit of the quadcolor camera sensitivities to the color matching functions and (2) provides sensitivity in the wavelength region where the sensitivities of RGB channels are small. Munsell color chips under illuminant D65 were used as samples. Compared with the case without the filter, the mean spectral reflectance rms error and the mean color difference (ΔE₀₀) using the quadcolor camera with the optimized notch filter reduced from 0.00928 and 0.3062 to 0.0078 and 0.2085, respectively; compared with the case of using the D5100 camera, these two mean metrics reduced by 56.3%. Full article

This work presents a comprehensive technological study of dielectric resonator-based microstrip filters (DRMFs), encompassing the design, fabrication, and rigorous characterization of the $T{E}_{01\delta }$ mode. Through systematic coupling analysis, we demonstrate filters featuring novel input–output coupling techniques and innovative implementations of both transmission zeros (4-2-0 configuration) and equalization zeros (4-0-2 configuration), specifically designed for demanding space and radar receiver applications, while the loaded quality factor ($Q_L$) and insertion loss do not match those of dielectric resonator cavity filters (DRCFs), our solution significantly surpasses conventional microstrip filters (MFs), achieving $Q_L$> 3000 compared to typical $Q_L$≈ 200 for coupled-line MFs in X-band. The fabricated filters exhibit exceptional performance as follows: input reflection ($S_{11}$) below −18 dB (4-2-0) and −16.5 dB (4-0-2), flat transmission response ($S_{21}$), and out-of-band rejection exceeding −30 dB. Mechanical tuning enables precise control of input–output coupling, inter-resonator coupling, cross-coupling, and frequency synthesis, while equalization zeros provide tailored group delay characteristics. This study positions DRMFs as a viable intermediate technology for high-performance RF systems, bridging the gap between conventional solutions. Full article

Underwater object detection with Synthetic Aperture Sonar (SAS) images faces many problems, including low contrast, blurred edges, high-frequency noise, and missed small objects. To improve these problems, this paper proposes a high-accuracy underwater object detection algorithm for SAS images, named the HAUOD algorithm. First, considering SAS image characteristics, a sonar preprocessing module is designed to enhance the signal-to-noise ratio of object features. This module incorporates three-stage processing for image quality optimization, and the three stages include collaborative adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement, non-local mean denoising, and frequency-domain band-pass filtering. Subsequently, a novel C2fD module is introduced to replace the original C2f module to strengthen perception capabilities for low-contrast objects and edge-blurred regions. The proposed C2fD module integrates spatial differential feature extraction, dynamic feature fusion, and Enhanced Efficient Channel Attention (Enhanced ECA). Furthermore, an underwater multi-scale contextual attention mechanism, named UWA, is introduced to enhance the model’s discriminative ability for multi-scale objects and complex backgrounds. The proposed UWA module combines noise suppression, hierarchical dilated convolution groups, and dual-dimensional attention collaboration. Experiments on the Sonar Common object Detection dataset (SCTD) demonstrate that the proposed HAUOD algorithm achieves superior performance in small object detection accuracy and multi-scenario robustness, attaining a detection accuracy of $95.1\%$, which is $8.3\%$ higher than the baseline model (YOLOv8n). Compared with YOLOv8s, the proposed HAUOD algorithm can achieve $6.2\%$ higher accuracy with only $50.4\%$ model size, and reduce the computational complexity by half. Moreover, the HAUOD method exhibits significant advantages in balancing computational efficiency and accuracy compared to mainstream detection models. Full article

The present work proposes a novel fractional-order multifunction filter topology in current-mode (CM), which is designed based on the Modified Current Feedback Operational Amplifier (MCFOA). The proposed design simultaneously generates fractional-order low-pass (FO-LPF), high-pass (FO-HPF), and band-pass (FO-BPF) outputs while utilizing an optimized set of essential active and passive elements, thereby ensuring simplicity, cost efficiency, and compatibility with integrated circuits (ICs). The fractional-order feature allows precise control over the transition slope between the passband and the stopband, enhancing design flexibility. PSpice simulations validated the filter’s theoretical performance, confirming a 1 kHz cut-off frequency, making it suitable for VLF applications such as military communication and submarine navigation. Monte Carlo analyses demonstrate robustness against parameter variations, while a low THD, a wide dynamic range, and low power consumption highlight its efficiency for high-precision, low-power applications. This work offers a practical and adaptable approach to fractional-order circuit design, with significant potential in communication, control, and biomedical systems. Full article

In this paper, we implement a compact switchable bandpass filter on a 50 Ω microstrip line. The proposed structure consists of an input/output stage with one end terminated at 50 Ω, a C-shaped-open loop resonator, and two L-shaped-open loop resonators. The proposed filter operates in four different modes depending on the on/off combination of the five PIN diodes. Each mode includes a dual-band pass filter (DB-BPF) designed for the 1.4 GHz and 5.1 GHz bands, another DB-BPF covering the 2.4 GHz and 4.2 GHz bands, a wideband BPF with a bandwidth ranging from 2 to 4.5 GHz, and an all-pass filter (APF) that allows all frequencies to pass through. The proposed structure is extremely compact because it is implemented on a 50 Ω line without any additional space. Full article

High-frequency (HF) injection is a widely used technique for low-speed implementation of position sensorless permanent magnet synchronous motor control. A key component of this technique is the tracking loop control system, which extracts rotor position error and utilizes proportional–integral regulation as a position observer for estimating the rotor position. Generally, this process relies on band-pass filters (BPFs) and low-pass filters (LPFs) to modulate signals in the quadrature current to obtain rotor position error information. However, limitations in filter accuracy and dynamic response lead to prolonged convergence times and timing inconsistencies in the estimation process, which affects real-time motor control performance. To address these issues, this study proposes an exponential moving average (EMA)-based scheme for rotor position error extraction, offering a rapid response under dynamic conditions such as direction reversals, step speed changes, and varying loads. EMA is used to pass the original rotor position information carried by the quadrature current signal, which contains HF components, with a specified smoothing factor. Then, after the synchronous demodulation process, EMA is employed to extract rotor position error information for the position observer to estimate the rotor position. Due to its computational simplicity and fast response in handling dynamic conditions, the proposed method can serve as an alternative to BPF and LPF, which are commonly used for rotor position information extraction, while also reducing computational burden and improving performance. Finally, to demonstrate its feasibility and effectiveness in improving rotor position estimation accuracy, the proposed system is experimentally validated by comparing it with a conventional system. Full article

Advances in microfluidics, optogenetics and electronics have enabled the study of dynamically controlled inputs on cellular fate. Here, we applied a microfluidic system to deliver periodic inputs of growth factors to pheochromocytoma cells and measured the extent of premature differentiation as a function of input frequency. Epidermal growth factor-triggered differentiation peaked at two cycles/hour, while nerve growth factor-triggered differentiation peaked at one cycle/hour. To interpret the results, we analyzed a published model that attributed pheochromocytoma cell differentiation to the linear combination of activated enzymes extracellular signal-regulated kinase (ERK), cAMP response element binding protein (CREB), protein kinase B (AKT) and c-Jun N-terminal kinase (JNK) at specific times after step input stimulation. Transfer functions for enzyme activation were derived from the published time-domain activation kinetics and these transfer functions were combined in a parallel architecture as a predictor of neurite outgrowth, as a function of input frequency. Qualitative agreement was observed between the model and the experiments. Full article

A miniaturized hyperspectral camera, developed by integrating a linear variable band-pass filter (LVBPF) with an image sensor, was installed on the TIRSAT 3U CubeSat, launched on 17 February 2024 by Japan’s H3 launch vehicle. The satellite and its onboard hyperspectral camera conducted on-orbit experiments and successfully acquired hyperspectral data from multiple locations. The required attitude control for the hyperspectral mission was also achieved. CubeSat-based hyperspectral missions often face challenges in image alignment due to factors such as parallax, distortion, and limited attitude stability. This study presents solutions to these issues, supported by actual observational hyperspectral data. To verify the consistency of the hyperspectral data acquired by TIRSAT and processed using the proposed method, a validation analysis was conducted. Full article

Despite the proliferation of methods for detecting AC arc faults, current solutions often fall short in balancing cost efficiency, real-time performance, and implementation flexibility. This paper proposes a novel joint detection method based on robust local mean decomposition (RLMD) and pulse density analysis. The method leverages a dual-path analog signal processing framework: the low-frequency component of the current transformer (CT) signal is digitized and decomposed using RLMD to extract statistical feature indicators, while the high-frequency component is fed into a high-speed comparator to generate TTL pulses, from which the pulse density is calculated within a sliding time window. By combining the characteristic quantities derived from RLMD with the temporal pulse density, the proposed scheme achieves accurate and efficient detection of arc faults. Experimental results validate the approach, demonstrating high detection probability, adjustable sensitivity, low power consumption, and cost-effectiveness. These attributes underscore the method’s practical relevance and engineering significance in intelligent fault monitoring systems. Full article

Advanced multi-channel acoustic emission (AE) monitoring systems often rely on complex and costly architectures, especially those requiring custom FPGA-based hardware. In this work, we present a digital signal processing (DSP)-based approach to high-performance AE acquisition, implemented using a simplified analog front-end (AFE) and a commercially available synchronous data acquisition (DAQ) card (NI USB-6356). This design eliminates the need for specialized FPGA development, improving accessibility and reducing system complexity. A key feature of the system is the replacement of traditional analog filters with a software-defined digital band-pass filtering module implemented in LabVIEW. This allows for real-time or post-processing filtering with adjustable parameters, enhancing flexibility in data analysis. The system supports 8-channel synchronous sampling at 1.25 MS/s, and performance evaluations demonstrate a dynamic range of 79.22 dB and a signal-to-noise ratio (SNR) of 85.39 dB. These results confirm the system’s ability to maintain high fidelity in AE signal acquisition without the need for dedicated hardware filtering or custom DAQ hardware. The proposed method offers a practical and validated alternative for multi-channel AE monitoring, with potential applications in structural health monitoring, materials testing, and other engineering domains. Full article

In modern optical coating production, optical monitoring technology is an indispensable component. The traditional monochromatic monitoring technology used in current commercial and research institutions is usually only for a specific wavelength and cannot fully represent the characteristics of the film in the entire spectral range. Moreover, for non-quarter-wave coating systems (such as multilayer or complex coating systems), a thickness change in a single coating may have a significant effect on the performance of the entire coating system. In this case, it may be difficult to use monochromatic monitoring to accurately determine the thickness of each layer, resulting in reduced monitoring accuracy. At present, although broadband optical monitoring can be monitored over a wide wavelength range, the stop-plating time may be misjudged due to error accumulation during the coating process. To solve these problems, a broadband optical monitoring method with an improved error compensation mechanism is proposed in this paper. An optimal function that combines the absolute error and shape similarity of the transmission spectrum is designed, and the transmission spectrum is optimized by the limited random search method. In addition, a breakpoint algorithm based on parabolic error curve prediction is designed for the first time in this paper, which avoids the problem of excessive deposition thickness encountered by traditional broadband monitoring methods in the automatic coating processes. To verify the effectiveness of the proposed method, a set of hardware verification platforms based on broadband optical monitoring is designed in this paper, and a 30-layer shortwave-pass filter is constructed as an example. Compared with the traditional time monitoring method (CTMM), the proposed broadband optical monitoring method (PBMM) has significant advantages in terms of the matching degree between the transmission spectrum and the target spectrum, as well as the average transmittance in the low-pass band. In summary, the broadband optical monitoring method with an improved error compensation mechanism proposed in this paper provides an effective solution for high-precision optical coating production and has high practical application value and research significance. Full article

This study presents a novel method for dynamically tuning singular states in one-dimensional (1D) photonic lattices (PLs) using air-slit-based structural modifications. Singular states, arising from symmetry-breaking-induced resonance radiation, generate diverse spectral features through interactions between resonance modes and background radiation. By strategically incorporating air slits to break symmetry in 1D PLs, we demonstrated effective control of resonance positions, enabling dual functionalities including narrowband band pass and notch filtering. These singular states originate from asymmetric guided-mode resonances (aGMRs), which can be interpreted by analytical modeling of the equivalent slab waveguide. Moreover, the introduction of multiple air slits significantly enhances spectral tunability by inducing multiple folding behaviors in the resonance bands. This approach allows for effective manipulation of optical properties through simple adjustments of air-slit displacements. This work provides great potential for designing multifunctional photonic devices with advanced metamaterial technologies. Full article

Edge-computing applications demand ultra-low-power architectures for both feature extraction and classification tasks. In this manuscript, a Keyword Spotting (KWS) system tailored for energy-constrained portable environments is proposed. A 16-channel analog filter bank is employed for audio feature extraction, followed by a digital Gated Recurrent Unit (GRU) classifier. The filter bank is behaviorally modeled, making use of second-order band-pass transfer functions, simulating the analog front-end (AFE) processing. To enable efficient deployment, the GRU classifier is trained using a Learned Step Size (LSQ) and Look-Up Table (LUT)-aware quantization method. The resulting quantized model, with 4-bit weights and 8-bit activation functions (W4A8), achieves 91.35% accuracy across 12 classes, including 10 keywords from the Google Speech Command Dataset v2 (GSCDv2), with less than 1% degradation compared to its full-precision counterpart. The model is estimated to require only 34.8 kB of memory and 62,400 multiply–accumulate (MAC) operations per inference in real-time settings. Furthermore, the robustness of the AFE against noise and analog impairments is evaluated by injecting Gaussian noise and perturbing the filter parameters (center frequency and quality factor) in the test data, respectively. The obtained results confirm a strong classification performance even under degraded circuit-level conditions, supporting the suitability of the proposed system for ultra-low-power, noise-resilient edge applications. Full article