Search Results (715)

Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges such as weathering, vegetation cover, and spectral overlap in complex sedimentary rock regions. This study proposes a hierarchical multi-feature random forest algorithm based on Feature-Preserved Compressive Sampling (FPCS). Using 3D laser point cloud data from the Manas River outcrop in the southern margin of the Junggar Basin as the test area, we integrate graph signal processing and multi-scale feature fusion to construct a high-precision lithology identification model. The FPCS method establishes a geologically adaptive graph model constrained by geodesic distance and gradient-sensitive weighting, employing a three-tier graph filter bank (low-pass, band-pass, and high-pass) to extract macroscopic morphology, interface gradients, and microscopic fracture features of rock layers. A dynamic gated fusion mechanism optimizes multi-level feature weights, significantly improving identification accuracy in lithological transition zones. Experimental results on five million test samples demonstrate an overall accuracy (OA) of 95.6% and a mean accuracy (mAcc) of 94.3%, representing improvements of 36.1% and 20.5%, respectively, over the PointNet model. These findings confirm the robust engineering applicability of the FPCS-based hierarchical multi-feature approach for point cloud lithology identification. Full article

In this paper a new single-input independent multiple-output universal tunable filter employing second-generation current conveyors (CCII) and second-generation voltage conveyors (VCII) as active elements is presented. The proposed filter has been analyzed at transistor level, using a CMOS standard AMS 0.35 μm technology, and implemented using discrete components based on the commercially available AD844. A detailed mathematical analysis is carried out, considering also parasitic impedances and non-ideal parameters. The low-pass, band-pass, and high-pass responses are simultaneously obtained and experimentally verified at 10 kHz central frequency where the voltage gain is about 27 dB for each output. THD analysis has been performed to evaluate the proposed work. Full article

This paper introduces a compact analog configuration that concurrently realizes a mixed-mode biquadratic filter and a dual-mode quadrature oscillator (QO) by employing a single differential differencing gain amplifier (DDGA) and all-grounded passive components. The proposed design supports four fundamental operation modes—voltage-mode (VM), current-mode (CM), trans-impedance-mode (TIM), and trans-admittance-mode (TAM)—utilizing the same circuit topology without structural modifications. In filter operation, it offers low-pass, high-pass, band-pass, band-stop, and all-pass responses with orthogonal and electronic pole frequency and quality factor. In oscillator operation, it delivers simultaneous voltage and current quadrature outputs with independent tuning of oscillator frequency and condition. The grounded-component configuration simplifies layout and enhances its suitability for monolithic integration. Numerical simulations in a 0.18-μm CMOS process with ±0.9 V supply confirm theoretical predictions, demonstrating precise gain-phase characteristics, low total harmonic distortion (<7%), modest sensitivity to 5% component variations, and stable operation from −40 °C to 120 °C. These results, combined with the circuit’s low component count and integration suitability, suggest strong potential for future development in low-power IoT devices, adaptive communication front-ends, and integrated biomedical systems. Full article

The harmonized multi-sensor satellite data assists users by providing seamless analysis-ready data with enhanced temporal resolution. The Harmonized Landsat Sentinel (HLS) product has gained popularity due to the seamless integration of Landsat OLI and Sentinel-2 MSI, achieving a temporal resolution of 2.8 to 3.5 days. However, applications that require monitoring intervals of less than three days or cloudy data can limit the usage of HLS data. Gaofen-1 (GF-1) Wide Field of View (WFV) data provides the capacity further to enhance the data availability by harmonization with HLS. In this study, GF-1/WFV data is harmonized with HLS by employing deep learning-based conditional Generative Adversarial Networks (cGANs). The harmonized WFV data with HLS provides an average temporal resolution of 1.5 days (ranging from 1.2 to 1.7 days), whereas the temporal resolution of HLS varies from 2.8 to 3.5 days. This enhanced temporal resolution will benefit applications that require frequent monitoring. Various processes are employed in HLS to achieve seamless products from the Operational Land Imager (OLI) and Multispectral Imager (MSI). This study applies 6S atmospheric correction to obtain GF-1/WFV surface reflectance data, employs MFC cloud masking, resamples the data to 30 m, and performs geographical correction using AROP relative to HLS data, to align preprocessing with HLS workflows. Harmonization is achieved without using BRDF normalization and bandpass adjustment like in the HLS workflows; instead, cGAN learns cross-sensor reflectance mapping by utilizing a U-Net generator and a patchGAN discriminator. The harmonized GF-1/WFV data were compared to the reference HLS data using various quality indices, including SSIM, MBE, and RMSD, across 126 cloud-free validation tiles covering various land covers and seasons. Band-wise scatter plots, histograms, and visual image color quality were compared. All these indices, including the Sobel filter, histograms, and visual comparisons, indicated that the proposed method has effectively reduced the spectral discrepancies between the GF-1/WFV and HLS data. Full article

In this paper, we present an extremely compact eleven-state microwave filter with four concentric split-ring resonators (SRRs). Reconfigurability is achieved by switching off either single or multiple SRRs, thereby obtaining different triple-band, dual-band, and single-band configurations from the initial quad-band topology. Switches are placed on the vertical branches of SRRs in order to minimize the additional insertion loss. As switching elements, we first use traditional RF switches—PIN diodes—and then examine the integration of non-volatile RF switches—memristors—into filter design. Memristors’ ability to remember previous electrical states makes them a main building block for designing circuits that are both energy-efficient and adaptive, opening a new era in electronics and artificial intelligence. As RF memristors are not commercially available, PIN diodes are used for experimental filter verification. Afterwards, we compare the filter characteristics realized with PIN diodes and memristors to present capabilities of memristor technology. Memristors require no bias, and their parasitic effects are modeled with low resistance for the ON state and low capacitance for the OFF state. Measured performances of all obtained configurations are in good agreement with the simulations. The filter footprint area is 26 mm × 29 mm on DiClad substrate. Full article

The bulk linearization technique is a design strategy used to extend the linear region of a metal oxide semiconductor field effect transistor (MOSFET) by increasing its saturation voltage through a composite structure and a gate biasing circuit. This allows us to develop compact and flexible pseudo-resistor elements for integrated circuit designs. In this paper we propose a new simple yet effective design approach, focused on the biasing circuit, that optimizes area, offset, and power consumption without altering the design complexity of the original solution. Post-layout simulations verify the presented design strategy, which is then applied for designing a band-pass filter for neural action potential acquisition. Results of harmonic distortion and noise analysis strengthen the validity of the proposed strategy. Full article

In this study, a novel synthesis method for bandpass filters is proposed. The method relies on Richard’s transform and avoids approximations in circuit realizations. Thus, proper frequency responses are obtained for bandpass filters with bandwidths ranging from narrow to wide. In the presented approach, a method for removing the input and output inverters/transformers is proposed and is used to show how classic parallel-coupled resonator filters can be designed using the proposed method. Also, a degree of freedom is introduced that allows the overall impedance level of the fabricated filter to be tuned, which is used to tune the frequency response of the filter to the theoretical one. Both narrow-band and wideband solutions in terms of impedance inverter realization are discussed in the paper. The theoretical investigations are confirmed by an experimental realization of two bandpass filters with parallel-coupled shorted resonators. Full article

Kinesthetic motor imagery (KMI), the mental rehearsal of a motor task without its actual performance, constitutes one of the most common techniques used for brain–computer interface (BCI) control for movement-related tasks. The effect of neural injury on motor cortical activity during execution and imagery remains under investigation in terms of activations, processing of motor onset, and BCI control. The current work aims to conduct a post hoc investigation of the event-related potential (ERP)-based processing of KMI during BCI control of anthropomorphic robotic arms by spinal cord injury (SCI) patients and healthy control participants in a completed clinical trial. For this purpose, we analyzed 14-channel electroencephalography (EEG) data from 10 patients with cervical SCI and 8 healthy individuals, recorded through Emotiv EPOC BCI, as the participants attempted to move anthropomorphic robotic arms using KMI. EEG data were pre-processed by band-pass filtering (8–30 Hz) and independent component analysis (ICA). ERPs were calculated at the sensor space, and analysis of variance (ANOVA) was used to determine potential differences between groups. Our results showed no statistically significant differences between SCI patients and healthy control groups regarding mean amplitude and latency (p < 0.05) across the recorded channels at various time points during stimulus presentation. Notably, no significant differences were observed in ERP components, except for the P200 component at the T8 channel. These findings suggest that brain circuits associated with motor planning and sensorimotor processes are not disrupted due to anatomical damage following SCI. The temporal dynamics of motor-related areas—particularly in channels like F3, FC5, and F7—indicate that essential motor imagery (MI) circuits remain functional. Limitations include the relatively small sample size that may hamper the generalization of our findings, the sensor-space analysis that restricts anatomical specificity and neurophysiological interpretations, and the use of a low-density EEG headset, lacking coverage over key motor regions. Non-invasive EEG-based BCI systems for motor rehabilitation in SCI patients could effectively leverage intact neural circuits to promote neuroplasticity and facilitate motor recovery. Future work should include validation against larger, longitudinal, high-density, source-space EEG datasets. Full article

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article

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

This article investigates how to exploit the high-frequency mmWave for 5G-advanced and beyond, which requires new filters for the wide bandpass and its multi-sub-band. Based on the substrate-integrated waveguide (SIW), spoof surface plasmon polariton (SSPP), and metastructures, like complementary split-ring resonators (CSRRs), the development of a wide bandpass filter and a multi-sub-band filter is proposed, along with an experimental realization to verify the model. The upper and lower cutoff frequencies of the wide bandpass are controlled through an SIW-SSPP structure, whereas the corresponding wide bandpass and its multi-sub-band filters are designed through incorporating new metastructures. The frequency range of 24.25–29.5 GHz, which covers the n257, n258, and n261 bands for 5G applications, was selected for verification. The basic SIW-SSPP wide bandpass structure of 24.25–29.5 GHz was designed first. Then, by incorporating an Archimedean spiral configuration, the insertion loss within the passband was reduced from 1 dB to 0.5 dB, while the insertion loss in the high-frequency stopband was enhanced from 40 dB to 70 dB. Finally, CSRRs were integrated to effectively suppress undesired frequency components within the bandpass, thereby achieving multi-sub-band filters with low insertion losses with a triple-sub-band filter of 0.5 dB, 0.7 dB, and 0.8 dB in turn. The experimental results showed strong agreement with the design scheme, thereby confirming the rationality of the design. Full article

An external power build-up cavity of a line-narrowed 407-nm laser diode for Raman gas analysis was demonstrated to possess good gas detection capabilities. By employing an ordinary laser diode without anti-reflection coating or and a bandpass interference filter in an external cavity resonance, the laser linewidth was narrowed by resonant optical feedback, and tens of watts of external cavity power were built up. The coupling mechanism between the semiconductor laser and the external cavity are discussed, as well as the noise background in the experimental results. The Raman spectrum of ambient air was analyzed, achieving a methane detection limit of 1 ppm. Full article

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

Computational imaging spectrometers using broad-bandpass filter arrays with distinct transmission functions are promising implementations of miniaturization. The number of filters is limited by the practical factors. Compressed sensing is used to model the system as linear underdetermined equations for hyperspectral imaging. This paper proposes the following method: parallel dictionary reconstruction and fusion for spectral recovery in computational imaging spectrometers. Orthogonal systems are the dictionary candidates for reconstruction. According to observation of ground objects, the dictionaries are selected from the candidates using the criterion of incoherence. Parallel computations are performed with the selected dictionaries, and spectral recovery is achieved by fusion of the computational results. The method is verified by simulating visible-NIR spectral recovery of typical ground objects. The proposed method has a mean square recovery error of ≤1.73 × 10⁻⁴ and recovery accuracy of ≥0.98 and is both more universal and more stable than those of traditional sparse representation methods. Full article