Search Results (102)

Background: The acoustic recognition accuracy of Unmanned Aerial Vehicles (UAVs) is severely degraded by strong gas discharge noise in power substations and overhead transmission lines. Methods: This paper proposes a UAV acoustic recognition method based on adaptive time–frequency denoising and multichannel feature fusion. The method integrates adaptive Wiener filtering with time–frequency masking techniques, employs a six-channel microphone array for collaborative noise reduction, and constructs a CNN-LSTM hybrid deep learning model for UAV type recognition. Results: Experimental results demonstrate that the two-stage denoising achieves a signal-to-noise ratio (SNR) improvement of 19.08 dB and a spectral fidelity of 0.89. Under extreme noise conditions at −10 dB SNR, the proposed method achieves 87.4% recognition accuracy, representing a 23.7% improvement over the traditional MFCC-SVM method and a 12.3% improvement over single-stage denoising strategies. In unknown environments, the recognition accuracy remains at 87–91%, exhibiting strong robustness and generalization ability. Conclusions: The proposed method effectively mitigates strong discharge noise interference and provides a reliable acoustic recognition solution for UAV intrusion detection in power facilities. Full article

This article presents one possible method for modelling the electromagnetic radiation spectrum. This work aims to model a radiation source by combining two spectrums and obtaining a different spectrum as a result. To achieve this, the spectrum of a commercially available LED and a filter that transmits electromagnetic radiation at wavelengths above 475 nm were used. The article presents a methodical approach to designing the resulting spectral irradiance graph and the individual component spectral irradiance graphs. The originality of this method lies in transmitting the spectrum of one diode and combining it with an unfiltered diode. The irradiance values obtained on the sensor array depend on the electromagnetic radiation power of the light source and the distance between the sensor array and the light source’s centre. The simulation results for the combined spectrum were 13 W/m². This work may influence the design of new industrial illuminators. Full article

This work presents a characterization method for cameras with trichromatic RGB color filter array and polarization layer (RGB-P) sensor-based imaging devices. Such sensors enable the reconstruction of color and polarization of registered scene elements, which is an important requirement in computer vision. We will present spectral responsivity measurements, which reveal different sensitivities for various color and polarization channels. Furthermore, we will discuss and model an observed chromaticity shift in registered camera signals for polarized irradiance. Both lead to inaccurate estimation of color and polarization features. In order to overcome these issues, we will present a neural-network-based model for color and polarization feature reconstruction. Essentially, it considers spectral sensitivity for polarized irradiance. Furthermore, the model takes into account that, for visualization, the color signals have to be a linear combination of polarization channels. Models were trained for selected natural and synthetic reflectance sets, as well as commonly used lighting. We evaluated the resulting performance, which yielded robust results. The method can be employed for an estimation of color and polarization features for RGB-P imaging devices. Applications can be found in photography, as well as machine and computer vision, in which object surface color rendering plays a major role. Full article

Recent advances in metasurface-based research have enabled significant reductions in the size and weight of optical devices. By employing metallic nanostructures with subwavelength dimensions, color filtering can be achieved through phenomena such as extraordinary optical transmission (EOT), which allows specific bands of visible light to pass through. However, conventional EOT-based color filters often suffer from strong side peaks outside the desired transmission band, degrading color purity and hindering accurate color reproduction. In this study, we propose an ultrathin, polarization-independent color filter based on a nanohole array that utilizes the EOT effect while effectively suppressing unwanted side peaks. To achieve this, we introduce a modified design in which additional metallic triangular edges are placed around a hole in a conventional hole array. This configuration suppresses higher-order diffraction modes and enables selective transmission at RGB wavelengths, thereby improving spectral selectivity and overall color performance. Full article

Lensless on-chip microscopy enables high-throughput, wide-FOV imaging; however, the Bayer color filter array (CFA) in standard color sensors spatially multiplexes spectral channels, introducing sub-sampling and spectral crosstalk that degrade phase retrieval. We propose a Wirtinger Poly-Gradient Solver (WPGS) for quantitative phase reconstruction with Bayer-filtered color sensors under sequential Red–Green–Blue Light-Emitting Diode (RGB-LED) illumination. The method combines Transport of Intensity Equation (TIE)-based initialization with polychromatic Wirtinger optimization to suppress CFA-induced artifacts and enable pixel super-resolution (PSR). Experiments resolve a $2.76 \mu \mathrm{m}$ linewidth using a $1.85 \mu \mathrm{m}$ pixel-pitch sensor, exceeding the nominal Nyquist limit imposed by pixel sampling. We further demonstrate label-free imaging of HeLa cells and unstained tissue sections, supporting high-throughput digital pathology and offering potential for longitudinal biological observation. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article

The spatial resolution of spectral imaging systems is fundamentally constrained by hardware trade-offs, and the availability of large-scale annotated spectral datasets remains limited. This study presents a comprehensive evaluation of super-resolution (SR) methods across interpolation-based, CNN-based, GAN-based, and diffusion-based approaches. Using a synthetic 30-band spectral representation reconstructed from RGB with the MST++ model as a proxy ground truth, we arrange non-adjacent triplets as three-channel PNG inputs to ensure compatibility with existing SR architectures. A unified pipeline enables reproducible evaluation at ×2, ×4, and ×8 scales on 50 unseen images, with performance assessed using PSNR, SSIM, and SAM. Results confirm that bicubic interpolation remains a spectrally reliable baseline; shallow CNNs (SRCNN, FSRCNN) generalize well without fine-tuning; and ESRGAN improves spatial detail at the expense of spectral accuracy. Diffusion models (SR3, ResShift, SinSR), evaluated in a zero-shot setting without spectral-domain adaptation, exhibit unstable performance and require spectrum-aware training to preserve spectral structure effectively. The findings underscore a persistent trade-off between perceptual sharpness and spectral fidelity, highlighting the importance of domain-aware objectives when applying generative SR models to spectral data. This work provides reproducible baselines and a flexible evaluation framework to support future research in spectral image restoration. Full article

Real-time monitoring of hazardous gas emissions in open environments remains a critical challenge. Conventional spectrometers and filter wheel systems acquire spectral and spatial information sequentially, which limits their ability to capture multiple gas species and dynamic dispersion patterns rapidly. A High-Resolution Snapshot Multispectral Imaging System (HRSMIS) is proposed to integrate high spatial fidelity with multispectral capability for near real-time plume visualization, gas species identification, and concentration retrieval. Operating across the 7–14 $\mathsf{\mu }$m spectral range, the system employs a dual-path optical configuration in which a high-resolution imaging path and a multispectral snapshot path share a common telescope, allowing for the simultaneous acquisition of fine two-dimensional spatial morphology and comprehensive spectral fingerprint information. Within the multispectral path, two $5\times 5$ microlens arrays (MLAs) combined with a corresponding narrowband filter array generate 25 distinct spectral channels, allowing concurrent detection of up to 25 gas species in a single snapshot. The high-resolution imaging path provides detailed spatial information, facilitating spatio-spectral super-resolution fusion for multispectral data without complex image registration. The HRSMIS demonstrates modulation transfer function (MTF) values of at least 0.40 in the high-resolution channel and 0.29 in the multispectral channel. Monte Carlo tolerance analysis confirms imaging stability, enabling the real-time visualization of gas plumes and the accurate quantification of dispersion dynamics and temporal concentration variations. Full article

The use of Multispectral (MS) imaging is growing fast across many research fields. However, one of the obstacles researchers face is the limited availability of multispectral image databases. This arises from two factors: multispectral cameras are a relatively recent technology, and they are not widely available. Hence, the development of an image database is crucial for research on multispectral images. This study takes advantage of two high-end MS cameras in visible and near-infrared based on filter array technology developed in the PImRob platform, the University of Burgundy, to provide a freely accessible database. The database includes high-resolution MS images taken from different plants and weeds, along with annotated images and masks. The original raw images and the demosaicked images have been provided. The database has been developed for research on demosaicking techniques, segmentation algorithms, or deep learning for crop/weed discrimination. Full article

Multispectral imaging plays an important role in fields such as environmental monitoring and industrial inspection. To meet the demands for high spatial resolution, portability, and multi-scenario use, this study presents a reconfigurable 2 × 3 multispectral camera-array imaging system. The system features a modular architecture, allowing for the flexible exchange of lenses and narrowband filters. Each camera node is equipped with an FPGA that performs real-time sensor control and data preprocessing. A companion host program, based on the GigE Vision protocol, was developed for synchronous control, multi-channel real-time visualization, and unified parameter configuration. End-to-end performance verification confirmed stable, lossless, and synchronous acquisition from all six 3072 × 2048-pixel resolution channels. Following field alignment, the 16 mm lens achieves an effective 4.7 MP spatial resolution. Spectral profile measurements further confirm that the system exhibits favorable spectral response characteristics. The proposed framework provides a high-resolution and flexible solution for portable multispectral imaging. Full article

Recent studies have highlighted the key role of lighting in regulating circadian rhythms and its impact on human health. Exposure to blue light, especially at specific times of day, is now quantified using the melanopic Equivalent Daylight Illuminance (m-EDI) parameter, defined in the CIE S 026 standard. This parameter is proportional to the integral, in the visible range, of the spectral power distribution and the melanopic sensitivity function, which peaks near 490 nm, and is similar to a Gaussian distribution. Low-cost spectrometric sensors using photodiode arrays and narrowband filters offer a cost-effective way to estimate m-EDI through a weighted sum of channel responses. However, due to inherent sensor variability, individual calibration is recommended. The standard approach involves multiple linear regression to fit the sensor’s output to reference values using a set of test light sources. This method is easy to implement but depends heavily on the selection of calibration illuminants, which must outnumber the channels. This paper analyzes the sensitivity of this method to the sensor’s spectral response and the choice of calibration sources. A revised calibration approach is proposed, selectively discarding channels to reduce deviations from the target response. Applied to several sensors, this method significantly improves calibration accuracy and robustness, reducing the RMS error for several test LEDs from 17.6 to 1.36 lux. Full article

A novel airborne pathogen detection method, based on Flashing Ratchet Potential (FRP) and Electric Current Spectroscopy (ECS), is presented. The system employs a precisely engineered asymmetric electrode array to generate controlled directional transport of oxygen ions (O₂⁻•), produced via thermionic emission and three-body electron attachment. As these ions interact with airborne particles in the detection zone, measurable perturbations in the ECS profile emerge, yielding distinct spectral signatures that indicate particle presence. Proof-of-concept experiments, using standardized talcum powder aerosols as surrogates for viral-scale particles, established optimal operating parameters of 6 V potential and 600 kHz modulation frequency, with reproducible detection signals showing a relative shift of 4.5–13.4% compared to filtered-air controls. The system’s design concept incorporates humidity-resilient features, intended to maintain stability under varying environmental conditions. Together with the proposed size selectivity (50–150 nm), this highlights its potential robustness for real-world applications. To the best of our knowledge, this is the first demonstration of an open-air electro-ratchet transport system coupled with electric current spectroscopy for bioaerosol monitoring, distinct from prior optical or electrochemical airborne biosensors, highlighting its promise as a tool for continuous environmental surveillance in high-risk settings such as hospitals, airports, and public transit systems. Full article

To evaluate the prospects of multi-antenna GNSS buoy and mooring array in ocean altimetry satellite calibration, experiments are conducted in the ocean around Qianliyan islet in China’s Yellow Sea. The trials aim to validate the feasibility of establishing an ocean altimetry satellite calibration site while assessing the performance of relevant calibration equipment. Utilizing one multi-antenna GNSS buoy system and one mooring array operating for over 20 days, the experiment incorporates continuous GNSS observation data from Qianliyan islet’s permanent station. Results reveal that high-frequency sea surface height (SSH) signals exhibit periods approaching or below 10 s, with the designed low-pass filter effectively attenuating these high-frequency components. Significant differences emerge in the power spectra of filtered SSH measurements between instruments: high-frequency signals detected by the mooring array demonstrate greater spectral concentration and lower signal intensity than those recorded by the GNSS buoy. Through multi-day synchronized observations, the height datum for mooring array SSH measurements is obtained, revealing average standard deviation of 2.76 cm in filtered SSH differences between platforms—validating both the system design and data processing methodology. This experiment successfully demonstrates the performance of calibration equipment, preliminarily verifies the effectiveness of ground-based calibration data processing techniques, and further confirms the technical viability of establishing an ocean altimetry satellite calibration site around Qianliyan islet. Full article

The integration of a pixelated Fabry–Pérot filter array onto the image sensor enables on-chip snapshot multispectral imaging, significantly reducing the size and weight of conventional spectral imaging equipment. However, a traditional Fabry–Pérot cavity, based on metallic or dielectric layers, exhibits a narrow bandwidth, which restricts their utility in broader applications. In this work, we propose novel Fabry–Pérot filter structures that employ dielectric thin films for phase modulation, enabling single-peak filtering across a broad operational wavelength range from 400 nm to 1100 nm. The proposed structures are easy to fabricate and compatible with complementary metal-oxide-semiconductor (CMOS) image sensors. Moreover, the structures show low sensitivity to oblique incident angles of up to 30° with minimal wavelength shifts. This advanced Fabry–Pérot filter design provides a promising pathway for expanding the operational wavelength of snapshot spectral imaging systems, thereby potentially extending their application across numerous related fields. Full article

To improve the spatial processing performance in the low frequency and long range shallow water communication system, a multi-mode joint equalization scheme is proposed, which combines modal depth function estimation, mode filtering, and multi-input equalization. This method first estimates the modal depth function of the effective modes by Singular Value Decomposition (SVD) of Cross Spectral Density Matrix (CDSM), then separates the influence of each mode on the continuous-time signal by the vertical array mode filtering without any prior information. After these pre-processings, the separated signal is only affected by the single channel mode, and the output Signal-to-Noise Ratio (SNR) is enhanced, and channel delay spread is reduced simultaneously. All the separated parts are then sent to a multi-input equalizer to compensate for the channel fading between different modes.Simulation results verify that compared with single channel equalization after beamforming and multichannel equalization, the proposed multi-mode joint equalization can obtain 3 dB and 6 dB gain, respectively. Experimental results also show that the proposed equalization can achieve lower Bit Error Rate (BER) and higher output SNR. Full article