Search Results (94)

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

Noise in Pulsar Timing Array (PTA) data is commonly modeled as a mixture of white and red noise components. While the former is related to the receivers, and easily characterized by three parameters (EFAC, EQUAD and ECORR), the latter arises from a mix of hard to model sources and, potentially, a stochastic gravitational wave background (GWB). Since their frequency ranges overlap, GWB search methods must model the non-GWB red noise component in PTA data explicitly, typically as a set of mutually independent Gaussian stationary processes having power-law power spectral densities. However, in searches for continuous wave (CW) signals from resolvable sources, the red noise is simply a component that must be filtered out, either explicitly or implicitly (via the definition of the matched filtering inner product). Due to the technical difficulties associated with irregular sampling, CW searches have generally used implicit filtering with the same power law model as GWB searches. This creates the data analysis burden of fitting the power-law parameters, which increase in number with the size of the PTA and hamper the scaling up of CW searches to large PTAs. Here, we present an explicit filtering approach that overcomes the technical issues associated with irregular sampling. The method uses adaptive splines, where the spline knots are included in the fitted model. Besides illustrating its application on real data, the effectiveness of this approach is investigated on synthetic data that has the same red noise characteristics as the NANOGrav 15-year dataset and contains a single non-evolving CW signal. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. 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

Accurate real-time temperature measurement under extreme thermal-pressure conditions remains challenging in aerospace. Sapphire fiber Bragg gratings (FBGs), exhibiting temperature measurement capabilities up to 1900 °C, demonstrate suitability for such extreme environments. However, the development of a high-performance demodulation system capable of processing sapphire FBG signals over wide spectral ranges at elevated speeds remains a technical challenge. This study presents a real-time FBG signal demodulation system that incorporates an all-dielectric subwavelength grating edge filter. The designed grating, comprising a TiO₂/Si₃N₄ subwavelength unit array, modulates Mie-type electric and magnetic multipole resonances to achieve precisely tailored transmission and reflection spectra. Simulation results indicate that the grating exhibits low ohmic loss, excellent linearity, complementary transmission/reflection characteristics, a wide linear range, and angular-dependent tunability. The designed edge-filter-based demodulation system incorporates dual single-point detectors to simultaneously monitor the transmitted and reflected signals. Leveraging the functional relationship between the center wavelength of the FBG and the detected signals, this system enables high-speed, wide-range interrogation of the center wavelength, thus facilitating real-time demodulation for wide-range temperature sensing. The proposed method and system are validated through theoretical modeling, offering an innovative approach for sapphire FBG signal demodulation under extreme thermal-pressure conditions. Full article

Recent advances in spectral imaging have enabled snapshot acquisition, as a means to mitigate the impracticalities of spectral imaging, e.g., expert operators and cumbersome hardware. Snapshot spectral imaging, e.g., in technologies like spectral filter arrays, has also enabled higher temporal resolution at the expense of the spatio-spectral resolution, allowing for the observation of temporal events. Designing, realising, and deploying such technologies is yet challenging, particularly due to the lack of clear, user-meaningful quality criteria across diverse applications, sensor types, and workflows. Key research gaps include optimising raw image processing from snapshot spectral imagers and assessing spectral image and video quality in ways valuable to end-users, manufacturers, and developers. This paper identifies several challenges and current opportunities. It proposes considering them jointly and suggests creating a new unified snapshot spectral imaging paradigm that would combine new systems and standards, new algorithms, new cost functions, and quality indices. Full article

Spatial multispectral imaging technology can selectively image in specific spectral bands, and the filter wheel is a core component for multispectral selection. At present, there are relatively few types of spectral bands for the filter wheel under limited space/weight constraints. Addressing the challenges presented by this issue, this paper introduces an innovative design approach for the development of a double-layer or even multi-layer filter wheel that is operated by a solitary motor in conjunction with a differential gear mechanism, enabling a vast array of spectral segment combinations within a highly compact layout. A detailed design is implemented for the double-layer filter wheel, including comprehensive modal and dynamic analyses. The results of the modal analysis attested to the structural stability of the component, and the outcomes of the dynamic analysis validated the component’s timely and reliable switching capabilities. A prototype was meticulously crafted and subjected to rigorous testing. The switching functionality was validated during these tests, concurrently affirming the accuracy of the finite element analysis results. Additionally, spectral and application testing confirmed the number of spectral segments and the practical utility of the components. The research presented in this article introduces an innovative design concept for multispectral imaging filter-wheel mechanisms, providing a valuable reference and profound insights for the design and arrangement of a double-layer or even multi-layer filter wheel. Full article

Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN. Full article

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 $\mathrm{d}$$\mathrm{B}$ for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 $\mathrm{d}$$\mathrm{B}$ to 16 $\mathrm{d}$$\mathrm{B}$, respectively. Full article