Narrow-band Spectrum Research Articles

Wideband Tuning and Deep-Tissue Spectral Detection of Indium Phosphide Nano-Laser Particles.

Laser particles (LPs) emitting narrowband spectra across wide spectral ranges are highly promising for high-multiplex optical barcoding. Here, we present LPs based on indium phosphide (InP) nanodisks, operating in the near-infrared wavelength range of 740-970 nm. Utilizing low-order whispering gallery resonance modes in size-tuned nanodisks, we achieved an ultrawide color palette with 27% bandwidth utilization and nanometer-scale linewidth. The minimum laser size was 430 nm in air and 560 nm within the cytoplasm, operating at mode order 4 or 5. We further demonstrated spectral detection of laser peaks with high signal-to-background ratios in highly-scattering media, including 1-cm-thick chicken breast tissue and blood vessels in live mice.

Leveraging Fog Computing for Security‐Aware Resource Allocation in Narrowband Internet of Things

ABSTRACTNarrowband Internet of Things (NB‐IoT) is LPWAN operating using narrowband spectrum in IoTs, requiring low data rates and long battery life. Since NB‐IoT does not support handover, the requirement to sustain network connectivity during mobility may result in fake base station connections, hence applications are limited to stationary use‐cases. Researchers propose extending NB‐IoT in mobile applications, owing to higher signal quality and battery life, despite DoS attacks due to low bandwidth. As NB‐IoT is resource‐constrained, resources must be allocated based on application with data processing offloaded to optimise performance. Cloud servers, being centralised and memory‐intensive, may result in increased computational delay, lower throughput, and DoS attacks in NB‐IoT. Hence, in this paper, we implement fog computing, a decentralised technology, providing scalability, reduced bandwidth, and enhanced privacy, to provide distributed processing. Additionally, we develop secure handover protocols for private and service‐provider‐controlled fog networks under normal and cell‐splitting conditions to minimise fake base station attacks and provide seamless handover. We introduce reputation‐based mechanisms to determine device integrity and differentiate faulty behaviour and attacks. Further, we implement real‐time application‐aware resource allocation and QoS‐based load‐balancing using deep learning to distribute data processing between devices, fog and cloud servers. We simulate and prototype protocols on iFogSim2 and Raspberry Pi 4. Security of the fog computing framework is validated against various attacks and formally verified using Scyther. Evaluation shows that our approach consumes 12% and 43.75% lower power and communication overhead and approximately 6 and 16 times lower execution time and memory compared with existing solutions, thus making our approach lightweight.

Single snapshot-based miniaturized mid-infrared spectrometer with a dielectric metasurface

Mid-infrared spectroscopy is critical for applications like environmental monitoring and medical diagnostics, as it includes the fundamental vibrational frequencies of many molecular bonds, enabling detailed chemical analysis. We developed a miniature mid-infrared spectrometer using a fully dielectric metasurface operating in the 3–5 µm range. By combining simple structural units, design flexibility was enhanced, resulting in rich spectral data. The spectrometer was evaluated using narrowband, broadband, and transmission spectra of real materials. We detected the spectral responses of metasurface arrays and determined the unknown spectrum using an algorithm based on Tikhonov regularization with generalized cross-validation. By reconstructing multiple narrow spectra obtained through rotating the filters, spectral resolution intervals of approximately 10 nm were achieved.

Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning.

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model's R2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status.

Particle swarm optimization of high Q factor interdigitated E-shaped metamaterial for refractive index sensing

Metamaterials for refractive index sensing applications have garnered significant attention in recent years. However, achieving an optimal balance between high sensitivity and quality factor, which together determine the Figure of Merit of sensors, necessitates extensive optimization efforts. In this paper, we present the metaheuristic optimization of a refractive index-based plasmonic sensor operating at a wavelength of 1500 nm, which has the potential to open new avenues for applications in biomedical sensing and beyond. Particle Swarm Optimization is utilized to maximize the performance of the proposed infrared metamaterial sensor. The proposed design features two overlapping E-shaped silver patches placed on top of a grounded Al2O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathrm {Al_{2}O_{3}}$$\\end{document} substrate. This configuration results in a narrow-band absorption spectrum with peak resonances caused by the confinement of the electric field within the gaps of the E-shaped metallic arms. The metaheuristic optimization process yielded sensor dimensions that achieve a high sensitivity of 834.7 nm/RIU,Q = 865 and a Figure of Merit of 481.34 RIU-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{RIU}^{-1}$$\\end{document}, demonstrating outstanding performance in cancer cell detection.

The Origins of Narrow Spectra of Fast Radio Bursts

Observations find that some fast radio bursts (FRBs) have extremely narrowband spectra, i.e., Δν/ν 0 ≪ 1. We show that, when the angular size of the emission region is larger than the Doppler beaming angle, the observed spectral width (Δν/ν 0) exceeds 0.58 due to the high-latitude effects for a source outside the magnetosphere, even when the spectrum in the source’s comoving frame is monochromatic. The angular size of the source for magnetospheric models of FRBs can be smaller than the Doppler beaming angle, in which case this geometric effect does not influence the observed bandwidth. We discuss various propagation effects to determine if any could transform a broad-spectrum radio pulse into a narrow spectrum signal at the observer’s location. We find that plasma lensing and scintillation can result in a narrow bandwidth in the observed spectrum. However, the likelihood of these phenomena being responsible for the narrow observed spectra with Δν/ν 0 < 0.58 in the fairly large observed sample of FRBs is exceedingly small.

A Dual-Stream Deep Learning-Based Acoustic Denoising Model to Enhance Underwater Information Perception

Estimating the line spectra of ship-radiated noise is a crucial remote sensing technique for detecting and recognizing underwater acoustic targets. Improving the signal-to-noise ratio (SNR) makes the low-frequency components of the target signal more prominent. This enhancement aids in the detection of underwater acoustic signals using sonar. Based on the characteristics of low-frequency narrow-band line spectra signals in underwater target radiated noise, we propose a dual-stream deep learning network with frequency characteristics transformation (DS_FCTNet) for line spectra estimation. The dual streams predict amplitude and phase masks separately and use an information exchange module to swap learn features between the amplitude and phase spectra, aiding in better phase information reconstruction and signal denoising. Additionally, a frequency characteristics transformation module is employed to extract convolutional features between channels, obtaining global correlations of the amplitude spectrum and enhancing the ability to learn target signal features. Through experimental analysis on ShipsEar, a dataset of underwater acoustic signals by hydrophones deployed in shallow water, the effectiveness and rationality of different modules within DS_FCTNet are verified.Under low SNR conditions and with unknown ship types, the proposed DS_FCTNet model exhibits the best line spectrum enhancement compared to methods such as SEGAN and DPT_FSNet. Specifically, SDR and SSNR are improved by 14.77 dB and 13.58 dB, respectively, enabling the detection of weaker target signals and laying the foundation for target localization and recognition applications.

Intelligent Gesture Recognition Based on Screen Reflectance Multi-Band Spectral Features.

Human-computer interaction (HCI) with screens through gestures is a pivotal method amidst the digitalization trend. In this work, a gesture recognition method is proposed that combines multi-band spectral features with spatial characteristics of screen-reflected light. Based on the method, a red-green-blue (RGB) three-channel spectral gesture recognition system has been developed, composed of a display screen integrated with narrowband spectral receivers as the hardware setup. During system operation, emitted light from the screen is reflected by gestures and received by the narrowband spectral receivers. These receivers at various locations are tasked with capturing multiple narrowband spectra and converting them into light-intensity series. The availability of multi-narrowband spectral data integrates multidimensional features from frequency and spatial domains, enhancing classification capabilities. Based on the RGB three-channel spectral features, this work formulates an RGB multi-channel convolutional neural network long short-term memory (CNN-LSTM) gesture recognition model. It achieves accuracies of 99.93% in darkness and 99.89% in illuminated conditions. This indicates the system's capability for stable operation across different lighting conditions and accurate interaction. The intelligent gesture recognition method can be widely applied for interactive purposes on various screens such as computers and mobile phones, facilitating more convenient and precise HCI.

Two-wavelength non-dispersive infrared sensing systems using stacked electrically controllable photonic-crystal band-pass filters.

We present a scheme of non-dispersive infrared (NDIR) sensing utilizing stacked, two-wavelength electrically controllable bandpass filters that integrate multiple quantum wells (MQWs) and photonic crystals. By appropriately designing the MQWs and photonic crystals, we realize electrically controllable filters that exhibit symmetric, narrow-band transmittance spectra with high transmittance at two target wavenumbers (1035 cm-1 and 1085 cm-1) and demonstrate independent modulation of the transmittance at each target wavenumber when the two filters are stacked. In addition, by using the proposed stacked filters, we construct two-wavelength mechanical-chopper-free NDIR systems with a single detector and demonstrate its ability to robustly measure the gas concentration even when the absolute optical power incident on the detector is varied.

DUV electroluminescence from Bi3+-doped bi-phase yttrium phosphate

Trivalent-doped tetragonal xenotime yttrium orthophosphate phosphors have attracted significant attention as promising materials for deep ultraviolet (DUV) luminescence. In this study, we achieved DUV electroluminescence (EL) using Bi3+-doped bi-phase yttrium phosphate (YPB) phosphor under an AC-driven sinusoidal waveform. Phosphor powder was synthesized via a solid-state reaction with an optimal Bi3+ concentration. The phosphor exhibited a mixed phase of xenotime tetragonal YPO4 and monoclinic Y(PO3). We suggest that the mixed phase within the phosphor particles plays a crucial role in enhancing light generation via a bipolar field emission mechanism in the developed powder-based EL device. It emitted a narrow-band DUV emission spectrum peaking at 243 nm, which was attributed to the 3P1 → 1S0 transition of the Bi3+ ions within the mixed host lattices. Moreover, it has a lower voltage application than the excimer lamp DUV sources. The application of the YPB phosphor in EL devices presents an appealing outlook for future advancements in the development of DUV sources.

Peripheral carbazole units-decorated MR emitter containing B−N covalent bond for highly efficient green OLEDs with low roll-off

Boron−nitrogen doped multiple resonance (BN-MR) emitters, characterized by B−N covalent bonds, offer distinctive advantages as pivotal building blocks for facile access to novel MR emitters featuring narrowband spectra and high efficiency. However, there remains a scarcity of exploration concerning synthetic methods and structural derivations to expand the library of novel BN-MR emitters. Herein, we present the synthesis of a BN-MR emitter, tCz[B−N]N, through a one-pot borylation reaction directed by the amine group, achieving an impressive yield of 94%. The emitter is decorated by incorporating two 3,6-di-t-butylcarbazole (tCz) units into a B−N covalent bond doped BN-MR parent molecule via para-C−π−D and para-N−π−D conjugations. This peripheral decoration strategy enhances the reverse intersystem crossing process and shifts the emission band towards the pure green region, peaking at 526 nm with a narrowband full-width at half maximum (FWHM) of 41 nm. Consequently, organic light emitting diodes (OLEDs) employing this emitter achieved a maximum external quantum efficiency (EQEmax) value of 27.7%, with minimal efficiency roll-off. Even at a practical luminance of 1000 cd∙m−2, the device maintains a high EQE value of 24.6%.

SDR implementation of wideband spectrum sensing using machine learning

SummaryNew cognitive radio (CR) systems require high throughput and bandwidth. Hence, CR users need to detect wide frequency bands of the radio spectrum to exploit unused frequency channels. This paper proposes a new wideband spectrum sensing (WBSS) detection approach based on machine learning (ML) for scanning subchannels. The originality of the proposed approach is to detect spectrum opportunities using a narrowband spectrum sensing (NBSS) method‐based support vector machine (SVM) classification and two features: energy and goodness of fit (GoF). The simulation results show that the proposed WBSS approach‐based ML presents a higher probability of detection than the WBSS approach‐based conventional detectors, even at low signal‐to‐noise ratio (SNR). Finally, the software defined radio (SDR) implementation validates the proposed WBSS approach for real detection scenarios.

Evaluation method for the color preference of lighting traditional Chinese paintings in museum based on light source spectral analysis

To evaluate the lighting color preference on traditional Chinese paintings based on the spectral power distributions (SPDs) of light sources, an experiment to examine the color preference of 40 representative SPDs illuminating two typical traditional Chinese paintings was performed in a 1:1 simulated exhibition hall. By decomposing the data, the influence trend from the different narrowband spectra on the color preference was obtained, and two key spectra with a significant influence on the color preference were determined (WLP = 425 nm with FWHM = 20 nm and WLP = 525 nm with FWHM = 30 nm). With an evaluation model using the relative spectral areas of S425 and S525, the calculation of the lighting color preference for traditional Chinese painting illumination based on SPD was achieved. Our results could provide a mathematical tool for the comparison and selection of light sources for the illumination of traditional Chinese paintings and the development and design of specific light sources.

Sum rule comparison of narrowband and broadband sum frequency generation spectra and comparison with theory

Earlier sum frequency generation (SFG) experiments involve one infrared and one visible laser, and a measurement of the intensity of the response, yielding data on the surface sensitive properties of the sample. Recently, both the real and imaginary components of the susceptibility were measured in two different sets of experiments. In one set, a broadband infrared laser was used, permitting observations at very short times, while in another set the infrared laser was narrowband, permitting higher spectral resolution. The differences in the spectrum obtained by the two will be most evident in studying narrow absorption bands, e.g., the band due to dangling OH bonds at a water interface. The direct comparisons in the integrated amplitude (sum rule) of the imaginary part of the dangling OH bond region differ by a factor of 3. Due to variations in experimental setup and data processing, corrections were made for the quartz reference, Fresnel factors, and the incident visible laser wavelength. After the corrections, the agreement differs now by the factors of 1.1 within broadband and narrowband groups and the two groups now differ by a factor of 1.5. The 1.5 factor may arise from the extra heating of the more powerful broadband laser system on the water surface. The convolution from the narrowband SFG spectrum to the broadband SFG spectrum is also investigated and it does not affect the sum rule. Theory and narrowband experiments are compared using the sum rule and agree to a factor of 1.3 with no adjustable parameters.

Spectral Wave Characterization Of The Pacific Coast Of Costa Rica

The results of the analysis of the wave measurement campaigns on the Pacific coast of Costa Rica are presented. After analyzing more than 14000 spectral shapes from sea states, it has been determined that many of them present 2 or more swell peaks, so it was proposed to perform a wave characterization in which 8 different types of spectra were defined to include all the data collected. Also, verifications were carried out in these sea states to check the reliability of many of the approximations usually used in coastal engineering that assume narrow-band spectra, such as the JONSWAP type.

Efficient narrowband organic light-emitting diodes based on B,O embedded multi-resonance emitters containing B-N covalent bonds

Multi-resonance (MR) thermally activated delayed fluorescence (TADF) materials emerge rapidly in recent years, and are expected to be an effective solution for ultra-high-resolution displays. Solving the main issues of low yield and dangerous lithium reagent for synthesis, extending the material diversity and figuring out the structure–activity relationship would all be beneficial to promote their commercialization. Herein, two easy-to-access MR-TADF emitters, BO-N1 and BO-N2, are constructed by integrating B,O-based MR polycyclic aromatic with B-N covalent bonds. The B-N covalent bonds are formed through simple amine-directed borylation with high yield over 90 %, which significantly improves yield, simplifies procedure and effectively avoids hazardous lithiation compared to the traditional methods. Two emitters exhibit bright green emissions in doped films with full width at half maximum (FWHM) of 28 and 24 nm, and high photoluminescence quantum yields (PLQY) of 85 % and 90 %. The green electroluminescent device of BO-N2 achieves narrow-band spectrum with FWHM of 29 nm, maximum brightness of 10480 cd m−2 and external quantum efficiency (EQE) of 20.1 %.

Application of machine learning on complete vehicle data for interior vibration prediction based on transfer paths in narrowband spectrum

Interior noise and vibration are critical quality criteria for vehicles in the automotive sector. The latter is essential in driving comfort, where many development resources are used for the design and the evaluation. As a result, testing the entire vehicle is very important to assess vibration and ride comfort. The assembly of the entire vehicle is carried out virtually using transfer functions derived either from simulations or from measurements. Since simulations of a complete vehicle are still challenging,transfer functions are usually measured with considerable effort. Therefore, limiting the necessary measurements to specific components is desirable to be efficient and to save costs. In this work, the authors use an artificial neural network to predict the interior vibration profiles of a complete vehicle based on full test drives at different speeds and road conditions. Triaxial acceleration measurements serve as the database. In addition, a criterion is proposed to select essential sensors for the learning process. The results show that if the data is handled carefully, many sensors can be discarded, and the network can predict accurate acceleration spectra for virtual sensors at various test conditions.

Enhanced thermal emission from metal-free, fully epitaxial structures with epsilon-near-zero InAs layers

We introduce a novel method to create mid-infrared (MIR) thermal emitters using fully epitaxial, metal-free structures. Through the strategic use of epsilon-near-zero (ENZ) thin films in InAs layers, we achieve a narrow-band, wide-angle, and p-polarized thermal emission spectra. This approach, employing molecular beam epitaxy, circumvents the complexities associated with current layered structures and yields temperature-resistant emission wavelengths. Our findings contribute a promising route towards simpler, more efficient MIR optoelectronic devices.

Ultra-narrowband light absorption with dual metamaterial ring resonator patches

In this paper, by utilizing ring resonator patches (RRPs) within metamaterial structures, a new approach is introduced to showcase the enhancement of light absorption in ultra-narrowband conditions. The utilization of a slit ring resonator enables the excitation of resonance modes characterized by standing wave patterns. The estimation of absorption peak positions can be reliably achieved through the application of the Fabry-Pérot (F–P) cavity model. Attaining absorption with a narrowband spectrum necessitates the simultaneous elimination of reflectance by achieving perfect impedance matching and the suppression of transmission via the metallic layer. The desirable characteristics of ultra narrow bandwidth can be preserved by changing the geometrical parameters of the RRPs. When working as a refractive-index sensor, the RRPs-based metamaterial showcases remarkable sensing capabilities owing to its consistent and reliable enhancement of ultra-narrowband absorption.

Enhanced spectral attention and adaptive spatial learning guided network for hyperspectral and LiDAR classification

Although the data fusion of hyperspectral images (HSI) and light detection and ranging (LiDAR) has provided significant gains for land-cover classification, it also brings technical obstacles (i.e., it is difficult to capture discriminative local and global spatial-spectral from redundant data and build interactions between heterogeneous data). In this paper, a classification network named enhanced spectral attention and adaptive spatial learning guided network (ESASNet) is proposed for the joint use of HSI and LiDAR. Specifically, first, by combining a convolutional neural network (CNN) with the transformer, adaptive spatial learning (ASL) and enhanced spectral learning (ESL) are proposed to learn the spectral-spatial features from the HSI data and the elevation features from the LiDAR data in the local and global receptive field. Second, considering the characteristics of HSI with a continuous, narrowband spectrum, ESL is designed by adding enhanced local self-attention to enhance the mining of the spectral correlations across the adjacent spectrum. Finally, a feature fusion module is proposed to ensure an efficient information exchange between HSI and LiDAR during spectral features and spatial feature fusion. Experimental evaluations on the HSI-LiDAR dataset clearly illustrate that ESASNet performs better in feature extraction than the state-of-the-art methods. The code is available at https://github.com/AirsterMode/ESASNet.