Spectral Enhancement Research Articles

Retraction Note: Antenna optical communication spectral enhancement with energy analysis of photovoltaic cell microgrid environmental application

Retraction Note: Antenna optical communication spectral enhancement with energy analysis of photovoltaic cell microgrid environmental application

Spectral Flux Enhancement of X Rays for Addressing Ultranarrow Nuclear Transitions

Spectral Flux Enhancement of X Rays for Addressing Ultranarrow Nuclear Transitions

Evaluation of GOCE/GRACE and combined global geopotential models using GNSS/levelling data over Nigeria

AbstractGlobal Geopotential Models (GGMs) provide valuable information about Earth’s gravity field functionals, such as geoid heights and gravity anomalies. However, ground-based datasets are required to validate these GGMs at the regional and local scales. In this study, we validated the accuracy of GGMs by comparing them with ground-based Global Navigation Satellite System (GNSS)/levelling data for the first time in Nigeria. We employed two validation scenarios: with and without considering spectral consistency using the spectral enhancement method (SEM) to incorporate high and very high frequencies of the gravity field spectrum from the combined global gravity field model (XGM2019e_2159) and the residual terrain model (RTM) derived from the Shuttle Radar Topography Mission (SRTM) data, respectively. The results of this evaluation confirmed that the application of SEM improved the assessment of the GGM solutions in an unbiased manner. Integrating XGM2019e_2159 and SRTM data to constrain the high-frequency component of geoid heights in Gravity Field and Steady-State Ocean Circulation Explorer (GOCE)-based GGMs led to an improvement of approximately 10% in reducing the standard deviation (STD) relative to when SEM was not applied. GO_CONS_GCF_2_TIM_R6 at spherical harmonics (SH) of up to degree and order 260 demonstrated the lowest STD when compared to GO_CONS_GCF_2_DIR_R6 and GO_CONS_GCF_2_SPW_R5, with a reduction from 0.380 m without SEM application to 0.342 m with SEM implementation. In addition, four transformation models, namely, linear, four-parameter, five-parameter, and seven-parameter models, were evaluated. The objective is to mitigate the reference system offsets between the GNSS/levelling data and the GGMs and to identify the particular parametric model with the smallest STD across all GGMs. This effort reduced the GGMs misfits to GNSS/levelling to 0.30 m, representing a 15.3% decrease in STD. Notably, the XGM2019e_2159 model provides this improvement.

Spectral efficiency enhancement and energy optimization in 5G networks via stochastic optimization-inspired by modified slime mould algorithm

Spectral efficiency enhancement and energy optimization in 5G networks via stochastic optimization-inspired by modified slime mould algorithm

Spectral domain strategies for hyperspectral super-resolution: Transfer learning and channel enhance network

As the network structures continue to innovate and evolve, significant achievements have been achieved in hyperspectral image super-resolution tasks. However, how to further explore the spectral domain potential from prior knowledge and channel-enhanced structures to achieve better performance has inspired the following two works: Firstly, to systematically compare prior knowledge of spectral with spatial domain for HSI-SR tasks, four transfer learning strategies are proposed. The superior performance of the Relevant Channel/Random Space (RCRS) strategy reveals the importance of spectral feature reconstruction in HSI-SR tasks. Meanwhile, an interesting phenomenon has been observed that even without training on real datasets, the model can already exhibit a core or even decent super-resolution capability based solely on prior knowledge of above four strategies. Secondly, a dual-branch channel network with complementary channel feature extraction (CCFE) and adjacent channel feature extraction (ACFE) module is designed for spectral feature enhancement, which demonstrate superior performance compared to state-of-the-art methods on six datasets. To conclude, the effectiveness of RCRS strategy with pseudo prior channel knowledge on seven dual-input and eight single-input networks, as well as superiority of the proposed channel-enhanced network indicate the importance of spectral properties for HSI-SR tasks.

Fourier coded aperture transform rapid spectral imaging

The Fourier coded aperture transform hyperspectral imaging system (FCTS), which we proposed in our previous work, has excellent physical interpretability compared to other encoding methods. Due to the excellent energy concentration characteristic of Fourier encoding, it can achieve high-quality spectral imaging even under undersampling conditions. However, since the grayscale encoding method limits the system's sampling speed, we propose a binary sampling Fourier-encoded spectral imaging method. Besides, to address the significant errors of reconstructed spectral image at low sampling rates, we construct a spectral image enhancement network, which introduces spatial and channel attention modules to successively enhance the spatial and spectral information of the reconstructed spectral images. During the spectral image refinement and enhancement stage, band grouping is performed to alleviate the difficulty in feature extraction and make the training process more stable. The effectiveness of our proposed method has been validated on both public datasets and our own datasets. When the sampling times are reduced by an order of magnitude, on the three public datasets, the PSNR improved by 10.79 dB, the SSIM increased by 0.35, and the SAM decreased by 12.1° on average. On our own datasets, the PSNR improved by 12 dB, the SSIM increased by 0.24, and the SAM decreased by 6.5°.

Lycopene detection in cherry tomatoes with feature enhancement and data fusion

Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving RP2, RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.

Theoretical study of tip-enhanced photoluminescence of single molecule in nanocavity formed by apex protrusion and substrate

Tip-enhanced photoluminescence techniques, characterized by ultra-high detection sensitivity and spatial resolution, have exhibited unprecedented capabilities in exploring optoelectronic phenomena and unraveling intrinsic physical mechanism at the single-molecule level. To suppress the quenching of molecular luminescence and achieve high-resolution optical imaging, a rational design of the tip-substrate configuration is required. In this paper, we systematically investigate the tip-enhanced photoluminescence characteristics of single molecules located within the plasmonic nanocavity formed by the tip protrusion and the substrate from a theoretical perspective. The impacts of electron tunneling and electron-hole pair creation on localized electric field and quantum yield are discussed using the quantum correction model and nonlocal dielectric theory, respectively. The crucial roles of atomic-scale protrusion at the tip apex and the decoupling layer on the substrate in enhancing vertical and horizontal electric fields as well as quantum yields have been emphasized. The results indicate that in smaller sub-nanometer cavities, electron tunneling and nonlocal dielectric effects greatly weaken the electric field intensity and fluorescence quantum yield. Meanwhile, due to the sharp lightning rod effect, the presence of atomic-level protrusion at the tip apex can provide a strong and highly localized plasmonic field. We find that the added NaCl layer not only blocks charge transfer between molecules and the substrate but also effectively prevents fluorescence quenching. The maximum fluorescence and Raman enhancement factors near the tip apex approach up to 5 and 12 orders of magnitude, respectively, achieving sub-nanometer spatial resolution. Finally, compared to vertical electric field and dipole, sharp protrusion can excite a stronger horizontal electric field and couple with horizontal dipoles to yield a higher quantum yield, resulting in a three orders of magnitude higher in the fluorescence enhancement of horizontal dipole compared to the tip without protrusion. Our theoretical research not only confirms the experimental results of spectral enhancement with the proximity of the tip to the molecule in tip-enhanced photoluminescence, but also provides effective guidance for a deeper understanding of the mechanism of tip-enhanced fluorescence and Raman spectroscopy, as well as optimizing the experimental system.

Arrays of ZnO/MoS2 nanorods and MoS2 nanotubes for spectral enhancement and self-cleaning

Structure and heterojunction of substrates are widely regarded as critical factors to improve Raman detection and recyclibility, but the exact roles are rarely explored for the mixed contributions. Herein, the arrays of ZnO/MoS2 coaxial nanorods (NRs) and MoS2 nanotubes (NTs) were successfully constructed for the surface-enhanced Raman scattering (SERS). The influences of NR and NT length on the morphology, light absorption and emission, as well as SERS performance were systematically investigated. Both the arrays grown for 40 min displayed optimal SERS performance, in which MoS2 NT array owned stronger enhancement with a detection limit of 5 × 10−7 M than that of 1 × 10−6 M for ZnO/MoS2 NR array. The NTs also effectively enhanced photocatalysis, in which rhodamine 6G pollutant was completely degraded within 140 min under ultraviolet light, surpassing that by the NRs. The analysis revealed that band alignment and structure characteristic of the specimens played critical roles in the spectral enhancement and self-cleaning, while the nanostructure surpassed heterojunction as it supplied void interior for extending light propagation and more contact area for adsorbing pollutants. These results would contribute to a better understanding of the Raman enhancement and could be extended to the design of SERS substrates to harness their potential application.

A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification.

Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for real-time applications. To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel segmentation method to build fusion adjacency matrices at various scales, which improves the graph convolution efficiency and node representations. Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions. Meanwhile, the CNN branch uses a convolutional network with an attention mechanism to focus on detailed features of local areas. By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages complementary features to fully learn rich spatial-spectral information. Our experimental results demonstrate that the proposed method outperforms existing advanced approaches in terms of classification efficiency and accuracy across three benchmark data sets.

Development of a semi-supervised machine learning based noise filter for quantum cascade laser-coupled mid-infrared spectrometer

The rapid and accurate quantitative determination of molecular rovibrational spectral features is highly desirable for applications in fundamental high-resolution spectroscopy, trace chemical sensing, and environmental monitoring. But significant spectral noise can impede the accuracy and precision of such spectral measurements as well as the precise classification of fine and hyperfine spectral fingerprints. Nevertheless, the denoising of weak rovibrational spectral data has long been a computational and experimental challenge. Here, we develop a new semi-supervised machine learning (SSML) denoising method combining unsupervised spectral eigenvector space dimensionality reduction of the spectral data matrix exploiting principal component analysis with supervised Fourier domain residual analysis for enhancing high-resolution rovibrational spectral signal quality. We discussed the detailed implementation of the SSML algorithm on the real experimental mid-infrared rovibrational spectral features of nitrogen dioxide (NO2) in the gas-phase acquired from a quantum cascade laser-coupled cavity ring-down spectroscopic method. Our results confirm the robustness and feasibility of the SSML approach and could lead to a wide range of pertinent applications in infrared spectroscopy including precise spectral signal analysis, concentration retrieval and performance enhancement of laser-coupled spectroscopic sensors.

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

Understanding the spatial and temporal distribution of small water bodies is essential for managing water resources, crafting conservation policies, and preserving watershed ecosystems and biodiversity. However, existing studies often rely on a single remote sensing data source (optical or microwave), focusing on large-scale, flat areas and lacking comprehensive monitoring of small water bodies in complex terrain. Therefore, considering the complementary advantages of multisource remote sensing (multispectral and SAR), this paper proposes a multispectral and SAR fusion algorithm, named Multispectral and SAR Fusion algorithm (MASF), to better capture the remote sensing characteristics of small water bodies in complex areas. Based on this, a dataset containing spectral, texture, and geometric features is constructed, and multi-scale segmentation and random forest algorithms are applied for identification of small water bodies in complex terrain. The results demonstrate that the proposed fusion algorithm MASF exhibits minimal spectral distortion (SAM < 3.5, ERGAS <21, RMSE <0.01) and robust spatial feature enhancement (PSNR >40, SSIM >0.999, CC > 0.99). The Overall Accuracy (OA) and Kappa coefficients for both experimental areas surpassed 0.9. For rivers and reservoirs, both Producer's Accuracy (PA) and User's Accuracy (UA) exceeded 0.9. The UA for agricultural ponds exceeded 0.8. Comparative analysis with three other types of water-related data products shows that the freshwater identification results in this study have certain advantages in local small water bodies. Our research holds significant implications for the utilization of water resources in mountainous areas, prevention and control of floods and floods, as well as the development of aquaculture industry.

Transferable adversarial attack based on sensitive perturbation analysis in frequency domain

Recently, transferable adversarial attacks on deep neural networks (DNNs) have attracted significant attention. Although existing adversarial attacks have achieved high attack success rates in white-box scenarios, they perform poorly in black-box attack scenarios. To address the issue of low transferability in black-box adversarial attacks, we propose a frequency-sensitive perturbation-based black-box adversarial attack method to enhance transferability performance from the perspective of Fourier domain sensitivity. Initially, the Fourier sensitivity heatmaps of multiple substitute models are integrated to identify the common sensitive frequency region. Subsequently, the perturbations in the frequency domain are optimized through joint loss, thereby constraining the sensitive region and limiting the perturbations in the non-sensitive region. Finally, in each iteration, a spectral model enhancement is performed to simulate additional substitute models and reduce the spectral discrepancy between the substitute and target models. Extensive experimental results on benchmark datasets demonstrated the effectiveness of the proposed method. Compared to existing black-box adversarial attack methods, the proposed approach generates targeted and non-targeted adversarial samples with higher transfer success rates and good invisibility, thereby confirming its superiority.

Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure

Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks.

Regional evaluation of global geopotential models and three types of digital elevation models with ground-based gravity and GNSS/levelling data using several techniques over Sudan

Abstract To evaluate the performance of the global geopotential models (GGMs) in a more unbiased way, ground-based gravity and GNSS/levelling datasets are highly required. In this study, the eight latest releases of the satellite-only and combined GGMs are evaluated on the regional scale using the available terrestrial gravity and GNSS/Levelling data over Sudan, considering the spectral consistency issue by applying the spectral enhancement method (SEM). The evaluation process consists of three stages: firstly, the eight GGMs are evaluated globally with each other by using different degree variances in terms of geoid heights, gravity anomalies, and signal-to-noise ratio (SNR); secondly, the GGMs are compared against the Earth Gravitational Model 2008 (EGM2008) on a regional scale over Sudan; thirdly, apply the SEM strategy by incorporating high (SEM_WITHOUT_RTM technique) and very-high (SEM technique) frequencies of the gravity field spectrum from the EGM2008 and high-resolution residual terrain model (RTM), respectively. For reliable robustness of the latter evaluation process, three different DEMs are used, namely, SRTM30, ASTER30, and GTOPO30. Our findings on the evaluation process using SEM_WITHOUT_RTM technique show improved gravity anomalies solutions regarding differences of standard deviations (STD) from 19–20.7 mGal to about 14 mGal. When applying the SEM technique, more improvements are achieved, providing STD differences in gravity anomalies and geoid heights of about 12 mGal and 45 cm, respectively. Among the three applied DEMs, it has been found that despite the slight refinements, the ASTER30 and GTOPO30 models show better performance than the SRTM30 model.

Engineering the Impact of Phonon Dephasing on the Coherence of a WSe_{2} Single-Photon Source via Cavity Quantum Electrodynamics.

Emitter dephasing is one of the key issues in the performance of solid-state single-photon sources. Among the various sources of dephasing, acoustic phonons play a central role in adding decoherence to the single-photon emission. Here, we demonstrate that it is possible to tune and engineer the coherence of photons emitted from a single WSe_{2} monolayer quantum dot via selectively coupling it to a spectral cavity resonance. We utilize an open cavity to demonstrate spectral enhancement, leveling, and suppression of the highly asymmetric phonon sideband, finding excellent agreement with a microscopic description of the exciton-phonon dephasing in a truly two-dimensional system. Moreover, the impact of cavity tuning on the dephasing is directly assessed via optical interferometry, which points out the capability to utilize light-matter coupling to steer and design dephasing and coherence of quantum emitters in atomically thin crystals.

Multi-label feature selection via spectral clustering-based label enhancement and manifold distribution consistency

Multi-label feature selection via spectral clustering-based label enhancement and manifold distribution consistency

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

The S-transform is a fundamental time-frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0-3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

A theoretical study of the signal enhancement mechanism of coaxial DP-LIBS

In the field of dual-pulse laser-induced breakdown spectroscopy (DP-LIBS) research, the pursuit of methods for determining pulse intervals and other parameters quickly and conveniently in order to achieve optimal spectral signal enhancement is paramount. To aid researchers in identification of optimal signal enhancement conditions and more accurate interpretation of the underlying signal enhancement mechanisms, theoretical simulations of the spatiotemporal processes of coaxial DP-LIBS-induced plasma have been established in this work. Using a model based on laser ablation and two-dimensional axisymmetric fluid dynamics, plasma evolutions during aluminum–magnesium alloy laser ablation under single-pulse and coaxial dual-pulse excitations have been simulated. The influences of factors, such as delay time, laser fluence, plasma temperature, and particle number density, on the DP-LIBS spectral signals are investigated. Under pulse intervals ranging from 50 to 1500 ns, the time evolutions of spectral line intensity, dual-pulse emission enhancement relative to the single-pulse results, laser irradiance, spatial distribution of plasma temperature and species number density, as well as laser irradiance shielded by plasma have been obtained. The study indicates that the main reason behind the radiation signal enhancement in coaxial DP-LIBS-induced plasma is attributed to the increased species number density and plasma temperature caused by the second laser, and it is inferred that the shielding effect of the plasma mainly occurs in the boundary layer of the stagnation point flow over the target surface. This research provides a theoretical basis for experimental research, parameter optimization, and signal enhancement tracing in DP-LIBS.