Volume 0,Issue 1,2025 Table of Contents
2025(1):1-6.
DOI: https://doi.org/10.1007/s11801-025-3270-2
Abstract:
Broadband and perfect terahertz absorber based on multilayer metamaterial using cross-ring patterned structures is proposed and investigated. The structure of the absorber is double absorption layers consisting of a chromium cross ring and eight isosceles right triangles. The unique structure of the double absorbing layers excites the electric dipole multimode resonance, giving rise to high absorption performance. Meanwhile, the influence of construal parameters on absorber behavior is also discussed. The numerical results show that the absorption achieves over 90% ranging from 2.45 THz to 6.25 THz and 99% absorption in the range of 3.7—5.3 THz. The realization of broadband and perfect absorber is described using the impedance matching principle. It is obviously found that the absorber is insensitive to the high angle of incidence for both transverse electric (TE) and transverse magnetic (TM) polarizations. Compared with the former reports, this absorber has remarkable improved absorption efficiency and smaller period. The terahertz absorber may be found applications in the fields of energy capture and thermal detection.
Broadband and perfect terahertz absorber based on multilayer metamaterial using cross-ring patterned structures is proposed and investigated. The structure of the absorber is double absorption layers consisting of a chromium cross ring and eight isosceles right triangles. The unique structure of the double absorbing layers excites the electric dipole multimode resonance, giving rise to high absorption performance. Meanwhile, the influence of construal parameters on absorber behavior is also discussed. The numerical results show that the absorption achieves over 90% ranging from 2.45 THz to 6.25 THz and 99% absorption in the range of 3.7—5.3 THz. The realization of broadband and perfect absorber is described using the impedance matching principle. It is obviously found that the absorber is insensitive to the high angle of incidence for both transverse electric (TE) and transverse magnetic (TM) polarizations. Compared with the former reports, this absorber has remarkable improved absorption efficiency and smaller period. The terahertz absorber may be found applications in the fields of energy capture and thermal detection.
2025(1):7-12.
DOI: https://doi.org/10.1007/s11801-025-3283-x
Abstract:
We demonstrated a new type of MAX phase material, chromium titanium aluminum carbide (Cr2TiAlC2) polymer film, to generate a passively Q-switched erbium-doped fiber laser (EDFL). The film thickness was measured to be around 45 µm, which was fabricated using the embedding method with polyvinyl alcohol (PVA) polymer as hoster. The saturable absorber (SA) film demonstrates a dual-wavelength passively Q-switched EDFL which operates at 1 531 nm and 1 560.19 nm, respectively. The Q-switching pulse duration could be varied from 2.46 µs to 770 ns, while the repetition rate varied from 92.76 kHz to 106.6 kHz with an increasing input pumping range from 154 mW to 300 mW. The maximum output power and pulse energy of 15.05 mW and 141.18 nJ were obtained at the maximum input power of 300 mW, respectively.
We demonstrated a new type of MAX phase material, chromium titanium aluminum carbide (Cr2TiAlC2) polymer film, to generate a passively Q-switched erbium-doped fiber laser (EDFL). The film thickness was measured to be around 45 µm, which was fabricated using the embedding method with polyvinyl alcohol (PVA) polymer as hoster. The saturable absorber (SA) film demonstrates a dual-wavelength passively Q-switched EDFL which operates at 1 531 nm and 1 560.19 nm, respectively. The Q-switching pulse duration could be varied from 2.46 µs to 770 ns, while the repetition rate varied from 92.76 kHz to 106.6 kHz with an increasing input pumping range from 154 mW to 300 mW. The maximum output power and pulse energy of 15.05 mW and 141.18 nJ were obtained at the maximum input power of 300 mW, respectively.
2025(1):13-20.
DOI: https://doi.org/10.1007/s11801-025-3239-1
Abstract:
In this paper, we present a metamaterial structure of Dirac and vanadium dioxide (VO2) and investigate its optical properties using the finite-difference time-domain (FDTD) technique. Using the phase transition feature of VO2, the design can realize active tuning of the plasmon induced transparency (PIT) effect at terahertz frequency, thereby converting from a single PIT to a double-PIT. When VO2 is in the insulating state, the structure is symmetric to obtain a single-band PIT effect. When VO2 is in the metallic state, the structure turns asymmetric to realize a dual-band PIT effect. This design provides a reference direction for the design of actively tunable metamaterials. Additionally, it is discovered that the transparent window’s resonant frequency and the Fermi level in this structure have a somewhat linear relationship. In addition, the structure achieves superior refractive index sensitivity in the terahertz band, surpassing 1 THz/RIU. Consequently, the design exhibits encouraging potential for application in refractive index sensors and optical switches.
In this paper, we present a metamaterial structure of Dirac and vanadium dioxide (VO2) and investigate its optical properties using the finite-difference time-domain (FDTD) technique. Using the phase transition feature of VO2, the design can realize active tuning of the plasmon induced transparency (PIT) effect at terahertz frequency, thereby converting from a single PIT to a double-PIT. When VO2 is in the insulating state, the structure is symmetric to obtain a single-band PIT effect. When VO2 is in the metallic state, the structure turns asymmetric to realize a dual-band PIT effect. This design provides a reference direction for the design of actively tunable metamaterials. Additionally, it is discovered that the transparent window’s resonant frequency and the Fermi level in this structure have a somewhat linear relationship. In addition, the structure achieves superior refractive index sensitivity in the terahertz band, surpassing 1 THz/RIU. Consequently, the design exhibits encouraging potential for application in refractive index sensors and optical switches.
2025(1):21-27.
DOI: https://doi.org/10.1007/s11801-025-3273-z
Abstract:
A process for purifying aqueous solutions containing heavy and toxic metals such as chromium (Cr) has been investigated. One of the extremely harmful pollutants in rivers and seawater is the heavy metal ions due to their direct impacts on human, animals and plants are hexavalent Cr (VI). Consequently, highly sensitive sensor to detect Cr is essential. Surface plasmon resonance (SPR) technique has attracted huge research interest in detecting heavy metals specifically. In this study, three types of prism-based SPR sensor, gold (Au)/silver (Ag), Au/polyaniline (PANI) and Au/ titanium dioxide (TiO2) nanostructured films, are investigated as potential sensing material to detect the presence of Cr (VI) ions in water. The base Au layer with thickness of 48.3 nm is deposited on a glass slide for all sensors. For Au/Ag, Au/PANI nanofibers and Au/TiO2 sensor films, the Cr (VI) concentration is varied from 1 ppm to 15 ppm with sensitivity of 0.270 °ppm-1, 0.082 °ppm-1 and 0.039 °ppm-1, respectively. Based on these results, the Au/PANI nanofibers are the most sensitive to Cr (VI) among the tested sensing materials.
A process for purifying aqueous solutions containing heavy and toxic metals such as chromium (Cr) has been investigated. One of the extremely harmful pollutants in rivers and seawater is the heavy metal ions due to their direct impacts on human, animals and plants are hexavalent Cr (VI). Consequently, highly sensitive sensor to detect Cr is essential. Surface plasmon resonance (SPR) technique has attracted huge research interest in detecting heavy metals specifically. In this study, three types of prism-based SPR sensor, gold (Au)/silver (Ag), Au/polyaniline (PANI) and Au/ titanium dioxide (TiO2) nanostructured films, are investigated as potential sensing material to detect the presence of Cr (VI) ions in water. The base Au layer with thickness of 48.3 nm is deposited on a glass slide for all sensors. For Au/Ag, Au/PANI nanofibers and Au/TiO2 sensor films, the Cr (VI) concentration is varied from 1 ppm to 15 ppm with sensitivity of 0.270 °ppm-1, 0.082 °ppm-1 and 0.039 °ppm-1, respectively. Based on these results, the Au/PANI nanofibers are the most sensitive to Cr (VI) among the tested sensing materials.
2025(1):28-34.
DOI: https://doi.org/10.1007/s11801-025-4027-7
Abstract:
The fiber optic sensing technology provides data support in structural health monitoring of the macro facilities, including design, construction, and maintenance of bridges, tunnels, ports and other infrastructures. In this paper, a distributed vibration sensing system is proved to be responsive to a single touch over a 1.8-m-long equivalent fiber segment, covering a vibration frequency from 5 Hz to 25 kHz. The sensing fiber was arranged as an S type layout on the bridge to recognize the standing state, windblown disturbance, and walking vibration. Moreover, the knocking and climbing events are recognized fiber laying spinning lines and hanging on the fences, respectively. The demonstration shows an accurate positioning and sensitive vibration monitoring applied on the automated three-dimensional (3D) printed bridge, which is applicable to all kinds of 3D printed facilities as intelligent sensory neuro-networks.
The fiber optic sensing technology provides data support in structural health monitoring of the macro facilities, including design, construction, and maintenance of bridges, tunnels, ports and other infrastructures. In this paper, a distributed vibration sensing system is proved to be responsive to a single touch over a 1.8-m-long equivalent fiber segment, covering a vibration frequency from 5 Hz to 25 kHz. The sensing fiber was arranged as an S type layout on the bridge to recognize the standing state, windblown disturbance, and walking vibration. Moreover, the knocking and climbing events are recognized fiber laying spinning lines and hanging on the fences, respectively. The demonstration shows an accurate positioning and sensitive vibration monitoring applied on the automated three-dimensional (3D) printed bridge, which is applicable to all kinds of 3D printed facilities as intelligent sensory neuro-networks.
2025(1):35-42.
DOI: https://doi.org/10.1007/s11801-025-3245-3
Abstract:
Aiming at the problem that the current traffic safety helmet detection model can’t balance the accuracy of detection with the size of the model and the poor generalization of the model, a method based on improving you only look once version 5 (YOLOv5) is proposed. By incorporating the lightweight GhostNet module into the YOLOv5 backbone network, we effectively reduce the model size. The addition of the receptive fields block (RFB) module enhances feature extraction and improves the feature acquisition capability of the lightweight model. Subsequently, the high-performance lightweight convolution, GSConv, is integrated into the neck structure for further model size compression. Moreover, the baseline model’s loss function is substituted with efficient insertion over union (EIoU), accelerating network convergence and enhancing detection precision. Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.
Aiming at the problem that the current traffic safety helmet detection model can’t balance the accuracy of detection with the size of the model and the poor generalization of the model, a method based on improving you only look once version 5 (YOLOv5) is proposed. By incorporating the lightweight GhostNet module into the YOLOv5 backbone network, we effectively reduce the model size. The addition of the receptive fields block (RFB) module enhances feature extraction and improves the feature acquisition capability of the lightweight model. Subsequently, the high-performance lightweight convolution, GSConv, is integrated into the neck structure for further model size compression. Moreover, the baseline model’s loss function is substituted with efficient insertion over union (EIoU), accelerating network convergence and enhancing detection precision. Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.
2025(1):43-50.
DOI: https://doi.org/10.1007/s11801-025-3275-x
Abstract:
In this study, we present a unified sparsity-driven framework that significantly enhances motion deblurring performance by integrating two key components:a custom-designed dataset and a low-rank module (LRM). This framework leverages the inherent sparsity of per-pixel blur kernels to bolster both deblurring accuracy and model interpretability. Firstly, we propose an adaptive-basis decomposition-based deblurring (ADD) approach, which constructs a tailored training dataset to enhance the generalization capacity of the deblurring network. The ADD framework adaptively decomposes motion blur into sparse basis elements, effectively addressing the intricacies associated with non-uniform blurs. Secondly, an LRM is proposed to improve the interpretability of deblurring models as a plug-and-play module, primarily designed to identify and harness the intrinsic sparse features in sharp images. A series of ablation studies have been conducted to substantiate the synergistic advantages of combining the proposed ADD with the LRM for overall improvement in deblurring efficacy. Subsequently, we empirically demonstrate through rigorous experimentation that incorporating the LRM into an existing Uformer network leads to substantial enhancement in reconstruction performance. This integration yields a sparsity-guided low-rank network (SGLRN). Operating under the overarching principle of sparsity, SGLRN consistently outperforms state-of-the-art methods across multiple standard deblurring benchmarks. Comprehensive experimental results, assessed through quantitative metrics and qualitative visual evaluations, provide compelling evidence of its effectiveness. The overall deblurring results are available at Google Drive.
In this study, we present a unified sparsity-driven framework that significantly enhances motion deblurring performance by integrating two key components:a custom-designed dataset and a low-rank module (LRM). This framework leverages the inherent sparsity of per-pixel blur kernels to bolster both deblurring accuracy and model interpretability. Firstly, we propose an adaptive-basis decomposition-based deblurring (ADD) approach, which constructs a tailored training dataset to enhance the generalization capacity of the deblurring network. The ADD framework adaptively decomposes motion blur into sparse basis elements, effectively addressing the intricacies associated with non-uniform blurs. Secondly, an LRM is proposed to improve the interpretability of deblurring models as a plug-and-play module, primarily designed to identify and harness the intrinsic sparse features in sharp images. A series of ablation studies have been conducted to substantiate the synergistic advantages of combining the proposed ADD with the LRM for overall improvement in deblurring efficacy. Subsequently, we empirically demonstrate through rigorous experimentation that incorporating the LRM into an existing Uformer network leads to substantial enhancement in reconstruction performance. This integration yields a sparsity-guided low-rank network (SGLRN). Operating under the overarching principle of sparsity, SGLRN consistently outperforms state-of-the-art methods across multiple standard deblurring benchmarks. Comprehensive experimental results, assessed through quantitative metrics and qualitative visual evaluations, provide compelling evidence of its effectiveness. The overall deblurring results are available at Google Drive.
2025(1):51-56.
DOI: https://doi.org/10.1007/s11801-025-3278-7
Abstract:
Although guided image filtering (GIF) is known for preserving edges and fast computation, it may produce inaccurate outputs in depth map restoration. In this paper, a novel confidence-weighted GIF called mutual-structure weighted GIF (MSWGIF) is proposed, which replaces the mean filtering strategy in GIF during handling overlapping windows. The confidence value is composed of a depth term and a mutual-structure term, where the depth term is utilized to protect the edges of the output, and the mutual-structure term helps to select accurate windows during the structure characteristics of the guidance image are transferred to the output. Experimental results show that MSWGIF reduces the root mean square error (RMSE) by an average of 12.37%, and the average growth rate of correlation (CORR) is 0.07% on average. Additionally, the average growth rate of structure similarity index measure (SSIM) is 0.34%.
Although guided image filtering (GIF) is known for preserving edges and fast computation, it may produce inaccurate outputs in depth map restoration. In this paper, a novel confidence-weighted GIF called mutual-structure weighted GIF (MSWGIF) is proposed, which replaces the mean filtering strategy in GIF during handling overlapping windows. The confidence value is composed of a depth term and a mutual-structure term, where the depth term is utilized to protect the edges of the output, and the mutual-structure term helps to select accurate windows during the structure characteristics of the guidance image are transferred to the output. Experimental results show that MSWGIF reduces the root mean square error (RMSE) by an average of 12.37%, and the average growth rate of correlation (CORR) is 0.07% on average. Additionally, the average growth rate of structure similarity index measure (SSIM) is 0.34%.
2025(1):57-64.
DOI: https://doi.org/10.1007/s11801-025-3280-0
Abstract:
The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases, with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer (CRC). Although deep learning holds promise in mitigating missed polyp rates, modern endoscopy examinations pose additional challenges, such as image blurring and atomizing. This study explores lightweight yet powerful attention mechanisms, introducing the spatial-channel transformer (SCT), an innovative approach that leverages spatial channel relationships for attention weight calculation. The method utilizes rotation operations for inter-dimensional dependencies, followed by residual transformation, encoding inter-channel and spatial information with minimal computational overhead. Extensive experiments on the CVC-ClinicDB polyp detection dataset, addressing endoscopy pitfalls, underscore the superiority of our SCT over other state-of-the-art methods. The proposed model maintains high performance, even in challenging scenarios.
The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases, with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer (CRC). Although deep learning holds promise in mitigating missed polyp rates, modern endoscopy examinations pose additional challenges, such as image blurring and atomizing. This study explores lightweight yet powerful attention mechanisms, introducing the spatial-channel transformer (SCT), an innovative approach that leverages spatial channel relationships for attention weight calculation. The method utilizes rotation operations for inter-dimensional dependencies, followed by residual transformation, encoding inter-channel and spatial information with minimal computational overhead. Extensive experiments on the CVC-ClinicDB polyp detection dataset, addressing endoscopy pitfalls, underscore the superiority of our SCT over other state-of-the-art methods. The proposed model maintains high performance, even in challenging scenarios.
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