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We report the flexible on-target delivery of 800 nm wavelength, 5 GW peak power, 40 fs duration laser pulses through an evacuated and tightly coiled 10 m long hollow-core nested anti-resonant fiber by positively chirping the input pulses to compensate for the anomalous dispersion of the fiber. Near-transform-limited output pulses with high beam quality and a guided peak intensity of 3 PW/cm2 were achieved by suppressing plasma effects in the residual gas by pre-pumping the fiber with laser pulses after evacuation. This appears to cause a long-term removal of molecules from the fiber core. Identifying the fluence at the fiber core-wall interface as the damage origin, we scaled the coupled energy to 2.1 mJ using a short piece of larger-core fiber to obtain 20 GW at the fiber output. This scheme can pave the way towards the integration of anti-resonant fibers in mJ-level nonlinear optical experiments and laser-source development.
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Optical fibers with a low thermal coefficient of delay (TCD) have been developed for frequency and timing transmission/distribution. However, their temperature sensitivity changes as a function of temperature and, to date, no study of such fibers has demonstrated improved performance over extended temperature ranges, especially at sub-zero temperatures. Here, we show that a hollow core fiber (HCF) with a thin acrylate coating can have a TCD within ±2.0 ps/km/°C over a broad temperature range from -150°C to +60°C. In addition, this thinly coated HCF can be fully insensitive to temperature around -134°C, making it of interest, e.g., for laser stabilization close to cryogenic temperatures.
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In the original publication of our research article "Hollow core fiber Fabry-Perot interferometers with reduced sensitivity to temperature" [Opt. Lett.47, 2510 (2022)10.1364/OL.456589OPLEDP0146-9592], we identified an error that requires correction. The authors sincerely apologize for any confusion that may have arisen from this error. The correction does not affect the overall conclusions of the paper.
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We demonstrate a 3× thermal phase sensitivity reduction for a hollow-core fiber (HCF) Fabry-Perot interferometer by winding the already low temperature sensitivity HCF on to a spool made from an ultralow thermal expansion material. A record low room temperature fiber coil phase thermal sensitivity of 0.13â ppm/K is demonstrated. The result is of particular interest in reducing the thermal sensitivity of HCF-based Fabry-Perot interferometers (for which existing thermal sensitivity reduction methods are not applicable). Our theoretical analysis predicts that significantly lower (or even zero) thermal sensitivity should be achievable when a spool with a slightly negative coefficient of thermal expansion is used. We also suggest a method to fine-tune the thermal sensitivity and analyze it with simulations.
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One of the major challenges in the deployment of quantum communications (QC) over solid-core silica optical fiber is the performance degradation due to the optical noise generated with co-propagating classical optical signals. To reduce the impact of the optical noise, research teams are turning to new and novel architectures of solid-core and hollow-core optical fiber. We studied the impact when co-propagating a single-photon level (850 nm) and two classical optical signals (940 nm and 1550 nm) while utilizing a nested antiresonant nodeless fiber (NANF) with two low-loss windows. The 940 nm signal was shown to impact the single-photon measurement due to the silicon detector technology implemented; however, multiplexing techniques and filtering could reduce the impact. The 1550 nm signal was shown to have no detrimental impact. The results highlight that both high bandwidth optical traffic at 1550 nm and a QC channel at 850 nm could co-propagate without degradation to the QC channel.
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The optical phase accumulated when light propagates through an optical fiber changes with temperature. It has been shown by various authors that this thermal phase sensitivity is significantly smaller in hollow core fibers (HCFs) than in standard single-mode fibers (SSMFs). However, there have been considerable differences in the level of sensitivity reduction claimed, with factors in the range ×3 to ×20 improvement for HCFs relative to SSMFs reported. Here we show experimentally that this large variation is likely attributable to the influence of fiber coating, which is exacerbated in HCFs with a relatively thin silica glass outer wall (e.g., the wall thickness is typically just 20 µm in a 125 µm diameter HCF). Further, we show that the coating also causes the optical phase stability to suffer from relaxation effects, which have not been previously discussed in the HCF literature, to the best of our knowledge. In addition to demonstrating these relaxation effects experimentally, we analyze them through numerical simulations. Our results strongly suggest that they originate from the viscoelastic properties of the coating. To minimize the adverse effects of the coating, we have fabricated a HCF with a relatively thick wall (â¼50µm) and a very thin coating (10 µm). This results in an almost 30-fold reduction in HCF thermal phase sensitivity relative to SSMFs - a significantly lower sensitivity than in previous reports. Moreover, our thinly coated HCF exhibits no discernable relaxation effects while maintaining good mechanical properties.
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Using micro-optic collimator technology, we present compact, low-loss optical interconnection devices for hollow core fibers (HCFs). This approach is one of the key manufacturing platforms for commercially available fiber optic components and most forms of HCFs can readily be incorporated into this platform without the need for any substantial or complicated adaptation or physical deformation of the fiber structure. Furthermore, this technique can provide for very low Fresnel reflection interconnection between solid-core fiber and HCF and in addition provides a hermetic seal for HCFs, which can be a critical issue for many HCF applications. In this paper, several exemplar HCF components are fabricated with low insertion loss (0.5-2 dB), low Fresnel reflection (-45 dB) and high modal purity (>20 dB) using various state-of-the-art HCFs.
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We report the first extruded tellurite antiresonant hollow core fibers (HC-ARFs) aimed at the delivery of mid-infrared (Mid-IR) laser radiation. The preform extrusion fabrication process allowed us to obtain preforms with non-touching capillaries in a single step, hence minimizing thermal cycles. The fibers were fabricated from in-house synthetized tellurite glass (containing Zn, Ba and K oxides) and co-drawn with a fluorinated ethylene propylene (FEP) polymer outer layer to improve their mechanical properties and protect the glass from humidity. The fabricated HC-ARFs transmit in the Mid-IR spectral range from 4.9 to 6 µm. We measured losses of â¼8.2, 4.8 and 6.4 dB/m at 5 µm, 5.6 µm and 5.8 µm, respectively in two different fibers. These losses, which are dominated by leakage mostly arising from a non-uniform membrane thickness, represent the lowest attenuation reported for a tellurite-based HC-ARF to date. The fibers present good beam quality and an M2 factor of 1.2. Modelling suggests that by improving the uniformity in the capillary membrane thickness losses down to 0.05 dB/m at 5.4 µm should be possible, making this solution attractive, for example, for beam delivery from a CO laser.
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Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architecture, called Res-BRNet, for brain tumor classification using magnetic resonance imaging (MRI) scans. Res-BRNet employs a systematic combination of regional and boundary-based operations within modified spatial and residual blocks. The spatial blocks extract homogeneity, heterogeneity, and boundary-related features of brain tumors, while the residual blocks significantly capture local and global texture variations. We evaluated the performance of Res-BRNet on a challenging dataset collected from Kaggle repositories, Br35H, and figshare, containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Res-BRNet outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), and precision (0.9822). Our results suggest that Res-BRNet is a promising tool for brain tumor classification, with the potential to improve the accuracy and efficiency of clinical diagnosis and treatment planning.
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COVID-19, a novel pathogen that emerged in late 2019, has the potential to cause pneumonia with unique variants upon infection. Hence, the development of efficient diagnostic systems is crucial in accurately identifying infected patients and effectively mitigating the spread of the disease. However, the system poses several challenges because of the limited availability of labeled data, distortion, and complexity in image representation, as well as variations in contrast and texture. Therefore, a novel two-phase analysis framework has been developed to scrutinize the subtle irregularities associated with COVID-19 contamination. A new Convolutional Neural Network-based STM-BRNet is developed, which integrates the Split-Transform-Merge (STM) block and Feature map enrichment (FME) techniques in the first phase. The STM block captures boundary and regional-specific features essential for detecting COVID-19 infectious CT slices. Additionally, by incorporating the FME and Transfer Learning (TL) concept into the STM blocks, multiple enhanced channels are generated to effectively capture minute variations in illumination and texture specific to COVID-19-infected images. Additionally, residual multipath learning is used to improve the learning capacity of STM-BRNet and progressively increase the feature representation by boosting at a high level through TL. In the second phase of the analysis, the COVID-19 CT scans are processed using the newly developed SA-CB-BRSeg segmentation CNN to accurately delineate infection in the images. The SA-CB-BRSeg method utilizes a unique approach that combines smooth and heterogeneous processes in both the encoder and decoder. These operations are structured to effectively capture COVID-19 patterns, including region-homogenous, texture variation, and border. By incorporating these techniques, the SA-CB-BRSeg method demonstrates its ability to accurately analyze and segment COVID-19 related data. Furthermore, the SA-CB-BRSeg model incorporates the novel concept of CB in the decoder, where additional channels are combined using TL to enhance the learning of low contrast regions. The developed STM-BRNet and SA-CB-BRSeg models achieve impressive results, with an accuracy of 98.01%, recall of 98.12%, F-score of 98.11%, Dice Similarity of 96.396%, and IOU of 98.85%. The proposed framework will alleviate the workload and enhance the radiologist's decision-making capacity in identifying the infected region of COVID-19 and evaluating the severity stages of the disease.
Assuntos
COVID-19 , Radiologia , Humanos , COVID-19/diagnóstico por imagem , Radiografia , Tomografia Computadorizada por Raios X , Aprendizagem , Processamento de Imagem Assistida por ComputadorRESUMO
There are a host of applications in communications, sensing, and science, in which analogue signal transmission is preferred over today's dominant digital transmission. In some of these applications, the advantage is in lower cost, while in others, it lies in superior performance. However, especially for longer analogue photonics links (up to 10 s of km), the performance is strongly limited by the impairments arising from using standard single-mode fibres (SSMF). Firstly, the three key metrics of analogue links (loss, noise figure, and dynamic range) tend to improve with received power, but this is limited by stimulated Brillouin scattering in SSMF. Further degradation is due to the chromatic dispersion of SSMF, which induces radio-frequency (RF) signal fading, increases even-order distortions, and causes phase-to-intensity-noise conversion. Further distortions still, are caused by the Kerr nonlinearity of SSMF. We propose to address all of these shortcomings by replacing SSMFs with hollow-core optical fibres, which have simultaneously six times lower chromatic dispersion and several orders of magnitude lower nonlinearity (Brillouin, Kerr). We demonstrate the advantages in this application using a 7.7 km long hollow-core fibre sample, significantly surpassing the performance of an SSMF link in virtually every metric, including 15 dB higher link gain and 6 dB lower noise figure.
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For over 50 years, pure or doped silica glass optical fibres have been an unrivalled platform for the transmission of laser light and optical data at wavelengths from the visible to the near infra-red. Rayleigh scattering, arising from frozen-in density fluctuations in the glass, fundamentally limits the minimum attenuation of these fibres and hence restricts their application, especially at shorter wavelengths. Guiding light in hollow (air) core fibres offers a potential way to overcome this insurmountable attenuation limit set by the glass's scattering, but requires reduction of all the other loss-inducing mechanisms. Here we report hollow core fibres, of nested antiresonant design, with losses comparable or lower than achievable in solid glass fibres around technologically relevant wavelengths of 660, 850, and 1060 nm. Their lower than Rayleigh scattering loss in an air-guiding structure offers the potential for advances in quantum communications, data transmission, and laser power delivery.