Channel Image Research Articles

A novel approach to authentication of highbush and lowbush blueberry cultivars using image analysis, traditional machine learning and deep learning algorithms

AbstractThe objective of this study was to classify blueberry cultivars based on image texture parameters using models built using traditional machine learning and deep learning algorithms. The blueberries belonging to highbush cultivars (‘Bluecrop’, ‘Herbert’, ‘Jersey’, and ‘Nelson’) and lowbush cultivars (‘Emil’ and ‘Putte’) were subjected to imaging using a digital camera. The texture parameters from blueberry images in color channels R, G, B, L, a, b, X, Y, Z, U, V, and S were determined. After selection image textures were used to build models for the classification of all highbush and lowbush blueberry cultivars, and highbush blueberry cultivars and lowbush blueberry cultivars, separately. In the case of distinguishing all cultivars, such as ‘Bluecrop’, ‘Herbert’, ‘Jersey’, and ‘Nelson’, ‘Emil’ and ‘Putte’, the classification accuracy reached 92.33% for a model built using a deep learning algorithm. Models built to distinguish only highbush cultivars provided an average accuracy of up to 91.25% (WiSARD). For models developed to classify two lowbush cultivars, an average accuracy reaching 96% (WiSARD) was found. The applied procedure can be used in practice to distinguish blueberry cultivars before their consumption or processing.

Use of ResNets for HLB Disease Detection on Orange Leaves Using Terrestrial Multispectral Images

Abstract. Huanglongbing (HLB) is a bacterial disease transmitted by different vectors of sap-sucking insects. It affects all crops of citrus trees, decreasing the values of those fruits in the market and eventually the decay of orchards. In Brazil, the world's leading orange producer, citriculture faces severe issues with HLB and substantial economic loss. Technical means of scanning the orchards with high-throughput becomes essential for the sustainability of this industry. In this study, we propose to investigate an operational strategy consisting of scanning large portions of foliage (the canopy of one tree or more) in which there can few early foliage symptoms. It is proposed to investigate deep learning tools to solve this complex binary classification problem. The study is based on a dataset comprising 1,297 terrestrial multispectral (14 channels) images captured at high spatial resolution in a commercial orange orchard in Brazil. It is proposed to adapt and retrain standard neural network architectures, namely ResNets18 and ResNets34, to process such images. Our analysis reveals promising results, with both models demonstrating convergence and achieving stable performance. Notably, ResNet18 outperformed ResNet34, achieving an accuracy of 76.45% compared to 66.79% from ResNet34. These findings suggest that deep neural network methods can effectively manage non-radiometrically calibrated data and accurately distinguish images with HLB symptoms from healthy plants . However, with reduced datasets and limited possibilities for transfer learning and fine-tuning, it seems that only reasonable sized networks can be trained. Thus, more advanced state-of-the-art tools of the are still challenging to deploy for agricultural multi- or hyperspectral data.

Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.

Photon-counting computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among different channel images. In addition, reconstruction of each channel image suffers photon count starving problem. To make full use of the correlation among different channel images to suppress the data noise and enhance the texture details in reconstructing each channel image, this paper proposes a tensor neural network (TNN) architecture to learn a multi-channel texture prior for PCCT reconstruction. Specifically, we first learn a spatial texture prior in each individual channel image by modeling the relationship between the center pixels and its corresponding neighbor pixels using a neural network. Then, we merge the single channel spatial texture prior into multi-channel neural network to learn the spectral local correlation information among different channel images. Since our proposed TNN is trained on a series of unpaired small spatial-spectral cubes which are extracted from one single reference multi-channel image, the local correlation in the spatial-spectral cubes is considered by TNN. To boost the TNN performance, a low-rank representation is also employed to consider the global correlation among different channel images. Finally, we integrate the learned TNN and the low-rank representation as priors into Bayesian reconstruction framework. To evaluate the performance of the proposed method, four references are considered. One is simulated images from ultra-high-resolution CT. One is spectral images from dual-energy CT. The other two are animal tissue and preclinical mouse images from a custom-made PCCT systems. Our TNN prior Bayesian reconstruction demonstrated better performance than other state-of-the-art competing algorithms, in terms of not only preserving texture feature but also suppressing image noise in each channel image.

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of themammogram based CADx schemes.However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels. Thus, it is needed to convert the grayscale mammograms into three channel images for the input of deep transfer models. This study aims to developa novel pseudo color image generation methodwhich utilizesthe mass contour information to enhance the classification performance.Accordingly, a total of 830 breast cancer cases were retrospectively collected, which contains 310 benign and 520 malignant cases, respectively. For each case, a total of four regions of interest (ROI) are collected from the grayscale images captured for both the CC and MLO views of the two breasts. Meanwhile, a total of seven pseudo color image sets are generated as the input of the deep learning models, which arecreated through a combination of the original grayscale image, a histogram equalized image, a bilaterally filtered image, and a segmented mass. Accordingly, the output features from four identical pre-trained deep learning models are concatenated and then processed by a support vector machine-based classifier to generate the final benign/malignant labels. The performance of each image set was evaluated and compared. The results demonstrate that the pseudo color sets containing the manually segmented mass performed significantly better than all other pseudo color sets, which achieved an AUC (area under the ROC curve) up to 0.889 ± 0.012 and an overall accuracy up to 0.816 ± 0.020, respectively. At the same time, the performance improvement is also dependent on the accuracy of the mass segmentation. The results of this study support our hypothesis that adding accurately segmented mass contours can provide complementary information, thereby enhancing the performance of the deep transfer model in classifying suspicious breast lesions.

Spectral and optic-metric methods of monitoring parameters of plasma channels caused by discharge currents between metals granules in working liquids

Introduction. Spark-erosion processing of metals and alloys granules in working liquids is the basis of a several technological processes. Efficiency of energy use in them and parameters of the resulting product largely depend on the accuracy of stabilization and regulation of pulse power in each plasma channel between the granules. To achieve this, until now only the voltage and current of the discharge pulses in the entire layer of granules have been controlled. Problem. The measurement methods, which are used, are not effective enough for monitoring the parameters of individual plasma channels and predicting the size distribution of eroded metals particles at the stage of their formation. The aim of the work is to develop a method for determining the volumes of components of plasma channels in layers of metals granules during their spark-erosion treatment to predict the size distribution of eroded metal particles at the stage of their formation, as well as to simplify the method of spectrometric analysis of the elemental composition of substances surrounding plasma channels for the operational prediction of the chemical composition of resulting products. Methodology. A series of experiments were carried out on spark-erosion processing of Al and Ag granules layers in distilled water. Using a digital camera, images of the plasma channels in them were obtained. Based on the theory of pulsed electrical breakdown of liquid dielectrics, an analysis of the components of plasma channels was carried out. Using the specialized ToupView program, the volumes of equivalent ellipsoids of rotation were determined, approximating the halos of colored radiation likely arising from streamers, as well as the spark cores of plasma channels emitting white light. The shades of the resulting radiation were studied for several metals and working liquids. The obtained data were compared with the known results of spectrometric studies for the same elements excited by similar mechanisms. Results. The theory of discharge-pulse systems for spark-erosion processing of granular conductive media has been developed in the direction of new methods for monitoring the parameters of discharge pulses and predicting the chemical composition and size distribution parameters of eroded metal particles at the stage of their production. An optic-metric method has been developed for determining the volumes of halos and cores of plasma channels. A simplified spectral method for determining the chemical composition of erosion particles based on the shade of the resulting radiation was proposed. Originality. The developed new optic-metric method makes it possible to obtain information about almost every plasma channel, which refines predictions of the size distribution of erosion particles. To implement the method, general-purpose hardware and specialized software that is freely available are used. The developed method of simplified spectral analysis of excited atoms makes it possible to make preliminary predictions of the chemical composition of the obtained erosion particles already at the stage of their formation without the use of expensive specialized equipment. Practical significance. The ratio of the volumes of halos and cores of plasma channels between Al and Ag granules in distilled water was measured. An analysis of the emission spectra of plasma channel halos between Al, Ag and Cu granules in distilled water, Fe in ethyl alcohol, Ni-Mn-Ga and Ti-Zr-Ni alloys in liquid nitrogen, and Ti-Zr-Ni in liquid argon was carried out. Based on spectrometry data, the resulting shades of these radiations were substantiated and their description in the RGB system is given. References 56, table 1, figures 4.

Novel Far-Red Fluorescent 1,4-Dihydropyridines for L-Type Calcium Channel Imaging.

Upregulation of L-type calcium channels (LTCCs) is implicated in a range of cardiovascular and neurological disorders. Therefore, the development of toolboxes that unlock fast imaging protocols in live cells is coveted. Herein, we report a library of first-in-class far-red small-molecule-based fluorescent ligands (FluoDiPines), able to target LTCCs. All fluorescent ligands were evaluated in whole-cell patch-clamp and live-cell Ca2+ imaging whereby FluoDiPine 6 was found to be the best candidate for live-cell fluorescence imaging. Low concentration of FluoDiPine 6 (50 nM) and a quick labeling protocol (5 min) are successfully applied to fixed and live cells to image LTCCs with good specificity.

IRDNet: An image-defogging algorithm based on dark channel prior

Abstract Image-defogging, as an important part of computer vision, has been widely used in intelligent driving, target recognition, satellite detection, underwater exploration and so on. Improving the performance of the defogging algorithm based on deep learning has practical significance for the completion of high-level vision tasks. This paper proposes an IRDNet algorithm by improving the image deep learning algorithm based on the RefineDNet framework and designing a new deep learning network structure. The proposed algorithm combines dark channel prior knowledge and atmospheric degradation model to decompose the input fogged images into dark channel images and degraded images, and then carries out feature extraction and detail enhancement for the two images by convolutional neural network. IRDNet introduces a fully connected convolutional structure, attention mechanism, and a pyramid structure to improve the performance of the overall defogging network. By testing on OTS datasets, we compare the IRDNet algorithm with traditional defogging methods, end-to-end deep learning defogging methods, and prior knowledge-guided deep learning defogging methods. Test results show that the performance of IRDNet is better than other methods, and the defogging network shows richer details of texture and more realistic colors.

A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection.

Learning traffic as videos: A spatio-temporal VAE approach to periodic traffic raster data imputation

For modern Intelligent Transportation System (ITS), data missing during traffic raster acquisition can be inevitable because of the loop detector malfunction or signal interference. Nevertheless, missing data imputation is meaningful due to the periodic spatio-temporal characteristics and individual randomness of traffic raster data. In this paper, traffic raster data collected from all spatial regions at each time interval are considered as a multiple channel image. Accordingly, the traffic raster data over a period of time can be regarded as video, on which an unsupervised generative neural network called MSST-VAE (Multiple Streams Spatial Temporal-VAE) is proposed for traffic raster data imputation, and this model can even robustly performs at varied missing rates while many other approaches fail to conduct. Two major innovations can be summarized in MSSTVAE: Firstly, it uses multiple periodic streams of Variational Auto-Encoders (VAEs) with Sylvester Normalizing Flows (SNFs), which shows strong generalization ability. Secondly, after the traffic raster data are transferred into videos, an ECB (Extraction-and-Calibration Block) consisting of dilated P3D gated convolution and multi-horizon attention mechanism is employed to learn global-local-granularity spatial features and long-short-term temporal features. Extensive experiments on three real traffic flow datasets validate that MSST-VAE outperforms other classical traffic imputation models with the least imputation error.

AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation

Sea fog reduces visibility to less than 1 km and is a major cause of maritime accidents, particularly affecting the navigation of small fishing vessels as it forms when warm, moist air moves over cold water, making it difficult to predict. Traditional visibility measurement tools are costly and limited in their real-time monitoring capabilities, which has led to the development of video-based algorithms using cameras. This study introduces the Approximating and Eliminating the Airlight–Reduced DCP (AEA-RDCP) algorithm, designed to address the issue where sunlight reflections are mistakenly recognized as fog in existing video-based sea fog intensity measurement algorithms, thereby improving performance. The dataset used in the experiment is categorized into two types: one consisting of images unaffected by sunlight and another consisting of maritime images heavily influenced by sunlight. The AEA-RDCP algorithm enhances the previously researched RDCP algorithm by effectively eliminating the influence of atmospheric light, utilizing the initial stages of the Dark Channel Prior (DCP) process to generate the Dark Channel image. While the DCP algorithm is typically used for dehazing, this study employs it only to the point of generating the Dark Channel, reducing computational complexity. The generated image is then used to estimate visibility based on a threshold for fog density estimation, maintaining accuracy while reducing computational demands, thereby allowing for the real-time monitoring of sea conditions, enhancing maritime safety, and preventing accidents.

Deep learning permits imaging of multiple structures with the same fluorophores

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

Experimental Study of Electromagnetic Interference from Concentrated Discharge Channels within the Soil to Adjacent Directly Buried Cables during Lightning Current Inflow to the Ground

Independent lightning rods are often installed in substation grounding systems for lightning protection. The concentrated discharge channel formed when a lightning current flows to the ground through a grounding electrode will cause electromagnetic interference to the directly buried secondary cable. And the three-dimensional structure of the discharge channel will affect the transient electromagnetic field distribution, thereby affecting the electromagnetic transients on cable shields. In order to explore the influence of soil discharge phenomena on the electromagnetic interference of the directly buried secondary cable, in this paper, we carried out experiments on cables in two different grounding modes, single-ended and double-ended grounding, and captured image of soil discharge channels. The results show that the cable grounding mode will affect the coupling mode that causes the shielding layer current. The relative spatial position of the soil discharge channel and the cable has a significant impact on the magnitude of the shielding layer current under both grounding modes. The water content and salt content of the soil also have different degrees of influence on the coupling current of the shielding layer in different grounding modes.

Remote sensing data fusion approach for estimating forest degradation: a case study of boreal forests damaged by Polygraphus proximus

In the context of global climate change and rising anthropogenic loads, outbreaks of both endemic and invasive pests, pathogens, and diseases pose an increasing threat to the health, resilience, and productivity of natural forests and forest plantations worldwide. The effective management of such threats depends on the opportunity for early-stage action helping to limit the damage expand, which is difficult to implement for large territories. Recognition technologies based on the analysis of Earth observation data are the basis for effective tools for monitoring the spread of degradation processes, supporting pest population control, forest management, and conservation strategies in general. In this study, we present a machine learning-based approach for recognizing damaged forests using open source remote sensing images of Sentinel-2 supported with Google Earth data on the example of bark beetle, Polygraphus proximus Blandford, polygraph. For the algorithm development, we first investigated and annotated images in channels corresponding to natural color perception—red, green, and blue—available at Google Earth. Deep neural networks were applied in two problem formulations: semantic segmentation and detection. As a result of conducted experiments, we developed a model that is effective for a quantitative assessment of the changes in target objects with high accuracy, achieving 84.56% of F1-score, determining the number of damaged trees and estimating the areas occupied by withered stands. The obtained damage masks were further integrated with medium-resolution Sentinel-2 images and achieved 81.26% of accuracy, which opened the opportunity for operational monitoring systems to recognize damaged forests in the region, making the solution both rapid and cost-effective. Additionally, a unique annotated dataset has been collected to recognize forest areas damaged by the polygraph in the region of study.

Automated image processing algorithm for 3D OCT images of fouling in spacer-filled membrane filtration channels

OCT imaging is an important technique to study fouling in spacer-filled channels of reverse osmosis systems for seawater desalination. However, OCT imaging of membrane filtration channels with feed spacers is challenging because the spacer material can be (partly) transparent, making it difficult to detect and possibly mistaken for fouling, and the longer optical pathway through the spacer material distorts the image below the spacer. This study presents an automated 3D OCT image processing method in MATLAB for visualization and quantification of biofouling in spacer-filled channels. First, a spacer template of arbitrary size and rotation was generated from a CT scan of the feed spacer. Second, background noise and file size were reduced by representing the OCT image with a list of discrete reflectors. Finally, the spacer template was overlayed with the feed spacer in the 3D OCT image, enabling automated visualization of the feed spacer and correction of the distortions. Moreover, the method allows the selection of datasets with the same location relative to the position of the spacer, enabling systematic comparison between datasets and quantitative analysis.•A spacer template of arbitrary size and rotation was generated from a CT scan.•The background noise was removed, and the file size was reduced by representing the OCT dataset with a list of discrete reflectors.•The spacer template was overlayed with the feed spacer in the 3D OCT image.

Simultaneous Multifocal Plane Fourier Ptychographic Microscopy Utilizing a Standard RGB Camera.

Fourier ptychographic microscopy (FPM) is a computational imaging technology that can acquire high-resolution large-area images for applications ranging from biology to microelectronics. In this study, we utilize multifocal plane imaging to enhance the existing FPM technology. Using an RGB light emitting diode (LED) array to illuminate the sample, raw images are captured using a color camera. Then, exploiting the basic optical principle of wavelength-dependent focal length variation, three focal plane images are extracted from the raw image through simple R, G, and B channel separation. Herein, a single aspherical lens with a numerical aperture (NA) of 0.15 was used as the objective lens, and the illumination NA used for FPM image reconstruction was 0.08. Therefore, simultaneous multifocal plane FPM with a synthetic NA of 0.23 was achieved. The multifocal imaging performance of the enhanced FPM system was then evaluated by inspecting a transparent organic light-emitting diode (OLED) sample. The FPM system was able to simultaneously inspect the individual OLED pixels as well as the surface of the encapsulating glass substrate by separating R, G, and B channel images from the raw image, which was taken in one shot.

Weak Value Amplification Based Optical Sensor for High Throughput Real-Time Immunoassay of SARS-CoV-2 Spike Protein.

The demand for accurate and efficient immunoassays calls for the development of precise, high-throughput analysis methods. This paper introduces a novel approach utilizing a weak measurement interface sensor for immunoassays, offering a solution for high throughput analysis. Weak measurement is a precise quantum measurement method that amplifies the weak value of a system in the weak interaction through appropriate pre- and post-selection states. To facilitate the simultaneous analysis of multiple samples, we have developed a chip with six flow channels capable of conducting six immunoassays concurrently. We can perform real-time immunoassay to determine the binding characteristics of spike protein and antibody through real-time analysis of the flow channel images and calculating the relative intensity. The proposed method boasts a simple structure, eliminating the need for intricate nano processes. The spike protein concentration and relative intensity curve were fitted using the Log-Log fitting regression equation, and R2 was 0.91. Utilizing a pre-transformation approach to account for slight variations in detection sensitivity across different flow channels, the present method achieves an impressive limit of detection(LOD) of 0.85 ng/mL for the SARS-CoV-2 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, with a system standard deviation of 5.61. Furthermore, this method has been successfully verified for monitoring molecular-specific binding processes and differentiating binding capacities.

Modeling the Propagation of Slow Magnetoacoustic Waves in a Multistranded Coronal Loop

We study the propagation properties of slow magnetoacoustic waves in a multithermal coronal loop using a 3D MHD model, for the first time. A bundle of 33 vertical cylinders, each of 100 km radius, randomly distributed over a circular region of radius 1 Mm, is considered to represent the coronal loop. The slow waves are driven by perturbing the vertical velocity (v z ) at the base of the loop. We apply forward modeling to the simulation results to generate synthetic images in the coronal channels of the Solar Dynamics Observatory/Atmospheric Imaging Assembly. Furthermore, we add appropriate data noise to enable direct comparison with the real observations. It is found that the synthetic images at the instrument resolution show noncospatial features in different temperature channels in agreement with previous observations. Time–distance maps are constructed from the synthetic data to study the propagation properties. The results indicate that the oscillations are only visible in specific channels, depending on the temperature range of the plasma existing within the loop. Additionally, the propagation speed of slow waves is also found to be sensitive to the available temperature range. Overall, we propose that the cross-field thermal properties of coronal structures can be inferred using a combination of numerical simulations and observations of slow magnetoacoustic waves.

Multichannel Sandstone Thin Sections Identification Based on Improved DeepLab V3 Plus Neural Network.

As an unconventional natural gas resource, tight sandstone gas is primarily stored in the minuscule pores between rocky sand grains. A thorough understanding of the pore structure characteristics of tight sandstone reservoirs is essential for formulating an extraction plan and enhancing the efficiency of gas field development. The pore structure and mineral composition in the sandstone can be directly observed by thin sections. Nevertheless, previous approaches for the automated identification of sandstone thin sections exhibit certain limitations including slow identification, low accuracy, and challenges in the recognition of particle sizes. To achieve more accurate and convenient mineral component identification, this study introduces a multichannel identification method built upon the enhanced DeepLab V3 Plus model. Initially, all 224 × 224 × 3 cross-polarized light (CPL) and orthogonal polarized light (XPL) sandstone thin sections were amalgamated into 224 × 224 × 6 multichannel (six channels) images. Subsequently, multiple networks were employed to train the three polarized data sets, and the optimal semantic segmentation architecture and data set were selected through filtering. Following that, embedding the attention mechanism into the semantic segmentation network enhanced the identification accuracy. Ultimately, mineral sizes were calculated to enable more precise classification and naming of sandstone thin sections. The results show that the new method outperforms in terms of recognition accuracy, achieving 89.8% for Mean PA and 81.2% for Mean IOU. The novel approach's enhanced level of detailing enables more precise identification of mineral composition and pore structure, a crucial aspect in evaluating reservoirs and predicting oil and gas production. It can also provide new insights into identifying and categorizing other thin sections with similar compositions.

Advancements in Cholangioscopy: A Case Report with a New Cholangioscope System and Review of Literature

AbstractThe field of cholangioscopy is undergoing rapid evolution, marked by continuous innovation. The advent of SpyGlass cholangioscopy has initiated a new chapter, transforming the practice with advancements that include single-operator procedures and the introduction of disposable cholangioscopes. Despite these strides, significant needs remain, including enhancement of optical imaging, improving maneuverability, and expansion of therapeutic channels. This article delves into the recent progress within cholangioscopy, with a particular focus on the clinical applications of the Leinzett Lan-EP-3516 cholangioscope for the management of portal biliopathy. We present a detailed case study demonstrating how this cutting-edge cholangioscope—distinguished by its various sizing options and compatibility with portable devices for immediate imaging—played an important role in the successful treatment of a distal common biliary duct stricture and choledocholithiasis. The device's cost-effectiveness and superior imaging quality signify a notable advancement in cholangioscopic technology, facilitating enhanced treatment pathways for patients with complex biliary disorders. This case exemplifies the dynamic and expanding utility of cholangioscopy in therapeutic interventions and underscores its escalating significance in clinical settings.

A reconstruction and recovery network-based channel estimation in high-speed railway wireless communications

The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development. Due to the considerable mobility of trains and the complex nature of the environment, the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics, which presents significant hurdles in terms of channel estimation. In addition, the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation. To address the aforementioned issues, this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network. In this method, the time-frequency response of the channel is considered as a two-dimensional image. The Fast Super-Resolution Convolution Neural Network (FSRCNN) is used to first reconstruct channel images. Next, the Denoising Convolution Neural Network (DnCNN) is applied to reduce the channel noise and improve the accuracy of channel estimation. Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method, while also exhibiting reduced algorithmic complexity.