Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention.

Sparse-view CT reconstruction is a challenging ill-posed inverse problem, where insufficient projection data leads to degraded image quality with increased noise and artifacts. Recent deep learning approaches have shown promising results in CT reconstruction. However, existing methods often neglect projection data constraints and rely heavily on convolutional neural networks, resulting in limited feature extraction capabilities and inadequate adaptability.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction.

Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings.To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework.

[Electrocardiogram classification algorithm based on CvT-13 and multimodal image fusion].

Electrocardiogram (ECG) signal is an important basis for the diagnosis of arrhythmia and myocardial infarction. In order to further improve the classification effect of arrhythmia and myocardial infarction, an ECG classification algorithm based on Convolutional vision Transformer (CvT) and multimodal image fusion was proposed. Through Gramian summation angular field (GASF), Gramian difference angular field (GADF) and recurrence plot (RP), the one-dimensional ECG signal was converted into three different modes of two-dimensional images, and fused into a multimodal fusion image containing more features.

A 3D reconstruction based on an unsupervised domain adaptive for binocular endoscopy.

In minimally invasive surgery, endoscopic image quality plays a crucial role in surgery. Aiming at the lack of a real parallax in binocular endoscopic images, this article proposes an unsupervised adaptive neural network. The network combines adaptive smoke removal, depth estimation of binocular endoscopic images, and the 3D display of high-quality endoscopic images.

COVID-19 CT image segmentation method based on swin transformer.

Owing to its significant contagion and mutation, the new crown pneumonia epidemic has caused more than 520 million infections worldwide and has brought irreversible effects on the society. Computed tomography (CT) images can clearly demonstrate lung lesions of patients. This study used deep learning techniques to assist doctors in the screening and quantitative analysis of this disease.

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey.

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient's body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications.

Multi-modal feature-fusion for CT metal artifact reduction using edge-enhanced generative adversarial networks.

Computed Tomography (CT) imaging is one of the most widely-used and cost-effective technology for organ screening and diseases diagnosis. Because of existence of metallic implants in some patients, the CT images acquired from these patients are often corrupted by undesirable metal artifacts, which causes severe problem of metal artifact. Although there have been proposed many methods to reduce metal artifact, reduction is still challenging and inadequate, and results are suffering from symptom variance, second artifact and poor subjective evaluation.

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection.

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image.

An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of Visual Sensors.

Text recognition in natural scene images has always been a hot topic in the field of document-image related visual sensors. The previous literature mostly solved the problem of horizontal text recognition, but the text in the natural scene is usually inclined and irregular, and there are many unsolved problems. For this reason, we propose a scene text recognition algorithm based on a text position correction (TPC) module and an encoder-decoder network (EDN) module.

A Software Defined Radio Evaluation Platform for WBAN Systems.

In recent years, the Wireless Body Area Network (WBAN) concept has attracted significant academic and industrial attention. WBAN specifies a network dedicated to collecting personal biomedical data from advanced sensors that are then used for health and lifestyle purposes. In 2012, the 802.

Classification for Single-Trial N170 During Responding to Facial Picture With Emotion.

Whether an event-related potential (ERP), N170, related to facial recognition was modulated by emotion has always been a controversial issue. Some researchers considered the N170 to be independent of emotion, whereas a recent study has shown the opposite view. In the current study, electroencephalogram (EEG) recordings while responding to facial pictures with emotion were utilized to investigate whether the N170 was modulated by emotion.

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model.

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies.

Capsule Design for Blue Light Therapy against Helicobacter pylori.

A photo-medical capsule that emits blue light for Helicobacter pylori treatment was described in this paper. The system consists of modules for pH sensing and measuring, light-emitting diode driver circuit, radio communication and microcontroller, and power management. The system can differentiate locations by monitoring the pH values of the gastrointestinal tract, and turn on and off the blue light according to the preset range of pH values.

[Development of physiological monitors based on the Zigbee technology for hyperbaric oxygen chambers].

This paper introduces a monitor that can monitor five physiological parameters (ECG, blood pressure, spo2, respiration and temperature) based on Wireless Sensor Networks. The monitor will be applied to hyperbaric oxygen chambers. After acquisition, the signal will be displayed on the LCD screen of the monitor terminal in the cabin.