Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and ease establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated long-distance transmission limited coverage base stations (BSs), emerging as a powerful paradigm both communication services. Furthermore, incorporating simultaneously transmitting reflecting...

Rapid and accurate identification of cancerous areas during surgery is crucial for guiding surgical procedures reducing postoperative recurrence rates. Dynamic Cell Imaging (DCI) has emerged as a promising alternative to traditional frozen section pathology, offering high-resolution displays tissue structures cellular characteristics. However, challenges persist in segmenting DCI images using deep learning methods, such color variation artifacts between patches whole slide images, the...

A long-standing goal of AI systems is to perform complex multimodal reasoning like humans. Recently, large language models (LLMs) have made remarkable strides in such multi-step on the modality solely by leveraging chain thought (CoT) mimic human thinking. However, transfer these advancements contexts introduces heightened challenges, including but not limited impractical need for labor-intensive annotation and limitations terms flexibility, generalizability, explainability. To evoke CoT...

Plant density is a significant variable in crop growth. estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms well-established procedure. However, flight companies for wheat are typically executed at early development stages. Further exploration required to estimate the plant after tillering stage, which crucial following growth This study proposed model, DeNet, highly accurate tillering. The validation results presented that (1) DeNet with global-scale...

Abstract The detection of strip steel surface defects is critical to ensuring the quality products. Many deep learning-based methods have been presented and can achieve outstanding performance. However, most these ignore frequency information among defect areas, which plays an important role in detection. This paper proposes a learning method further improve segmentation effects based on existing methods, called low-pass U-Net. Since are located high-frequency we implement filter before...

Optical Coherence Tomography Angiography (OCTA) is a non-invasive and non-contacting imaging technique providing visualization of microvasculature retina optic nerve head in human eyes vivo. The adequate image quality OCTA the prerequisite for subsequent quantification retinal microvasculature. Traditionally, score based on signal strength used discriminating low quality. However, it insufficient identifying artefacts such as motion off-centration, which rely specialized knowledge need...

Optical Coherence Tomography Angiography (OCTA) is a non-invasive and non-contacting imaging technique, which can visualize the microvasculature of retina optic nerve head in human eyes vivo. The adequate image quality OCTA prerequisite for downstream quantification retinal microvasculature. Conventionally, score based on signal strength. Yet, it insufficient identifying artifacts such as motion off-centration, rely specialized knowledge relies tediously time-consuming manual identification....

Abstract Tool wear is unavoidable during machining, which one of the most common tool failure modes. It significant to evaluate state quickly and effectively for timely change strategy. The cutting vibration signals after show strong non-Gaussian characteristics. Higher order spectrum a powerful analyzing characteristics signals, can restrain noise provide more information than classical power analysis. This paper presents milling monitoring method based on higher entropy. Due large amount...

The coexistence of lung cancer and pulmonary tuberculosis (TB) is rare, the clinical radiological features are always similar between TB. In present case, a non-small cell patient with an epidermal growth factor receptor (EGFR)-sensitive mutation was diagnosed TB during treatment tyrosine kinase inhibitor (TKI) because discrepant confusing responses among different lesions. Therefore, we should combine characteristics pathological microbiological tests to confirm diagnosis or cancer. It safe...

The study of coastal processes is critical for the protection and development beach amenities, infrastructure, properties. Many studies evolution rely on data collected using remote sensing show that can be characterized by a finite number "beach states". However, due to practical constraints, long-term displaying all states are rare. Additionally, when dataset available, accuracy classification not entirely objective since it depends operator. To address this problem, we hourly images...

Background and Objective Coronary artery disease remains a leading cause of mortality among individuals with cardiovascular conditions. The therapeutic use bioresorbable vascular scaffolds (BVSs) through stent implantation is common, yet the effectiveness current BVS segmentation techniques from Intravascular Optical Coherence Tomography (IVOCT) images inadequate. Methods This paper introduces an enhanced approach using novel Wavelet-based U-shape network to address these challenges. We...

This paper presents an intelligent prediction method for aeration capacity of biochemical tank sewage treatment. Firstly, the data collected in field is processed from actual treatment plant and set obtained through correlation analysis. Secondly, after optimizing model parameters, RF, GBDT, LGB LR models are established respectively to obtain forecasting capabilities each model. Furthermore, fusion Stacking introduced by using GBDT as first layer second layer. Experimental results show that...

Summary As an important method to diagnose gastric cancer, pathological sections images (GPSI) are hard and time‐consuming be recognized even by experienced doctor. An efficient was designed detect cancer in magnified (20 × ) GPSI using deep learning technology. A novel DenseNet architecture applied, modified with a multistage attention module (MSA‐DenseNet). To develop this model focusing on features, two‐stage‐input adopted select more semantic information of cancer. Moreover, the...

On account of the poor anti-nose performance and complicated calibration process in existing algorithms vehicle camera self-calibration, a method based on single octagonal template is proposed this paper. This takes advantage character cross ratio harmonic conjugate projective geometry relevant reasoning Laguerre theory. With method, information about size, locations motion states isn't needed while reducing movement times template. Meanwhile, precision corner detection can reach sub-pixel...

The purpose of super-resolution reconstruction is to reconstruct a high-resolution image from one or more low resolution images. In this paper, we propose an improved generative adversarial networks based on the attention model. model can be used extract important features and suppress unimportant features, so as ensure quality network optimize structure generator in (GAN). experimental results show that Set5 datasets, could use less residual block train spending time by using That also...

Abstract Quickly and accurately tracing neuronal morphologies in large-scale volumetric microscopy data is a very challenging task. Most automatic algorithms for multi-neuron whole brain are designed under the Ultra-Tracer framework, which begins of neuron from its soma traces all signals via block-by-block strategy. Some image blocks easy their reconstructions accurate, some others difficult inaccurate or incomplete. The former called low Tracing Difficulty Blocks (low-TDBs), while latter...

High‐density crowd counting in natural scenes is an extremely difficult and challenging research subject computer vision. Although the algorithm based on convolutional neural network has achieved significantly better results than traditional algorithm, most of them tend to focus local features images, obtain rich global contextual dependencies. To solve this problem, a dual attention module multi‐label fully are proposed study. Moreover, authors improve by following multiple perspectives....