Since 2020, the China Earthquake Early Warning Network (CEEWN) has collected extensive ground motion data using high-density microelectromechanical system (MEMS) sensors and force-balanced accelerographs (FBAs). This offers a valuable opportunity for studying near-fault developing regional models (GMMs) in China. However, concerns about usability of acceleration records from low-cost MEMS have limited their application. We aimed to assess CEEWN by analyzing noise levels, usable bandwidth,...

Change vector analysis (CVA) and post-classification change detection (PCC) have been the most widely used change-detection methods. However, CVA requires sound radiometric correction to achieve optimal performance, PCC is susceptible accumulated classification errors. Although in posterior probability space (CVAPS) was developed resolve limitations of CVA, uncertainty remote sensing imagery limits performance CVAPS owing three major problems: 1) mixed pixels, 2) identical ground cover type...

Acoustic neuroma has similarities with other intracranial tumors in imaging manifestations and location of incidence, misdiagnosis often occurs clinical practice. This paper uses a mask region convolution neural network (Mask RCNN) to classify acoustic neuromas. The T1WI-SE sequence MRI images 120 patients our hospital were collected. Based on preprocessing, the improved feature pyramid networks (FPN) algorithm Mask RCNN comprehensive training conducted, classification effects different...

The classification of high-resolution remote sensing images finds widespread applications, yet achieving accurate land types often relies heavily on labeled samples. However, obtaining samples is a challenging and time-consuming task. To mitigate the algorithm's dependence reduce computational time resource consumption, we propose an approach combining context-aggregated transformer-based network (TSNet) with differentiable feature clustering method for unsupervised image classification....

It is essential to build good image representations for many computer vision tasks. In this study, the authors propose a hierarchical spatial pyramid max pooling method based on scale‐invariant feature transform (SIFT) features and sparse coding, which builds through network. includes three parts: SIFT features’ extraction, coding pooling. To mimic visual cortex, is, firstly, performed original in patches, distils extracts most distinctive significant feature, SIFT‐pooled each local patch,...

Designing diffractive waveguides for head-mounted displays requires wide-angle conical diffraction analysis of multiple gratings. In this work, waveguide design using the relative direction cosine space, which extends space to a involving refractive indices and can describe grating through various media, is demonstrated. A was fabricated with periods 382 270 nm, generated monochromatic virtual image in green light (520 nm). The maximum field view measured as 39° 0.5° deviation from center view.

Points on maps that stand for geographic objects such as settlements are generally connected by road networks. However, in the existing algorithms point cluster simplification, points usually viewed discrete or their distances considered Euclidean spaces, and therefore generalization results obtained these sometimes unreasonable. To take roads into consideration so clusters can be simplified appropriate ways, network Voronoi diagram is used a new algorithm proposed this paper. First,...

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map suitable for global threshold. However, vigorous user intervention required to achieve optimal performance. Therefore, eliminate retain the merit of CVAPS, unsupervised...

This letter proposes a novel dual-output pulse coupled neural network model (DPCNN). The new is applied to obtain more stable texture description in the face of geometric transformation. Time series, which are computed from output binary images DPCNN, employed as translation-, rotation-, scale-, and distortion-invariant features. In experiments, DPCNN has been well tested by using Brodatz's album VisTex database. Several existing models compared with proposed model. experimental results,...

The electronic property and aromaticity of endohedrally doped clusters are investigated using the density‐functional theory (DFT) within hybrid B3LYP method. calculated results reveal that two have high thermodynamic stability reflected by reaction energy. At same time, it could be hoped their may arise from closed‐shell spherical with eight π ‐electrons satisfying counting rule N = 1. A popular nucleus‐independent chemical shifts (NICSs) calculation on basis magnetic shieldings is also...

Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus medical image fusion tasks. Unfortunately, PCNNs difficult to directly apply multispectral fusion, especially when the spectral fidelity is considered. A key problem that most methods using usually focus on selection mechanism either in space domain or transform domain, rather than a details injection mechanism, which of utmost importance fusion. Thus, novel pansharpening PCNN model...

Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, spatial available hyperspectral is inferior, severely affecting accuracy ground object identification. In paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen scale multispectral imagery. Firstly, a SAM-CC strategy designed assign bands bands. Subsequently, improved PCNN (IPCNN)...

Software performance evaluation in multimedia communication systems is typically formulated into a multi-layered client-server queuing network (MLCSQN) problem. However, the existing analytical methods to MLCSQN model cannot provide satisfactory solution terms of accuracy, convergence and consideration interlocking effects. To this end, paper proposes heuristic solving method for boost prediction distributed software systems. The core concept referred as basic model, which can be further...

Purpose The purpose of this study is to examine the predictive effects social context and its interaction with individual differences on job crafting behaviors. Specially, paper draws purposeful work behavior theory outline how four characteristics (social support, interdependence, outside organization feedback from others) moderation neuroticism predict task crafting, relational cognitive crafting. Design/methodology/approach current examined as antecedents moderating were explored well. By...

This paper presents a secure group communication scheme to relieve the bottleneck problem of vehicular authentication efficiency using batch verification accelerate speed during construction phase. We use certificateless public key cryptosystem encryption overcome escrow problem. Specific revocation malicious vehicles, bloom filter is applied issuing information revoked vehicles instead time-consuming list. By performance evaluation, proposed outperforms previously reported schemes in terms...

A robust reversible data hiding scheme for multispectral image is proposed in this paper. Different from the existing lossless schemes which use statistical quantity of arithmetic difference blocks, method shifts transform domain histogram to get a better performance images. The average coefficients 3D-IWT (Integer Wavelet Transform) block used embed and achieve robustness. secret message can be extracted without location map or overhead information. experimental results demonstrate that...

The detection of change in remote-sensing images is broadly applicable to many fields. In recent years, both supervised and unsupervised methods have demonstrated excellent capacity detect changes high-resolution images. However, most these are sensitive noise, their performance significantly deteriorates when dealing with that been contaminated by mixed random noises. Moreover, require samples manually labeled for training, which time-consuming labor-intensive. This study proposes a new...