Due to the outbreak of lung infections caused by coronavirus disease (COVID-19), humans have face an unprecedented and devastating global health crisis. Since chest computed tomography (CT) images COVID-19 patients contain abundant pathological features closely related this disease, rapid detection diagnosis based on CT is great significance for treatment blocking spread disease. In particular, segmentation lung-infected area can quantify evaluate severity However, due blurred boundaries low...

Automatic segmentation of lesion areas in dermoscopic images is a crucial step computer-aided skin examination and diagnosis systems. Efficient accurate benefits the quantitative analysis diseases, such as melanoma, dermatofibroma, seborrheic keratosis so on. However, practical clinical diagnosis, some exhibit large-scale changes, fuzzy irregular boundaries, low contrast between background, leading to potential errors. To overcome this difficulty, we propose novel network called ADF-Net,...

Abstract In recent years, great achievements have been made in graph convolutional network (GCN) for non-Euclidean spatial data feature extraction, especially the skeleton-based extraction. However, fixed structure determined by adjacency matrix usually causes problems such as weak modeling ability, unsatisfactory generalization performance, excessively large number of model parameters, and so on. this paper, a spatially adaptive residual (SARGCN) is proposed action recognition based on...

In order to make the classification and regression of single-stage detectors more accurate, an object detection algorithm named Global Context You-Only-Look-Once v3 (GC-YOLOv3) is proposed based on (YOLO) in this paper. Firstly, a better cascading model with learnable semantic fusion between feature extraction network pyramid designed improve accuracy using global context block. Secondly, information be retained screened by combining three different scaling maps together. Finally,...

With the development of graph convolutional network (GCN) over recent years, skeleton-based action recognition has achieved satisfactory results. However, some existing GCN-based models were complex because lots parameters in models. Moreover, a large proportion extraction methods for temporal feature could not effectively extract features. To address this problem, lightweight channel-topology based adaptive (LC-AGCN), is proposed paper. And it includes three innovative and important blocks....

In the medical field, Alzheimer’s disease (AD), as a neurodegenerative brain which is very difficult to diagnose, can cause cognitive impairment and memory decline. Many existing works include variety of clinical neurological psychological examinations, especially computer-aided diagnosis (CAD) methods based on electroencephalographic (EEG) recording or MRI images by using machine learning (ML) combined with different preprocessing steps such hippocampus shape analysis, fusion embedded...

For skeleton-based action recognition, graph convolutional networks (GCN) have absolute advantages. Existing state-of-the-art (SOTA) methods tended to focus on extracting and identifying features from all bones joints. However, they ignored many new input which could be discovered. Moreover, GCN-based recognition models did not pay sufficient attention the extraction of temporal features. In addition, most had swollen structures due too parameters. order solve problems mentioned above, a...

Graph convolutional networks (GCNs), which extend neural (CNNs) to non-Euclidean structures, have been utilized promote skeleton-based human action recognition research and made substantial progress in doing so. However, there are still some challenges the construction of models based on GCNs. In this paper, we propose an enhanced adjacency matrix-based graph network with a combinatorial attention mechanism (CA-EAMGCN) for recognition. Firstly, matrix is constructed expand model's perceptive...

Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion have achieved certain success, challenges remain accurately delineating the boundaries of regions with irregular shapes, blurry edges, and occlusions by artifacts. To address these issues, a multi-attention codec network selective dynamic fusion (MASDF-Net) is proposed this study. In network, we use pyramid vision...

In recent years, great progress has been made in the recognition of skeletal behaviors based on graph convolutional networks (GCNs). most existing methods, however, fixed adjacency matrix and structure are used for skeleton data feature extraction spatial dimension, which usually leads to weak modeling ability, unsatisfactory generalization performance, an excessive number model parameters. Most these methods follow ST-GCN approach temporal inevitably a non-key frames, increasing cost...

This paper implements two kinds of memristor-based colpitts oscillators, namely, the circuit where memristor is added into feedback network oscillator in parallel and series, respectively. First, a MULTISIM simulation for memristive built, an emulator constructed by some off-the-shelf components utilized to replace memristor. Then physical system implemented terms circuit. Circuit experimental study show that this can exhibit periodic, quasi-periodic, chaotic behaviors with certain...

Inspired from the promising performances achieved by recurrent neural networks (RNN) and convolutional (CNN) in action recognition based on skeleton, this paper presents a deep network structure which combines both CNN for classification RNN to achieve attention mechanism human interaction recognition. Specifically, module is utilized give various levels of frames different weights, employed extract high-level spatial temporal information skeleton data. These two modules seamlessly form...

A simple circuit based on a memristor and constructed from colpitts oscillator is investigated in this paper. The evolutions of impedance current-voltage characteristics the are studied, phase trajectories state variables presented, bifurcation diagrams with different parameters initial conditions illustrated. With variances memristor, shows dynamic behaviors such as period, period doubling bifurcation, chaos, etc. And there coexist periodic windows some regions chaos. Numerical simulation...

A memristor-based fractional order circuit derived from Chua's topology is presented. The dynamic properties of this such as phase trajectories, time evolution characteristics state variables are analyzed through the approximation method operator. In addition, it clearly describes relationships between impedance variation memristor and varying mobility doped region in different parameters. Finally, a periodic system driven by another chaotic synchronized to via linear error feedback technique.

Automatic segmentation of lesion areas in dermatoscopic images is a key step computer-aided medical image diagnosis systems. However, this type presents challenges such as blurry boundaries the target and significant variations target. Therefore, it remains difficult task. At same time, real-time performance also crucial factor, generating accurate results quickly can assist professionals making timely correct decisions. We propose lightweight fast network based on Transformer CNN, called...

Based on the combination of space time modulation and chaotic frequency hopping (FH) techniques, a novel scheme differential space-time for slow FH system is proposed. This collects strong points coding spread spectrum technology. It high immune to interception prediction by generating sequence as pseudo-noise (PN) code in system. Meanwhile, it mitigates multipath fading effects through obtaining diversity gain. Simulation results bit error rate (BER) demonstrate its effectiveness.

Spectral regrowth of a code division multiple access (CDMA) signal passed through weakly nonlinear circuit are analyzed using power series and statistical method The yields an analytical expression for the autocorrelation function output as input transformed by behavioral model amplifier. It provides helpful predictions inter-modulation in wireless communication systems with distortion effects. This estimation spectral CDMA have been used design transceiver systems.