The main objective of this paper is to use deep neural networks decode the electroencephalography (EEG) signals evoked when individuals perceive four types motion stimuli (contraction, expansion, rotation, and translation). Methods for single-trial multi-trial EEG classification are both investigated in study. Attention mechanisms a variant recurrent (RNNs) incorporated as decoding model. emphasize task-related responses reduce redundant information EEG, whereas RNN learns feature...

This paper presents a white-box intention-aware decision-making for the handling of interactions between pedestrian and an automated vehicle (AV) in unsignalized street crossing scenario. Moreover, design framework has been developed, which enables parameterization decision-making. is designed such manner that it can understand pedestrians urban traffic react accordingly to their intentions. That way, human-like response actions ensured, leading higher acceptance AVs. The core notion this...

Background: Histone H1.5 has been considered as a novel cancer marker its expression is associated with various human cancers. The objective of this study was to explore the effects phosphorylation in Ras-driven growth and migration glioma cells.Methods: plasmids for wide-type Ras or RasG12V/Y40C were transfected into A172 cells. levels phosphorylated AKT H1.5T10ph tested by Western blot. on cells determined MTT, soft-agar colony formation, transwell assay. qRT-PCR ChIP assay utilized assess...

Graph embedding training models access parameters sparsely in a “one-hot” manner. Currently, the distributed graph neural network is learned by data parallel with parameter server, which suffers significant performance and scalability problems. In this article, we analyze problems characteristics of kind on GPU clusters for first time, find that fixed model scattered among different machine nodes are major limiting factor efficiency. Based our observation, develop an efficient system called...

Template matching is an important and challenging task in remote sensing computer vision. Existing template methods often fail the presence of complex nonrigid deformation, occlusion, background clutter. In this letter, inspired by Siamese trackers, we propose end-to-end method that based on network. Different from traditional methods, our treats as a classification-regression task. It more robust to clutter, deformation. Moreover, introduce channel-attention mechanism cross correlation...

Convolution computing is one of the primary time consuming part convolutional neural networks (CNNs). State art use samll, 3 × filters. Recent work on Winograd convolution can reduce computational complexity a lot, making fast. But existing implementations limited to small tiles, i.e. F(4 4, 3) and F(2 2, where 4 2 are tile sizes output channels filter size, single precision data. In this paper, we propose an optimized mixed F(6 6, implementation NVIDIA Ampere GPUs using Tensor Cores. Our...

Graph neural networks (GNNs) are prevalent to deal with graph-structured datasets, encoding graph data into low dimensional vectors. In this paper, we present a fast training network framework, i.e., WholeGraph, based on multi-GPU distributed shared memory architecture. Whole-Graph consists of partitioning the and corresponding node or edge features multi-GPUs, eliminating bottleneck communication between CPU GPUs during process. And different is implemented by GPUDirect Peer-to-Peer (P2P)...

In this paper, we present an augmented radial basis function (RBF) neural network (ARBFNN) to linearize a wideband Doherty RF power amplifier with strong memory effects., A 51-dBm and 25 MHz mixed test signal were utilized validate the performance of ARBFNN nonlinear model predistorter. The validation results illustrated that outperforms polynomial (MP) real-valued time-delay (RVTDNN) 3 5 dB improvements in normalized mean square error respectively. predistorter exhibits significant...

Hyperspectral remote sensing has been widely used in mineral identification using the particularly useful short-wave infrared (SWIR) wavelengths (1.0 to 2.5 μm). Current mapping methods are easily limited by sensor’s radiometric sensitivity and atmospheric effects. Therefore, a simple algorithm (SMMA) based on combined application with multitype diagnostic SWIR absorption features for hyperspectral data is proposed. A total of nine calculated, respectively, from airborne visible/infrared...

Image fusion is the process of combining multiple input images from single or imaging modalities into a fused image, which expected to be more informative for human machine perception as compared any images. In this paper, we propose novel method based on deep learning fusing infrared and visible images, named LBP-based proportional generative adversarial network (LPGAN). image task, preservation structural similarity gradient information contradictory, it difficult both achieve good...

In this paper, a cooperative decision-making is presented, which suitable for intention-aware automated vehicle functions. With an increasing number of highly and autonomous vehicles on public roads, trust very important issue regarding their acceptance in our society. The most challenging scenarios arise at low driving speeds these vehicles, where interactions with vulnerable road users likely occur. Such must be addressed by the automation vehicle. novelties paper are adaptation general...

Database intrusion detection technology is an important part of the database security. The paper presents a new method based on event sequence clustering. Firstly, aiming at computing similarity two SQL statement sequences, improved edit distance function defined. corresponding clustering results are obtained by computed similarity. Secondly, attack sequences detected calculating between user's operation and cluster center. association analyzed. At last, experimental show that our approach...

With the rapid progress in industrial field, equipment fault identification plays a crucial role improving production efficiency and reducing operating costs. This article proposes specialized efficient method system based on big data correlation analysis. achieves precise of faults by finely collecting preprocessing operation data, deeply exploring potential association rules between data. The proposed this not only improves accuracy recognition, but also significantly enhances recognition...

A nonlinear energy sink (NES) has such advantages as controlling broader band responses and better robustness than conventional control devices like tuned mass dampers (TMDs). In this research, a cubic stiffness NES mitigating the dynamic of multi-degree-of-freedom structure under white noise, harmonic seismic excitations was tested using shake table, influences parameters on vibration mitigation effects were investigated. The test results indicate that same acceleration noise TMDs, even...