AIMC Topic: Neural Networks, Computer

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WaSR-A Water Segmentation and Refinement Maritime Obstacle Detection Network.

IEEE transactions on cybernetics
Obstacle detection using semantic segmentation has become an established approach in autonomous vehicles. However, existing segmentation methods, primarily developed for ground vehicles, are inadequate in an aquatic environment as they produce many f...

High-Performance Siamese Network for Real-Time Tracking.

Sensors (Basel, Switzerland)
Target tracking algorithms based on deep learning have achieved good results in public datasets. Among them, the network tracking algorithm based on Siamese tracking has a high accuracy and fast speed, which has attracted significant attention. Howev...

Polarization Domain Spectrum Sensing Algorithm Based on AlexNet.

Sensors (Basel, Switzerland)
In this paper, we propose a spectrum sensing algorithm based on the Jones vector covariance matrix (JCM) and AlexNet model, i.e., the JCM-AlexNet algorithm, by taking advantage of the different state characteristics of the signal and noise in the pol...

A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots.

Sensors (Basel, Switzerland)
Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challeng...

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Physics in medicine and biology
Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.We propose a novel deep graph reasoning model to learn from multi-order neighborhood t...

Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing.

Scientific reports
This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-f...

Deep graph level anomaly detection with contrastive learning.

Scientific reports
Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern and feature information are different from most normal graphs in a graph set, which is rarely studied by other researchers but has significant application value...

Deep learning system for paddy plant disease detection and classification.

Environmental monitoring and assessment
Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier...

Uncertainty-aware self-supervised neural network for livermapping with relaxation constraint.

Physics in medicine and biology
.T1ρmapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can mapT1ρfrom a reduced number ofT1ρweighted images but requires significant amounts of high-quality training data....

Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation.

PLoS computational biology
Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with period...