Original and high-quality papers are invited from the potential authors. The topics include, but are not limited to:

Image Processing

Sensing, Representation, Modepng, and Registration
Image Segmentation
Image Denoising
Image Filtering
Image Restoration
Image Enhancement
Multi-resolution Processing
Compression, Coding, and Transmission
Detection, Recognition, and Classification
Computational Imaging
Color, Multi-spectral, and Hyper-spectral Imaging
Stereoscopic, Multi-view, and 3D Processing
Image and Video Quapty Models
Motion Estimation
Image Registration
Image and Video Analysis
Computer Vision
Pattern Recognition
Robotics and Vision
Image Scanning, Display and Printing
Shape and Image Retrieval
Feature Extraction
Medical Imaging

Communications

Cognitive Radio and AI-Enabled Networks
Communication and Information System Security
Communication QoS, Reliability and Modeling
Communication Software and Multimedia
Communication Theory
Green Communication Systems and Networks
IoT and Sensor Networks
Mobile & Wireless Networks
Next-Generation Networking and Internet
Optical Networks & Systems
Signal Processing for Communications
Wireless Communications
Aerial Communications
Big Data
Cloud Computing, Networking and Storage
E-Health
Full-Duplex Communications
Machine Learning for Communications
Molecular, Biological and Multi-Scale Communications
Quantum Communications & Computing
Satellite and Space Communications
Smart Grid Communications
Social Networks
	
Machine Learning

Natural Language Processing
Machine learning methods
Learning and adaptive control
Learning/adaption of recognition and perception
Learning for Handwriting Recognition
Learning in Image Pre-Processing and Segmentation
Learning in process automation
Learning of appropriate behaviour
Learning of action patterns
Learning robots
Feature extractions
Support vector machines (SVM)
Least-squares SVM (LS-SVM)
Twin SVM (TWSVM)
Extreme learning machine (ELM)
Artificial neural network (ANN)
Classification techniques
Reinforce learning
Deep learning
Representation Learning and Deep Learning
Scene Analysis and Understanding
Neural Generative Models, Autoencoders, GANs
Optimization and Learning Methods