Automatic modulation recognition based on CNN and GRU

CNN-Based Automatic Urinary Particles Recognition

The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases. Manual urine sediment examination is labor-intensive, subjective and time-consuming, and the traditional automatic algorithms often extract the hand-crafted features for recognition. Instead of using the hand-crafted features, in this paper, we explo...

Auto Analysis of Customer Feedback using CNN and GRU Network

Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is in great demand. Users may write feedbacks in any language, and hence mining appropriate information often becomes intractable. Especially in a traditional feature-based supervis...

Automatic analogue modulation recognition

For several reasons, modulation recognition is extremely important in communication intelligence (COMINT). In this paper, a global procedure for recognition of analogue modulation types is developed. Computer simulations for different types of band-limited analogue modulated signals corrupted by band-limited Gaussian noise have been carried out. Expressions for the instantaneous amplitude and p...

Automatic MRI Prostate Segmentation based CNN-ASM

Action Recognition with Image Based CNN Features

Most of human actions consist of complex temporal compositions of more simple actions. Action recognition tasks usually relies on complex handcrafted structures as features to represent the human action model. Convolutional Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for designing handcrafted features. Usually, the output of the last layer in CNN (a layer before t...