Real-time LabVIEW Fruit Recognition Using Deep Learning that helps speed-up checking out process in supermarkets and preventing thieves.

We are going to design a Neural Network classifier to recognize IRIS flowers. This Neural Network classifier is deployed directly into Arduino devices for real-time applications.

We are going to design a time series modeling system using Bayesian Neural Network. This system will then be deployed directly into Arduino devices for real-time forecasting applications.

This project aims to design a Thought Control System that allows severely disabled people to navigate their wheelchairs with just the power of their brain. By using a two-channel wireless EEG system, human brain signal is acquired and then recognized by a machine learning technique.

This video demonstrates how to use deep learning in LabVIEW to design a real-time fruit detection application that can correctly recognize different types of fruits. More importantly, the expensive NI Vision Development Module is not required in order to develop this native deep learning LabVIEW application.

This video demonstrates a new way of designing a deep learning neural network by using friendly user interface software DLHUB. No Python code is required during the deep learning design process. Model verification is supported to check if the constructed deep learning model is valid. Model evaluation and model testing are also supported. The trained deep learning model will be exported directly into LabVIEW, LabVIEW NXG, C, C++, C# environments for real-time deployment.

This video provides step by step guide of designing a Bayesian Neural Network that can classify the sex of a crab based on six physical measurements. This trained Neural Network will then be deployed directly into a LabVIEW application for deployment.

In this example, we are going to design a sale prediction system for a game company to estimate the total earning of their new game based on its attributes. Based on historical sale data, we will design a prediction system using machine learning. Most importantly, ANNHUB allows exported this prediction system directly to LabVIEW for actual production

This video demonstrates that ANSCENTER object detection LabVIEW APIs are used to detect fruit using Faster R-CNN, YOLO, or SSD object detection models. Neither Python engine nor NI vision development module is required. Only low-quality webcam with a picture control can be used to deliver fruit detection task for the auto check-out application.

This video demonstrates how to design, train, and evaluate a deep learning model for LabVIEW applications in 5 simple steps. No Python and text-based programming skills are required to construct a deep learning model. The designer also does not require in-depth deep learning knowledge to design any deep learning models properly.

In this video, we are going to demonstrate how to use deep learning object detection in LabVIEW using Deep Learning Single Shot Detector (SSD) model for an auto checkout application. With a simple LabVIEW application, we can turn an ordinary webcam into a smart camera. Ths application can be deployed in real-time targets for production purposes.

We are going to demonstrate that by using ANSCENTER artificial intelligence software, we can turn standard CCTV cameras into smart cameras to assist emergency evacuation procedures effectively. We took the small part of the “Emergency Evacuation Mock Drill Highlights Karama Office” Youtube video, and we applied the ANSCENTER real-time object detection algorithm to detect human presence in the building. We can identify the number of people in the building and their behaviors in fire and emergencies. So we can inform them and guide them to evacuate the building effectively and safely. With that counting feature, we would know how many people are still in the building. Therefore, we will have effectively evacuation plan to rescue those trapped people safely. The system also can identify rescuers, so that will inform the rescue team to locate a missing person. By applying our cutting-edge technology, we can turn an ordinary camera system into a useful system that can save lives

We are going to demonstrate step by step to design a deep learning classifier using the transfer learning technique in DLHUB. We will follow the blog on the ANSCENTER website with the Fruit Recognition example. The idea is to use a transfer learning technique to simplify the deep learning design process.

In this video, we are going to use ANNHUB machine learning software to design a performance prediction system for secondary school students. We use the dataset, from Paulo Cortez's and Alice Silva's works, that contains student's attributes and his/her grades (G1, G2). By using machine learning, we will be able to predict a student's final grade (G3) based on his/her attributes and other midterm grades (G1 and G2).

In this video, we are going to design a deep learning model that can detect Pneumonia based on chest X-Ray images. We use the X-Ray dataset from Paul Mooney uploaded on the Kaggle website. This database contains X-Ray images taken from healthy people and people with Pneumonia (infected by virus or bacteria).

We are going to use ANSCENTER FaceAPI to design a Face Detection Application in LabVIEW using an ordinary webcam. There are only four simple functions in this FaceAPI library required. Let started to test the Face Detection application. In this application, we use a Logitech USB webcam to acquire a person's image. The FaceAPI allows use to detect face features that represents a person's unique identity.