Aggie Ocean Discovery

Aggie Ocean Discovery

Dylan Blakeslee

Dylan Blakeslee

College Station, Texas

Developing Artificially Intelligent Autonomous Underwater research vehicles to understand the ocean's environment

Virtual Reality, Robotics, Artificial Intelligence

Description

The Aggie Ocean Discovery Team is a multidisciplinary group of engineering students based in College Station at Texas A&M University. We are working with industry leaders to develop next generation ocean research vessels, equipped with Artificial Intelligence (AI) and a plethora of cutting edge technology. Our long term mission is to deploy Autonomous Underwater Vehicles (AUVs) around the world for research and accessible education.

Gallery

Links

AGGIE OCEAN DISCOVERY RESEARCH TEAM

Default user avatar 57012e2942

Siddharth N. created project Gesture Recognition System for soldiers

Medium d177d58c e0e0 4930 a7fd 546020c23721

Gesture Recognition System for soldiers

It is a gesture classification system for soldiers where cameras cannot be used.The system I made can classify 40 of the standard army gesture.It uses a support vector machine to classify the gestures.I created my own dataset for training. The list of gestures contain static as well as dynamic gestures. Different algorithms in terms of number of features was used for classifying them. The list of gestures can be found here: https://www.zombiehunters.org/wiki/index.php/Military_Hand_Signals Further improvement will be made using wireless modules and increasing mobility.

Medium img 20171008 033022

Burhan K. updated status

Medium img 20171008 033022

Burhan KAYA

AI , Cyber Security, IOT

Hello I'm an Electrical and Electronics Engineer. I do research on artificial intelligence and IOT. Besides this, I have a project with artificial intelligence algorithms. Therefore, using MODIVUS will contribute to me. I do not want to be deprived of such an impossibility. I'm glad you helped me.

Medium profilev2

Chaplin M. updated status

Medium profilev2

Chaplin Marchais

Well its now 7am and I have been up for about 30 hours.... but the image recognition is now actually working in azure!! Now time to take a power nap and then do some optimization with Intel's awesome suite of tools!

Medium profilev2

Chaplin M. updated status

Medium profilev2

Chaplin Marchais

Fifth night in a row I find myself still in front of the computer at 2AM.... I think I got up at-least twice today though!! Progress... Oh well, the future doesn't build itself! ...... yet....

Medium mol

Moloti N. created project Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

Medium 18657e03 c017 4a3b b485 57589b45e7a5

Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

We propose the use of Drones to help communities enhance their security initiatives, to identify criminals during the day and at night. We use multiple sensors and computer vision algorithms to be able to recognize/detect motion and content in real-time, then automatically send messages to community members cell phones about the criminal activities. Hence, community members may be able to stop house breakings before they even occur.

Machine Intelligence Algorithm Design Methodology

AMCnet: https://github.com/AfricaMachineIntelligence/AMCnet https://devmesh.intel.com/projects/africa-motion-content-network-amcnet

We propose a deep neural network for the prediction of future frames in natural video sequences using CPU. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. The model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. The model we aim to build should be end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human AVA and UCF-101 datasets. We show state-of-the art performance in comparison to recent approaches. This is an end-to-end trainable network architecture running on the CPU with motion and content separation to model the spatio-temporal dynamics for pixel-level future prediction in natural videos.

// We then use this AMCnet pretrained model on the Video feed from the DJI Spark drone, integrated with the Movidius NCS to accelerate real-time object detection neural networks.

See More

No users to show at the moment.

No users to show at the moment.