Artificial Intelligence

Get the most out of your training, scoring, algorithms and frameworks on Intel® architecture for Deep Learning and Artificial Intelligence.

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Shreyasi P. updated status

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Shreyasi Pal

Hello!

I am interested in the fields of Artificial Intelligence, Machine Learning and Computer Vision. I am currently working on a project titled Application of Deep Learning in 3D Ear Biometrics. Basically the CNN model feeds on 3D point cloud of the ear to recognize the subject.

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Daniel T. created project BR - Obstacle Detection

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BR - Obstacle Detection

Implementation of algorithm that performs a detection and classification of Obstacles that exist without means of automation, in real time. Among the Possible obstacles, the initial objective is a classification between automotive vehicles and pedestrians. Knowledge of artificial intelligence and Computational vision will be known, but the specific techniques are determined without results Project development, as it will depend on the student's learning.

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Daniel T. created project CAVSIM

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CAVSIM

In recent years, research over information exchange between vehicles has been growing, with the goal to improve safety and efficiency in traffic. This project describes the development of a multi-agent system for simulation of connected vehicles in road crossings, in order to remove the need for traffic lights. The system was developed using the C# programming language and Boris.NET platform for communication between agents. The user can specify the desired environments using JSON files which are read at the simulation startup. A 2D graphical interface was also created to view the simulation, so that it can follow an agent or stay at a map position.

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Daniel T. created project E-MOTIV

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E-MOTIV

This work presents the development of a prototype for the recognition of universal facial expressions. This technic provides an alternative way of gathering data from the user, being able for usage as an input way for information systems. For faces detection and extraction of their characteristics technics of Computer Vision and Digital Image Processing are employed, implemented by the dlib library with Intel RealSense. The classification into facial expressions is performed by an Artificial Neural Network, of the multilayer perceptron kind.

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Adam M. created project TASS PVL Computer Vision Hub

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TASS PVL Computer Vision Hub

DESCRIPTION:

TASS PVL is a sister project to the original TASS Hub project. As with TASS Hub, TASS PVL is a local server which homes an IoT connected A.I. powered by the Intel® Computer Vision SDK Beta. The hub can connect to multiple IP cameras and two Realsense cameras. First, the program detects if there is a face, or faces, present in the frames, and if so passes the frames through the trained model to determine whether the face is a known person or an intruder. In the event of a known person or intruder, the server communicates with the IoT JumpWay which executes the relevant commands that set by rules, for instance, controlling other devices on the network or raising alarms in applications etc.

INTEL® TECHNOLOGY

TASS PVL uses the following Intel technologies:

  • Intel® Core i7 NUC
  • Intel® Computer Vision SDK Beta
  • Intel® Realsense (R200,F200)

IOT CONNECTIVITY:

The IoT connectivity is managed by the TechBubble IoT JumpWay, the TechBubble Technologies IoT PaaS which primarily, at this point, uses secure MQTT protocol. Rules can be set up that can be triggered by sensor values/warning messages/device status messages and identified known people or intruder alerts. These rules allow connected devices to interact with each other autonomously, providing an automated smart home/business environment.

ARTIFICIAL INTELLIGENCE:

TASS PVL uses the Intel Computer Vision SDK Beta to provide the system with Artificial Intelligence. For other uses of A.I. used in the sister project TASS PVL, follow this link.

INTELLILAN MANAGEMENT:

The IntelliLan Management Console/Applications are essentially IoT JumpWay applications, capable of controlling all IntelliLan devices on their network and communicating with the IoT JumpWay. Users can use the console and manage their devices using their voice which is powered by TIA, an A.I. agent developed to assist home and business owners to use TechBubble web and IoT systems.

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Prajjwal B. created project Anomaly detection Using General Adversarial Networks

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Anomaly detection Using General Adversarial Networks

Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.Performing unsupervised learning to identify anomalies in imaging data as candidates for markers. By using deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, we can achieve high accuracy in anomaly detection. Medical imaging enables the observation of markers correlating with disease status, and treatment response. Generative model will generate anomalies . The training procedure for Generative model is to maximize the probability of discriminative model making a mistake.

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Abinash Kumar

Park Street area, Kolkata, West Bengal, India

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Jeffin TJ

1001 Fort Crook Rd N, Bellevue, NE 68005, USA

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Jim Kao

United States

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Amita Kapoor

Lodhi Road, Near Airforce Bal Bharati School, New Delhi, Delhi 110003, India

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Urvishkumar Patel

I am 5th semester electrical engineering student with dream to change the world using artificial intelligence and robotics.

Gujarat, India

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saloni gupta

I am beginner in this area of artificial intelligence,machine learning and deep learning.

India

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OnTheWall

Thumb paul langdon Paul Langdon

Created: 01/13/2017

MagicMirror+ for Intel Joule - Adding Face/Voice Recognition & Control using RealSense, Voice and...

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Triton UAS

Default user avatar 57012e2942 Marco Flowers

Created: 03/03/2017

Triton UAS is an engineering student organization at UC San Diego that develops deep learning sys...

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