Project Kidney 3D Printed

Project Kidney 3D Printed

FABIO CARVALHO

FABIO CARVALHO

, State of Sergipe

To build a 3D Printed Kidney from Human Cells culture tissue.

Robotics, Artificial Intelligence, Internet of Things, Virtual Reality, Android, Networking, Intel RealSense™

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Description

This is an active endeavor on spreading this independent campaign, which address a very noble public health problem: the lack of organs to kidney transplantation and ultimately by creating a prototype printer able to build a 3d printed living tissue organ.

Although, many researchers are, at this moment, doing all that's humanly possible to achieve this goal, and maybe saving more than 10.000.000 lives that are in risk just now all over the world due to renal chronic failure, is obvious as much people join with new projects, closer we, mankind, are going to get from achieving this object: autologous organs 3d printed and transplanted.

We, as human race, already have the individual tools need to perform the task already. But individual tools are not, by themselves, just sufficient. It is imperative the thinking and open discussion on innovative strategies.

Years ago, some individual researchers have decided, by themselves, to build 3d printers. Safe, cheap and technology available worldwide. Then we now have: reprap, prusa, mendel (reprap.org). They were individuals that needed to do, but they left the development opened for others to build and Internet Community helped because these goals are useful to everybody.

So, why not build a tissue 3d printer able to build any organ we choose to build, in a solo, eclectic, world wide, open, community?

Our main goal is to build a functional 3d tissue printer prototype kidney. Next step, is transplantation of a 120 cc of weight prototype, capable of maintaining in vivo serum creatinine levels below 1.5 mg/dL and a creatinine clearance greater then 90 ml/min/1,73m2.

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Indiegogo's raising funds campaign

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

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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.

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amber p. updated status

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amber pande

Hi Everybody, I am Amber Pande, pursuing undergraduate in Computer Science in India. Java, C, Python, SQL, C++, HTML5 are the programming languages that I have worked with until now.

However, my fields of interest beside core programming are Artificial Intelligence and Gaming. Currently, I am working on a Hackathon project.about Deep Learning The topic of my project is to develop a Web based software for inspection of various buildings from Life and Fire Safety Point of view.

Moreover, for my further study, I am planning to go with Artificial Intelligence and Research for my Master's Degree. Also, I am planning to make a career move as a Deep Learning (AI) research from a core developer.

I am willing to learn a lot from this platform and also do the optimum use of the resources and share my knowledge with the AI communities in India.

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Rohan S. updated status

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Rohan Sen

Recently I'm working virtual reality and android simultaneously, as I am intern in virtual and augmented reality related company so getting training in AR and VR

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Kerem Kurban

Having a biological background made me understand the complexity of intra&inter-cellular networks. Now I'm trying to implement my knowledge to ML, DL&AI networks, especially in neuroscientific studies.

Bebek Mh., Cevdet Paşa Cd., 34342 Beşiktaş/Istanbul, Turkey