Friday, June 18, 2021

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Thursday, July 4, 2019

Line tracking and following using computer vision - Robotex CY 2019

The research laboratory of the Department of Computer Science of Neapolis University Paphos Intelligent Systems Lab in cooperation with Cypriot start-up company Robotics Lab has designed, created and presented at the Robotex Cyprus 2019 robotics competition a new type of robotic vehicle that can autonomously track and follow a black line using computer vision technologies and infrared sensors. The robot was designed, implemented and programmed as an attempt to present a new and innovative idea that fuses the input from a computer camera with the raw inputs of infrared sensors.

The proposed algorithm uses methods from closed-loop control theory, open-loop control theory, and Fuzzy Logic control theory. The importance of the aforementioned experiment lies in the fact that the researchers managed to implement the new technology in their autonomous robotic vehicle using a low-end 16 MHz microcontroller from an Arduino Nano board, a 60 frames per second camera produced by Pixy and an array of 8 analog Infrared sensors made by Pololu. The rest of the parts were either designed from scratch and 3D printed or designed and manufactured specifically for this project.

The robot competed and won the Line Following challenge during the ROBOTEX CY 2019 robotics competition in the Universities category, on a very difficult 20-meter long track. The robot also achieved the 3rd over-all fastest time in the competition that leads to a 3rd place in the Best of the Best category.

The ROBOTEX CY 2019 robotics competition took place this past weekend at the University of Cyprus Sports Center. This was the 3rd year in a row that the competition is held in Cyprus, with this year's competitors exceeding 1000 individuals.

Thursday, March 28, 2019

A.I. Is Flying Drones (Very, Very Slowly)

A drone from the University of Zurich is an engineering and technical marvel. It also moves slower than someone taking a Sunday morning jog.

At the International Conference on Intelligent Robots and Systems in Madrid last October, the autonomous drone, which navigates using artificial intelligence, raced through a complicated series of turns and gates, buzzing and moving like a determined and oversized bumblebee. It bobbed to duck under a bar that swooshed like a clock hand, yawed left, pitched forward and raced toward the finish line. The drone, small and covered in sensors, demolished the competition, blazing through the course twice as fast as its nearest competitor. Its top speed: 5.6 miles per hour.

A few weeks earlier, in Jeddah, Saudi Arabia, a different drone, flown remotely by its pilot, Paul Nurkkala, shot through a gate at the top of a 131-foot-high tower, inverted into a roll and then dove toward the earth. Competitors trailed behind or crashed into pieces along the course, but this one swerved and corkscrewed through two twin arches, hit a straightaway and then blasted into the netting that served as the finish line for the Drone Racing League’s world championship. The winning drone, a league-standard Racer3, reached speeds over 90 miles per hour, but it needed a human to guide it. Mr. Nurkkala, known to fans as Nurk, wore a pair of goggles that beamed him a first-person view of his drone as he flew it.

Thursday, March 21, 2019

NVIDIA Research project uses AI to instantly turn drawings into photorealistic images

NVIDIA Research has demonstrated GauGAN, a deep learning model that converts simple doodles into photorealistic images. The tool crafts images nearly instantaneously, and can intelligently adjust elements within images, such as adding reflections to a body of water when trees or mountains are placed near it.

The new tool is made possible using generative adversarial networks called GANs. With GauGAN, users select image elements like 'snow' and 'sky,' then draw lines to segment an image into different elements. The AI automatically generates the appropriate image for that element, such as a cloudy sky, grass, and trees.

As NVIDIA reveals in its demonstration video, GauGAN maintains a realistic image by dynamically adjusting parts of the render to match new elements. For example, transforming a grassy field to a snow-covered landscape will result in an automatic sky change, ensuring the two elements are compatible and realistic.

GauGAN was trained using millions of images of real environments. In addition to generating photorealistic landscapes, the tool allows users to apply style filters, including ones that give the appearance of sunset or a particular painting style. According to NVIDIA, the technology could be used to generate images of other environments, including buildings and people.

Talking about GauGAN is NVIDIA VP of applied deep learning research Bryan Catanzaro, who explained:

This technology is not just stitching together pieces of other images, or cutting and pasting textures. It's actually synthesizing new images, very similar to how an artist would draw something.

NVIDIA envisions a tool based on GauGAN could one day be used by architects and other professionals who need to quickly fill a scene or visualize an environment. Similar technology may one day be offered as a tool in image editing applications, enabling users to add or adjust elements in photos.

The company offers online demos of other AI-based tools on its AI Playground.

Friday, February 15, 2019 uses AI to generate endless fake faces

The ability of AI to generate fake visuals is not yet mainstream knowledge, but a new website — — offers a quick and persuasive education.

The site is the creation of Philip Wang, a software engineer at Uber, and uses research released last year by chip designer Nvidia to create an endless stream of fake portraits. The algorithm behind it is trained on a huge dataset of real images, then uses a type of neural network known as a generative adversarial network (or GAN) to fabricate new examples.

“Each time you refresh the site, the network will generate a new facial image from scratch,” wrote Wang in a Facebook post. He added in a statement to Motherboard: “Most people do not understand how good AIs will be at synthesizing images in the future.”

The underlying AI framework powering the site was originally invented by a researcher named Ian Goodfellow. Nvidia’s take on the algorithm, named StyleGAN, was made open source recently and has proven to be incredibly flexible. Although this version of the model is trained to generate human faces, it can, in theory, mimic any source. Researchers are already experimenting with other targets. including anime characters, fonts, and graffiti.

Thursday, February 14, 2019

How artificial intelligence is shaking up the job market

The future of work is usually discussed in theoretical terms. Reports and opinion pieces cover the full spectrum of opinion, from the dystopian landscape that leaves millions unemployed, to new opportunities for social and economic mobility that could transform society for the better.

The World Economic Forum’s The Future of Jobs 2018 aims to base this debate on facts rather than speculation. By tracking the acceleration of technological change as it gives rise to new job roles, occupations and industries, the report evaluates the changing contours of work in the Fourth Industrial Revolution.

One of the primary drivers of change identified is the role of emerging technologies, such as artificial intelligence (AI) and automation. The report seeks to shed more light on the role of new technologies in the labour market, and to bring more clarity to the debate about how AI could both create and limit economic opportunity. With 575 million members globally, LinkedIn’s platform provides a unique vantage point into global labour-market developments, enabling us to support the Forum's examination of the trends that will shape the future of work.

Our analysis uncovered two concurrent trends: the continued rise of tech jobs and skills, and, in parallel, a growth in what we call “human-centric” jobs and skills. That is, those that depend on intrinsically human qualities.