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Sensing and Signal Processing


Lidar and Pedestrian Safety

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While there are many modes and preferences when it comes to transportation, every person is a pedestrian at some point in their day. Unfortunately, being a pedestrian can be hazardous. For instance, in 2018 in the United States, there was a more than three percent increase in the number of pedestrians killed in traffic crashes. According to the National Highway Traffic Safety Administration, the number totaled 6,283 deaths, which was the highest since 1990. Accidents involving pedestrians are preventable tragedies.


Huawei investigates the future of healthcare technology for developing countries

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Huawei's TECH4ALL initiative aims to ensure nobody is left behind in the digital world by encouraging digital inclusion programmes and empowering technology adoption globally. The project is similar to some of the work happening within academia across Europe, where research projects are focused on harnessing technology for societal good. Professor van Ginneken, Professor of Medical Image Analysis at Radboud University Medical Centre in The Netherlands, is introducing digitized healthcare solutions to developing countries and believes that in ten years' time, all hospital pathology departments will be digitized. When did your work in medical imaging begin? I studied physics and completed a PhD in medical image analysis in 1996, developing computer programs that analyse chest x-rays using artificial intelligence (AI). At the end of the 1990s we wanted to put digital chest x-ray units with AI software in countries where there was a lot of tuberculosis, because it accommodates faster, more widespread screening, without the need to develop images on film.


BrainChip Brings AI to the Edge and Beyond - Gestalt IT

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Until now, Artificial Intelligence processing has been a centralized function. It featured massive systems with thousands of processors working in parallel. But researchers have discovered that lower-precision operations work just as well for popular applications like speech and image processing. This opens the door to a new generation of cheap and low-power machine learning chips from companies like BrainChip. Machine learning is incredibly challenging, with massive data sets and power-hungry processors.


AI Halloween Avatars! StyleGAN2 Generator Reveals Your Inner Zombie

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Want to see what you'd look like as a zombie? Forget makeup; now there's a GAN for that. The popular StyleGAN (Style Generative Adversarial Network) is a GAN architecture extension open-sourced by Nvidia in 2019 that can generate impressively photorealistic images while enabling user control over image style. This year's new and improved StyleGAN2 has redefined the state-of-the-art in image generation -- and has also inspired a number of fun and creative pursuits with faces. StyleGAN tech inspired last month's viral Toonify Yourself website, which was created by a couple of independent developers and turns selfies into adorable big-eyed cartoon characters. Now, just in time for costume season, another indie developer has taken facial image transfer tech to the opposite end of the cuteness spectrum, building a zombie generator.


Global Big Data Conference

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Continental AG is taking a minority stake in AEye Inc., a Dublin, California-based developer of LiDAR technology, in order to bring its autonomous vehicle technology to commercial vehicles sooner. Specifically, AEye, founded in 2013, has developed a long-range LiDAR system that can detect vehicles at a distance of more than 300 meters and pedestrians at more than 200 meters. Continental hopes the investment will enhance its current short-range LiDAR technology that is slated to go into production by the end of 2020. Then the AEye system would be deployed in a automotive passenger or commercial vehicle later this decade. "We now have optimum short-range and long-range LiDAR technologies with their complimentary sets of benefits under one roof," said Frank Petznick, head of Continental's advanced driver assistance systems, in a statement.


Technological innovations of AI in medical diagnostics

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However, as IDTechEx has reported previously in its article'AI in Medical Diagnostics: Current Status & Opportunities for Improvement', image recognition AI's current value proposition remains below the expectations of most radiologists. Over the next decade, AI image recognition companies serving the medical diagnostics space will need to test and implement a multitude of features to increase the value of their technology to stakeholders across the healthcare setting. Radiologists have a range of imaging methods at their disposal and may need to utilise more than one to detect signs of disease. For example, X-ray and CT scanning are both used to detect respiratory diseases. X-rays are cheaper and quicker, but CT scanning provides more detail about lesion pathology due its ability to form 3D images of the chest.


Roadmap to Computer Vision - KDnuggets

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Computer Vision (CV) is nowadays one of the main application of Artificial Intelligence (eg. In this article, I will walk you through some of the main steps which compose a Computer Vision System. We will now briefly walk through some of the main processes our data might go through each of these three different steps. When trying to implement a CV system, we need to take into consideration two main components: the image acquisition hardware and the image processing software. One of the main requirements to meet in order to deploy a CV system is to test its robustness.


Image Data Augmentation in Python

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To be very precise and clear, if you have 100 images in a dataset. You want to train your model but 100 is a very less number for a model to train and provide accurate results. So, it would be great if you don't have to manually collect more images or download them more from the internet to increase the size of your dataset. What if you can convert those 100 images into 500 or more images and increase your dataset size multiple times. This process is known as Image data augmentation.


Digital Healthcare in Latin America

Communications of the ACM

The healthcare system in Latin America (LATAM) has made significant improvements in the last few decades. Nevertheless, it still faces significant challenges, including poor access to healthcare services, insufficient resources, and inequalities in health that may lead to decreased life expectancy, lower quality of life, and poor economic growth. Digital Healthcare (DH) enables the convergence of innovative technology with recent advances in neuroscience, medicine, and public healthcare policy.a In this article, we discuss key DH efforts that can help address some of the challenges of the healthcare system in LATAM focusing on two countries: Brazil and Mexico. We chose to study DH in the context of Brazil and Mexico as both countries are good representatives of the situation of the healthcare system in LATAM and face similar challenges along with other LATAM countries. Brazil and Mexico have the largest economies in the region and account for approximately half of the population and geographic territory of LATAM.11


Imaging Sciences R&D Laboratories in Argentina

Communications of the ACM

We use the term imaging sciences to refer to the overarching spectrum of scientific and technological contexts which involve images in digital format including, among others, image and video processing, scientific visualization, computer graphics, animations in games and simulators, remote sensing imagery, and also the wide set of associated application areas that have become ubiquitous during the last decade in science, art, human-computer interaction, entertainment, social networks, and many others. As an area that combines mathematics, engineering, and computer science, this discipline arose in a few universities in Argentina mostly in the form of elective classes and small research projects in electrical engineering or computer science departments. Only in the mid-2000s did some initiatives aiming to generate joint activities and to provide identity and visibility to the discipline start to appear. In this short paper, we present a brief history of the three laboratories with the most relevant research and development (R&D) activities in the discipline in Argentina, namely the Imaging Sciences Laboratory of the Universidad Nacional del Sur, the PLADEMA Institute at the Universidad Nacional del Centro de la Provincia de Buenos Aires, and the Image Processing Laboratory at the Universidad Nacional de Mar del Plata. The Imaging Sciences Laboratorya of the Electrical and Computer Engineering Department of the Universidad Nacional del Sur Bahía Blanca began its activities in the 1990s as a pioneer in Argentina and Latin America in research and teaching in computer graphics, and in visualization.