Navigating cluttered environments is a challenging task for any mobile system. Existing approaches for ground-based mobile systems primarily focus on small wheeled robots, which face minimal constraints with overhanging obstacles and cannot manage steps or stairs, making the problem effectively 2D. However, navigation for legged robots (or even humans) has to consider an extra dimension. This paper proposes a tailored scene representation coupled with an advanced trajectory optimisation algorithm to enable safe navigation. Our 3D navigation approach is suitable for any ground-based mobile robot, whether wheeled or legged, as well as for human assistance. Given a 3D point cloud of the scene and the segmentation of the ground and non-ground points, we formulate two Gaussian Process distance fields to ensure a collision-free path and maintain distance to the ground constraints. Our method adeptly handles uneven terrain, steps, and overhanging objects through an innovative use of a quadtree structure, constructing a multi-resolution map of the free space and its connectivity graph based on a 2D projection of the relevant scene. Evaluations with both synthetic and real-world datasets demonstrate that this approach provides safe and smooth paths, accommodating a wide range of ground-based mobile systems.

We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. At a system power consumption of 5.6 W, we measure a total energy consumption of 192 µJ for the ASIC and achieve a classification time of 276 µs per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (93.7 0.7) % at (14.0 1.0) % false positives. The system is directly applicable to edge inference applications due to its small size, power envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC to be operated reliably outside a specialized lab setting. In future applications, the system allows for a combination of conventional machine learning layers with online learning in spiking neural networks on a single neuromorphic platform.

This paper investigates different approaches to build and use digital human avatars toward interactive Virtual Co-presence (VCP) environments. We evaluate the evolution of technologies for creating VCP environments and how the advancement in Artificial Intelligence (AI) and Computer Graphics affect the quality of VCP environments. We categorize different methods in the literature based on their applications and methodology and compare various groups and strategies based on their applications, contributions, and limitations. We also have a brief discussion about the approaches that other forms of human representation, rather than digital human avatars, have been utilized in VCP environments. Our goal is to fill the gap in the research domain where there is a lack of literature review investigating different approaches for creating avatar-based VCP environments. We hope this study will be useful for future research involving human representation in VCP or Virtual Reality (VR) environments. To the best of our knowledge, it is the first survey research that investigates avatar-based VCP environments. Specifically, the categorization methodology suggested in this paper for avatar-based methods is new.

IRVINE, CA, June 17, 2021 (GLOBE NEWSWIRE) -- via NewMediaWire -- Vita Mobile Systems, Inc. (OTC PINK: VMSI), a technology company focused on digital imaging in mobile devices, collection and management of big data and development of artificial intelligence, today announced it has signed a non-binding letter of intent (LOI) to acquire a technology company with proprietary Artificial Intelligence software. The acquisition would see VMSI absorb a company with a proprietary Artificial Intelligence (AI) Resource Engine designed to analyze geolocation-based information and trends to address many of the economic and social concerns the world is facing as it transitions into a new post-pandemic society. "As we emerge from an unprecedented year, we are excited and optimistic for this acquisition and for the future on VMSI. The pandemic reshaped the world, its industries and how people interact and connect. One of the most significant changes the pandemic brought was a revolution in technology used to bring people together virtually. VMSI's products originally had a focus on events where people would come together physically, but the pandemic changed the way people interact and we changed with it. We re-aligned our technology suite to meet the changes and, today, VMSI is well positioned to capitalize on the need for immediate geolocation-based information that has been re-enhanced by the pandemic. During the past months, management has also worked on developing strategic partnerships and evaluating acquisition candidates that would complement our technology foundation," stated Sean Guerrero, CEO of Vita Mobile Systems.

The migration to utilizing AI and ML in mobile systems locally'in memory' has happened very fast within a few short years. We've been reading about the tremendous developments in AI and ML achieved by Apple the latest iPhone 11 and Tesla in their new neural network chip to help achieve autonomous driving in their cars within the next year or two. Now, Gyrfalcon Technology Inc. (GTI) has developed an AI Neural Accelerator that enables smartphones like the LG Q70 to benefit from high performance & low power all at a much lower price point. We expect to see hundreds of products using GTI AI Accelerator chips before too long. Artificial Intelligence (AI) and Machine Learning (ML) have been around a long time but are gaining new popularity due to the ability to get these technologies to do some amazing things like beat anyone at chess, recognize someone walking in public from millions of stored faces and other problems which lend themselves to problems that require a lot of parallel processing.

Energy is limited in mobile systems, however, so for this possibility to become a viable opportunity, energy usage must be conservative. The Low-Power Image Recognition Challenge (LPIRC) is the only competition integrating image recognition with low power. LPIRC has been held annually since 2015 as an on-site competition. To encourage innovation, LPIRC has no restriction on hardware or software platforms: the only requirement is that a solution be able to use HTTP to communicate with the referee system to retrieve images and report answers. Each team has 10 minutes to recognize the objects in 5,000 (year 2015) or 20,000 (years 2016 and 2017) images.

There's no doubt that artificial intelligence is being used by more and more industries. As Information Age points out, Enhancing E-commerce, calculating valuations, inventory management and of course enabling driverless cars are among the many uses of artificial intelligence. With this in mind, which tech stocks should investors buy to cash in on this trend? Although Nvidia Corporation (NASDAQ:NVDA) now appears to be the prime beneficiary of the machine learning aspect of artificial intelligence, Nvidia stock has soared over 200% in the last year and likely already reflects a great deal of revenue from AI. Consequently, investors should wait for a better entry point before buying Nvidia stock. But four other tech stocks Intel Corporation (NASDAQ:INTC), Delphi Automotive PLC (NYSE:DLPH), Visteon Corp (NYSE:VC), and Baidu Inc (NASDAQ:BIDU) -- are definitely poised to get a meaningful boost from AI and should be bought at current levels.