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Eve Door and Window review: This smart, Thread-enabled door and window sensor is only for HomeKit users


Eve Systems has been adding more and more Thread-enabled smart gadgets to its portfolio, including the Eve Aqua sprinkler controller (which we've previously reviewed) and the Eve Energy smart plug (ditto). Now comes Eve Door & Window, a HomeKit- and Thread-capable contact sensor, and it's as easy to set up and use as Eve's Aqua and Energy products. With able assistance from the Eve app, Eve Door & Window supports powerful automations and lets you take a deep dive into when, and how often, your doors and windows have been opened and closed. At $40, however, Eve Door & Window is mighty expensive for a contact sensor, and while it does support HomeKit, it doesn't work with Alexa or Google Assistant, which means only Apple users need apply. You can also configure it to trigger lighting scenes when you arrive home, or to turn down the thermostat when someone opens the window, so it's geared more toward home automation than home security; for the latter, you're on your own in terms of integrating the sensor with a third-party security system.

AI-controlled sensors could save lives in 'smart' hospitals and homes


As many as 400,000 Americans die each year because of medical errors, but many of these deaths could be prevented by using electronic sensors and artificial intelligence to help medical professionals monitor and treat vulnerable patients in ways that improve outcomes while respecting privacy. "We have the ability to build technologies into the physical spaces where health care is delivered to help cut the rate of fatal errors that occur today due to the sheer volume of patients and the complexity of their care," said Arnold Milstein, a professor of medicine and director of Stanford's Clinical Excellence Research Center (CERC). Milstein, along with computer science professor Fei-Fei Li and graduate student Albert Haque, are co-authors of a Nature paper that reviews the field of "ambient intelligence" in health care -- an interdisciplinary effort to create such smart hospital rooms equipped with AI systems that can do a range of things to improve outcomes. For example, sensors and AI can immediately alert clinicians and patient visitors when they fail to sanitize their hands before entering a hospital room. AI tools can be built into smart homes where technology could unobtrusively monitor the frail elderly for behavioral clues of impending health crises.

How edge AI can make enterprises more agile


The pandemic has accelerated the adoption of edge computing, or computation and data storage that's located close to where it's needed. According to the Linux Foundation's State of the Edge report, digital health care, manufacturing, and retail businesses are particularly likely to expand their use of edge computing by 2028. This is largely because of the technology's ability to improve response times and save bandwidth while enabling less constrained data analysis. While only about 10% of enterprise-generated data is currently created and processed outside a traditional datacenter or cloud, that's expected to increase to 75% by 2022, Gartner says. Internet of things (IoT) devices alone are expected to create over 175 zettabytes of data in 2025.



A rising senior at San Diego State University, computer engineering major James Bunnell will soon have the distinction of having contributed to five scientific papers as an undergraduate. He is now focused on machine learning to assess the best options for materials to be used in sensors that will be embedded in the brain to help patients with debilitating movement disorders such as Parkinson's Disease. Bunnell is a first-generation college student whose parents worked for the Federal Aviation Administration. Their experience changed his own career trajectory, leading him to embark on a pathway to research. When Bunnell transferred to SDSU from Palomar College, he benefited from two programs designed to provide academic support and opportunities for students looking to pursue STEM career pathways: Advancing Navy STEM Workforce through Education and Research (ANSWER) and Math, Engineering, Science Achievement (MESA) programs.

How Data Labeling Services Empower Self-Driving Industry 2021? -- Part4


If you are not as paranoid as Musk, automatic driving may not need to divide any technical routes, but only need to optimize the technology. But standing on the opposite side of lidar, Tesla may have missed the best time to develop fully autonomous driving. Lidar is not to replace millimeter-wave radar and vision, but to match with other sensors as a heterogeneous sensor. Through these three different sensors, a heterogeneous fusion can be made to ensure the overall perception security and improve sensitivity and accuracy. Different from the traditional mechanical rotary lidar, Suteng, a Chinese company mainly adopt MEMS technology, which has the advantages of small volume, easy integration, low energy consumption, and low cost.

Does Uncle Sam Really Want You?


Uncle Sam doesn't really want a gangly 18-year-old soldier to stand guard outside the gate of a military base, rather he wants a wide-area motion imagery (WAMI) system that provides surveillance, reconnaissance, and intelligence-gathering using specialized software and camera systems to detect and track hundreds of people and vehicles all at the same time over a city-sized area. Uncle Sam doesn't really want a blurry eyed, half asleep and distracted human pilot flying in circles trying to find camouflaged bad guys on the ground, rather he wants a multispectral system, that can see things invisible to human eyes, consisting of four high-definition cameras covering five spectral bands; a three-color diode pump laser designator and rangefinder; laser spot search and track capability; automated sensor and laser bore sight alignment; three-mode target tracker., and MTS sensors that offers multiple fields of view, electronic zoom, and multimode video tracking. Uncle Sam doesn't really want more spies in trench coats that lurk in dark corners vaping, rather he wants persistent surveillance systems that collect and integrate data from specific geographic areas with data on activities that happened there at specific dates and times. This capability requires a spatiotemporal analytic method to recognize trends and patterns from large, diverse data sets. These data sets identify activities: events and transactions conducted by entities (people or vehicles) in an area, while documenting patterns of life and alerting to unusual events.

RoboCup Logistics League: Interview with Sebastian Eltester


This year, RoboCup will be taking place from 22-28 June as a fully remote event with RoboCup competitions and activities taking place all over the world. The RoboCup Logistics League (RCLL) is a sub-league of the RoboCup Industrial League. It focuses on in-factory logistics applications. The goal is for a team of autonomous robots to assemble products on demand, using a set of production machines. Each team comprises up to three autonomous robots which can produce using seven machines.

PhD Scholarship – Learning to sense: Next generation photonic sensors enabled by machine learning Job at University of South Australia in Adelaide, Australia


Become an expert and make a difference to society. The University of South Australia (UniSA) is Australia's University of Enterprise. We are South Australia's largest university and one of the very best young universities in the world. At UniSA, we are authentic, resilient, and influential - and we deliver results. We pride ourselves on our dynamic and agile culture, which embraces challenges and thrives on breaking new ground.

Artificial Intelligence in Facility Management


Facility management is the part of the business that has always been under pressure to'do more for less' and to deliver the magic 10% cost savings that the core business demands of it. As businesses start the slow road to recovery and begin to emerge from the pandemic and enforced lockdowns, facility management and its associated costs will again be under the microscope. Traditionally, these cost savings have come from market testing, outsourcing, re-tendering, re-scoping, head count reduction and other areas of efficiencies that have by now, challenged the simultaneous demand for improved service quality and performance. Whist technology has played an important part of facility management for some time now, through a hunger for data to measure performance and through BIM and SMART or intelligent buildings, enabling informed decisions to be made, there is now a new opportunity for the use of technology in facility management and this is arguably the biggest opportunity yet. Facility management is involved across every organisation, and markets across both the private and public sectors and in commercial and non-commercial entities.

AI at the Edge: New Machine Learning Engine Deploys Directly on Sensors - News


ONE Tech, an AI and ML-driven company specializing in Internet of Things (IoT) solutions for network operators, enterprises, and more, has announced new capabilities of its MicroAI Atom product. MicroAI Atom is part of ONE Tech's Micro AI product line, and it now has the ability to train and run AI models at the edge, enabling a variety of individuals and entities to "reduce the costs of bringing intelligence to the edge and endpoint by at least 80 percent." According to ONE Tech's press release, MicroAI is a machine learning algorithm that is embedded into microcontroller units and operates a recursive analysis of device behavior. More specifically, MicroAI collects data from internal device sensors and utilizes a semi-supervised learning approach to come up with a complete view of device behavior. Semi-supervised learning refers to a machine learning method that makes use of some labeled data with a much larger amount of unlabeled data during the model's training phase. Other machine learning methods can either employ a fully supervised approach that would consist of using only labeled training data or a fully unsupervised approach that would consist of using only unlabeled training data.