The widespread adoption of IoT has driven the development of cyber-physical systems (CPS) in industrial environments, leveraging Industrial IoTs (IIoTs) to automate manufacturing processes and enhance productivity. The transition to autonomous systems introduces significant operational costs, particularly in terms of energy consumption. Accurate modeling and prediction of IIoT energy requirements are critical, but traditional physics- and engineering-based approaches often fall short in addressing these challenges comprehensively. In this paper, we propose a novel methodology for benchmarking and analyzing IIoT devices and applications to uncover insights into their power demands, energy consumption, and performance. To demonstrate this methodology, we develop a comprehensive framework and apply it to study an industrial CPS comprising an educational robotic arm, a conveyor belt, a smart camera, and a compute node. By creating micro-benchmarks and an end-to-end application within this framework, we create an extensive performance and power consumption dataset, which we use to train and analyze ML models for predicting energy usage from features of the application and the CPS system. The proposed methodology and framework provide valuable insights into the energy dynamics of industrial CPS, offering practical implications for researchers and practitioners aiming to enhance the efficiency and sustainability of IIoT-driven automation.

OYSTER BAY, NY--Advances in machine learning technology will help propel sales of smart cameras for machine vision applications to 197 million units and a total value of $35 billion by 2027, according to global technology intelligence firm ABI Research. "The shift from machines that can automate simple tasks to autonomous machines that can'see' to optimize elements for extended periods will drive new levels of industrial innovation. This is the innovation that machine learning offers to machine vision. Machine learning can augment classic machine vision algorithms by employing the range and reach of neural network models, thus expanding machine vision far beyond visual inspection and quality control," explains David Lobina, artificial intelligence and machine learning analyst at ABI Research. Smart cameras, embedded sensors and powerful computers can bring machine learning analyses to every process step.

In Stanley Kubrick's masterful film 2001: A Space Odyssey, the supercomputer HAL 9000 (heuristically programmed algorithmic computer) uses artificial intelligence to detect emotion and suffering, and controls all of a spaceship's systems, including its crew. The new labour monitoring practices we are seeing emerge today – with the stated aim of improving the working environment – appear just as outlandish. Take, for example, Canon's Beijing office, which has installed smart cameras that prevent any action from being performed (such as scheduling a meeting, accessing certain rooms, etc.) unless they detect a smile. In Europe, some companies are offering their employees the chance to participate in business-related trials which involve supplying them with glasses that establish emotion indicators. One example is the Shore app, developed by the Fraunhofer Institute for Integrated Circuits IIS in Germany, and which is used in Google's'smart glasses'.

Ever scrolled through Instagram and spotted yourself in the background of a friend's photo looking a bit rough? A new AI-designed camera could fix the problem for you before it even happens -- though it's more intended as a solution for surveillance cameras than your pal's smartphone. Digital cameras are everywhere, and they see you a lot, thanks to facial recognition tech, body motion tracking, and medical imaging. These cameras pick up myriad details daily, resulting in massive troves of data and raising serious privacy concerns. To try and streamline what some of these lenses see, a group of researchers from the University of California, Los Angeles, has created cameras that can be taught to snap images of important objects while simultaneously erasing others from the shot.

On June 25th, BBC Click aired a feature on how artificial intelligence and smart cameras can help reduce flytipping. Featuring iDefigo and The South London Partnership, the feature includes a piece from iDefigo co-founder Scott Wattie. There has been a surge in the illegal dumping of rubbish during the pandemic. With over a million reported incidents across the UK and clear-up costs of over £10 million, pressure is mounting on Local Authorities to take action to reduce these figures. A number of councils have turned to tech to combat the problem.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The expanding use of surveillance cameras, whether in service of public safety, health monitoring or commercial operations, has heightened concerns about privacy. These days, it seems people's movements will be captured on CCTV cameras regardless of where they go. The number of surveillance systems in use has grown, with no signs of slowing down. According to the U.S. Bureau of Labor Statistics, the number of surveillance camera installations in the U.S. grew from 47 million to 85 million from 2015 to 2021, an increase of 80%.

When students in suburban Atlanta returned to school for in-person classes amid the pandemic, they were required to mask up, like in many places across the US. Yet in this 95,000-student district, officials took mask compliance a step further than most. Through a network of security cameras, officials harnessed artificial intelligence to identify students whose masks drooped below their noses. "If they say a picture is worth a thousand words, if I send you a piece of video – it's probably worth a million," said Paul Hildreth, the district's emergency operations coordinator. "You really can't deny, 'Oh yeah, that's me, I took my mask off.'"

PowerPoint presentations may change in the next few months, as Microsoft integrates live and pre-recorded video into presentations you view and create. Microsoft's engineering teams are always hard at work launching features, and today marks Microsoft's spring 2022 update of sorts, on a variety of different subjects. Microsoft announced a new Surface Hub-specific webcam, updated features to Teams and other productivity apps, and some specific improvements to how Microsoft deals with workers who are returning to the office. For that matter, Microsoft also released a survey noting that many workers aren't all that interested in returning to work, either preferring to work remotely or as a hybrid of at-home and in-person work. PowerPoint touches many different lives and careers (even holiday parties) so it's not surprising that two of the most important announcements involve it.

It was the kind of message Connor McCluskey loves to find in his inbox. As a member of the product innovation team at FirstEnergy Corp. -- an electric utility serving 6 million customers from central Ohio to the New Jersey coast -- his job is to find technologies that open new revenue streams or cut costs. In the email, Chris Ricciuti, the founder of Noteworthy AI, explained his ideas for using edge computing to radically improve how utilities track their assets. For FirstEnergy, those assets include tens of millions of devices mounted on millions of poles across more than 269,000 miles of distribution lines. Ricciuti said his startup aimed to turn every truck in a utility's fleet into a smart camera that takes pictures of every pole it passes.

You can't keep a fitness company down. Following some rough financial news from one of the companies that thrived during the pandemic as many took up at-home workouts, Peloton is looking to bounce back, like a burpee, but more business-like. It just announced the Peloton Guide, a strength-training camera system that looks like Kinect, hooks up to your TV and uses machine learning to understand your movements. The movement tracker feature is compatible with hundreds of existing Peloton strength classes. The idea is to encourage users to carry out all of the exercises in a class and keep up with instructors (but it's not a big deal if you can't stick to the instructor's pace).