Machine learning for embedded systems has been gaining a lot of momentum over the past several years. For embedded developers, machine learning was something that data scientists were concerned with and something that lived up on the cloud, far from the resource-constrained microcontrollers that embedded developers work with on a daily basis.
What seems like almost overnight, however, machine learning is suddenly finding its way to microcontroller and edge devices.
To some developers, this may seem baffling or at least intriguing.
But why is machine learning so important to embedded developers now? Let’s explore a few possibilities.
We’re entering a world in which WiFi and Bluetooth may no longer be the best communication technologies for Internet of Things (IoT) applications.
The IoT is gaining more ground each year. Experts project there will be 75 billion connected devices by 2025.
To support this incredible demand for bandwidth, new players have entered the IoT arena. For many industries, including supply chain, agriculture, healthcare, energy, and urban planning, Low-Power Wide-Area Networks (LPWANs) are a much better fit.
A synthetic material inspired by the natural protective shell of bacteria, could someday yield a plethora of new technologies with applications in biofuel production, medicine and the industrial sector.
Researchers from the lab of Cheryl Kerfield at Michigan State University have designed a genetically engineered shell based on natural structures and the principles of protein evolution in bacteria.
Bacteria is comprised of nanometer-sized factories that have a number of different jobs, including making nutrients and isolating toxic materials that can cause harm, but all include a common exterior shell made of protein tiles.
Big 5G Event — Roughly five years ago, the global wireless industry touted 5G as something that we wouldn’t see until around 2020. And then everyone got 5G fever.
As a result, wireless engineers — spurred on by the whips of their paycheck signatories — finalized a barebones version of the 5G standard in 2017 so that operators like SK Telecom and AT&T could get to market more than a year ahead of that initial schedule.
But now, here in the middle of 2019, today’s 5G networks in the US don’t inspire much confidence.
Big 5G Event — The promise of 5G cellular technology offers a lot of potential for public safety applications, but the road to real-world implementations is long and bumpy.
While 5G could help jumpstart real-time information gathering, interpretation and sharing — which could greatly assist public safety efforts — the messy details of how to fund, build and operate the systems that provide such a connectivity upgrade are still being worked through.
The UK’s National Audit Office (NAO), which aims to ensure public money isn’t being wasted on government-funded projects, has slammed the lack of progress being made on and cost overruns of the delayed 4G-based Emergency Services Network (ESN), which is intended to replace the existing Airwave service.
The Home Office, the government department running the project, now forecasts that ESN will cost a staggering £9.3 billion (US$12.1 billion), a full £3.1 billion ($4 billion) more than originally budgeted.
Of this, £1.4 billion ($1.8 billion) is being spent on just keeping Airwave going until ESN is ready.
As 5G networks start to go live, mobile operators are working on one technology that’s less sexy than connected cars or gigabit-speed video but could cause a lot of headaches if it doesn’t work.
The issue is timing: Most carriers in North America rely on GPS to tell their cell sites what time it is. That works for timing and clock synchronization in 4G, but 5G networks have much more strict requirements, and getting out of sync could cause a site to go down.
The problem is one of the biggest worries for carriers implementing 5G transport networks, said Heavy Reading analyst Sterling Perrin, who led a panel on the topic at the Big 5G Event in Denver this week.
There’s a new business model in town, and it’s changing the contours of the sales landscape. Whereas companies used to subscribe to a B2B (business-to-business) or B2C (business-to-consumer) model, the recent desire to innovate at a competitive pace has inspired many companies to try a B2B2C approach.
Kris Goldhair, strategic account director at KBMax, explains why more manufacturers are looking to technologies like configure, price, quote (CPQ) to help sales teams reduce their close times, automate processes and efficiently respond to the call for mass customization in a B2B2C era.
In April of 2018, quietly and with little fanfare at the time, U.S. employers entered a fundamentally new era for talent management: the “talent inversion.”
For two decades, ending in April 2018, every monthly BLS report showed more unemployed workers than (non-farm) job openings.
By the end of January 2019, there were 6.5 million unemployed workers in the US, while at that same time, there were 7.6 million unfilled jobs.
This upside-down ratio wherein there are more unfilled jobs than qualified unemployed workers to take them is generally acknowledged as a national “skills gap”, “skills mismatch”, or “talent inversion”.
At the present time, the trend is continuing and the gap is growing.