Category Archives: Blog

Since the pandemic hit our shores earlier this year, we’ve been keeping our eye out for ways in which the electronics industry has responded. Several of our earlier posts have addressed this. In Tech Takes on the Coronavirus, I wrote about two Italian innovators who’d come up with a way to use 3D printers to create valves needed for ventilators. Oura Rings Enlisted in the Fight Against COVID talked about this technology, which the NBA planned to use to keep players in their bubble safe. Most of us have gotten used to social distancing, and in this post I took at look at some applications that enable social distancing in a retail setting.

EDN shares our interest in, and they recently asked a provocative questions: “Has the electronics industry done enough tackling the pandemic?”

To answer this and other questions, EDN/AspenCore pulled together a series on “real design solutions related to diagnostics, treatment, surveillance and prevention of the spread of the novel coronavirus,” which I’ve been reading on EE Times.

In this post, I’m providing a very brief summary of what’s in first article in their series. (The remaining articles will be fodder for future posts.)

Majeed Ahmad offered a “sneak peek into the brand-new design ecosystem built around the fight against the coronavirus pandemic.” He began with automated ways to detect and maintain social distancing, and the role that photoelectric proximity sensors, combined with miniature programmable logic controllers, is playing. The sensors determine which way the traffic is flowing, and the PLCs counts the number of people coming and going and provides red light-green light data.

The next element of the coronavirus design ecosystem he introduces is COVID testing via camera. Using thermal imaging cameras to take someone’s body temperatures is a fast, efficient, and contact-free way to figure out who’s running a fever. An alarm is triggered, and the person with an elevated temperature can be taken aside for further screening.

A third area focuses on pre-diagnostic screening, and I found this one particularly interesting. The one Majeed writes about is predicated on analyzing samples of coughing sounds to “identify unique cough patterns associated with COVID-19 infections.”

SensiML, a subsidiary of QuickLogic, which develops software solutions for ultra-low-power IoT end-points, is crowdsourcing the cough samples from healthcare facilities to create large datasets. Next, it’s going to apply the AI-based cough analysis algorithms to these datasets to build an efficient COVID-19 screening mechanism.

Given that we all get a bit panicked when someone near us starts coughing – even if they’re wearing a mask and six-feet away – it will be great if someone could figure out how to detect a possible COVID infection as opposed to a tickle in the throat.

The final part of the ecosystem Majeed sees emerging is wireless patches for contact tracing. These wearables would be equipped with temperature sensing chips. “The low-cost and no-touch temperature sensing tags can remotely sense body temperature and ensure that people with symptoms—having placed the patch on the skin—can stay at home.”

So far, all the articles I’ve read in this series have been worth the read. The electronics industry isn’t going to cure COVID-19, but it’s certainly going to help us work our way through the pandemic while we await a viable vaccine. And it will help us be better prepared to respond to anything that happens in the future.

 

As a car enthusiast, and a devoted techie, I’ve been an avid follower of all the news about autonomous vehicles. While we’re definitely on the road to autonomous vehicles, the road is long and winding. And there are speedbumps along the way. But as everyone who’s sat behind the wheel – hands off! – while their car perfectly executed a parallel parking maneuver knows, we’ll get there.

While a lot of media attention has gone to experiments with fully autonomous driving, “getting there” is going to arrive thanks to incremental changes that have been making their way into consumer vehicles for a while now: cruise control, alerts when you’re veering out of your lane, backing up guidance. The Society of Automotive Engineers has established a six-level continuum that defines automated driving. It ranges from Level 0, where the driver is in full control (but with light assistance like automatic emergency braking), up to Level 5, where the driving is 100% left to the control system, and the human “driver” is for all intents and purposes a passenger.

The available technology currently puts us at Level 2. At this level, the driver still needs to pay attention and take care of the driving tasks. But it includes Active Driving Assistance, with adaptive, or dynamic, cruise control (with speed automatically adjusted so that the vehicle stays a safe distance from the vehicles ahead) and lane keeping assistance working together.

How’s Active Driving Assistance working out? Well, according to AAA, the state-of-the-art puts things at a considerable distance from 100% reliability.

AAA automotive researchers found that over the course of 4,000 miles of real-world driving, vehicles equipped with active driving assistance systems experienced some type of issue every 8 miles, on average. Researchers noted instances of trouble with the systems keeping the vehicles tested in their lane and coming too close to other vehicles or guardrails. AAA also found that active driving assistance systems, those that combine vehicle acceleration with braking and steering, often disengage with little notice – almost instantly handing control back to the driver. A dangerous scenario if a driver has become disengaged from the driving task or has become too dependent on the system. (Source: AAA)

For their study, AAA tested out active driving assistance systems in routine scenarios, under both real-world and closed-course settings.

On public roadways, nearly three-quarters (73%) of errors involved instances of lane departure or erratic lane position. While AAA’s closed-course testing found that the systems performed mostly as expected, they were particularly challenged when approaching a simulated disabled vehicle. When encountering this test scenario, in aggregate, a collision occurred 66% of the time and the average impact speed was 25 mph.

Their recommendation? When it comes to Active Driving Assistance, we’re still in the early development stage. So proceed with caution.

Moving technology that’s not quite there yet is dangerous, especially given the tendency to oversell new tech. Because of this, drivers may not fully grasp that they still need to stay engaged and be ready to take over full control. Another problem with getting Active Driving Assistance into the public realm is that bad experiences will turn folks off to acceptance of autonomous driving – even when it becomes more perfected. As it stands, a 2020 AAA survey found that only 12% of drivers “would trust riding in a self-driving car.” All things considered, this is not surprising. But the automotive industry doesn’t want to encounter a stall in acceptance once the technology is more proven.

In the meantime, Active Driving Assistance is a step along the way to the fully autonomous vehicle. Can’t wait for more!

 

If you’ve been in a grocery, pharmacy, or big box store recently – or lined up outside a Starbucks – you’ve seen the duct-taped X’s telling you where to stand to keep your social distance from the person in front of you. And if you’ve been taking walks – or just walking down the aisle in the grocery store – you’ve probably gotten pretty good at gauging just what six feet away looks like.

Not surprisingly, as the pandemic continues on and, with it social distancing requirements, there will be technology-based solutions that can save us from having to rely on the tape measure and the eyeball.

I recently came across a post on the topic, from the perspective of retail applications, written by Andrea Berry of STMicroelectronics and published on embedded.com.

Presence-detection solutions are not one-size-fits-all. The core sensors and technologies required to accomplish the variety of features and use cases are as diverse as the applications and environments in which they are used. In each case, system architectures must take into consideration environmental factors; the shape, structure, and layout of the space; power availability; node-to-node communications requirements and constraints; and data and information security.

Here are the areas that Berry notes are likely candidates for technical solutions, and what those solutions might look like:

Occupancy-density indication

Many stores limit the number of shoppers who can be in the store at the same time. But once they’re in there, how do you keep them from jamming the aisles and clumping together? In stores that are set up using aisle-based layouts – and this covers pretty much all grocery stores, chain pharmacies, and big-box stores, but not department stores or boutique shops:

Simple motion-detection technology, similar to what is used in home security systems (typically based off of passive infrared, time-of-flight, microwave, mirror optic, or ultrasonic sensors, or a combination thereof), could be used at each end of the aisle to determine when someone is entering or exiting.

Alerts could be triggered if someone were entering the wrong way – many stores are now using tape arrows to indicate which direction you should be rolling your cart in – or if the capacity of the aisle was reached and shoppers would not be able to socially distance. This type of solution wouldn’t do anything to prevent anyone from getting in your space when you’re reaching for that last box of pasta, but it could be used to keep shoppers moving in the right direction, and limiting the number of shoppers in each aisle.

“Designing a variety of proximity sensing building blocks requires a range of sensor, power management, and connectivity solutions.”

Absolute social-distancing measurement

There are a number of different technologies that can be used to solve the social distancing problem: optical sensors, radar/LIDAR, pressure sensors, microphone arrays, RFID localization and zoning, Wifi-Bluetooth.

Given the presence of security cameras, stores may already have the infrastructure in place to put optical sensors in place.

Image processing may be performed at the edge by the sensor node, with real-time alert at the sensor if there was an occupancy exception in that area. …Both visual and thermal optical sensors could be used in such an application.

Like optical sensors, “radar and LiDAR could be used to accurately pinpoint the location of every person within a given space.” The advantage here is that they are longer range than optical sensors, so you’d need fewer of them. The disadvantage is that stores with aisle formats have a lot of obstructions, so deployment could be tricky.

Pressure sensors (MEMS, strain gauge) could be embedded in the flooring to detect when shoppers are getting too close to each other for comfort, but this would be a costly solution that might be overkill when it comes to social distance monitoring.

Microphone arrays, with ultrasonic microphones picking up the sound a shopper’s making, could work for social distancing but the work (signal conditioning, intelligence processing) involved in implementing a solution may not be worth it.

Carts and baskets could be given an RFID tag, which could be used to determine where those carts and baskets are. But, as anyone who’s ever done any shopping knows, people do from time to time step away from their carts, and it doesn’t do much good, health-wise, if the carts are socially distanced but the living, breathing shopper aren’t.

While there are privacy issues involved, Wi-Fi and Bluetooth location determination could be easily implemented, given that nearly everyone has a cellphone with them and given that many stores already track patrons entering when their mobile starts looking for a Wi-Fi signal. Not only could a Wi-Fi approach be useful for social distancing, but it could also be used for contact tracing if needed.  

Social distancing requirements will likely be with us for a long time to come. So far, we’ve been relying on old-fashioned methods to maintain it. It won’t be long before the technology to enable social distancing comes along.

Like those belonging to pretty much everyone else, the electronics devices and gadgets I rely on occasionally run out of juice. I forget to charge my phone. Or my smart watch. I misjudge how long my laptop has been running untethered, and now I’m finding that I’m running on empty. Sure, it’s not as terrible as realizing that the gas gauge is telling you that you have 32 miles left in the tank – and passing a sign on the highway informing you that the next rest stop is 34 miles away. Which means pulling off the highway near some pokey town – when all you want to do is get home! No, charging those devices tends to be a pretty quick and painless fix. Annoying when the battery runs out, but generally a problem that’s easy enough to remedy.

This is true for personal devices, and to a slightly lesser extent true for devices used in commercial or industrial settings. But there are some situations in which the devices are deployed in an environment where it’s not so simple to recharge or replace their sensors when the batteries start to fade. Think of, say, the security perimeter around a large energy, agricultural or military installation that’s remote and isolated.

Here, drones may be coming to the rescue.

In a recent Spectrum/IEEE article, writer Michelle Hampton discusses a system through which drones in flight can recharge sensors:

A new system is designed to make the charging process easier by using unmanned aerial vehicles (UAVs) to deliver power using radio waves during a flyby. A specialized antenna on the sensor harvests the signals and converts them into electricity.

The researchers behind this, including Joseph Contantine and others at American University of Beirut, were looking at using radio frequency ways for recharging sensors, but they were facing a significant challenge in terms of getting close enough to the sensor to give it a good charge. The answer they came up with was the UAV, which can get up close and personal with a sensor, hovering right above it, and following “an optimized trajectory that maximizes energy transfer.”

In the proposed approach, a UAV transmits radio frequencies to each sensor, which has an antenna for detecting the signals. The signals are then conveyed to a rectifier, which converts the signals into electricity. This power can be used to charge the sensor and/or activate it.

The UAV can also selectively target sensors.

The UAV supports both charging and wakeup (which can be done at a greater distance: 27.5 meters, as opposed to a distance of 1.2 meters when charging is involved.

Now, the team is working to develop a load-independent rectenna (antenna and rectifier) that maintains high efficiency across a wide range of loads and frequencies. 

This solution would be overkill, but I have to admit I’m getting a kick out of imagining a drone flying around my house, stopping to charge the batteries in all the devices I somehow forgot to plug in to the charger!