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Nvidia’s top scientist develops open-source ventilator that can be built with $400 in readily available parts

by coronavirusCOVID-19GadgetsHardwareHealthStanford UniversityTC

Nvidia Chief Scientist Bill Dally has released an open-source ventilator hardware design he developed in order to address the shortage resulting from the global coronavirus pandemic. The mechanical ventilator design developed by Dally can be assembled quickly, using off-the-shelf parts with a total cost of around $400 — making it an accessible and affordable alternative to traditional, dedicated ventilators, which can cost $20,000 or more.

The design created by Dally strives for simplicity, and basically includes just two central components — a solenoid valve and a microcontroller. The design is called the OP-Vent, and in the video below you can see how bare-bones it is in terms of hardware compared to existing alternatives, including some of the other more complex emergency-use ventilator designs developed in response to COVID-19.

Dally’s design, which was developed using input from mechanical engineers and doctors, including Dr. Andrew Moore, a chief resident at Stanford University and Dr. Bryant Lin, a medical devices expert and company co-founder, can be assembled in as little as five minutes, and is small enough to fit in a Pelican case for easy transportation and potability. It also employs fewer parts and uses less energy than similarly simple designs that adapt the manual breather bags used by paramedics in emergency response.

Next up for the design is getting it cleared by the FDA under the agency’s Emergency Use Authorization program for COVID-19 equipment, and then seeking manufacturing partners to pursue large-scale manufacturing.

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R&D Roundup: Sweat power, Earth imaging, testing ‘ghostdrivers’

by Artificial IntelligencecoronavirusCOVID-19GadgetsHealthLidarmachine learningScienceSpaceTCTechnology

I see far more research articles than I could possibly write up. This column collects the most interesting of those papers and advances, along with notes on why they may prove important in the world of tech and startups.

This week: one step closer to self-powered on-skin electronics; people dressed as car seats; how to make a search engine for 3D data; and a trio of Earth imaging projects that take on three different types of disasters.

Sweat as biofuel

Monitoring vital signs is a crucial part of healthcare and is a big business across fitness, remote medicine and other industries. Unfortunately, powering devices that are low-profile and last a long time requires a bulky battery or frequent charging is a fundamental challenge. Wearables powered by body movement or other bio-derived sources are an area of much research, and this sweat-powered wireless patch is a major advance.

A figure from the paper showing the device and interactions happening inside it.

The device, described in Science Robotics, uses perspiration as both fuel and sampling material; sweat contains chemical signals that can indicate stress, medication uptake, and so on, as well as lactic acid, which can be used in power-generating reactions.

The patch performs this work on a flexible substrate and uses the generated power to transmit its data wirelessly. It’s reliable enough that it was used to control a prosthesis, albeit in limited fashion. The market for devices like this will be enormous and this platform demonstrates a new and interesting direction for researchers to take.

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Indian smartphone market grew by 4% in Q1, but projected to decline by 10% this year

by AppleAsiacoronavirusCOVID-19GadgetsMobileMotorolaSamsung ElectronicsXiaomi

India has emerged as one of the fastest growing smartphone markets in the last decade, reporting growth each quarter even as handset shipments slowed or declined elsewhere globally. But the world’s second largest smartphone is beginning to feel the coronavirus heat, too.

The Indian smartphone market grew by a modest 4% year-over-year in the quarter that ended on March 31, research firm Counterpoint said Friday evening. The shipment grew annually in January and February, when several firms launched their smartphones and unveiled aggressive promotional plans.

But in March the shipment saw a 19% year-over-year dip, the firm said. Counterpoint estimated that the smartphone shipments in India will decline by 10% this year, compared to a 8.9% growth in 2019 and 10% growth in 2018.

The research firm also cautioned that India’s lockdown, ordered last month, has severely slowed down the local smartphone industry and it may take seven to eight months to get back on track. Currently, only select items such as grocery products are permitted to be sold in India.

Prachir Singh, Senior Research Analyst at Counterpoint Research, said the COVID-19 impact on India was relatively mild until mid-March. “However, economic activities declined as people save money in expectation of an extended period of uncertainty and an almost complete lockdown. Almost all smartphone manufacturing has been suspended. Further, with the social distancing norms, factories will be running at lower capacities even after the lockdown is lifted,” he said.

Overall, 31 million smartphone units shipped in India in Q1 2020. Chinese smartphone maker Xiaomi, which has held the tentpole position in what has become its biggest market globally for more than two years, widened its lead to command 30% of the market.

Vivo’s share grew to 17%, up from 12% during the same period last year. Samsung, which once led the Indian market, now sits at the third spot with 16% market share, down from 24% in Q1 2019. Apple maintained its recent momentum and grew by a strong 78% year-over-year in Q1 this year. It now commands 55% of the premium smartphone segment (handsets priced at $600 or above.).

More than 100 smartphone plants in India assemble or produce about 700,000 to 800,000 handsets a day, some of which are exported outside of the country. But the lockdown has halted the production and could cost the industry more than $3 billion to $4 billion in direct loss this year.

“We often draw parallels between India and China. But in China, their factories have adopted automation at various levels, something that is not the case in India,” said Tarun Pathak, a senior analyst at Counterpoint, earlier this week.

China, where smartphone sales declined by 38% annually in February this year, has already started to see recovery. Xiaomi said last month that its phone factories were already operating at more than 80% of their capacity. Globally, smartphone shipment declined by 14% in February, according to Counterpoint.

Apple said to sell Macs powered by in-house ARM-based chips as early as 2021

by AppleGadgetsHardwareInteliPadiPadsiPhonemacosSteve JobsTC

Apple’s long-rumored Mac ARM chip transition could happen as early as next year, according to a new report from Bloomberg. The report says that Apple is currently working on three Mac processors based on the design of the A14 system-on-a-chip that will power the next-generation iPhone. The first of the Mac versions will greatly exceed the speed of the iPhone and iPad processors, according to the report’s sources.

Apple and CMU researchers demo a low friction learn-by-listening system for smarter home devices

by AppleArtificial IntelligenceGadgetsmachine learning

A team of researchers from Apple and Carnegie Mellon University’s Human-Computer Interaction Institute have presented a system for embedded AIs to learn by listening to noises in their environment without the need for up-front training data or without placing a huge burden on the user to supervise the learning process. The overarching goal is for smart devices to more easily build up contextual/situational awareness to increase their utility.

The system, which they’ve called Listen Learner, relies on acoustic activity recognition to enable a smart device, such as a microphone-equipped speaker, to interpret events taking place in its environment via a process of self-supervised learning with manual labelling done by one-shot user interactions — such as by the speaker asking a person ‘what was that sound?’, after it’s heard the noise enough time to classify in into a cluster.

A general pre-trained model can also be looped in to enable the system to make an initial guess on what an acoustic cluster might signify. So the user interaction could be less open-ended, with the system able to pose a question such as ‘was that a faucet?’ — requiring only a yes/no response from the human in the room.

Refinement questions could also be deployed to help the system figure out what the researchers dub “edge cases”, i.e. where sounds have been closely clustered yet might still signify a distinct event — say a door being closed vs a cupboard being closed. Over time, the system might be able to make an educated either/or guess and then present that to the user to confirm.

They’ve put together the below video demoing the concept in a kitchen environment.

In their paper presenting the research they point out that while smart devices are becoming more prevalent in homes and offices they tend to lack “contextual sensing capabilities” — with only “minimal understanding of what is happening around them”, which in turn limits “their potential to enable truly assistive computational experiences”.

And while acoustic activity recognition is not itself new, the researchers wanted to see if they could improve on existing deployments which either require a lot of manual user training to yield high accuracy; or use pre-trained general classifiers to work ‘out of the box’ but — since they lack data for a user’s specific environment — are prone to low accuracy.

Listen Learner is thus intended as a middle ground to increase utility (accuracy) without placing a high burden on the human to structure the data. The end-to-end system automatically generates acoustic event classifiers over time, with the team building a proof-of-concept prototype device to act like a smart speaker and pipe up to ask for human input. 

“The algorithm learns an ensemble model by iteratively clustering unknown samples, and then training classifiers on the resulting cluster assignments,” they explain in the paper. “This allows for a ‘one-shot’ interaction with the user to label portions of the ensemble model when they are activated.”

Audio events are segmented using an adaptive threshold that triggers when the microphone input level is 1.5 standard deviations higher than the mean of the past minute.

“We employ hysteresis techniques (i.e., for debouncing) to further smooth our thresholding scheme,” they add, further noting that: “While many environments have persistent and characteristic background sounds (e.g., HVAC), we ignore them (along with silence) for computational efficiency. Note that incoming samples were discarded if they were too similar to ambient noise, but silence within a segmented window is not removed.”

The CNN (convolutional neural network) audio model they’re using was initially trained on the YouTube-8M dataset  — augmented with a library of professional sound effects, per the paper.

“The choice of using deep neural network embeddings, which can be seen as learned low-dimensional representations of input data, is consistent with the manifold assumption (i.e., that high-dimensional data roughly lie on a low-dimensional manifold). By performing clustering and classification on this low-dimensional learned representation, our system is able to more easily discover and recognize novel sound classes,” they add.

The team used unsupervised clustering methods to infer the location of class boundaries from the low-dimensional learned representations — using a hierarchical agglomerative clustering (HAC) algorithm known as Ward’s method.

Their system evaluates “all possible groupings of data to find the best representation of classes”, given candidate clusters may overlap with one another.

“While our clustering algorithm separates data into clusters by minimizing the total within-cluster variance, we also seek to evaluate clusters based on their classifiability. Following the clustering stage, we use a unsupervised one-class support vector machine (SVM) algorithm that learns decision boundaries for novelty detection. For each candidate cluster, a one-class SVM is trained on a cluster’s data points, and its F1 score is computed with all samples in the data pool,” they add.

“Traditional clustering algorithms seek to describe input data by providing a cluster assignment, but this alone cannot be used to discriminate unseen samples. Thus, to facilitate our system’s inference capability, we construct an ensemble model using the one-class SVMs generated from the previous step. We adopt an iterative procedure for building our ensemble model by selecting the first classifier with an F1 score exceeding the threshold, 𝜃&'( and adding it to the ensemble. When a classifier is added, we run it on the data pool and mark samples that are recognized. We then restart the cluster-classify loop until either 1) all samples in the pool are marked or 2) a loop does not produce any more classifiers.”

Privacy preservation?

The paper touches on privacy concerns that arise from such a listening system — given how often the microphone would be switched on and processing environmental data, and because they note it may not always be possible to carry out all processing locally on the device.

“While our acoustic approach to activity recognition affords benefits such as improved classification accuracy and incremental learning capabilities, the capture and transmission of audio data, especially spoken content, should raise privacy concerns,” they write. “In an ideal implementation, all data would be retained on the sensing device (though significant compute would be required for local training). Alternatively, compute could occur in the cloud with user-anonymized labels of model classes stored locally.”

You can read the full paper here.

Samsung’s Galaxy Watch blood pressure monitoring app approved by South Korean regulators

by GadgetsSamsungTCWearables

Samsung Electronics announced today that its blood pressure monitoring app for Galaxy Watches has been approved by South Korean regulators. Called the Samsung Health Monitor, the app will be available for the Galaxy Watch Active2 during the third quarter, at least in South Korea, and added to upcoming Galaxy Watch devices.

TechCrunch has contacted Samsung for more information on when the app, which uses the Galaxy Watch Active2’s advanced sensor technology, will be available in other markets.

It was cleared by South Korea’s Ministry of Food and Drug Safety for use as an over-the-counter, cuff-less blood pressure monitoring app. The app first has to be calibrated with a traditional blood pressure cuff, then it monitors blood pressure through pulse wave analysis. Users need to recalibrate the app at least once every four weeks.

According to a recent report by IDC, in the last quarter of 2019, Samsung wearables ranked third in terms of shipments, behind Apple and Xiaomi, with volume driven by its Galaxy Active watches. Samsung has sought to differentiate its smartwatches with a focus on health and fitness monitoring, including sleep trackers.

Samsung’s Galaxy Watch Active 2 is a solid smartwatch for Android users

VanMoof-S3-X3-2

VanMoof introduces new S3 and X3 electric bikes

by Gadgets

VanMoof is releasing a new generation of its electric bike. In many ways, the VanMoof S3 and its smaller version the VanMoof X3 are refined versions of the VanMoof Electrified S2 and X2. It features an updated motor, hydraulic brakes and a familiar design.

If you’re not familiar with VanMoof bikes, the company has been building electric bikes with some smart features, such as an anti-theft system. There’s an integrated motion detector combined with an alarm, a GPS chip and cellular connectivity. If you declare your bike as stolen, the GPS and cellular chips go live and you can track your bike in the VanMoof app.

The company wants to control as much of the experience as possible, which means that it designs the bike in house, sells it on its website and in its own stores. 80% of orders happen on the website and VanMoof now has nine stores around the world. The company has sold 120,000 bikes over the years.

The S3 and X3 still feature the iconic triangular-shaped futuristic-looking frame. The electric motor has been updated — it is more powerful, more responsive, quieter and smaller. You’re not going to constantly switch from one gear to another as there’s an electronic gear shifting system — it has been updated from two gears to four gears. All you have to do is jump on the bike and start pedaling.

A big improvement compared to the previous generation is that the S3 and X3 now feature hydraulic brakes instead of mechanical disc brakes. And you’ll find the good old boost button on the handlebar to get an extra burst of acceleration when you need it.

When it comes to design, the saddle has been redesigned, the coating on the bike is now matte and you’ll see a lot of changes across the board. The only difference between the S3 and X3 is that the S3 is designed for taller people while the X3 is designed for smaller people. Unfortunately, it looks like the battery is still not removable.

The company is trying to control the supply chain as much as possible. It works with a small set of suppliers to manufacture custom components and then tries to cut out as many middleperson as possible to bring costs down. The VanMoof S3 and X3 cost $1,998/€1,998 but the company could raise the introductory price in the future due to pressure on the supply chain.

Here’s a video of the previous generation VanMoof Electrified X2 we shot a couple of months ago:

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3D-printed glasses startup Fitz is making custom protective eyewear for healthcare workers

by coronavirusCOVID-19GadgetsHardwareHealthiPadStartupsTC

A lot of startups have answered the call for more personal protective equipment (PPE) and other essentials to support healthcare workers in their efforts to curb the spread and impact of COVID-19. One of those is direct-to-consumer 3D-printed eyewear brand Fitz, which is employing its custom-fit glasses technology to build protective, prescription specs for front-line healthcare workers in need of the best protection they can get.

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The $99 Mendel Air Sensor uses data to help you grow better veggies (or weed)

by GadgetsHardwareTC

The Mendel Air Sensor app is the first app I open every morning. Before Reddit, before Gmail, before NYT. I roll over, grab my phone and check my plants. I don’t know if there’s a higher honor I can bestow on an app.

The Mendel Air Sensor is a game-changer for indoor growers. It offers a sophisticated suite of sensors that collects critical information about growing conditions. With a price of $99, there’s very little else on the market that offers the same sort of data collection at an affordable price.

The company behind the Mendel Air Sensor started by building similar sensors for at-home aquariums. This group knows data collection and teamed up with an experienced manufacturer to develop and ship the Mendel Air Sensor.

I know very little about growing plants indoors. I’ve watched some YouTube videos, read a lot of blog posts and asked friends for advice. And yet I have a small growing operation in my basement: tomatoes, romaine lettuce, carrots and, you know, other leafy greens.

Several weeks in, I’m starting to appreciate the data behind growing plants. There’s a lot to consider, from the temperature to types and amount of light, to humidity and how the plants react to humidity through a calculation to determine the vapor pressure deficit (VPD).

I have a Mendel Air Sensor hanging in one grow tent (pictured at the top), and it’s my new obsession. The small green device collects four data points every 15 minutes and displays the information through a web app or smartphone app. This is allowing me to fine-tune the controlled environment through exhaust fans, light placement and humidifier levels.

As I’ve found, it’s critical to watch this data throughout the day. I’ve yet to stabilize the environment to a point where I set it and forget it. About twice a day, because of the Mendel Air Sensor, I make slight changes to the growing tent, which results in dramatic changes to the environment. Without access to this data, I wouldn’t know something is off until the plant shows warning signs — and as I understand it, that’s when it’s too late.

At $99, it’s a good value, and there are only a few competitors in the space. Most are double or triple the price, though their charting products seem more mature.

CEO Nate Levine tells TechCrunch Mendel started as a 50/50 partnership with another bootstrapped company, RapidLED out of the Bay Area. This company has sold lights for indoor growers for the last few years and already has an established base of customers in this field. But Levine didn’t start to build a product for monitoring plants; instead, he created, FishBit, a product for monitoring aquariums.

The parallels between the two markets helped Levine’s team jump into the indoor gardening space. As Levine told TechCrunch, the consumer demands are similar, and like with aquariums, indoor growers are increasingly looking for ways to increase capabilities. Instead of keeping fish alive, though, they’re trying to get more tomatoes. Or weed.

Levine said that unlike with aquariums, indoor growers can be less stingy with their cash, though, right now, with cannabis, margins are slim. There isn’t a gold rush, he said, but noted that the cannabis market, in particular, is at the right spot for companies to launch new products.

The company is marketing the same product to home growers, and commercial growers thought this could be a challenge with the current web app. It lacks robust features found on other products. For a small grower like me, it’s okay, but I expect commercial customers expect better logging, more detailed analysis and a variable monitoring cycle instead of just every 15 minutes.

To make it available for international users, the company needs to swap out the USB power supply.

Don’t call this is a pivot. Or at least Levine doesn’t call it a pivot. As he told TechCrunch, if he goes back to the original pitch deck, the company is still driving at the same goal for FishBit, and everything the team learns on Mendel is implemented in FishBit, too. The goal is to build an entire product line of smart hardware and software for the indoor grower.

RapidLED approached Levine and the team at an aquarium conference and offered to build the hardware if Levine could make the software. My plants are happy that the two companies forged the partnership.

As for my plants, I’ve learned a few things because of the Mendel Air Sensor. First, my grow lights put out much more heat than I expected, and I need to dump the cheap set and get a name brand unit. Second, the humidity was much lower than I had expected, so I added a humidifier. Finally, monitoring the VPD is much easier than it seems if the calculations are automated.

Growing plants is hard, but it’s easier with the data from the Mendel Air Sensor.