Sensors, MCUs, AI and More
From medical monitoring portables to wearable social distance alarms to store occupancy counters, an entire range of embedded devices have emerged to serve the demands of the COVID-19 era. System designers are leveraging the latest MCU, sensor and wireless technologies to build their COVID-19 devices.
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2020 was a year that turned the world upside-down, as the novel coronavirus pandemic spread worldwide infecting millions with the disease caused by the virus: COVID-19. From the earliest months when the pandemic was spreading to worldwide scale, to the last months of 2020 when vaccine approval and distribution plans were underway, embedded technologies were constantly finding themselves in a diverse variety of solutions designed to address, in most cases, quite new problems.
These solutions encompassed a wide range of areas. Included were temperature sensing devices, social distancing alarms, ventilator reference designs, AI-based detectors for measuring symptom trends and even identifying mask-wearing in crowds. The building blocks for these device designs comprise microcontrollers (MCUs), sensors, Bluetooth-based SoCs, FPGAs, GPUs, AI-modeling software and more. Over the past 12 months, those technologies have formed the backbone enabling system designs aimed at managing all aspects of the COVID-19 era.
WIRELESS TEMPERATURE SENSOR
Clearly COVID-19 has raised the importance of continuous temperature monitoring, especially inside hospitals. In November, Nordic Semiconductor announced that a Polish medical startup, Warmie, is employing a Nordic nRF52810 System-on-Chip (SoC) in a small (3.2cm × 2.5cm × 0.7cm), high accuracy (±0.1ºC), battery-powered, wireless temperature sensor that is designed to rapidly detect changes in body temperature (Figure 1). Nordic says it is the first device of its kind to be certified for use in hospital emergency rooms (EU IIb medical device classification).
Warmie designed a Nordic nRF52810 SoC in a small (3.2cm x 2.5cm x 0.7cm), high accuracy (±0.1ºC), battery-powered, wireless temperature sensor that is designed to rapidly detect changes in body temperature. It is the first device of its kind to be certified for use in hospital emergency rooms (EU IIb medical device classification).
Up to one in 10 people develop infections after having an operation in a hospital, says Nordic. If not caught early enough, these can go from a local infection to a whole body one that is much harder to treat with antibiotics and requires extended and costly re-hospitalization. The Warmie Sensor allows localized continuous temperature and infection monitoring of post-operative surgical wounds both in and out of hospital, as well as of hospitalized COVID-19 patients. It also enables continuous monitoring of large numbers of patients simultaneously.
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Unlike many medical devices, the Warmie Sensor is also so simple and relatively low cost ($60, €50) that it can be easily used by non-professional consumers, such as concerned parents with young children as well as patients themselves who want to ensure their own health after leaving hospital. Because of the extreme low power consumption of the Nordic nRF52810, users are able to achieve a typical battery life of six months.
The company says its Warmie Sensor is not just a simple thermometer but a highly accurate medical device that is capable of not only single-point temperature measurements but also evaluating patterns of temperature changes that can be spread over several parts of the body. The Warmie Sensor is also designed to be extremely easy to integrate into other telemedicine systems using, for example, wireless heart rate monitors or pulse oximeters.
BLUETOOTH-BASED WEARABLE
Gone are the days when the idea of social distancing was an abstract concept. The coronavirus pandemic has made the world very much aware of the positive mitigating benefits of keeping a 6′ or greater distance when in a public situation. Embedded technology has addressed these needs too. Along those lines, in September U-blox confirmed that its Bluetooth 5 modules are being built into wearable devices developed by Electronic Precepts. Helping to combat the global COVID-19 pandemic, its TDS-50 is available as either a wristband or a pendant (Figure 2).
The TDS-50 gives wearers visual and vibrational alerts if another TDS-50 wearer comes within a 2-meter distance of them for over 45 seconds. The unit embeds U‑blox’s ANNA-B112.
The device provides a highly-effective track and trace solution, with data being directly stored on the device then periodically sent to a web server. In addition, through the social distancing function that is also featured, wearers are given visual and vibrational alerts if another TDS-50 wearer comes within a 2-meter distance of them for over 45 seconds. The device can be used anywhere, from schools to businesses or airport settings.
The unit embeds U-blox’s ANNA-B112 module. With dimensions of just 6.5mm × 6.5mm × 1.2mm, this space-saving, highly integrated and pre-approved system-in-package (SiP) is optimized for situations where a swift turnaround is mandated. It is based on Nordic’s nRF52832 chip-level Bluetooth technology, with a 64MHz Arm Cortex-M4 processing resource plus 512KB of embedded flash memory. Supporting 1.4Mb/s data rates, it is highly suited to wearables applications and does not impact heavily on battery reserves.
The TDS-50 units needed a compact form factor to make them comfortable for the user to wear. They also had to support ultra-low power operation, with the ability to deliver passive scanning (for social distancing purposes) over a period of up to 240 hours. Consequently, the wireless communication module specified had to meet both of these key criteria fully. Given the immediacy of the COVID-19 crisis, it was crucial as well that the solution could be brought to market very rapidly—with the constituent RF electronics being quick and simple to incorporate into the design and having all the necessary standards approvals in place, says U-blox.
UWB-BASED WEARABLE
Ultra-Wideband (UWB) technology is an obvious choice for use in social distancing wearable solutions. Along those lines, in November Renesas Electronics and Altran announced that they had co-developed a wearable solution for social distancing based on UWB technology (Figure 3). Earlier in 2020, Renesas announced it had licensed UWB technology from 3db Access AG, a fabless semiconductor company specializing in secure UWB low power chips to augment Renesas MCUs.
This wearable solution for social distancing is based on UWB technology. The platform’s form factor, a wristwatch, combines the Renesas Synergy S128 MCU featuring HMI capacitive touch with licensed secure ranging UWB technology.
The platform’s form factor, a wristwatch, combines the Renesas Synergy S128 MCU featuring HMI capacitive touch with the licensed secure ranging UWB technology. Unlike other social distancing wearables based on technologies such as Bluetooth Low Energy (BLE), Renesas’ UWB chipset with Low Rate Pulse (LRP) can operate on 10× lower power consumption than competing UWB chips and measure distances with an accuracy of 10cm or less—the precision necessary for social distancing applications. The wristwatch’s safe distance is user-configurable. The wearer is alerted by LEDs and haptic feedback when a second device is detected within this range. Renesas says it will begin sampling the UWB chipset during the second half of 2021.
As co-developer and system integrator, Altran will leverage the UWB-based platform along with other internal assets to develop additional wearable solutions for social distancing as well as related location-based applications for clients across a broad range of markets. The social distancing wristwatch will be showcased in Altran’s innovation lab.
TEMPERATURE SENSING WEARABLE
Temperature sensors embedded in wearable devices are also critical to today’s fight against the coronavirus. In October, Nordic Semiconductor announced that CWD Innovations, Nordic’s first official module partner in India, selected Nordic’s nRF52832 BLE SoC to provide the short-range wireless connectivity and core processing power for its SmartTemp+ continuous body temperature monitoring system (Figure 4).
SmartTemp+ is a safe alternative to infrared, digital and mercury-based thermometers. The platform is designed to support the frontline of the healthcare industry during the COVID-19 pandemic by enabling contactless measurement and continuous monitoring of patient temperature. It embeds Nordic’s nRF52832 BLE SoC.
According to the company, SmartTemp+ is a safe alternative to infrared, digital and mercury-based thermometers for use in healthcare, government and home environments. The platform is designed to support the frontline of the healthcare industry during the COVID-19 pandemic by enabling contactless measurement and continuous monitoring of patient temperature. Fever is a symptom in around 88% of confirmed COVID-19 cases, according to the World Health Organization (WHO). The SmartTemp+ also enables configurable alerts when body temperature rises above or falls below the patient’s appropriate thresholds and GPS tagging and geofencing for patients under quarantine.
The chip’s processor and memory resources allow it to run complex BLE applications. Once attached to the patient’s axial region (including the head, neck and chest), the 3cm-diameter wearable device uses a built-in temperature sensor to reliably and accurately measure the patient’s body temperature over a period of 15 days with no additional intervention.
The nRF52832 SoC’s powerful 32-bit Arm Cortex M4 processor is designed to support the Floating Point (FP) and Digital Signal Processing (DSP) computations typical of high-end wireless applications. The microprocessor, combined with the SoC’s generous memory allocation (512KB flash memory and 64KB RAM), provides ample processing resources to supervise the SmartTemp+’s advanced edge computing algorithms.
Using the Nordic SoC-enabled BLE connectivity, the temperature data is automatically transmitted every 60 seconds (or other configured advertising frequency) from the device to a Bluetooth 4.0 (and later) smartphone, from where the user can view the information and self-monitor through the iOS and Android SmartTemp+ companion app. Alternatively, the data can be transmitted to a proprietary gateway within a hospital using BLE, then relayed to a cloud-based dashboard where the information can be monitored by healthcare professionals or health authorities. This enables early intervention in cases of COVID-19 as well as case mapping across the wider community.
RADAR-BASED ENTRANCE COUNTER
Another important need driven by the COVID-19 pandemic is the ability to track the number of people entering public buildings. Governmental regulations all over the world have driven an urgent need for solutions to secure social distancing in public buildings in order to support slowing down the spread of COVID-19. With that in mind, Infineon Technologies developed a system that counts people while entering and leaving buildings or rooms and ensures social distancing at the same time. The smart entrance counter solution is a miniaturized, discrete radar board (20mm × 15mm) that accurately and anonymously counts people with one single 60GHz radar sensor and integrated software (Figure 5). A traffic light system informs personnel if an entry is allowed or not.
Infineon’s smart entrance counter solution is a miniaturized, discrete radar board (20 mm × 15 mm) that accurately and anonymously counts people with one single 60 GHz radar sensor and integrated software.
Infineon Technologies says its smart entrance counter solution is a closed system. On the one hand, it prevents overcrowding, on the other hand, it enables businesses to keep their operations running. And most importantly, due to the use of radar technology, personal data are 100% protected. The system counts a person, but does not know who it is, says the company.
The volume for this kind of solution could amount to 90 million units globally, predicts the company. Infineon’s Smart entrance counter solution with XENSIV 60GHz radar sensor works contactless and can easily be installed on the side or ceiling of an entrance or exit. It can be implemented in all kinds of building types, such as public buildings, retail and grocery stores, restaurants, schools or corporate spaces—for example cafes, offices and the like.
The radar sensor has broader applications such as controlling the various elements of a smart building optimally, such as ventilation, lighting systems, screens, automatic doors, smart home devices, or security systems including cameras—basically any device that must be able to detect motions in the surroundings accurately. With that in mind, Infineon has made available a radar demo board with the XENSIV BGT60LTR11AIP, a fully integrated microwave motion sensor with built-in antennas.
The all-in-one solution comes in a size- and cost-efficient package and requires neither RF expertise nor external signal processing when implemented. Therefore, it simplifies the design-in and shortens the time to market. The BGT60LTR11AIP offers numerous advantages over classic PIR-based motion sensors such as higher sensitivity, which enables to detect smallest movements and proximity direction. Radar also allows detection through non-metallic materials, allowing for design flexibility for the end product. The radar demo kit BGT60LTR11AIP can be ordered now, the single chip will be available in spring 2021.
SPEEDING DEVICES TO MARKET
The COVID-19 pandemic has put pressure on medical device manufacturers to design and get new devices to market faster than ever before. Serving such needs in October Maxim Integrated rolled out its Health Sensor Platform 3.0 (HSP 3.0). Also called as MAXREFDES104#, this ready-to-wear wrist form factor reference design monitors blood oxygen saturation (SpO2), electrocardiogram (ECG), heart rate (HR), body temperature and motion (Figure 6).
The Health Sensor Platform 3.0 (HSP 3.0) is a ready-to-wear wrist form factor reference design that monitors blood oxygen saturation (SpO2), electrocardiogram (ECG), heart rate (HR), body temperature and motion.
Included algorithms provide HR, heart-rate variability (HRV), respiration rate (RR), SpO2, body temperature, sleep quality and stress level information at clinical-grade levels. It allows wearable designers to start collecting data immediately, saving at least six months over building these devices from scratch. Designed for wrist-based form factors, HSP 3.0 can be adapted for other dry electrode form factors such as chest patches and smart rings.
Compared to its predecessor, Health Sensor Platform 2.0 (HSP 2.0), the HSP 3.0 adds optical SpO2 measurement and dry-electrode capability to the ECG. As a result, it can enable end solutions to monitor cardiac heart and respiratory issues for management of ailments like chronic obstructive pulmonary disease (COPD), infectious diseases (such as COVID-19), sleep apnea and atrial fibrillation (AFib). Compared to its predecessor, the narrower form factor and enhanced optical architecture of HSP 3.0 improves signal acquisition quality and uses upgraded MCU, power, security and sensing ICs. The reference design includes complete optical and electrode designs, along with algorithms to meet clinical requirements.
HSP 3.0 or MAXREFDES104# includes the following sensor, power management, MCU and algorithm products: the MAX86176: an optical photoplethysmography (PPG) and electrical ECG analog front end (AFE); the MAX20360: battery management power management IC (PMIC); the MAX32666: a BLE-enabled, ultra-low power MCU with two Arm Cortex-M4F cores and an additional SmartDMA which permits running the BLE stack independently; the MAX32670: An MCU dedicated to Maxim’s PPG algorithms of pulse rate, SpO2, HRV, RR, sleep quality monitoring and stress monitoring; and the MAX30208: a 2mm × 2mm, low-power, high-accuracy digital temperature sensor.
COLLECTING COUGH DATA
Embedded, AI and IoT technologies have also played key roles in solutions that study trends of COVID-19 symptoms. In an example, in May SensiML (a subsidiary of QuickLogic) announced a collaboration on an effort to use its AI technology to help predict whether people are showing symptoms of COVID-19 infection.
One such capability involves using crowdsourcing to collect cough sounds from a large number of volunteers and then analyzing that data combined with other datasets from the consortium using the SensiML Analytics Toolkit to identify the unique cough patterns associated with COVID-19 infections (Figure 7). The goal of the initiative is to give businesses, governments, healthcare and other public facilities access to multi-sensor, pre-diagnostic screening mechanisms to help slow the spread of the disease.
SensiML has collaborated with several organizations to use its AI technology to help predict whether people are showing symptoms of COVID-19 infection. This involves collecting data and analyzing that data combined with other datasets using the SensiML Analytics Toolkit.
According to SensiML, the initiative is supported by a consortium of companies, universities and health organizations including Asymmetric Return Capital, SkyWater Technology and Upward Health. In addition to its work with the consortium to build an enhanced screening application, SensiML plans to open-source its own crowdsourced cough sound dataset for researchers at large to access.
The concept of utilizing AI for pre-diagnostic screening of cough acoustic samples has been studied and validated in recent published academic research and is supported by ongoing projects at multiple esteemed universities. Early published results suggest that using AI to identify coughs as a COVID-19 screening mechanism has significant potential, because the pathomorphology of the disease is distinctive from that of other respiratory diseases.
To support the crowdsourcing effort, the company has set up a web-based data collection page which explains the contribution process and includes a brief (voluntary) questionnaire. No user-identifying information is collected and all data submitted is anonymous. Those interested in contributing a cough sample as part of the study can visit: https://sensiml.com/covid-19 [1]. Healthy individuals, those having other respiratory conditions, and those suspected or confirmed to have an active COVID-19 infection are all encouraged to contribute.
AI FOR MEDICAL DESIGNS
AI and deep learning are playing important roles not only in healthcare in general, but also in the design of medical devices. In October, Xilinx introduced a fully functional medical X-ray classification deep-learning model and a reference design kit, in association with Spline.AI on Amazon Web Services (AWS). The UltraScale+ Healthcare AI Starter Kit is a smart and scalable solution for pneumonia and COVID-19 prediction, using Vitis-AI and AWS IoT Greengrass with Xilinx ZCU104 FPGA board as the edge device (Figure 8).
Co-developed with Spine.AI on Amazon Web Services (AWS), Xilinx’s UltraScale+ Healthcare AI Starter Kit is a smart and scalable solution for pneumonia and COVID-19 prediction using Vitis-AI and AWS IoT Greengrass with Xilinx ZCU104 FPGA board as the edge device.
The kit provides a highly scalable development platform that is extremely cost-effective and suitable for many hospital, ambulance or hospital-on-wheels use-cases. Designed to enable healthcare researchers to develop a radiology flow that can help to improve COVID-19 diagnostics applications, it also provides two sets of deep learning models for pneumonia and COVID-19 prediction based on chest X-Ray image processing.
The collaboratively developed solution uses an open-source model, which runs on a Python programming platform on a Xilinx Zynq UltraScale+ MPSoC device, meaning it can be adapted by researchers to suit different application specific requirements. Medical diagnostic, clinical equipment makers and healthcare service providers can use the open-source design to rapidly develop and deploy trained models for many clinical and radiological applications in a mobile, portable or point-of-care edge device with the option to scale using the cloud.
MORE WITH AI MODELING
AI-based solutions are also coming from GPU technology vendor Nvidia. In May, the company announced AI models to help the medical community better track, test and treat COVID-19. The AI models, developed jointly with the National Institutes of Health (NIH), can help researchers study the severity of COVID-19 from chest CT scans and develop new tools to better understand, measure and detect infections.
The models were made available in the latest release of Clara Imaging on the NGC software hub (Figure 9). Jointly developed by Nvidia’s Medical Imaging applied research team and clinicians and data scientists at the NIH through a cooperative research and development agreement, the models used data from locations with high rates of COVID-19 infections, including China, Italy, Japan and the United States. The AI models were built using the Nvidia Clara application framework for medical imaging. Nvidia Clara contains domain-specific AI training and deployment workflow tools that allowed Nvidia and NIH to develop the models in under three weeks.
Running on a variety of hardware solutions, Nvidia’s Clara application framework leverages the edge stack to deliver all the necessary tools for healthcare application developers.
Nvidia Clara Imaging released Clara Train MMARs for CT Lung segmentation and COVID-19 Chest CT Classification, providing researchers with a state-of-the art implementation and ability to optimize these models with the Clara Train application framework. Also available is the Clara Deploy reference pipeline for COVID-19 Chest CT Classification to provide researchers with a reference deployment pipeline, which can be seamlessly evaluated for localized data.
FACE MASK DETECTOR
In an example of the Clara Imaging technology put to use, in August Nvidia announced that it has developed Nvidia Clara Guardian, an application framework and partner ecosystem that simplifies the development and deployment of smart sensors with multimodal AI in healthcare facilities.
Clara Guardian comes with a collection of healthcare-specific, pretrained models and reference applications that are powered by GPU-accelerated application frameworks, toolkits and SDKs. You can use Nvidia Transfer Learning Toolkit (TLT) to develop highly accurate, intelligent video analytics (IVA) models with zero coding and use the Nvidia DeepStream SDK to deploy multi-platform scalable video analytics.
In the announcement, Nvidia shared information on experiments using TLT to train a face mask detection model and then using the DeepStream SDK to perform efficient, real-time deployment of the trained model (Figure 10). Face mask detection systems are now increasingly important, especially in smart hospitals for effective patient care. They’re also important in stadiums, airports, warehouses and other crowded spaces where foot traffic is heavy and safety regulations are critical to safeguarding everyone’s health. Nvidia provides access to the recipe and scripts to build your own app using the Nvidia-AI-IOT/face-mask-detection GitHub repo [2]. The full tutorial is on the Nvidia Developer Blog [3].
Nvidia Transfer Learning Toolkit (TLT) can be used to develop highly accurate, intelligent video analytics (IVA) models with zero coding and use the Nvidia DeepStream SDK to deploy multi-platform scalable video analytics. Nvidia has shared information experiments using TLT to train a face mask detection model and then using the DeepStream SDK to perform efficient, real-time deployment of the trained model.
VENTILATOR REFERENCE DESIGN
In August, Trinamic Motion Control, now part of Maxim Integrated, announced the TMC4671+TMC6100-TOSV-REF design board, a BLDC servo driver capable of 12V to 36V with up to 6A RMS to accommodate medical ventilator and respiratory system design (Figure 11). To shorten design cycles, the open-source module features a session border controller connector in Raspberry Pi style and space for a pressure sensor add-on board.
The TMC4671+TMC6100-TOSV-REF design board is a BLDC servo driver capable of 12V to 36V with up to 6A RMS to accommodate medical ventilator and respiratory system design. To shorten design cycles, the open-source module features a session border controller connector in Raspberry Pi style and space for a pressure sensor add-on board.
Medical ventilator design must deploy sensor technology for monitoring and reacting to changes in pressure, flow, volume, respiration rate and other parameters, says Trinamic. With that in mind, the reference design board includes a Hall sensor interface and connectors for an optional pressure sensor add-on board. As a result, important pressure data can be directly visualized on a Raspberry Pi with touchscreen using the free firmware developed by Trinamic for the reference design board. Hardware and software are open-source, meeting the goals of the Trinamic Open-Source Ventilator (TOSV) project, which led to this reference design.
The reference design shows engineers how to build medical ventilators using readily available components, bypassing the longer lead times of traditional components. The design uses a dynamically controlled high-RPM turbine BLDC motor to allow the use of pressure and volume sensors to enable both pressure-controlled and flow-controlled ventilation modes. These important features enable engineers to address the immediate need for technologically advanced ventilators, which is being driven by the rapidly changing COVID-19 crisis, says Trinamic.
The company’s previous experience with controlling CPAP device turbines showed that providing fast and dynamic control of low-induction BLDC motors can be exceptionally challenging. It requires careful consideration of the trade-off between high switching frequency and current ripples, and their consequent switching and stator losses, respectively.
Thanks to the embedded TMC4671 servo controller IC, the module generates a pulse-width modulation (PWM) frequency and a current controller clock of 100kHz independent of the MCU. This approach reduces the system’s current consumption by up to 15%, compared with a frequency of 25kHz, without compromising performance.
An add-on board for I2C and analog sensors supports pressure sensors offered by various manufacturers. The TMC4671+TMC6100-TOSV-REF plugs into a single-board computer to access the user interface and high-level control functions. Following the example of the TOSV project, the module uses Raspberry Pi with touchscreen display. The complete TMC4671+TMC6100-TOSV-REF reference board is available now through Trinamic’s distribution channels. All hardware, firmware, and software are open source under the MIT license.
VACCINE MONITORING KITS
Some (although not all) of the early COVID-19 vaccines must be contained in extreme low temperature containers during transport and distribution. That temperature must be monitored. Addressing that need, in November distributor CAS Datalogger announced four temperature monitoring kits designed specifically for new COVID-19 vaccine applications at -70°C. Utilizing proven TandD TR-7 series data loggers, they include everything needed for monitoring dry ice and ultra-cold transport and storage of the vaccine.
The Wi-Fi and Bluetooth Dry Ice or Ultra-Low Freezer Monitoring Kit includes a TR 75wb, a two-channel logger for thermocouple sensors with support for WiFi and Bluetooth communication (Figure 12). The Ethernet Dry Ice or Ultra-Low Freezer Monitoring Kit includes a TR-75nw, a two-channel logger for thermocouple sensors with support for LAN networked communication.
The Wi-Fi and Bluetooth Dry Ice or Ultra-Low Freezer Monitoring Kit includes a TR-75wb, a two-channel logger for thermocouple sensors with support for Wi-Fi and Bluetooth communication. They include everything needed for monitoring dry ice and ultra-cold transport and storage of COVID-19 vaccines.
Each kit includes a TR-75wb or TR-75nw, 1 or 2 temperature probes, 1 or 2 nylon thermal buffers, a mounting bracket, a power supply, a NIST traceable calibration certificate and AA batteries for primary or backup power. The free TandD WebStorage Service software, is a cloud-based website for the display and management of recorded data. It provides high/low alarming and quick access to settings.
Recorded data can be easily displayed on a dashboard or as a graph for fast and accurate reporting. The Wi-Fi and Bluetooth Dry Ice or Ultra-Low Freezer Monitoring Kit also includes access to the free TandD Thermo mobile app, available on Android and iOS devices, allowing users to view and manage their data anywhere in the world. 
RESOURCES
[1] https://sensiml.com/covid-19
[2] https://github.com/NVIDIA-AI-IOT/face-mask-detection
[3] https://developer.nvidia.com/blog/implementing-a-real-time-ai-based-face-mask-detector-application-for-covid-19
CAS Dataloggers | www.dataloggerinc.com
Infineon Technologies | www.infineon.com
Maxim Integrated | www.maximintegrated.com
Nordic Semiconductor | www.nordicsemi.com
Nvidia | www.nvidia.com
Renesas Electronics | www.renesas.com
SensiML | www.sensiml.com
Trinamic Motion Control | www.trinamic.com
U-blox | www.u-blox.com
Xilinx | www.xilinx.com
PUBLISHED IN CIRCUIT CELLAR MAGAZINE • JANUARY 2021 #366 – Get a PDF of the issue
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Jeff served as Editor-in-Chief for both LinuxGizmos.com and its sister publication, Circuit Cellar magazine 6/2017—3/2022. In nearly three decades of covering the embedded electronics and computing industry, Jeff has also held senior editorial positions at EE Times, Computer Design, Electronic Design, Embedded Systems Development, and COTS Journal. His knowledge spans a broad range of electronics and computing topics, including CPUs, MCUs, memory, storage, graphics, power supplies, software development, and real-time OSes.
