The Future of Biomedical Signal Analysis Technology

Biomedical signals obtained from the human body can be beneficial in a variety of scenarios in a healthcare setting. For example, physicians can use the noninvasive sensing, recording, and processing of a heart’s electrical activity in the form of electrocardiograms (ECGs) to help make informed decisions about a patient’s cardiovascular health. A typical biomedical signal acquisition system will consist of sensors, preamplifiers, filters, analog-to-digital conversion, processing and analysis using computers, and the visual display of the outputs. Given the digital nature of these signals, intelligent methods and computer algorithms can be developed for analysis of the signals. Such processing and analysis of signals might involve the removal of instrumentation noise, power line interference, and any artifacts that act as interference to the signal of interest. The analysis can be further enhanced into a computer-aided decision-making tool by incorporating digital signal processing methods and algorithms for feature extraction and pattern analysis. In many cases, the pattern analysis module is developed to reveal hidden parameters of clinical interest, and thereby improve the diagnostic and monitoring of clinical events.Figure1

The methods used for biomedical signal processing can be categorized into five generations. In the first generation, the techniques developed in the 1970s and 1980s were based on time-domain approaches for event analysis (e.g., using time-domain correlation approaches to detect arrhythmic events from ECGs). In the second generation, with the implementation of the Fast Fourier Transform (FFT) technique, many spectral domain approaches were developed to get a better representation of the biomedical signals for analysis. For example, the coherence analysis of the spectra of brain waves also known as electroencephalogram (EEG) signals have provided an enhanced understanding of certain neurological disorders, such as epilepsy. During the 1980s and 1990s, the third generation of techniques was developed to handle the time-varying dynamical behavior of biomedical signals (e.g., the characteristics of polysomnographic (PSG) signals recorded during sleep possess time-varying properties reflecting the subject’s different sleep stages). In these cases, Fourier-based techniques cannot be optimally used because by definition Fourier provides only the spectral information and doesn’t provide a time-varying representation of signals. Therefore, the third-generation algorithms were developed to process the biomedical signals to provide a time-varying representation, and   clinical events can be temporally localized for many practical applications.

This essay appears in Circuit Cellar 315, October 2016. Subscribe to Circuit Cellar to read project articles, essays, interviews, and tutorials every month!

These algorithms were essentially developed for speech signals for telecommunications applications, and they were adapted and modified for biomedical applications. The nearby figure illustrates an example of knee vibration signal obtained from two different knee joints, their spectra, and joint time-frequency representations. With the advancement in computing technologies, for the past 15 years, many algorithms have been developed for machine learning and building intelligent systems. Therefore, the fourth generation of biomedical signal analysis involved the automatic quantification, classification, and recognition of time-varying biomedical signals by using advanced signal-processing concepts from time-frequency theory, neural networks, and nonlinear theory.

During the last five years, we’ve witnessed advancements in sensor technologies, wireless technologies, and material science. The development of wearable and ingestible electronic sensors mark the fifth generation of biomedical signal analysis. And as the Internet of Things (IoT) framework develops further, new opportunities will open up in the healthcare domain. For instance, the continuous and long-term monitoring of biomedical signals will soon become a reality. In addition, Internet-connected health applications will impact healthcare delivery in many positive ways. For example, it will become increasingly effective and advantageous to monitor elderly and chronically ill patients in their homes rather than hospitals.

These technological innovations will provide great opportunities for engineers to design devices from a systems perspective by taking into account patient safety, low power requirements, interoperability, and performance requirements. It will also provide computer and data scientists with a huge amount of data with variable characteristics.

The future of biomedical signal analysis looks very promising. We can expect  innovative healthcare solutions that will improve everyone’s quality of life.

Sridhar (Sri) Krishnan earned a BE degree in Electronics and Communication Engineering at Anna University in Madras, India. He earned MSc and PhD degrees in Electrical and Computer Engineering at the University of Calgary. Sri is a Professor of Electrical and Computer Engineering at Ryerson University in Toronto, Ontario, Canada, and he holds the Canada Research Chair position in Biomedical Signal Analysis. Since July 2011, Sri has been an Associate Dean (Research and Development) for the Faculty of Engineering and Architectural Science. He is also the Founding Co-Director of the Institute for Biomedical Engineering, Science and Technology (iBEST). He is an Affiliate Scientist at the Keenan Research Centre at St. Michael’s Hospital in Toronto.

The Hunt for Power Remote Sensing

With the advent of the Internet of Things (IoT), the need for ultra-low power passive remote sensing is on the rise for battery-powered technologies. Always-on motion-sensing technologies are a great option to turn to. Digital cameras have come light years from where they were a decade ago, but low power they are not. When low-power technologies need always-on remote sensing, infrared motion sensors are a great option to turn to.

Passive infrared (PIR) sensors and passive infrared detectors (PIDs) are electronic devices that detect infrared light emitted from objects within their field of view. These devices typically don’t measure light per se; rather, they measure the delta of a system’s latent energy. This change generates a very small potential across a crystalline material (gallium nitride, cesium nitrate, among others), which can be amplified to create a usable signal.

Infrared technology was built on a foundation of older motion-sensing technologies that came before. Motion sensing was first utilized in the early 1940s, primarily for military purposes nearing the end of World War II. Radar and ultrasonic detectors were the progenitors of motion-sensing technologies seen today, relying on reflecting sound waves to determine the location of objects in a detection environment. Though effective for its purpose, its use was limited to military applications and was not a reasonable option for commercial users.

This essay appears in Circuit Cellar 314 (September 2016).

The viability of motion detection tools began to change as infrared-sensing options entered development. The birth of modern PIR sensors began towards the end of the sixties, when companies began to seek alternatives to the already available motion technologies that were fast becoming outdated.

The modern versions of these infrared motion sensors have taken root in many industries due to the affordability and flexibility of their use. The future of motion sensors is PID, and it has several advantages over its counterparts:

  • Saving Energy—PIDs are energy efficient. The electricity required to operate PIDs is minimal, with most units actually reducing the user’s energy consumption when compared to other commercial motion-sensing devices.
  • Inexpensive—Cost isn’t a barrier to entry for those wanting to deploy IR motion sensing technology. This sensor technology makes each individual unit affordable, allowing users to deploy multiple sensors for maximum coverage without breaking the bank.
  • Durability—It’s hard to match the ruggedness of PIDs. Most units don’t employ delicate circuitry that is easily jarred or disrupted; PIDs are routinely used outdoors and in adverse environments that would potentially damage other styles of detectors.
  • Simple and Small—The small size of PIDs work to their advantage. Innocuous sensors are ideal for security solutions that aren’t obtrusive or easily noticeable. This simplicity makes PIDs desirable for commercial security, when businesses want to avoid installing obvious security infrastructure throughout their buildings.
  • Wide Lens Range—The wide field of vision that PIDs have allow for comprehensive coverage of each location in which they are placed. PIDs easily form a “grid” of infrared detection that is ideal for detecting people, animals, or any other type of disruption that falls within the lens range.
  • Easy to Interface With—PIDs are flexible. The compact and simple nature of PIDs lets the easily integrate with other technologies, including public motion detectors for businesses and appliances like remote controls.

With the wealth of advantages PIDs have over other forms of motion-sensing technology, it stands to reason that PIR sensors and PIDs will have a place in the future of motion sensor development. Though other options are available, PIDs operate with simplicity, energy-efficiency, and a level of durability that other technologies can’t match. Though there are some exciting new developments in the field of motion-sensing technology, including peripherals for virtual reality and 3-D motion control, the reliability of infrared motion technology will have a definite role in the evolution of motion sensing technology in the years to come.

As the Head Hardware Engineer at Cyndr (, Kyle Engstrom is the company’s lead electron wrangler and firmware designer. He specializes in analog electronics and power systems. Kyle has bachelor’s degrees in electrical engineering and geology. His life as a rock hound lasted all of six months before he found his true calling in engineering. Kyle has worked three years in the aerospace industry designing cutting-edge avionics.

New Highly integrated Hall sensors

Infineon Technologies recently introduced a new family of Hall sensors targeted at cost-effective, compact designs. Available as latch and switch-type devices, the sensors in the TLx496x family have precise switching points, stable operation, and a low power consumption.Infineon TLx496x

The TLx496x Hall sensors consume no more than 1.6 mA. In addition, they have an integrated Hall element, a voltage regulator (to power the Hall element and the active circuits), choppers (ensure that the temperature remains stable), an oscillator, and an output driver.


The TLE496x-xM series is well suited for automotive applications (e.g., power windows and sunroofs, trunk locks, and windshield wipers) with an operating voltage of 3 to 5.5 V. The TLI496x-xM series units function like the TLE496x-xM units, but it is specified for a temperature range of –40° to 125°C and is JESD47 qualified. The TLI496x-xM is used in BLDC motors in e-bikes and fans in PCs and in electric drives in building automation. The TLV496x-xTA/B series was specifically developed for the cost-effective, contactless positioning. Typical applications are BLDC motors in home appliances (e.g., dishwashers), compressors in air-conditioners, and more. Despite the pressure to cut costs, these applications need very precise Hall latches or Hall switches (unipolar/bipolar) for temperatures ranging from –40 °C to 125 °C. The TLV496x-xTA/B versions have a power consumption of 1.6 mA and an ESD protection up to 4 kVH Human Body Model (HBM). The output has overcurrent protection and automatically switches off at high temperatures.

The Hall sensors of all three series are available in high volume. Development support includes online simulation tools and application manuals.


Source: Infineon Technologies

Super-Compact, Nine-Axis Motion Sensor

Bosch Sensortec recently announced the launch of the compact BMX160 nine-axis motion sensor, which a great option for small, power-constrained applications ranging from “smart” wearables to virtual reality (VR) devices. Housed in a compact 2.5 × 3 × 0.95 mm3 package, the sensor combines advanced accelerometer, gyroscope, and geomagnetic sensor technologies.Bosch-BMX160

The BMX160’s features, specs, and benefits:

  • Compact size: 2.5 × 3 × 0.95 mm3
  • Reduces power consumption below 1.5 mA
  • Enables Android wearable applications that rely on sensor data
  • You can use the sensor with the Bosch Sensortec BSX sensor data fusion software library.
  • Pin- and register-compatibility with the six-axis BMI160 IMU s
  • Built-in power management unit

BMX160 samples are now available for development partners.

Source: Bosch Sensortec

The Future of Sensor Technology for the IoT

Sensors are at the heart of many of the most innovative and game-changing Internet of Things (IoT) applications. We asked five engineers to share their thoughts on the future of sensor technology.

ChrisCantrellCommunication will be the fastest growth area in sensor technology. A good wireless link allows sensors to be placed in remote or dynamic environments where physical cables are impractical. Home Internet of Things (IoT) sensors will continue to leverage home Wi-Fi networks, but outdoor and physically-remote sensors will evolve to use cell networks. Cell networks are not just for voice anymore. Just ask your children. Phones are for texting—not for talking. The new 5G mobile service that rolls out in 2017 is designed with the Internet of Things in mind. Picocells and Microcells will better organize our sensors into manageable domains. What is the best cellular data plan for your refrigerator and toaster? I can’t wait for the TV commercials. — Christopher Cantrell (Software Engineer, CGI Federal)

TylerSensors of the future will conglomerate into microprocessor controlled blocks that are accessed over a network. For instance, weather sensors will display temperature, barometric pressure, humidity, wind speed, and direction along with latitude, longitude, altitude, and time thrown in for good measure, and all of this will be available across a single I2C link. Wide area network sensor information will be available across the Internet using encrypted links. Configuration and calibration can be done using webpages and all documentation will be stored online on the sensors themselves. Months’ worth of history will be saved to MicroSD drives or something similar. These are all things that we can dream of and implement today. Tomorrow’s sensors will solve tomorrow’s problems and we can really only make out the barest of glimpses of what tomorrow will hold. It will be entertaining to watch the future unfold and see how much we missed. — David C. Tyler (Retired Computer Scientist)

Quo vadis electronics? During the past few decades, electrical engineering has gone through an unprecedented growth. As a result, we see electronics to control just about everything around us. To be sure, what we call electronics today is in fact a symbiosis of hardware and software. At one time every electrical engineer worth his salt had to be able to solder and to write a program. A competent software engineer today may not understand what makes the hardware tick, just as a hardware engineer may not understand software, because it’s often too much for one person to master. In most situations, however, hardware depends on software and vice versa. While current technology enables us to do things we could not even dream about just a few years ago, when it comes to controlling or monitoring physical quantities, we remain limited by what the data sensors can provide. To mimic human intellect and more, we need sensors to convert reality into electrical signal. For that research scientists in the fields of physics, chemistry, biology, mathematics, and so forth work hard to discover novel, advanced sensors. Once a new sensor principle has been found, hardware and software engineers will go to work to exploit its detection capabilities in practical use. In my mind, research into new sensors is presently the most important activity for sustaining progress in the field of electronic control. — George Novacek (Engineer, Columnist, Circuit Cellar)

GustafikIt’s hard to imagine the future of sensors going against the general trend of lower power, greater distribution, smaller physical size, and improvements in all of the relevant parameters. With the proliferation of small connected devices beyond industrial and specialized use into homes and to average users (IoT), great advances and price drops are to be expected. Tech similar to that, once reserved for top-end industrial sensor networks, will be readily available. As electrical engineers, we will just have to adjust as always. After years of trying to avoid the realm of RF magic, I now find myself reading up on the best way to integrate a 2.4-GHz antenna onto my PCB. Fortunately, there is an abundance of tools, application notes, and tutorials from both the manufacturers and the community to help us with this next step. And with the amazing advances in computational power, neural networks, and various other data processing, I am eager to see what kind of additional information and predictions we can squeeze out of all those measurements. All in all, I am looking forward to a better, more connected future. And, as always, it’s a great time to be an electrical engineer. — David Gustafik (Hardware Developer, MicroStep-MIS)

MittalMiniature IoT, sensor, and embedded technologies are the future. Today, IoT technology is a favorite focus among many electronics startups and even big corporations. In my opinion, sensor-based medical applications are going to be very important in our day-to-day lives in the not-so-distant future. BioMEMS sensors integrated on a chip have already made an impact in industry with devices like glucometers and alcohol detectors. These types of BioMEMS sensors, if integrated inside mobile phones for many medical applications, can address many human needs. Another interesting area is wireless charging. Imagine if you could charge all your devices wirelessly as soon as you walk into your home. Wouldn’t that be a great innovation that would make your life easier? So, technology has a very good future provided it can bring out solutions which can really solve human needs. — Nishant Mittal (Master’s Student, IIT Bombay, Mumbai)

Low-Power 12 DOF Bluetooth Smart Sensor Development Platform

Dialog Semiconductor now offers a small, low-power 12 Degrees-of-Freedom (DOF) wireless smart sensor development kit for Internet of Things (IoT) applications, such as wearables, virtual reality, 3-D indoor mapping, and navigation. The DA14583 SmartBond Bluetooth Smart SoC is combined with Bosch Sensortec’s gyroscope, accelerometer, magnetometer, and environmental sensors. A 16 mm × 15 mm PCB is supplied as a dongle in a plastic housing. Current consumption is only 1.3 mA (typical) when streaming sensor data; it’s less than 110 µA in advertising mode and under 11 µA in power-save mode.Dialog DS025

The complementary software development kit (SDK) includes Dialog’s SmartFusion smart sensor library for data acquisition, auto-calibration, and sensor data fusion. It runs on the DA14583’s embedded Cortex M0 processor. The DA14583 has an ARM Cortex-M0 baseband processor with an integrated ultra-low power Bluetooth Smart radio. The development kit includes the following Bosch sensors: a BMI160 six-axis inertial measurement unit, a BMM150 three-axis geomagnetic field sensor, and a BME280 integrated environmental unit, which measures pressure, temperature, and humidity.

Source: Dialog Semiconductor

Heart Rate Monitoring Sensor Solution

Silicon Labs recently announced an optical heart rate-sensing solution for wrist-based heart rate monitoring (HRM) applications. The new Si1144 HRM solution includes a low-power optical sensor module paired with an EFM32 Gecko microcontroller running Silicon Labs’s advanced HRM algorithm. The compact Si1144 sensor module integrates an optical sensor, green LED, an ADC, LED drivers, control logic, and an I2C digital interface, making it a good option for power-sensitive wearables.Silicon Labs Si1144



The Si1144-AAGX HRM module’s features:

  • Accurately senses weak blood flow signals on the wrist
  • Choice of two algorithms to support static HRM and optional dynamic motion-compensated HRM using data from an external accelerometer
  • HRM solution pairs optical module with a Pearl Gecko microcontroller
  • Fully integrated HRM IC with green LED lens, high-sensitivity photodiode, low-noise ADC, LED drivers, optical blocking, and host communications/interrupts
  • Two LED drivers
  • Ultra-low-power consumption for long battery life (less than 500 nA standby current with 1.71-to-3.6-V supply voltage)
  • I2C serial communications, up to 3.4-Mbps data rate
  • 10-lead 4.9 × 2.85 × 1.2 mm LGA module package

Samples and production quantities of the Si1144-AAGX HRM module currently available. The Si1144 module along with Silicon Labs’s HRM algorithm costs $2.82 in 10,000-unit quantities. The HRM44-GGG-PS development board costs $57.60.

Source: Silicon Labs

Robots with a Vision

Machine chine vision is a field of electrical engineering that’s changing how we interact with our environment, as well as the ways by which machines communicate with each other. Circuit Cellar has been publishing articles on the subject since the mid-1990s. The technology has come a long way since then. But it’s important (and exciting) to regularly review past projects to learn from the engineers who paved the way for today’s ground-breaking innovations.

In Circuit Cellar 92, a team of engineers (Bill Bailey, Jon Reese, Randy Sargent, Carl Witty, and Anne Wright) from Newton Labs, a pioneer in robot engineering, introduced readers to the M1 color-sensitive robot. The robot’s main functions were to locate and carry tennis balls. But as you can imagine, the underlying technology was also used to do much more.

The engineering team writes:

Machine vision has been a challenge for AI researchers for decades. Many tasks that are simple for humans can only be accomplished by computers in carefully controlled laboratory environments, if at all. Still, robotics is benefiting today from some simple vision strategies that are achievable with commercially available systems.

In this article, we fill you in on some of the technical details of the Cognachrome vision system and show its application to a challenging and exciting task—the 1996 International AAAI Mobile Robot Competition in Portland, Oregon… In 1996, the contest was for an autonomous robot to collect 10 tennis balls and 2 quickly and randomly moving, self-powered squiggle balls and deliver them to a holding pen within 15 min.

In M1’s IR sensor array, each LED is fired in turn and detected reflections are latched by the 74HC259 into an eight-bit byte.

In M1’s IR sensor array, each LED is fired in turn and detected reflections are latched by the 74HC259 into an eight-bit byte.

At the time of the conference, we had already been manufacturing the Cognachrome for a while and saw this contest as an excellent way to put our ideas (and our board) to the test. We outfitted a general-purpose robot called M1 with a Cognachrome and a gripper and wrote software for it to catch and carry tennis balls… M1 follows the wall using an infrared obstacle detector. The code drives two banks of four infrared LEDs one at a time, each modulated at 40 kHz.

The left half of M1’s infrared sensor array is composed of a Sharp GP1U52X infrared detector sandwiched between four infrared LEDs

The left half of M1’s infrared sensor array is composed of a Sharp GP1U52X infrared detector sandwiched between four infrared LEDs

Two standard Sharp GP1U52X infrared remote-control reception modules detect reflections. The 74HC163/74HC238 combination fires each LED in turn, and the ’HC259 latches detected reflections. This system provides reliable obstacle detection in the 8–12″ range.

The figure above shows the schematic. The photo shows the IR sensors.

The system provides only yes/no information about obstacles in the eight directions around the front half of the robot. However, M1 can crudely estimate distance to large obstacles (e.g., walls) via patterns in the reflections. The more adjacent directions with detected reflections, the closer the obstacle probably is.

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SimpleLink Simplifies IoT Prototyping

Texas Instruments recently introduced the next-generation SimpleLink SensorTag development kit, which enables the fast integration of sensor data with wireless cloud connectivity.TI Simplelink

Features of the new SensorTags include:

  • Flexible development with wireless connectivity options including Bluetooth low energy, 6LoWPAN and ZigBee based on the SimpleLink ultra-low power CC2650 wireless microcontroller
  • 10 integrated low-power sensors
  • New DevPack plug-in modules that extend the kits’ functionality and programmability
  • Out of the box capabilities with a free iOS or Android app
  • Connect to the cloud in minutes via TI’s IoT cloud ecosystem including IBM’s Bluemix IoT Foundation
  • Available TI Design reference designs, including 3-D print files of the SensorTag enclosures, that enable you to reuse the SensorTags for new designs

The SensorTag kits come with ready-to-use protocol stacks, a free Code Composer Studio IDE license, online training, and 24/7 online TI E2E community support. In addition, TI’s cloud-based software development tools provide instant access to examples, documentation, software and even an integrated development environment (IDE) all from the convenience of the web.

Expanding the standards supported by the SensorTag, there will be two different development kit versions:

  • The multi-standard SensorTag, based on the SimpleLink ultra-low power CC2650 wireless MCU, supports development for Bluetooth Smart, 6LoWPAN and ZigBee. This SensorTag has a unique feature that enables developers to change between different 2.4-GHz technologies by simply loading new software images directly from the SensorTag app over-the-air. When the SensorTag is used as a ZigBee and 6LoWPAN device, it connects to the cloud via a BeagleBone Black gateway. For Bluetooth Smart development, it connects via a smartphone.
  • The Wi-Fi SensorTag will allow users to demo the SimpleLink CC3200 wireless MCU. Further details and availability information will be coming soon. Start developing today with the CC3200 solution with these development tools.
  • Both SensorTags come with 10 integrated low-power sensors including the TI OPT3001 precision ambient light sensor, TI HDC1000 integrated humidity and temperature sensor and TI TMP007 contactless IR thermopile sensor. Additional sensors include a nine-axis motion sensor (gyroscope, compass and accelerometer), altimeter/ pressure sensor, digital microphone, and magnet sensor.

New to the next-generation CC2650 SensorTag is the ability for developers to customize their kit to fit their design with new DevPack plug-in modules. DevPacks available today include:

  • The $15 Debug DevPack is based on the TM4C1294 microcontroller (MCU) to add debug capabilities to the SensorTag. Plug it into the DevPack expansion header and debug the SensorTag with Code Composer Studio IDE, TI Cloud Tools, or IAR embedded workbench for ARM.
  • The Display (watch) DevPack adds a 1.35 inch ultra-low power graphical display to the SensorTag. The Watch DevPack is designed for development of smartwatches, refrigerator displays and any other application that has a need for a remote display.
  • The LED Audio DevPack consists of four high power multi-color LEDs and a 4W audio amplifier powered by a micro-USB for home automation and smart lighting applications.
  • Create your own! If developers cannot find a specific DevPack to fit their needs, they can create their own by downloading the Build Your Own DevPack guide.

The new SimpleLink multi-standard CC2650 SensorTag (CC2650STK) is available now for $29 in the TI Store and authorized distributors. Related software for each connectivity standard is also available:

  • Bluetooth Smart software
  • 6LoWPAN software
  • ZigBee software

The SimpleLink SensorTag DevPacks are also available on the TI Store and through TI authorized distributors. The Debug DevPack (CC-DEVPACK-DEBUG) costs $15. The Display DevPack (DEVPACK-WATCH) costs $19. The LED Audio DevPack (DEVPACK-LED-AUDIO) is $19. Pricing and availability for the SimpleLink Wi-Fi CC3200 SensorTag will be coming later in 2015.

New Motion Module for Easy Motion Monitoring

Microchip Technology announced at the Embedded World conference in Germany the MM7150 Motion Module, which combines Microchip’s SSC7150 motion co-processor combined with nine-axis sensors. Included in compact form factor are an accelerometer, magnetometer, and gyroscope.  With a simple I2C connection to most MCUs/MPUs, embedded applications and Internet of Things (IoT) systems can easily tap into the module’s advanced motion and position data.Microchip MM7150

The SSC7150 motion co-processor is preprogrammed with sensor fusion algorithms that intelligently filter, compensate, and combine the raw sensor data to provide highly accurate position and orientation information.  The small module self-calibrates during operation utilizing data from the prepopulated sensors—Bosch BMC150 (six-axis digital compass) and the BMG160 (three-axis gyroscope).

The single-sided MM7150 motion module is easily soldered down during the manufacturing process. You can develop motion applications for a variety of products with Microchip’s MM7150 PICtail Plus Daughter Board.  The MM7150 Motion Module is well suited for a wide range of applications: embedded (e.g., portable devices and robotics), industrial (e.g., commercial trucks, industrial automation, and patient tracking), and consumer electronics (e.g., IoT, remote controls, and wearable devices).

The MM7150 is supported by the MM7150 PICtail Plus Daughter Board (AC243007, $50) that plugs directly  into Microchip’s Explorer 16 Development Board (DM240001, $129) to enable quick and easy prototyping utilizing Microchip’s extensive installed base of PIC microcontrollers.

The 17 mm × 17 mm MM7150 is priced at $26.76 each in 1,000-unit quantities.

Source: Microchip Technology





Flexible I/O Expansion for Rugged Applications

WynSystemsThe SBC35-CC405 series of multi-core embedded PCs includes on-board USB, gigabit Ethernet, and serial ports. These industrial computers are designed for rugged embedded applications requiring extended temperature operation and long-term availability.

The SBC35-CC405 series features the latest generation Intel Atom E3800 family of processors in an industry-standard 3.5” single-board computer (SBC) format COM Express carrier. A Type 6 COM Express module supporting a quad-, dual-, or single-core processor is used to integrate the computer. For networking and communications, the SBC35-CC405 includes two Intel I210 gigabit Ethernet controllers with IEEE 1588 timestamping and 10-/100-/1,000-Mbps multispeed operation. Four Type-A connectors support three USB 2.0 channels and one high-speed USB 3.0 channel. Two serial ports support RS-232/-422/-485 interface levels with clock options up to 20 Mbps in the RS-422/-485 mode and up to 1 Mbps in the RS-232 mode.

The SBC35-CC405 series also includes two MiniPCIe connectors and one IO60 connector to enable additional I/O expansion. Both MiniPCIe connectors support half-length and full-length cards with screw-down mounting for improved shock and vibration durability. One MiniPCIe connector also supports bootable mSATA solid-state disks while the other connector includes USB. The IO60 connector provides access to the I2C, SPI, PWM, and UART signals enabling a simple interface to sensors, data acquisition, and other low-speed I/O devices.

The SBC35-CC405 runs over a 10-to-50-VDC input power range and operates at temperatures from –40°C to 85°C. Enclosures, power supplies, and configuration services are also available.

Linux, Windows, and other x86 OSes can be booted from the CFast, mSATA, SATA, or USB interfaces, providing flexible data storage options. WinSystems provides drivers for Linux and Windows 7/8 as well as preconfigured embedded OSes.
The single-core SBC35-CC405 costs $499.

Winsystems, Inc.

Bluetooth Low Energy Changes the “Wireless Landscape”

In 2010, the Bluetooth Special Interest Group (SIG) took Nokia’s existing Wibree standard and renamed it Bluetooth Low Energy (BLE). In doing so, it combined the latest in a series of evolutionary engineering improvements with brute-force market pressure to change the wireless landscape.

Adding BLE to the Bluetooth 4.0 specification has spurred rapid adoption. In fact, the SIG predicts that 90% of Bluetooth-enabled smartphones will support BLE by 2018. Before this wide adoption, a Wibree-based product had to include both sides of the radio link. Now a BLE-based device can ship with the assumption that the customer already owns the receiving half. This enables system architects to consider the user interface (UI) to be a software problem, not a hardware one. Hardware UIs are expensive and their power requirements are many orders of magnitude higher. BLE-based design can cut total product costs by more than half and increase usability by leveraging the customer’s smartphone. This provides a high-resolution screen, an already familiar user experience, and an Internet connection essentially for free.

The Mooshimeter displays a car startup transient.

The Mooshimeter displays a car startup transient. (Photo courtesy of Mooshim Engineering)

Wibree’s main technical value proposition is its extremely small power draw. Our company, Mooshim Engineering, offers the Mooshimeter, a wireless multimeter and data logger that uses your smartphone as a display. The transceiver we use for the Mooshimeter consumes a little less than 100 µW average draw to both send broadcast announcements every few seconds and listen for wake-up requests. This is roughly 10 to 100 times more power than a quartz watch, but 10 to 100 times less power than the watch’s backlight. Like the wristwatch, this draw is extremely peaky and depends heavily on usage. Products that only need to transmit can pull as little as 155 µJ per announcement. This provides more than a year of standby time.

Using 100-μW average draw as a starting point and assuming perfect power conversion, power could be provided by 2 to 4 mg per day of storage with a rechargeable lithium-ion battery; 1 to 3 g per day of storage with a supercapacitor; 10 mm2 of solar cells placed in a good spot outdoors; 5 cm2 of skin contact using thermal harvesters (e.g., a narrow but secure wristband); vibration harvesters, either on our limbs or in heavy industrial settings; or –10 dBm of wireless power transfer. An alkaline AA battery could ideally provide four years of service, although its self-discharge is more than 10% of the energy budget.

These power levels enable devices to have a high level of energy independence and become truly wireless—no data wire and no power wire. Thus architects can explore new relationships among devices, their environments, and their users. Connectors don’t compromise environmental seals, and frequent recharging doesn’t compromise the user’s experience.

The 100-μW  budget assumes the device just periodically announces its existence (as with wireless tethers and remote wake-up). But in uses that require more interesting payloads, the value proposition may be that the wireless link can fade into the noise of the energy budget. Remoting the user interface can also save energy, as even a dim indicator LED draws a milliwatt.

BLE is gaining its heaviest traction in electronic wearables, where users are likely to have BLE-enabled smartphones and a willingness to try new technologies. Fitness aids are enjoying early success because their sensor payloads are relatively low power and they address a large user base.

Medical wearables will take longer because of regulatory concerns and the user base. Diabetics may carry several screens with them, and often these devices will use proprietary radio protocols. Moving to a standard protocol could reduce the carry burden and provide a more secure data link. Standardization improves security through the principle of “given enough eyeballs, all bugs are shallow.”

Home appliances may be third-wave BLE adopters. The power draw is irrelevant here. A high-efficiency transformer wastes 10,000 times more power than the radio uses. It likely won’t eliminate the need for a hardware user interface either. Who wants to load an app to microwave their dinner? The convincing use case is to provide powerful diagnostic and monitoring capabilities. A refrigerator can tell a user’s phone when it needs a new filter. Washing machines can push notifications. Smoke detectors will proactively demand replacement batteries.

Until 2010, Wibree and its competitors offered incrementally improved energy independence. But BLE’s rapid market growth offers an inexpensive and unobtrusive way for system architects to provide new and compelling user experiences.


Eric VanWyk

Eric VanWyk

Eric VanWyk, who wrote this essay for Circuit Cellar, is co-founder of Mooshim Engineering and an adjunct instructor at Franklin W. Olin College of Engineering in Needham, MA, where he earned his BSc in Electrical and Computer Engineering in 2007. His background is in educational robotics, short-range wireless, and medical device development. Eric and his business partner, James Whong, have joined the rapidly growing number of innovators developing hardware and sensor add-ons that take advantage of Bluetooth Low Energy (BLE) 4.0 in today’s mobile devices. Their crowdfunded Mooshimeter is a multichannel circuit testing meter that uses a smartphone or tablet, via BLE, as a wireless, high-resolution graphical display.

A Coding Interface for an Evaluation Tool

John Peck, a test engineer at Knowles Electronics in Itasca, IL, has used ASCII interfaces to test equipment since he was a graduate student.

“I love test equipment with open, well-documented, ASCII command sets,” he says. “The plain text commands give a complicated instrument a familiar interface and an easy way to automate measurements.”

So when Peck needed to automate the process of reading his ultrasonic range finder’s voltage output, he wanted an ASCII interface to a voltmeter. He also wanted the meter to convert volts into distance, so he added an Atmel AVR Butterfly microcontroller into the mix (see Photo 1). “I thought it would be easy to give it a plain text interface to a PC,” he says.

Atmel AVR Butterfly

Atmel AVR Butterfly

The project became a bit more complex than he expected. But ultimately, Peck says, he came up came up with “a simple command interface that’s easy to customize and extend. It’s not at the level of a commercial instrument, but it works well for sending a few commands and getting some data back.”

If you would like to learn more about how to send commands from a PC to the AVR Butterfly and the basics of using the credit card-sized, single-board microcontroller to recognize, parse, and execute remote commands, read Peck’s article about his project in Circuit Cellar’s May issue.

In the italicized excerpts below, he describes his hardware connections to the board and the process of receiving remote characters (the first step in receiving remote commands). Other topics you’ll find in the full article include setting up a logging system to understand how commands are being processed, configuring the logger (which is the gatekeeper for messages from other subsystems), recognizing and adding commands to extend the system, and sending calibration values.

Peck programmed his system so that it has room to grow and can accommodate his future plans:

“I built the code with AVR-GCC, using the -Os optimization level. The output of avr-gcc –version is avr-gcc (Gentoo 4.6.3 p1.3, pie-0.5.1) 4.6.3.

“The resulting memory map file shows a 306-byte .data size, a 49-byte .bss size, and a 7.8-KB .text size. I used roughly half of the AVR Butterfly’s flash memory and about a third of its RAM. So there’s at least some space left to do more than just recognizing commands and calibrating voltages.”

“I’d like to work on extending the system to handle more types of arguments (e.g., signed integers and floats). And I’d like to port the system to a different part, one with more than one USART. Then I could have a dedicated logging port and log messages wouldn’t get in the way of other communication. Making well-documented interfaces to my designs would help me with my long-term goal of making them more modular.”

These are the connections needed for Atmel’s AVR Butterfly. Atmel’s AVRISP mkII user’s guide stresses that the programmer must be connected to the PC before the target (AVR Butterfly board).

Figure 1: These are the connections needed for Atmel’s AVR Butterfly. Atmel’s AVRISP mkII user’s guide stresses that the programmer must be connected to the PC before the target (AVR Butterfly board).

The AVR Butterfly board includes an Atmel ATmega169 microcontroller and some peripherals. Figure 1 shows the connections I made to it. I only used three wires from the DB9 connector for serial communication with the PC. There isn’t any hardware handshaking. While I could also use this serial channel for programming, I find that using a dedicated programmer makes iterating my code much faster.

A six-pin header soldered to the J403 position enabled me to use Atmel’s AVRISP mkII programmer. Finally, powering the board with an external supply at J401 meant I wouldn’t have to think about the AVR Butterfly’s button cell battery. However, I did need to worry about the minimum power-on reset slope rate. The microcontroller won’t reset at power-on unless the power supply can ramp from about 1 to 3 V at more than 0.1 V/ms. I had to reduce a filter capacitor in my power supply circuit to increase its power-on ramp rate. With that settled, the microcontroller started executing code when I turned on the power supply.

After the hardware was connected, I used the AVR downloader uploader (AVRDUDE) and GNU Make to automate building the code and programming the AVR Butterfly’s flash memory. I modified a makefile template from the WinAVR project to specify my part, programmer, and source files. The template file’s comments helped me understand how to customize the template and comprehend the general build process. Finally, I used Gentoo, Linux’s cross-development package, to install the AVR GNU Compiler Collection (AVR-GCC) and other cross-compilation tools. I could have added these last pieces “by hand,” but Gentoo conveniently updates the toolchain as new versions are released.


Figure 2: This is the program flow for processing characters received over the Atmel AVR Butterfly’s USART. Sending a command terminator (carriage return) will always result in an empty Receive buffer. This is a good way to ensure there’s no garbage in the buffer before writing to it.

To receive remote commands, you begin by receiving characters, which are sent to the AVR Butterfly via the USART connector shown in Figure 1. Reception of these characters triggers an interrupt service routine (ISR), which handles them according to the flow shown in Figure 2. The first step in this flow is loading the characters into the Receive buffer.

Figure 3: The received character buffer and pointers used to fill it are shown. There is no limit to the size of commands and their arguments, as long as the entire combined string and terminator fit inside the RECEIVE_BUFFER_SIZE.

Figure 3: The received character buffer and pointers used to fill it are shown. There is no limit to the size of commands and their arguments, as long as the entire combined string and terminator fit inside the RECEIVE_BUFFER_SIZE.

Figure 3 illustrates the Receive buffer loaded with a combined string. The buffer is accessed with a pointer to its beginning and another pointer to the next index to be written. These pointers are members of the recv_cmd_state_t-type variable recv_cmd_state.

This is just style. I like to try to organize a flow’s variables by making them members of their own structure. Naming conventions aside, it’s important to notice that no limitations are imposed on the command or argument size in this first step, provided the total character count stays below the RECEIVE_BUFFER_SIZE limit.

When a combined string in the Receive buffer is finished with a carriage return, the string is copied over to a second buffer. I call this the “Parse buffer,” since this is where the string will be searched for recognized commands and arguments. This buffer is locked until its contents can be processed to keep it from being overwhelmed by new combined strings.

Sending commands faster than they can be processed will generate an error and combined strings sent to a locked parse buffer will be dropped. The maximum command processing frequency will depend on the system clock and other system tasks. Not having larger parse or receive buffers is a limitation that places this project at the hobby level. Extending these buffers to hold more than just one command would make the system more robust.

Editor’s Note: If you are interested in other projects utilizing the AVR Butterfly, check out the Talk Zombie, which won “Distinctive Excellence” in the AVR Design Contest 2006 sponsored by ATMEL and administered by Circuit Cellar. The ATmega169-based multilingual talking device relates ambient temperature and current time in the form of speech (English, Dutch, or French). 

A Workspace for Microwave Imaging, Small Radar Systems, and More

Gregory L. Charvat stays very busy as an author, a visiting research scientist at the Massachusetts Institute of Technology (MIT) Media Lab, and the hardware team leader at the Butterfly Network, which brings together experts in computer science, physics, and electrical engineering to create new approaches to medical diagnostic imaging and treatment.

If that wasn’t enough, he also works as a start-up business consultant and pursues personal projects out of the basement-garage workspace of his Westbrook, CT, home (see Photo 1). Recently, he sent Circuit Cellar photos and a description of his lab layout and projects.

Photo 1

Photo 1: Charvat, seated at his workbench, keeps his equipment atop sturdy World War II-era surplus lab tables.

Charvat’s home setup not only provides his ideal working conditions, but also considers  frequent moves required by his work.

Key is lots of table space using WW II surplus lab tables (they built things better back then), lots of lighting, and good power distribution.

I’m involved in start-ups, so my wife and I move a lot. So, we rent houses. When renting, you cannot install the outlets and things needed for a lab like this. For this reason, I built my own line voltage distribution panel; it’s the big thing with red lights in the middle upper left of the photos of the lab space (see Photo 2).  It has 16 outlets, each with its own breaker, pilot lamp (not LED).  The entire thing has a volt and amp meter to monitor power consumption and all power is fed through a large EMI filter.

Photo 2: This is another view of the lab, where strong lighting and two oscilloscopes are the minimum requirements.

Photo 2: This is another view of the lab, where strong lighting and two oscilloscopes are the minimum requirements.

Projects in the basement-area workplace reflect Charvat’s passion for everything from microwave imaging systems and small radar sensor technology to working with vacuum tubes and restoring antique electronics.

My primary focus is the development of microwave imaging systems, including near-field phased array, quasi-optical, and synthetic-aperture radar (SAR). Additionally, I develop small radar sensors as part of these systems or in addition to. Furthermore, I build amateur radio transceivers from scratch. I developed the only all-tube home theater system (published in the May-June 2012 issues of audioXpress magazine) and like to restore antique radio gear, watches, and clocks.

Charvat says he finds efficient, albeit aging, gear for his “fully equipped microwave, analog, and digital lab—just two generations too late.”

We’re fortunate to have access to excellent test gear that is old. I procure all of this gear at ham fests, and maintain and repair it myself. I prefer analog oscilloscopes, analog everything. These instruments work extremely well in the modern era. The key is you have to think before you measure.

Adequate storage is also important in a lab housing many pieces for Charvat’s many interests.

I have over 700 small drawers full of new inventory.  All standard analog parts, transistors, resistors, capacitors of all types, logic, IF cans, various radio parts, RF power transistors, etc., etc.

And it is critical to keep an orderly workbench, so he can move quickly from one project to the next.

No, it cannot be a mess. It must be clean and organized. It can become a mess during a project, but between projects it must be cleaned up and reset. This is the way to go fast.  When you work full time and like to dabble in your “free time” you must have it together, you must be organized, efficient, and fast.

Photos 3–7 below show many of the radar and imaging systems Charvat says he is testing in his lab, including linear rail SAR imaging systems (X and X-band), a near-field S-band phased-array radar, a UWB impulse X-band imaging system, and his “quasi-optical imaging system (with the big parabolic dish).”

Photo 3: This shows impulse rail synthetic aperture radar (SAR) in action, one of many SAR imaging systems developed in Charvat’s basement-garage lab.

Photo 3: This photo shows the impulse rail synthetic aperture radar (SAR) in action, one of many SAR imaging systems developed in Charvat’s basement-garage lab.

Photo 4: Charvat built this S-band, range-gated frequency-modulated continuous-wave (FMCW) rail SAR imaging system

Photo 4: Charvat built this S-band, range-gated frequency-modulated continuous-wave (FMCW) rail SAR imaging system.

Photo 5: Charvat designed an S-band near-field phased-array imaging system that enables through-wall imaging.

Photo 5: Charvat designed an S-band near-field phased-array imaging system that enables through-wall imaging.

Photo 5: Charvat's X-band, range-gated UWB FMCW rail SAR system is shown imaging his bike.

Photo 6: Charvat’s X-band, range-gated UWB FMCW rail SAR system is shown imaging his bike.

Photo 7: Charvat’s quasi-optical imaging system includes a parabolic dish.

Photo 7: Charvat’s quasi-optical imaging system includes a parabolic dish.

To learn more about Charvat and his projects, read this interview published in audioXpress (October 2013). Also, Circuit Cellar recently featured Charvat’s essay examining the promising future of small radar technology. You can also visit Charvat’s project website or follow him on Twitter @MrVacuumTube.