If you’re planning to develop IoT applications for the EFM32 Giant Gecko or Pearl Gecko, you’re probably already thinking about using a real-time operating system.
It’s quite true that many embedded developers can get by with less sophisticated software based on a simple loop. But the latest EFM32 microcontrollers are packed with complex peripherals that require correspondingly complex application software. And designing IoT devices means dealing with both elevated user expectations and challenging design requirements. All this means that it’s become increasingly difficult for your projects to succeed without an operating system.
So how to get started? It can be daunting to make the sudden jump from bare-metal programming to kernel-based application development. So help you overcome that hurdle, we're producing a ten-episode video series to help smooth the way: Getting Started with Micrium OS.
The series is hosted by Matt Gordon, who has spent more than 10 years helping developers learn how to maximize the potential of the Micrium real-time operating system. He helped establish the Micrium training program, and is currently RTOS Product Manager at Silicon Labs.
The first episodes in the series starts with some basic information about what a kernel does and how kernel-based applications are structured. Matt covers initialization, how the kernel performs task scheduling, and how context switches pass control of the CPU from one task to another. Later in the series, Matt will discuss synchronization, resource protection, and inter-task communication. The series will leave you with a cohesive picture of real-time kernels and Micrium OS.
That’s not all: this series is supplemented with some of the best developer documentation ever produced for embedded systems programming. You can visit https://doc.micrium.com to learn much more about kernel-based application development and the networking and communication stacks that make up Micrium OS.
The Micrium OS kernel is available for free download through Simplicity Studio v4. To download and to find out more about Micrium OS, visit: https://www.silabs.com/support/getting-started/micrium-os
To find the series on YouTube, visit: https://goo.gl/JQ4UaV
And be sure to subscribe to the Silicon Labs YouTube channel to receive notifications of new episodes! https://www.youtube.com/user/ViralSilabs
Check out the first video in the series here:
Although not an entirely new concept, the smart meter market continues to be a major global growth market based on the device’s ability to greatly improve efficiencies for both utility companies and consumers. Markets and Markets estimates the smart meter market to be worth $12.79 billion (2017), and it is expected to grow at a CAGR rate of 9.34 percent from 2017-2022.
Interestingly, the first smart meter was developed pre-Internet, in the 1970s, and it wasn’t until the mid-nineties after the U.S. National Energy Policy Act, and similar utility deregulation efforts across the globe, that smart metering really took off. Widespread deregulation set-up a market-driven pricing environment for utility companies, creating an immediate demand for utility companies to understand the energy consumption rate of their customers in order to keep their costs down, hence a crucial need for smart meters was born.
Modern day smart meters record and report, via a communications network, the consumption of electricity, gas, water, or heating/cooling. By obtaining this level of consumption detail in real-time, utilities can simultaneously reduce costs while increasing customer satisfaction, making smart meter deployments a valuable investment for any type of utility company. Smart meters also play a key role in helping regions meet aggressive climate goals set-up by state and federal governments in many countries.
The benefits are obvious, but from a designer perspective, the types of metering technologies are vast and require detailed knowledge of the market.
The most common type of smart meters use one-way, transmit only communications and are called Automatic Meter Reading (AMC). These meters started out as walk-by or drive-by meters, but eventually have become fully automated with wireless capability, running on a Wide Area Network (WAN).
Advanced Metering Infrastructure (AMI) meters are two-way communications networks that not only produce a reading, but control the meter and equipment and allow the utility to connect or disconnect customers; monitor and anticipate usage changes, allowing for a smart grid operation; and enable software and security updates.
Traditional metrology equipment was used in the earliest smart meters, but today almost all new smart meter designs use electronic equipment, referred to in the industry as static meters.
Electricity meters are probably what most people think of when they hear the term smart meter, and there are two primary kinds of electricity meters. Current Transformers (CT) were the original meter, though now a wide range of MCU-based meters exist, which don’t have the problems associated with transformer-based meters, such as the tendency to get saturated with heavy currents and the susceptibility to tampering.
One of the more popular types of smart meters deployed extensively in Europe and urban areas are Heat Cost Allocator (HCA) devices. These meters are typically used in multi-tenant residential and commercial buildings, and enable a fair cost allocation of a shared heating system, giving tenants heating bills proportional to their usage of the heating system. This meter is hailed by energy conservationists, as it encourages users to reduce consumption, unlike a flat heating bill that doesn’t reward tenants for reduced energy consumption behavior.
In-Home Displays (IHD) is another desired piece of metering, and IHDs are common in homes part of the GB Smart Energy program in North America. These devices have direct wireless connections to the smart meters in the home, and typically use a Zigbee mesh network to display varying utility cumulative and real-time usage rates.
To no surprise to embedded designers, there are numerous communications technologies to choose from when designing a smart meter.
Typical installations use a sub-GHz Field Area Network (FAN) with a star or mesh topology, though another popular option is using equipment with WAN capabilities built directly into the meter with a M2M connection using 2G, 3G or 4G. The new NarrowBand IoT standard has improved the power and cost performance of this approach, creating numerous unlicensed band Low Power Wide Area Network (LPWAN) technology providers. Another major communications network is the Zigbee-based Home Area Network (HAN), which is already deployed in more than 23 million homes in the U.K. The HAN meters have a built-in Zigbee radio, and come with an IHD.
Though, Wi-Fi, Bluetooth and Z-Wave are nowhere to be found in smart meter deployments, due primarily to power constraints. But Bluetooth Low Energy is a viable option if based on a 2.4 GHz radio using a multi-protocol SoC, such as a Silicon Labs Mighty Gecko.
The Power Play
Power is not an issue for electricity meters since they have their own power supply, but power becomes a pivotal issue for heating, gas, and water meters. Specialized lithium batteries have been created for smart meters in recent years – lasting close to 20 years - but not all markets embrace these batteries. China is a good example, as it requires utility customers to replace their double AA batteries every 12-18 months.
Maximizing battery life is an important part of smart meter designs, making the underlying technology components critical to creating a high-performance smart meter unburdened by power restrictions.
Whatever smart meter electronic design pursued, smart meters will continue to prove their worth as a highly efficient way for utilities to compete and run more efficiently, consumers to save money, and societies at large to reduce their environmental footprint.
Morrie Altmejd, a senior staff engineer at Silicon Labs, wrote this article that recently appeared in Electronic Products Magazine.
Designing and implementing an optical heart rate monitoring (HRM) system, also known as photoplethysmography (PPG), is a complex, multidisciplinary project. Design factors include human ergonomics, signal processing and filtering, optical and mechanical design, low-noise signal receiving circuits and low-noise current pulse creation.
Wearable manufacturers are increasingly adding HRM capabilities to their health and fitness products. Integration is helping to drive down the cost of sensors used in HRM applications. Many HRM sensors now combine discrete components such as analog front ends (AFE), photodetectors and light-emitting diodes (LEDs) into highly integrated modules. These modules enable a simpler implementation that reduces the cost and complexity of adding HRM to wearable products.
Wearable form factors are steadily changing too. While chest straps have effectively served the health and fitness market for years, HRM is now migrating to wrist-based wearables. Advances in optical sensing technology and high-performance, low-power processors have enabled the wrist-based form factor to be viable for many designs. HRM algorithms also have reached a level of sophistication to be acceptable in wrist form factors. Other new wearable sensing form factors and locations are emerging, such as headbands, sport and fitness clothing, and earbuds. However, the majority of wearable biometric sensing will be done on the wrist.
No two HRM applications are alike. System developers must consider many design tradeoffs: end-user comfort, sensing accuracy, system cost, power consumption, sunlight rejection, how to deal with many skin types, motion rejection, development time and physical size. All of these design considerations impact system integration choices, whether to use highly integrated module-based solutions or architectures incorporating more discrete components.
Figure 1 shows the fundamentals of measuring heart rate signals, which depend on the heart rate pressure wave being optically extracted from tissue. Figure 1 shows the travel path of the light entering the skin. The expansion and contraction of the capillaries, caused by the heart rate pressure wave, modulates the light signal injected into the tissue by the green LEDs. The received signal is greatly attenuated by the travel through the skin and is picked up by a photodiode and sent to the electronic subsystem for processing. The amplitude modulation due to the pulse is detected (filtering out motion noise), analyzed and displayed
Figure 1. Principles of operation for optical heart rate monitoring.
A fundamental approach to HRM system design uses a custom-programmed, off-the-shelf microcontroller (MCU) that controls the pulsing of external LED drivers and simultaneously reads the current output of a discrete photodiode. Note that the current output of the photodiode must be converted to voltage to drive most analog-to-digital (A/D) blocks. The Figure 2 schematic shows the outline of such a system. Note that the I-to-V converter creates a voltage equal to VREF at 0 photodiode current, and the voltage decreases with increasing current.
The current pulses generally used in heart rate systems are between 2 mA and 300 mA depending on the color of the subject’s skin and the intensity of sunlight with which the desired signal needs to compete. The infrared (IR) radiation in sunlight passes through skin tissue with little attenuation, unlike the desired green LED light, and can swamp the desired signal unless the green light is very strong or unless an expensive IR blocking filter is added. Generally speaking, the intensity of the green LED light where it enters the skin is between 0.1x and 3x the intensity of sunlight. Due to heavy attenuation by the tissue, the signal that arrives at the photodiode is quite weak and generates just enough current to allow for a reasonable signal-to-noise ratio (SNR) (70 to 100 dB) due to shot noise even in the presence of perfect, noise-free op amps and A/D converters. The shot noise is due to the finite number of electrons received for every reading that occurs at 25 Hz. The photodiode sizes used in the design are between 0.1 mm2 and 7 mm2. However, above 1 mm there are diminishing returns due to the effect of sunlight.
Figure 2. The basic electronics required to capture optical heart rate.
The difficult and costly function blocks to implement in an optical heart rate system design, as shown in Figure 2, are the fast, high-current V-to-I converters that drive the LED, a current to voltage converter for the photodiode and a reliable algorithm in the MCU that sequences the pulses under host control. A low-noise (75 - 100 dB SNR) 300 mA LED driver that can be set to very low currents down to 2 mA while still creating very narrow light pulses down to 10 µs is an expensive block to achieve with discrete op amps.
The narrow pulses of light down to 10 µs shown in Figure 2 allow the system to tolerate motion and sunlight. Typically two fast light measurements are made for each 25 Hz sample. One measurement is taken with the LEDs turned off and one with the LEDs turned on. The calculated difference removes the effect of ambient light and gives the desired raw optical signal measurement that is, most importantly, insensitive to flickering background light.
The short duration of the optical pulses both allows and requires a relatively strong light pulse. It is essential to stay brighter than the sunlight signal, which may be present and not allow the PPG signal carrier to be dwarfed by the sunlight signal. If the sunlight signal is larger than the PPG carrier, then although it may be removed by subtraction, the signal can be so large that external modulation such as swinging an arm in and out of shadow can create difficult-to-remove artifacts. As a result, systems that use low-current LED drivers and large photodiodes to compensate suffer severely from motion artifacts in bright light situations
Much of the desired HRM sensing functionality is available pre-designed and integrated into a single device. Packing most of this functionality into one piece of silicon results in a relatively small 3 mm x 3 mm package that can even integrate the photodiode (PD) itself.
Figure 3 shows an example of a schematic with an Si118x optical sensor from Silicon Labs. This HRM design is relatively easy to implement. The engineer just needs to focus on the optical portion of the design, which includes optical blocking between the parts on the board and coupling the system to the skin.
Figure 3. An integrated heart rate sensor requiring only external LEDs.
While the approach shown in Figure 3 results in a high-performance HRM solution, it is not as small or power efficient as some designers would like. To achieve an even smaller solution, the LED die and the control silicon must be integrated into a single package that incorporates all essential functions including the optical blocking and the lenses that improve the LED output. Figure 4 illustrates this more integrated approach, based on a Silicon Labs Si117x optical sensor.
No external LEDs are required for this HRM design. The LEDs and photodiode are all internal to the module, which can be installed right below the optical ports at the back of a wearable product such as a smartwatch. This advantageous approach enables a shorter distance between the LEDs and the photodiode than is possible with a discrete design. The reduced distance allows operation at extremely low power due to lower optical losses traversing the skin.
Integrating the LEDs also addresses the issue of light leakage between the LEDs and the photodiode. As a result, the designer does not have to add optical blocking to the printed circuit board (PCB). The alternative to this approach is to handle the blocking with plastic or foam inserts and special copper layers on the PCB.
Figure 4. A highly integrated HRM sensor module incorporating all essential components.
There is one more part of an HRM design that the developer does not necessarily need to create: the HRM algorithm. This software block residing on the host processor is quite complex due to the signal corruption that occurs during exercise and motion in general. End-user motion often creates its own signal that spoofs the actual heart rate signal and is sometimes falsely recognized as the heart rate beat.
If a wearable developer or manufacturer does not have the resources to develop the algorithm, third-party vendors provide this software on a licensed basis. Silicon Labs also offers a heart rate algorithm for its Si117x/8x optical sensors that can be compiled to run on most host processors.
It is up to the designer to decide how much integration is right for the HRM application. The developer can simplify the design process and speed time to market by opting for a highly integrated module-based approach using a licensed algorithm. Developers with in-depth optical sensing expertise, time and resources may opt to use separate components (sensors, photodiodes, lenses, etc.) and do their own system integration, and even create their own HRM algorithm. Ultimately, when it comes to HRM system design, the developer has a choice of doing it all or purchasing it all.
This week we’re at APEC 2018 and we’ve just introduced two new PoE powered device families designed for best-in-class efficiency and integration for the IoT. Power-over-ethernet is ideally suited for application that require both power and data at a device connected to an Ethernet switch. A couple of the advantages include lower equipment costs and lower installation costs compared to separate data cables and power cables. It also makes use of the massive installed base of UTP cabling for wired Ethernet networks, and is part of IEEE’s 802.3at Ethernet standard, which specifies the technical requirements for the safe and reliable distribution of power over the same CAT-5 UTP cabling.
Our new Si3406x and Si3404 devices offer the highest level of integration available for high-voltage devices on a single power delivery chip and support IEEE 802.3at PoE+ power functionality, power conversion options with up to 90 percent efficiency, robust sleep/wake/LED support modes, and electromagnetic interference (EMI) performance. These features will help developers reduce system cost and help them get to market faster with high-power, high-efficiency PoE PD-powered applications.
Designers face a number of challenges in creating new devices, including low power conversion efficiency, electromagnetic interference problems, oversized PCBs with a lot of BOM, and running out of headroom on power. The Si3406x and Si3404 can help relieve all of these through high efficiency, proven EMI results with suppression and control techniques, superior BOM integration, and 30W power headroom.
IP cameras are a good use case because two cables are needed; one for power and one for data. With PoE, these two cables are combined into one. With a complete power supply built with Si3406x or Si3404 PD devices, designers can focus on their more value-added portions of an IP Camera design.
The growth of the IoT is raising demand for PoE+ connectivity across application areas, and the increasing popularity of the PoE+ standard, coupled with the requirement to support 30 W designs, these parts represent the next movement in PD interface solutions for homes, businesses, and industrial environments.
The Si3406x family integrates control and power management functions needed for a PoE+ PD applications, converting the high voltage supplied over a 10/100/1000BASE-T Ethernet connection to a regulated, low-voltage output supply. The highly integrated architecture minimizes printed circuit board (PCB) footprint and external BOM cost by enabling the use of economical external components while maintaining high performance.
Its high-power PoE+ capabilities also make it possible to develop advanced IoT products including IP cameras with pan/tilt/zoom and heater elements and newer protocol 802.11 wireless access points that demand much from power supplies. The Si3406x family’s on-chip current-mode-controlled switching regulator supports multiple isolated and non-isolated power supply topologies. This flexibility, along with Silicon Labs’ comprehensive PoE/PD reference designs, makes it easier and faster for developers to deploy critical power supply subsystems.
The S3406x and Si3404 Family bring a large number of additional benefits over our previous, single offering of Si3402.