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Network processor from STMicroelectronics enhances security

Thu, Sep 24, 2020

Combining Bluetooth 5.0 performance with enhanced privacy and security features, the BlueNRG-2N network processor is convenient and scalable, says STMicroelectronics.

The Bluetooth 5.0-certified network coprocessor is pre-programmed and ready for connecting to a host controller to provide Bluetooth connectivity. It simplifies the manufacture of products, says ST, and also allows the developer to scale the performance, features and cost of the host system independently. It can be used in a range of products such as smart medical wearables, PC peripherals, remote controls, lighting, industrial and home automation devices, and allows designers to optimise their choice of microcontroller to meet specific model requirements.

The Bluetooth enhancements featured in BlueNRG-2N include support for data length extension, which accelerates over the air (OTA) firmware updates by as much as 2.5 times, claims ST, and raises data transfers to 700kbit/s at the application level. The BlueNRG-2N also has support for Bluetooth LE Privacy 1.2, which allows it to change address frequently without host-processor involvement to prevent unwanted tracking and with minimal impact on system power consumption.

BlueNRG-2N is programmed with a digitally signed Bluetooth LE stack, which saves manufacturing costs yet maintains the design flexibility to upgrade via OTA updates. For cyber security, built-in image authentication technology always checks the stack before starting to allow only signed firmware images to run.

Power consumption is lower compared with previous BlueNRG generations, with low transmit and receive current and drawing just 900nA in shutdown mode with the Bluetooth Low Energy stack running. At the same time, the device maintains robust radio performance, with +8dBm programmable RF output power and up to a 96dB link budget.

BlueNRG-2N is the latest member in ST’s BlueNRG family of Bluetooth Low Energy chips for wireless system design. It is the dedicated network co-processor within the BlueNRG family. It shares key features with the BlueNRG-2 Bluetooth 5.0-certified SoC, which also contains a programmable Arm Cortex-M0 microcontroller to host the main application and Bluetooth connectivity on the same device. ST Microelectronics also offers developers the STM32 online development environment (ODE), which includes the STM32CubeMX GUI plug in, designed for developers to kick-start projects.

BlueNRG-2N is in full production and is included in ST’s 10-year industrial product-longevity program. The devices are available in a 2.66 x 2.56mm WLCSP34 chip-scale packages (BlueNRG-234N) or 5.0 x 5.0mm QFN32 (BlueNRG-232N).

https://www.st.com

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Semtech provide low power connectivity to Amazon Sidewalk

Wed, Sep 23, 2020

Long range (LoRa) devices from Semtech provide long range, low bandwidth connectivity for Amazon Sidewalk, the crowd sourced wireless network that can simplify device set up, extend range and provide limited functionality even when devices fall offline. It is free of charge to customers.

LoRa devices are defined by the long range, low power operation which make it particularly suitable for low bandwidth outdoor and indoor smart home and smart community products.

Semtech’s LoRa platform in the Amazon Sidewalk network extends the range of a customer’s home network to connect both outdoor and indoor, low bandwidth smart home products, including smart lights, pet trackers, sensors for asset tracking, smart irrigation, and additional low cost devices needed for residential use. With extended range, mobility and low power consumption, the LoRa platform enables the rapid deployment of a vast range of secure, low touch consumer IoT applications, says Semtech.

“The collaboration with Amazon solidifies that LoRa is the de facto platform for IoT LPWAN applications and expands LoRa to new consumer applications,” said Mohan Maheswaran, Semtech’s president and CEO.

The global smart home market is expected to grow by a factor of five to reach more than $192 billion in 2023, a rapid increase from the $41 billion in revenues in 2018 (IHS Markit). Networks such as Bluetooth and Wi-Fi, are limited in connectivity and range whereas cellular (e.g. 5G) is needed for mission critical, long distance, guaranteed delivery of data, but can be too costly and power hungry for battery operated devices.

Semtech’s LoRa devices are widely adopted long range, low power solutions for IoT that gives telecomms companies, IoT application makers and system integrators the feature set necessary to deploy low cost, interoperable IoT networks, gateways, sensors, module products, and IoT services worldwide. IoT networks based on the LoRaWAN specification have been deployed in 100 countries and Semtech is a founding member of the LoRa Alliance, the fastest growing IoT Alliance for Low Power Wide Area Network applications.

http://www.semtech.com

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Maxim reduces cost and size for dynamic hand gesture sensing

Wed, Sep 23, 2020

Dynamic gesture sensing for automotive applications can be enabled at what is claimed to be the industry’s lowest cost and smallest size using the MAX25205 data acquisition system says Maxim Integrated Products.

The MAX25205 provides swipe and hand-rotation sensing at 10 times lower cost and up to 75 per cent smaller size than time of flight (ToF) camera-based systems, claims Maxim Integrated.

Featuring integrated optics and a 6 x 10 infrared (IR) sensor array, the MAX25205 detects swipe and hand rotation gestures without the complexity of ToF cameras to improve driver safety. Gesture and proximity sensing is expected to replace knobs and touchscreens for infotainment, phone, side mirror, climate, trunk, sunroof and reading lamp controls in vehicles.

The gesture and proximity sensing sensor is supplied in a 4.0 x 4.0mm chip, which is up to 75 per cent smaller than ToF camera-based solutions, claims Maxim. It is intended for use to complement voice commands, for use when voice command is not effective. It also means that automotive displays do not become smeared with fingerprints as drivers and passengers use the touchscreens.

According to Maxim, the MAX25205 allows developers to avoid complex software development and maintenance programs. The integration of a 60 photo diode array, LED driver and internal LDO result in a form factor that is “significantly smaller” than ToF camera solutions, says the company. It can be paired with a small microcontroller, rather than the larger microprocessors that more complex solutions require.

The versatile device delivers nine gestures, including swipe, rotations, air link and 3×2 proximity zones with low lag time in a single chip. Alternative solutions require three chips and a complicated microprocessor, says Maxim.

The MAX25205 is available at Maxim’s website and the company also offers an evaluation kit, the MAX25205EVKIT#.

Maxim Integrated develops analogue and mixed-signal products and technologies to make systems smaller and smarter, with enhanced security and increased energy efficiency. Its customers are used by customers in the automotive, industrial, healthcare, mobile consumer and cloud data centre markets.

http://www.maximintegrated.com

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SensiML automates dataset management via Google’s TensorFlow Lite

Tue, Sep 22, 2020

Users of TensorFlow Lite for Microcontrollers can leverage the SensiML Analytics Toolkit which seamlessly integrates with Google’s TensorFlow Lite for Microcontrollers to reduce errors and to build more efficient models even more quickly, says SensiML.

The company, a subsidiary of QuickLogic, develops AI tools for intelligent IoT endpoints. IoT developers working with Google’s TensorFlow Lite for Microcontrollers open source neural network inference engine can leverage SensiML’s powerful automated data labelling and pre-processing capabilities to reduce dataset errors, build more efficient edge models, and do so more quickly, says SensiML.

Following the standardised workflow within the model building pipeline, developers can collect and label data using the SensiML Data Capture Lab, and also create data pre-processing and feature engineering pipelines using the SensiML Analytics Studio. They can additionally perform classification using TensorFlow Lite for Microcontrollers to develop smart sensor algorithms capable of running on low power IoT endpoint devices.

TensorFlow Lite for Microcontrollers is a version of TensorFlow Lite from Google, which has been specifically designed to implement machine learning (ML) models on microcontrollers and other memory-limited devices. According to SensiML, the Analytics Toolkit delivers the easiest and most transparent set of developer tools for the creation and deployment of edge AI sensor algorithms for IoT devices. The tightly coupled integration of SensiML and Google’s TensorFlow enables developers to build intelligent sensor AI algorithms capable of running autonomously on IoT edge devices, adds SensiML.

“Without high-quality datasets, it is difficult to train models that can perform reliably in production. We talk a lot about how critical datasets are to building sensor-based edge AI applications, but currently don’t have a good solution for creating them,” said Pete Warden technical lead of Google’s TensorFlow mobile team. “SensiML’s Data Capture Lab fills this gap in the industry by providing tools to quickly collect and annotate high-quality sensor datasets,” he added.

Referring to the TensorFlow Lite for Microcontrollers framework, Chris Rogers, CEO of SensiML says it is intended for running neural network models on embedded devices. “SensiML’s end-to-end workflow automates the tedious and error-prone data cleansing and labeling process as well as pre-processing and feature extraction providing a powerful new capability for TensorFlow Lite for Microcontrollers users to improve performance and productivity for their edge AI projects,” he said.

The SensiML Analytics Toolkit with support for TensorFlow Lite for Microcontrollers is available now from SensiML.

https://sensiml.com/tensorflow-lite

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Network processor from STMicroelectronics enhances security

Semtech provide low power connectivity to Amazon Sidewalk

Maxim reduces cost and size for dynamic hand gesture sensing

SensiML automates dataset management via Google’s TensorFlow Lite

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