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MEMS barometric pressure sensors are slim and compact

Tue, Sep 28, 2021

Able to measure a height difference as small as 50mm, the ICP-20100 low noise MEMS barometric pressure sensors have a compact footprint for use in smartphones, tablets, drones, augmented reality / virtual reality (AR / VR) equipment and smart home appliances.

The InvenSense ICP-20100 sensors combine a barometric pressure and a temperature sensor in a 2.0 x 2.0 x 0.8mm package. The low noise pressure sensing capabilities, with a range of 30 to 110 kPa, allows the device to detect altitude changes of less than 50mm.

The performance extends InvenSense’s SmartPressure family and offers multiple input voltage levels of 1.2V, 1.8V and 3.3V. There is also a choice of interfaces, namely I²C, I3CSM and SPI. The ICP-20100 can be configured to achieve low noise or low power performance.

The SmartPressure family uses a capacitive MEMS architecture to deliver lower power consumption and lower noise than competing pressure sensors technologies, explains InvenSense. In addition to operation over a wide temperature range, the ICP-20100 pressure sensor can deliver the measurement accuracies required by applications such as 3D geolocation and emergency location service (E911), mobile indoor / outdoor navigation, sport and fitness activity tracking and altitude-hold in drones. The sensor’s low power consumption assists in extending battery life for always-on applications, for example in the IoT. The pressure sensor temperature co-efficient offset is ±0.5 Pa/ degrees C.

A development kit, the DK-20100, and evaluation platform with software are also available.

The ICP-20100 joins the existing ICP-10125, ICP-10101, and ICP-10111 pressure sensors in the SmartPressure family.

Target applications in addition to smartphones and tablets are sports, and fitness activity monitoring devices, altitude control for drones and aerial toys, gaming equipment, including virtual reality. The ICP-20100 can also be used for indoor / outdoor navigation, for example in detecting, floors in a lift or steps. In smart home appliances, they can be used in robotic vacuum cleaners and other equipment which needs sensing capabilities.

TDK was established in 1935 to commercialise ferrite, a key material in electronic and magnetic products. The company’s portfolio features passive components such as ceramic, aluminium electrolytic and film capacitors, as well as magnetics, high-frequency and piezo and protection devices. There are also sensors and sensor systems such as temperature and pressure, magnetic, and MEMS sensors, power supplies and energy devices.

Products are marketed under the product brands TDK, Epcos, InvenSense, Micronas, Tronics and TDK-Lambda. TDK focuses on demanding markets in automotive, industrial and consumer electronics, and information and communication technology. The company has a network of design and manufacturing locations and sales offices in Asia, Europe, and in North and South America.

https://invensense.tdk.com

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MicroAI teams up with Renesas to bring ML / AI to RA microcontrollers

Mon, Sep 27, 2021

Training machine learning (ML) models directly in the embedded environment is now possible following MicroAI’s collaboration with Renesas to integrate the former’s MicroAI AtomML technology in the latter’s RA microcontroller range.

It is claimed to result in the first time microcontrollers can train ML models directly in the embedded environments.

The MicroAI-powered microcontrollers can be used in industrial, commercial and consumer systems and devices to adopt edge AI (artificial intelligence). Embedding intelligence at the source of the data, lowers operational costs as well as the expenses around connectivity in ‘smart’ machines and IoT devices, says MicroAI.

Mohammed Dogar, senior director of global business development, Renesas, said: “The industry has been asking to bring more insight and intelligence into the performance of their assets closer to the source of the data, and, working with MicroAI, we have a solution.”

MicroAI is a patented ML algorithm that lives directly on a machine or IoT device, providing deep insight into the behaviour, health and performance of the equipment or devices. Typical examples are robotic welding arms used in automotive assembly lines or for greenhouse gas efficiency in agriculture. These can often face unexpected downtime and static maintenance schedules, resulting in unnecessary costs in terms of lost production and service charges because operators can only when a problem occurs. Creating more visibility into the operation of manufacturing lines, allows asset owners and manufacturers can make adjustments to reduce those events to keep equipment and operations running smoothly.

The Renesas RA (Renesas Advanced) microcontrollers are 32-bit devices with Arm Cortex-M33, -M23 and -M4 processor cores. They are PSA-certified for IoT hardware, software and devices.

In response to calls for predictive insights into machinery, equipment and production systems, MicroAI’s chief executive officer, Yasser Khan, says: “Working with Renesas, MicroAI is delivering that capability by utilising our technology to bring machine learning to microcontrollers, providing the ability to train machine learning models directly in the embedded environment.”

http://www.micro.ai

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Lattice updates mVision stack for 4K video processing

Mon, Sep 27, 2021

Higher image signal processing (ISP) is delivered via the updated mVision software stack, released by Lattice Semiconductor.

The mVision stack supports 4K video data processing and LPDDR4 memory for popular vision and high speed data interfaces. Developers can implement popular high speed communication and display interfaces to accelerate embedded vision performance with low power consumption for edge applications including machine vision, robotics, advanced driver assistance systems (ADAS), video surveillance and

The mVision v2.1 has support for the LPDDR4 DRAM memory standard. With up to eight programmable SerDes lanes capable of speeds up to 10.3 Gbits per second, the Nexus FPGAs are claimed to deliver the highest system bandwidth in their class, enabling popular communication and display interfaces such as 10 Gigabit Ethernet, PCI Express, SLVS-EC, CoaXPress, and HBR3 DisplayPort (at up to 8.1 Gbits per second per lane).

Signal bridging and duplication reference designs have also been added. The latest reference designs help developers convert legacy video standards commonly used in industrial applications to the more widely-used MIPI standard. The reference designs include MIPI to parallel conversion, parallel to MIPI conversion, MIPI CSI-2 to LVDS conversion, one-to-N MIPI duplicator and support for the Nexus FPGAs. The ISP support from partner Helion enables high resolution, high frame rate UHD cameras and drones, adds Lattice.

Industry analyst Bob O’Donnell with TECHnalysis Research explains: “LPDDR4 memory provides device designers with a great range of different capacities / densities, speeds, and power requirements that can be matched to specific applications. Because of its low power nature, LPDDR4 memory is particularly well-suited for embedded and machine vision in battery-powered devices or other applications where thermal management is a challenge.”

While embedded and machine vision applications can realise new user experiences in industrial, automotive and consumer markets, supporting them at the edge requires a balance of processing performance and the design’s power consumption and physical constraints, adds Mark Hoopes, director of industrial and automotive segment marketing at Lattice Semiconductor. The mVision stack simplifies and accelerates embedded vision solution development and this version 2.1 leverages the company’s Nexus FPGAs to support high performance interfaces and faster processing at low power, he says.

http://www.latticesemi.com

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Smallest Arduino Pro board brings sensing to the edge

Mon, Sep 27, 2021

Claimed to be the smallest Arduino board to date, Nicla Sense ME has been created by Arduino Pro and Bosch Sensortec. It makes sensing and intelligence at the edge accessible to all, says Bosch Sensortec.

The Sense ME is the first product in the Nicla family. It combines low-power sensor nodes with the ability to handle AI and machine learning (ML) on the edge. Dimensions are just 22.86 x 22.86mm.

The Nicla Sense ME has a nine degrees of freedom (DoF) smart motion sensor and a 4DoF environmental sensor with AI capabilities. It uses Bosch Sensortec’s BHI260AP artificial intelligence (AI) sensor system with integrated motion sensor, BMM150 magnetometer, BMP390 pressure sensor, and the BME688 four-in-one gas sensor with AI and integrated high linearity and high accuracy pressure, humidity and temperature sensors.

The board can sense and process different types of data on the edge and reduces latency and power consumption for more privacy without minimal bandwidth, says Bosch. Arduino Pro’s Adriano Chinello describes it as “a tiny board with a really great mix of sensors combined with high computational power, opening up a whole new range of applications leveraging on sensor fusion. Smart building automation, mobile and wearable devices, industrial and professional equipment are key targets”.

Power-saving operation and clear programming structure make the small board suitable for research projects, rapid prototyping and development. It is included in the starter kit provided to all teams participating in the Bosch Sensortec’s 2021 IoT Innovation Challenge, an online competition for students.

“Bosch Sensortec’s self-learning AI smart motion sensor, the environmental sensor with AI capabilities and all the other sensors allow a broad range of applications to address the different segments of the IoT market. This way developing intelligent, low-power and scalable edge sensing applications is easier than ever before” says Dr. Stefan Finkbeiner, CEO of Bosch Sensortec.

The Nicla Sense ME can be powered by a battery and used as a complete standalone board, or attached to an Arduino board to expand its capabilities. It is equipped with Arduino’s fast deployment and easy configuration and is also future-proof, says Bosch, as it allows for additional sensors. It is compatible with upcoming Nicla products, as well as the Arduino Pro MKR and Portenta families.

https://www.bosch-sensortec.com

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MEMS barometric pressure sensors are slim and compact

MicroAI teams up with Renesas to bring ML / AI to RA microcontrollers

Lattice updates mVision stack for 4K video processing

Smallest Arduino Pro board brings sensing to the edge

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