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Water-resistant MEMS pressure sensors meet consumer budgets

Wed, Mar 13, 2019

Water-resistant MEMS pressure sensors, the LPS33W series from STMicroelectronics, exhibit chemical compatibility, stability, and accuracy for use in applications as diverse as fitness trackers and other wearables, vacuum cleaners, and general-purpose industrial sensing.

The IPx8-rated LPS33W is protected by a viscous potting gel inside the cylindrical metal package to withstand salt water, chlorine, bromine, detergents (such as hand soap and shampoo, e-liquids) and light industrial chemicals such as n-pentane. The package lid provides high corrosion resistance, and the cylindrical form factor is easy to use with o-rings in applications that require a sealed enclosure, says ST.

The proprietary gel formula, together with the built-in signal-conditioning ASIC, ensure class-leading 0.008hPa RMS pressure noise thereby allowing outstanding measurement resolution, claims ST. Susceptibility to reflow-soldering stress during assembly is also extremely low, drifting less than ±2hPa and recovering normal accuracy in 72 hours. This is claimed to be more than twice the speed of other sensors. Temperature compensation keeps accuracy within ±3hPa over the operating range from 0 to +65 degrees C.

The LPS33W operates at just 15 microA in high-performance mode, with a 3.0 microA low-power mode and 1.0 microA power-down to help maximise runtime of battery-powered devices. A 128-bit FIFO stores up to 40 slots of 32-bit pressure and temperature data. This saves power by minimising intervention from the host microcontroller. A low-pass filter and I2C and SPI digital interfaces are also built-in.

The LPS33W is in mass production now, in a 3.3 x 2.9mm cylindrical metal case.

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Arrow Electronics has teamed up with Shiratech to create the Body Cam 1.0

Fri, Mar 08, 2019

Arrow Electronics has teamed up with Shiratech to create the Body Cam 1.0 and Icomox development platforms for image streaming and industrial condition monitoring, respectively. .

Body Cam 1.0 is a live-streaming open platform based on a quad-core Arm Cortex-A7 processor with built-in LTE modem based on a Quectel SC20 SoC.

It contains a fully integrated 13Mpixel camera module based on an ON Semiconductor sensor, wi-fi and Bluetooth 4.1 connectivity. GNSS satellite positioning is also included.

Context awareness comes from Analog Devices’ ADUX1020 multifunction photometric sensor with gesture and proximity detection and ADXL343BCCZ 10/13-bit accelerometer. External interfaces include a two-lane MIPI-CSI2 input for a rear-facing camera, micro USB port, analogue microphone input, and Micro SD and Micro SIM slots.

Body Cam 1.0 comes with a simple one-button camera control, plus on/off and audio-volume buttons, for rapid development of smart products for baby monitoring, security surveillance, emergency services, and independent living. It is housed in a plastic enclosure that attaches to clothing for field tests.

The Icomox (Intelligent Condition-Monitoring Box) low-power platform integrates vibration, magnetic-field, temperature and sound sensors for detecting faults in a variety of industrial equipment, assets, and structures.

The selection of integrated sensors includes Analog Devices’ ADXL356 low-power low-noise vibration sensor and ADT7410 16-bit -55 degrees C to +155 degrees C temperature sensor, as well as a magnetic-field sensor and Mems microphone.

Running on the Analog Devices ADuCM4050 application processor, and featuring Analog Devices’ LTC5800-IPM wireless transceiver, Icomox offers an open sensor-to-cloud platform that combines high-performance sensing, embedded software and analytics for early failure detection, and configurable warnings and event timestamping for each sensor.

The LTC5800-IPM is a complete 2.4 GHz, IEEE 802.15.4e SoC with embedded SmartMesh IP networking software for flexible connectivity and high network reliability in challenging RF conditions often encountered in tough industrial environments.

Icomox comes with on-board analytics software for early fault detection.

Optional Cloud applications allow access to other advanced analytics capabilities.

http://www.arrow.com

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Automatic direction-finding antenna pinpoints rogue transmissions

Tue, Mar 05, 2019

Now available from Link Microtek is an automatic direction-finding (DF) antenna that allows the source of unauthorised or interfering transmissions to be located rapidly and accurately, even in urban areas where signal reflections can make this a challenging task.

Recent drone activity at Gatwick and Heathrow airports has served to highlight the disruption that can be caused when illicit transmissions continue unchecked, and the new ADFA 1 antenna could be used as part of a solution to help security and communications professionals deal with such situations as quickly as possible.

The device is also suits telecommunication or defence applications.

Manufactured by Narda Safety Test Solutions, the ADFA 1 antenna covers the frequency range 200MHz to 2.7GHz and is designed for use with the company’s SignalShark portable real-time spectrum analyser.

There is no need for a laptop computer. By means of a strong magnetic mount, the antenna can be attached to the roof of any normal vehicle to enable a series of random bearings to be taken in the suspected area.

Each bearing cycle achieves a typical accuracy of 1 degree and takes 1.2 milliseconds, thereby ensuring reliable measurements even for pulsed signals or transmissions of very short duration.

The results can be displayed by SignalShark numerically or with live visualisation of the transmitter location in the form of a heat map. In addition, the ADFA 1 determines the elevation angle of the signal bearing, allowing the location of the source to be narrowed down to an individual floor level.

Users of the new antenna can also observe the broadband spectrum at the same time as determining the signal bearing, which enables them to continue tracking a source that suddenly changes channel.

At the heart of the ADFA 1 is an array of nine antenna elements around an omnidirectional reference element, optimally arranged to achieve reliable measurement results.

The antenna works on the principle of measuring the phase difference between the nine elements and the central reference.

As well as the vehicle mounting kit, there is an optional tripod with quick-release coupling and level indicators, which allows easy set-up of the antenna for DF measurements to trace interference from a semi-fixed location, as is commonly required in military applications.

Housed in a radome measuring 480 (D) x 219 (H) mm, the ADFA 1 antenna weighs 5.6kg and is sealed to IP55 standard to prevent the ingress of moisture and dust.

It has an operating temperature range of -40 to +65 degrees C and when mounted on a vehicle roof can withstand wind speeds of up to 130km/h.

http://www.linkmicrotek.com

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Image recognition SoC includes deep neural network accelerator

Fri, Mar 01, 2019

An image recognition SoC for automotive applications has been announced by Toshiba Electronics Europe. It implements a deep learning accelerator at 10 times the speed and four times the power efficiency of Toshiba’s earlier heterogeneous multi-core SoC for image recognition which was introduced at the 2015 IEEE International Solid-State Circuits Conference (ISSCC).

It is designed for advanced driver assistance systems (ADAS), such as autonomous emergency braking, which require increasingly advanced capabilities. Implementing them requires an image recognition SoC that can recognise road traffic signs and road situations at high speed with low power consumption.

Deep neural networks (DNN), algorithms modelled after the neural networks of the brain, perform recognition processing more accurately than conventional pattern recognition and machine learning, and is widely expected to be used in automotive applications. However, DNN-based image recognition with conventional processors takes time, as it relies on a huge number of multiply-accumulate (MAC) calculations. DNN with conventional high-speed processors also consumes too much power, adds Toshiba.

 To overcome this, it has developed a DNN accelerator that implements deep learning in hardware. It is defined by three features: parallel MAC units, reduced DRAM access and reduced SRAM access.

The ViscontiTM5 SoC has four processers, each with 256 MAC units to boost DNN processing speed. Conventional SoCs have no local memory to keep temporal data close to the DNN execution unit, they also consume a lot of power accessing local memory and when loading the weight data used for the MAC calculations. In Toshiba’s SoC, SRAM is implemented close to the DNN execution unit, and DNN processing is divided into sub-processing blocks to keep temporal data in the SRAM, reducing DRAM access. Additionally, Toshiba has added a decompression unit to the accelerator. Weight data, compressed and stored in DRAM in advance, are loaded through the decompression unit. This reduces the power consumption involved in loading weight data from DRAM, explains Toshiba.

Finally, conventional deep learning needs to access SRAM after processing each layer of DNN, which consumes too much power. The accelerator has a pipelined layer structure in the DNN execution unit of DNN, allowing a series of DNN calculations to be executed by one SRAM access.

The ViscontiTM5 SoC complies with ISO26262, the global standard for functional safety for automotive applications.

Sample shipments of Toshiba’s image-recognition processor will begin in September 2019.

 http://www.toshiba.semicon-storage.com  

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