Energy Micro has added a generic low
energy sensor interface to its EFM32 microcontroller product family. The
Lesense function block enables autonomous monitoring of up to 16 external
sensors in the microcontroller's sub-microamp Deep Sleep mode.
Able to run independently of the EFM32's ARM Cortex(TM)-M3 core, Lesense can be
used to create highly integrated, ultra low power, sensor solutions.
Particularly suitable for battery operated systems, the sensor interface is
designed to operate with virtually any type of analog sensor control scheme,
including capacitive, inductive and resistive types.
Among a variety of uses, Lesense can be configured to support autonomous
capacitive touch pad- and slider based products, and gas and water metering
products relying on inductive rotation sensors.
The Lesense function block will first be made available in Energy
Gecko microcontroller family, sampling and in volume during Q1'2011. Pin and
software compatible with the bigger Gecko microcontrollers, the Tiny Gecko
provides users with a wide range of low power peripheral function blocks,
including an 8-channel, 12-bit ADC using 350ÂµA at full resolution and
1Msamples/sec conversion rate, and a low energy UART consuming as little as
150nA, and a new 8x20 segment LCD controller.
In an age of globalization and rapid changes through scientific progress, two of our societies' (and economies') main concerns are to satisfy the needs and wishes of the individual and to save precious resources. Cloud computing caters to both of these.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.