Tag Archives: sensor

Sensitive Magnetic-Field Sensor Has Low Noise

Although applications for magnetic sensors cover a vast field, ranging from the gigantic magnetic resonance imaging or MRI systems to sensing tiny gear-teeth, they are one of the most overlooked or misunderstood among the modern sensors in use. Researchers are constantly on the lookout for increasingly small but more sensitive magnetic-field sensors. However, sensitivity alone is not the only qualifying parameter for such sensors—low-level transducers require to be low-noise as well.

That is exactly what researchers at Brown University have developed. Their magnetic sensor is not only sensitive, it exhibits a very low noise level. With support from the National Science Foundation, the researchers have developed a device that, as a part of an arrangement of a magnetic immunoassay, looks for pathogens in fluid systems using magnetism. They claim that as the device is extremely small, millions of such sensors can fit on a single chip.

The basic principle behind the sensor is the Hall effect. In a Hall effect sensor, passing a direct current through it when the sensor is perpendicular to a magnetic field, causes the development of a voltage at right angles to the current path. The presence and magnitude of the magnetic field directly influence the presence and magnitude of the voltage.

The researchers at Brown University have developed a variation of the Hall effect sensor and have named it the Anomalous Hall Effect or AHE, and this occurs in ferromagnetic materials only. The difference between the two effects is that while the conventional Hall effect is the result of charge on electrons, the anomalous Hall effect is due to electron spin.

As electrons with various spins orient themselves in different directions, the AHE detects this with a small but definite voltage. Incidentally, magnetic fields cause many interesting phenomena on atomic particles. For instance, MRI systems capture signal source emissions related to the magnetic moment of the hydrogen nucleus.

The researchers fabricated the device as an ultra-thin film made of ferromagnetic materials like boron, iron, and cobalt, with electron spins arranged in in-plane anisotropy—meaning, the electron spins align themselves in the plane of the film. However, exposing the film to a high temperature and a strong magnetic field can change the spin of the electrons to perpendicular anisotropy, and their alignment turns perpendicular to the film.

Equalizing the two anisotropies results in a reorientation of the electron spins when the material encounters any external magnetic field, providing a reorientation voltage across the AHE. Compared to a conventional Hall-effect sensor, an AHE sensor is about 20X more sensitive.

The thickness of the AHE device offers a tradeoff in performance. A thick film requires a strong magnetic field to reorient the spins, resulting in a reduction in sensitivity. However, in a thin film, the electrons tend to reorient their spins by themselves, reducing the usefulness of the sensor. The researchers tried many thicknesses and found 0.9 nm thickness worked the best.

As magnetic anisotropy is highly dependent on temperature, researchers are using temperature to fine-tune a single magnetic AHE sensor, thereby achieving very low levels of intrinsic noise during its operation.

Differential Pressure with a Tiny Sensor

Process control requires system operators to monitor and control the condition and movement of liquids and gases. Several instruments are available for this, allowing measurement and monitoring of variables, and these fall under the categories of pressure, temperature, level, and flow. Among the pressure-gage category, differential-pressure gages receive the widest recognition for being the largest specialty type – useful in filtration, flow, and level measurements.

While standard pressure gages measure pressure at a single point in a system, differential pressure gages measure pressures at two points and display the difference on a single dial. This makes it easy for the operator to know at a glance, which of the two points is at a higher pressure, and by how much. Use of differential pressure gages greatly reduces operator error, protecting expensive equipment. They reduce operator training and maintenance time, thereby improving process efficiency.

For instance, differential pressure gages are popularly applied in filtration. In this process, a filter separates unwanted contaminants or particles from a gas or liquid system. However, with the progress of the process, the filter becomes increasingly clogged, leading to a drop in efficiency and pressure at the outlet.

It would seem enough to use a single standard pressure gage at the outlet to monitor the health of the filter and assess the time for its inspection and replacement. However, the situation is complicated, as most processes do not maintain a steady working pressure. Several factors are responsible for this, such as compressor or pump on-off cycles or valve open-close cycles, causing wide pressure fluctuations in most processes. For many systems, operators expect such fluctuations of pressure as normal, within limits.

Using two standard pressure gages, one at the input and the other at the output, introduces two additional problems for the operator. First, this compounds the accuracy errors resulting from the two gages as against error from one gage. Second, the operator needs training in reading the two gages, then subtracting the readings, and finally, interpreting the result. History shows many operators do not truly understand the importance of the calculation.

Installing one differential pressure gage using the same taps at the filter inlet and outlet solves all the problems listed above. The accuracy goes up as the rate of error drops. Additionally, the operator does not have to rely on mathematics to understand and interpret the reading – most differential pressure gage dials feature a red arc to indicate the clogging of the filter.

The SDP3x differential pressure sensor from Sensirion is a tiny device. Its dimensions are only 5x8x5 mm, making it one of the smallest of its kind, but with countless new possibilities of applications. It is well suited for use in portable medical devices as well as in consumer electronics.

Users can choose between an analog signal output and a digital one from two versions of the fully calibrated and temperature-compensated differential pressure sensor. The digital sensor, the SDP31, comes with an I2C interface, while the analog sensor, the SDP36, offers an analog output signal. The sensors have a sampling rate of 2 KHz with a resolution of 16-bits, and a measurement range of +/-500 Pa with a span accuracy of 3% of the reading.

What if Your Life was Speech Activated?

Although we mostly use speech when interacting with other human beings, interacting with machines using speech is still a distant dream. So far, human-to-machine communication technology has been reserved for science fiction movies. However, many are working to provide groundwork for transforming that vision to reality. For instance, speech recognition software, such as Apple’s Siri for the iPhone 4s, is now quite popular. Yet, there are several challenges to address and many kinks to be smoothened out related to voice authentication and voice-activated commands.

VocalZoom, a startup based in Israel, utilizes military technology and develops proprietary optical sensors to map out vibrations emanating from people when they speak. Their HMC or human-to-machine sensor is coupled to an acoustic microphone voice signal. They translate the output to a machine-readable sound signal. The system delivers a speech-recognition technology that is highly accurate and unparalleled in the market today.

VocalZoom approached the problem of speech recognition in an entirely different way. They came across a military technology commonly used for eavesdropping – a laser microphone to sense vibrations on windows. Designers at VocalZoom surmised that if windows vibrate when people speak, surely other things did too. Their research led them to facial skin vibrations because of voice. They created a special low-cost sensor small enough to measure facial vibrations similar to the way microphones did. Their speech recognition system uses microphones, audio processors and the special sensor.

The special sensor is actually an interferometer to measure distance and velocity. Therefore, it can be used as a microphone for measuring vibrations of audiobe used for 3D imaging, proximity sensing, biometric authentication, tapping detection and accurate heart-rate detection. The multifunction sensor has a very wide dynamic range useful for implementation in many applications, for instance, to measure vibrations in engines, industrial printers, or turbines.

A typical sensor for measuring distance and velocity, such as time-of-flight based sensors, use an emitter and a detector. However, designers at VocalZoom use a laser for both purposes. That means their interferometer is of a super low-cost design that practically has no optical component. However, they had to cope with noise issues and it was necessary to develop noise reduction methods when using the sensor with speech recognition systems.

The noise reduction methods used by VocalZoom often use optical sensors to improve speech recognition. They have reached a stage where in an environment with a lot of background noise, they can reduce the results of the speech recognition or voice authentication to a very low error rate.

In actual practice, the laser is directed at the face of the person talking. It measures vibrations that are in the order of tens and hundreds of nanometers, not usually picked up by normal sensors. As the laser measurements are so precise, other surrounding noise does not interfere with the micro-measurements of the skin, which are then converted into clear audio.

Very soon, you will be able to use the optical laser technology of VocalZoom together with Siri or Google Voice and other voice-recognition applications for a wholly different experience.

How does an Android process sense motion?

The Android 4.4 Operating System from Google is able to track your motion in real-time. You can test this with the Google-map application when traveling – your current position as shown on the map will shift as you move. Although this was feature available earlier as well, Google has mandated that 4.4 version onwards, Android will be using this function in the background while it has turned the application processor off. Google has introduced this change to save battery life.

To comply with this mandate, manufacturers will now have to offload this function from the application processor and transfer it to a sensor hub. In anticipation of this mandate from Google, InvenSense has already transferred those functions into their patented DMP or Digital Motion Processor, which they have announced as their six-axis MEMS combo processor for an accelerometer and a gyroscope. Therefore, smart sensors will be providing the real-time contextual awareness functions in the background of your smartphone, while its screen is switched off.

This can be done in one of two ways. One of them may be to allow several new sensor functions to be run in a sensor hub. However, this has the disadvantage of adding cost to the product. A much better way, followed by InvenSense, is to include the processing within the sensor itself, which means smartening up the sensors. The MPU-8515 is a six-axis digital motion processor developed by InvenSense for this purpose.

Inside the MPU-6515, there is a three-axis gyroscope along with a three-axis accelerometer housed within the same package. With an enhanced version of their DMP built into their MPU, InvenSense is able to handle the specific functions that the Android operating system mandates running external to the application processor. With the MPU-6515, sensors can remain on for more time and supply more real-time data for location and context awareness yet reduce battery consumption.

In practice, the Android operating system shuts down the application processor when there is no activity input from the screen. It wakes up only when it receives a significant motion interrupt while rejecting false triggers to switch the application processor back on. Significant motions include pedometric functions such as detecting and counting steps while running in the background.

Processing information accurately when the application processor is turned off involves inertial location tracking. That requires processing rotation vectors involving six axes. The MPU-6515 does this by amalgamating the outputs of three axes from the gyroscope and three axes from the accelerometer sensors and buffering them periodically between the significant motion interrupts using a new batch mode.

The MPU-6515 can work in both modes – with a hub or in a hub less mode. This additional functionality is helpful for situations where the Android operating system has turned off both the application processor and the hub. Using this combo gyroscope and accelerometer chip with enhanced digital motion processor, InvenSense has been able to enhance its handling of contextual awareness for the Android operating system.

Manufacturers can easily use the MPU-6515, measuring a mere 3x3x0.9 mm, in smartphones, wearables, tablets and in devices for Internet of Things. Those using the earlier device from InvenSense, the MPU-6500 can easily replace the older chip as both are pin-compatible.