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In recent years Artificial intelligence (AI) has become a technology that global companies are desperately trying to take advantage of, as it is one of the most emerging and competitive technologies. However, a lot of AI technologies focus on the software, with operating speeds low which makes them a poor fit for mobile devices. For this reason big companies are focusing on developing AI with low power and high speeds, hoping to make AI fit for mobile use.   Professor Hoi-Jun Yoo of the Department of Electrical Engineering, along with his research team and collaboration with start-up company, UX Factory Co, has developed a semiconductor chip, CNNP (CNN Processor), which runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. Consisting of two different formats, the K-Eye series is available as a wearable type and a dongle type. The wearable type device can be used with a smartphone via Bluetooth, and it can operate for more than 24 hours with its internal battery. By conveniently hanging the K-Eye around their necks users can check information about people by using their smartphone or smart watch, which connects K-Eye and allows users to access a database via their smart devices. A smartphone with K-EyeQ, the dongle type device, can recognise and share information about users at any time.  It works by recognising an authorised user looking at the screen, which then automatically turns the smartphone on, without a fingerprint, passcode or iris authentication. The smartphone cannot be tricked by the user’s photograph, as it can distinguish whether an input face is coming from a saved photograph versus a real person. Other distinct features are carried out by the K-Eye series. Detecting a face at first and then recognising it is one, and it is possible to maintain ‘Always-on’ status with low power consumption of less than 1mW. The research team devised two key technologies to complete this: an image sensor with ‘Always-on’ face detection and the CNNP face recognition chip.  The ‘Always-on’ image sensor, the first key technology, is able to determine if there is a face in its camera range. Then, it can capture frames and set the device to operate only when a face exists, reducing the standby power significantly. Additionally the face detection sensor combines analogue and digital processing to reduce power consumption. Using this approach, the analogue processor, combined with the CMOS Image Sensor array, distinguishes the background area from the area likely to include a face, and the digital processor then detects the face only in the selected area. Therefore, it becomes effective in terms of frame capture, face detection processing, and memory usage.    Following this the second key technology, CNNP, is able to achieve incredibly low power consumption, by optimising a convolutional neural network (CNN) in the areas of circuitry, architecture, and algorithms. Specially designed to enable data to be read in a vertical direction as well as in a horizontal direction, the on-chip memory integrated in CNNP also has immense computational power with 1024 multipliers and accumulators operating in parallel and is capable of directly transferring the temporal results to each other without accessing to the external memory or on-chip communication network. Additionally, convolution calculations with a two-dimensional filter in the CNN algorithm are approximated into two sequential calculations of one-dimensional filters to achieve higher speeds and lower power consumption.  CNNP achieved 97% high accuracy but consumed only 1/5000 power of the GPU thanks to these new technologies. Face recognition can be performed with only 0.62mW of power consumption, and the chip can show higher performance than the GPU by using more power.  Developed by Kyeongryeol Bong, a PhD student under Professor Yoo, these chips were presented at the International Solid-State Circuit Conference (ISSCC) held in San Francisco earlier this year. CNNP, which has the lowest reported power consumption in the world, has achieved a huge amount of attention, which has led to the development of the present K-Eye series for face recognition.  Professor Yoo commented: “AI - processors will lead the era of the Fourth Industrial Revolution. With the development of this AI chip, we expect Korea to take the lead in global AI technology.”  Ref.MT9V022 OV05633

 Omron Electronic Components has extended its non-contact MEMS thermal sensor range with a new narrow-field version specifically designed to provide accurate non-contact measurements of an objects’ surface temperature for industrial control, medical and building automation systems. Omron is relaunching its full range of MEMS thermal sensors in Europe, including wider field versions ideal for detecting room occupancy and similar applications.   The new Omron D6T-1A-02 is a super-sensitive infra-red (IR) temperature sensor that makes full use of proprietary Omron MEMS sensing technology. It can measure the surface temperature of an object between -40 up to +80°C in the target area with an accuracy of +/-1.5°C and a resolution of 0.06°C.  The device includes a state-of-the-art MEMS thermopile, a sensor ASIC (Application Specific Integrated Circuit) and a signal processing microprocessor in a tiny package of only 12.0mm x 11.6mm x 9.2mm. The D6T-1A-02 features a narrow field of view of 26.5 degrees square, allowing it to accurately assess the surface temperature of a specific object in this area. Features also include a digital I2C output which offers excellent noise immunity (measured as noise equivalent temperature difference) of 140mK. The Omron D6T thermal sensor is also ideal for building automation applications, measuring the temperature in a room, or detecting occupancy even when people are stationary. For these applications, Omron is offering versions with a wider field of view. These include a 1x1 device, the D6T-1A-01, with a field 58 degrees square. A 4x4 version and a 1x8 version are also available. These ultra-sensitive sensors are an outstanding alternative to pyroelectric sensors or PIR detectors in home automation, building automation, healthcare, security and industrial applications, which often fail to distinguish between an unoccupied space and a stationary person. While standard thermal sensors are only able to measure temperature at one contact point, the D6T range can measure the temperature of an entire area contactlessly. Signals generated by infrared rays are extremely weak. To achieve reliable detection, Omron has developed and manufactured every part of the new high sensitivity thermal sensor in-house, from the MEMS sensors to the ASICs and other application-specific parts. The technology behind Omron’s D6T thermal sensors combines a MEMS micro-mirror structure for efficient IR radiation detection with a high-performance silicon lens to focus the infrared rays onto its thermopiles. The ASIC then uses proprietary algorithms to make the necessary computations and convert sensor signals into digital I2C outputs. All components were developed in-house and are fabricated in Omron’s own MEMS facilities. Ref.KY66-G6SK-2-DC5KY66-G5LA-14-DC5

Detecting temperature is an important function of skin. Snakes can use their skin to track warm-blooded prey, even in the dark. Now a highly sensitive, flexible sensor film could also make this characteristic available for robots and prosthetics. Whether in factories, the office or the kitchen: Robots continue to encroach on various aspects of our lives. When it comes to safety, that increases requirements for the “man-machine interface” considerably. Which is why developing sensitive robot skins has become a hot topic in robot research. After all, “collisions” can only be avoided if you can “see” your counterpart—as quickly and accurately as possible. Methods for doing so range from image processing using mechanical devices to contact-free sensor solutions. For example, scientists at the Technical University of Munich (TUM) have been working on artificial skin made of small hexagon plates with infrared, temperature and acceleration sensors. The infrared sensors register when things come close to the robot. Researchers at ETH Zurich and the California Institute of Technology (Caltech) are pursuing a more natural approach. Their temperature sensor is based on the plant material pectin. Like the snake’s extremely sensitive pit organ that can sense a mammal’s warm body up to a meter away, it can precisely measure temperatures to one hundredth of a degree. That is twice as sensitive as human skin.(A highly sensitive sensor film for robots measures temperatures with an accuracy of one hundredth of a degree. .)(Image: Caltech) “Cyber wood” as a temperature sensorDiscovering the artificial “snake organ” was actually a coincidence. It turns out that the electrical conductivity of cell walls in trees depends on temperature. That is because of the plant material pectin, which can also be used in the kitchen as a gelling agent for puddings and jams. Measurements that were taken on a type of “cyber wood” made of pectin and carbon nanotubes revealed that the higher the temperature, the more free calcium ions were formed at the contact points between two sugar molecules. Electrical conductivity increases proportionally. That is how the sensor idea was born. All that was missing was the “skin”. The answer: A 20-micrometer-thick film made of simple pectin gel laced with a calcium solution. Safe human-robot collaborationInitial testing revealed that the ultrathin transparent film that can be formed into nearly any shape can measure temperatures from 10 to 50 degrees Celsius with a precision of one hundredth of a degree. Supposedly, the “prey” that was used was a teddy bear—fresh from the microwave. To spatially resolve hot or cold sensations like human skin, researchers attached several electrodes along the long and short sides of a piece of “skin” measuring 25 square centimeters. The resulting grid made it possible to determine the position of temperature changes at specific locations. The “snake skin” is extremely easy to make and is more robust and less prone to interference than existing flexible temperature sensors equipped with transistors. After improving the computer algorithms used to analyze the electrode signals and improving the electrical contacts, the “snake skin” should be ready for a field trial in robotics or prosthetics. Ref.KY32-DS18B20KY45-LM61CIM3XKY45-LM35DT

Texas Instruments (TI) introduced the industry's first differential inductive switch, with a dual-coil architecture that automatically compensates for variations in temperature and component aging. The LDC0851 detects the presence or absence of conductive material by using a simple coil drawn on a printed circuit board (PCB). This unique approach enables low-cost, highly reliable switching implementations for a variety of uses including buttons, knobs, door open/close detection, and speed and directional sensing in personal electronics, appliances, industrial equipment and communications applications. The LDC0851 provides a temperature-stable switching accuracy of better than 1 percent of the sensor coil diameter, removing the need for production calibration and minimizing part-to-part variation. Unlike alternative sensing technologies, the LDC0851's contactless and magnet-free design is immune to dirt, dust or other environmental factors, providing designers a reliable, low-cost solution. The device joins TI's distinctive portfolio of inductive-sensing integrated circuits (ICs) including the LDC1614 family of multichannel inductance-to-digital converters.  Key features and benefits of the LDC0851: ·Stable switching threshold:The differential architecture maintains the switching threshold across variations in temperature, humidity and other environmental factors, as well as providing immunity to component aging for stable, long-term performance. ·High accuracy:The device can deliver better than 1 percent switching accuracy, which is up to 10 times more accurate than magnetic sensor-based designs, reducing the need for production calibration. ·High reliability:The device's immunity to nonconductive contaminants such as oil, dirt and dust can help extend product lifetimes and reduce replacement costs. The solution is also unaffected by direct current (DC) magnetic fields, ensuring robust operation and reliability in a wide range of environments. ·Low power:Duty cycling of the LDC0851 allows for less than 20-µA average current consumption at 10 samples per second, which is up to five times lower than competitive solutions. Tools and support to jump-start design The LDC0851EVM evaluation module helps designers easily configure the LDC0851 and start designing it into a system without programming.（The LDC0851EVM evaluation module.）An incremental rotary encoder reference design (TIDA-00828) demonstrates the LDC0851 in a simple 32-position rotary-knob design. Using only two LDC0851 inductive switches, the system can track rotation position and direction, and designers can easily scale the number of encoder positions up or down.（TIDA-00828 Inductive Sensing 32-Position Encoder Knob ReferenceDesign using the LDC0851.） System designers can start their inductive-sensing design in minutes with TI's WEBENCH Coil Designer. This online tool simplifies sensor-coil design based on application and system requirements. The optimized design is exportable to a variety of computer-aided design (CAD) programs to quickly incorporate the sensor coil into an overall system layout. Ref.KY362-LDC0851EVMKY362-LDC1614EVM     

Eight years ago, Ted Adelson’s research group at MIT’s CSAIL unveiled a new sensor technology, called GelSight, that uses physical contact with an object to provide a remarkably detailed 3D map of its surface. Now, by mounting GelSight sensors on the grippers of robotic arms, two MIT teams have given robots greater sensitivity and dexterity. The researchers presented their work in two papers at the International Conference on Robotics and Automation.In one paper, Adelson’s group uses the data from the GelSight sensor to enable a robot to judge the hardness of surfaces it touches — a crucial ability if household robots are to handle everyday objects.In the other, Russ Tedrake’s Robot Locomotion Group at CSAIL uses GelSight sensors to enable a robot to manipulate smaller objects than was previously possible.The GelSight sensor is, in some ways, a low-tech solution to a difficult problem. It consists of a block of transparent rubber — the “gel” of its name — one face of which is coated with metallic paint. When the paint-coated face is pressed against an object, it conforms to the object’s shape.The metallic paint makes the object’s surface reflective, so its geometry becomes much easier for computer vision algorithms to infer. Mounted on the sensor opposite the paint-coated face of the rubber block are three colored lights and a single camera.“[The system] has colored lights at different angles, and then it has this reflective material, and by looking at the colors, the computer … can figure out the 3D shape of what that thing is,” explains Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences.In both sets of experiments, a GelSight sensor was mounted on one side of a robotic gripper, a device somewhat like the head of a pincer, but with flat gripping surfaces rather than pointed tips.For an autonomous robot, gauging objects’ softness or hardness is essential to deciding not only where and how hard to grasp them but how they will behave when moved, stacked, or laid on different surfaces. Tactile sensing could also aid robots in distinguishing objects that look similar.In previous work, robots have attempted to assess objects’ hardness by laying them on a flat surface and gently poking them to see how much they give. But this is not the chief way in which humans gauge hardness.Rather, our judgments seem to be based on the degree to which the contact area between the object and our fingers changes as we press on it. Softer objects tend to flatten more, increasing the contact area.The MIT researchers adopted the same approach. Wenzhen Yuan, a graduate student in mechanical engineering and first author on the paper from Adelson’s group, used confectionary molds to create 400 groups of silicone objects, with 16 objects per group. In each group, the objects had the same shapes but different degrees of hardness, which Yuan measured using a standard industrial scale.Then she pressed a GelSight sensor against each object manually and recorded how the contact pattern changed over time, essentially producing a short movie for each object. To both standardise the data format and keep the size of the data manageable, she extracted five frames from each movie, evenly spaced in time, which described the deformation of the object that was pressed.Finally, she fed the data to a neural network, which automatically looked for correlations between changes in contact patterns and hardness measurements. The resulting system takes frames of video as inputs and produces hardness scores with very high accuracy.Yuan also conducted a series of informal experiments in which human subjects palpated fruits and vegetables and ranked them according to hardness. In every instance, the GelSight-equipped robot arrived at the same rankings. Ref:KY45-AT42QT1110-AUKY45-STMPE1208SQTRKY45-MPR032EPR2

In the future, a new sensor cable could be used to protect airports, industrial complexes and people’s yards without a great deal of expense. It registers even the tiniest changes in the Earth’s magnetic field.Plenty of things go unnoticed by our senses. One of them is the Earth’s magnetic field. Unlike migratory birds and sea turtles, we need technical aids to make use of it. Like the good old compass. It has been helping seamen navigate the seven seas since the 12th century. Sort of a primitive precursor of today’s magnetic field sensors. Then in 1832, mathematician and physicist Carl Friedrich Gauss laid the cornerstone for modern magnetic sensor technology when he developed a method for measuring both the direction and intensity of the Earth’s magnetic field.Since then, magnetic-field sensors have become quite important because they make entirely new solutions for difficult measuring tasks possible. And not just for research and industry—also for our private lives. For example, they help determine location and position in our smartphones. And with Apps such as Telemeter 11th, they even turn a digital Swiss knife into a metal detector.Magnetic burglar alarmSaarland University’s “All-round Warning Alarm” is based on a similar principle. When attached to fencing, a thin magnetic field sensor cable can tell whether the wind, a bird or wire cutters are “interacting” with the wire mesh. Buried in the ground of future traffic-guidance systems, it can tell which direction automobiles are driving. Not even smartphones or zippers can go undetected. That is because everything within a few meters that influences the Earth’s magnetic field is registered by highly sensible magnetic field sensors and transmitted to a smartphone via Bluetooth.The sensor cable is flexible and can be adapted to a wide variety of requirements, and it consumes very little electricity. It is also practically wear free, and measurements do not dependent on weather conditions. In addition, no data is stored and the sensor system has proved a hard nut for hackers to crack.The technology is based on the fact that the Earth’s weak magnetic field (approx. 50 microtesla) is always everywhere. And that every ferromagnetic object measurably disturbs this field for magnetic field sensors with sensitivities in the nanotesla range. Corresponding electronics and algorithms then determine metallic properties, size and direction of motion. Every type of “disturbance” has its own magnetic fingerprint.Magnetic field sensors for every purposeResearchers have been working a “magnetic” recognition systems for a good 15 years. As part of the development process, experiments were conducted with so-called AMR (anisotropic magnetoresistance) and GMR (giant magnetoresistance) sensors. The latter can be found in billions of read heads in hard disk drives, and the physicists who discovered them, Peter Gruenberg from Forschungszentrum Jülich and Albert Fert from Université Paris-Sud, were awarded the Nobel Prize in Physics in 2007. Both work sensors are based on the so-called magnetoresistance effect, which says that ferromagnetic materials change their resistance in a magnetic field.Burglars in a “tunnel”The first trials with the third member of the magnetoresistance team, i.e. GMI (giant magnetoimpedence) sensors, are now underway in Saarland. In this case, impedance (alternating current resistance) depends on the intensity of an applied, relatively weak external magnetic field.But that’s not all: The prototype recently introduced by Saarland University researchers uses a fourth variant, i.e. TMR (tunnel magnetoresistance) sensors, which have only been commercially available for a short time. As the word “tunnel” suggests, these magnetic field sensors make use of quantum mechanics effects. Due to their high change in resistance of 20%, TMRs are extremely interesting for a number of applications. They are provided by Sensitec(Germany), one of the partners in this project, which is sponsored by Germany’s Federal Ministry of Research.With the exception of the AMR effect, all magnetoresistance effects were discovered after 1988. In other words, this is still a relatively new, rapidly growing research sector with the prospect of extraordinary sensor solutions for various electronics sectors in the years to come. It will be interesting to see what happens! Ref:KY45-HMR3400KY45-HMC6343KY45-HMC2003