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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

Train delays due to leaves on the line could be a thing of the past if a prototype developed at the University of Birmingham is adopted by railway networks.Every year, thousands of commuters endure the frustration of Autumn delays caused by the accumulation of leafy slush on train tracks – and these problems usually reach their peak in mid-November, when leaf loss is coupled with high levels of moisture in the air or on the ground.Lee Chapman, Professor of Climate Resilience from the University, was inspired by the Internet of Things, which uses a range of innovative power, communication and sensing technology to aggregate real-time, on the ground, data.Funded by EPSRC and the Rail Safety and Standards Board, he worked with Alta Innovations, the University of Birmingham's technology transfer company, to transform the concept into a reality. His new technology, called AutumnSense, uses low-cost sensors to continuously measure the level of moisture on the railway line at potentially thousands of sites across the network. By linking this data with a leaf-fall forecast, operators can identify where and when the risk is greatest. This allows the precise and efficient use of automated treatment trains, which can clear the lines before the morning rush hour starts. His team are now testing the next element of the solution which is a low-cost method to count the number of leaves remaining on the trees.Professor Chapman's team had previously developed low-cost devices that are fitted to lamp-posts, and transmit data on road surface temperatures, to show precisely where road gritting is needed, and where it isn't. The road technology, called WinterSense, is currently being tested by commercial partners and is expected to be in mass production by the end of this winter.Professor Chapman said, "One of the major issues with road and rail safety is that hazardous conditions are usually highly localised. For remedial actions to be efficient, and demonstrate 'best value' for the taxpayer, resources should be deployed where they are needed, rather than in a blanket fashion."He is marketing AutumnSense and WinterSense through AltaSense, an operating division of Alta Innovations, and hopes to incorporate by Autumn 2017.He said, "Even though leaf loss and damp conditions can largely be predicted - and despite automated treatment trains working round the clock from October to December - a windy, rainy night still causes havoc for commuters. We have run an initial trial of AutumnSense on a stretch of London Underground tracks that are above ground, and are hoping to move quickly towards a fuller network wide trial."Wet leaves pose a very real safety challenge for train operators, potentially doubling the breaking distance and causing signalling issues, or 'disappearing trains' on the rail control systems due to the electrically insulating effect of the leaves which can prevent operation of track circuits. Leaves on the line are only an issue when they are mixed with moisture or dew, creating a slippery, Teflon-like substance. Reference:KY45-D7E-1KY45-BU-27135-000KY45-1005447-1 

The Telecontrolli capacitive rain sensor detects precipitation rate, current precipitation intensity and its end with precision and effectiveness, avoiding false positives which can cause inappropriate operations (objects such as dirt or other particles that in preexisting optical sensors cause a reflection mimicking the one of rain and limit its effectiveness in rapidly responding to light rain).Capacitive sensing is a technology based on the change in capacity determined by the change of the dielectric constant of the material separating the electrical conductors (plates) of the capacitor following the action of external agents.Capacitive sensing is becoming a popular technology to replace optical detection methods and mechanical designs for applications like proximity/gesture detection, material analysis and rain/humidity detection, because it offers more reliable and accurate measurements than optical ones.The sensor has IDT electrodes on one side of the alumina substrate, while a resistive heater and a temperature sensor are placed on the opposite side. The rain sensitive area, which in dry conditions assumes the nominal value of 100pF. Moreover in presence of the rain, the capacitance goes to high values compared to dry conditions and the ratio changing is over 300%.The integral and configurable heater is provided to ensure that the detection surface dries quickly, protecting the surface from fog, condensed moisture and frost. It also can be disabled when power consumption is critical.The alumina substrate and the glass sensitive layer makes the sensor immune to water and moisture absorption, ensuring high resistance and long duration.Moreover, thanks to the thermal conductivity of the ceramic, the heat emitted by the heater on the back of the sensor is immediately transferred to the upper surface, making the sensor more sensitive than other technologies.Furthermore, while resistive rain sensors are sensible to corrosion and contamination, the Telecontrolli capacitive rain sensor doesn't suffer from these disadvantages, making it suitable for any type of application - including irrigation systems for agriculture, automotive wiper systems, weather stations, and applications for home and building automation.  

New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain.Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses; a key component that every ANN requires in large numbers.In the study, published in Nature Communications, the Southampton research team experimentally demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data.Memristors are electrical components that limit or regulate the flow of electrical current in a circuit and can remember the amount of charge that was flowing through it and retain the data, even when the power is turned off.Lead author Dr Alex Serb, from Electronics and Computer Science at the University of Southampton, said: "If we want to build artificial systems that can mimic the brain in function and power we need to use hundreds of billions, perhaps even trillions of artificial synapses, many of which must be able to implement learning rules of varying degrees of complexity. Whilst currently available electronic components can certainly be pieced together to create such synapses, the required power and area efficiency benchmarks will be extremely difficult to meet -if even possible at all- without designing new and bespoke 'synapse components'."Memristors offer a possible route towards that end by supporting many fundamental features of learning synapses (memory storage, on-line learning, computationally powerful learning rule implementation, two-terminal structure) in extremely compact volumes and at exceptionally low energy costs. If artificial brains are ever going to become reality, therefore, memristive synapses have to succeed."Acting like synapses in the brain, the metal-oxide memristor array was capable of learning and re-learning input patterns in an unsupervised manner within a probabilistic winner-take-all (WTA) network. This is extremely useful for enabling low-power embedded processors (needed for the Internet of Things) that can process in real-time big data without any prior knowledge of the data.Co-author Dr Themis Prodromakis, Reader in Nanoelectronics and EPSRC Fellow in Electronics and Computer Science at the University of Southampton, said: "The uptake of any new technology is typically hampered by the lack of practical demonstrators that showcase the technology's benefits in practical applications. Our work establishes such a technological paradigm shift, proving that nanoscale memristors can indeed be used to formulate in-silico neural circuits for processing big-data in real-time; a key challenge of modern society."We have shown that such hardware platforms can independently adapt to its environment without any human intervention and are very resilient in processing even noisy data in real-time reliably. This new type of hardware could find a diverse range of applications in pervasive sensing technologies to fuel real-time monitoring in harsh or inaccessible environments; a highly desirable capability for enabling the Internet of Things vision."Reference:KY259-SDUS5EB-002GKY259-SDUS5AB-002GKY259-SDUS5AB-001G 

IBM is telling the world about something quite ambitious: TrueNorth's neurons could revolutionize system architecture. Dharmendra S. Modha, IBM Fellow, has given us the overview of what TrueNorth is all about in his report in IBM Research.Six years ago, he said, IBM and university partners began their effort to build a brain-inspired computer.Phase 0 turned into Phase 1, Phase 2, and Phase 3— from neuroscience to super computing to a new architecture, to a new programming language to algorithms, applications, and now, new chip, which is TrueNorth.He offered some numbers, which are bit daunting for those not accustomed to the "neuromorphic" world of computer research.Modha said, "we have shrunk the neurosynaptic core by 15-fold in area and 100-fold in power, and have tiled 4,096 cores via an on- IC chip network to create TrueNorth—with one million neurons and 256 million synapses."Cade Metz, Wired senior staff writer, went beyond the numbers to describe on Monday what he saw. "Dharmendra Modha walks me to the front of the room so I can see it up close. About the size of a bathroom medicine cabinet, it rests on a table against the wall, and thanks to the translucent plastic on the outside, I can see the computer chips and the circuit boards and the multi-colored lights on the inside. It looks like a prop from a '70s sci-fi movie, but Modha describes it differently. 'You're looking at a small rodent,' he says. He means the brain of a small rodent—or, at least, the digital equivalent."(The machine at the front of the room is really 48 separate machines, each built around its own TrueNorth processors, Metz wrote.)Not surprisingly, several websites took to the rodent comparison to report that IBM had come up with a "rat brain"-like chip that might power the phones of tomorrow.Modha, meanwhile, spelled out the applications that might result. "The architecture can solve a wide class of problems from vision, audition, and multi-sensory fusion."Making smartphones, as Wired put it, "hyper-smart"? That would be one effect. Modha said, "On one hand, with portable devices: think smart phones, sensor networks, self-driving automobiles, robots, public safety, medical imaging, real-time video analysis, signal processing, olfactory detection, and digital pathology. On the other hand, with synaptic supercomputers: —think multimedia processing on the cloud."Reporter Mike Murphy in Quartz on Tuesday talked about the technology itself which is turning the corner: "While current chips are excellent at analyzing information in sequential order, the new 'neuromorphic' types of chips Modha's team are working on are better suited to finding patterns in information—like the right side of the brain."Traditional chips follow instructions, whereas IBM's new chip manages 'spikes'—rather like spikes in electrical activity in an organic brain," Murphy said.TrueNorth, with all its comparisons, is not a brain but it is a step toward a digital brain. "Let's be clear: we have not built the brain, or any brain," said Modha in the IBM Research report. "We have built a computer that is inspired by the brain. The inputs to and outputs of this computer are spikes. Functionally, it transforms a spatio-temporal stream of input spikes into a spatio-temporal stream of output spikes."Collaboration with Samsung was critical in gaining access to their advanced 28nm foundry process, he said. This allowed balancing the low active power of the architecture with matching low power of the underlying silicon technology.He added, "I am immensely grateful to our 200+ collaborators since 2008—spanning eight IBM labs and fabs, five universities, one start-up, and two Department of Energy laboratories. Finally, DARPA's mandate, metrics, and investment were absolutely vital."