Robotic comfort systems have been developed which use fans to deliver heated/cooling air to building occupants to provide greater levels of personal comfort.  However, current robotic systems rely on surveys asking individuals about their comfort state through a web interface or app.  This reliance on user feedback becomes impractical due to survey fatigue on the part of the user.  Researchers at the University of California, Berkeley have developed a system which uses a visible light camera located on the nozzle of a robotic fan to detect human facial features (e.g., eyes, nose, and lips).  Images from a co-located thermal camera are then registered onto the visible light image and temperatures of different facial features are captured and used to infer the comfort state of the individual.  Accordingly, the fan/heater system blows air with a specific velocity and temperature toward the occupant via a closed-loop feedback control.  Since the system can track a person in an environment, it addresses issues with prior data collection systems that needed occupants to be positioned in a specific location.

Covalent organic frameworks (COFs) are 2D or 3D extended periodic networks assembled from symmetric, shape persistent molecular 5 building blocks through strong, directional bonds. Traditional COF growth strategies heavily rely on reversible condensation reactions that guide the reticulation toward a desired thermodynamic equilibrium structure. The requirement for dynamic error correction, however, limits the choice of building blocks and thus the associated mechanical and electronic properties imbued within the periodic lattice of the COF.   UC Berkeley researchers have demonstrated the growth of crystalline 2D COFs from a polydisperse macromolecule derived from single-layer graphene, bottom-up synthesized quasi one-dimensional (1D) graphene nanoribbons (GNRs). X-ray scattering and transmission electron microscopy revealed that 2D sheets of GNR-COFs self-assembled at a liquid-l quid interface stack parallel to the layer boundary and exhibit an orthotropic crystal packing. Liquid-phase exfoliation of multilayer GNR-COF crystals gave access to large area bilayer and trilayer cGNR-COF films. The functional integration of extended 1D materials into crystalline COFs greatly expands the structural complexity and the scope of mechanical and physical materials properties.

This invention creates a new graphene nanoribbons (GNR)-based transistor technology capable of pushing past currently projected limits in the operation of digital electronics for combining high current (i.e. high speed) with low-power and high on/off ratio. The inventors describe the design and synthesis of molecular precursors for low band gap armchair graphene nanoribbons (AGNRs) featuring a width of N=11 and N=15 carbon atoms, their growth into AGNRs, and their integration into functional electronic devices (e.g. transistors). N is the number of carbon atoms counted in a chain across the width and perpendicular to the long axis of the ribbon.

The real-time position and mobility of a user is key to providing personalized location-based services (LBSs) – such as navigation. With the pervasiveness of GPS-enabled mobile devices (MDs), LBSs in outdoor environments is common and effective. However, providing equivalent quality of LBSs using GPS in indoor environments can be problematic. The ubiquity of both WiFi in indoor environments and WiFi-enabled MDs, makes WiFi a promising alternative to GPS for indoor LBSs. The most promising approach to establishing a WiFi-based indoor positioning system requires the construction of a high quality radio map for an indoor environment. However, the conventional approach for making the radio map is labor intensive, time-consuming, and vulnerable to temporal and environmental dynamics. To address this situation, researchers at UC Berkeley developed an approach for automatic, fine-grained radio map construction and adaptation. The Berkeley technology works both (a) in free space – where people and robots can move freely (e.g. corridors and open office space); and (b) in constrained space – which is blocked or not readily accessible. In addition to its use with WiFi signals, this technology could also be used with other RF signals – for example, in densely populated and built-up urban areas where it can be suboptimal to only rely on GPS.

Capacitive-gap transduced micromechanical resonators routinely post Q several times higher than piezoelectric counterparts, making them the preferred platform for HF and low-VHF (e.g. 60-MHz) timing oscillators, as well as very narrowband (e.g. channel-select) low-loss filters. However, the small electromechanical coupling (as gauged by the resonator's motion-to-static capacitance ratio, Cx/Co) of these resonators at higher frequency prevents sub-mW GSM reference oscillators and complicates the realization of wider bandwidth filters. To address this situation, researchers at UC Berkeley developed a capacitive-gap transduced radial mode disk resonator with reduced mass and stiffness. This novel Berkeley disk resonator has a measured electromechanical coupling strength (Cx/Co) of 0.56% at 123 MHz without electrode-to-resonator gap scaling. This is an electromechanical coupling strength improvement of more than 5x compared with a conventional radial contour-mode disk at the same frequency. This increase should help improve the passbands of channel-select filters targeted for low power wireless transceivers and lower the power of MEMS-based oscillators.  

With the emergence of the Internet of Things in smart homes and buildings, determining the identity and mobility of people are key to realizing personalized, context-aware and location-based services - such as adjusting lights and temperature as well as setting preferences of electronic devices in the vicinity. Conventional electronic user identification approaches either require proactive cooperation by users or deployment of dedicated infrastructure. Consequently, existing approaches are intrusive, inconvenient, or expensive to ubiquitously implement. For example: biometric identification requires specific hardware and physical interaction; and vision-based (video) approaches need favorable lighting and introduce privacy issues. To address this situation, researchers at UC Berkeley developed an identification system that uses existing, pervasive WiFi infrastructure and users' WiFi-enabled devices. The innovative Berkeley technology cleverly leverages attributes such as the MAC address and RSS of users' WiFi-enabled devices. Furthermore, the Berkeley approach is facilitated by an unsupervised learning scheme that maps each user identification with associated WiFi-enabled devices. This technology could serve as a vital underpinning for practical personalized context-aware and location-based services in the era of the Internet of Things.

In our electronically connected society, human identification systems are critical to secure authentication, and also enabling for tailored services to individuals. Conventional human identification systems, such as biometric-based or vision-based approaches, require either the deployment of dedicated infrastructure, or the active cooperation of users to carry devices. Consequently, pervasive implementation of conventional human identification systems is expensive, inconvenient, or intrusive to privacy. Recently, WiFi infrastructure, and associated WiFi-enabled mobile and IoT devices have become ubiquitous, and correspondingly, have enabled many context-aware and location-based services. To address the challenges of human identification systems and take advantage of the popularity of WiFi, researchers at UC Berkeley developed a human identification system based on analyzing signals from existing WiFi-enabled devices. This novel device-free approach uses WiFi signal analysis to reveal the unique, fine-grained gait patterns of individuals as the "fingerprint" for human identification.

A sub-skin-depth (nanoscale metallization) thin film antenna is shown that is monolithically integrated with an array of neural recording electrodes on a flexible polymer substrate. The structure is intended for long-term biometric data and power transfer such as electrocorticographic neural recording in a wireless brain-machine interface system. The system includes a microfabricated thin-film electrode array and a loop antenna patterned in the same microfabrication process, on the same or on separate conductor layers designed to be bonded to an ultra-low power ASIC.

96 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The ∼10% optical contrast of graphene on specialized substrates like oxide-capped silicon substrates, together with the high-throughput and noninvasive features of optical microscopy, have greatly facilitated the use and research of graphene research for the past decade.  However, substantially lower contrast is obtained on transparent substrates. Visualization of nanoscale defects in graphene, e.g., voids, cracks, wrinkles, and multilayers, formed during either growth or subsequent transfer and fabrication steps, represents yet another level of challenge for most device substrates.     UC Berkeley researchers have developed a facile, label-free optical microscopy method to directly visualize graphene on transparent inorganic and polymer substrates at 30−40% image contrast per graphene layer.  Their noninvasive approach overcomes typical challenges associated with transparent substrates, including insulating and rough surfaces, enables unambiguous identification of local graphene layer numbers and reveals nanoscale structures and defects with outstanding contrast and throughput. We thus demonstrate in situ monitoring of nanoscale defects in graphene, including the generation of nano-cracks under uniaxial strain, at up to 4× video rate.  

Realizing the potential of massive sensor networks requires overcoming cost and power challenges. When sleep/wake strategies can adequately limit a network node's sensor and wireless power consumption, then the power limitation comes down to the real-time clock (RTC) that synchronizes sleep/wake cycles. With typical RTC battery consumption on the order of 1µW, a low-cost printed battery with perhaps 1J of energy would last about 11 days. However, if a clock could bleed only 10nW from this battery, then it would last 3 years. To attain such a clock, researchers at UC Berkeley developed a mechanical circuit that harnesses squegging to convert received RF energy (at -58dBm) into a local clock while consuming less than 17.5nW of local battery power. The Berkeley design dispenses with the conventional closed-loop positive feedback approach to realize an RCT (along with its associated power consumption) and removes the need for a sustaining amplifier altogether. 

Wireless sensors and the Internet of Things (IoT) have the potential to greatly impact society. Millimeter-scale wireless microsystems are the foundation of this vision. Accordingly, to realize this potential, these microsystems must be extremely low-cost and energy autonomous. Integrating wireless sensing systems on a single silicon chip with zero external components is a key advancement toward achieving those cost and energy requirements.  Almost all commercial microsystems today use off-chip quartz technology for precise timing and frequency reference. The quartz crystal (XTAL) is a bulky off-chip component that puts a size limitation on miniaturization and adds to the cost of the microsystem. Alternatively, MEMS technology is showing promising results for replacing the XTAL in space-constrained applications. However, the MEMS approach still requires an off-chip frequency reference and the resulting packaging adds to the cost of the microsystem.  To achieve a single-chip solution, researchers at UC Berkeley developed: (1) an approach to calibrating the frequency of an on-chip inaccurate relaxation oscillator such that it can be used as an accurate frequency reference for low-power, crystal-free wireless communications; and (2) a novel ultra-low power radio architecture that leverages the inaccurate on-chip oscillator, operates on energy harvesting, and meets the 1% packet error rate specification of the IEEE 802.15.4 standard. 

This case helps reinvent the transistor by building on the success of Berkeley’s 3D FinFET/Trigate/Tri-Gate methods and devices, with increased focus on the negative capacitance of the MOS-channel and ferroelectrics, and an unconventional effective oxide thickness approach to the gate dielectric. Proof of concept devices have been demonstrated at 30nm gate length and allow for use of thinner ferroelectric films than 2D negative capacitance transistors (e.g. see http://digitalassets.lib.berkeley.edu/techreports/ucb/text/EECS-2014-226.pdf ). The devices also performed at low operating voltage which lowers operating power.

Energy Isolation Lock out Tag out (“LOTO”) is a series of CalOSHA and FedOSHA code compliance requirements and is the primary means by which equipment must be rendered “safe” prior to allowing personnel to work on the equipment.  LOTO codes require equipment-specific written procedures identifying all types of energy sources needed to operate the equipment as well as the energy-isolation methods and locations of utility disconnects, stored energy, etc. In addition, every LOTO procedure must be annually verified to confirm the written procedure is still accurate to the equipment.   Whereas current LOTO procedures are typically hand-written or using other time-consuming processes, UC Berkeley authors have created software allowing users to retrieve LOTO procedures in real-time guiding the end-user through a logical thought process to allow them to identify all energy sources and safety processes, and equipment needed.  

The ability to see depth is a key visual function, as three-dimensional vision is used to guide body movements. Although many visual cues are used to infer spatial relationships, depth perception relies primarily on stereopsis, or the perception of depth based on differences in the images in the two eyes. More than 5% of the US population, however, is unable to see in three dimensions due to stereo-blindness and stereo-anomaly. Without depth perception, basic activities such as catching a ball or driving a car are not possible. Current therapeutic methods to address this issue include a set of eye-training exercises that aim to equalize the input from the eyes to the brain, which are collectively called orthoptics.   Researchers at UC Berkeley have developed an orthoptic method to train stereo depth perception. This method includes devices and systems for implementation, and it can be used in the home. 

Trillions of sensors are envisioned to achieve the potential benefits of the internet of things.  Realizing this potential requires wireless sensors with low power requirements such that there might never be a need to replace a sensor’s power supply (e.g. battery) over the lifetime of that device.  The battery lifetime of wireless communications devices is often governed by power consumption used for transmitting, and therefore transmit power amplifiers used in these devises are important to their commercial success.  The efficiencies of these power amplifiers are set by the capabilities of the semiconductor transistor devices that drive them.  To achieve improved efficiencies, researchers at UC Berkeley have developed a novel method and structure for realizing a zero-quiescent power trigger sensor and transceiver based on a micromechanical resonant switch.  This sensor/transceiver is unique in its use of a resonant switch (“resoswitch”) to receive an input, amplify it, and finally deliver power to a load.  This novel technology also greatly improves short-range communication applications, like Bluetooth.  For example, with this technology, interference between Bluetooth devices would be eliminated.  Also, Miracast would work, despite the presence of interfering Bluetooth signals.

Each university, company or hospital that has a license to work with radioactive materials or is authorized to use x-ray machines is required to train its radioactive material or x-ray machine users. UC Berkeley has developed a radiation safety online training course made up of 7 training modules, which fulfill this training requirement. This safety course can be used by other organizations that are interested in fulfilling this requirement in an interactive and engaging way.   

Information and communication technologies rely on integrated circuits (ICs) or “chips.” Increased integration has improved system performance and energy efficiency, and lowered the manufacturing cost per component. Moore’s Law predicts that the number of transistors on an IC will double every two years, yet industry experts predict that we are reaching economic limits of traditional circuit patterning processes. Photolithographic patterning is best suited to print linear features that are evenly spaced. The smaller or more complex the shape, the more likely the printed pattern will be blurred and unusable. Although multiple-patterning techniques can be used to increase feature density on ICs, they bring a high additional cost to the process. This means that the most advanced ICs available today have a high density of features, but are restricted to having simple patterns and are increasingly expensive to produce. Without innovations in production techniques, Moore’s Law will reach its end in the near future.  To address this issue, researchers at UC Berkeley have developed a one-step method to increase feature density on chips. This method is capable of achieving arbitrarily small feature size, and self-aligns to pre-existing features on the surface formed by other techniques. 

Almost 170 million people in the United States (~55% of the total U.S. population) wear vision correction. Of this population, more than 63 million people (53%) up to age 64 have presbyopic vision. Eyeglasses have been the primary tool to correct such aberrations since the 13th century. In more modern times, contact lenses and refractive surgery have become viable alternatives to wearing eyeglasses. Unfortunately, these approaches require the observer to either use eyewear or undergo surgery, which is often uncomfortable and costly, and can lead to complications, in the case of surgery. To address these challenges, researchers at the University of California, Berkeley, and MIT have developed vision correcting screen technology which involves digitally modifying the content of a display so that the display can be seen in sharp focus by the user without requiring the use of eyeglasses or contact lenses. By leveraging specialized optics in concert with proprietary prefiltering algorithms, the display architecture achieves significantly higher resolution and contrast than prior approaches to vision-correcting image display. The teams have successfully demonstrated light field displays at low cost backed by efficient 4D prefiltering algorithms, producing desirable vision-corrected imagery even for higher-order aberrations that are difficult to be corrected with conventional approaches like eyeglasses.

The tracking scanning laser ophthalmoscope (TSLO) provides fast and accurate measurements of fixational eye motion with flexible field of views. Currently, this system is the most accurate, fast and functional eye-tracking system used in a standard ophthalmic instrument. At a basic research level, the benefits of accurate eye-tracking are especially useful for delivering stimuli to targeted retinal locations as small as a single cone. In the clinical domain, advances in imaging and tracking technology help render accurate images which can lead to better outcomes in treating eye disease. Scanning laser ophthalmoscopy (SLO) uses both a horizontal and vertical scanner to image a specific region of the retina. Current state of the art tracking SLO systems are only suitable for observing a narrow field of view (FOV < five degrees) and will lose signal with certain types of eye motion. This is problematic for patients suffering from varying retinal or neurological disorders, where unstable fixation hinders accurate eye-tracking and image acquisition. These include retinal diseases of the macula such as: age-related macular degeneration, or neurological disorders such as: Alzheimer's and Parkinson's disease. In cases such as these, it would be desirable to capture a larger field of view whose image quality is sufficient to track the retina for larger and more rapid eye movements. To help address this problem, researchers at the University of California, Berkeley have developed systems, software, and methods for an image-based high-performance TSLO. Early laboratory experimentation results suggest significantly enhanced eye-tracking in terms of: sampling uniformity of eye motion traces, detection of eye rotation, increased frame rate of image capture, expandable/adjustable FOV, stabilization accuracy of 0.66 arcminutes, and tracking accuracy of 0.2 arcminutes or less across all frequencies. The Berkeley system and techniques show promise for observing detailed structural and functional changes in the eye as a result of age and/or disease like never before.

The increasing role of wireless technology is driving the need for reducing power consumption of wireless devices. The high-Q SAW and FBAR vibrating mechanical devices used for current RF band-pass filters are responsible for significant power savings. Still, there is room for improvement. To address this situation, researchers at UC Berkeley have developed an active resonator system with tunable quality factor, frequency, and impedance. Coupling two or more of these Berkeley resonators together enables construction of filters with arbitrarily small adjustable bandwidths and tunable insertion loss thereby achieving significant advantage over traditional filters constructed from passive resonators.

Supercapacitors are attractive energy storage devices due to their high-power capabilities and robust cycle lifetimes.   “Super” capacitors are named in part because the electrodes are composed of materials with high specific surface area and the distance between the electrode and electrochemical double layer is very small compared to standard capacitors.  A variety of porous silicon nanowires have been developed for use as supercapacitors electrodes by maximizing the specific surface area of active materials.  Although the use of Si is attractive due to its wide-spread adoption by microelectronics industry and due to its abundance, Si nanowires are highly reactive and dissolve rapidly when exposed to mild saline solutions.  Previously, silicon carbide thin films were used to protect the porous silicon nanowires, but the coatings were 10’s of nm thick and while they successfully mitigated Si degradation during electrochemical cycling in aqueous electrolytes, they also resulted in pore blockage and a large decrease in energy storage potential.   Researchers at UC Berkeley have developed methods and materials to improve porous silicon nanowires by overcoming the above limitations.  The resulting nanowires have an ultrathin carbon coating preserving the pore structure while mitigating Si degradation.  The resulting supercapacitor electrodes have the highest capacitance (and hence energy storage) per projected area to date.   

Data storage outsourcing has become one of the most popular applications of cloud computing, offering benefits such as economies of scale, flexible accessibility, efficiency, and allowing companies to focus on their primary business activities. Due to the increase in percentage of services conducted online and number of mobile internet connections, demand for data storage continues to grow. Customers in this industry are primarily concerned with authenticated storage and data retrievability. Although many efficient proof of retrievability technologies have been developed for static data, only two dynamic technologies exist. However, both are too expensive to implement in practice due to the fact that they require a high level of bandwidth. To address this problem, researchers have developed a dynamic proof of reliability scheme that requires 300 times less bandwidth than currently available technologies. This innovative technology makes dynamic proof retrievability of data practical and efficient, and thus attractive for the industry implementation. This technology gives clients of cloud storage providers assurance that their data has not been modified and that no data loss has occurred.

There is a long-standing problem of how to switch piezoelectric filters when used in switchable filter banks -- such as needed in RF channel-selection. To address this problem, researchers at UC Berkeley have developed a method and structure for a piezoelectric resonator with tunable transfer function -- i.e. tunable gain. This Berkeley resonator's gain is tunable to many values -- including values that are low enough to consider the device to be "off" relative to the background signal. Accordingly, this approach enables on/off switching of piezoelectric resonators; and it thereby obviates the need for separate low loss switches, which otherwise would be needed in series with piezoelectric resonators to switch them on and off -- adding insertion loss and raising system gain. In addition, this ability to adjust filter gain makes it possible for the resonator to control low power gain in a receiver front-end.

The reduction of power supply voltage with each new generation of CMOS technology continues to complicate the design of charge pumps needed for high voltage applications that integrate into systems alongside transistor chips -- such as the increasing number of MEMS-based gyroscopes, timing oscillators, and gas sensors. Moreover, the aggressive scaling in CMOS resulting in lower dielectric and junction breakdown voltages has compelled the use of customized CMOS processes -- including increased gate oxide thickness and/or added deep-n-wells. Clearly, advances in transistor technology are moving in the opposite direction of the needs of high voltage MEMS applications. To address this trend, researchers at UC Berkeley have developed a MEMS-based charge pump. This design avoids the turn-on voltage and breakdown limitation of CMOS. With much higher breakdown voltages than transistor counterparts, the demonstrated MEMS charge pump implementation should eventually allow voltages higher than 50V desired for capacitive-gap transduced resonators that currently dominate the commercial MEMS-based timing market.

A core problem in computer vision and graphics applications is recovering a model of the attributes that create an image. This model is comprised of the intrinsic scene properties: the collection of shapes, paints and lights which together create an image. Conventional methods for recovering intrinsic scene properties rely on multiple observations of the same scene in order to over-constrain the problem. By comparison, recovering these properties from a single image is vastly more difficult. To address this challenge, researchers at UC Berkeley have developed algorithms that infer the most likely intrinsic scene properties of a single image. Furthermore, the researchers have developed corresponding software that can render the scene with adjusted properties such as a different viewpoint, paints, lights, and shapes.