Radiation detectors are typically instrumented with low noise preamplifiers that generate voltage pulses in response to energy deposits from particles (x-rays, gamma-rays, neutrons, protons, muons, etc.). This preamplifier signal must be further processed in order to improve the signal to noise ratio, and then subsequently estimate various properties of the pulse such as the pulse amplitude, timing, and shape. Historically, this “pulse processing” was carried out with complex, purpose-built analog electronics. With the advent of digital computing and fast analog to digital converters, this type of processing can be carried out in the digital domain.There are a number of commercial products that perform “hardware” digital pulse processing. The common element among these offerings is that the pulse processing algorithms are implemented in hardware (typically an FPGA or high performance DSP chip). However this hardware approach is expensive, and it's hard to tailor for a specific detector and application.To address these issues, researchers at UC Berkeley developed a solution that performs the pulse processing in software on a general purpose computer, using digital signal processing techniques. The only required hardware is a general purpose, high speed analog to digital converter that's capable of streaming the digitized detector preamplifier signal into computer memory without gaps. The Berkeley approach is agnostic to the hardware, and is implemented in such a way as to accommodate various hardware front-ends. For example, a Berkeley implementation uses the PicoScope 3000 and 5000 series USB3 oscilloscopes as the hardware front-end. That setup has been used to process the signal from a number of semiconductor and scintillator detectors, with results that are comparable to analog and hardware digital pulse processors.In comparison to current hardware solutions, this new software solution is much less expensive, and much more easily configurable. More specifically, the properties of the digital pulse shaping filter, trigger criteria, methods for estimating the pulse parameters, and formatting/filtering of the output data can be adjusted and tuned by writing simple C/C++ code.

Advances in Augmented and Virtual Reality (AR/VR) headsets and displays have introduced alternative systems of immersive and context aware communications platforms.  However, one key factor that can cause a major bottleneck in future AR/VR communication is the limited space surrounding the user in the real world.  In Augmented Reality, unlimited spatial data can be imported to the user’s current surrounding.  Many of these virtual objects do not hold spatial limitations to themselves and are only restricted to the user’s real world surrounding constraints.  They can be visualized, augmented and placed anywhere necessary in the space, as long as they are within the users’ environmental boundaries.   However, this one-way spatial limitation between virtual and real objects does not always apply in communication applications where two or more users, all having spatial discrete constraints, are interacting with each other in a spatial setting.  All parties of the tele-conference (or other communication methods) hold unique spatial limitations (room size, furniture settings, etc.) and consequently their virtual doubles or Avatars may not be able practice the same spatial relationship and arrangement between the real-world spaces and their corresponding boundaries for all parties.  This would result in misalignment of head and body gestures, spatial sound errors and other micro expression errors due to the incorrect positioning of each member of the virtual call.   UC researchers have developed a search and recommendation process which can identify mutual accessible boundaries of all the parties of a communication setting (AR conference calls, virtual calls, tele-immersion, etc.) and provide each user the exact location to position itself and where to move surrounding objects so that all parties of the call can hold a similar spatial relationship to each other with minimum effort.  Such process would allow all members of the virtual call to augment other members in their own spaces, by considering the spatial limitations of all participants in the virtual/augmented reality call.    The process facilitates promoting remote communication in all consumer levels, in both commercial and personal settings.  It would also benefit remote workplace procedures, allowing workers and employees to communicate efficiently together, without accessing large commercial spaces.  Preserving micro-gestures and expressions in another feature of this process, maintaining different attributions of social interactions and effective communications.

Many areas of intellectual property law involve subjective judgments regarding confusion or similarity. For example, in trademark or trade dress lawsuits a key factor considered by the court is the degree of visual similarity between the trademark or product designs under consideration. Such similarity judgments are nontrivial, and may be complicated by cognitive factors such as categorization, memory, and reasoning that vary substantially across individuals. Currently, three forms of evidence are widely accepted: visual comparison by litigants, expert witness testimonies, and consumer surveys. All three rely on subjective reports of human responders, whether litigants, expert witnesses, or consumer panels. Consequently, all three forms of evidence potentially share the criticism that they are subject to overt (e.g. conflict of interest) or covert (e.g. inaccuracy of self-report) biases.To address this situation, researchers at UC Berkeley developed a technology that directly measures the mental state of consumers when they attend to visual images of consumer products, without the need for self-report measures such as questionnaires or interviews. In so doing, this approach reduces the potential for biased reporting.  

Spatial computing experiences are constrained by the real-world surroundings of the user.  In such experiences, augmenting virtual objects to existing scenes require a contextual approach, where geometrical conflicts are avoided, and functional and plausible relationships to other objects are maintained in the target environment.  Yet, due to the complexity and diversity of user environments, automatically calculating ideal positions of virtual content that is adaptive to the context of the scene is considered a challenging task.    UC researchers have developed a framework which augments scenes with virtual objects using an explicit generative model to learn topological relationship from priors extracted from a real-world and/or synthetic 3D datasets.  Primarily designed for spatial computing applications, SceneGen extracts features from rooms into a novel spatial representation which encapsulates positional and orientational relationships of a scene which captures pairwise topology between objects, object groups, and the room.  The AR application iteratively augments objects by sampling positions and orientations across a room to create a probabilistic heat map of where the object can be placed.  By placing objects in poses where the spatial relationships are likely, we are able to augment scenes that are realistic. 

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.

Electromagnetic noise from surfaces is one of the limiting factors for the performance of solid state and trapped ion quantum information processing architectures. This noise introduces gate errors and reduces the coherence time of the systems. Accordingly, there is great commercial interest in reducing the electromagnetic noise generated at the surface of these systems.Surface treatment using ion bombardment has shown to reduce electromagnetic surface noise by two orders of magnitude. In this procedure ions usually from noble gasses are accelerated towards the surface with energies of 300eV to 2keV. Until recently, commercial ion guns have been repurposed for surface cleaning. While these guns can supply the ion flux and energy required to prepare the surface with the desired quality, they are bulky and limit the laser access, making them incompatible with the requirements for ion trap quantum computing.To address this limitation, UC Berkeley researchers have developed an ion gun that enables in-situ surface treatment without sacrificing high optical access, enabling in situ use with a quantum information processor.

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 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.