Researchers at the University of California, Davis have developed a sensor for measuring food spoilage of nuts, seeds, and oils. It measures volatile organic compounds as a biomarker of food spoilage through a simple device in only three minutes.

Researchers at the University of California, Davis have developed a device for multi-dimensional data extraction at the molecular level to allow one to simultaneously detect the presence of a single-molecule electrically, and to extract a chemical fingerprint to identify that molecule optically.

The disclosed methods offer a robust approach to accurately quantify skin optical properties across different skin tones, facilitating improved diagnosis, monitoring, and treatment in dermatology.

Volatile organic compounds (VOCs) are released by various products and during various processes. Ethanol is one such VOC that is released as an important byproduct of alcoholic fermentation. Ethanol emitted during fermentation can be estimated using the amount of liquid lost during storage. The instrumentation needed to accurately quantify ethanol emissions is specialized and costly. Researchers at UC Santa Cruz have developed low-cost VOC sensors that are useful for the wine industry, among others.

 The dynamic patterning of 3D optical point clouds has emerged as a key enabling technology in volumetric processing across a number of applications. In the context of biological microscopy, 3D point cloud patterning is employed for non-invasive all-optical interfacing with cell ensembles. In augmented and virtual reality (AR/VR), near-eye display systems can incorporate virtual 3D point cloud-based objects into real-world scenes, and in the realm of material processing, point cloud patterning can be mobilized for 3D nanofabrication via multiphoton or ultraviolet lithography. Volumetric point cloud patterning with spatial light modulators (SLMs) is therefore widely employed across these and other fields. However, existing hologram computation methods, such as iterative, look-up table-based and deep learning approaches, remain exceedingly slow and/or burdensome. Many require hardware-intensive resources and sacrifices to volume quality.To address this problem, UC Berkeley researchers have developed a new, non-iterative point cloud holography algorithm that employs fast deterministic calculations. Compared against existing iterative approaches, the algorithm’s relative speed advantage increases with SLM format, reaching >100,000´ for formats as low as 512x512, and optimally mobilizes time multiplexing to increase targeting throughput. 

Brief description not available

Brief description not available

The inventors have developed a scalable laser aperture that emits light perpendicular to the surface. The aperture can, in principal, scale to arbitrarily large sizes, offering a universal architecture for systems in need of small, intermediate, or high power. The technology is based on photonic crystal apertures, nanostructured apertures that exhibit a quasi-linear dispersion at the center of the Brillouin zone together with a mode-dependent loss controlled by the cavity boundaries, modes, and crystal truncation. Open Dirac cavities protect the fundamental mode and couple higher order modes to lossy bands of the photonic structure. The technology was developed with an open-Dirac electromagnetic aperture, known as a Berkeley Surface Emitting Laser (BKSEL).  The inventors demonstrate a subtle cavity-mode-dependent scaling of losses. For cavities with a quadratic dispersion, detuned from the Dirac singularity, the complex frequencies converge towards each other based on cavity size. While the convergence of the real parts of cavity modes towards each other is delayed, going quickly to zero, the normalized complex free-spectral range converge towards a constant solely governed by the loss rate of Bloch bands. The inventors show that this unique scaling of the complex frequency of cavity modes in open-Dirac electromagnetic apertures guarantees single-mode operation of large cavities. The technology demonstrates scaled up single-mode lasing, and confirmed from far-field measurements. By eliminating limits on electromagnetic aperture size, the technology will enable groundbreaking applications for devices of all sizes, operating at any power level. BACKGROUND Single aperture cavities are bounded by higher order transverse modes, fundamentally limiting the power emitted by single-mode lasers, as well as the brightness of quantum light sources. Electromagnetic apertures support cavity modes that rapidly become arbitrarily close with the size of the aperture. The free-spectral range of existing electromagnetic apertures goes to zero when the size of the aperture increases. As a result, scale-invariant apertures or lasers has remained elusive until now.  Surface-emitting lasers have advantages in scalability over commercially widespread vertical-cavity surface-emitting lasers (VCSELs). When a photonic crystal is truncated to a finite cavity, the continuous bands break up into discrete cavity modes. These higher order modes compete with the fundamental lasing mode and the device becomes more susceptible to multimode lasing response as the cavity size increases. 

Brief description not available

Brief description not available

Brief description not available

Researchers at the University of California, Davis have developed an optical transceiver that uses compressive sensing to reduce bandwidth requirements and improve signal resolution.

This technology utilizes inter-subject measurement of brain activity for the purpose of matching individuals. In particular, the invention measures the similarity and differences in neural activity patterns between interacting individuals (either in person or online) as a signature measurement for their matching capabilities. Relevant applications can be in the world of human resources (e.g., building collaborative teams), patient-therapist matching and others. The application relies on the utilization of both custom and commercial devices for measuring brain activity.

In developed countries, buildings demand a large percentage of a region's energy-generating requirements. This has led to an urgent need for efficient buildings with reduced energy requirements. In office buildings, lighting takes up 20% to 45% of the total energy consumption. Furthermore, the adoption of smart lighting control strategies such as daylight harvesting is shown to reduce lighting energy use by 30% to 50%.For most closed-loop lighting control systems, the real-time data of the daylight level at areas of interest (e.g., the office workbench) are the most important inputs. Current state-of-the-art solutions use dense arrays of luxmeters (photosensors) to monitor the daylight environment inside buildings. The luxmeters are placed on either workbenches, or ceilings and walls near working areas. Digital cameras are used in controlled laboratory environments and occasionally in common buildings to evaluate glare resulting from excessive daylight. The disadvantage of these sensor-based approaches is that they're expensive to install and commission. Additionally, the sample area of these sensors is limited to either the area of the luxmeters or the view of the cameras. Consequently, many sensors are needed to measure the daylight in a large office space.To address this situation, researchers at UC Berkeley developed a portable cyber-physical system for real time, daylight evaluation in buildings, agriculture facilities, and solar farms (collectively referred to as "structures").

Researchers at the University of California, Davis have developed a method of eliminating electrical current mismatches in the charge pumps of phase-locked loops (PLL) systems - thereby increasing their power efficiency and phase detection capabilities.

Researchers at UC Berkeley have developed techniques for optical voltage sensing of power grids as well voltage sensing within a human or animal subject. The safe, accurate and economical measurement of time-varying voltages in electric power systems poses a significant challenge. Current systems for measuring power grid voltages typically involve instrument transformers. Although these systems are accurate and robust to environmental conditions, they are bulky, heavy, and expensive, thus limiting their use in microgrids and sensing applications. An additional drawback is that some designs explode when they fail. Optical methods for direct measurement of high voltages have gained attention in recent years, mainly due to the high available bandwidth, intrinsic electrical isolation, and the potential for low cost and remote monitoring. Stage of Research The inventors have developed a low-Q resonant optical cavity-based voltage sensor based on a piezoelectric AIN thin film that transduces a voltage applied across the piezo terminals into a change in the resonant frequency of the cavity. This sensor can be fabricated with high yield and low cost (<$1), which makes it uniquely well-suited to reduce the cost of grid voltage measurement.

Scanning tunneling microscopy (STM) techniques and associated spectroscopic (STS) methods, such as dI/dV point spectroscopy, have been widely used to measure electronic structures and local density of states of molecules and materials with unprecedented spatial and energy resolutions. However, the quality of dI/dV spectra highly depends on the shape of the probe tips, and atomically sharp tips with well-defined apex structures are required for obtaining reliable spectra. In most cases, STS measurements are performed in ultra-high vacuum  and low temperature (4 K) to minimize disturbances. Advance tip preparation and constant in situ tip conditioning are required before and during the characterization of target molecules and materials. A common way to prepare STM tips is to repetitively poke them on known and bare substrates (i.e. coinage metals or silicon) to remove contaminations and to potentially coat the tip with substrate atoms. The standard dI/dV spectra of the substrate is then used as a reference to determine whether the tip is available for further experiments. However, tip geometry changes during the poking process are unpredictable, and consequently tip conditioning is typically slow and needs to be constantly monitored. Therefore, it restricts the speed of high-quality STM spectroscopic studies. In order to make efficient use of instrument idle time and minimize the research time wasted on tip conditioning, UC Berkeley researchers developed software based on Python and machine learning that can automate the time-consuming tip conditioning processes. The program is designed to do tip conditioning on Au(111) surfaces that are clean or with low molecular coverage with little human intervention. By just one click, the program is capable of continued poking until the tip can generate near-publication quality spectroscopic data on gold surfaces. It can control the operation of a Scienta Omicron STM and automatically analyze the collected topographic images to find bare Au areas that are large enough for tip conditioning. It will then collect dI/dV spectra at selected positions and use machine learning models to determine their quality compared to standard dI/dV spectra for Au20 and determine if the tip is good enough for further STS measurements. If the tip condition is not ideal, the program will control the STM to poke at the identified positions until the machine learning model predicts the tip to be in good condition.

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.

Researchers at the University of California, Davis have developed an amplifier technology that boosts power output in order to improve data transmission speeds for high-frequency communications.

UCLA researchers in the Department of Electrical and Computer Engineering have developed a wirelessly powered, flexible sensor that detects pipe leaks over long distances.

UCLA Researchers in the Departments of Bioengineering and Mathematics have developed a method to generate uniform, thermodynamically stabilized microdroplets with digitizable solid structures.

This invention leverages the nuclease activity of CRISPR proteins for the direct, sensitive detection of specific nucleic acid sequences. This all-in-one detection modality includes an internal Nuclease Chain Reaction (NCR), which possesses an amplifying, feed-forward loop to generate an exponential signal upon detection of a target nucleic acid.Cas13 or Cas12 enzymes can be programmed with a guide RNA that recognizes a desired target sequence, activating a non-specific RNase or DNase activity. This can be used to release a detectable label. On its own, this approach is inherently limited in sensitivity and current methods require an amplification of genetic material before CRISPR-base detection. 

UCLA researchers in the Department of Electrical and Computer Engineering have developed a terahertz (THz) detector that utilizes the micro-Doppler effect to detect vibrations and long-distance sounds.

UCLA researchers in the Department of Electrical and Computer Engineering have developed a highly sensitive, wearable hormone monitoring platform.

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a bioinspired, thin and flexible liquid metal filled resistive PDMS microchannel shear force sensing skin.