Dynamic Vision Sensors (DVS), also known as event cameras or neuromorphic sensors, enable extremely high temporal resolution and dynamic range compared to traditional sensors. However, DVS pixels only capture changes in intensity, which discards all static information. To overcome this issue, an additional photosensor array is needed either (1) in a two-sensor system or (2) combined into a single sensor with two-pixel technologies (DAVIS346). In both cases, the resulting system is bulkier, more complex to design, and more expensive to manufacture. UC Berkeley researchers have developed an event-based imaging system that can capture static intensity, thereby eliminating the need of such two-pixel technologies by extracting underlying static intensity information directly from DVS pixels. The researchers have also demonstrated the feasibility of this approach through the analysis of noise statistics in event cameras.

 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. 

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Researchers at the University of California, Davis have developed a reconfigurable radiator array that produces a high frequency directed beam via uninterrupted, scalable, electronic beam steering.

Researchers at UCI have developed a novel method to combine common CCD (charge-coupled device) cameras with mid-infrared (MIR) technology in order to create an affordable and accessible spectroscopic camera for biochemical imaging.

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 terahertz (THz) detector that utilizes the micro-Doppler effect to detect vibrations and long-distance sounds.

Professor Bir Bhanu and colleagues at the  University of California, Riverside, have  developed a robust vehicle classification system based  on video images from the rear-side view of a vehicle. This  system classifies a vehicle into one of four  classes: sedan, pick-up truck, SUV/minivan,  and unknown. The system validates detected moving objects by a simple frame differencing approach. Table I shows the false alarm percentages over the different methods used to classify vehicles. Table II shows that the UCR method has the highest accuracy when compared to other known methods.       Figure 1 is the Dynamic Bayesian Network structure created by extracting data from surveillance. In conjunction with the car being spotted the Left Tail Light (LTL), License Plate (LP), Right Tail Light (RTL), and Rear Dimensions (RD) are identified simultaneously and set within the Dynamic Bayesian Network to accurately classify and identify the vehicle.    

This materials platform enables flexible engineering of infrared (IR) emissivity and development of thermal radiation devices beyond the Stefan-Boltzmann law. The materials structure is based on thin films of vanadium oxide (VO2) with judiciously designed graded W doping across a thickness less than the skin depth of electromagnetic screening (~100 nm). The infrared emissivity can be engineered to decrease in an arbitrary manner from ~ 0.75 to ~ 0.35 over a temperature range up to 50 C near room temperature. The large range of emissivity tuning and flexible adjustability is beyond the capability of regular materials or structures. This invention provides a new platform for unprecedented manipulation of thermal radiation and IR signals with a wide variety of applications, such as:  The emissivity can be programmed to precisely counteract the T^4 dependence in the Stefan-Boltzmann law and achieve a temperature dependent thermal radiation. Such a design enables a mechanically flexible and power-free infrared camouflage, which is inherently robust and immune to drastic temporal fluctuation and spatial variation of temperature. By tailoring structure and composition, the materials platform can create a surface with robust and arbitrary IR temperature image, regardless of the actual temperature distribution on the targets. This design of infrared "decoy" not only passively conceals the real thermal activity of the object, but also intentionally fools the camera with a counterfeited image. The materials platform can achieve strong temperature dependence of reflectivity over a broad wavelength from near-IR to far-IR, which is promising for high-sensitivity remote temperature sensing by thermoreflectance imaging, or active reflectance modulation of IR signals. 

UCLA researchers in the Department of Electrical Engineering have developed a sub-carrier successive approximation radar (SAR) system with a sufficiently high accuracy to capture three-dimensional images of objects concealed either under the clothing of a person, or within small packages. 

UCLA researchers in the Department of Electrical Engineering have developed a novel approach for terahertz (THz) quantum-cascade (QC) lasers to achieve scalable output power, high quality diffraction limited, and directive output beams.

Researchers at the University of California, Davis have developed a device-free, WiFi based framework that can isolate individual identity, from a small group of users, simply by observing variations in WiFi signals through a user’s gait.

Background: Transportation and vehicle classification systems are becoming smarter and more automated. For example, electronic toll collection systems have been introduced and drivers are not required to stop, eliminating road delays. New technologies have also been added to these systems that enable service providers to acquire data on what type of vehicles are utilizing their amenities as well as vehicle identification for safety & control purposes.  Brief Description: UCR Researchers have developed a method and system for vehicle classification using video imaging. This novel invention entails a vehicle ground clearance measurement system along with a video camera that captures a travelling vehicle and categorizes it into a vehicle class. The cameras on current methods and systems rely on side views of the vehicle, which can easily be obstructed by other vehicles.

UCLA researchers in the Department of Chemistry & Biochemistry have developed a novel image filtering algorithm that removes image noise while preserving image features with unprecedented fidelity.   

Professor Mona Jarrahi in the UCLA Department of Electrical Engineering has developed a technique for operating continuous-wave (CW) terahertz imaging and spectroscopy systems based on photoconductive terahertz sources and/or detectors that uses a low-duty-cycle optical pump, achieving high radiation powers and detection sensitivities without causing thermal breakdown, as well as higher quality image and spectra data.

Researchers at the University of California, Davis have developed a method using standard mobile device sensors assisting with facial authentication to overcome the limitations faced by current methods.

Improved reliability of fingerprint authentication is achieved through a unique vascular fingerprint which increases accuracy and verifies liveness.

Ringer distinguishes flexible regions from rigid regions of biomolecules such as drug receptors. To assess the generality and significance of the weak secondary peaks of uniquely modeled residues, we ran Ringer on 402 high-resolution (<=1.5 Å) crystal structures from the Protein Data Bank. Omit electron-density maps were analyzed to reduce the effects of model bias. When applied after refinement is considered complete, Ringer discovers polymorphism at over 3.5 times the frequency that is currently modeled in the PDB. Multiple conformers are found for >18% of unbranched residues in a test set of 402 high-resolution structures, in addition to the 5.1% that are already modeled. More than a method for enhancing crystallographic refinement, however, Ringer is best used as a tool for systematically detecting low-occupancy structural features. The hidden conformational substates identified using Ringer provide clues to the functional roles of protein structural polymorphism and to assess the response of protein side chain distributions to perturbations including ligand binding, temperature changes and mutations. In calmodulin, for example, Ringer identifies side chains that undergo conformational population inversions and side-chain rigidification upon peptide binding, linking the structure to dynamic properties. Similarly, in human proline isomerase, Ringer was used to define the nature of a coupled conformational switch in the free-enzyme that defines motions that occur during turnover. In both cases, the alternate conformations identified by Ringer provided structural insights not available from any other experimental technique. Link to overview of Ringer software

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