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Preserving Protein Function Via Statistically Random Heteropolymers

Protein-based materials have the potential to change the current paradigm of materials science. However, it still remains a challenge to preserve protein hierarchical structure and function while making them readily processable. Protein structure is inherently fluid, and it is this property that contributes to their fragility outside of their native environment. Through the use of rationally designed statistically random heteropolymers, it is possible to stabilize proteins at each hierarchical level and process them in organic solvents, a common need for materials fabrication. The chemical and architectural complexities of statistically random heteropolymers provide a modular platform for tunable protein-polymer-solvent interactions. This provides opportunities not offered by small molecule surfactants or amphiphilic block copolymers. Through evaluation of horseradish peroxidase and green fluorescent protein structure, we show that statistically random heteropolymers can stabilize enzymes. Allowing for activity retention when stored in organic solvent, over 80% activity was observed after 24 hours. Furthermore, horseradish peroxidase and chymotrypsin proteins, when encapsulated in statistically random heteropolymers, are still accessible to their substrates while remaining inaccessible to the denaturing organic solvent. Statistically random heteropolymers have potential in creating stimuli-reponsive materials and nanoreactors composed of proteins and synthetic materials.

Real-time, Passive Non-Line-of-Sight Imaging with Thermal Camera by Exploiting Bidirectional Reflectance Distribution Function

UCLA researchers in the Department of Electrical and Computer Engineering have developed a Non-line-of-sight (NLOS) Imaging System using low cost thermal cameras that enable 3D recovery of NLOS heat source for imaging around corners.

Low-Intrusion Plasma Probe

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed an inexpensive and easily implemented plasma diagnostic tool, the Low-Intrusion Probe.

Lambda-Reservoir Computing

UCLA researchers in the Department of Electrical and Computer Engineering have developed a Spectral Reservoir Computer that processes data using nonlinear optical interactions.

A Method for Characterization of Device and Material and Communication at Thz Frequencies

UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel method for real-time detection and characterization of pulsed THz waveforms that features differential detection of high sensitivity, and phase diversity to overcome the dispersion penalty for wideband operation.

Hydrogel Thin Film-Based Dynamic Structural Color System for Sensing, Camouflage, and Adaptive Optics

UCLA researchers from the Department of Material Science and Engineering have developed a novel hydrogel color system that can be used for dynamic sensing, camouflage, and adaptive optics.

Spectro-Temporal Lidar

UCLA researchers in the Department of Electrical and Computer Engineering have developed a LIDAR sensor that collects high frame-rate 3D measurements for autonomous vehicle and robotics applications.

Multifaceted III-Nitride Surface-Emitting Laser

Improved laser capability using III-Nitride VCSELs as the illumination source for sensing applications of a fluorescent sample.

Sub-Carrier Successive-Approximation Mm-Wave Radar For High-Resolution 3D Imaging

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. 

External Cavity Laser Based Upon Metasurfaces

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.

Facial Recognition & Vehicle Logo Super-Resolution System

Background: The video surveillance market is projected to grow annually at 17% and reach $42B by 2020. Video surveillance is a popular tool to track and monitor movement of people and vehicles to provide protection and discover information for investigations. Current technologies are competent in capturing images but not with high definition. Therefore, a more advanced security system that is smarter and multidimensional is needed.  Brief Description: UCR Researchers have developed a novel method and system for unified face representation for individual recognition in surveillance videos along with vehicle recognition. They extracted facial images from a video, generated an emotion avatar image (EAI) and computed features using their innovative algorithms. Low-resolution vehicle images can also be enhanced by using their super-resolution algorithms to produce high-resolution images. Existing technologies can only take frontal images but this new technology can handle out-of-plane, rotated images.

A Video Based Hierarchical Vehicle Classification System

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.

An Ultra-Sensitive Method for Detecting Molecules

To-date, plasmon detection methods have been utilized in the life sciences, electrochemistry, chemical vapor detection, and food safety. While passive surface plasmon resonators have lead to high-sensitivity detection in real time without further contaminating the environment with labels. Unfortunately, because these systems are passively excited, they are intrinsically limited by a loss of metal, which leads to decreased sensitivity. Researchers at the University of California, Berkeley have developed a novel method to detect distinct molecules in air under normal conditions to achieve sub-parts per billion detection limits, the lowest limit reported. This device can be used detecting a wide array of molecules including explosives or bio molecular diagnostics utilizing the first instance of active plasmon sensor, free of metal losses and operating deep below the diffraction limit for visible light.  This novel detection method has been shown to have superior performance than monitoring the wavelength shift, which is widely used in passive surface plasmon sensors. 

Highly Accurate Occupancy Estimation Using RF Signals and Wi-Fi

A framework that counts the number of people in an area based on RF signals and a Wi-Fi card or network. 

Distributed Scalable Interaction Paradigm for Multi-User Interaction Across Tiled Multi-Displays

The technology is a method for multiple users to interact simultaneously with multiple tiled displays.Under this technology, multiple users are allowed to interact with a tiled display with a distributed registration technique.It features easy scalability across different applications, modalities and users and user interactions involve hand gestures or are laser-based.

Image Filtering Algorithm for Enhanced Noise Removal and Feature Preservation

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.   

Crystal Laser Wakefield Accelerator and Its Applications

The technology is a development of a more efficient particle accelerator in terms of energy, cost and space considerations. It is used in particle acceleration applications (cancer treatment, manufacture of components for electronic devices, etc.) The technology is an ultra-compact particle accelerator and particle source. The properties include: Laser Wakefield Accelerator in a solid medium, i.e. crystal in which the Laser Wakefield by charged particle beam bunch. The driver is a high intensity pulsed x-ray. The technology applicable to electron, proton, and ion acceleration and can be used for ultra-compact particle source (neutrons, muons, and neutrinos)

A Neuromorphic Robot that Interacts with People Through Tactile Sensing and Bi-directional Learning

The device is an interactive neuromorphic robot, using to mimic neuro-biological architectures and learning.Properties include:a spiking neural network to control robot behavior, inexpensive parts which are easily available, and two-way learning and behavior shaping.The technology is autonomous, highly mobile, and includes on-board measurement equipment.

Cacophony: A Framework for Next Generation Software Sensors

The technology is a software architecture for providing robust predictions for software systems from cloud sourced data points. Properties include:the ability to “wrap” existing software sensors with additional services. The technology is used by executing software on a cloud based server and manipulating data points from user update systems, such as Waze, and provide predictive services around these data points.

Low-Duty-Cycle Continuous-Wave Photoconductive Terahertz Imaging and Spectroscopy Systems

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.

Sensor-Assisted Facial Authentication System For Smartphones

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.

Superhydrophobic Induced High Numerical Plastic Lenses

The application of novel manufacturing techniques, chemical modifications and alternative materials produces the next generation of lenses. These lenses are inexpensive, contain improved numerical aperture and can be easily manufactured. Overall, these improvements create new applications for miniaturized optical and optical electronic devices.

System And Method For Capturing Vital Vascular Fingerprint

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

Self-Calibrating Micro-Fabricated Resonant Load Cells

The technology is a cost-efficient and highly sensitive micro-mechanical test frames for the characterization of small-scale materials and structures. It is designed for a manufacturing process and self-calibration procedure for the practical use of MEMS resonant sensors as ultra-sensitive load cells. The properties of the technology include:cost-effective fabrication and implementation, load cells with unprecedented combinations of resolution and range, the ability for load cells to be mounted on hybrid micro-mechanical test frames or integrated with on-chip actuators, and the calibration involves no external instrumentation.

Chip-Based Droplet Sorting

Microfluidic devices are poised to revolutionize environmental, chemical, biological, medical and pharmaceutical detectors and diagnostics. The term “microfluidic devices” loosely describes the new generation of instruments that mix, react, count, fractionate, detect, and characterize samples in a micro-electro-mechanical system (MEMS) circuit manufactured through standard semiconductor lithography techniques. Although a wide array of microfluidic technologies are currently available, novel MEMS fluidic systems are needed as scientists continue to work with smaller sample volumes and desire devices with increased sensitivity and effectiveness. Researchers at the University of California, Irvine have developed a unique non-contact system for sorting monodisperse water-in-oil emulsion droplets in a microfluidic device. The technology can be coupled to other on-chip processes to increase device efficiency by sorting out un-reacted droplets.

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