Learn more about UC TechAlerts – Subscribe to categories and get notified of new UC technologies

Browse Category: Imaging > Other

Categories

[Search within category]

High-Frequency Imaging and Data Transmission Using a Re-configurable Array Source with Directive Beam Steering

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.

(SD2021-087) Bioinspired Wet Adhesives: Suction discs for adhesion to rough, delicate, and wet surfaces

Adhesion involves highly complex and hierarchical structures in nature, and by understanding the biological intricacies of such adhesive structures, one can improve engineered adhesives. The role of reversible adhesion in both the natural world and in engineering is to temporarily bind to a surface, providing the opportunity to detach and re-attach as needed. In nature, animals use attachment to enhance their fitness.  In robotics, reversible adhesion enables improved manipulation and locomotion by managing contact at the interface between the robot and its environment.

2-D Polymer-Based Device for Serial X-Ray Crystallography

Researchers at the University of California, Davis have developed a single-use chip for the identification of protein crystals using X-ray based instruments.

Protein Inhibitor of Type II-A CRISPR-Cas System

The inventors have discovered three protein inhibitors of the type II-A CRISPR-Cas system that specifically inhibit Cas9 from staphylococcus aureus. This finding is of potential importance to many companies in the CRISPR space. 

Protein Inhibitor of Type VI-B CRISPR-Cas System

The inventors have discovered the first protein inhibitor of the type VI-B CRISPR-Cas system. By controlling this CRISPR system, one could possibly ameliorate the toxicity and off-target cleavage activity observed with the use of the type VI CRISPR system. Moreover, these proteins can also serve as an antidote for instances where the use of CRISPR-Cas technology poses a safety risk. Additionally, this technology can also be used for engineering genetic circuits in mammalian cells. This finding is of potential importance to many companies in the CRISPR space. 

Deep Learning Techniques For In Vivo Elasticity Imaging

Imaging the material property distribution of solids has a broad range of applications in materials science, biomechanical engineering, and clinical diagnosis. For example, as various diseases progress, the elasticity of human cells, tissues, and organs can change significantly. If these changes in elasticity can be measured accurately over time, early detection and diagnosis of different disease states can be achieved. Elasticity imaging is an emerging method to qualitatively image the elasticity distribution of an inhomogeneous body. A long-standing goal of this imaging is to provide alternative methods of clinical palpation (e.g. manual breast examination) for reliable tumor diagnosis. The displacement distribution of a body under externally applied forces (or displacements) can be acquired by a variety of imaging techniques such as ultrasound, magnetic resonance, and digital image correlation. A strain distribution, determined by the gradient of a displacement distribution, can be computed (or approximated) from measured displacements. If the strain and stress distributions of a body are both known, the elasticity distribution can be computed using the constitutive elasticity equations. However, there is currently no technique that can measure the stress distribution of a body in vivo. Therefore, in elastography, the stress distribution of a body is commonly assumed to be uniform and a measured strain distribution can be interpreted as a relative elasticity distribution. This approach has the advantage of being easy to implement. The uniform stress assumption in this approach, however, is inaccurate for an inhomogeneous body. The stress field of a body can be distorted significantly near a hole, inclusion, or wherever the elasticity varies. Though strain-based elastography has been deployed on many commercial ultrasound diagnostic-imaging devices, the elasticity distribution predicted based on this method is prone to inaccuracies.To address these inaccuracies, researchers at UC Berkeley have developed a de novo imaging method to learn the elasticity of solids from measured strains. Our approach involves using deep neural networks supervised by the theory of elasticity and does not require labeled data for the training process. Results show that the Berkeley method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy and missing measurements.

Gigahertz Bandwidth Asic For Time-Resolved Frequency Domain Optical Metrology

Inventors from UC Irvine and Beckman Laser Institute have created a low-cost, compact technology for in vivo optical imaging of biological tissues.The technology has the ability to be adapted into a wearable device for bedside imaging in hospitals and clinics.

Real-Time Imaging in Low Light Conditions

Prof. Luat Vuong and colleagues from the University of California, Riverside have developed a method for imaging in low light and low signal-to-noise conditions. This technology works by using a dense neural network to reconstruct an object from intensity-only data and efficiently solves the inverse mapping problem without performing iterations with each image and without deep learning schemes. This network operates without learned stereotypes with low computational complexity, low reconstruction latency, decreased power consumption, and robust resistance to disturbances compared to current imaging technologies. Fig 1: Theoretical/simulation accuracy for multi-vortex arrays - 3,5,7 correspondingly using the dense single layer neural net, in comparison to convolutional NN and a single layer NN using conventional imaging. The SNR is provided for the conventional imaging scheme.  

Embedded Power Amplifier

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.

Polarization-Sensitive Optical Coherence Tomography Using a Polarization-Insensitive Detector

A polarization-sensitive optical coherence tomography (PS-OCT) is a common approach to non-invasively imaging in biomedical applications. The inventors have come up with a new way of creating a PS-OCT that is cheaper and simpler.

Vehicle Make and Model Identification

Prof. Bir Bhanu and his colleagues from the University of California, Riverside have developed a method for  analyzing real-time video feed of vehicles from a rear  view  perspective to identify the make and model of a vehicle. This method works by using a software system for detecting the Regions-of-Interest (ROIs) of moving vehicles and moving shadows, computing structural and other features and using a vehicle make and model database for vehicle identification. The system performs calculations based on factors found in all vehicles, so it is reliable regardless of vehicle color and type. The system is compatible with low resolution video feed, so it is able to analyze video feed in real-time. Thus, this technology holds potential for innovating fields like vehicle surveillance, vehicle security, class-based vehicle tolling, and traffic monitoring where reliable real-time video analysis is needed.  Figure 1: Example of the direct rear view of moving vehicles.  

Vibration Sensing and Long-Distance Sounding with THz Waves

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.

Vehicle Logo Identification in Real-Time

Brief description not available

Design Of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks

UCLA researchers in the Department of Electrical and Computer Engineering have developed a diffractive neural network that can process an all-optical, 3D printed neural network for deep learning applications.

High External-Efficiency Nanofocusing for Lens-Free Near-Field Optical Microscopy

Profs. Ruoxue Yan, Ming Liu, and their colleagues from the University of California, Riverside have developed a two-step sequential broadband nanofocusing technique with an external nanofocusing efficiency of ~50% over nearly all the visible range on a fibre-coupled nanowire scanning probe. By integrating this with a basic portable scanning tunneling microscope, the technology captured images with spatial resolution as low as one nanometer at high resolution. The high performance and vast versatility offered by this fibre-based nanofocusing technique allows for the easy incorporation of nano-optical microscopy into various existing measurement platforms.  Fig. 1: High-resolution NSOM mapping. a, scanning tunnelling microscope topographic image of single wall carbon nanotubes on a gold film. Top inset: cross-sectional profile along the dashed line. Bottom inset: the possible configurations of the bundle.  

Materials Platform for Flexible Emissivity Engineering

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. 

Simultaneous Acquisition Of Multiple Epi-Fluorescence Micrographs

UCSF researchers have developed a novel microscope system which allows multiple images of a single sample to be acquired simultaneously.  These images are structured and, when coupled with conventional structured illumination image processing methods, can provide video rate, super-resolution micrographs. 

Graphene Oxide Based Affinity Grids As Sample Supports For High- Resolution Cryo Electron Microscopy.

Inventors at UCSF have developed a novel and economical method to produce a new generation of Cryo-EM sample grids that bind purify and protect biomolecule samples.

Head-Mounted Display EEG Device

Diagnosis and detection of progression of neurological disorders remain challenging tasks. For example, a validated portable objective method for assessment of degenerative diseases would have numerous advantages compared to currently existing methods to assess functional loss in the disease. An objective EEG-based test would remove the subjectivity and decision-making involved when performing perimetry, potentially improving reliability of the test. A portable and objective test could be done quickly at home under unconstrained situations, decreasing the required number of office visits and the economic burden of the disease. In addition, a much larger number of tests could be obtained over time. This would greatly enhance the ability of separating true deterioration from measurement variability, potentially allowing more accurate and earlier detection of progression. In addition, more precise estimates of rates of progression could be obtained.

System For Fast Multi-Photon Imaging Using Spectrally Diffracted Excitation

UCLA researchers in the Department of Electrical Engineering have developed a new system for fast multi-photon imaging using spectrally diffracted excitation.

Chronoprints: Identifying Adulterated Samples in Food and Drug Safety

Prof. Will Grover and his colleague at the University of California have developed a method to identify adulterated drugs and foods by observing how they behave when disturbed by temperature changes or other causes. Images of the sample’s behavior as it freezes over time are captured and processed into chronoprints.  Chronoprints are fundamentally bitmap images of samples on a computer, and it is possible to leverage existing image analysis and comparison techniques that have been already developed to analyze Chronoprints. Fig. 1 Producing a "chronological fingerprint" or chronoprint capturing how six samples (in this example, authentic and adulterated samples of an over-the-counter liquid cold medicine) respond to a perturbation over space and time (in this case, a rapidly changing temperature gradient). (A) A microfluidic thermometer chip containing the samples is partially immersed in liquid nitrogen to establish a rapidly changing temperature gradient along the chip. (B) The chip contains six samples (red) loaded in microfluidic channels that run parallel to the dynamic temperature gradient. (C) An inexpensive USB microscope records a video of the physical changes in the samples as they react to the dynamic temperature gradient.  Fig. 2 By reducing each channel image to a single column of pixels, and then placing these columns side-by-side, we create a bitmap image (the sample’s chronoprint) that captures how the sample changes over space (the y-axis) and time (the x-axis). Finally, by comparing the chronoprints of all six samples in the chip, we can determine whether the samples are either likely the same or definitely different.  

Method for Concentration and Formulation of Radiopharmaceuticals

Researchers at the UCLA Department of Medical and Molecular Pharmacology have developed a compact microfluidic device that is able to achieve rapid concentration and/or reformulation of PET tracers after HPLC purification.

Incorporation of Mathematical Constraints in Methods for Dose Reduction and Image Enhancement in Tomography

UCLA researchers have developed an algorithm that enables construction of 3D images from tomographic data through iterative methods with the incorporation of mathematical constraints. This methodology is an improvement over conventional techniques as it allows for radiation dose reduction and improved resolution.

Quantitative Multiparametric PET/CT Imaging for Nonalcoholic Fatty Liver Diseases

Researchers at the University of California, Davis have developed a quantitative imaging method to detect and characterize liver inflammation for diagnosing a wide spectrum of nonalcoholic fatty liver diseases (NAFLDs).

Multi-Frequency Harmonic Acoustography for Target Identification and Border Detection

UCLA researchers in the Department of Bioengineering, Electrical Engineering, and Head and Neck Surgery have developed a novel ultrasound-based imaging technique that can be used to analyze tumor margins during surgery.

  • Go to Page: