This technology revolves around Optical Coherence Tomography (OCT), a noninvasive imaging method that provides detailed cross-sectional images of tissue microstructure and blood flow. OCT utilizes either time domain (TDOCT) or Fourier domain (FDOCT) approaches, with FDOCT offering superior sensitivity and speed. Doppler OCT combines Doppler principles with OCT to visualize tissue structure and blood flow concurrently. Additionally, polarization-sensitive OCT detects tissue birefringence. Advanced methods aim to enhance the speed and sensitivity of Doppler OCT, crucial for various clinical applications such as ocular diseases and cancer diagnosis. Swept source FDOCT systems further improve imaging capabilities by increasing range and sensitivity. Overall, this technology represents significant advancements in biomedical imaging, offering insights into both structural and functional aspects of tissue physiology.

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Researchers at the University of California, Davis have developed an invention that utilizes an integrated Raman scanning head and machine vision for high throughput chemical analysis of liquid biopsy samples.

This patent application describes methods for non-invasive, label-free imaging of the cellular immune response in human skin using a nonlinear optical imaging system.

Volumetric additive manufacturing and vat-polymerization 3D printing methods rapidly solidify freeform objects via photopolymerization, but problematically raises the local temperature in addition to degree-of-conversion (DOC). The generated heat can critically affect the printing process as it can auto-accelerate the polymerization reaction, trigger convection flows, and cause optical aberrations. Therefore, temperature measurement alongside conversion state monitoring is crucial for devising mitigation strategies and implementing process control. Traditional infrared imaging suffers from multiple drawbacks such as limited transmission of measurement signal, material-dependent absorptions, and high background signals emitted by other objects. Consequently, a viable temperature and DOC monitoring method for volumetric 3D printing doesn’t exist.To address this opportunity, UC Berkeley researchers have developed a tomographic imaging technique that detects the spatiotemporal evolution of temperature and DOC during volumetric printing. The invention lays foundations for the development of volumetric measurement systems that uniquely resolve both temperature and DOC in volumetric printing.This novel Berkeley measurement system is envisaged as an integral tool for existing manufacturing technologies, such as computed axial lithography (CAL, Tech ID #28754), and as a new research tool for commercial biomanufacturing, general fluid dynamics, and more.

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.

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 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. 

Large format active or deformable mirrors can enable optical applications that are difficult to achieve with more conventional-sized deformable mirrors. In particular, adaptive secondary mirrors (ASMs) can be integrated into telescopes and provide adaptive optics corrections. However, making facesheets for ASMs is challenging. Current facesheet fabrication processes are costly and risky. Hot forming approaches for forming curved facesheets have been developed, but these methods typically require a mold for the facesheet to slump into.

Plant viral nanoparticles (plant VNPs) are promising biogenetic nanosystems for the delivery of therapeutic, immunotherapeutic, and diagnostic agents. The production of plant VNPs is simple and highly scalable through molecular farming in plants. Some of the important advances in VNP nanotechnology include genetic modification, disassembly/reassembly, and bioconjugation. Although effective, these methods often involve complex and time-consuming multi-step protocols.

Researchers at the University of California, Davis have developed a large area sensor array for ultrasound imaging systems that utilizes high-bandwidth piezoelectric sensors and modular design elements.

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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.

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.

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

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.

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.

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.

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.  

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.

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.

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. Two Step alignment: (a) Target image (b) Query Image, (c) Vertical alignment cost (Hotter colors indicate higher cost) and solution (blue line), (d) Horizontal alignment alignment cost and so- lution, (e) Aligned query, (f) Query aligned with SIFTflow Cumulative match characteristic for top 20 retrievals demonstrating significant improvement over the baseline

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.

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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.