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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UCR faculty, Prof. Hamed Mohsenian-Rad and his team, has developed an innovative algorithm, using compensation theorem, that uses synchronized voltage and current phasor measurements data from micro-phasor measurement units (micro-PMU) to identify with high accuracy the location of events in a distribution grid – with as little as 2 micro-PMUs. In addition to the data from micro-PMUs, their algorithm only requires access to basic grid topology and line impedance data – data that is readily available in the electric utility databases.

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Power amplifier performance for emerging 5G mm-wave systems poses significant challenges for output power, efficiency and linearity. Efficiency in backoff is a key concern, given the peak-to-average power ratio of order 6-9dB for 5G signals. As a result, considerable attention has been given to composite amplifiers featuring backoff efficiency enhancement, particularly Doherty amplifiers. Adaptive bias circuits have been previously developed for use with power amplifiers at low microwave frequencies (for example, 1-2GHz as applied in 2G, 3G and 4G cellular networks).  Direct application of these techniques is not straightforward at higher frequencies, such as 28GHz as used for 5G wireless communications, because the transistors have less gain at the high frequencies. 

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This technology computationally optimizes the upcycling of critical metals in deep eutectic solvents with molecular dynamics, artificial intelligence, and experimental approaches.

Researchers at the University of California, Davis have developed a highly efficient microchannel polymer heat exchanger in a compact and lightweight design.

This technology shows three different forms of bauxite to be effective adsorbents for phosphate ions. 1. Mildly processed bauxite (MPB), which is essentially ball-milled raw bauxite ore, 2. Thermally activated bauxite (TAB), which is ball-milled bauxite ore subjected to 300 C roasting, and 3. Acid treated thermally activated bauxite (ATAB), which is ball-milled bauxite ore subjected to 300 C roasting and subsequent acid treatment using 5M HCl.  These three different forms of bauxite are shown to adsorb phosphate in high amounts from solutions containing a range of initial phosphate concentrations, 5 ppm to 631 ppm.  ATAB shows the highest adsorption density, demonstrating a value of 50 mg of PO4-/g ATAB at pH=6TAB shows an adsorption density of 25 mg PO4-/g TAB at pH=6  There are two industry standard materials for phosphate adsorption, activated magnesia (MgO), and activated alumina (Al2O3). For comparison, activated magnesia (MgO) demonstrates an adsorption capacity of 25 mg PO4-/g at pH=6. Activated alumina (Al2O3) shows an adsorption capacity of 11 mg PO4-/g at pH=6 (reference: Journal of Environmental Chemical Engineering 5 C(2017) 3181–31893183).  Phosphate, a finite and dwindling resource mined from phosphate rock, is a critical nutrient in modern agriculture, which is applied as fertilizer to ensure adequate plant growth. The inventors provide a cost-effective, environmentally-friendly method for recovering phosphate from agricultural runoff and other wastewater and delivering the recovered phosphate in a targeted and controlled manner to agriculture and farm sectors.

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 collaborated with colleagues at the University of Washington and Emory University to develop a DNA-based, memory and data storage technology that integrates seamlessly with semiconductor-based technologies and conventional electronic devices.

The inventors have developed a new flexible motion sensing method that exploits nonlinear intermodulation of MRI receiver coil preamplifiers to sense the motion of a subject in an MRI scanner without on-subject hardware. The method transmits two tones at two different frequencies, f1 and f2, designed to be received at frequency f_BPT by the receiver via intermodulation, where f1 and f2 are much greater than the MRI center frequency. These signals are picked up by the receiver coils, mixed at the pre-amplification stage by intermodulation, then digitized by the receiver chain. The method is 20 times more sensitive to motion than the state-of-the-art Pilot Tone (PT) method of motion sensing. The inventors have demonstrated the method with second order intermodulation. Additionally, more transmitters can be used, each with a different set of frequencies. Higher frequency tones enable greater sensitivity to subject motion. This method enables the detection of motion at multiple temporal and spatial scales, for example, breathing and rigid motion of the head. The method is used simultaneously with conventional MR imaging and does not adversely impact the signal-to-noise ratio (SNR) of the acquired MR image. The method has been demonstrated using inexpensive consumer grade hardware for the 2.4GHz ISM band as a proof-of-concept. Since the MR signal is small (< -30dBm), little transmit power is necessary to induce an intermodulation signal similar in amplitude to the MR signal.

The inventor has developed systems and methods for performing continuous 3D roll-based additive manufacturing. This invention is distinct from roll-based micro/nanomanufacturing methods such as imprint lithography, gravure printing, and photo-roll lithography because it enables production of high aspect ratio reentrant features and voids in a single step that are difficult or even impossible with the existing methods.

The inventor has developed novel algorithms and metrology methodologies, including real-time in-situ imaging of part formation, in computed axial lithography printing (CALP). CALP is a form of continuous 3D roll-based additive manufacturing which is distinct from roll-based micro/nanomanufacturing methods such as imprint lithography, gravure printing, and photo-roll lithography because it enables production of high aspect ratio reentrant features and voids in a single step that are difficult or even impossible with the existing methods.

Prof. Konstantinos Karydis’ lab at the University of California, Riverside has developed a new active resilient quadrotor (ARQ), which incorporates passive springs within its frame to absorb shocks and survive collisions.  Each arm of the quadrotor is equipped with sensors to accurately and rapidly detect the location (in the drone’s frame) and intensity of a collision.  In addition, a recovery controller that enables the drone to sustain flight after collision with objects like wall, poles, or moving objects. The technology has been proven on the quadrotor however it may be applied to drones with more than four arms. Fig 1: Instances of the novel ARQ drone detecting and recovering from colllisions in (a) and (b) and from collision with a wall (c) and (d). Fig 2: shows ARQ detecting and recovering from a passive collision. (a) ARQ hovers. (b) Collision starts and the ARQ arm absorbs the shock. (c) recovery control starts and there is a body interfering with the ARQ’s flight path. (d) ARQ is stabilized and hovering again.  

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.

The inventors have developed two new CasX gene-editing platforms (DpbCasXv2 and PlmCasXv2) through rationale structural engineering of the CasX protein and gRNA, which yield improved in vitro and in vivo behaviors. These platforms dramatically increase DNA cleavage activity and can be used as the basis for further improving CasX tools.The RNA-guided CRISPR-associated (Cas) protein CasX has been reported as a fundamentally distinct, RNA-guided platform compared to Cas9 and Cpf1. Structural studies revealed structural differences within the nucleotide-binding loops of CasX, with a compact protein size less than 1,000 amino acids, and guide RNA (gRNA) scaffold stem. These structural differences affect the active ternary complex assembly, leading to different in vivo and in vitro behaviors of these two enzymes.

Photodetectors for infrared light suffer from low performance and high cost which hampers commercial applications. The researchers have engineered a method to boost the performance of any current photodetectors, especially within the infrared region, using quantum dots.   The researchers have demonstrated world record performance for sensing and detection.

Researchers at the University of California, Davis have developed absorptive bandpass filters that enable improved passband flatness and good impedance matching both in-band and out-of-band.

Deep Neural Networks (DNNs) are revolutionizing a wide range of services and applications such as language translation , transportation , intelligent search, e-commerce, and medical diagnosis. These benefits are predicated upon delivery on performance and energy efficiency from hardware platforms. With the diminishing benefits from general-purpose processors, there is an explosion of digital accelerators for DNNs. Mixed-signal acceleration is also gaining traction. Albeit low-power, mixed signal circuitry suffers from limited range of information encoding, is susceptible to noise, imposes Analog to Digital (A/D) and Digital to Analog (D/A) conversion overheads, and lacks fine-grained control mechanism. Realizing the full potential of mixed-signal technology requires a balanced design that brings mathematics, architecture, and circuits together.

UCLA researchers in the Department of Bioengineering have developed a novel machine learning assisted wearable sensor system for the direct translation of sign language into voice with high performance.