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Sonification-Facilitated Cognitive Training System to Enhance Visual Learning and Memory

UCLA researchers in the Department of Psychology have developed a new cognitive training tool to enhance visual learning and memory using sound.

Near-Realistic Sports Motion Analysis and Activity Monitoring

UCLA researchers in the Department of Computer Science have developed a new technology to fight the growing obesity epidemic by encouraging exercise in video games.

Methods and System for Large-Scale Dream Data in Immersive Multisensory Environment: Acquisition, Analysis, Modeling and Interpretation & Applications

UCLA researchers in the Department of Electrical Engineering have developed the Dream Brain System, an immersive Virtual Reality platform that collects dream data for therapeutic, scientific and experimental use. By capturing relevant dream data through multimodal signals recollected by the user, the Dream Brain System greatly advances conventional dream reporting techniques by providing effective dream recollection and interpretation.

Use of Augmented Reality for Enhanced & Efficient Communication Technologies

A communication interaction paradigm based on augmented reality that enables a remote collaborator to control his/her viewpoint onto a remote scene and communication information with visual references such as identifying objects, locations, directions, spatial instructions, etc.

Energy-Efficient All-Optical Nanophotonic Computing

Researchers at the University of California, Davis, have developed a new computing and signal processing platform based on nanophotonics and nanoelectronics to decrease power consumption and improve overall computing speed with all-optical inputs and outputs.

Predictive Optimization Of Pharmeceutical Efficacy

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a machine learning platform to virtually screen combinatorial drug therapies.

Data Shepherding: Cache Design For Future Large Scale Chips

The ability of a central processing unit to store frequently-used data in nearby, easily accessible cache data banks has revolutionized computational performance, though their effective implementation in multicore processors has become a technological challenge. Researchers at UCI have developed a new means of data caching that is fully applicable to multicore processors, and offers reduced memory access time over standard techniques.

Automated Reconstruction Of The Cardiac Chambers From MRI

This is a fast, fully automated method to accurately model a patient’s left heart ventricle via machine learning algorithms.

Developing Physics-Based High-Resolution Head And Neck Biomechanical Models

UCLA researchers in the Department of Radiation Oncology at the David Geffen School of Medicine have developed a new computational method to model head and neck movements during medical imaging/treatment procedures.

A Hundred Tiny Hands

100 Tiny Hands is an experiential learning program that imparts science, technology, engineering, and math (“STEM”) education to children ages six to twelve using storybook-inspired curriculum coupled with interactive educational “toolboxes.”

Monolithically Integrated Implantable Flexible Antenna for Electrocorticography and Related Biotelemetry Devices

A sub-skin-depth (nanoscale metallization) thin film antenna is shown that is monolithically integrated with an array of neural recording electrodes on a flexible polymer substrate. The structure is intended for long-term biometric data and power transfer such as electrocorticographic neural recording in a wireless brain-machine interface system. The system includes a microfabricated thin-film electrode array and a loop antenna patterned in the same microfabrication process, on the same or on separate conductor layers designed to be bonded to an ultra-low power ASIC.

Robust Visual-Inertial Sensor Fusion For Navigation, Localization, Mapping, And 3D Reconstruction

UCLA researchers in the Computer Science Department have invented a novel model for a visual-inertial system (VINS) for navigation, localization, mapping, and 3D reconstruction applications.

Dsp-Sift: Domain-Size Pooling For Image Descriptors For Image Matching And Other Applications

UCLA researchers in the Computer Science Department have invented a novel modification to the scale-invariant feature transform (SIFT) algorithm that shows significant improvement for computer vision applications.

Metal-free affinity media/agents for the selective capture of histidine-rich peptide sequences

The present invention utilizes metal-free synthetic polymer-based materials for the purification of peptides and proteins containing or being fused with histidine-rich sequences, which does not damage the function of the target protein and is less costly.

Grouping Algorithm For Touchscreen Finger Position Detection

UCLA researchers in the Department of Electrical Engineering developed a new grouping algorithm for touchscreen finger position detection.

Cross-Layer Robust Header Compression (ROHC) Compressor Design

Researchers at the University of California, Davis have developed a ROHC compressor that adaptively adjusts the compression level based on an underlying Partially Observable Markov Decision Process (POMDP) model.

Energy Efficient Trigger Word Detection via Accelerometer Data

Researchers at the University of California, Davis have developed an energy-efficient voice monitoring technique for smart devices, such as smartphones and wearables, based on accelerometer data.

Direct Optical Visualization Of Graphene On Transparent Substrates

96 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The ∼10% optical contrast of graphene on specialized substrates like oxide-capped silicon substrates, together with the high-throughput and noninvasive features of optical microscopy, have greatly facilitated the use and research of graphene research for the past decade.  However, substantially lower contrast is obtained on transparent substrates. Visualization of nanoscale defects in graphene, e.g., voids, cracks, wrinkles, and multilayers, formed during either growth or subsequent transfer and fabrication steps, represents yet another level of challenge for most device substrates.     UC Berkeley researchers have developed a facile, label-free optical microscopy method to directly visualize graphene on transparent inorganic and polymer substrates at 30−40% image contrast per graphene layer.  Their noninvasive approach overcomes typical challenges associated with transparent substrates, including insulating and rough surfaces, enables unambiguous identification of local graphene layer numbers and reveals nanoscale structures and defects with outstanding contrast and throughput. We thus demonstrate in situ monitoring of nanoscale defects in graphene, including the generation of nano-cracks under uniaxial strain, at up to 4× video rate.  

Fluid management device / fluid delivery system

Researchers at UC Irvine have developed a fluid delivery device. This delivery device simplifies the process of intravenous drug delivery to allow for an automated, efficient, and error free intravenous drug administration.

Realization Of Artificial Magnetic Skyrmions At Room Temperature

Researchers at University of California – Davis have developed a novel method to achieve artificial magnetic skyrmions at room temperature. The invention is suitable for exploration of magnetic skyrmions towards highly energy efficient magnetic information storage, such as high density magnetic recording, magnetic sensors, non-volatile magnetic memory and logic devices

Efficient Encoding of Genomic Data Using Deduplication

With today’s technology, storage of genome sequence data relies heavily on compression, using techniques such as Lempil, ziv and gziv, which are commonly stored in the file formats .bam or .sam forms. Current techniques use standard reference genomes, such as HG19, compiled from a variety of human genomes. The results of many small reads are aligned and stored along with their quality data stores. The impact of whole genome sequencing, particularly in clinical treatment of cancer, will rapidly consume available storage. In 2010, 13 million Americans had cancer; with the existing technology, a single whole genome sequence for each person would be 39 exabyte’s, equal to 39,000 petabytes, 39 million terabytes or 39 billion gigabytes. There simply isn't a storage system that large, as storage capacity only grows at a rate of less than 20% per year. 

Method for Exactly Transferring Graded Information in a Neuromorphic Circuit

A method whereby information encoded in spiking activity or current amplitude of a population of neurons may be transferred to a second population of neurons or simulated neurons.

Fast Frequency Estimator (FFE)

The problem of estimating and tracking the frequency of a weak sinusoidal signal occurs in many areas of signal processing. There are a variety of approaches to estimate the frequency of a harmonic signal. The most common approach is to directly measure the time difference between zero crossings and the number of cycles per second. However, this approach is very sensitive to signal noise. Solutions to overcome this problem have been proposed, such as the Fourier transform technique, correlation, the least square error technique, recursive algorithms, chirp Z transform (CZT), adaptive notch filters, and Kalman filtering that estimates instantaneous frequency of the signal. The Kalman filter is a recursive stochastic technique that gives an optimal estimation of state variables of given linear dynamic system from noisy measures. Moreover, the filter must always deal with the inherent nonlinearity and with extreme noise levels. Coincidentally, the Kalman filter also gives a time-varying gain, which is not amendable to frequency domain analysis. 

A Robust Hybrid Control Algorithm for a Single-Phase DC/AC Inverter

Future energy distribution systems must be capable of interconnecting highly variable sources of electricity into the existing grid. The development of “Smart Grid” is needed due to increasing electricity demands and the need regulate input power sources. A particular challenge already impacting deployment of diverse renewable electric sources is the need to regulate the highly variable power these sources generate. While single-phase DC/AC inverters using Pulse WIdth Modulation (PWM) are one of the most common topologies used in power conversion, PWM is not robust with respect to changes in the DC input voltage. PWM also suffers from harmonic distortions that are less and less acceptable to downstream consumers of the power. One of the main shortcomings of converters controlled by PWM-based algorithms is that they are not robust to changes in the input DC voltage, which limits their use in renewable energy applications.

A Learning-Based Approach For Filtering Monte Carlo Noise

UC Santa Barbara researchers have evaluated the complex relationship between the filter parameters and noisy scene data using a nonlinear regression model, a multilayer perceptron neural network.

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