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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.

Bit-Parallel Vector Composability For Neural Acceleration

Conventional neural accelerators rely on isolated self‐sufficient functional units that perform an atomic operation while communicating the results through an operand delivery‐aggregation logic. Each single unit processes all the bits of their operands atomically and produce all the bits of the results in isolation.  

Mixed-Signal Acceleration Of Deep Neural Networks

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, mixedsignal 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.

Contextual Augmentation Using Scene Graphs

Spatial computing experiences are constrained by the real-world surroundings of the user.  In such experiences, augmenting virtual objects to existing scenes require a contextual approach, where geometrical conflicts are avoided, and functional and plausible relationships to other objects are maintained in the target environment.  Yet, due to the complexity and diversity of user environments, automatically calculating ideal positions of virtual content that is adaptive to the context of the scene is considered a challenging task.    UC researchers have developed a framework which augments scenes with virtual objects using an explicit generative model to learn topological relationship from priors extracted from a real-world and/or synthetic 3D datasets.  Primarily designed for spatial computing applications, SceneGen extracts features from rooms into a novel spatial representation which encapsulates positional and orientational relationships of a scene which captures pairwise topology between objects, object groups, and the room.  The AR application iteratively augments objects by sampling positions and orientations across a room to create a probabilistic heat map of where the object can be placed.  By placing objects in poses where the spatial relationships are likely, we are able to augment scenes that are realistic. 

A Fully Integrated Stretchable Sensor Arrays for Wearable Sign Language Translation To Voice

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.

A Battery-Less Wirelessly Powered Frequency-Swept Spectroscopy Sensor

UCLA researchers in the Department of Electrical and Computer Engineering have developed a wirelessly powered frequency-swept spectroscopy sensor.

ScatterMIMO: Enabling Virtual MIMO with Smart Surfaces. ScatterMIMO is a programmable smart surface that contains phase shifters to change the wireless channel

In the last decade, the bandwidth expansion and MIMO spatial multiplexing have promised to increase data throughput by orders of magnitude. However, we are yet to enjoy such improvement in real-world environments, as they lack rich scattering and preclude effective MIMO spatial multiplexing.

Training Platform for Transoral Robotic Surgery

UCLA researchers in the Departments of Bioengineering and Head & Neck Surgery have developed a novel robotic platform for the training of transoral surgery.

Predictive Controller that Optimizes Energy and Water Used to Cool Livestock

Researchers at the University of California, Davis have developed a controller that applies environmental data to optimizing operations of livestock cooling equipment.

Pulsed-Coherent Electronic Front End for Detection and Ranging

Researchers in the UCLA Department of Electrical and Computer Engineering have developed a Light Detection and Ranging (LiDAR) device capable of high resolution, high acquisition measurements with minimized walk error and adjustable detection quality.

An Antenna Design Method to Realize Endfire Radiation with Vertical Polarization

Researchers from the UCLA Department of Electrical and Computer Engineering have developed a method to realize endfire radiation with vertical polarization on low-profile and compact resonant antennas, allowing for high selective frequency responses and low energy cost.

Athermal Nanophotonic Lasers

Researchers at the University of California, Davis have developed a nanolaser platform built from materials that do not exhibit optical gain.

Multi-Wavelength, Nanophotonic, Neural Computing System

Researchers at the University of California, Davis have developed a multi-wavelength, Spiking, Nanophotonic, Neural Reservoir Computing (SNNRC) system with high-dimensional (HD) computing capability.

Reticulation Of Macromolecules Into Crystalline Networks

Covalent organic frameworks (COFs) are 2D or 3D extended periodic networks assembled from symmetric, shape persistent molecular 5 building blocks through strong, directional bonds. Traditional COF growth strategies heavily rely on reversible condensation reactions that guide the reticulation toward a desired thermodynamic equilibrium structure. The requirement for dynamic error correction, however, limits the choice of building blocks and thus the associated mechanical and electronic properties imbued within the periodic lattice of the COF.   UC Berkeley researchers have demonstrated the growth of crystalline 2D COFs from a polydisperse macromolecule derived from single-layer graphene, bottom-up synthesized quasi one-dimensional (1D) graphene nanoribbons (GNRs). X-ray scattering and transmission electron microscopy revealed that 2D sheets of GNR-COFs self-assembled at a liquid-l quid interface stack parallel to the layer boundary and exhibit an orthotropic crystal packing. Liquid-phase exfoliation of multilayer GNR-COF crystals gave access to large area bilayer and trilayer cGNR-COF films. The functional integration of extended 1D materials into crystalline COFs greatly expands the structural complexity and the scope of mechanical and physical materials properties.

Wearable Monitor of Attentional Integrity and Mental Strain

UCLA researchers in the Department of Psychiatry & Biobehavioral Sciences have developed a novel brain monitoring device that can be worn inconspicuously.

Low Energy and Noise Sub-Sampling Phase-Locked Loop

Phase locked loops are widely employed in radio, telecommunications, computers and other electronic applications. They can be used to demodulate a signal, recover a signal from a noisy communication channel, generate a stable frequency at multiples of an input frequency, or distribute precisely timed clock pulses in digital logic circuits such as microprocessors. Researchers at the University of California, Davis have invented a novel, sub-sampling phase-locked, loop (SSPLL) that uses a sub-sampling lock detector (SSLD) to monitor the harmonic selected by the SSPLL. This technology requires lower energy consumption and reduces signal noise.

Fast Deep Neural Network (DNN) Training/Execution on Hardware Platforms

With the growing range of applications for Deep Neural Networks (DNNs), the demand for higher accuracy has directly impacted the depth of the state-of-the-art models. Although deeper networks are shown to have higher accuracy, they suffer from drastically long training time and slow convergence speed with high computational complexity.

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.

Method and Apparatus for Movement Therapy Gaming System

Rehabilitation therapy, while an important tool for the long term recovery of patients affected by brain injury or disease, is expensive and requires one-on-one attention from a certified healthcare professional. UCI researchers have developed a computer-based system that provides arm movement therapy for patients. The system allows patients to independently practice hand and arm movements, improving therapeutic outcomes, while reducing hospital visits and cost for both patients and healthcare providers.

Low Band Gap Graphene Nanoribbon Electronic Devices

This invention creates a new graphene nanoribbons (GNR)-based transistor technology capable of pushing past currently projected limits in the operation of digital electronics for combining high current (i.e. high speed) with low-power and high on/off ratio. The inventors describe the design and synthesis of molecular precursors for low band gap armchair graphene nanoribbons (AGNRs) featuring a width of N=11 and N=15 carbon atoms, their growth into AGNRs, and their integration into functional electronic devices (e.g. transistors). N is the number of carbon atoms counted in a chain across the width and perpendicular to the long axis of the ribbon.

Advanced Power Management IC’s for Li-Ion Powered Mobile & IoT Devices

Most modern mobile, wearable, and Internet of Things (IoT) devices utilize Li-ion batteries as power supplies. Since the 2.8-4.2V Li-ion output voltage range is not compatible with the 0.6-1.0V voltage requirements of most system-on-chips (SoCs) implemented in scaled CMOS, a DC-DC converter, typically implemented as a discrete power management integrated circuit (PMIC), is placed between the battery and the load.

Cloud- enabled Wireless pH Monitoring in Laboratory Sample Vials

A team of inventors at UCI have developed a miniaturized, wireless pH sensing system capable of monitoring the pH of laboratory samples in real-time with cloud-enabled connections for data collection. The sensor is designed to fit into the caps of standard sample vials, providing continuous measurements and eliminating the need to open vials during sensing.

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