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Thz Radiation Detector Using Bilayers Of Antiferromagnet And Heavy Metal Films

Background A practical implementation that can generate and detect electromagnetic radiation in the range 0.1 to 10 Terahertz is important in the advancement of biomedicine, security systems, ultrafast 5G & 6G communication, etc. Technology Prof. Jing Shi and his research team have developed a novel, compact and scalable semiconductor integrated circuit technology (IC) for Terahertz (THz) detection. The innovation is a thin film device that is an Anti-Ferro Magnetic (AFM) and Heavy Metal (HM) bilayer - thin film structure that offers broad frequency tunability and scalability. Benefits Broader and tunable frequency responses beyond 1THzCompact & ScalableEase of implementation in semiconductor IC technologyCost effective   Principle: resonant absorption of THz radiation drives spins in anti-ferromagnet (AFM) into precession, which is detected by heavy metal (HM) as a DC voltage.

Plant-Specific and Agricultural Field/Orchard/Crop Optimization Using Aerial Image Processing

Researchers at the University of California, Davis have developed a system that combines large datasets of aerial imagery with artificial intelligence to acquire per-plant analytics and predict crop yields. The system is a scalable per-tree yield prediction model for nut crops, provides large-scale canopy profile analytics in 3D, and the next generation of aerial image analytics for agriculture.

Compressive High-Speed Optical Transceiver

Researchers at the University of California, Davis have developed an optical transceiver that uses compressive sensing to reduce bandwidth requirements and improve signal resolution.

Low-Cost, Multi-Wavelength, Camera System that Incorporates Artificial Intelligence for Precision Positioning

Researchers at the University of California, Davis have developed a system consisting of cameras and multi-wavelength lasers that is capable of precisely locating and inspecting items.

In-Sensor Hardware-Software Co-Design Methodology of the Hall Effect Sensors to Prevent and Contain the EMI Spoofing Attacks

Researchers at UCI have developed a novel co-design methodology of hardware-software architecture used for protecting Hall sensors found in autonomous vehicles, smart grids, industrial plants, etc…, against spoofing attacks.There are currently no comprehensive measures in place to protecting Hall sensors.

Integrated Virtual Reality and Audiovisual Display Support System for Patients in a Prone Position

Researchers at the University of California, Davis have developed an integrated virtual reality and audiovisual support system that increases the comfort of patients who are undergoing diagnostic tests or medical procedures in the prone and other positions.

Reducing Electrical Current Variations in Phase-Locked Loop Systems

Researchers at the University of California, Davis have developed a method of eliminating electrical current mismatches in the charge pumps of phase-locked loops (PLL) systems - thereby increasing their power efficiency and phase detection capabilities.

Smart Suction Cup for Adaptive Gripping and Haptic Exploration

Vacuum grippers are widely used in industry to handle objects via suction pressure. Unicontact suction cups are commonly used for gripping because they are simple to operate and can handle a variety of items, including those that are delicate, large, or inaccessible to jaw grippers. However, suction cup grippers have challenges such as planning a contact location and inertial force-induced grasping failure. To address these challenges, UC Berkeley researchers developed a tactile sensing technology for smart suction cups. This Berkeley sensing technology can detect suction contact and prevent suction cup grasp failures. It can perform tactile sensing of object properties such as roughness or porosity that might lead to grasping failures before they happen. If a grasp failure does happen, the technology gains additional information about why and how the failure occurred to prevent similar failures in future attempts. Sensing occurs quickly, such that robot behavior can remain fast while increasing performance, efficiency and reliability.  As compared with other robotic grasping sensing technologies, this smart suction cup technology is affordable, resilient and easy to service. The cup is manufactured using the same process as other suction cups, and electronics are simple and located away from the point-of-contact and protected from damage or hazardous exposure.

Phased-Locked Loop Coupled Array for Phased Array Applications

Researchers at the University of California, Davis have developed a phased-locked loop coupled array system capable of generating phase shifts in phased array antenna systems - while minimizing signal losses.

(SD2019-340) Collaborative High-Dimensional Computing

Internet of Things ( IoT ) applications often analyze collected data using machine learning algorithms. As the amount of the data keeps increasing, many applications send the data to powerful systems, e.g., data centers, to run the learning algorithms . On the one hand, sending the original data is not desirable due to privacy and security concerns.On the other hand, many machine learning models may require unencrypted ( plaintext ) data, e.g., original images , to train models and perform inference . When offloading theses computation tasks, sensitive information may be exposed to the untrustworthy cloud system which is susceptible to internal and external attacks . In many IoT systems , the learning procedure should be performed with the data that is held by a large number of user devices at the edge of Internet . These users may be unwilling to share the original data with the cloud and other users if security concerns cannot be addressed.

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.

(SD2020-367) 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.

(SD2020-422) 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.

Compact Ion Gun for Ion Trap Surface Treatment in Quantum Information Processing Architectures

Electromagnetic noise from surfaces is one of the limiting factors for the performance of solid state and trapped ion quantum information processing architectures. This noise introduces gate errors and reduces the coherence time of the systems. Accordingly, there is great commercial interest in reducing the electromagnetic noise generated at the surface of these systems.Surface treatment using ion bombardment has shown to reduce electromagnetic surface noise by two orders of magnitude. In this procedure ions usually from noble gasses are accelerated towards the surface with energies of 300eV to 2keV. Until recently, commercial ion guns have been repurposed for surface cleaning. While these guns can supply the ion flux and energy required to prepare the surface with the desired quality, they are bulky and limit the laser access, making them incompatible with the requirements for ion trap quantum computing.To address this limitation, UC Berkeley researchers have developed an ion gun that enables in-situ surface treatment without sacrificing high optical access, enabling in situ use with a quantum information processor.

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.

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