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Telehealth-Mediated Physical Rehabilitation Systems and Methods

The use of telemedicine/telehealth increased substantially during the COVID-19 pandemic, leading to its accelerated development, utilization and acceptability. Telehealth momentum with patients, providers, and other stakeholders will likely continue, which will further promote its safe and evidence-based use. Improved healthcare by telehealth has also extended to musculoskeletal care. In a recent study looking at implementation of telehealth physical therapy in response to COVID-19, almost 95% of participants felt satisfied with the outcome they received from the telehealth physical therapy (PT) services, and over 90% expressed willingness to attend another telehealth session. While telehealth has enhanced accessibility by virtual patient visits, certain physical rehabilitation largely depends on physical facility and tools for evaluation and therapy. For example, limb kinematics in PT with respect to the shoulder joint is difficult to evaluate remotely, because the structure of the shoulder allows for tri-planar movement that cannot be estimated by simple single plane joint models. With the emergence of gaming technologies, such as videogames and virtual reality (VR), comes new potential tools for virtual-based physical rehabilitation protocols. Some research has shown digital game environments, and associated peripherals like immersive VR (iVR) headsets, can provide a powerful medium and motivator for physical exercise. And while low-cost motion tracking systems exist to match user movement in the real world to that in the virtual environment, challenges remain in bridging traditional PT tooling and telehealth-friendly physical rehabilitation.

Hybrid Guided-Wave And Free-Space System For Broadband Integrated Light Delivery

Photonic integrated circuits (PICs) have emerged as an encouraging platform for many fields due to their compact size, phase stability, and can be mass produced in semiconductor foundries at low cost. As such, PIC enabled waveguide-to-free-space beam delivery has been demonstrated towards ion trap quantum computing, atomic clocks, optical tweezers, and more. Grating couplers are commonly used, as through careful design, they can generate diffraction-limited focused spots into free space from a waveguide input. However, they suffer from many drawbacks – they have a narrow optical bandwidth, limited efficiency, are sensitive to light polarization and the emission angle is sensitive to fabrication variation.Quantum systems require stable delivery of multiple wavelengths, often spanning the near ultraviolet (NUV), visible, and near infrared (NIR) spectrum, to multiple locations tens to hundreds of micrometers above the PIC. This requirement exacerbates the pitfalls of grating couplers; their single-wavelength operation necessitates multiple gratings per unit cell. With more gratings to fabricate, fabrication variance takes a greater toll on device performance. UC Berkeley researchers have devised a new approach and device to deliver light from in-plane waveguides to out-of-plane free space beams in a low-loss, broadband manner. In particular, this device is used for controlling qubits in a trapped ion quantum computer, but in general the system is suitable for other integrated beam delivery applications.

Fast Electromigration Analysis For Multi-Segment Interconnects Using Hierarchical Physics-Informed Neural Network

Prof. Sheldon Tan and his team have developed a new hierarchical learning-based electro-migration analysis method called HierPINN-EM to solve for multi-segment interconnects in VLSI chips. HierPINN-EM provides much better accuracy, faster training speeds and faster inference speeds compared to current state-of-the-art techniques. 

Field-Programmable Ising Machines (FPIM)

Certain difficult optimization problems, such as the traveling salesman problem, can be solved using so-called analog Ising machines, in which electronic components (such as certain arrangements of diodes or electronic switches) implement an analog of a well-studied physical system known as an Ising machine. The problem is recast so that its solution can be read off from the lowest-energy configuration of the analog Ising machine, a state which the system will naturally evolve towards. While promising, this methodology suffers major drawbacks. Firstly, the number of subunits, known as “spins”, in the analog Ising machines, as well as the number of connections between these subunits, can grow substantially with problem size. Secondly, existing implementations of this principle rely on chip constructions which are optimized for one or a few problems, and are not sufficiently reprogrammable to be repurposed efficiently for other applications. To address these problems, researchers at UC Berkeley have developed a device known as a Field-programmable Ising machine which can be adapted to implement an analog Ising machine using a variety of hardware designs, such as the diodes and switches mentioned above. These Ising machines can be effectively reprogrammed to efficiently solve a wide array of problems across various domains. The inventors have shown that this design can be applied to SAT (“Satisfiability”) problems, a class known to be similar to the traveling salesman problem, in that the number of spins needed and their level of connectivity do not grow too quickly with problem size.

Integrated Microlens Coupler For Photonic Integrated Circuits

Silicon photonics is increasingly used in an array of communications and computing applications. In many applications, photonic chips must be coupled to optical fibers, which remains challenging due to the size mismatch between the on-chip photonics and the fiber itself. Existing approaches suffer from low alignment tolerance, sensitivity to fabrication variations, and complex processing, all of which hinder mass manufacture.To address these problems, researchers at UC Berkeley have developed a coupling mechanism between a silicon integrated photonic circuit and an optical fiber which uses a microlens to direct and collimate light into the fiber. Researchers have demonstrated that this device can achieve low coupling loss at large alignment tolerances, with an efficient and scalable manufacturing process analogous to existing manufacture of electronic integrated circuits. In particular, because the beam is directed above the silicon chip, this method obviates dry etching or polishing of the edge of the IC and allows the silicon photonics to be produced by dicing in much the same way as present electronic integrated circuits.

Functionalized Sila-Adamantane

Brief description not available

Livesynthesis: Towards An Interactive Synthesis Flow

In digital circuit design, synthesis is a tedious and time consuming task. Designers wait several hours for relatively small design changes to yield synthesis results. 


Electronic circuits are growing in complexity every year. Existing workflows that optimize the design and placement of circuit components are laborious and time-consuming though. Incremental design changes that target device optimization can take many hours to render. Streamlined design workflows that are both fast and able to optimize performance are needed to keep pace with these device improvements. A UC Santa Cruz researcher has developed a new technique, SMatch, to shorten design workflow times with minimal QoR impact. 

Chromium Complexes Of Graphene

Brief description not available

Corf: Coalescing Operand Register File For Graphical Processing Units

Modern Graphical Processing Units (GPUs) consist of several Streaming Multiprocessors (SM) – each has its own Register File (RF) and a number of integers, floating points and specialized computational cores. GPU program is decomposed into one or more cooperative thread arrays that are scheduled to the SMs. GPUs invest in large RFs to enable fine-grained and fast switching between executing groups of threads. This results in RFs being the most power hungry components of the GPU. The RF organization substantially affects the overall performance and energy efficiency of the GPU.

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.

Superlattice, Ferroic Order Thin Films For Use As High/Negative-K Dielectric

With the two-dimensional scaling of silicon field-effect transistors reaching fundamental limits, new functional improvements to transistors, as well as novel computing paradigms and vertical device integration at the architecture-level, are currently under intense study. Gate oxides play a critical role in this endeavor, as it’s a common performance booster for all devices, including silicon, new channel materials with potential for higher performance, and even materials suitable for three-dimensional integrated transistors.With the scaling of lateral dimensions in advanced transistors, an increased gate capacitance is desirable both to retain the control of the gate electrode over the channel and to reduce the operating voltage. To pursue these performance gains, UC Berkeley researchers invented a new heterostructure insulator material where: 1) the material possesses specific ferroic order such as ferroelectricity/anti-ferroelectricity or a mixture of both; 2) the overall dielectric property such as the permittivity is determined by the stacking order of different layers rather than exact volume fraction of the constituents; and 3) the material is composed of one or several repetition of ultra thin superlattice periods ranging from a few angstroms to 3 nm.

Precision Graphene Nanoribbon Wires for Molecular Electronics Sensing and Switch

The inventors have developed a highly scalable multiplexed approach to increase the density of graphene nanoribbon- (GNR) based transistors. The technology forms a single device/chip (scale to 16,000 to >1,000,000 parallel transistors) on a single integrated circuit for single molecule biomolecular sensing, electrical switching, magnetic switching, and logic operations. This work relates to the synthesis and the manufacture of molecular electronic devices, more particularly sensors, switches, and complimentary metal-oxide semiconductor (CMOS) chip-based integrated circuits.Bottom-up synthesized graphene nanoribbons (GNRs) have emerged as one of the most promising materials for post-silicon integrated circuit architectures and have already demonstrated the ability to overcome many of the challenges encountered by devices based on carbon nanotubes or photolithographically patterned graphene. The new field of synthetic electronics borne out of GNRs electronic devices could enable the next generation of electronic circuits and sensors.  

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.

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