Prof. John Franchak and his team have developed a prototype system that accurately classifies an infant's body position.

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.

Common nuclear imaging techniques include computed tomography (CT), single photon emission CT (SPECT), and positron emission tomography (PET). PET differs from other nuclear imaging techniques in that it can visualize both functional and biological activities, including detection of metabolism within human tissues. PET is especially good for imaging patients with cancer, or brain or heart conditions. At low energies, when positrons collide with electrons near the radionuclide decay, Gamma rays (annihilation photons) are created. Gammas originating from the same electron-positron annihilation are generated exclusively in an entangled Bell state. Gammas which do not share an annihilation origin event, such as randoms, are not entangled. Additionally, a gamma which undergoes an internal scatter becomes decoherent (unentangled) from its pair, such as the gammas found in the scattered coincidence pairs. Scattered and random events degrade the image quality. Recently, quantum-based techniques utilizing entanglement of annihilation photons has been recognized as one approach to address scatter and random and to optimize the signal to noise (SNR) ratio.

 The dynamic patterning of 3D optical point clouds has emerged as a key enabling technology in volumetric processing across a number of applications. In the context of biological microscopy, 3D point cloud patterning is employed for non-invasive all-optical interfacing with cell ensembles. In augmented and virtual reality (AR/VR), near-eye display systems can incorporate virtual 3D point cloud-based objects into real-world scenes, and in the realm of material processing, point cloud patterning can be mobilized for 3D nanofabrication via multiphoton or ultraviolet lithography. Volumetric point cloud patterning with spatial light modulators (SLMs) is therefore widely employed across these and other fields. However, existing hologram computation methods, such as iterative, look-up table-based and deep learning approaches, remain exceedingly slow and/or burdensome. Many require hardware-intensive resources and sacrifices to volume quality.To address this problem, UC Berkeley researchers have developed a new, non-iterative point cloud holography algorithm that employs fast deterministic calculations. Compared against existing iterative approaches, the algorithm’s relative speed advantage increases with SLM format, reaching >100,000´ for formats as low as 512x512, and optimally mobilizes time multiplexing to increase targeting throughput. 

Although GPS can be used for localization outdoors, indoor environments (office buildings, shopping malls, transit hubs) can be particularly challenging for many of the general population, and especially for blind walkers. GPS-denied environments have received considerable attention in recent years as our population’s digital expectations grow. To address GPS-denied environments, various services have been explored, including technology based on Bluetooth low energy (BLE), Wi-Fi, and camera. Drawbacks with these approaches are common, including calibration (fingerprinting) overhead using Wi-Fi, beacon infrastructure costs using BLE, and unoccluded visibility requirements in camera-based systems. While localization and wayfinding using inertial sensing overcomes these challenges, large errors with accumulated drift are known. Moreover, the decoupling of the orientation of the phone from the direction of walking, as well as accurately detecting walker’s velocity and detecting steps and measuring stride lengths, have also been challenges for traditional pedestrian dead reckoning (PDR) systems. Relatedly, blind walkers (especially those who do not use a dog guide) often tend to veer when attempting to walk in a straight line, and this unwanted veering may generate false turn detections with such inertial methods.

Measurement of electrical activity in nervous tissue has many applications in medicine, but the implantation of a large number of sensors is traditionally very risky and costly. Devices must be large due to their necessary complexity and power requirements, driving up the risk further and discouraging adoption. To address these problems, researchers at UC Berkeley have developed devices and methods to allow small, very simple and power-efficient sensors to transmit information by backscatter feedback. That is, a much more complex and powerful external interrogator sends an electromagnetic or ultrasound signal, which is modulated by the sensor nodes and reflected back to the interrogator. Machine learning algorithms are then able to map the reflected signals to nervous activity. The asymmetric nature of this process allows most of the complexity to be offloaded to the external interrogator, which is not subject to the same constraints as implanted devices. This allows for larger networks of nodes which can generate higher resolution data at lower risks and costs than existing devices.

The inventors have developed a scalable laser aperture that emits light perpendicular to the surface. The aperture can, in principal, scale to arbitrarily large sizes, offering a universal architecture for systems in need of small, intermediate, or high power. The technology is based on photonic crystal apertures, nanostructured apertures that exhibit a quasi-linear dispersion at the center of the Brillouin zone together with a mode-dependent loss controlled by the cavity boundaries, modes, and crystal truncation. Open Dirac cavities protect the fundamental mode and couple higher order modes to lossy bands of the photonic structure. The technology was developed with an open-Dirac electromagnetic aperture, known as a Berkeley Surface Emitting Laser (BKSEL).  The inventors demonstrate a subtle cavity-mode-dependent scaling of losses. For cavities with a quadratic dispersion, detuned from the Dirac singularity, the complex frequencies converge towards each other based on cavity size. While the convergence of the real parts of cavity modes towards each other is delayed, going quickly to zero, the normalized complex free-spectral range converge towards a constant solely governed by the loss rate of Bloch bands. The inventors show that this unique scaling of the complex frequency of cavity modes in open-Dirac electromagnetic apertures guarantees single-mode operation of large cavities. The technology demonstrates scaled up single-mode lasing, and confirmed from far-field measurements. By eliminating limits on electromagnetic aperture size, the technology will enable groundbreaking applications for devices of all sizes, operating at any power level. BACKGROUND Single aperture cavities are bounded by higher order transverse modes, fundamentally limiting the power emitted by single-mode lasers, as well as the brightness of quantum light sources. Electromagnetic apertures support cavity modes that rapidly become arbitrarily close with the size of the aperture. The free-spectral range of existing electromagnetic apertures goes to zero when the size of the aperture increases. As a result, scale-invariant apertures or lasers has remained elusive until now.  Surface-emitting lasers have advantages in scalability over commercially widespread vertical-cavity surface-emitting lasers (VCSELs). When a photonic crystal is truncated to a finite cavity, the continuous bands break up into discrete cavity modes. These higher order modes compete with the fundamental lasing mode and the device becomes more susceptible to multimode lasing response as the cavity size increases. 

Brief description not available

Brief description not available

Researchers from UC San Diego have developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system in the form of a VR headset tracking, that provides real-time accurate 3D indoor localization.

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.

Indoor localization has been studied for nearly two decades fueled by wide interest in indoor navigation, achieving the necessary decimeter-level accuracy. However, there are no real-world deployments of WiFi-based user localization algorithms, primarily because these algorithms are infrastructure dependent and therefore assume the location of the Access Points, their antenna geometries, and deployment orientations in the physical map. In the real world, such detailed knowledge of the location attributes of the access point is seldom available, thereby making WiFi localization hard to deploy.   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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides context, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of wellestablished platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven’t caught up because of the lack of reliable mapping and positioning frameworks (and lack of integration between the two). SLAM methods construct maps that aren’t tagged with locations. Wi-Fi positioning lacks maps, and is also prone to environmental errors. In contrast, indoor navigation even with significant interest from industry and academia lacks further behind.  We cannot use our smartphone to navigate to a conference room in a new building or to find a product of interest in a shopping mall. The primary reason for the poor indoor navigation system is the unavailability of indoor localization augmented maps and floor plans. On one hand, Google and a few other providers make indoor floor plans for airports, malls, and famous buildings, those floor-plans have to be created manually and often need to updated as floor plans change and they lack details such as the position of furniture and other obstacles. On the other hand, besides mapping, ability to position users’ location on these indoor maps is necessary for indoor navigation  

Researchers at the University of California, Irvine have developed a novel algorithm that is designed to be integrated with current multi-tone continuous wave (MTCW) lidar technology in order to enhance the capability of lidar to acquire range(distance) of fast-moving targets as well as simultaneous velocimetry measurements.

There is a wide range of applications for gyroscopes, including: inertial navigation, stabilization, maintaining direction. Many of these applications require low noise. One approach to reducing noise is to increase the mass of the gyroscope transducer. However, this generally comes with increased size and cost. Mode-matched gyroscopes avoid these penalties. These gyroscopes are based on transducers with high quality factor Q. Provided that the resonance frequencies of the drive and sense axes are equal, the noise is suppressed by the quality factor Q. The Q-factor of typical gyroscopes ranges from 1000 to several million, offering dramatic noise reduction. The required precision of mode-matching, which is on the order of 1/Q, presents an implementation challenge. For example, in a mode-matched gyroscope with Q=106, the relative deviation of the frequencies of oscillation of the drive and sense mode must be 10 6. This level of precision is not attainable by typical transducer fabrication techniques such as MEMS or trimming.This innovation presents an alternative approach for continuously monitoring the split between the resonances of the drive and sense modes. While also based on a periodic calibration signal, it does not suffer from corruption of or from the rate measurement. Consequently, the frequency of the calibration signal can be chosen independently of the bandwidth of the rate input and instead set by the required tracking bandwidth of the mode split estimate. The latter is typically dominated by environmental variations such as temperature and on the order of 1Hz or less in typical implementations.

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.

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.

UCLA researchers in the Department of Electrical and Computer Engineering have developed a terahertz (THz) detector that utilizes the micro-Doppler effect to detect vibrations and long-distance sounds.

UCLA researchers in the Department of Electrical and Computer Engineering have developed an antenna design procedure that can realize devices with beam scanning at a fixed frequency on a single element antenna.

Determining the location of a transmitting party in a communication network normally requires a number of identifying factors. The transmitting party can be located through triangulating their signal, using the signal’s arrival at several receivers to determine the transmission’s origin. One can also determine the location of a receiver by comparing the receipt of multiple transmitter signals; however, in both scenarios the transmitter and receiver must employ clock synchronization and signal time-of-travel information to accurately compute relative localization between the two.   The Global Positioning System is a well-known example of this process, leveraging known time (and synchronizing to that time) and position of each GPS satellite to deduce the location of a receiver.  

Teleoperation brings the advantage of remote control and manipulation to distant locations or harsh or constrained environments. The system allows operators to send commands from a remote console, traditionally called a master device, to a robot, traditionally called a slave device, and offers synchronization of movements. This allows the remote user to operate as if on-site, making teleoperational systems an ideal and often only solution to a wide range of applications such as underwater exploration, space robotics, mobile robots, and telesurgery. The main technical challenge in realizing remote telesurgery (and similarly, all remote teleoperation) is the latency from the communication distance between the master and slave. This delay causes overshoot and oscillations in the commanded positions, and are observable and statistically significant in as little as 50msec of round trip communication delay. Predictive displays are virtual reality renderings, generally designed for space operations, that show a prediction of the events to follow in a short amount of time. It can be used to overcome the negative effects of delay by giving the operator immediate feedback from a predicted environment. Furthermore, it does not suffer stability issues that arise with delayed haptic feedback. Early predictive displays included manipulation of the Engineering Test Satellite 7 from ground control where the round trip delay can be up to 7sec and Augmented Reality (AR) rendering where the prediction is overlaid on raw image data. These strategies can be applied to telesurgery, but require overcoming the unique challenges in calculating and tracking the 3D environment for a full environment prediction, which includes non-rigid material such as tissue. Furthermore, prior work in the surgical robotics community highlights the need for active tracking rather than only relying on kinematic calibrations to localize the slave due to the millimeter scale of a surgical operation and the often utilized cable driven actuation.

Object detection and ranging is a fundamental task for several applications such as autonomous vehicles, atmospheric observations, 3D imaging, topography and mapping. UCI researchers have developed a light detection and ranging (LIDAR) system which makes use of frequency modulated continuous waves (FMCW) with several simultaneous radiofrequency tones for improved speed of measurement while maintaining robust spatial information. 

Professor Jonathan Hopkins and colleagues have developed amechanical programmable metamaterial consisting of an array of actively, independently controlled micro-scale unit cells. This technology allows for the application of materials which have instantly changeable, programmable properties that can exceed those of conventional, existing materials.

With the emergence of the Internet of Things in smart homes and buildings, determining the identity and mobility of people are key to realizing personalized, context-aware and location-based services - such as adjusting lights and temperature as well as setting preferences of electronic devices in the vicinity. Conventional electronic user identification approaches either require proactive cooperation by users or deployment of dedicated infrastructure. Consequently, existing approaches are intrusive, inconvenient, or expensive to ubiquitously implement. For example: biometric identification requires specific hardware and physical interaction; and vision-based (video) approaches need favorable lighting and introduce privacy issues. To address this situation, researchers at UC Berkeley developed an identification system that uses existing, pervasive WiFi infrastructure and users' WiFi-enabled devices. The innovative Berkeley technology cleverly leverages attributes such as the MAC address and RSS of users' WiFi-enabled devices. Furthermore, the Berkeley approach is facilitated by an unsupervised learning scheme that maps each user identification with associated WiFi-enabled devices. This technology could serve as a vital underpinning for practical personalized context-aware and location-based services in the era of the Internet of Things.

UCLA researchers in the Department of Mechanical & Aerospace Engineering and the Department of Pathology & Lab Medicine have proposed a new platform technology to actuate and sense force propagation in real-time for large sheets of cells.

UCLA researchers in the Department of Chemistry and Biochemistry & Physics and Astronomy have developed a novel method to lithograph two polished solid surfaces by using a simple mechanical alignment jig with piezoelectric control and a method of pressing them together and solidifying a material.