Sensors are used in a multitude of applications from molecular biology, chemicals detection to wireless communications. Researchers at the University of California Irvine have invented a new type of electronic circuit that utilizes exceptional points of degeneracy to improve the sensitivity of signal detection.

This technology introduces a novel, cost-effective solution for improving the accuracy of pedestrian navigation systems under extreme conditions.

This technology introduces a novel approach to MEMS integration, utilizing folding techniques to enable special distribution of sensor components and co-integration of multiple physical sensor modalities with control electronics in a compact formfactor.

Researchers at the University of California, Davis have developed a haptic interface designed to aid visually impaired individuals in navigating their environment using their portable electronic devices.

This technology introduces a novel, cost-effective solution for improving the accuracy of pedestrian navigation systems under extreme conditions.

      While robots have proven effective in enhancing the precision and time efficiency of MRI-guided interventions across various medical applications, safety remains a formidable challenge for robots operating within MRI environments. As the robots assume full control of medical procedures, the reliability of their operation becomes paramount. Precise control over robot forces is particularly crucial to ensure safe interaction within the MRI environment. Furthermore, the confined space in the MRI bore complicates the safe operation of human-robot interaction, presenting challenges to maneuverability. However, there exists a notable scarcity of force-controlled robot actuators specifically tailored for MRI applications.       To overcome these challenges, UC Berkeley researchers have developed a novel MRI-compatible rotary series elastic actuator module utilizing velocity-sourced ultrasonic motors for force-controlled robots operating within MRI scanners. Unlike previous MRI-compatible SEA designs, the module incorporates a transmission force sensing series elastic actuator structure, while remaining compact in size. The actuator is cylindrical in shape with a length shorter than its diameter and integrates seamlessly with a disk-shaped motor. A precision torque controller enhances the robustness of the invention’s torque control even in the presence of varying external impedance; the torque control performance has been experimentally validated in both 3 Tesla MRI and non-MRI environments, achieving a settling time of 0.1 seconds and a steady-state error within 2% of its maximum output torque. It exhibits consistent performance across low and high external impedance scenarios, compared to conventional controllers for velocity-sourced SEAs that struggle with steady-state performance under low external impedance conditions.

         Recent mass evacuation events, including the 2018 Camp Fire and 2023 Maui Fire, have demonstrated shortcomings in our communication abilities during natural disasters and emergencies. Individuals fleeing dangerous areas were unable to obtain fast or accurate information pertaining to open evacuation routes and faced traffic gridlocks, while nearby communities were unprepared for the emergent situation and influx of persons. Climate change is increasing the frequency, areas subject to, and risk-level associated with natural hazards, making effective communication channels that can operate when mobile network-based systems and electric distribution systems are compromised crucial.         To address this need UC Berkeley researchers have developed a mobile network-free communication system that can function during natural disasters and be adapted to most communication devices (mobile phones and laptops). The self-organized, mesh-based and low-power network is embedded into common infrastructure monitoring device nodes (e.g., pre-existing WSN, LoRa, and other LPWAN devices) for effective local communication. Local communication contains dedicated Emergency Messaging and “walkie-talkie” functions, while higher level connectivity through robust gateway architecture and data transmission units allows for real-time internet access, communication with nearby communities, and even global connectivity. The system can provide GPS-free position information using trilateration, which can help identify the location of nodes monitoring important environmental conditions or allowing users to navigate.

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.

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. This technology revolutionizes remote sensing by providing high precision, single-shot simultaneous ranging and velocimetry measurements without the need for sweeping.  

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.

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.