Spectral machine vision collects both the spectral and spatial dependence (x,y,λ) of incident light, containing potentially useful information such as chemical composition or micro/nanoscale structure.  However, analyzing the dense 3D hypercubes of information produced by hyperspectral and multispectral imaging causes a data bottleneck and demands tradeoffs in spatial/spectral information, frame rate, and power efficiency. Furthermore, real-time applications like precision agriculture, rescue operations, and battlefields have shifting, unpredictable environments that are challenging for spectroscopy. A spectral imaging detector that can analyze raw data and learn tasks in-situ, rather than sending data out for post-processing, would overcome challenges. No intelligent device that can automatically learn complex spectral recognition tasks has been realized.       UC Berkeley researchers have met this opportunity by developing a novel photodetector capable of learning to perform machine learning analysis and provide ultimate answers in the readout photocurrent. The photodetector automatically learns from example objects to identify new samples. Devices have been experimentally built in both visible and mid-infrared (MIR) bands to perform intelligent tasks from semiconductor wafer metrology to chemometrics. Further calculations indicate 1,000x lower power consumption and 100x higher speed than existing solutions when implemented for hyperspectral imaging analysis, defining a new intelligent photodetection paradigm with intriguing possibilities.

A revolutionary approach to enhancing data recovery in distributed systems through flexible storage codes.

An AI-powered platform for the generation of automated electronic patient anatomy education models, providing surgeons with clinically relevant patient anatomy data.

Researchers at the University of California, Davis have developed an innovative technology designed to directly measure fetus blood oxygen saturation level through the maternal abdomen from the onset of labor until birth, thereby improving fetal health outcomes.

This technology introduces an advanced scheduling strategy for optimizing multi-model AI workloads on heterogeneous chiplet-based multi-chip modules (MCMs), aiming at maximizing performance efficiency.

Researchers at the University of California Davis have developed a technology that enables the provable editing of DNNs (deep neural networks) to meet specified safety criteria without altering their architecture.

Researchers at the University of California, Davis have developed a technology that introduces a novel approach to hardware security using photonic physically unclonable functions for true random number generation and biometric ID.

Researchers at the University of California, Davis, have developed a memory system that uses optical interconnects.

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.

Researchers at the University of California, Davis have developed a technology that provides a novel method for decoding biosignals into speech, enhancing communication for individuals with speech impairments.

      The demand for power electronics with smaller volumes, lighter weights, and lower cost has motivated ongoing investigation into alternative power passive component technologies. Miniaturization of power converters is bottlenecked by magnetics, whose power densities fundamentally reduce at small scales. Capacitors exhibit much more favorable densities at small sizes, but efficient voltage regulation and galvanic isolation are difficult to achieve without magnetics. Therefore piezoelectric components have emerged as compelling alternative passive components for power electronics. However,  their high-performance capabilities have been limited to applications of high load impedance due to the high characteristic of piezoelectric resonators (PRs) themselves.       To overcome this challenge, UC Berkeley researchers have developed novel piezoelectric resonator (PR) designs based on overtones, with enhanced power densities and reduced optimal load impedances. The overtone PRs have been demonstrated to have comparable efficiency to fundamental-mode PRs, while their capabilities for power handling density and lower optimal load impedances are increased. Use of overtone PRs can expand the utility of piezoelectrics to a wider scope of power electronics.

      Probabilistic Seismic Hazard Analysis (PSHA) has become a foundational method for determining seismic design levels and conducting regional seismic risk analyses for insurance risk analysis, governmental hazard mapping, critical infrastructure planning, and more. PSHA traditionally relies on two computationally intensive approaches: Riemann Sum and conventional Monte Carlo (MC) integration. The former requires fine slices across magnitude, distance, and ground motion, and the latter demands extensive synthetic earthquake catalogs. Both approaches become notably resource intensive for low-probability seismic hazards, where achieving a COV of 1% for a 10−4 annual hazard probability may require 108 MC samples.       UC Berkeley researchers have developed an Adaptive Importance Sampling (AIS) PSHA, a novel framework to approximate optimal importance sampling (IS) distributions and dramatically reduce the number of MC samples to estimate hazards. Efficiency and accuracy of the proposed framework have been validated against Pacific Earthquake Engineering Research Center (PEER) PSHA benchmarks covering various seismic sources, including areal, vertical, and dipping faults, as well as combined types. Seismic hazards are calculated up to 3.7×104 and 7.1×103 times faster than Riemann Sum and traditional MC methods, respectively. Coefficients of variation (COVs) are below 1%. Enhanced “smart” AIS PSHA variants are also available that outperform “smart” implementations of Riemann Sum by a factor of up to 130.

Optical atomic clocks have taken a giant leap in recent years, with several experiments reaching uncertainties at the 10−18 level. The development of synchronized clock networks and transportable clocks that operate in extreme and distant environments would allow clocks based on different atomic standards or placed in separate locations to be compared. Such networks would enable relativistic geodesy, tests of fundamental physics, dark matter searches, and more. However, the leading neutral-atom optical clocks operate on wavelengths of 698 nm (Sr) and 578 nm (Yb). Light at these wavelengths is strongly attenuated in optical fibers, posing a challenge to long-distance time transfer. Those wavelengths are also inconvenient for constructing the ultrastable lasers that are an essential component of optical clocks. To address this problem, UC Berkeley researchers have developed a new, laser-cooled neutral atom optical atomic clock that operates in the telecommunication wavelength band. The leveraged atomic transitions are narrow and exhibit much smaller black body radiation shifts than those in alkaline earth atoms, as well as small quadratic Zeeman shifts. Furthermore, the transition wavelengths are in the low-loss S, C, and L-bands of fiber-optic telecommunication standards, allowing the clocks to be integrated with robust laser technology and optical amplifiers. Additionally, the researchers have identified magic trapping wavelengths via extensive studies and have proposed approaches to overcome magnetic dipole-dipole interactions. Together, these features support the development of fiber-linked terrestrial clock networks over continental distances.

         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.

Since the advent of asynchronous packet-based networks in communication and information technology, the topic of clock synchronization has received significant attention due to the temporal requirements of packet-based networks for the exchange of information. In more recent years, as distributed packet-based networks have evolved in terms of size, complexity, and, above all, application scope, there has been a growing need for new clock synchronization schemes with tractable design conditions to meet the demands of these evolving networks. Distributed applications such as robotic swarms, automated manufacturing, and distributed optimization rely on precise time synchronization among distributed agents for their operation. For example, in the case of distributed control and estimation over networks, the uncertainties of packet-based network communication require timestamping of sensor and actuator messages in order to synchronize the information to the evolution of the dynamical system being controlled or estimated. Such a scenario is impossible without the existence of a common timescale among the non-collocated agents in the system. In fact, the lack of a shared timescale among the networked agents can result in performance degradation that can destabilize the system. Moreover, one cannot always assume that consensus on time is a given, especially when the network associated to the distributed system is subject to perturbations such as noise, delay, or jitter. Hence, it is essential that these networked systems utilize clock synchronization schemes that establish and maintain a common timescale for their algorithms. With the arrival of more centralized protocols came motivated leader-less, consensus-based approaches by leveraging the seminal results on networked consensus in (e.g., Cao et al. 2008). More recent approaches (Garone et al. 2015, Kikuya et al. 2017) employ average consensus to give asymptotic results on clock synchronization under asynchronous and asymmetric communication topology. Unfortunately, a high number of iterations of the algorithm is often required before the desired synchronization accuracy is achieved. Furthermore, the constraint on asymmetric communication precludes any results guaranteeing stability or robustness. Lastly, these approaches suffer from over-complexity in term of both computation and memory allocation. Moreover, both synchronous and asynchronous scenarios require a large number of iterations before synchronization is achieved. Finally, the algorithm subjects the clocks to significant non-smooth adjustments in clock rate and offset that may prove undesirable in certain application settings.

Researchers at the University of California, Davis have developed an automated harvesting system using predictive scheduling for crop-transport robots, reducing manual labor, and increasing harvesting efficiency.

Researchers at the University of California, Davis have developed an autonomous crop transportation robot to aid field workers during harvest.

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.

The exchange of information among nodes in a communications network is based upon the transmission of discrete packets of data from a transmitter to a receiver over a carrier according to one or more of many well-known, new or still developing protocols. In this context, a protocol consists of a set of rules defining how the nodes interact with each other based on information sent over the communication links. Often, multiple nodes will transmit a packet at the same time and a collision occurs. During a collision, the packets are disrupted and become unintelligible to the other devices listening to the carrier activity. In addition to packet loss, network performance is greatly impacted. The delay introduced by the need to retransmit the packets cascades throughout the network to the other devices waiting to transmit over the carrier. Therefore, packet collision has a multiplicative effect that is detrimental to communications networks. As a result, multiple international protocols have been developed to address packet collision, including collision detection and avoidance. Within the context of wired Ethernet networks, the issue of packet collision has been largely addressed by network protocols that try to detect a packet collision and then wait until the carrier is clear to retransmit. Emphasis is placed in collision detection, i.e., a transmitting node can determine whether a collision has occurred by sensing the carrier. At the same time, the nature of wireless networks prevents wireless nodes from being able to detect a collision. This is the case, in part, because in wireless networks the nodes can send and receive but cannot sense packets traversing the carrier after the transmission has started. Another problem arises when two transmitting nodes are out of range of each other, but the receiving node is within range of both. In this case, a transmitting node cannot sense another transmitting node that is out of communications range. IEEE 802.11 protocols are the basis for wireless network products using the Wi-Fi brand and are the world's most widely used wireless computer networking standards. With IEEE 802.11 packet collision features come deficiencies, like fairness. 802.11’s approach to certain parameters after each successful transmission may cause the node who succeeds in transmitting to dominate the channel for an arbitrarily long period of time. As a result, other nodes may suffer from severe short-term unfairness. Also, the current state of the network (e.g., load) is something that also should be factored. In general, there is a need for techniques to recognize network patterns and determine certain parameters that are responsive to those network patterns.

Real-time radiation monitoring is critical for public health and emergency response. High-frequency monitoring can generate large amounts of data for dozens of radioactive isotopes though. There is a growing demand for compact radiation detection devices that are also able to quickly and autonomously process these large datasets for anomalies. A UC Santa Cruz researcher has developed machine learning software that synthesizes real-time radiation monitoring data in situ to detect radioactive anomalies.

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Researchers at the University of California, Davis have developed several blockchain paradigms that provide new approaches and expand on existing protocols to improve performance in large-scale blockchain implementations.

Researchers at the University of California, Davis have developed an approach for integrating quantum computers into the existing internet backbone.

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.

Researchers at the University of California, Davis have collaborated with colleagues at the University of Washington and Emory University to develop a DNA-based, memory and data storage technology that integrates seamlessly with semiconductor-based technologies and conventional electronic devices.