Researchers at the University of California Davis have developed a system designed to predict and prevent ATV rollovers, enhancing rider safety.

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.

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Many people have learned to appreciate the advent of GPS based navigational applications in our daily lives through the use of street level navigation, and many more loathe the same applications when using them to navigate established public transportation systems. Many of these travelers become confused and frustrated when attempting to understand and act on the directions given to them by such existing applications that primarily focus on large-scale street navigation, especially if the user has a visual or cognitive impairment. Several existing applications will not even attempt to aid someone in the navigation of say, a metro, train or bus station, and instead simply inform the user of the label of the route that the application intends the user to take. Without any small-scale directions many people find themselves struggling to figure out what platform or boarding zone they need to use to get on their preferred method of transportation, as well as how to get to these platforms and boarding zones in the first place. These transit hubs, plazas, malls, and the like have long been a pain in the side of developers and users alike when it comes to navigation. Innovation has long been overdue in this space concerning small scale transit plaza navigation, with major players holding large market shares in navigation not even attempting to address this longstanding problem. The only existing application to offer indoor navigation offers very limited as well as inconsistent functionality including only two-dimensional indoor mapping, due to manually uploaded floor plans that are only available in the first place from partnering locations. This has continued to be an issue due to a lack of adoption by existing locations, as each location is required to draw out their floor plan on an antiquated image file and submit it for approval. Solving this problem would ease a large amount of stress for those navigating in areas they are not familiar with, as well as saving time that could possibly make the difference between a missed train and a nearly missed train.

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.

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.

 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. 

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The field of autonomous transportation is rapidly evolving to operate in diverse settings and conditions. However, as the number of autonomous vehicles on the road increases the complexity of the computations needed to safely operate all of the autonomous vehicles grows rapidly. across multiple vehicles, this creates a very large volume of computations that must be performed very quickly (e.g., in real or near-real time).   Thus, treating each autonomous vehicle as an independent entity may result in inefficient use of computing resources, as many redundant data collections and computations may be performed (e.g., two vehicles in close proximity may be performing computations related to the same detected object). To address this issue, researches at UC Berkeley proposed algorithms for the management and exchange of shared information across nearby and distant vehicles.According to the proposed arrangement, autonomous vehicles may share data collected by their respective sensor systems with other autonomous vehicles and adjust their operations accordingly in a manner that is more computationally efficient. This can not only increase safety but at the same time reduce computational load required by each individual vehicle.

The field of autonomous transportation is rapidly evolving to operate in diverse settings and conditions.  Critical to the performance of autonomous vehicles is the ability to detect other objects in the autonomous vehicle’s vicinity and adjust accordingly. To do so, many autonomous vehicles utilize a variety of sensors, including sonar. Although these sensor systems have been shown to improve the safety of autonomous vehicles by reducing collisions, the sensor systems tend to be computationally inefficient.  For instance, the sensor systems may generate large volumes of data that must be processed quickly (e.g., in real or near-real time).  The performance of excessive computations may delay the identification and deployment of necessary resources and actions and/or increase the cost of hardware on the vehicle making it less financially appealing to the consumer. Researches at UC Berkeley proposed algorithms for temporally and spectrally adaptive sonar systems for autonomous vehicles. These allow utilization of existing sonar system in an adaptive manner and in interface with existence hardware/software employed on autonomous vehicles. 

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.

Need to transport sturdy adjustable workbenches for use at sea or other temporary work spaces that need anchoring to walls or floors and you can't find a commercially available source?

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The number one cause of train derailments globally are unidentified track defects which accumulate over time under the heavy loads and weathering to which rail is exposed. For the last 100 years rail inspection has sought to identify these structural defects before they can pose a serious threat to regular rail traffic. Unfortunately, rail inspection has required specialized low-speed testing cars which can only operate at less than 25% the normal speed of a train. These inspection cars must coordinate their work around planned outages of the rail line, impacting normal rail traffic. Due to this inconvenience, rail defects are typically repaired in real-time, as identified, vs. being prioritized as to potential seriousness and repaired in order of likelihood to cause a future accident.

While cellular phone networks are not designed for navigation, they are abundant in urban environments which are known to challenge GPS signals.  University of California, Riverside researchers integrated signals-of-opportunity from mobile phone networks to provide autonomous vehicles with precise navigational information.

Nanocrystalline iron nitride is an important soft magnetic material; however, conventional methods of production don’t exist. Synthesis of dense nanocrystalline iron nitrides is not possible by simply annealing elemental iron in NH3 at temperatures in excess of 600° C since g’-Fe4N and other iron nitrides are unstable above 600°C and will decompose. Sandia researchers have discovered that by using a two-step reactive milling process and high pressure spark plasma sintering (SPS) they can quickly and efficiently fabricate bulk g’-Fe4N parts.

This technology combines air layer frictional drag reduction (ALDR) with super hydrophobic surfaces (SHS) to achieve frictional drag reduction of ALDR with significantly reduced gas flux. Thus, enabling increased net energy savings. The stable air layer is achieved with lesser gas flux when utilizing a SHS.Periodic air layers may replenish SHS, enabling drag reduction with reduced energy cost. Combinations of SHS and regular or other non-SHS surface may be used to control spreading of gas, thus facilitating formation of ALDR using discrete gas injection points better than previously achievable. Such surface variations could also be used to preferentially guide gas towards or away from propulsion, depending on desired outcome. By controlling ALDR regionally or globally on a surface, with or without SHS, this technology modifies flow around a hull. This mediates forces on partially or fully submerged objects, enabling control of flow patterns, resistance, steering, and/or dynamics.

Researchers at the University of California, Davis have developed a new flight mechanism that offers vertical take-off and landing (VTOL) capability and cruising speeds comparable with fixed wing unmanned aerial vehicles (UAV).

As improvements in technology allow for construction of bigger, more uniquely designed skyscrapers, bridges, and motorways that can carry greater loads and are seismically sound, current cement composites are being pushed to their performance limits. Now more than ever, assessing damage to cement composite structures is of integral importance. However, traditional methods can be destructive, subjective, and may not detect previously existing damage, which can be invisible to the naked eye or hidden beneath structural surfaces. Addition of conductive additives, such as carbon nanotubes (CNTs) to cementitious composites attributes both load-bearing and damage self-sensing properties to the composites. However, current formulations and methods for producing these multifunctional cement composites require specialized equipment, are labor, time, and capital intensive, and are not scalable.

The invention relates to the field of micromachined gyroscopes, and in particular to inertial micromachined transducers for measurement of angular rotation rate of an object. A three-degrees of freedom (DOF) MEMS inertial micromachined gyroscope with nonresonant actuation with a drive direction, sense direction and a direction perpendicular to the drive and sense directions comprises a planar substrate, a 2-DOF sense-mode oscillator coupled to the substrate operated at a flattened wide-bandwidth frequency region, and a 1-DOF drive mode oscillator coupled operated at resonance in the flattened wide-bandwidth frequency region to achieve large drive-mode amplitudes.