An intent-aware, dual-stream AI architecture that adapts compute allocation and inference depth on embedded platforms, balancing rapid triage and detailed analysis for real-time visual understanding.

Researchers at the University of California, Davis have developed a navigation system that constructs a sensing map from wireless signal observations and pedestrian deadreckoning (PDR) data to enable accurate indoor navigation without relying on traditional geographic localization maps.

A novel laser-based method to create self-aligned electrodes with increased capacitance for improved performance of hemispherical resonator gyroscopes.

An innovative technology that greatly enhances navigational system performance by reducing dependence on unreliable signals for a wide range of navigation-reliant products.

A novel LTE-based navigation system that enhances positioning accuracy and reliability by jointly estimating time-of-arrival (TOA) and direction-of-arrival (DOA) of signals.

Position-sensitive radiation detection has been used in semiconductor detector development for decades. Traditional approaches have relied on segmented electrodes to achieve spatial resolution. Conventional semiconductor radiation detectors utilize segmented electrodes where each electrode segment is physically separated and individually read out to determine the position of radiation interactions. Traditional segmented electrode designs have long suffered from highly non-uniform electric fields within the detector volume, particularly at electrode edges and corners. These field concentrations can cause premature breakdown and inconsistent charge collection. This non-uniformity can also lead to position-dependent signal variations, pulse time dispersion, and potential electrical connections between adjacent electrodes from radiation damage. Moreover, common approaches to manufacturing of segmented electrodes requires precise mask alignment and complex fabrication processes, resulting in higher production costs and reduced yields.

A novel method to extract navigation observables from Starlink LEO satellite signals enabling precise positioning without additional infrastructure.

A novel framework enabling submeter-level accurate unmanned aerial vehicle (UAV) navigation using cellular carrier phase measurements with and without a base station.

A novel navigation framework enabling accurate positioning using unknown signals of opportunity without relying on Global Navigation Satellite System (GNSS).

A novel navigation framework utilizing low Earth orbit (LEO) satellite signals to provide accurate positioning where traditional Global Navigation Satellite System (GNSS) signals fail.

This technology enables precise navigation by opportunistically using 5G new radio (NR) signals without requiring dedicated positioning transmissions or direct network communication.

A novel navigation framework leveraging LTE cellular signals enables sub-meter level accurate UAV positioning in GNSS-challenged environments.

A novel receiver architecture and navigation framework leveraging Doppler measurements from low Earth orbit (LEO) satellites to provide accurate positioning where Global Navigation Satellite System (GNSS) signals are unreliable or unavailable.

WiFi-based indoor positioning has been a widely researched area for the past five years, with systems traditionally relying on signal telemetry data including Received Signal Strength Indicator (RSSI), Channel State Information (CSI), and Fine Timing Measurement (FTM). However, adoption in practice has remained limited due to environmental challenges including signal fading, multipath effects, and interference that significantly impact positioning accuracy. Existing machine learning approaches typically require extensive manual feature engineering, preprocessing steps like filtering and data scaling, and struggle with missing or incomplete telemetry data while lacking flexibility across heterogeneous environments. Furthermore, there is currently no unified model capable of handling variations in telemetry data formats from different WiFi device vendors, use-case requirements, and environmental conditions, forcing practitioners to develop separate models for each specific deployment scenario.

HydraFusion is a modular, selective sensor fusion framework designed to enhance performance and efficiency in multi-sensory computing systems across diverse contexts.

EnergyShield is a pioneering framework designed to optimize energy consumption through safe, intelligent offloading of deep neural network computations for autonomous vehicles.

A revolutionary approach to enhancing the safety and efficiency of autonomous vehicles through advanced scene-graph embeddings.

Vehicle platooning technology is an evolving segment within the broader movement towards more intelligent transportation, specifically relating to autonomous vehicles. Some early concepts dates back to the 1970s with projects like Electronic Route Guidance System developed by the U.S. Federal Highway Administration, which used a destination-oriented approach with roadside units to decode vehicle inputs and provide routing instructions. Subsequent initiatives such as the California Partners for Advanced Transportation Technology program demonstrated vehicles traveling in close formation guided by magnets embedded in roadways. The landscape has since evolved from individual vehicle automation concepts to more sophisticated vehicle-to-vehicle (V2V) communication schemes to enable coordinated movements. More recent industry developments have been driven by advancements in 5G technology, V2V communication protocols, and enhanced safety requirements. Current systems face control stability challenges, particularly as platoon size increases, with research showing that system stabilizability degrades and can lose stability entirely in infinite vehicle formations. Moreover, issues with V2V communication reliability persist, including frequent intermittent connectivity problems and wireless interference, limiting wider adoption. Additional challenges include the fundamental trade-off between fuel efficiency and safety margins, where shorter inter-vehicle distances improve aerodynamic benefits but increase collision risk.

Outdoor wireless sensor and camera networks are important for environmental monitoring and public-safety surveillance, yet their real-world deployment still relies heavily on expert intuition and exhaustive simulations that fail to scale in many landscapes. Traditional coverage-maximization techniques evaluate every candidate position for every node while factoring in every other node, the task complexity becomes intractable as node count or terrain granularity grows. The challenge is sharper in three-dimensional topographies where ridges, valleys, and plateaus block line-of-sight and invalidate two-dimensional heuristics. Moreover, once nodes are in the field, relocating them is slow and costly if new blind spots emerge or missions evolve.

A groundbreaking system that enables navigation in GPS-denied environments by using intelligent systems to mimic biological systems that recognize locations through visual cues and perform contextually appropriate actions.

This technology represents a significant leap in radar systems, offering millimeter-scale range resolution and high angular resolution.

A novel circuit design promoting enhanced sensitivity for electromagnetic sensing through exceptional points of degeneracy.

This technology enhances indoor pedestrian localization accuracy using LTE signals by mitigating multipath errors through synthetic aperture navigation.

An innovative approach to indoor localization using LTE signals and IMU data, enhancing accuracy and reliability for navigation.

An innovative approach to vehicle localization and mapping using lidar and cellular LTE data, enhancing accuracy without relying on GNSS signals.