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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.

POEM is a groundbreaking 3D bioprinting technology enabling high-resolution, multi-material, and cell-laden structure fabrication with enhanced cell viability.

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

An innovative design automation methodology leveraging graph neural networks to enhance integrated circuit security by mitigating hardware threats and protecting intellectual property.

A revolutionary method for improving the efficiency and quality of reprogramming adult cells into stem cells or other therapeutically relevant cell types via adhesome gene manipulation.

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.

This technology revolutionizes hydrogen production by using induction heating for catalytic methane decomposition, significantly increasing hydrogen yield.

Revolutionizing cement manufacturing through an energy-efficient electrochemical method that produces calcium hydroxide with reduced CO2 emissions.

Researchers at the University of California, Davis have developed a technology that introduces a highly adaptable, lightweight robotic end effector designed for complex manipulation tasks in automation.

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A novel self-powered mechanical cleaner designed to enhance the efficiency of solar panels by regularly removing dust and debris.

A novel device leveraging centrifugal microfluidics to accelerate bacterial growth and rapidly determine antibiotic susceptibility.

Revolutionizing the upcycling of carboxylic acid-based chemical waste products to aldehyde derivatives using engineered biological systems.

A verbatim phoneme recognition framework that transcribes what a person actually says, including accents and dysfluencies, to provide precise feedback for pronunciation training.

This technology leverages X-ray-induced acoustic phenomena for real-time, in-line verification of photon beam location and dose during cancer radiotherapy.

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.

This technology offers a novel approach to navigation by using LTE signals, providing a viable alternative to traditional GPS systems.

Researchers at the University of California, Davis have developed a sustainable alternative to Portland cement by utilizing alkali-activated binders (AAB) with biomass ash, significantly reducing greenhouse gas emissions.

This technology enables precise, selective manipulation of magnetically barcoded materials, distinguishing them from background magnetic materials

This technology uses smartphones to monitor a building's structural health by recording ambient vibrations. The data is processed to determine structural parameters, establishing a baseline to identify changes that may indicate damage, and trigger inspections.

Introducing a groundbreaking orthogonal redox cofactor, NMN+, to revolutionize redox reaction control in biomanufacturing.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter "organoid") have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Modern methods of interfacing with organoids involve any combination of encoding information, decoding information, or perturbing the underlying dynamics through various timescales of plasticity. Our knowledge of biological learning rules has not yet translated to reliable methods for consistently training neural tissue in goal-directed ways. In vivo training methods commonly exploit principles of reinforcement learning and Hebbian learning to modify biological networks. However, in vitro training has not seen comparable success, and often cannot utilize the underlying, multi-regional circuits enabling dopaminergic learning. Successfully harnessing in vitro learning methods and systems could uniquely reveal fundamental mesoscale processing and learning principles. This may have profound implications, from developing targeted stimulation protocols for therapeutic interventions to creating energy-efficient bio-electronic systems.

       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.