Single-particle/single-molecule tracking (SPT) is a key tool for quantifying molecular motion in cells and in vitro. Wide-field SPT, in particular, can yield super-resolution mapping of physicochemical parameters and molecular interactions at the nanoscale, especially when integrated with single-molecule localization microscopy techniques like photoactivation and fluorophore exchange. However, wide-field SPT is often limited to the slow (<10 μm2/s) diffusion of molecules bound to membranes, chromosomes, or the small volume of bacteria, in part due to the ~10 ms framerate of common single-molecule cameras like electron-multiplying charge-coupled devices (EM-CCDs); for unbound diffusion in the mammalian cell and in solution, a molecule readily diffuses out of the <1 μm focal range of high-numerical-aperture objective lenses within 10 ms. While recent advances such as ultra-highspeed intensified CMOS cameras, feedback control by locking onto a molecule, trapping, and tandem excitation pulse schemes address the framerate issue, each also introduces drawbacks in light/signal efficiency, speed, uninterrupted diffusion paths, and/or trajectory resolution, e.g., number of time points.      UC Berkeley researchers have overcome these myriad challenges by introducing spatially-encoded dynamics tracking (SpeedyTrack), a strategy to enable direct microsecond wide-field single-molecule tracking/imaging on common microscopy setups. Wide-field tracking is achieved for freely diffusing molecules at down to 50 microsecond temporal resolutions for >30 timepoints, permitting trajectory analysis to quantify diffusion coefficients up to 1,000 um2/s. Concurrent acquisition of single-molecule diffusion trajectories and Forster resonance energy transfer (FRET) time traces further elucidates conformational dynamics and binding states for diffusing molecules. Moreover, spatial and temporal information is deconvolved to map long, fast single-molecule trajectories at the super-resolution level, thus resolving the diffusion mode of a fluorescent protein in live cells with nanoscale resolution. Already substantially outperforming existing approaches, SpeedyTrack stands out further for its simplicity—directly working off the built-in functionalities of EM-CCDs without the need to modify existing optics or electronics.

       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.

      Molecular dynamics (MD) is a computational materials science modality widely used in academic and industrial settings for materials discovery and more. A critical aspect of modern MD calculations are machine learning interatomic potentials (MLIPs), which learn from reference quantum mechanical calculations and predict the energy and forces of atomic configurations quickly. MLIPs allow for more accurate and comprehensive exploration of material/molecular properties at-scale. However, state-of-the-art MLIP methods mostly use a short-range approximation, which may be sufficient for describing properties of homogeneous bulk systems but fail for liquid-vapor interfaces, dielectric response, dilute ionic solutions with Debye-Huckel screening, and interactions between gas phase molecules. Short-range MLIPs neglect all long-range interactions, such as Coulomb and dispersion interactions.      To address the current shortcoming, UC Berkeley researchers have developed a straightforward and efficient algorithm to account for long-range interactions in MLIPs. The algorithm can predict system properties including those with charged, polar or apolar molecular dimers, bulk water, and water-vapor interfaces. In these cases standard short-range MLIPs lead to unphysical predictions, even when utilizing message passing algorithms. The present method eliminates artifacts while only about doubling the computational cost. Furthermore, it can be incorporated into most existing MLIP architectures, including potentials based on local atomic environments such as HDNPP, Gaussian Approximation Potentials (GAP), Moment Tensor Potentials (MTPs), atomic cluster expansion (ACE), and MPNN (e.g., NequIP, MACE).

      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.

As the density and performance of electronics continues to increase, thermal challenges have become a primary concern. Removing heat from electronic components can be extremely challenging, given their small size, electrical activity, and mechanical constraints. This necessitates the design of cooling solutions for a wide variety of electronic designs in applications such as datacenters, renewables, aircraft, etc. To address this problem, researchers at UC Berkeley have developed a thermal test vehicle (TTV) for characterizing the performance of electronics cooling solutions under a wide variety of operating conditions. All of the TTV circuitry required to perform measurements and temperature estimations can be included on one printed circuit board (PCB). This represents a simple, highly flexible approach for thermal test vehicle design. The overall size of the array can be scaled to any desired amount. This novel TTV represents a simple, highly flexible approach for thermal test vehicle design. Furthermore, its use of standard commercial electronic components allows for a vast reduction in cost compared to existing commercial solutions.

      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.

      Miniaturization and performance of power electronics is fundamentally limited by magnetic components, whose power densities inherently reduce at small scales. Piezoelectric resonators (PRs), which store energy in the mechanical compliance and inertia of a piezoelectric material, offer various advantages for power conversion including high quality factors, planar form factors, opportunity for batch fabrication, and potential for integration. Contrary to magnetic components, PRs have increased power handling densities at small scales. Noteworthy advancements have been made in magnetic-less, PR-based power converter designs, demonstrating significant achievements in both power density (up to 5.7 kW/cm3) and efficiency (up to >99%). However, while PRs are promising alternative passive components, they cannot be used as drag-and-drop replacements for magnetics; achieving high performance in a PR-based converter requires complicated control of multi-stage switching sequences. A need exists for more practical ways to leverage piezoelectrics in power conversion without such added complexity.      To address this challenge, UC Berkeley researchers have developed a piezoelectric component that may be leveraged to directly emulate the dynamics of a magnetic component. The “active inductor” can serve as a drag-and-drop replacement for bulky magnetic inductors in power converters. Power density and efficiency of underlying piezoelectrics are preserved while the design complexity associated with piezoelectric-based power converters is simplified. Detailed models and control strategies for the piezoelectric-based active inductors have been developed and usage demonstrated in a classic buck converter. The active inductor is further validated with closed-loop simulation results and open-loop experimental results, confirming its inductor-like behavior.

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.

Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primarily focuses on either transcription or detection, and the performance of each aspect remains limited.To address this problem, UC Berkeley researchers have developed a new unconstrained dysfluency modeling (UDM) approach that addresses both transcription and detection in an automatic and hierarchical manner. Furthermore, a simulated dysfluent dataset called VCTK++ enhances the capabilities of UDM in phonetic transcription. The effectiveness and robustness of UDM in both transcription and detection tasks has been demonstrated experimentally.UDM eliminates the need for extensive manual annotation by providing a comprehensive solution.

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.

Existing approaches to visualizing 3D models are capable of producing highly detailed representations of 3D scenes with precision and significant compositional control, but they also require a significant amount of time and expertise by the user to create and configure. Recent developments in generative machine learning (GML) have brought about systems that are capable of quickly producing convincing synthetic images of objects, people, landscapes, and environments, without the need for a 3D model, but these are difficult to precisely control and compose. Therefore, current methods cannot directly relate to detailed 3D models with the fidelity required for many applications, including architecture, product/industrial design, and experience design. To address this opportunity, UC Berkeley researchers have developed a new, GML-integrated 3D modeling and visualization workflow. The workflow streamlines the visualization process by eliminating arduous and time-consuming aspects while maintaining important points of user control. The invention is tailored for the production of “semantically-guided” visualizations of 3D models by coupling the detailed compositional control offered by 3D models with the unique facility of defining visual properties of geometry using natural language. The invention allows designers to more rapidly, efficiently, and intuitively iterate on designs.

Certain difficult optimization problems, such as the traveling salesman problem, can be solved using so-called analog Ising machines, in which electronic components (such as certain arrangements of diodes or electronic switches) implement an analog of a well-studied physical system known as an Ising machine. The problem is recast so that its solution can be read off from the lowest-energy configuration of the analog Ising machine, a state which the system will naturally evolve towards. While promising, this methodology suffers major drawbacks. Firstly, the number of subunits, known as “spins”, in the analog Ising machines, as well as the number of connections between these subunits, can grow substantially with problem size. Secondly, existing implementations of this principle rely on chip constructions which are optimized for one or a few problems, and are not sufficiently reprogrammable to be repurposed efficiently for other applications. To address these problems, researchers at UC Berkeley have developed a device known as a Field-programmable Ising machine which can be adapted to implement an analog Ising machine using a variety of hardware designs, such as the diodes and switches mentioned above. These Ising machines can be effectively reprogrammed to efficiently solve a wide array of problems across various domains. The inventors have shown that this design can be applied to SAT (“Satisfiability”) problems, a class known to be similar to the traveling salesman problem, in that the number of spins needed and their level of connectivity do not grow too quickly with problem size.

This invention is designed to monitor and analyze cardiovascular health data in real-time. The system comprises a user device equipped with cardiac sensors to detect and record cardiovascular data from a patient. This data, along with the patient's demographic information, is transmitted to a cloud-based platform where it is analyzed by a machine learning model. The model classifies the cardiovascular data and sends the classification results to healthcare workers for further action. This innovative system represents a significant advancement in the field of cardiovascular health monitoring, leveraging modern technology to enhance patient care and streamline healthcare processes.

Metamaterials are constructed from regular patterns of simpler constituents known as unit cells. These engineered metamaterials can exhibit exotic mechanical properties not found in naturally occurring materials, and accordingly they have the potential for use in a variety of applications from running shoe soles to automobile crumple zones to airplane wings. Practical design using metamaterials requires the specification of the desired mechanical properties based on understanding the precise unit cell structure and repeating pattern. Traditional design approaches, however, are often unable to take advantage of the full range of possible stress-strain relationships, as they are hampered by significant nonlinear behavior, process-dependent manufacturing errors, and the interplay between multiple competing design objectives. To solve these problems, researchers at UC Berkeley have developed a machine learning algorithm in which designers input a desired stress-strain curve that encodes the mechanical properties of a material. Within seconds, the algorithm outputs the digital design of a metamaterial that, once printed, fully encapsulates the desired properties from the inputted stress-strain curve. This algorithm produces results with a fidelity to the desired curve in excess of 90%, and can reproduce a variety of complex phenomena completely inaccessible to existing methods.

Silicon photonics is increasingly used in an array of communications and computing applications. In many applications, photonic chips must be coupled to optical fibers, which remains challenging due to the size mismatch between the on-chip photonics and the fiber itself. Existing approaches suffer from low alignment tolerance, sensitivity to fabrication variations, and complex processing, all of which hinder mass manufacture.To address these problems, researchers at UC Berkeley have developed a coupling mechanism between a silicon integrated photonic circuit and an optical fiber which uses a microlens to direct and collimate light into the fiber. Researchers have demonstrated that this device can achieve low coupling loss at large alignment tolerances, with an efficient and scalable manufacturing process analogous to existing manufacture of electronic integrated circuits. In particular, because the beam is directed above the silicon chip, this method obviates dry etching or polishing of the edge of the IC and allows the silicon photonics to be produced by dicing in much the same way as present electronic integrated circuits.

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.

The inventors have discovered a new way to generate ultrafast back-gating, by leveraging the surface band bending inherent to many semiconductor materials. This new architecture consists of a standard bulk semiconductor material and a layered material on the surface. Optical pulses generate picosecond time-varying electric fields on the surface material. The inventors have successfully applied this method to a quantum well Rashba system, as this is considered today one of the most promising candidates for spin-based devices, such as the Datta Das spin-transistor. The technology can induce an ultrafast gate and drive time-dependent Rashba and quantum well dynamics never observed before, with switching faster than 10GHz. This approach minimizes lithography and will enable light-driven electronic and spintronics devices such as transistors, spin-transistors, and photo-controlled Rashba circuitry. This method can be applied with minimal effort to any two-dimensional material, for both exfoliated and molecular beam epitaxy grown samples. Electric field gating is one of the most fundamental tuning knobs for all modern solid-state technology, and is the foundation for many solid-state devices such as transistors. Current methods for in-situ back-gated devices are difficult to fabricate, introduce unwanted contaminants, and are unsuited for picosecond time-resolved electric field studies.  

With the two-dimensional scaling of silicon field-effect transistors reaching fundamental limits, new functional improvements to transistors, as well as novel computing paradigms and vertical device integration at the architecture-level, are currently under intense study. Gate oxides play a critical role in this endeavor, as it’s a common performance booster for all devices, including silicon, new channel materials with potential for higher performance, and even materials suitable for three-dimensional integrated transistors.With the scaling of lateral dimensions in advanced transistors, an increased gate capacitance is desirable both to retain the control of the gate electrode over the channel and to reduce the operating voltage. To pursue these performance gains, UC Berkeley researchers invented a new heterostructure insulator material where: 1) the material possesses specific ferroic order such as ferroelectricity/anti-ferroelectricity or a mixture of both; 2) the overall dielectric property such as the permittivity is determined by the stacking order of different layers rather than exact volume fraction of the constituents; and 3) the material is composed of one or several repetition of ultra thin superlattice periods ranging from a few angstroms to 3 nm.

The inventors have developed a highly scalable multiplexed approach to increase the density of graphene nanoribbon- (GNR) based transistors. The technology forms a single device/chip (scale to 16,000 to >1,000,000 parallel transistors) on a single integrated circuit for single molecule biomolecular sensing, electrical switching, magnetic switching, and logic operations. This work relates to the synthesis and the manufacture of molecular electronic devices, more particularly sensors, switches, and complimentary metal-oxide semiconductor (CMOS) chip-based integrated circuits.Bottom-up synthesized graphene nanoribbons (GNRs) have emerged as one of the most promising materials for post-silicon integrated circuit architectures and have already demonstrated the ability to overcome many of the challenges encountered by devices based on carbon nanotubes or photolithographically patterned graphene. The new field of synthetic electronics borne out of GNRs electronic devices could enable the next generation of electronic circuits and sensors.  

Vacuum grippers are widely used in industry to handle objects via suction pressure. Unicontact suction cups are commonly used for gripping because they are simple to operate and can handle a variety of items, including those that are delicate, large, or inaccessible to jaw grippers. However, suction cup grippers have challenges such as planning a contact location and inertial force-induced grasping failure. To address these challenges, UC Berkeley researchers developed a tactile sensing technology for smart suction cups. This Berkeley sensing technology can detect suction contact and prevent suction cup grasp failures. It can perform tactile sensing of object properties such as roughness or porosity that might lead to grasping failures before they happen. If a grasp failure does happen, the technology gains additional information about why and how the failure occurred to prevent similar failures in future attempts. Sensing occurs quickly, such that robot behavior can remain fast while increasing performance, efficiency and reliability. As compared with other robotic grasping sensing technologies, this smart suction cup technology is affordable, resilient and easy to service. The cup is manufactured using the same process as other suction cups, and electronics are simple and located away from the point-of-contact and protected from damage or hazardous exposure.

The power demands on data centers are large and increasing rapidly. This is straining data center economic and environment impacts, and in turn driving improvements in data center power efficiencies. Data centers have been widely adopting 48 V intermediate bus architectures due to higher efficiency, good flexibility, and reduced cost. However, a major challenge in such systems is the conversion from the 48 V bus to the extreme low voltage and high current operating levels of server CPUs and GPUs.To address this challenge, UC Berkeley researchers developed a multi-phase hybrid power converter architecture. The Berkeley design uses hybrid converter topologies. A switched-capacitor network is smartly merged with a switched-inductor network, resulting in circuit component number reduction and soft-charging operation of the capacitors. Furthermore, the Berkeley architecture integrates a multi-phase control technique to achieve a higher conversion ratio of the switched-capacitor network, which can further improve the overall system efficiency without increasing the circuit size.  

Imaging the material property distribution of solids has a broad range of applications in materials science, biomechanical engineering, and clinical diagnosis. For example, as various diseases progress, the elasticity of human cells, tissues, and organs can change significantly. If these changes in elasticity can be measured accurately over time, early detection and diagnosis of different disease states can be achieved. Elasticity imaging is an emerging method to qualitatively image the elasticity distribution of an inhomogeneous body. A long-standing goal of this imaging is to provide alternative methods of clinical palpation (e.g. manual breast examination) for reliable tumor diagnosis. The displacement distribution of a body under externally applied forces (or displacements) can be acquired by a variety of imaging techniques such as ultrasound, magnetic resonance, and digital image correlation. A strain distribution, determined by the gradient of a displacement distribution, can be computed (or approximated) from measured displacements. If the strain and stress distributions of a body are both known, the elasticity distribution can be computed using the constitutive elasticity equations. However, there is currently no technique that can measure the stress distribution of a body in vivo. Therefore, in elastography, the stress distribution of a body is commonly assumed to be uniform and a measured strain distribution can be interpreted as a relative elasticity distribution. This approach has the advantage of being easy to implement. The uniform stress assumption in this approach, however, is inaccurate for an inhomogeneous body. The stress field of a body can be distorted significantly near a hole, inclusion, or wherever the elasticity varies. Though strain-based elastography has been deployed on many commercial ultrasound diagnostic-imaging devices, the elasticity distribution predicted based on this method is prone to inaccuracies.To address these inaccuracies, researchers at UC Berkeley have developed a de novo imaging method to learn the elasticity of solids from measured strains. Our approach involves using deep neural networks supervised by the theory of elasticity and does not require labeled data for the training process. Results show that the Berkeley method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy and missing measurements.

Computer-based, internet-connected, audio/video meeting platforms have become pervasive worldwide, especially since the 2020 emergence of the COVID-19 pandemic lockdown. These meeting platforms include Cisco Webex, Google Meet, GoTo, Microsoft Teams, and Zoom. However, those popular platforms are optimized for meetings in which all the participants are attending the meeting online, individually. Accordingly, those platforms have shortcomings when used for hybrid meetings in which some participants are attending together in-person and others attending online. Also, the existing platforms are problematic for large meetings in big rooms (e.g. classrooms) in which most or all of the participants are in-person. To address those suboptimal meet platform situations, researchers at UC Berkeley conceived systems, methods, algorithms and other software for a meeting platform that's optimized for hybrid meetings and large in-person meetings. The Berkeley meeting platform offers a user experience that's familiar to users of the conventional meeting platforms. Also, the Berkeley platform doesn't require any specialized participant hardware or specialized physical room infrastructure (beyond standard internet connectivity).

Radiation detectors are typically instrumented with low noise preamplifiers that generate voltage pulses in response to energy deposits from particles (x-rays, gamma-rays, neutrons, protons, muons, etc.). This preamplifier signal must be further processed in order to improve the signal to noise ratio, and then subsequently estimate various properties of the pulse such as the pulse amplitude, timing, and shape. Historically, this “pulse processing” was carried out with complex, purpose-built analog electronics. With the advent of digital computing and fast analog to digital converters, this type of processing can be carried out in the digital domain.There are a number of commercial products that perform “hardware” digital pulse processing. The common element among these offerings is that the pulse processing algorithms are implemented in hardware (typically an FPGA or high performance DSP chip). However this hardware approach is expensive, and it's hard to tailor for a specific detector and application.To address these issues, researchers at UC Berkeley developed a solution that performs the pulse processing in software on a general purpose computer, using digital signal processing techniques. The only required hardware is a general purpose, high speed analog to digital converter that's capable of streaming the digitized detector preamplifier signal into computer memory without gaps. The Berkeley approach is agnostic to the hardware, and is implemented in such a way as to accommodate various hardware front-ends. For example, a Berkeley implementation uses the PicoScope 3000 and 5000 series USB3 oscilloscopes as the hardware front-end. That setup has been used to process the signal from a number of semiconductor and scintillator detectors, with results that are comparable to analog and hardware digital pulse processors.In comparison to current hardware solutions, this new software solution is much less expensive, and much more easily configurable. More specifically, the properties of the digital pulse shaping filter, trigger criteria, methods for estimating the pulse parameters, and formatting/filtering of the output data can be adjusted and tuned by writing simple C/C++ code.