Recent ucla_ece_oapdeposits items

PERCEL: A Re-Writable NVM CIM Incorporating a CTT-Based Per-Cell DAC

Tue, 12 May 2026 00:00:00 +0000

Compute in memory (CiM) accelerators perform matrix vector multiplications (MVMs) directly inside memory arrays, reducing data movement and improving both energy efficiency and throughput for AI workloads. To reduce the number of conversions, recent designs use multi-bit compute cells. Nevertheless, practical multi-bit CiM still faces a tension between accuracy, efficiency, and re-writeability, since multi-level NVM based designs suffer from nonlinearity and poor re-writeability, while multi-level activation based DRAM / SRAM macros are limited by mismatch and low accuracy. This work introduces a per-cell DAC based CiM macro that combines the density of multi-level NVM with fully re-writable DRAM weights to break the trade-off. Each bit cell embeds a compact 6-bit CTT based current mode DAC, calibrated in-situ through a write verify write loop, together with a 1T(1C) embedded DRAM. Post-layout simulations of a 576×256 macro in 22nm FDSOI project 49.9 8b-TOPS/W, and 8.96 8b-TOPS/mm2,...

Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases.

Fri, 10 Apr 2026 00:00:00 +0000

Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaround times, narrow dynamic ranges for the tested biomarkers, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Here, we present a deep learning-enhanced dual-mode multiplexed vertical flow assay (xVFA) with a portable optical reader and a neural network-based quantification pipeline. This optical sensor integrates colorimetric and chemiluminescent detection within a single paper-based cartridge to complementarily cover a large dynamic range (spanning ~6 orders of magnitude) for both low- and high-abundance biomarkers, while maintaining quantitative accuracy. Using 50 µL of serum, the optical...

Diagnostic x-ray source using electrons produced by a 100 J-class picosecond laser

Wed, 1 Apr 2026 00:00:00 +0000

Many laser-based high-energy-density science (HEDS) facilities have one or more short-pulse (sub- to few-picosecond) laser beams for diagnostics. For the past decade, we have been developing a novel x-ray probing capability using such picosecond lasers interacting with an underdense plasma to produce relativistic electrons. The ultimate goal of these experiments is to demonstrate a new type of x-ray backlighter using the short-pulse ARC laser at the National Ignition Facility (NIF). Before this diagnostic is fielded at the NIF, it is critical to demonstrate the viability and reproducibility of the x-ray source on comparable high-power short-pulse laser systems. We present experiments that were carried out with the OMEGA EP laser at the University of Rochester’s laboratory for laser energetics. In these experiments, high-energy electrons are produced through a combination of the self-modulation instability and direct laser acceleration in an underdense gas jet. These electrons...

Dictionary Learning for Phase-Less Beam Alignment Codebook Design in Multipath Channels

Wed, 11 Feb 2026 00:00:00 +0000

Large antenna arrays are critical for reliability and high data rates in wireless networks at millimeter-wave and sub-terahertz bands. While traditional methods for initial beam alignment for analog phased arrays scale beam alignment overhead linearly with the array size, compressive sensing (CS) and machine learning (ML) algorithms can scale logarithmically. CS and ML methods typically utilize pseudo-random or heuristic beam designs as compressive codebooks. However, these codebooks may not be optimal for scenarios with uncertain array impairments or multipath, particularly when measurements are phase-less or power-based. In this work, we propose a novel dictionary learning method to design codebooks for phase-less beam alignment given multipath and unknown impairment statistics. This codebook learning algorithm uses an alternating optimization with block coordinate descent to update the codebooks and Monte Carlo trials over multipath and impairments to incorporate a-priori knowledge...

MicroBayesAge: a maximum likelihood approach to predict epigenetic age using microarray data

Wed, 3 Dec 2025 00:00:00 +0000

Certain epigenetic modifications, such as the methylation of CpG sites, can serve as biomarkers for chronological age. Previously, we introduced the BayesAge frameworks for accurate age prediction through the use of locally weighted scatterplot smoothing (LOWESS) to capture the nonlinear relationship between methylation or gene expression and age, and maximum likelihood estimation (MLE) for bulk bisulfite and RNA sequencing data. Here, we introduce MicroBayesAge, a maximum likelihood framework for age prediction using DNA microarray data that provides less biased age predictions compared to commonly used linear methods. Furthermore, MicroBayesAge enhances prediction accuracy relative to previous versions of BayesAge by subdividing input data into age-specific cohorts and employing a new two-stage process for training and testing. Additionally, we explored the performance of our model for sex-specific age prediction which revealed slight improvements in accuracy for male patients,...

Roadmap on basic research needs for laser technology

Wed, 3 Dec 2025 00:00:00 +0000

Motivated by the profound impact of laser technology on science, arising from an increase in focused light intensity by seven orders of magnitude and flashes so short electron motion is visible, this roadmap outlines the paths forward in laser technology to enable the next generation of science and applications. Despite remarkable progress, the field confronts challenges in developing compact, high-power sources, enhancing scalability and efficiency, and ensuring safety standards. Future research endeavors aim to revolutionize laser power, energy, repetition rate and precision control; to transform mid-infrared sources; to revolutionize approaches to field control and frequency conversion. These require reinvention of materials and optics to enable intense laser science and interdisciplinary collaboration. The roadmap underscores the dynamic nature of laser technology and its potential to address global challenges, propelling progress and fostering sustainable development. Ultimately,...

Quantum cascade laser: 30 years of discoveries

Wed, 19 Nov 2025 00:00:00 +0000

Quantum cascade laser: 30 years of discoveries

Tunable Metasurface External-Cavity Quantum Cascade Lasers up to 5.74 THz

Wed, 19 Nov 2025 00:00:00 +0000

Vertical-external-cavity surface-emitting lasers based on amplifying quantum-cascade metasurfaces are demonstrated in the 5–6 THz range for the first time. Enhanced parasitic coupling to lossy surface modes requires updated design guidelines, while elevated losses and lower available material gain limit the scaling of the metasurface period from previous designs. A series of metasurface devices with varying periods employing both uniform and focusing metasurfaces is fabricated and characterized. Reducing the metasurface period below 70% of the free-space wavelength enables devices with superior performance, achieving pulsed operation up to 5.74 THz with peak output powers of 1.3 mW, maximum operating temperatures up to 83 K, and continuous-wave operation up to 54 K with 23 μW of output power. In the best case, single-mode tuning from 5.23–5.73 THz, which corresponds to a 9.1% fractional tuning, is realized with near-Gaussian far-field profiles maintained across the entire range....

Fast and Robust Multivariate Estimator Based on Heavy-Tailed Cauchy Uncertainties

Wed, 24 Sep 2025 00:00:00 +0000

The multivariate Cauchy estimator (MCE) is an analytic and recursive state estimation algorithm derived using Bayes’s rule and applied to discrete-time dynamic systems. Unlike the Kalman filter, the MCE models its additive measurement and process noises as Cauchy distributed rather than Gaussian. Due to its complexity, the initially introduced MCE was limited to small-dimensional problems running for short time horizons. In this paper, we propose several enhancements to the MCE algorithm, which, when used collectively, yield a fast and robust multivariate estimation algorithm for linear and nonlinear dynamic systems of a moderate state-space dimension. These contributions allow, for the first time, the MCE to be tested in linear and nonlinear estimation applications. We demonstrate its advantages compared to the Extended Kalman Filter/unscented Kalman filter.

Optical generative models

Wed, 10 Sep 2025 00:00:00 +0000

Generative models cover various application areas, including image and video synthesis, natural language processing and molecular design, among many others^1-11. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge^12-14. Here we present optical generative models inspired by diffusion models⁴, where a shallow and fast digital encoder first maps random noise into phase patterns that serve as optical generative seeds for a desired data distribution; a jointly trained free-space-based reconfigurable decoder all-optically processes these generative seeds to create images never seen before following the target data distribution. Except for the illumination power and the random seed generation through a shallow encoder, these optical generative models do not consume computing power during the synthesis of the images. We report the optical generation of monochrome and multicolour images...

A closed loop fully automated wireless vagus nerve stimulation system

Mon, 4 Aug 2025 00:00:00 +0000

Vagus nerve stimulation (VNS) has been explored as a treatment for a range of conditions, including epilepsy, cardiovascular disorders, drug-resistant depression, chronic pain, and obesity. Conventionally, VNS is administered using an open-loop approach, in which trained personnel adjust stimulation parameters. Medical supervision is necessary to minimize adverse effects, such as severe bradycardia, which can significantly interfere with daily activities. This requirement limits the feasibility of VNS in unsupervised settings and highlights the need for an automated control system. To address this limitation, we introduce the fully automated wireless VNS (FAW-VNS) system, which dynamically adjusts stimulation parameters to maintain steady-state operation while minimizing bradycardia. The FAW-VNS system operates in real-time and consists of a biocompatible, miniaturized, wirelessly powered implant equipped with cuff electrodes; a handheld device for power delivery and stimulation...

Multi-mode THz quantum-cascade VECSELs based on disordered metasurfaces.

Wed, 30 Jul 2025 00:00:00 +0000

Quantum-cascade vertical-external-cavity surface-emitting-lasers (VECSELs) based on disordered amplifying metasurfaces are demonstrated and explored as potential broadband, multi-mode THz sources. The disorder is introduced along one spatial axis of the metasurface by pseudo-randomly varying the width of its resonant ridge antennas. Compared to a quantum-cascade (QC) VECSEL based on a uniform metasurface, the disordered structure supports much more localized transverse modes with reduced spatial overlap within the QC gain material. This localization is hypothesized to facilitate the spatial hole burning of the gain material and, therefore, enable multi-mode lasing, particularly for short cavities on the order of a few wavelengths. Several devices have been fabricated and shown to differ from uniform QC-VECSELs in a few key ways, possessing highly nonlinear light-current characteristics, angle-dependent emission spectra and broadband multi-mode lasing. At most, 17 modes are simultaneously...

Harmonic and Subharmonic RF Injection Locking of THz Metasurface Quantum-Cascade VECSEL

Wed, 30 Jul 2025 00:00:00 +0000

Harmonic and subharmonic RF injection locking is demonstrated in a terahertz (THz) quantum-cascade vertical-external-cavity surface-emitting laser (QC-VECSEL). By tuning the RF injection frequency around integer multiples and submultiples of the cavity round-trip frequency, different harmonic and subharmonic orders can be excited in the same device. Modulation-dependent behavior of the device has been studied with recorded lasing spectral broadening and locking bandwidths in each case. In particular, harmonic injection locking results in the observation of harmonic spectra with bandwidths over 200 GHz. A semiclassical Maxwell-density matrix formalism has been applied to interpret QC-VECSEL dynamics, which aligns well with experimental observations.

Design for Telecom-Wavelength Quantum Emitters in Silicon Based on Alkali-Metal-Saturated Vacancy Complexes

Wed, 18 Jun 2025 00:00:00 +0000

Defect emitters in silicon are promising contenders as building blocks of solid-state quantum repeaters and sensor networks. Here, we investigate a family of possible isoelectronic emitter defect complexes from a design standpoint. We show that the identification of key physical effects on quantum defect state localization can guide the search for telecom-wavelength emitters. We demonstrate this by performing first-principles calculations on the Q center, predicting its charged sodium variants possessing ideal emission wavelength near the lowest-loss telecom bands and ground state spin for possible spin-photon interface and nanoscale spin sensor applications yet to be explored in experiments.

Spatial quantum-interference landscapes of multi-site-controlled quantum dots coupled to extended photonic cavity modes

Sat, 26 Apr 2025 00:00:00 +0000

A compact platform to integrate emitters in a cavity-like support is to embed quantum dots (QDs) in a photonic crystal (PhC) structure, making them promising candidates for integrated quantum photonic circuits. The emission properties of QDs can be modified by tailored photonic structures, relying on the Purcell effect or strong light-matter interactions. However, the effects of photonic states on spatial features of exciton emissions in these systems are rarely explored. Such effect is difficult to access due to random positions of self-assembled QDs in PhC structures, and the fact that quantum well excitons’ wavefunctions resemble photonic states in a conventional distributed Bragg reflector cavity system. In this work, we instead observe a spatial signature of exciton emission using site-controlled QDs embedded in PhC cavities. In particular, we observe the detuning-dependent spatial repulsion of the QD exciton emissions by polarized imaging of the micro-photoluminescence,...

Machine learning in point-of-care testing: innovations, challenges, and opportunities

Fri, 11 Apr 2025 00:00:00 +0000

The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of next-generation POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective explores how ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of ML-enhanced POCT in clinical settings and provide a comprehensive overview...

Skin-interfaced multimodal sensing and tactile feedback system as enhanced human-machine interface for closed-loop drone control

Mon, 7 Apr 2025 00:00:00 +0000

Unmanned aerial vehicles have undergone substantial development and market growth recently. With research focusing on improving control strategies for better user experience, feedback systems, which are vital for operator awareness of surroundings and flight status, remain underdeveloped. Current bulky manipulators also hinder accuracy and usability. Here, we present an enhanced human-machine interface based on skin-integrated multimodal sensing and feedback devices for closed-loop drone control. This system captures hand gestures for intuitive, rapid, and precise control. An integrated tactile actuator array translates the drone's posture into two-dimensional tactile information, enhancing the operator's perception of the flight situation. Integrated obstacle detection and neuromuscular electrical stimulation-based force feedback system enable collision avoidance and flight path correction. This closed-loop system combines intuitive controls and multimodal feedback to reduce...

Self-powered electrotactile textile haptic glove for enhanced human-machine interface

Fri, 4 Apr 2025 00:00:00 +0000

Human-machine interface (HMI) plays an important role in various fields, where haptic technologies provide crucial tactile feedback that greatly enhances user experience, especially in virtual reality/augmented reality, prosthetic control, and therapeutic applications. Through tactile feedback, users can interact with devices in a more realistic way, thereby improving the overall effectiveness of the experience. However, existing haptic devices are often bulky due to cumbersome instruments and power modules, limiting comfort and portability. Here, we introduce a concept of wearable haptic technology: a thin, soft, self-powered electrotactile textile haptic (SPETH) glove that uses the triboelectric effect and gas breakdown discharge for localized electrical stimulation. Daily hand movements generate sufficient mechanical energy to power the SPETH glove. Its features-softness, lightweight, self-sustainability, portability, and affordability-enable it to provide tactile feedback...

An ICU-grade breathable cardiac electronic skin for health, diagnostics, and intraoperative and postoperative monitoring

Fri, 4 Apr 2025 00:00:00 +0000

Cardiovascular digital health technologies potentially outperform traditional clinical equipment through their noninvasive, on-body, and portable monitoring with mass cardiac data beyond the confines of inpatient settings. However, existing cardiovascular wearables have difficulty with providing medical-grade accuracy with a chronically comfortable and stable patient/consumer device interface for reliable clinical decision-making. Here, we develop an intensive care unit (ICU)-grade breathable cardiac electronic skin system (BreaCARES) for real-time, wireless, continuous, and comfortable cardiac care. BreaCARES enables a novel digital cardiac care platform for health care, outpatient diagnostics, stable intraoperative monitoring during heart surgery, and continuous and comfortable inpatient postoperative cardiac care, exhibiting ICU-grade accuracy while having superior anti-interference stability, portability, and long-term on-skin biocompatibility to the clinically and commercially...

Tandem metabolic reaction–based sensors unlock in vivo metabolomics

Wed, 2 Apr 2025 00:00:00 +0000

Mimicking metabolic pathways on electrodes enables in vivo metabolite monitoring for decoding metabolism. Conventional in vivo sensors cannot accommodate underlying complex reactions involving multiple enzymes and cofactors, addressing only a fraction of enzymatic reactions for few metabolites. We devised a single-wall-carbon-nanotube-electrode architecture supporting tandem metabolic pathway-like reactions linkable to oxidoreductase-based electrochemical analysis, making a vast majority of metabolites detectable in vivo. This architecture robustly integrates cofactors, self-mediates reactions at maximum enzyme capacity, and facilitates metabolite intermediation/detection and interference inactivation through multifunctional enzymatic use. Accordingly, we developed sensors targeting 12 metabolites, with 100-fold-enhanced signal-to-noise ratio and days-long stability. Leveraging these sensors, we monitored trace endogenous metabolites in sweat/saliva for noninvasive health monitoring,...

A 13.56-MHz 25-dBm-Sensitivity Inductive Power Receiver System-on-a-Chip With a Self-Adaptive Successive Approximation Resonance Compensation Front-End for Ultra-Low-Power Medical Implants

Wed, 2 Apr 2025 00:00:00 +0000

Battery-less and ultra-low-power implantable medical devices (IMDs) with minimal invasiveness are the latest therapeutic paradigm. This work presents a 13.56-MHz inductive power receiver system-on-a-chip with an input sensitivity of -25.4 dBm (2.88 μW) and an efficiency of 46.4% while driving a light load of 30 μW. In particular, a real-time resonance compensation scheme is proposed to mitigate resonance variations commonly seen in IMDs due to different dielectric environments, loading conditions, and fabrication mismatches, etc. The power-receiving front-end incorporates a 6-bit capacitor bank that is periodically adjusted according to a successive-approximation-resonance-tuning (SART) algorithm. The compensation range is as much as 24 pF and it converges within 12 clock cycles and causes negligible power consumption overhead. The harvested voltage from 1.7 V to 3.3 V is digitized on-chip and transmitted via an ultra-wideband impulse radio (IR-UWB) back-telemetry...

A multitask approach for automated detection and segmentation of thyroid nodules in ultrasound images

Wed, 19 Mar 2025 00:00:00 +0000

An increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9888 images, and annotated by collaborating radiologists. Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. However, the proposed multitask approach both detects suspicious images and segments potential nodules; this...

Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare

Tue, 18 Mar 2025 00:00:00 +0000

Skin-interfaced electronics (skintronics) have received considerable attention due to their thinness, skin-like mechanical softness, excellent conformability, and multifunctional integration. Current advancements in skintronics have enabled health monitoring and digital medicine. Particularly, skintronics offer a personalized platform for early-stage disease diagnosis and treatment. In this comprehensive review, we discuss (1) the state-of-the-art skintronic devices, (2) material selections and platform considerations of future skintronics toward intelligent healthcare, (3) device fabrication and system integrations of skintronics, (4) an overview of the skintronic platform for personalized healthcare applications, including biosensing as well as wound healing, sleep monitoring, the assessment of SARS-CoV-2, and the augmented reality-/virtual reality-enhanced human-machine interfaces, and (5) current challenges and future opportunities of skintronics and their potentials in clinical...

Rapidly self-healing electronic skin for machine learning–assisted physiological and movement evaluation

Thu, 27 Feb 2025 00:00:00 +0000

Emerging electronic skins (E-Skins) offer continuous, real-time electrophysiological monitoring. However, daily mechanical scratches compromise their functionality, underscoring urgent need for self-healing E-Skins resistant to mechanical damage. Current materials have slow recovery times, impeding reliable signal measurement. The inability to heal within 1 minute is a major barrier to commercialization. A composition achieving 80% recovery within 1 minute has not yet been reported. Here, we present a rapidly self-healing E-Skin tailored for real-time monitoring of physical and physiological bioinformation. The E-Skin recovers more than 80% of its functionality within 10 seconds after physical damage, without the need of external stimuli. It consistently maintains reliable biometric assessment, even in extreme environments such as underwater or at various temperatures. Demonstrating its potential for efficient health assessment, the E-Skin achieves an accuracy exceeding 95%, excelling...

Deep Learning‐Enhanced Chemiluminescence Vertical Flow Assay for High‐Sensitivity Cardiac Troponin I Testing

Wed, 26 Feb 2025 00:00:00 +0000

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible format. Here, high-sensitivity detection of cardiac troponin I (cTnI) is demonstrated through innovations in chemiluminescence-based sensing, imaging, and deep learning-driven analysis. This chemiluminescence vertical flow assay (CL-VFA) enables rapid, low-cost, and precise quantification of cTnI, a key cardiac protein for assessing heart muscle damage and myocardial infarction. The CL-VFA integrates a user-friendly chemiluminescent paper-based sensor, a polymerized enzyme-based conjugate, a portable high-performance CL reader, and a neural network-based cTnI concentration inference algorithm. The CL-VFA measures cTnI over a broad dynamic range covering six orders of magnitude and operates with 50 µL of serum per test, delivering results in 25 min. This system achieves a detection limit of 0.16 pg mL^-1 with an average coefficient...

Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI)

Mon, 17 Feb 2025 00:00:00 +0000

PurposeThis brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.MethodsWe briefly introduce explainability methods, including global and individual explanatory features, methods for imaging data and time series, as well as similarity classification, and unraveled rules and laws.ResultsGiven the increasing interest in artificial intelligence within the surgical field, we emphasize the critical importance of transparency and interpretability in the outputs of applied models.ConclusionTransparency and interpretability are essential for the effective integration of AI models into clinical practice.

Comparing P300 flashing paradigms in online typing with language models

Fri, 14 Feb 2025 00:00:00 +0000

The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. Since the system has a relatively slow typing speed, different stimulus presentation paradigms have been proposed designed to allow users to input information faster by reducing the number of required stimuli or increase signal fidelity. This study compares the typing speeds of the Row-Column, Checkerboard, and Combinatorial Paradigms to examine how their performance compares in online and offline settings. When the different flashing patterns were tested in conjunction with other established optimization techniques such as language models and dynamic stopping, they did not make a significant impact on P300 speller performance. This result could indicate that further performance improvements on the system lie beyond optimizing flashing patterns.

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning

Fri, 14 Feb 2025 00:00:00 +0000

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained neural network that digitally transforms dark-field images of unstained bacteria into their Gram-stained equivalents matching bright-field image contrast. After a one-time training, the virtual Gram staining model processes an axial stack of dark-field microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of virtual Gram staining on label-free bacteria samples containing Escherichia coli and Listeria innocua by quantifying the staining accuracy of the model and comparing the chromatic and morphological features of the virtually stained bacteria against their chemically stained...

Ultra-light antennas via charge programmed deposition additive manufacturing

Sun, 26 Jan 2025 00:00:00 +0000

The demand for lightweight antennas in 5 G/6 G communication, wearables, and aerospace applications is rapidly growing. However, standard manufacturing techniques are limited in structural complexity and easy integration of multiple material classes. Here we introduce charge programmed multi-material additive manufacturing platform, offering unparalleled flexibility in antenna design and the capability for rapid printing of intricate antenna structures that are unprecedented or necessitate a series of fabrication routes. Demonstrating its potential, we present a transmitarray antenna composed of an interconnected, multi-layered array of dielectric/conductive S-ring unit cells, reducing 94% mass of conventional antenna configurations. A fully printed circular polarized transmitarray system fed by a source and a Risley prism antenna system operating at 19 GHz both show close alignment between testing results and numerical simulations. This printing method establishes a universal...

Revealing the reaction path of UVC bond rupture in cyclic disulfides with ultrafast x-ray scattering

Sat, 18 Jan 2025 00:00:00 +0000

Disulfide bonds are ubiquitous molecular motifs that influence the tertiary structure and biological functions of many proteins. Yet, it is well known that the disulfide bond is photolabile when exposed to ultraviolet C (UVC) radiation. The deep-UV-induced S─S bond fragmentation kinetics on very fast timescales are especially pivotal to fully understand the photostability and photodamage repair mechanisms in proteins. In 1,2-dithiane, the smallest saturated cyclic molecule that mimics biologically active species with S─S bonds, we investigate the photochemistry upon 200-nm excitation by femtosecond time-resolved x-ray scattering in the gas phase using an x-ray free electron laser. In the femtosecond time domain, we find a very fast reaction that generates molecular fragments with one and two sulfur atoms. On picosecond and nanosecond timescales, a complex network of reactions unfolds that, ultimately, completes the sulfur dissociation from the parent molecule.

Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss

Thu, 16 Jan 2025 00:00:00 +0000

BACKGROUND: Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices. The challenge remains to develop a universal framework capable of improving image quality across different devices and conditions, independent of registration or specific organ characteristics. OBJECTIVE: This study aims to develop a robust framework that enhances the quality of ultrasound images, particularly those captured with compact, portable devices, which are often constrained by low quality due to hardware limitations. The framework...

Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial

Fri, 20 Dec 2024 00:00:00 +0000

OBJECTIVES: Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms. MATERIALS AND METHODS: We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring. RESULTS: We recruited 111 heart failure patients into the study. We found that the arm including...

Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT

Wed, 27 Nov 2024 00:00:00 +0000

CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists’ accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the...

Mormyroidea-inspired electronic skin for active non-contact three-dimensional tracking and sensing

Mon, 25 Nov 2024 00:00:00 +0000

The capacity to discern and locate positions in three-dimensional space is crucial for human-machine interfaces and robotic perception. However, current soft electronics can only obtain two-dimensional spatial locations through physical contact. In this study, we report a non-contact position targeting concept enabled by transparent and thin soft electronic skin (E-skin) with three-dimensional sensing capabilities. Inspired by the active electrosensation of mormyroidea fish, this E-skin actively ascertains the 3D positions of targeted objects in a contactless manner and can wirelessly convey the corresponding positions to other devices in real-time. Consequently, this E-skin readily enables interaction with machines, i.e., manipulating virtual objects, controlling robotic arms, and drones in either virtual or actual 3D space. Additionally, it can be integrated with robots to provide them with 3D situational awareness for perceiving their surroundings, avoiding obstacles, or tracking...

Plasma electron acceleration driven by a long-wave-infrared laser

Mon, 18 Nov 2024 00:00:00 +0000

Laser-driven plasma accelerators provide tabletop sources of relativistic electron bunches and femtosecond x-ray pulses, but usually require petawatt-class solid-state-laser pulses of wavelength λL ~ 1 μm. Longer-λL lasers can potentially accelerate higher-quality bunches, since they require less power to drive larger wakes in less dense plasma. Here, we report on a self-injecting plasma accelerator driven by a long-wave-infrared laser: a chirped-pulse-amplified CO2 laser (λL ≈ 10 μm). Through optical scattering experiments, we observed wakes that 4-ps CO2 pulses with < 1/2 terawatt (TW) peak power drove in hydrogen plasma of electron density down to 4 × 1017 cm−3 (1/100 atmospheric density) via a self-modulation (SM) instability. Shorter, more powerful CO2 pulses drove wakes in plasma down to 3 × 1016 cm−3 that captured and accelerated plasma electrons to relativistic energy. Collimated quasi-monoenergetic features in the electron output marked the onset of a transition from...

Haptic artificial muscle skin for extended reality

Wed, 13 Nov 2024 00:00:00 +0000

Existing haptic actuators are often rigid and limited in their ability to replicate real-world tactile sensations. We present a wearable haptic artificial muscle skin (HAMS) based on fully soft, millimeter-scale, multilayer dielectric elastomer actuators (DEAs) capable of significant out-of-plane deformation, a capability that typically requires rigid or liquid biasing. The DEAs use a thickness-varying multilayer structure to achieve large out-of-plane displacement and force, maintaining comfort and wearability. Experimental results demonstrate that HAMS can produce complex tactile feedback with high perception accuracy. Moreover, we show that HAMS can be integrated into extended reality (XR) systems, enhancing immersion and offering potential applications in entertainment, education, and assistive technologies.

Perfusion Collateral Index versus Hypoperfusion Intensity Ratio in Assessment of Collaterals in Patients with Acute Ischemic Stroke

Tue, 12 Nov 2024 00:00:00 +0000

BACKGROUND AND PURPOSE: Perfusion-based collateral indices such as the perfusion collateral index and the hypoperfusion intensity ratio have shown promise in the assessment of collaterals in patients with acute ischemic stroke. We aimed to compare the diagnostic performance of the perfusion collateral index and the hypoperfusion intensity ratio in collateral assessment compared with angiographic collaterals and outcome measures, including final infarct volume, infarct growth, and functional independence. MATERIALS AND METHODS: Consecutive patients with acute ischemic stroke with anterior circulation proximal arterial occlusion who underwent endovascular thrombectomy and had pre- and posttreatment MRI were included. Using pretreatment MR perfusion, we calculated the perfusion collateral index and the hypoperfusion intensity ratio for each patient. The angiographic collaterals obtained from DSA were dichotomized to sufficient (American Society of Interventional and Therapeutic Neuroradiology...

Speechformer-CTC: Sequential modeling of depression detection with speech temporal classification

Mon, 11 Nov 2024 00:00:00 +0000

Speech-based automatic depression detection systems have been extensively explored over the past few years. Typically, each speaker is assigned a single label (Depressive or Non-depressive), and most approaches formulate depression detection as a speech classification task without explicitly considering the non-uniformly distributed depression pattern within segments, leading to low generalizability and robustness across different scenarios. However, depression corpora do not provide fine-grained labels (at the phoneme or word level) which makes the dynamic depression pattern in speech segments harder to track using conventional frameworks. To address this, we propose a novel framework, Speechformer-CTC, to model non-uniformly distributed depression characteristics within segments using a Connectionist Temporal Classification (CTC) objective function without the necessity of input-output alignment. Two novel CTC-label generation policies, namely the Expectation-One-Hot and the...

Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification

Sat, 9 Nov 2024 00:00:00 +0000

Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 μL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification...

Biometric‐Tuned E‐Skin Sensor with Real Fingerprints Provides Insights on Tactile Perception: Rosa Parks Had Better Surface Vibrational Sensation than Richard Nixon

Thu, 7 Nov 2024 00:00:00 +0000

The dense mechanoreceptors in human fingertips enable texture discrimination. Recent advances in flexible electronics have created tactile sensors that effectively replicate slowly adapting (SA) and rapidly adapting (RA) mechanoreceptors. However, the influence of dermatoglyphic structures on tactile signal transmission, such as the effect of fingerprint ridge filtering on friction-induced vibration frequencies, remains unexplored. A novel multi-layer flexible sensor with an artificially synthesized skin surface capable of replicating arbitrary fingerprints is developed. This sensor simultaneously detects pressure (SA response) and vibration (RA response), enabling texture recognition. Fingerprint ridge patterns from notable historical figures - Rosa Parks, Richard Nixon, Martin Luther King Jr., and Ronald Reagan - are fabricated on the sensor surface. Vibration frequency responses to assorted fabric textures are measured and compared between fingerprint replicas. Results demonstrate...

A fully integrated breathable haptic textile

Thu, 7 Nov 2024 00:00:00 +0000

Wearable haptics serve as an enhanced media to connect humans and VR/robots. The inevitable sweating issue in all wearables creates a bottleneck for wearable haptics, as the sweat/moisture accumulated in the skin/device interface can substantially affect feedback accuracy, comfortability, and create hygienic problems. Nowadays, wearable haptics typically gain performance at the cost of sacrificing the breathability, comfort, and biocompatibility. Here, we developed a fully integrated breathable haptic textile (FIBHT) to solve these trade-off issues, where the FIBHT exhibits high-level integration of 128 pixels over the palm, great stretchability of 400%, and superior permeability of over 657 g/m²/day (moisture) and 40 mm/s (air). It is a stand-alone haptic system totally composed of stretchable, breathable, and bioadhesive materials, which empowers it with precise, sweating/movement-insensitive and dynamic feedback, and makes FIBHT powerful for virtual touching in broad...

Spatio-temporal breather dynamics in microcomb soliton crystals

Wed, 30 Oct 2024 00:00:00 +0000

Solitons, the distinct balance between nonlinearity and dispersion, provide a route toward ultrafast electromagnetic pulse shaping, high-harmonic generation, real-time image processing, and RF photonic communications. Here we uniquely explore and observe the spatio-temporal breather dynamics of optical soliton crystals in frequency microcombs, examining spatial breathers, chaos transitions, and dynamical deterministic switching – in nonlinear measurements and theory. To understand the breather solitons, we describe their dynamical routes and two example transitional maps of the ensemble spatial breathers, with and without chaos initiation. We elucidate the physical mechanisms of the breather dynamics in the soliton crystal microcombs, in the interaction plane limit cycles and in the domain-wall understanding with parity symmetry breaking from third-order dispersion. We present maps of the accessible nonlinear regions, the breather frequency dependences on third-order dispersion...

A Miniature Flexible Coil for High-SNR MRI of the Pituitary Gland

Wed, 25 Sep 2024 00:00:00 +0000

A Miniature Flexible Coil for High-SNR MRI of the Pituitary Gland

Neural network-based processing and reconstruction of compromised biophotonic image data

Thu, 19 Sep 2024 00:00:00 +0000

In recent years, the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of e.g., cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal, superior or alternative data. This strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic imaging. One of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed, critical for capturing fine dynamic biological processes. Additionally, this approach offers the prospect of simplifying hardware requirements and complexities, thereby making advanced imaging standards more accessible in terms of cost and/or size. This article...

Atomic dynamics of electrified solid–liquid interfaces in liquid-cell TEM

Tue, 10 Sep 2024 00:00:00 +0000

Electrified solid–liquid interfaces (ESLIs) play a key role in various electrochemical processes relevant to energy1–5, biology6 and geochemistry7. The electron and mass transport at the electrified interfaces may result in structural modifications that markedly influence the reaction pathways. For example, electrocatalyst surface restructuring during reactions can substantially affect the catalysis mechanisms and reaction products1–3. Despite its importance, direct probing the atomic dynamics of solid–liquid interfaces under electric biasing is challenging owing to the nature of being buried in liquid electrolytes and the limited spatial resolution of current techniques for in situ imaging through liquids. Here, with our development of advanced polymer electrochemical liquid cells for transmission electron microscopy (TEM), we are able to directly monitor the atomic dynamics of ESLIs during copper (Cu)-catalysed CO2 electroreduction reactions (CO2ERs). Our observation reveals...

Multiplexed All‐Optical Permutation Operations Using a Reconfigurable Diffractive Optical Network

Tue, 3 Sep 2024 00:00:00 +0000

Large-scale and high-dimensional permutation operations are important for various applications in, for example, telecommunications and encryption. Here, all-optical diffractive computing is used to execute a set of high-dimensional permutation operations between an input and output field-of-view through layer rotations in a diffractive optical network. In this reconfigurable multiplexed design, every diffractive layer has four orientations: (Formula presented.), (Formula presented.), (Formula presented.), and (Formula presented.). Each unique combination of these layers represents a distinct rotation state, tailored for a specific permutation operation. Therefore, a K-layer rotatable diffractive design can all-optically perform up to (Formula presented.) independent permutation operations. The original input information can be decrypted by applying the specific inverse permutation matrix to output patterns. The feasibility of this reconfigurable multiplexed diffractive design...

Data‐Class‐Specific All‐Optical Transformations and Encryption

Tue, 3 Sep 2024 00:00:00 +0000

Diffractive optical networks provide rich opportunities for visual computing tasks. Here, data-class-specific transformations that are all-optically performed between the input and output fields-of-view (FOVs) of a diffractive network are presented. The visual information of the objects is encoded into the amplitude (A), phase (P), or intensity (I) of the optical field at the input, which is all-optically processed by a data-class-specific diffractive network. At the output, an image sensor-array directly measures the transformed patterns, all-optically encrypted using the transformation matrices preassigned to different data classes, i.e., a separate matrix for each data class. The original input images can be recovered by applying the correct decryption key (the inverse transformation) corresponding to the matching data class, while applying any other key will lead to loss of information. All-optical class-specific transformations covering A → A, I → I, and P → I transformations...

All-optical phase conjugation using diffractive wavefront processing

Tue, 3 Sep 2024 00:00:00 +0000

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive...

Pyramid diffractive optical networks for unidirectional image magnification and demagnification

Tue, 3 Sep 2024 00:00:00 +0000

Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output field-of-view. Here, we present a pyramid-structured diffractive optical network design (which we term P-D2NN), optimized specifically for unidirectional image magnification and demagnification. In this design, the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification. This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction—achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. Furthermore, the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band...

Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning‐Designed Diffractive Polarization Transformer

Tue, 3 Sep 2024 00:00:00 +0000

Controlled synthesis of optical fields having nonuniform polarization distributions presents a challenging task. Here, a universal polarization transformer is demonstrated that can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs). This framework comprises 2D arrays of linear polarizers positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. After its deep learning-based training, this diffractive polarization transformer can successfully implement N_i N_o = 10 000 different spatially-encoded polarization scattering matrices with negligible error, where N_i and N_o represent the number of pixels in the input and output FOVs, respectively. This universal polarization transformation framework is experimentally...

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Tue, 27 Aug 2024 00:00:00 +0000

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

All-optical complex field imaging using diffractive processors

Wed, 21 Aug 2024 00:00:00 +0000

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging...

Comparing serial X-ray crystallography and microcrystal electron diffraction (MicroED) as methods for routine structure determination from small macromolecular crystals

Sun, 18 Aug 2024 00:00:00 +0000

Innovative new crystallographic methods are facilitating structural studies from ever smaller crystals of biological macromolecules. In particular, serial X-ray crystallography and microcrystal electron diffraction (MicroED) have emerged as useful methods for obtaining structural information from crystals on the nanometre to micrometre scale. Despite the utility of these methods, their implementation can often be difficult, as they present many challenges that are not encountered in traditional macromolecular crystallography experiments. Here, XFEL serial crystallography experiments and MicroED experiments using batch-grown microcrystals of the enzyme cyclophilin A are described. The results provide a roadmap for researchers hoping to design macromolecular microcrystallography experiments, and they highlight the strengths and weaknesses of the two methods. Specifically, we focus on how the different physical conditions imposed by the sample-preparation and delivery methods required...

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling

Fri, 16 Aug 2024 00:00:00 +0000

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale...

Unidirectional imaging using deep learning–designed materials

Thu, 15 Aug 2024 00:00:00 +0000

A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, B → A, the image formation would be blocked. We report the first demonstration of unidirectional imagers, presenting polarization-insensitive and broadband unidirectional imaging based on successive diffractive layers that are linear and isotropic. After their deep learning-based training, the resulting diffractive layers are fabricated to form a unidirectional imager. Although trained using monochromatic illumination, the diffractive unidirectional imager maintains its functionality over a large spectral band and works under broadband illumination. We experimentally validated this unidirectional imager using terahertz radiation, well matching our numerical results. We also created a wavelength-selective unidirectional imager, where two unidirectional imaging operations, in reverse directions, are multiplexed...

Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders

Tue, 6 Aug 2024 00:00:00 +0000

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with...

Optical injection locking of a THz quantum-cascade VECSEL with an electronic source.

Wed, 31 Jul 2024 00:00:00 +0000

Optical injection locking of a metasurface quantum-cascade (QC) vertical-external-cavity surface-emitting laser (VECSEL) is demonstrated at 2.5 THz using a Schottky diode frequency multiplier chain as the injection source. The spectral properties of the source are transferred to the laser output with a locked linewidth of ∼1 Hz, as measured by a separate subharmonic diode mixer, and a locking bandwidth of ∼300 MHz is achieved. The large locking range is enabled by the microwatt power levels available from modern diode multipliers. The interplay between the injected signal and feedback from external reflections is studied and demonstrated to increase or decrease the locking bandwidth relative to the classic locking range depending on the phase of the feedback.

A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV‑2 Using Machine Learning

Sat, 20 Jul 2024 00:00:00 +0000

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 μL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed...

Miniaturized Wirelessly Powered and Controlled Implants for Multisite Stimulation

Tue, 16 Jul 2024 00:00:00 +0000

This paper presents a miniaturized implant with a diameter of only 14 mm, which houses a novel System on Chip (SoC) enabling two voltage level stimulation of up to 16 implants using a single Tx coil. Each implant can operate at a distance of 80 mm in the air through the inductive resonant link. The SoC consumes only 27 μW static power and enables two channels with stimulation amplitudes of 1.8 V and 3.3 V and timing resolution of 100 μs. The SoC is implemented in the standard 180 nm complementary metal oxide semiconductor (CMOS) technology and has an area of 0.75 mm × 1.6 mm. The SoC comprises an RF rectifier, low drop-out regulator (LDO), error detection block, clock data recovery, finite state machine (FSM), and output stage. Each implant has a PCB-defined passcode, which enables the individual addressability of the implants for synchronized therapies. The implantable device weighs only 80 mg and sizes 20.1 mm³. Tolerance of up to 70° to angular...

Anisotropic 2D van der Waals Magnets Hosting 1D Spin Chains

Mon, 15 Jul 2024 00:00:00 +0000

The exploration of 1D magnetism, frequently portrayed as spin chains, constitutes an actively pursued research field that illuminates fundamental principles in many-body problems and applications in magnonics and spintronics. The inherent reduction in dimensionality often leads to robust spin fluctuations, impacting magnetic ordering and resulting in novel magnetic phenomena. Here, structural, magnetic, and optical properties of highly anisotropic 2D van der Waals antiferromagnets that uniquely host spin chains are explored. First-principle calculations reveal that the weakest interaction is interchain, leading to essentially 1D magnetic behavior in each layer. With the additional degree of freedom arising from its anisotropic structure, the structure is engineered by alloying, varying the 1D spin chain lengths using electron beam irradiation, or twisting for localized patterning, and spin textures are calculated, predicting robust stability of the antiferromagnetic ordering....

TinyNS: Platform-aware Neurosymbolic Auto Tiny Machine Learning

Sat, 6 Jul 2024 00:00:00 +0000

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this paper, we introduce TinyNS, the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators. TinyNS provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. TinyNS uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget....

Intertrust

Thu, 27 Jun 2024 00:00:00 +0000

An NDN network is made of named entities with various trust relations between each other. Entities are organized into trust zones. Each trust zone contains the entities under the same administrative control. This work-in-progress explores an approach to establishing trust relations between trust zones.

X-CHAR

Wed, 26 Jun 2024 00:00:00 +0000

End-to-end deep learning models are increasingly applied to safety-critical human activity recognition (HAR) applications, e.g., healthcare monitoring and smart home control, to reduce developer burden and increase the performance and robustness of prediction models. However, integrating HAR models in safety-critical applications requires trust, and recent approaches have aimed to balance the performance of deep learning models with explainable decision-making for complex activity recognition. Prior works have exploited the compositionality of complex HAR (i.e., higher-level activities composed of lower-level activities) to form models with symbolic interfaces, such as concept-bottleneck architectures, that facilitate inherently interpretable models. However, feature engineering for symbolic concepts-as well as the relationship between the concepts-requires precise annotation of lower-level activities by domain experts, usually with fixed time windows, all of which induce a heavy...

Continuous-wave GaAs/AlGaAs quantum cascade laser at 5.7 THz

Wed, 19 Jun 2024 00:00:00 +0000

Design strategies for improving terahertz (THz) quantum cascade lasers (QCLs) in the 5-6 THz range are investigated numerically and experimentally, with the goal of overcoming the degradation in performance that occurs as the laser frequency approaches the Reststrahlen band. Two designs aimed at 5.4 THz were selected: one optimized for lower power dissipation and one optimized for better temperature performance. The active regions exhibited broadband gain, with the strongest modes lasing in the 5.3-5.6 THz range, but with other various modes observed ranging from 4.76 to 6.03 THz. Pulsed and continuous-wave (cw) operation is observed up to temperatures of 117 K and 68 K, respectively. In cw mode, the ridge laser has modes up to 5.71 THz - the highest reported frequency for a THz QCL in cw mode. The waveguide loss associated with the doped contact layers and metallization is identified as a critical limitation to performance above 5 THz.

Multi-bounce self-mixing in terahertz metasurface external-cavity lasers

Wed, 19 Jun 2024 00:00:00 +0000

The effects of optical feedback on a terahertz (THz) quantum-cascade metasurface vertical-external-cavity surface-emitting laser (QC-VECSEL) are investigated via self-mixing. A single-mode 2.80 THz QC-VECSEL operating in continuous-wave is subjected to various optical feedback conditions (i.e., feedback strength, round-trip time, and angular misalignment) while variations in its terminal voltage associated with self-mixing are monitored. Due to its large radiating aperture and near-Gaussian beam shape, we find that the QC-VECSEL is strongly susceptible to optical feedback, which is robust against misalignment of external optics. This, in addition to the use of a high-reflectance flat output coupler, results in high feedback levels associated with multiple round-trips within the external cavity-a phenomenon not typically observed for ridge-waveguide QC-lasers. Thus, a new theoretical model is established to describe self-mixing in the QC-VECSEL. The stability of the device under...

A 0.4-4 THz p-i-n Diode Frequency Multiplier in 90-nm SiGe BiCMOS

Wed, 19 Jun 2024 00:00:00 +0000

A 0.4-4 THz p-i-n Diode Frequency Multiplier in 90-nm SiGe BiCMOS

Phase Locking of a THz QC-VECSEL to a Microwave Reference

Wed, 19 Jun 2024 00:00:00 +0000

Phase Locking of a THz QC-VECSEL to a Microwave Reference

Wavelength Scaling of Widely-Tunable Terahertz Quantum-Cascade Metasurface Lasers

Wed, 19 Jun 2024 00:00:00 +0000

Wavelength Scaling of Widely-Tunable Terahertz Quantum-Cascade Metasurface Lasers

Regioselective, catalytic 1,1-difluorination of enynes

Mon, 10 Jun 2024 00:00:00 +0000

Fluorinated small molecules are prevalent across the functional small-molecule spectrum, but the scarcity of naturally occurring sources creates an opportunity for creative endeavour in developing routes to access these important materials. Iodine(I)/iodine(III) catalysis has proven to be particularly well-suited to this task, enabling abundant alkene substrates to be readily intercepted by in situ-generated λ3-iodanes and processed to high-value (di)fluorinated products. These organocatalysis paradigms often emulate metal-based processes by engaging the π bond and, in the case of styrenes, facilitating fluorinative phenonium-ion rearrangements to generate difluoromethylene units. Here we demonstrate that enynes are competent proxies for styrenes, thereby mitigating the recurrent need for aryl substituents, and enabling highly versatile homopropargylic difluorides to be generated in an operationally simple manner. The scope of the method is disclosed, together with application...

TinyOdom

Mon, 10 Jun 2024 00:00:00 +0000

Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to...

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application

Sat, 8 Jun 2024 00:00:00 +0000

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques...

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