arXiv subjects

Shuo Gao

Publications and source records attributed to Shuo Gao.

31 records · Page 2Linked to original sources

arXiv2025

Wearable intelligent throat enables natural speech in stroke patients with dysarthria

Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven intelligent throat (IT) system that integrates throat muscle vibrations and carotid pulse signal sensors with large language model (LLM) processing to enable fluent, emotionally expressive communication. The system utilizes ultrasensitive textile strain sensors to capture high-quality signals from the neck area and supports token-level processing for real-time, continuous speech decoding, enabling seamless, delay-free communication. In tests with five stroke patients with dysarthria, IT's LLM agents intelligently corrected token errors and enriched sentence-level emotional and logical coherence, achieving low error rates (4.2% word error rate, 2.9% sentence error rate) and a 55% increase in user satisfaction. This work establishes a portable, intuitive communication platform for patients with dysarthria with the potential to be applied broadly across different neurological conditions and in multi-language support systems.

eess.AS↗

arXiv2025

Neuromorphic spatiotemporal optical flow: Enabling ultrafast visual perception beyond human capabilities

Optical flow, inspired by the mechanisms of biological visual systems, calculates spatial motion vectors within visual scenes that are necessary for enabling robotics to excel in complex and dynamic working environments. However, current optical flow algorithms, despite human-competitive task performance on benchmark datasets, remain constrained by unacceptable time delays (~0.6 seconds per inference, 4X human processing speed) in practical deployment. Here, we introduce a neuromorphic optical flow approach that addresses delay bottlenecks by encoding temporal information directly in a synaptic transistor array to assist spatial motion analysis. Compared to conventional spatial-only optical flow methods, our spatiotemporal neuromorphic optical flow offers the spatial-temporal consistency of motion information, rapidly identifying regions of interest in as little as 1-2 ms using the temporal motion cues derived from the embedded temporal information in the two-dimensional floating gate synaptic transistors. Thus, the visual input can be selectively filtered to achieve faster velocity calculations and various task execution. At the hardware level, due to the atomically sharp interfaces between distinct functional layers in two-dimensional van der Waals heterostructures, the synaptic transistor offers high-frequency response (~100 μs), robust non-volatility (>10000 s), and excellent endurance (>8000 cycles), enabling robust visual processing. In software benchmarks, our system outperforms state-of-the-art algorithms with a 400% speedup, frequently surpassing human-level performance while maintaining or enhancing accuracy by utilizing the temporal priors provided by the embedded temporal information.

cs.CV↗

arXiv2024

GEM: A GEneral Memristive Transistor Model

Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant attention due to their superior stability and operation flexibility compared to two-terminal memristors. However, the lack of a robust model that accurately captures their complex electrical behavior has hindered further exploration of their potential. In this work, we introduce the GEneral Memristive transistor (GEM) model to address this challenge. The GEM model incorporates time-dependent differential equation, a voltage-controlled moving window function, and a nonlinear current output function, enabling precise representation of both switching and output characteristics in memristive transistors. Compared to previous models, the GEM model demonstrates a 300% improvement in modeling the switching behavior, while effectively capturing the inherent nonlinearities and physical limits of these devices. This advancement significantly enhances the realistic simulation of memristive transistors, thereby facilitating further exploration and application development.

physics.app-ph↗

arXiv2024

High-Order Associative Learning Based on Memristive Circuits for Efficient Learning

Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framework with a biologically realistic structure. By utilizing memristors as synaptic modules and their state information to bridge different orders of associative learning, our design effectively establishes associations between multiple stimuli and replicates the transient nature of high-order associative learning. In Pavlov's classical conditioning experiments, our design achieves a 230% improvement in learning efficiency compared to previous works, with memristor power consumption in the synaptic modules remaining below 11 μW. In large-scale image recognition tasks, we utilize a 20*20 memristor array to represent images, enabling the system to recognize and label test images with semantic information at 100% accuracy. This scalability across different tasks highlights the framework's potential for a wide range of applications, offering enhanced learning efficiency for current memristor-based neuromorphic systems.

cs.NE↗

arXiv2024

Harnessing physical entropy noise in structurally metastable 1T' molybdenum ditelluride for true random number generation

True random numbers are essential in various research and engineering problems. Their generation depends upon a robust physical entropy noise. Here, we present true random number generation by harnessing the conductance noise probed in structurally metastable 1T' molybdenum ditelluride (MoTe2). The noise, well-fitting a Poisson process, is proved a robust physical entropy noise at low and even cryogenic temperatures. Noise characteristic analysis suggests the noise may originate from the polarization variations of the underlying ferroelectric dipoles in 1T' MoTe2. We demonstrate the noise allows for true random number generation, enabling their use as seed for generating high-throughput secure random numbers exceeding 1 Mbit/s, appealing for practical applications in, for instance, cryptography where data security is now a severe issue. As an example, we show biometric information safeguarding in neural networks by using the random numbers as mask, proving a promising data security measure in big data and artificial intelligence.

cs.CR↗

arXiv2024

Self-reconfigurable Multifunctional Memristive Nociceptor for Intelligent Robotics

Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribute, which indicates that the threshold of determination of a potentially hazardous stimulus relies on environmental knowledge. This critical attribute has been currently omitted, but it is highly desired for artificial nociceptors. Inspired by these shortcomings, this article presents, for the first time, a Self-Directed Channel (SDC) memristor-based self-reconfigurable nociceptor, capable of perceiving hazardous pressure stimuli under different temperatures and demonstrates key features of tactile nociceptors, including 'threshold,' 'no-adaptation,' and 'sensitization.' The maximum amplification of hazardous external stimuli is 1000%, and its response characteristics dynamically adapt to current temperature conditions by automatically altering the generated modulation schemes for the memristor. The maximum difference ratio of the response of memristors at different temperatures is 500%, and this adaptability closely mimics the functions of biological tactile nociceptors, resulting in accurate danger perception in various conditions. Beyond temperature adaptation, this memristor-based nociceptor has the potential to integrate different sensory modalities by applying various sensors, thereby achieving human-like perception capabilities in real-world environments.

physics.app-ph↗

arXiv2024

Real-Time State Modulation and Acquisition Circuit in Neuromorphic Memristive Systems

Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face challenges with the separation of state modulation and acquisition, leading to undesired time delays that impact real-time performance. To overcome this issue, we introduce a dual-function circuit that concurrently modulates and acquires memristor state information. This is achieved through two key features: 1) a feedback operational amplifier (op-amp) based circuit that ensures precise voltage application on the memristor while converting the passing current into a voltage signal; 2) a division calculation circuit that acquires state information from the modulation voltage and the converted voltage, improving stability by leveraging the intrinsic threshold characteristics of memristors. This circuit has been evaluated in a memristor-based nociceptor and a memristor crossbar, demonstrating exceptional performance. For instance, it achieves mean absolute acquisition errors below 1 Ω during the modulation process in the nociceptor application. These results demonstrate that the proposed circuit can operate at different scales, holding the potential to enhance a wide range of neuromorphic applications.

physics.app-ph↗

arXiv2024

Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning

Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as high accuracy, reduced workload, and improved accessibility, but it requires a large amount of expert-annotated data to build reliable models. To address this dilemma, we propose a general self-supervised machine learning framework that can handle diverse fundus diseases from unlabeled fundus images. Our method's AUC surpasses existing supervised approaches by 15.7%, and even exceeds performance of a single human expert. Furthermore, our model adapts well to various datasets from different regions, races, and heterogeneous image sources or qualities from multiple cameras or devices. Our method offers a label-free general framework to diagnose fundus diseases, which could potentially benefit telehealth programs for early screening of people at risk of vision loss.

eess.IV↗

arXiv2024

SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images

Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervised methods, bringing in a heavy workload to biomedical staff and hence suffering in expanding effective databases. To address this issue, in this article, we established a label-free method, name 'SSVT',which can automatically analyze un-labeled fundus images and generate high evaluation accuracy of 97.0% of four main eye diseases based on six public datasets and two datasets collected by Beijing Tongren Hospital. The promising results showcased the effectiveness of the proposed unsupervised learning method, and the strong application potential in biomedical resource shortage regions to improve global eye health.

eess.IV↗

arXiv2024

Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics

The demand for efficient edge vision has spurred the interest in developing stochastic computing approaches for performing image processing tasks. Memristors with inherent stochasticity readily introduce probability into the computations and thus enable stochastic image processing computations. Here, we present a stochastic computing approach for edge detection, a fundamental image processing technique, facilitated with memristor-enabled stochastic logics. Specifically, we integrate the memristors with logic circuits and harness the stochasticity from the memristors to realize compact stochastic logics for stochastic number encoding and processing. The stochastic numbers, exhibiting well-regulated probabilities and correlations, can be processed to perform logic operations with statistical probabilities. This can facilitate lightweight stochastic edge detection for edge visual scenarios characterized with high-level noise errors. As a practical demonstration, we implement a hardware stochastic Roberts cross operator using the stochastic logics, and prove its exceptional edge detection performance, remarkably, with 95% less computational cost while withstanding 50% bit-flip errors. The results underscore the great potential of our stochastic edge detection approach in developing lightweight, error-tolerant edge vision hardware and systems for autonomous driving, virtual/augmented reality, medical imaging diagnosis, industrial automation, and beyond.

cs.ET↗

arXiv2023

Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency

Our research presents a wearable Silent Speech Interface (SSI) technology that excels in device comfort, time-energy efficiency, and speech decoding accuracy for real-world use. We developed a biocompatible, durable textile choker with an embedded graphene-based strain sensor, capable of accurately detecting subtle throat movements. This sensor, surpassing other strain sensors in sensitivity by 420%, simplifies signal processing compared to traditional voice recognition methods. Our system uses a computationally efficient neural network, specifically a one-dimensional convolutional neural network with residual structures, to decode speech signals. This network is energy and time-efficient, reducing computational load by 90% while achieving 95.25% accuracy for a 20-word lexicon and swiftly adapting to new users and words with minimal samples. This innovation demonstrates a practical, sensitive, and precise wearable SSI suitable for daily communication applications.

eess.AS↗

arXiv2023

Intelligent machines work in unstructured environments by differential neuromorphic computing

Efficient operation of intelligent machines in the real world requires methods that allow them to understand and predict the uncertainties presented by the unstructured environments with good accuracy, scalability and generalization, similar to humans. Current methods rely on pretrained networks instead of continuously learning from the dynamic signal properties of working environments and suffer inherent limitations, such as data-hungry procedures, and limited generalization capabilities. Herein, we present a memristor-based differential neuromorphic computing, perceptual signal processing and learning method for intelligent machines. The main features of environmental information such as amplification (>720%) and adaptation (<50%) of mechanical stimuli encoded in memristors, are extracted to obtain human-like processing in unstructured environments. The developed method takes advantage of the intrinsic multi-state property of memristors and exhibits good scalability and generalization, as confirmed by validation in two different application scenarios: object grasping and autonomous driving. In the former, a robot hand experimentally realizes safe and stable grasping through fast learning (in ~1 ms) the unknown object features (e.g., sharp corner and smooth surface) with a single memristor. In the latter, the decision-making information of 10 unstructured environments in autonomous driving (e.g., overtaking cars, pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.

eess.SP↗

arXiv2023

Human Body Digital Twin: A Master Plan

A human body digital twin (DT) is a virtual representation of an individual's physiological state, created using real-time data from sensors and medical test devices, with the purpose of simulating, predicting, and optimizing health outcomes through advanced analytics and simulations. The human body DT has the potential to revolutionize healthcare and wellness, but its responsible and effective implementation requires consideration of various factors. This article presents a comprehensive overview of the current status and future prospects of the human body DT and proposes a five-level roadmap for its development. The roadmap covers the development of various components, such as wearable devices, data collection, data analysis, and decision-making systems. The article also highlights the necessary support, security, cost, and ethical considerations that must be addressed in order to ensure responsible and effective implementation of the human body DT. The proposed roadmap provides a framework for guiding future development and offers a unique perspective on the future of the human body DT, facilitating new interdisciplinary research and innovative solutions in this rapidly evolving field.

cs.AI↗