Communications engineering最新文献

Using human responses to optimize and thus personalize assistance enhances exoskeleton performance during locomotion. Current approaches lack efficiency, comfort, rapid deployability, and computation and actuation simplicity. Here we present a method that optimizes assistance within 2 min, 16 times faster than the state-of-the-art, by effectively imitating human joint moment while ensuring stability. Optimization of a unilateral ankle exoskeleton with off-board actuation produced gentler assistance (78.2% torque) while reducing muscle activity by 36.8% and metabolic cost by 20.4% than no assistance, comparable to state-of-the-art. The method was easily and effectively deployed across new gait conditions, to bilateral devices, to knee joints and also outdoors. It largely avoided the problems of existing methods with instantaneously measurable feedback, a non-aggressive tuning process, a reasonable tuning direction, and a non-parametric assistance formulation. By significantly reducing pre-research, operational, user physiological and psychological costs, this method largely elevates the accessibility level of effective, personalized and continuously tuned exoskeletons in everyday scenarios.

Advances in adaptive optics optical coherence tomography (AOOCT) have facilitated the three-dimensional assessment of structural and functional properties of individual retinal cells in the living human eye. However, even with diffraction-limited AOOCT systems, some cells in the living human retina can be difficult to resolve, especially when using near-infrared wavelengths of light (~1000 nm). We demonstrate that modifying the traditional AOOCT instrument design to enable annular illumination and sub-Airy disk detection results in improved imaging resolution beyond fundamental limits imposed by diffraction. We successfully applied this approach to in vivo human retinal imaging, achieving on average 36% improvement in lateral resolution beyond conventional imaging conditions, enabling improved visualization of the foveal cone and rod photoreceptor mosaics using AOOCT. These results demonstrate an effective strategy for improving lateral resolution in point-scanning AOOCT in a manner that is compatible with new and existing instruments.

Underwater object detection plays a crucial role in applications such as marine ecological monitoring and underwater rescue operations. However, challenges such as limited underwater data availability and low scene diversity hinder detection accuracy. In this paper, we propose the Underwater Layout-Guided Diffusion Framework (ULGF), a diffusion model-based framework designed to augment underwater detection datasets. Unlike conventional methods that generate underwater images by integrating in-air information, ULGF operates exclusively on a small set of underwater images and their corresponding labels, requiring no external data. We have publicly released the ULGF source code and the generated dataset for further research. Our approach enables the generation of high-fidelity, diverse, and theoretically infinite underwater images, substantially enhancing object detection performance in real-world underwater scenarios. Furthermore, we evaluate the quality of the generated underwater images, demonstrating that ULGF produces images with a smaller domain gap.

Traditional plastic and glass culture lacks physiological relevance, undermining predictive power in drug discovery. Organoids and organs-on-chip improve biomimicry but do not scale to high-throughput screening (HTS). Even simple hydrogel coatings in HTS plates suffer from curved menisci that disrupt seeding and imaging. We present HYDRA (HYDrogels by Robotic liquid-handling Automation), an automated method to fabricate thin, planar hydrogel films directly in standard plates. Liquid-handlers dispense sub-contact volumes without wall wetting; immediate re-aspiration pins the contact line, leaving a uniform layer with controlled stiffness and thickness. Using fish gelatin hydrogel, HYDRA produces meniscus-free coatings compatible with routine 96- and 384-well workflows and plate-scale quality control. HYDRA was validated through imaging-based dose-response assays with anticancer compounds, engineered epithelial monolayers, and long-term holographic and fluorescence microscopy. It preserved pharmacological sensitivity while supporting high-content imaging on soft, biomimetic substrates, offering a practical bridge between physiological relevance and HTS scalability for early in-vitro drug testing.

Seamless interaction between humans and Artificial Intelligence-empowered, battery-operated, miniaturized devices is reshaping wearable technology by forming an anthropomorphic artificial nervous system that demands high-speed, low-power connectivity. Besides being radiative, radio frequency links suffer absorption losses in non-line-of-sight scenarios and consume more than tens of milliwatts of power. Electro-quasistatic human body communication provides non-radiative links with  ~100X better energy efficiency and  ~30X superior signal confinement over radio wave-based wireless. However, it is limited by  ~60-70 dB path loss, limited bandwidth, and data rates ≤30 Mbps, insufficient for applications such as High definition streaming, and distributed computing at wearable sensor nodes. To overcome these challenges, we propose Body-Resonance Human Body Communication, which leverages the human body's transmission-line behavior in the near-intermediate field to enhance channel capacity by up to 30X. It achieves approximately 20 dB higher channel gain and a wider bandwidth compared to electro-quasistatic regime, supporting data rates of hundreds of Mbps. Experimental results validate low-loss (~40-50 dB), wideband body channels that are more than 10X less leaky than antenna-based wireless links. Body Resonance can potentially open up the possibilities of immersive augmented/virtual reality and cooperative on-body computing by enabling energy-efficient, high-speed wearable networks across healthcare, defense, and consumer electronics.

Silicon-based technologies have been researched extensively over the past few decades, but one ongoing problem has been bringing these technologies into the sub-THz regime. For wireless communications, these bands exhibit potential for massive improvements to bandwidth, data rates, etc., but overcoming the limits of operating frequency to allow devices to enter the sub-THz regime has proven challenging when designing working systems. Despite this, researchers have been developing novel ways to construct transceiver systems above 200 GHz. In this article, we showcase interesting designs that push the envelope towards more powerful, faster, and more useful silicon-based transceiver systems. We discuss transmitter systems grouped by modulation schemes as well as incoherent and coherent receiver systems. This allows us to point out the specific difficulties seen throughout these works and describe the direction needed to improve these systems. Finally, we discuss the future direction and application of silicon-based wireless communication systems as they move towards sub-THz regions.

Superconducting motors offer high power density, compactness, and efficiency for hydrogen-powered cryo-electric aircraft, but AC operation in cryogenic temperatures produces thermal losses that must be estimated accurately and rapidly at the design stage to optimize efficiency, minimize cryogenic heat load, and maximize specific power density. Traditional modeling approaches fall short-Finite-element is too slow/costly for system-level models, analytical models and look-up tables lack accuracy/flexibility, and earlier intelligent models gave only cycle-averaged (static) losses. Here we demonstrate AI can rapidly and accurately predict dynamic AC losses for superconducting propulsion motors. Using a large dataset of motor configurations, our AI-driven approach both predicts cycle-averaged and time-dependent morphology of instantaneous AC loss waveforms across various operating conditions and generalizes to unseen designs. Integrated into system-level model-based design, these AI-surrogate models enable rapid model trials, compliance checks, and the discovery of integration issues within simulated environments before propulsion motor deployment. Our deep learning-based model achieves a prediction time of less than 9 ms with a 99.97% accuracy (R²), making it suitable for system-level modeling of electric powertrains in hydrogen-powered cryo-electric aircraft. Furthermore, we benchmarked 14 AI and 2 mathematical fitting techniques for estimating average AC losses, providing comparative performance analysis. The results highlight that AI-based surrogate models enable high-accuracy, low-latency loss predictions to achieve optimal performance in superconducting propulsion motors in aircraft powertrain design.

Mechanical stress during the cycling process notably impacts the performance of lithium-ion batteries (LIBs), making it crucial to accurately monitor stress generation and propagation during battery operation. Traditional electrochemical-mechanical models are limited to the particle and electrode scales, and their parameter identification relies solely on voltage. Here, a multi-scale electrochemical-mechanical-thermal modelling framework with non-destructive parameter identification capabilities is proposed. This numerical model couples electrochemical reactions with thermal effects and links particle-scale strain to electrode-scale displacement. Diffusion-induced stress (DIS) is selected as a key indicator, combined with voltage, to analyze the sensitivity of 23 parameters. A voltage-strain multi-objective parameter identification strategy based on the Pareto front is employed to determine the key parameters. The framework demonstrates high fidelity, with the mean absolute percentage error for voltage and strain predictions below 1% and 3.6%, respectively. This work enables high-fidelity simulation of multi-physics behavior, provides an effective method for calibrating key parameters, and holds potential for establishing a reliable digital twin of LIBs.

In the past decades, active exoskeletons have been dedicated to reducing human effort, in particular to assist workers in occupational environments. However, this approach does not promote the learning of more ergonomic postures by workers, which is critical for the long-term prevention of musculoskeletal disorders. Alternatively, we propose the use of exoskeletons as biofeedback systems, generating task-relevant perturbations guiding users towards ergonomic postures. To test this approach, participants performed reach-to-hold movements towards a redundant target, allowing multiple final postures. We then introduced vibrations with posture-dependent intensity, generating a sensorimotor disturbance that canceled out either above or below each participant's nominal preferred posture. Interestingly, participants adapted to minimize the vibrations, whether it increased or decreased the gravity efforts, and retained the novel posture when it induced lower effort. Finally, all participants significantly reduced effort post-exposure. This work demonstrates the feasibility of using exoskeletons as biofeedback systems to improve posture, paving the path for applications in musculoskeletal disorders prevention.

Single-molecule localization microscopy achieves nanometer-scale resolution but is compromised by sample drift during image acquisition. Here we present reinforced optical cage systems, a novel approach that eliminates drift at its mechanical source rather than correcting it through complex image post-processing or fiducial markers. Reinforced optical cage systems employ perforated optomechanical components interconnected by tungsten-steel rods in a design proven by mechanical stability simulations. Our bench-top microscope, built with reinforced optical cage systems, demonstrated exceptional three-dimensional stability, with mean cumulative lateral drift of approximately 5 nanometers over 2 h in widefield fluorescence microscopy and 11-16 nanometers over 15 min in single-molecule localization microscopy, free from measurable axial drift. This development allows super-resolution microscopy to reach its full resolution without the necessity of sample drift correction, offering a straightforward, cost-effective, low-maintenance, and readily accessible solution to high-performance super-resolution microscopy. By addressing the fundamental issue of mechanical instability, reinforced optical cage systems enable improved precision instrumentation for the broader scientific and engineering community.