Lina Pu

Underwater acoustic reconfigurable intelligent surfaces modulate the electrical load impedance of a piezoelectric transducer to control the amplitude and phase of the reflected wave. Predicting this load-dependent response is challenging: one-dimensional Mason circuits neglect oblique-incidence and shear-vertical-wave effects in elastomer coatings, while full three-dimensional finite-element models are computationally expensive and offer limited physical insight. This paper presents a reduced-order semi-analytical model for a Tonpilz-inspired tunable reflector. The model combines a primary–shear-vertical elastodynamic state-space description of a polyurethane coating, angle-dependent water loading, and a lumped electromechanical Tonpilz model terminated by an external electrical load. It yields compact expressions for the angle- and frequency-dependent acoustic reflection coefficient. Under thin-coating, moderate-incidence, and strong tangential-clamping conditions, the model shows that the reflection coefficient becomes a linear-fractional (Möbius) image of a capacitance-normalized load parameter. From this structure, physical conditions for strong load-to-reflection tracking are derived and translated into concrete reflector-design guidelines. Validation against three-dimensional finite-element simulations and tank experiments near a series-resonant frequency of 28.4 kHz shows that the model captures the dominant resonance behavior, the load-dependent amplitude and phase trends, and the angle-dependent reflection characteristics. Remaining quantitative discrepancies in absolute phase and resonance sharpness are reported explicitly and attributed to reduced-order assumptions and simplified three-dimensional structural dynamics.

This article introduces a multilayered acoustic reconfigurable intelligent surface (ML-ARIS) architecture designed for the next generation of underwater communications. ML-ARIS incorporates multiple layers of piezoelectric material in each acoustic reflector, with the load impedance of each layer independently adjustable via a control circuit. This design increases the flexibility in generating reflected signals with desired amplitudes and orthogonal phases, enabling passive synthetic reflection using a single acoustic reflector. Such a feature enables precise beam steering, enhancing sound levels in targeted directions while minimizing interference in surrounding environments. Extensive simulations and tank experiments were conducted to verify the feasibility of ML-ARIS. The experimental results indicate that implementing synthetic reflection with a multilayer structure is indeed practical in real-world scenarios, making it possible to use a single reflection unit to generate reflected waves with high-resolution amplitudes and phases.

This article presents an underwater acoustical reconfigurable intelligent surface (UA-RIS) designed for long-range, high-speed, and environmentally friendly communication in oceanic environments. The proposed UA-RIS comprises multiple pairs of acoustical reflectors that utilize a synthetic reflection scheme to flexibly control the amplitude and phase of reflected waves. This capability enables precise beam steering to enhance or attenuate sound levels in specific directions. A prototype UA-RIS with 4 × 6 acoustical reflection units is constructed and tested in both tank and lake environments to evaluate performance. Experimental results using a continuous wave as the source signal demonstrate that the prototype is capable of effectively pointing reflected waves to targeted directions while minimizing sidelobes through synthetic reflection. Field tests reveal that deploying the UA-RIS on the sender side considerably extends communication ranges by 28% in deep water and 46% in shallow waters. Furthermore, with a fixed communication distance, positioning the UA-RIS at the transmitter side substantially boosts the receiving signal-to-noise ratio (SNR), with an average increase of 2.13 dB and peaks up to 2.92 dB. When positioned on the receiver side, the UA-RIS can expand the communication range in shallow and deep water environments by 40.6% and 66%, respectively. Moreover, placing the UA-RIS close to the receiver enhances SNR by an average of 2.56 dB, reaching up to 4.2 dB under certain circumstances.

Accurate decoding of motor intent from biosignals is an important step toward intuitive upper-limb prosthetic-control interfaces. We propose a novel high-dimensional multimodal deep learning framework that fuses surface electromyography (sEMG) and B-mode ultrasound (US) images to estimate metacarpophalangeal and proximal interphalangeal joint angles continuously. The framework employs a shared Encoder-Decoder-Regression architecture integrating convolutional neural networks (CNNs), transposed convolutions, an action-conditioned multi-head cross-attention module (ATT) that uses the commanded action as a query, and long short-term memory (LSTM) layers to jointly capture spatiotemporal features from both modalities. To improve cross-subject generalization and reduce data requirements for new users, we introduce a transfer learning strategy with parameter freezing. Experiments on data from seven able-bodied subjects show that, compared with sEMG-only and US-only baselines, the fusion model reduces test local root mean square error (RMSE) by 2.187° (23.385%) and 0.890° (11.054%), and increases test local correlation (Pearson's r) by 0.069 (10.02%) and 0.039 (5.48%) (p < 0.05), supporting the potential of multimodal fusion for future prosthetic-control interfaces. A preliminary validation on one amputee participant further supports the feasibility of applying the framework under a residual-limb sensing condition. Ablation studies further confirm that the full CNN+LSTM+ATT model achieves the best performance, reducing test local RMSE by 0.933° (11.524%) and increasing test local correlation by 0.033 (4.56%) (p < 0.05). Furthermore, fine-tuning the pretrained model with only 25% of a new subject's data yields performance comparable to full retraining, highlighting the framework's data efficiency.

This article explores the potential of underwater acoustic reconfigurable intelligent surfaces (UA-RIS) for facilitating long-range and eco-friendly communication in marine environments. Unlike radio frequency-based RIS (RF-RIS), which have been extensively investigated in terrestrial contexts, UA-RIS is an emerging field of study. The distinct characteristics of acoustic waves, including their slow propagation speed and potential for noise pollution affecting marine life, necessitate a fundamentally different approach to the architecture and design principles of UA-RIS compared to RF-RIS. Currently, there is a scarcity of real systems and experimental data to validate the feasibility of UA-RIS in practical applications. To fill this gap, this article presents field tests conducted with a prototype UA-RIS consisting of 24 acoustic elements. The results demonstrate that the developed prototype can effectively reflect acoustic waves to any specified directions through passive beamforming, thereby substantially extending the range and data rate of underwater communication systems.

This research contributes to long-term structural health monitoring (SHM) by exploring radio frequency energy-powered sensor nodes (RF-SNs) embedded in concrete. The RF-SN captures radio energy from a mobile radio transmitter for sensing and communication, which offers a cost-effective solution for consistent in-situ perception. To optimize the system performance across various situations, we've explored both active and passive communication methods. For the active RF-SN, we implement a specialized control circuit enabling the node to transmit data through ZigBee protocol at low incident power. For the passive RF-SN, radio energy is not only for power but also as a carrier signal, with data conveyed by modulating the amplitude of the backscattered radio wave. To address the challenge of significant attenuation of the backscattering signal in concrete, we utilize a square chirp-based modulation scheme for passive communication. This scheme allows the receiver to successfully decode the data even under a negative signal-to-noise ratio (SNR) condition. Performance modeling and optimization for both active and passive RF-SNs are provided in this study. The experimental results verify that an active RF-SN embedded in concrete at a depth of 13.5 cm can be effectively powered by a 915 MHz mobile radio transmitter with an effective isotropic radiated power (EIRP) of 32.5 dBm. This setup allows the RF-SN to send over 1 kilobyte of data within 10 seconds, with an additional 1.7 kilobytes every 1.6 seconds of extra charging. For the passive RF-SN buried at the same depth, continuous data transmission at a rate of 224 bps with a 3% bit error rate (BER) is achieved when the EIRP of the transmitter is 23.6 dBm.

The integration of energy harvesting capabilities into mobile edge computing (MEC) edge servers enables their deployment beyond the reach of electrical grids, expanding MEC services to isolated regions and geographically challenging terrains. However, the fluctuating nature of renewable energy sources, such as solar and wind, necessitates dynamic management of server computing power in response to variable energy harvesting rates. Unlike conventional models that assume predetermined amounts of harvested energy per time period, this study illustrates the complex interdependencies between server power consumption and variable energy harvesting rates due to battery charging characteristics. To address this, we introduce a novel energy harvesting model that comprehensively accounts for the interaction between computing power management and energy harvesting rates. We develop both offline and online offline optimal computing power management strategies aimed at maximizing the average computational capacity of edge servers. An analytical solution to the resulting nonlinear optimization problem is provided to determine the optimal computing power configurations. Simulation results indicate that the proposed strategy effectively balances energy harvesting rates and energy utilization, thereby enhancing computational performance in dynamic energy environments.

Energy-efficient federated learning (FL) is important for decentralized learning-based edge computing. The energy consumption of FL is largely affected by the efficiency of local training in edge devices and their communication efficiency to the central server. The majority of studies have focused on improving the latter while continuing to use traditional neural networks for local training. This can be computationally heavy for edge devices, especially given their limited energy supply and computation capabilities. In this paper, we propose a joint lightweight design and energy-saving algorithm (LDES) for efficient FL training and parameter uploading in the wireless edge. In the lightweight design of LDES, we investigate lightweight models in local training by deploying sparse or binary neural networks (SNN or BNN) at the edge, in order to reduce the uploaded data volume and the energy consumption. We develop an enhanced stochastic gradient descent algorithm with guaranteed convergence to handle the issues of non-smoothness and weight constraints (NSC-SGD) in training sparse models. In the energy-optimization part of LDES, we optimize power, bandwidth, and learning parameters to jointly minimize computing, uploading, and broadcasting energy. The numerical results show that LDES can reduce 56.21% energy consumption compared to benchmark FL schemes.

In traditional underwater acoustic positioning methods, reliance on systems such as Long Baseline (LBL) and Short Baseline (SBL) localization utilize transponders that actively transmit acoustic pulses. These systems are typically powered by cables and often result in constrictive deployment. The adoption of cable-free and battery-free transponders can greatly enhance the flexibility of deployment. This change facilitates large-scale implementation, thereby providing precise positioning services across extensive areas. However, localization with passive transponders faces unique challenges. It has been widely accepted that battery-free nodes are unsuitable for time-of-arrival (ToA) localization. The primary reason is the wakeup delay varies based on location and environment, which introduces an unpredictable offset into the time lapse between the transmitted and received pulses. To overcome this challenge, we model the wakeup delay of passive node, considering the hardware characteristics of the passive node and the multipath acoustic channel. Moreover, we propose a ToA-based localization framework specifically tailored for battery-free transponders. In this paper, we will verify our wakeup delay model and assess the performance of the proposed localization method through experimental results. This new approach marks a significant departure from the existing effort, making localization with passive transponders feasible.