Endovascular neural stimulation (ENS) offers a minimally invasive alternative to conventional intra-cranial implants. However, present ENS devices rely on long transvascular leads, which are prone to foreign body responses, breakage, higher impedance, and noisy input. In this study, we aimed to demonstrate the feasibility of fully wireless cortical stimulation using an ultra-flexible, leadless endovascular stimulator that can be delivered with standard neurointerventional techniques.

Approach. We designed an ENS implant integrating a miniaturized receiver coil, passive rectification circuitry, and an electrode pair on a flexible substrate that can be rolled into a catheter and self-expand against the vessel wall. Wireless power transfer was modeled and validated in vitro using sheep tissue to characterize inductive coupling, power transfer efficiency, and stimulation output over a range of coil separations (5-30 mm), vessel diameters (3-5 mm), and load impedances. In acute in vivo sheep experiments (n = 4), the device was placed either subdurally over motor cortex or endovascularly in the superior sagittal sinus, while an external transmitter (Tx) coil on the skull drove pulse-modulated bursts to control stimulation intensity and duration. 

Main Results. The ENS implant generated 3-11 V monophasic pulses across 1-4.7 kΩ loads at 20 mm separation and evoked neural responses in sheep. In vivo, controlled increases in Tx current produced corresponding changes in stimulation amplitude, and clear N1/N2 evoked potentials were observed for both subdural and endovascular stimulations at higher drive currents, whereas no responses were detected at low input levels or post-mortem.

Significance. This study provides the first in vivo demonstration of a fully wireless, catheter-deliverable endovascular cortical stimulator, capable of generating electrically evoked neural responses. This approach outlines a scalable path toward multi-site, leadless endovascular neuromodulation, potentially leads to fewer catastrophic failures, lower the risk of transvascular infection, and reduce crosstalk and noise in the stimulation waveforms.
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An important milestone in the path towards the 6 G wireless communication systems is presented in a recent work demonstrating a record-breaking THz wireless communication at a net rate of 27.84 Gbit/s over a 2.2 km wireless link using a carrier frequency of 335 THz lying in the most challenging atmospheric window.
Although biohybrid robots offer the potential for soft, adaptive actuation by harnessing living muscle, practical operation in cell culture environments is often limited by the requirement of immersed leads or cumbersome stimulation equipment. Here, we present a thin, miniaturized, wireless bioelectronic stimulator that can electrically drive biohybrid robots while maintaining stability in aqueous cell culture media. Built on a 50-µm liquid crystal polymer (LCP) substrate, the device integrates a planar receiving coil, interconnects, a diode-based rectifier, and a tank capacitor. This enables the device to convert an approximately 4.9-MHz radio-frequency (RF) input into pulsed direct current (DC), which is delivered through integrated stimulation electrodes. The stimulator has a footprint of ~ 32 mm² and a total thickness and mass of ~ 100 μm and ~ 7 mg, respectively. We integrated the stimulator with a nanopatterned carbon nanotube (CNT)/gelatin hydrogel fin seeded with human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to generate propulsion through fin flapping. By optimizing the thickness of the polydimethylsiloxane (PDMS) encapsulation layer, the density was tuned, and the robot remained freely floating and retained shape integrity during operation. This produced autonomous forward locomotion of 74.8 ± 16.4 μm s- 1. The stimulator generated distance-dependent output voltage pulses and enabled external pacing/modulation under the tested conditions, without a marked loss of cardiomyocyte attachment or α-actinin-positive sarcomeric organization. Together, these results provide a proof-of-concept compact, media-compatible, wireless bioelectronic interface toward closed-system biohybrid robotics.
Inductive sensors are widely implemented in industry due to their noncontact measurement capabilities and inherent immunity to environmental factors such as humidity and electrostatic charges. The rigid construction, however, confines the applications of inductive sensors to heavy machinery and robotics, with few uses in detecting subtle mechanical signals in biology. Here, we cover planar coils with structured magnetorheological elastomers (MRE) to yield a flexible inductive sensor that matches the soft mechanical properties of biological tissues. Multilayer microcavities within the MRE enable structural deformation under tiny fluctuations of pressure and strain, thereby modulating the magnetic reluctance surrounding the coil. Demonstrations in wearable monitoring of pulse, motion, and so forth, as well as implantable measurement of intracranial pressure (ICP) in a rat model, validate the capabilities of the wireless, continuous sensing in vivo. The materials, device architecture, and sensing mechanism offer a promising solution for fundamental and clinical research in biomechanics.
The trade-off between mechanical robustness and ionic conductivity in gel materials impedes their application in flexible electronics. Herein, a eutectogel is engineered via a synergistic strategy that integrates a ternary deep eutectic solvent (DES) (choline chloride/ethylene glycol/zinc chloride) with dynamic Zn2 + coordination. Through in situ photopolymerization of 1-vinylimidazole in the ternary DES, a dynamically cross-linked organic-inorganic hybrid network is constructed. Crucially, Zn2 + ions play a dual role: they form reversible Zn2 +-imidazole coordination sites, enhancing the mechanical properties with an elongation at break of 1100% and a Young's modulus of 0.23 MPa, while inducing coordination-driven densification of the amorphous network. This compaction effect tightens the polymer network without triggering crystallization, while accessible ion-transport pathways are retained within the amorphous network. Consequently, the eutectogel exhibits a high ionic conductivity of 0.38 mS cm- 1, overcoming the typical conductivity loss in high-strength gels. Using these properties, a flexible strain-sensing system with Bluetooth transmission is developed. It can capture real-time motor signals and convert them into visual commands, highlighting its potential for wireless assistive monitoring, particularly in rehabilitation for hemiplegic patients. This work provides a promising strategy for achieving a balance between mechanical robustness and ionic conductivity in soft materials by regulating the amorphous structure.
Data routing protocols play a vital role in Wireless Sensor Networks (WSNs). However, large network sizes and constrained resources demand more energy-efficient routing strategies. In this context, conventional routing protocols often show weak load balancing and inefficient energy use. Low-Energy Adaptive Clustering Hierarchy (LEACH) and Low-Energy Adaptive Clustering Hierarchy Centralized (LEACH-C) remain the two most widely adopted hierarchical routing protocols in WSNs. LEACH operates as a non-geographic distributed routing protocol, whereas LEACH-C is a geographic-based centralized routing protocol. Compared with flat routing protocols, both can prolong network lifetime, but they still suffer from limited energy efficiency. To address this limitation, we in this research proposed an enhanced LEACH protocol based on cluster configuration and Quantum Beluga Whale Optimization (QBWO-LEACH). During the setup phase, the central base station (BS) employs the proposed QBWO approach, which integrates Beluga Whale Optimization (BWO) with the strengths of quantum computing, to centrally organize the clusters. This process includes determining the cluster centroids, assigning cluster members, and evaluating cluster energy, cluster priority, and cluster lifetime. In the cluster heads (CHs) rotation phase, local clusters use the position and energy information of all cluster members to perform distributed CHs switching, distributing cluster energy approximately evenly among all members. In the steady-state phase, the relay forwarding of monitored data flows is implemented. Compared with traditional LEACH and other improved variants of the LEACH protocols, the comprehensive performance of the protocol proposed in the present research is found to be superior. We compare our proposed QBWO-LEACH with the existing LEACH protocols in terms of node survival, network residual energy, half node dies (HND), last node dies (LND), and first node dies (FND), in all four cases using both simulation and statistical analysis. QBWO-LEACH demonstrates an average improvement of 51.87% over LEACH, 17.69% over Particle Filter LEACH (PF-LEACH) and 4.31% over a 2-stage Genetic Algorithm-based LEACH (GA2-LEACH) in node survival and network residual energy in all four cases.
Implantable and wearable devices require antennas that are both miniaturized and efficient, yet conventional designs are constrained by narrow bandwidth and orientation sensitivity. We report overtone ultrawideband magnetoelectric (OUWB-ME) antennas that exploit higher-order acoustic modes in polished silicon substrates to achieve a 22.6-gigahertz -10-decibel bandwidth and overtone capability in the 3- to 4-gigahertz range. Packaged into "μBots," these magnetoelectric heterostructures bonded with silver nanoparticle inks maintain stable operation under biological loading. In vitro assays confirm the biocompatibility of aluminum nitride and the protective role of parylene encapsulation for iron-gallium. Ex vivo rat and human tissues reshape transmission and reflection spectra, with reproducible frequency windows near 3.3 and 3.9 gigahertz. μBots enable real-time audiovisual telemetry using software-defined radios and exhibit compatibility with 7-tesla magnetic resonance imaging. By combining wideband response, robustness to misalignment, and biocompatible packaging, OUWB-ME μBots provide a scalable platform for wireless bio-integrated communication and telemetry.
The concurrent operation of underwater wireless optical communication (UWOC) and artificial illumination represents a crucial functional requirement for underwater platforms. Nevertheless, intense background illumination significantly interferes with the performance of optical communication, leading to a reduction in the signal-to-noise ratio (SNR), an increase in bit-error rates (BER), and ultimately resulting in communication failure. We demonstrate a highly robust UWOC system that achieves an over 30 dB system-level equivalent tolerance to background illumination, including over 20 dB optical rejection and over 10 dB electrical-domain equivalent suppression. This system is enabled by a synergistic optical-electrical architecture comprising spectral avoidance, 5-ns gated sampling centered on the line-of-sight peak, and a real-time FPGA-implemented sliding-window adaptive decision threshold (ADTSW) algorithm. Under 300 W continuous illumination, we successfully achieved a 33 m underwater optical communication link with 20 Mbps in pool water and uninterrupted high-definition video streaming transmission between mobile platforms, in the South China Sea at the 17 m communication link with 3.125 Mbps. The proposed UWOC system offers a reliable solution for underwater data links in strong illumination environments.
Beamforming has emerged as an essential enabling technique for beyond 5G and future 6G systems because it improves spectral efficiency. However, optimizing antenna weights in beamforming is a highly nonlinear and multidimensional problem that traditional approaches struggle to solve. To address this, we offer a unique beamforming strategy based on the Caterpillar Fungus Optimization (CFO) algorithm, which strikes an optimal balance between exploration and exploitation, making it ideal for large-scale antenna systems. The CFO is inspired by the rare lifecycle of caterpillar fungus considering its soil exploration and parasitic behaviors. Its unique blend of wave-like and spiral search strategies, dual parasitism operators, and hybrid noise-handling makes it stand out among bio-inspired algorithms, enabling high accuracy and robustness in complex engineering optimization problems. The proposed scheme has two goals: first, to reduce the number of active antenna elements, thereby improving energy efficiency and reducing system complexity; and second, to suppress side lobe levels (SLL), which mitigate interference and improve communication performance. To assess its efficacy, the CFO-based method is compared to five established algorithms: Artificial Rabbits Optimizer (ARO), Whale Shark Optimization (WSO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Boomerang Aerodynamic Ellipse (BAE). According to simulation data, CFO maintains beamwidth deviations within 1% to 2% of the standard reference while achieving an average error reduction of up to 99.7% when compared to PSO and WSO. Furthermore, CFO outperforms all benchmark algorithms in terms of accuracy, and computing efficiency, delivering the lowest SLL deviations and the most steady convergence behavior. This paper provides a simulation-based beamforming optimization framework that employs the metaheuristic Caterpillar Fungus Optimization (CFO) algorithm for antenna array synthesis in beyond 5G and future 6G wireless systems.
In this work, a compact quad-element MIMO antenna demonstrating resonance at 6.39 GHz, 11.09 GHz, 14.69 GHz, 17.96 GHz encompassing C, X, and Ku bands, respectively within a single compact structure. The designed quad-element MIMO antenna employs a Y-shaped radiating monopole configuration with connected ground elements for easy system integration. Also, the designed MIMO antenna attains geometry driven Circular Polarization (CP) at specific elevation angles without any external phase-shifters/ parasitic structures, resulting in enhanced robustness against multipath fading and polarization mismatch. Further, the critical requirement of high inter-element isolation is maintained (Sij  < - 20 dB), suppressing mutual coupling effectively. The parameters of the presented quad port antenna design are also optimized using various machine learning (ML) algorithms and Gaussian regression process model is found to be most suitable providing best return loss performance. Finally, the designed quad port MIMO antenna is fabricated, and its prototype is tested to validate the simulated results. Further, the comprehensive analysis of diversity performance demonstrates ECC (≤ 0.001), DG (≈10 dB), CCL (< 0.2 bps/Hz), and TARC (> 9.99) parameters yielding an ergodic channel capacity exceeding 9 bps/Hz. The average efficiency is observed to be of 57% across all bands with a peak gain of 9.78 dBi at 17.96 GHz achieving highly integrated, reliable, promising solution for CP-MIMO communication systems making it a strong candidate for satellite and advanced wireless applications.
This paper presents design and development of 1-bit dual-mode metasurface for sub-6 GHz wireless communication systems. The design consists of four distinct configurations: two for transmission and two for reflection mode. Diode-based phase switching capability was initially considered in simulations, however the final prototype was fabricated with fixed stripline phase configuration. These striplines replicate diode ON/OFF states to validate the phase-switching logic to minimize the cost and design complexity. The proposed unit cell works effectively in both transmissive and reflective modes while maintaining a phase shift of 180° ± 20° throughout its operational bandwidth. Despite its multi-layer structure and dual-mode operation, the proposed unit cell has low insertion loss of around 0.4 dB and 0.21 dB in transmissive and reflective mode, respectively at the central frequency. The unit cell achieves 3 dB fractional bandwidth of 16% in transmissive mode and 12% in reflective mode. A 10 × 10 elements array simulation has been carried out to demonstrate pre-defined beam steering capabilities with ± 60° in both transmissive and reflective modes providing coverage of 240° out of complete 360°. The gain of the source antenna is enhanced by more than 6 dB across the operational bandwidth, with a fluctuation of 3 dB. The performance of the proposed metasurface is successfully validated through both numerical simulations and experimental results, with a good agreement observed in the overall radiation pattern and characteristics.
In wireless sensor networks (WSNs), transmission power adjustment has a direct impact on both node-level energy expenditure and overall network lifetime, while simultaneously shaping the communication conditions under which distributed data aggregation must operate. This coupling is especially important in energy-constrained settings with progressive node failures, where topology control affects not only connectivity but also the reliability of local information exchange. In this paper, we formulate the problem of distributed data aggregation in a WSN with strict local-information constraints, where nodes communicate only through one-hop broadcasts and adapt their transmission power according to the LINT protocol. The resulting communication graph is degree-regularized but not necessarily bidirectional, which makes the design and evaluation of aggregation protocols substantially different from the classical fixed-topology consensus setting. Within this formulation, we investigate the interplay between adaptive transmission power control and decentralized aggregation by comparing two local aggregation protocols, Metropolis and the Local Voting Protocol (LVP), under both adaptive-range and fixed-range communication regimes. Our contribution is twofold. First, we provide a problem formulation for joint adaptive communication and distributed aggregation in locally informed, energy-constrained WSNs. Second, using a simulation framework with a standard radio energy model, probabilistic reporting to a base station, and multiple node deployment scenarios, we show that the relative performance of aggregation protocols depends on the communication regime induced by power adjustment. In particular, Metropolis is competitive, and in some cases preferable, under fixed-range symmetric communication, whereas LVP yields more stable aggregation quality under LINT-based adaptive transmission power control while maintaining comparable network lifetime and energy efficiency.
Electrocorticography uses non-penetrating electrodes embedded in flexible substrates to record electrical activity from the surface of the brain. To use the technology to develop minimally invasive, high-bandwidth brain-computer interfaces, it will be necessary to improve the number of recording channels and the scalability of devices, which could be achieved by merging electrodes and electronics onto a single substrate. Here we report a 50-μm-thick, mechanically flexible micro-electrocorticography brain-computer interface that integrates a 256 × 256 array of electrodes, signal processing, data telemetry and wireless powering on a single complementary metal-oxide-semiconductor substrate. The device contains 65,536 recording electrodes, from which we can simultaneously record a selectable subset of up to 1,024 channels at a given time. Our chip is wirelessly powered, and when implanted below the dura, it can communicate bidirectionally with an external relay station outside the body. We show that the device can provide chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from the somatosensory, motor and visual cortices, decoding brain signals at high spatiotemporal resolution.
To address the challenging problem of collaborative optimization of communication delay and UAV load balancing in multi-Unmanned Aerial Vehicle (UAV)-assisted wireless rechargeable sensor networks, a dynamic threshold-enhanced diffusion proximal policy optimization algorithm (DTD-PPO) is proposed. Firstly, a multi-objective optimization model of multi-UAV-assisted WRSNs is constructed, and multi-dimensional constraints are incorporated to enhance the feasibility and practicality of the optimization solution. Secondly, a Markov Decision Process (MDP) framework is designed to balance the conflict between the dual objectives through dynamic weighting. To improve the exploration ability and training stability of the algorithm, the diffusion model is integrated into the PPO policy network, generating diversified actions through an adaptive noise-adding and denoising process. Additionally, a dynamic threshold strategy based on the normalized reward change rate is proposed to adjust the policy update magnitude in real-time. The effectiveness of our proposed algorithm is validated by using metrics of the data collection delay, UAV's flight distance deviation and the energy efficiency. The simulation results verify the superiority and robustness of DTD-PPO algorithm compared to the other benchmark methods.
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[This retracts the article DOI: 10.1007/s11277-021-08436-w.].
5G wireless networks have paved the way for intelligent, reliable communication networks with extremely high data rates, ultra-low latency, and highly reliable connectivity. With 6G wireless networks expected to offer up to 1 Tbps data rates with near-zero latency, wireless communication networks are expected to get smarter than ever. As a result, ultra-fast intelligent wireless networks are expected to power the hyper-connected intelligent world of tomorrow. Towards this direction, this paper provides a comprehensive overview of how machine learning (ML) techniques can be employed in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) networks over futuristic 6G wireless networks. Machine learning would enable 6G-enabled IoT/WSNs to operate autonomously, detect anomalies, optimize energy use, and respond to real-time data they sense and collect. Enabling technologies such as edge Artificial Intelligence (AI), satellite-assisted 6G, Intelligent Reflecting Surfaces (IRS), and terahertz communications are discussed. Furthermore, a novel architecture employing federated and distributed learning for IoT communication is presented to demonstrate low-latency, energy-efficient, and secure communication for distributed ML tasks. Results indicate the superiority of the proposed architecture over contemporary 5G-based architectures in terms of network intelligence, latency, and reliability. Finally, the paper discusses major challenges and future directions for realizing the promise of 6G for ML-powered IoT and WSNs.
This paper presents a Single-Inductor Bidirectional Converter (SIBC) for unified onboard Electric Vehicle (EV) charging, integrating grid, battery, and wireless ports within a single power stage. The topology enables native Grid-to-Vehicle (G2V), Wireless-to-Vehicle (W2V), and Vehicle-to-Wireless (V2W) operation without hardware reconfiguration, eliminating cascaded converter-inverter structures. Non-ideal steady-state and small-signal models are developed, revealing mode-dependent dynamics including a right-half-plane zero in V2W mode that constrains bandwidth. A two-loop Average Current-Mode Control (ACMC) is proposed to mitigate this limitation, achieving 40× bandwidth improvement over conventional voltage-mode control. Parametric sensitivity analysis of inductor equivalent series resistance establishes quantitative design boundaries for sustaining conversion efficiency above 90%. Scalability assessment to 3 kW operation demonstrates compatibility with silicon carbide devices and a bridgeless totem-pole PFC front-end achieving THD less than 4% and power factor higher than 0.99, satisfying IEC 61000-3-2 Class A with higher than 35 dB ripple rejection at the battery terminals. Experimental validation using a 136 W prototype achieves 93.4% transmitter and 95.07% receiver DC-DC efficiency, with an overall end-to-end efficiency of 75.03% including an 84.5% wireless link. A 1 kW interim hardware test confirms scalable operation, while simulation validates CC-CV battery charging compatibility. The results demonstrate that the SIBC architecture is not the dominant source of system losses and provides a compact, scalable foundation for advanced bidirectional EV charging systems.
Stretchable radio-frequency (RF) electronics underpin emerging wearable systems for body-centric communication, continuous health monitoring, and wireless power transfer. However, on-body stretchable antennas undergo multidirectional in-plane strain during natural motion, which detunes resonance and destabilizes wireless links. Existing strain-insensitive designs are typically effective only along prescribed loading directions and often compromise radiation performance. Here, we establish a systematic directional mechano-electromagnetic analysis framework for resonant planar antennas and introduce a dual-port multidirectional strain-insensitive antenna (DP-MSiA), whereby strain-insensitive resonance (shift ≤ 40 MHz at 2.45 GHz) is achieved under up to 45% strain across diverse in-plane directions. Based on the stable resonance and high realized gain of the DP-MSiA, we demonstrate strain-insensitive wireless energy harvesting with rectifiers under in-plane strain of varying direction and magnitude, as well as a strain-robust on-body communication system that sustains stable multimodal health-data transmission during natural motion. Our work opens new opportunities for creating deformation-insensitive electronics and enables integrated functionalities in wearable and embodied systems.
The integration of automation and electrochemical sensing is emerging as an important strategy to accelerate bioanalytical workflows, improve reproducibility, and reduce operator exposure to hazardous biological samples. Self-driving laboratories and automated analytical systems have attracted increasing attention in chemical and biomedical sciences due to their potential for scalable and high-throughput experimentation. However, most automated electrochemical platforms still rely on expensive robotic infrastructure and are often inaccessible for laboratories with limited resources. In addition, applications involving pathogenic microorganisms and 3D cell cultures require safer and more controlled analytical environments. Therefore, there remains a need for portable, low-cost, and semi-autonomous electrochemical systems capable of operating in microbiological and oncological settings. Herein, we report the development of the Carousel ElectroLab System (CELS), a portable and low-cost automated electrochemical platform integrating 3D-printed electrodes, Arduino-controlled carousel automation, and wireless communication with a miniaturized potentiostat. The system consists of eight fully 3D-printed electrochemical cells sequentially addressed for hands-free electrochemical measurements. Blue-laser treatment of the electrodes increased surface roughness and electrical conductivity, resulting in improved electrochemical performance and reproducibility (RSD <5%). As a proof-of-concept, the platform was applied in microbiological and oncological analyses. For microbiological applications, selective detection of Pseudomonas aeruginosa was achieved through electrochemical monitoring of pyocyanin (PYO), reaching a detection limit of 0.89 CFU mL-1 in King's A medium, with no significant response observed for other bacterial strains. In oncological studies, the system monitored doxorubicin-induced cytotoxicity in MCF-7 tumoroids by quantifying lactate dehydrogenase activity through NADH electrooxidation, enabling correlation between electrochemical signal and tumor cell death in 3D models. This work introduces a portable carousel-based electrochemical platform combining 3D printing, low-cost automation, and wireless electrochemical sensing for bioanalytical applications in controlled environments. The proposed CELS device represents a scalable and open-source alternative to conventional automated systems, enabling safer and reproducible analyses of pathogenic microorganisms and 3D tumor models. The modular architecture also provides a foundation for future integration of robotic fluidics and AI-assisted self-driving laboratory functionalities.