Communications engineering最新文献

With the continuous integration of advanced information and communication technologies into smart grids, the distribution network is undergoing a digital transformation, making the power distribution system increasingly complex. Edge computing shifts computation from the central control station to the distribution substations, thus enabling true distributed autonomy in power system operations. Taking an edge-computing-based digital substation as an example, this paper proposes a deep neural networks-based voltage regulation strategy for PV-rich distribution networks. However, executing tasks on resource-constrained edge devices faces several challenges, including data flow congestion, the inapplicability of conventional modelling and algorithm, and low computational efficiency. Therefore, we employ a unified weights neural network for Volt-Var control to achieve compression of the network parameters while still achieving differentiated action output. Furthermore, a carefully designed pipeline parallel computing structure is employed to simultaneously perform computations at different levels, further improving computational efficiency. The tested results show that, compared with existing methods, the proposed approach effectively mitigates voltage violations, improves storage efficiency and computational speed, and maintains robust performance under communication failures with partial observation, highlighting its resilience and potential for edge deployment.

Drone flight safety and operational efficiency are challenged in the landing phase, especially in windy conditions. While flight control should be a key focus, flight trajectory also plays a critical role in landing. Inspired by the multiple objectives, wind sensory capability, and skill learning of avian species, this study proposes a reinforcement learning-based trajectory planner for a drone to perform trajectory planning in the wind, aiming to balance multiple landing-related objectives based on onboard wind sensory capability and address the safety-operational efficiency dilemma of a landing site. Through four key experiments, this study demonstrates successful training, balanced landing performance, and strong generalization capability of the trajectory planner. The experiments highlight the importance of velocity sensory capability while indicating that wind sensory capability is less critical to the trajectory planner. The proposed framework with multiple objectives, wind sensory capability, and skill learning can benefit applications such as improving drone performance.

Layered composite structures inspired by biological tissues can exhibit out-of-plane negative Poisson's ratio, but identifying layups that maximize auxetic performance is challenging in high-dimensional designs. Here, we introduce an inverse design framework that searches for laminate layups with minimum Poisson's ratio. The approach combines multi-start resampling with machine learning-guided clustering to map layup families across layer numbers. Analytical relations from laminate mechanics link ply angles to effective properties, and computer simulations with laboratory measurements validate the predicted minima. The analysis resolves three layup categories, explains how shear-strain mismatch across bonded plies drives through-thickness auxetic expansion, and shows that simple symmetry rules reduce the search space. The framework reproduces previously reported minima and uncovers layups that approach lower Poisson's ratios under practical constraints. These results provide a physics-grounded, data-efficient route to engineer layered composite structures with strong auxetic responses and offer concise design rules for impact mitigation, vibration control, and flexible structures.

Gold-coated tilted fiber Bragg gratings have established themselves as powerful plasmonic biosensors, but their widespread deployment remains hindered by the need for costly, high-resolution interrogators and complex signal processing. Here, we demonstrate that tilted fiber Bragg gratings sensors can be effectively interrogated using a low-cost, coarsely resolved fiber Bragg grating interrogator with only 256 pixels spanning 45 nm, corresponding to a low resolution (~180 pm, 10 times coarser than standard interrogators). By applying a fast Fourier transform-based demodulation technique to the dense, comb-like cladding mode spectrum, we extract robust sensing information using only a narrow spectral window of a few tens of nanometers. This dramatically reduces hardware and computational requirements while preserving high sensitivity. We validate our approach in both refractometry and biosensing, targeting the clinically relevant biomarker Proteinase 3. Furthermore, we show that temperature cross-sensitivity can be compensated directly within this narrow spectral range by tracking a dedicated cladding mode resonance, eliminating the need to reference the Bragg mode. These advances pave the way for compact, cost-effective, and user-friendly plasmonic fiber sensor systems deployable in real-world biomedical environments.

Wireless communication at higher frequency bands has attracted research interest for fifth generation and beyond (5GB) wireless networks due to the large amount of unused bandwidth at these frequencies. However, there are substantial challenges associated with higher frequency bands due to the high path loss of the propagation environment and the high power consumption of the transceivers. Hybrid beamforming with massive multiple-input multiple-output (MIMO) has emerged as a solution to these problems by combining the performance and flexibility of digital beamforming with the energy efficiency of analog beamforming. Optical beamforming has recently been considered as an alternative to implement the analog component of a hybrid beamformer, which may offer improvements in size, weight and power consumption in comparison to conventional electronics. This paper proposes a new approach to implement an optical beamforming system based on photonic vector modulators using tunable photonic filters. Our experimental demonstration of the proposed optical beamformer shows that microring resonator (MRR)-based photonic vector modulators can be calibrated to achieve a root-mean-square (RMS) phase error of better than 2° and an amplitude error of 0.3 dB. Our findings identify a pathway to realize large-scale, fully-connected hybrid beamformers by leveraging compact and low loss photonic resonators.

Perovskite solar cells (PSCs) hold promise for high-efficiency photovoltaic technology but face commercialization challenges due to scaling difficulties. A common approach for scaling PSCs involves creating perovskite solar modules (PSMs) with subcells connected in series, using P1, P2, and P3 laser scribing process to reduce interconnection losses. In this study, a standard nanosecond pulse UV laser was used to perform these scribes. Here we demonstrated that, by employing a single 45 µm laser line for each scribe, it can significantly reduce the dead area, resulting in exceptionally high geometric fill factors (GFFs). In inverted PSMs with active areas of 4.0 cm² and 10.8 cm², it was reached GFFs of 99.3% and 98.8%, respectively. To the best of author's knowledge, this work demonstrates the first successful use of a single nanosecond laser source for continuous P1-P2-P3 scribing, achieving a dead area as low as 0.7% in a 4 cm² module.

The excitation and inhibition (E/I) balance of neural circuits is a crucial index of neurophysiological homeostasis associated with healthy brain functioning. Although several cutting-edge methods exist to assess E/I balance in an intact brain, they have inherent limitations, such as difficulties in tracking changes in E/I balance over time. To address this, we introduced neural-mass-model-based tracking using a data assimilation (DA) approach. While we previously demonstrated that sleep-dependent E/I changes could be estimated from electroencephalography (EEG) data, the neurophysiological validity of this method had not been directly evaluated. In this study, we developed an enhanced DA-based method and compared its E/I estimates with the concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) based measures. Our results revealed significant correlations between the DA-based estimates and TMS-EEG indices of E/I balance in the dorsolateral prefrontal cortex. These findings indicate that our computational approach provides neurophysiologically valid estimations of time-varying E/I balance.

We report on the design and fabrication of a circular pillar array as an interfacial barrier for microfluidic microphysiological systems (MPS). Traditional barrier interfaces, such as porous membranes and microchannel arrays, present limitations due to inconsistent pore size, complex fabrication and device assembly, and a lack of tunability using a scalable design. Our pillar array overcomes these limitations by providing precise control over pore size, porosity, and hydraulic resistance through simple modifications of pillar dimensions. Serving as an interface between microfluidic compartments, it facilitates cell aggregation for tissue formation and acts as a tunable diffusion barrier that mimics diffusion in vivo. We demonstrate the utility of barrier design to engineer physiologically relevant cardiac microtissues and a heterotypic model with vasculature within the device. The tunable properties offer significant potential for drug screening/testing and disease modeling, enabling comparisons of drug permeability and cell migration in MPS tissue with or without vasculature.

Current in silico dialyser models remain accurate only under narrowly constrained conditions. Advances in computing power and increasing levels of interdisciplinary collaboration, however, now set the stage for more robust computational haemodialyser models. In this review, we survey existing modelling approaches-long used to guide haemodialyser design and clinical nephrology practice-identify key unresolved challenges, and propose computational strategies poised to accelerate the development of dialyser technologies that better meet the needs of kidney failure patients.

Accurate estimation of core body temperature (CBT) is essential for physiological monitoring, yet current non-invasive methods lack statistically calibrated uncertainty estimates required for safety-critical use. Here we introduce a conformal deep learning framework for real-time, non-invasive CBT prediction with calibrated uncertainty, demonstrated in high-risk heat-stress environments. Developed from over 140,000 physiological measurements across six operational domains, the model achieves a test error of 0.29 °C, outperforming the widely used ECTemp™ algorithm with a 12-fold improvement in calibrated probabilistic accuracy and statistically valid prediction intervals. Designed for integration with wearable devices, the system uses accessible physiological, demographic, and environmental inputs to support practical, confidence-informed monitoring. A customizable alert engine enables proactive safety interventions based on user-defined thresholds and model confidence. By combining deep learning with conformal prediction, this approach establishes a generalizable foundation for trustworthy, non-invasive physiological monitoring, demonstrated here for CBT under heat stress but applicable to broader safety-critical settings.