Communications engineering最新文献

Magnetic particle imaging (MPI) is a tomographic imaging technique which determines the spatial distribution of magnetic nanoparticles (MNPs). Multi-contrast MPI provides the ability to detect environmental conditions of MNPs, such as temperature or viscosity. One parameter that has not been investigated but shows high potential for medical diagnosis is the pH value, as it is an indicator of inflamed or tumorous tissue. In this work, we present an approach to resolve the pH value using multi-contrast MPI. Our proof-of-concept is based on a stimuli-responsive, magnetic hydrogel that exhibits reversible swelling in response to a pH change. The pH contrast is generated indirectly via the pH-responsive hydrogel swelling modulating the signal of embedded MNPs. Magnetic particle spectrometry measurements show that the hydrogels' magnetic response correlates with the pH value, which could provide a new way of contactless pH monitoring. Finally, the feasibility of resolving different pH values in a multi-contrast MPI image is demonstrated.

Computational image enhancement for microscopy facilitates cutting-edge biological discovery. While promising, the commonly used deep learning methods are computationally expensive owing to the use of general-purpose architectures, which are inefficient for microscopy data. Here, we propose a sparsity-efficient neural network for image enhancement as a deep representation learning solution to inverse problems in imaging. To maximize accessibility, we developed a framework named DeBCR, consisting of a modular Python library and a user-friendly point-and-click DeBCR plugin for Napari, a popular bioimage analysis tool. We provide a detailed protocol for using the DeBCR as a library and a plugin, including data preparation, training, and inference. We compare the image restoration performance of DeBCR to ten current state-of-the-art models over four publicly available datasets spanning crucial modalities in advanced light microscopy. DeBCR demonstrates more robust performance in denoising and deconvolution tasks across all assessed microscopy modalities while requiring notably fewer parameters than existing models.

Parkinson's Disease (PD) involves not only α-synuclein pathology in dopaminergic neurons but also vascular impairments that remain underexplored due to limitations of traditional in vitro models. Here we present a microengineered 3D neurovascular midbrain model that reconstructs the capillary interface of substantia nigra dopaminergic neurons. In our proof-of-concept demonstration, we successfully recapitulated neuronal pathology in PD, including α-synuclein aggregation, inflammatory responses, and progressive neuronal degeneration, by exposing our model to specially generated PD-associated α-synuclein preformed-fibrils. Importantly, this engineering approach also enables the investigation of progressive vascular abnormalities in PD, such as endothelial dysfunction, barrier disruption, vascular regression, and the resulting impairment of blood flow. Our PD model establishes a tractable platform for investigating the multifaceted nature of the disease and understanding the complex interplay between neurodegeneration and vascular pathology, offering a unique tool for developing innovative therapeutic strategies that address both the neuronal and vascular components of PD pathology.

Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique that provides molecular-level information in vivo. To obtain this unique contrast, repeated acquisition at multiple frequency offsets is needed, resulting a long scanning time. In this study, we propose a hybrid strategy at k-space and image domain to accelerate CEST MRI to facilitate its wider application. In k-space, we developed a complementary undersampling strategy which enforces adjacent frequency offsets by acquiring different subregions of k-space. Both Cartesian and spiral k-space trajectories were applied to validate its effectiveness. In the image domain, we developed a multi-offset transformer reconstruction network that uses complementary information from adjacent frequency offsets to improve reconstruction performance. Additionally, we introduced a data consistency layer to preserve undersampled k-space and a differentiable coil combination layer to leverage multi-coil information. The proposed method was evaluated on rodent brain and multi-coil human brain CEST images from both pre-clinical and clinical 3 T MRI scanners. Compared to fully-sampled images, our method outperforms a number of state-of-the-art CEST MRI reconstruction methods in both accuracy and image fidelity. CEST maps, including amide proton transfer (APT) and relayed nuclear Overhauser enhancement (rNOE), were calculated. The results also showed close agreement with fully-sampled ones.

A parabolic coast or wall concentrates incoming waves at its focal point, creating a high‑energy zone ideal for enhanced capture. Yet, how to efficiently harvest this concentrated energy remains unclear. Here we propose designs of single- and dual-chamber Oscillating Water Column (OWC) chambers for enhancing wave energy capture. A time‑domain higher‑order boundary element method, grounded in nonlinear potential flow theory, is coupled with a nonlinear pneumatic model-calibrated via geometric scaling, dual‑chamber coupling, and focused‑wave boundary tests-to simulate OWC performance. Under parabolic focusing, a bimodal resonance yields peak power absorption up to 17 times that of an isolated device, and a leeward perforation design boosts the single‑chamber capture ratio to 25 times baseline. A dual‑chamber configuration with an added semicircular chamber further elevates total absorbed energy and widens the effective bandwidth. This work provides practical design guidance for efficient wave-energy devices operating in focused-wave environments.

Distillation is the most energy-consuming unit operation of the chemical industry, however, its decarbonization strategy necessitates laborious manual process simulation, optimization and carbon emission accounting. Here we established a reasoning agent consisting of a large language model (LLM) and an extensive tool set to automate learning material collection, process simulation, optimization and carbon emission accounting of a representative methanol and ethanol distillation case study. Then the agent automatically constructed a heat pump-assisted distillation process to save energy. The impact of three energy supply scenarios on the carbon emissions of distillation, namely, coal, natural gas and renewables, was evaluated. Combining the heat pump-assisted process and renewables could substantially reduce the carbon emission by 98% compared with the coal-based traditional distillation process. This study explored using reasoning agents to automate carbon emission and decarbonization intervention quantification, and facilitated high-resolution carbon emission models of the industry.

Truck platooning promises to enhance the efficiency of logistics, but commercial operation is hampered by safety and economic concerns. Human-lead truck platooning can mitigate these challenges by leveraging a human driver's expertise. However, existing human-lead truck platooning is limited to longitudinal control and lacks the lane-changing capability, which restricts logistical efficiency. To address this, we build upon previous research to propose a human-lead truck platooning method with lane-changing capability. The platoon leader is controlled by a skilled human driver, who is responsible for leading the following automated trucks. The human-lead platoon is enabled to cruise, lane-change, and obstacle avoidance, leveraging the driver's expertise to mitigate safety risks in long-tail scenarios. Drivers of the following trucks are not needed, reducing labor costs. The proposed method has been implemented in commercial operations at the world's largest port, Shanghai Yangshan Port, achieving an annual transport volume of 200,000 Twenty-foot Equivalent Units. It highlights a route for large-scale truck platooning implementation, potentially reshaping freight-transport operations.

Swarm control of autonomous underwater vehicles (AUVs) has been recognized as the foundation for marine exploration. However, the implementation of this task faces two major constraints: excessive communication energy demands and limited environmental perception capabilities. This article proposes a digital twin (DT)-driven swarm control of AUVs solution to overcome these limitations. We first create the digital replicas for each AUV by integrating the dynamics and environmental data. With the collected states from AUVs, a parameter estimator is proposed to predict the flow field, while a swarm networking protocol is designed to reduce the energy consumption. After that, an integral reinforcement learning (IRL)-based swarm controller is proposed to drive the virtual and real AUVs. Based on the interaction information between DT models and AUVs, the virtual-real error optimization algorithm is implemented to minimize the matching errors. Finally, the effectiveness of our solution is verified by the experimental results. These results demonstrate that the DT-driven swarm control of AUVs can improve the underwater situation awareness and prediction accuracy while reducing the communication energy consumption.

Large-volume 3D dense prediction is essential in industrial applications like energy exploration and medical image segmentation. However, existing deep learning models struggle to process full-size volumetric inputs at inference due to memory constraints and inefficient operator execution. Conventional solutions-such as tiling or compression-often introduce artifacts, compromise spatial consistency, or require retraining. Here we present a retraining-free inference optimization framework that enables accurate, efficient, whole-volume prediction without performance degradation. Our approach integrates operator spatial tiling, operator fusion, normalization statistic aggregation, and on-demand feature recomputation to reduce memory usage and accelerate runtime. Validated across multiple seismic exploration models, our framework supports full size inference on volumes exceeding 1024³ voxels. On FaultSeg3D, for instance, it completes inference on a 1024³ volume in 7.5 seconds using just 27.6 GB of memory-compared to conventional inference, which can handle only 448³ inputs under the same budget, marking a 13 × increase in volume size without loss in performance. Unlike traditional patch-wise inference, our method preserves global structural coherence, making it particularly suited for tasks inherently incompatible with chunked processing, such as implicit geological structure estimation. This work offers a generalizable, engineering-friendly solution for deploying 3D models at scale across industrial domains.

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.