Communications engineering最新文献

The nitrogen-vacancy (NV) center is a defect in diamond whose spin state can be read optically by exploiting its photoluminescence or electrically by exploiting its charge generation rate under illumination, both of which being spin-dependent. The latter method offers numerous opportunities in terms of integration and performance compared to conventional optical readout. Here, we investigate the physical properties of a graphitic-diamond-graphitic structure under illumination. We show how, for a type IIa diamond material, electron-hole pairs generated by an ensemble of NV centers lead to a p-type material upon illumination, making this all-carbon structure equivalent to two back-to-back Schottky diodes. We analyze how the reverse current flowing upon illumination changes as a function of bias voltage and radiofrequency-induced excitation of the NV ensemble spin resonances. Furthermore, we demonstrate how an additional field effect arising from the illumination scheme affects the reverse current, resulting in a photoelectrical signal that can exceed the optical signal under the same illumination conditions.

Magnetic Particle Imaging (MPI) is a preclinical imaging modality with potential for future clinical usage. The radiation-free guidance of endovascular interventions with MPI is especially promising. Here, we present a safety study on the heating of metallic medical implants during MPI measurements under realistic conditions in an extracorporeally-perfused cadaver model. The measurements were conducted by fiberoptic thermometers and showed no detectable heating of the tested endovascular devices in the cadaver model. A temperature increase of no more than 0.11 K was observed on the surface of the investigated proximal femoral nail. The in vitro testing of orthopedic prostheses (knee and hip) revealed a slight heating effect of 0.45 K. The dependence of heating on the applied excitation frequency was measured. Overall, the tested repertoire of implants did not heat by a clinically-relevant amount in a human-sized MPI-scanner under realistic conditions, indicating their safe usage in future clinical applications.

Radiation workers need accurate monitoring of X-ray exposure, but existing solutions are either inaccessible, expensive, or provide delayed feedback. We present OpenDosimeter ( www.opendosimeter.org ), an open hardware solution for real-time personal X-ray dose monitoring. Using a scintillator-based X-ray sensor on a custom board powered by a Raspberry Pi Pico, OpenDosimeter provides real-time feedback (1 Hz), data logging (10 hours), and battery-powered operation. A core innovation is that we calibrate the device using ²⁴¹Am found in ionization smoke detectors. Specifically, we use the γ-emissions to spectrally calibrate the dosimeter, then calculate the effective dose from X-ray exposure using the scintillator absorption efficiency and energy-to-dose coefficients derived from public tabulated data. We demonstrate that this transparent approach enables dose rate readings with linear response between 0.1-1000 μSv/h at  ± 25% accuracy, tested for energies up to 120 keV. The maximum dose rate readings are limited by pile-up effects when approaching count rate saturation (~77,000 counts per second at  ~13 μs average pulse-processing time). The total cost for making an OpenDosimeter is <$100, which, combined with its open design, enables cost-effective local reproducibility on a global scale. This paper complements the open-source documentation by explaining the underlying technology, algorithms, and areas for future improvement.

Electromagnetic waveguides are widely used in spacecraft, naval, electrical, and communication systems for transferring microwave energy. Conventional designs are typically rigid and bulky, offering little shape adaptability. This limits their use in confined spaces or systems requiring compact storage and high adaptability. Here we report highly shape-morphable origami electromagnetic waveguides that fold, deploy, and change shape. Inspired by origami folding techniques, including shopping bags and bellows designs, these waveguides demonstrate low-loss, robust microwave energy transmission in numerical and experimental studies. Results demonstrate that highly shape-morphable electromagnetic waveguides may effectively replace rigid counterparts including straight, twisted, and bent waveguides. This research employs a combined analytical and experimental framework to study the kinematics and mechanics of key geometries, particularly rectangular straight and twist bellows designs, offering rigorous structural design guidance. Engineering advancements and fundamental studies in this work lay the foundation for future adaptive microwave energy delivery waveguides.

Conventional dry filtration materials present a high initial efficiency but suffer from poor regenerability and limited functionality. Conversely, liquid-based purification strategies leverage the high capture potential of gas-liquid interfaces, but face challenges such as insufficient material strength, complex fabrication, and difficulties in large-scale application. Here, we proposed a porous ceramic-based bubble-enhanced filtration system (PCBEFS). The proposed system employs tunable porous ceramics as bubble generators in the water medium, thereby enabling highly efficient wet removal of particulate matter, while simultaneously providing air humidification, formaldehyde removal, and antibacterial functions. Furthermore, we established an empirical mathematical model linking pore structure, bubble characteristics, and removal performance, which elucidates the structure-activity relationship and presents insights into the capture mechanism jointly governed by porous ceramics and gas-liquid interfaces. PCBEFS addresses the limitations of the existing liquid-based purification systems and contributes substantially to multifunctional indoor air pollution control with strong engineering potential.

Event-based imaging is at the forefront of high-speed sensing applications due to the low latency of asynchronous detection and low data volume. Conventionally, events used in this form of sensing correspond to changes in intensity on the microsecond scale, this inherently makes the approach incompatible with scenarios where single photon detection is required such as single photon Light Detection and Ranging (LiDAR), and low-light-level imaging. Here we propose a new imaging modality which is driven instead by events generated from the detection of individual photons. We use a form of single-pixel imaging in which information from a 3-Dimensional scene is encoded entirely in the time-of-arrival of the photons such that each detected photon can be used to update an estimation of all transverse positions simultaneously. The image reconstruction is performed by a Spiking Convolutional Neural Network (SCNN) which has a natural complementarity with single photon detection that allows the scheme to run fully asynchronously and be driven by the detection of each individual photon. Our approach for processing LiDAR information asynchronously, driven by each detected photon, has the potential for minimal latency 3D imaging and sensing even in weak light conditions with applications in high speed target detection and robotics.

Transcranial magnetic stimulation combined with electroencephalography enables direct assessment of cortical excitability and connectivity via stimulation-evoked responses. While passive electrodes remain the gold standard, they require extensive preparation and are sensitive to contact quality, whereas active electrodes are easier to use but limited by increased height and larger decay artifacts. Here we introduce an ultra-thin active electrode system combined with hardware- and software-based artifact suppression. We collected electroencephalographic data from 10 healthy adults in Austria in 2024, recording brain responses with both active and passive electrodes during stimulation of the left primary motor cortex. We analyzed early and late responses for similarity and amplitude variability and found high consistency between electrode types, with stable waveforms after 20-30 trials. Response amplitudes did not differ significantly between electrode types. These findings demonstrate that active electrode systems can provide reliable and efficient recordings, supporting their broader use in stimulation-electroencephalography research.

Automatic object detection is increasingly used in the medical field to enhance clinical workflows before, during, and after diagnosis of various conditions. One example is prostate detection and prostate volume estimation, which can aid in triaging patients for prostate cancer through risk-stratification using prostate-specific antigen density. In this paper, a baseline prostate detection framework is presented, highlighting that current state-of-the-art object detection models can detect the prostate in difficult to interpret surface-based ultrasound images. A 5-fold cross-validation study returned intersection-over-union, precision, recall, F1, and average-precision values above $\mathrm{0.7}$ with real-time capabilities possible. Additionally, a simple size calculation based on the detection results showed high correlation with ground truth measurements, with Pearson Correlation Coefficients ranging from $\mathrm{0.55}$ to $\mathrm{0.84}$ for prostate volume estimates. These findings will contribute to the development of a real-time prostate detection and size estimation platform for prostate cancer risk-stratification to reduce unnecessary biopsy rates in healthcare systems.

The primary challenge in active object tracking (AOT) lies in maintaining robust and accurate tracking performance in the complex physical scenarios. Existing end-to-end frameworks based on deep learning and reinforcement learning often struggle with high computational costs, data dependency, and limited generalization, hindering their performance in practical applications. Although embodied intelligence (EI) is promising to enable agents to learn from physical interactions, it cannot tackle severe anomalies happened in the complex scenarios. In order to address this issue, here we propose a novel embodied learning method, called the Cognitive Embodied Learning (CEL), which is inspired by the dual decision-making system of the human brain. The CEL can dynamically switch between normal tracking and anomaly handling modes, supported by specialized modules including the anomaly cognition module, the rule reasoning module, and the anomaly elimination module. Moreover, we further introduce the categorical objective function to address function non-measurability and data confusion caused by severe anomalies. Extensive unmanned aerial vehicle anomaly active target tracking experiments in both simulated and real-world scenarios demonstrate the superior performance of our method. Compared to the state-of-the-art methods, the CEL achieves a 361.4% increase in the success rate and a 54.4% improvement of the task completion efficiency, which highlights the potential of CEL to advance the field of AOT and open new avenues for more robust and intelligent tracking systems in the challenging environments.

Low-grade thermal gradients in marine environments represent an underexploited energy source for autonomous sensing and monitoring. Converting such small temperature differences into usable electrical power remains a key challenge for ocean-deployed systems. We present a deployable thermomagnetic generator thoroughly characterized for marine-relevant energy harvesting. The device powers an internet-connected sensor and harvests ultra-low temperature differences akin to those at the ocean surface. It draws heat from water and rejects it to ambient air, operating optimally at a temperature difference (ΔT) of ~7.5 °C. Laboratory prototypes generated up to 6.7 mW at ΔT ~ 10 °C with gentle airflow (~1 m s^-1). A separate controlled wave-tank demonstration validated stable operation and sensor powering under marine-like boundary conditions. Given its voltage and power margins, the generator could sustain multiple sensor nodes. Scalability and material assessments identify modular deployment and non-rare-earth alternatives as pathways toward practical marine energy harvesting and low-grade waste-heat recovery.