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Abstract Microwave-optical interaction and its effective utilization are vital technologies at the frontier of classical and quantum sciences for communication, sensing, and imaging. Typically, state-of-the-art microwave-to-optical converters are realized by fiber and circuit approaches with multiple processing steps, and external powers are necessary, which leads to many limitations. Here, we propose a programmable metasurface that can achieve direct and high-speed free-space microwave-to-laser conversion. Moreover, it supports reverse conversion, achieving bidirectional operations. The programmable metasurface converter is realized by integrating subwavelength microwave resonant structures, MS junction and photoelectric PN junction components together, without connecting any direct-current supplies to provide driving bias. We further demonstrate the enormous potentials of the metasurface converter in cross-media links and develop a full-duplex air-water wireless communication system. Experimental results show that the bidirectional real-time data transmissions and exchanges are established through the air-water boundary. This work represents a decisive step towards microwave-optical interconversion on wireless and battery-free interfaces.

Cerebrovascular diseases, including ischemic strokes, hemorrhagic strokes, and vascular malformations, are major causes of morbidity and mortality worldwide. The advancements in neuroimaging techniques have revolutionized the field of cerebrovascular disease diagnosis and assessment. This comprehensive review aims to provide a detailed analysis of the novel imaging methods used in the diagnosis and assessment of cerebrovascular diseases. We discuss the applications of various imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and angiography, highlighting their strengths and limitations. Furthermore, we delve into the emerging imaging techniques, including perfusion imaging, diffusion tensor imaging (DTI), and molecular imaging, exploring their potential contributions to the field. Understanding these novel imaging methods is necessary for accurate diagnosis, effective treatment planning, and monitoring the progression of cerebrovascular diseases.

Abstract Background Chronic subdural hematomas (CSDHs) are one of the most common neurosurgical conditions. The standard surgical technique includes burr-hole craniostomy, followed by intraoperative irrigation and placement of subdural closed-system drainage. The drainage is generally removed after 48 h, which can be described as fixed-time drainage strategy. According to literature, the recurrence rate is 5–33% with this strategy. In our retrospective study, postoperative hematoma volume was found to significantly increase the risk of recurrence. Based on these results, an exhaustive drainage strategy is conducted to minimize postoperative hematoma volume and achieve a low recurrence rate and good outcomes. Methods This is a prospective, multicenter, open-label, blinded endpoint randomized controlled trial designed to include 304 participants over the age of 18–90 years presenting with a symptomatic CSDH verified on cranial computed tomography or magnetic resonance imaging. Participants will be randomly allocated to perform exhaustive drainage (treatment group) or fixed-time drainage (control group) after a one-burr hole craniostomy. The primary endpoint will be recurrence indicating a reoperation within 6 months. Discussion This study will validate the effect and safety of exhaustive drainage after one-burr hole craniostomy in reducing recurrence rates and provide critical information to improve CSDH surgical management. Trial registration Clinicaltrials.gov, NCT04573387. Registered on October 5, 2020.

Optical wireless communication (OWC) entitles many advantageous properties such as unlicensed spectrum, high electromagnetic compatibility, compared with its radio frequency wireless counterpart, which enables it to be a potential important driving factor for the diverse developments of future internet of things (IoT).Starting from the optical sources and optical devices, some basic principles and limitations of OWC were introduced.Then, some key technologies, including channel modeling, signal modulation, array communication and high-precision positioning were discussed.Based on those and according to the requirements on peak data rate, sensing precision, energy transfer and security and confidentiality of the next generation IoT, the role of OWC as a technological enabler for next generation IoT was analyzed.Finally, related conclusions were given.

A metasurface-based light-to-microwave transmitter, which can convert a light intensity signal to two microwave signals directly, was proposed to achieve the dual-channel data transmissions in light-to-microwave hybrid wireless communications.

ObjectiveChronic subdural hematoma (CSDH) is a common disease in neurosurgery, which usually occurs in the elderly. Seizure is one of the postoperative complications in CSDH patients and can affect patient outcomes. There is currently no consensus on whether antiepileptic drugs should be prescribed prophylactically. The aim of this study was to evaluate independent risk factors for postoperative seizures and unfavorable outcomes in CSDH patients.MethodsWe reviewed 1,244 CSDH patients who had undergone burr-hole craniotomy in this study. Patient clinical data, CT scan results, recurrence and outcome data were collected. We divided the patients into two groups based on whether they had a postoperative seizure. Percentages and χ2 tests were applied for categorical variables. Standard deviations and two-sided unpaired t-tests were applied for continuous variables. Stepwise logistic regression analyses were performed to identify the independent factors of postoperative seizures and unfavorable outcomes.ResultsThe incidence of seizures after CSDH surgery was 4.2% in this study. There was no significant difference in recurrence rate between seizure and non-seizure patients (p = 0.948), and the outcome of seizure patients was significantly poor (p

BackgroundThe geriatric nutritional risk index (GNRI) is a simple index for evaluating the nutrition status of elderly patients. Many investigations have demonstrated that this index is associated with the prognosis of several diseases. This study aims to identify the relationship between the GNRI and recovery in elderly mild traumatic brain injury (mTBI) patients.MethodsA total of 228 mTBI patients older than 65 years were included in this study. mTBI was defined as an injury to the brain with a loss of consciousness of 30 min or less, a duration of posttraumatic amnesia of

Abstract Low earth orbit constellation satellite communication has the characteristics of low propagation delay, low path loss, low launch cost and wide range of applications. Due to the low orbital altitude and the short orbital period of low earth orbit satellites, the relative position between satellites and gateway stations changes fast. As a result, the links between gateway stations and satellites need to be switched continuously. Based on the minimum handover frequency algorithm, this paper considers the balance of satellite workloads, and proposes a load balanced satellite handover strategy. In the proposed handover strategy, a joint optimization algorithm is employed, and the power allocation of the satellite is optimized to improve the system capacity. In the multi‐satellite connection model, an adaptive power allocation algorithm is proposed to guarantee the service quality of the system. Simulation results demonstrate the efficiency of the proposed satellite handover strategy.

Abstract Intelligent reflecting surface (IRS) is a new concept originating from metamaterials, which can achieve beamforming through controllable passive reflecting. This device makes it possible to engineer the wireless communication environment, and has drawn increasing attention. However, the associated channel models in current literature are mainly borrowed from conventional wireless channel models directly, omitting the unique features of IRS. In this paper, a geometry‐based stochastic channel model for IRS‐assisted wireless communication system is employed. The model has certain accuracy and low computational complexity. In particular, it captures the correlations of subchannels associated with different IRS elements, which is typically not considered in current works. Based on this channel model and the derived channel spatial correlation functions (CFs), an iterative reflection coefficients configuration method is proposed exploiting statistical channel state information to maximise the ergodic channel capacity. The impacts of the IRS spatial positions as well as the number of the IRS elements on the ergodic channel capacity is investigated through simulations. It is found that to obtain a larger ergodic channel capacity, the IRS should be placed in the vicinity of either the transmitter side or the receiver side, which is a useful guideline for practical deployment.

In this paper, we apply the advanced waveform universal filtered multi-carrier (UFMC) to a heterogeneous small-cell network, where both the inner-cell interference and the inter-cell interference are considered. By employing UFMC, a dynamic spectrum allocation scheme and a precoding scheme are proposed to mitigate the interference. The proposed UFMC based interference management schemes are analyzed in detail, and the achievable data rate of the proposed scheme is derived. Simulation results show that the proposed UFMC based schemes can efficiently mitigate the interference, and reduce the effect of frequency offsets.

The nonlinearity of light emitting diodes (LED) has restricted the bit error rate (BER) performance of visible light communications (VLC). In this paper, we propose model-driven deep learning (DL) approach using an autoencoder (AE) network to mitigate the LED nonlinearity for orthogonal frequency division multiplexing (OFDM)-based VLC systems. Different from the conventional fully data-driven AE, the communication domain knowledge is well incorporated in the proposed scheme for the design of network architecture and training cost function. First, a deep neural network (DNN) combined with discrete Fourier transform spreading (DFT-S) is adopted at the transmitter to map the binary data into complex I-Q symbols for each OFDM subcarrier. Then, at the receiver, we divide the symbol demapping module into two subnets in terms of nonlinearity compensation and signal detection, where each subnet is comprised of a DNN. Finally, both the autocorrelation of the learned mapping symbols and the mean square error of demapping symbols are taken into account simultaneously by the cost function for network training. With this approach, the LED nonlinearity and the interference introduced by the multipath channel can be effectively mitigated. The simulation results show that the proposed scheme exhibits better BER performance than some existing methods and further accelerates the training speed, which demonstrates the prospective and validity of DL in the VLC system.

In the biological nervous system, repetitive-pulse signals from different presynaptic neurons may correlate to produce a specific association at the postsynaptic neuron for complex neural encoding. Here, the fabrication and characterization of a triple-emitter light-induced synaptic device artificial synapse is proposed to mimic excitatory postsynaptic voltage (EPSV) summation of the biological nervous system. When repetitive-pulse signals with different periods are synchronously applied to three presynaptic terminals, temporal, and spatial EPSV summations lead to synaptic strengthening. Resonant spatiotemporal correlations occur because the continuous activities make the presynaptic inputs correlate with respect to each other. Experimental results demonstrate three different resonant EPSV correlations generated by a combination of each of the two repetitive-pulse signals, which agree well with the simulations based on the convolution operation and the nonlinear distortion function. These results suggest that the multiple-emitter light-induced artificial synapse is promising for the emulation of biological neural networks.

A multiple-quantum-well diode (MQW-diode) can function as a light-emitting diode or a photodiode. In particular, when appropriately biased, this dual-functioning device can simultaneously emit light and process information. Here, an MQW-diode of ring geometry is fabricated on a 1.64-mm-diameter suspended membrane by a combination of silicon separation and III-nitride backside thinning, and the 1.47-mm-diameter inner region enhances the overall light detection. When the MQW-diode operates under the simultaneous emission-detection mode, a three-stage amplifier circuit with a signal gain of 27.8 dB is used to recover the received signals, and the real-time spatial audio acquisition is experimentally demonstrated with the wire-bonded MQW-diode. This paper provides a promising path to produce multifunctional MQW-diodes toward the Internet of Things.

Existing algorithms on path planning with obstacles for unmanned aerial vehicles (UAVs) suffer from high computational complexity and unpredictability when the considered scenario is complicated. In this paper, we propose a novel path-planning algorithm for UAVs, which relies on continuously updating virtual regional field and its local gradients. The information of target regions and obstacles is incorporated in a virtual regional field. The algorithm circumvents the large number of variables to be optimized, and does not rely on any black boxes with unpredictable outputs. Real data show that the proposed algorithm can design a path with high coverage rate of the target region in a certain time duration, and guides the UAV to bypass the obstacles. The approach based on the regional field provides an option for low-cost hardwares, and reveals insights into the problem of path planning.

Affected by the complexity of the indoor environment, accurate indoor positioning is challenging in many localization based services (LBS). Recently, it has been recognized that, visible light communication (VLC) is promising for indoor navigation and positioning, due to the low implementation cost with marginal modification to the existing infrastructure and the possibility to achieve high accurate positioning results. Provided that the positions of the light emitting diodes (LEDs) are known to the receiver, the angle of arrival (AOA) of the light signal is able to be estimated by a camera embedded in a smart phone, and thus the position of the smart phone can be derived based on the triangulation. In this paper, the performance of the positioning accuracy is analyzed based on indoor positioning with VLC, and the analytical upper bound of location error is derived. Extensive simulation results have verified the theoretical analysis on the VLC-based localization approach in different indoor scenarios. In order to obtain better location performance, the principles of choosing reference LED and localization LED are also given.