In this study, a novel gradual initial decoding (GID) technique is introduced to improve implicit transmission with bit flipping (ITBF) and implicit transmission with collection decoding (ITCD) techniques introduced in [1]. Further, two additional decoding techniques, namely, feedback implicit decoding (FID) and iterative implicit decoding (IID) are introduced for further improvement. It is numerically demonstrated that all proposed techniques presented here can significantly improve performance in implicit transmission and increase the transmission rate on the implicit sequence.

Two neural networks with two features (SNR and mutual information) are applied to link adaptation that is targeted for meeting the WLAN standard. The usefulness of neural-network-based link adaptation is validated for the WLAN and also the performance (throughput and latency) is analysed to obtain some practical gain of link adaptation based on neural networks over the traditional approach.

High Resolution Range Profiles (HRRP) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non-cooperative circumstances. Currently, deep learning based models treat HRRP as sequences, which may lead to ignorance of the internal relationship of range cells. This letter introduces HRRPGraphNet, whose pivotal innovation is the transformation of HRRP data into a novel graph structure, utilizing a range cell amplitude-based node vector and a range-relative adjacency matrix. This graph-based approach facilitates both local feature extraction via one-dimensional convolution layers, global feature extraction through a graph convolution layer and a attention module. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet’s superior accuracy and robustness, particularly in limited training sample environments, underscoring the potential of graph-driven innovations in HRRP-based RATR. Codes are available at: https://github.com/MountainChenCad/HRRPGraphNet.

In this paper, a novel reconfigurable angular cone antenna that employs a rotatable fan ground plane for reconfiguration is introduced. By regulating the number and orientation of external grounds, the radiation range is progressively broadened, enabling the antenna to exhibit diverse radiation modes. Experimental results demonstrate that the antenna can successfully switch between three modes and validate seven distinct beam directions. These beam directions include: (1) a single ground plane at 45°, 135°, 225°, and 315°; (2) two ground planes at ±45°; and (3) four ground planes creating an omnidirectional radiation pattern. Notably, the antenna boasts a broad bandwidth of 60.66% (covering 1.2-4.9 GHz; VSWR < 2.0) which cover 2G/3G/4G/5G communication bands, while maintaining an antenna efficiency exceeding 90%, marking a significant performance enhancement.

This paper investigates the application of novel eye-tracking metrics to assess cognitive load in visual decision-making processes. In particular, it aims to contribute to understanding the cognitive process and developing complementary measures for evaluating visual behavior in neonatal pain assessment. We have carried out an eye-tracking study using the relative Explore-Exploit Ratio proposed and the application of the Task-Evoked Pupillary Response to evaluate pediatrician experts, non-experts, and parents while analyzing frontal facial faces of distinct newborns, before and after painful procedures, from a benchmark dataset considering areas of interest clinically relevant. The Tobii TX300 eye-tracking system recorded this data in a closed room with controlled lighting. Our results disclose that the visual attention described by the traditional metrics may not correspond directly to the respective fixation patterns and pupillary changes quantified for all the sample groups of participants investigated, highlighting statistically significant differences in the visual behavior between experts and non-experts in such vital decision-making tasks

This brief investigates the multi-point fault repair problem in active distribution networks, establishing resilience assessment metrics and constructing a fault repair mathematical model with road network coupling. An improved genetic algorithm is proposed, utilizing a comparison crossover operator and population information entropy. Compared to traditional algorithms, the comparison crossover operator preserves high-quality genes, while population information entropy maintains diversity, preventing the algorithm from converging on local optima. Simulation analysis demonstrates that the proposed fault repair method can restore normal power supply to distribution network lines within a short period and offers significant advantages in fault repair tasks.

This paper introduces a novel resource allocation algorithm, Priority-Aware Federated Resource Allocation (PAFRA), tailored for Power Line Communication (PLC) systems. Utilizing a federated learning framework, PAFRA optimizes the distribution of limited spectrum resources among multiple nodes within a residential environment. The algorithm employs a priority-based time slot allocation to manage subchannel conflicts and uses a Double Deep Q-Network (DDQN) for local training at each node, incorporating state, action, and reward configurations to refine transmission power and subchannel selections. Extensive simulations demonstrate that PAFRA significantly enhances system throughput across various Signal-to-Noise Ratio (SNR) levels, outperforming existing adaptive resource allocation strategies and random allocation methods. The findings highlight PAFRA’s ability to achieve superior performance in dynamic PLC environments, illustrating its potential to optimize network efficiency while adhering to strict regulatory emission standards.

Characterizing soil moisture (SM) around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated by two different microwave imaging techniques based on spatio-temporal measurements at various frequency bands. The study investigates the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN) algorithms for moisture classification based on these images in three scenarios: before clutter removal, after clutter removal, and after applying imaging techniques such as back projection and the Born approximation. We also explore the potentials of CNN and KNN for moisture estimation around the plant roots and in the presence of pebbles. The results demonstrate the more accurate moisture estimation using CNN when it is applied after clutter reduction considering back projection algorithm (BPA) as the imaging technique. The results indicate that using the Back Projection technique for image formation, combined with CNN for classification, improves leak detection accuracy by approximately 20% compared to other methods.

Voltage sags are one of the primary factors in power quality issues that lead to losses for sensitive users and reduce the operation resilience of distribution networks. However, due to the lack of accessibility in sensitive users' production information, accurately quantifying the resilience of distribution networks under the impact of voltage sags is challenging. In this letter, we first define an operation resilience index using a trapezoidal curve. Considering the varying tolerance levels of sensitive users to voltage sags, a feature indices system is estabilished using the adaptive S-transform, and a sample dataset is generated through the Monte Carlo method. Finally, we establish a mapping relationship between sag characteristics and operation resilience indices using the XGBoost-Stacking algorithm. This data-physics hybrid-driven model offers a quantitative approach for developing resilience enhancement strategies.

To address the issues of short flight duration and the inability to carry high-computation resources in small observation Unmanned Aerial Vehicle (UAV) due to limited energy and payload capacities, we proposes a deployment framework for an air-water surface collaborative observation system based on energy replenishment and computation offloading. In this framework, the UAV serve as platforms for observation tools, while Unmanned Surface Vehicle (USV) act as platforms for energy replenishment and edge computing nodes. The edge computing nodes process, analyze, and distribute the observation data received from the UAV. Upon receiving a charging signal, the UAV perform coordinated positioning with the USV using high-precision BeiDou positioning, land on the USV. After recharging, the UAV resume their observation tasks. Experimental results demonstrate that this framework prevents the small observation UAV from having to carry heavy computational loads during flight，and also, the small observation UAV can utilize the USV platform for cyclic recharging and takeoff. The findings of this study can be extended to the collaborative application of multiple UAVs and USVs, enabling broader and more sustained observations. This approach has significant potential for applications in environmental monitoring, disaster rescue, marine mapping, border patrol, and marine aquaculture capacity monitoring.

In this paper, we propose a new light field (LF) reconstruction network that increases LF angular resolution. The proposed method consists of two parts that extracts the initial feature map and refines the extracted initial feature map. In order to efficiently extract features from input images, we rearrange the input images into a macro-pixel image using pixel shuffle and then extract the initial feature map using successive convolution layers. The refinement network continuously extracts inherent correlation information using a dense back-projection structure. Finally, we are able to stably train the network and reconstruct high-quality LF images by connecting the initial feature map and the output of the refinement network with long skip connection. Simulation results show that the proposed network outperforms other existing methods in terms of execution time and reconstructed LF image quality.

The single transmitter and multiple receivers (STMR) mobile radar system is a special distributed radar system, which has stronger detection and survival capabilities. However, the high-speed movement of the radar platforms easily produce the across-range cell migration (RCM) of radar echo signal, which cause envelope shift effects within the coherent processing interval (CPI). In this paper, we proposed a motion compensation method for the STMR mobile radar system in the sea environment, to solve the problem of RCM. Firstly, the target points and clutter points with the same Doppler velocity are obtained by the radon Fourier transform (RFT) method. Secondly, using the range / Doppler difference between clutter and target under multi-radar views, the target and clutter points are separated by the spatial range mapping. The simulation results show that the method can effectively improve the coherent accumulation effect of the target in the sea environment with low signal-to-clutter ratio (SCR).

Quantum communication is expected to become the cornerstone of global communication systems, addressing critical issues of classical communication while providing unprecedented security and efficiency. A crucial aspect of advancing this field is quantum channel coding, which ensures data integrity by detecting and correcting errors specific to quantum systems. This research evaluates the performance of the three-qubit error correction code, the fundamental and simplest technique in quantum channel coding, for image transmission over error-prone channels. JPEG and HEIF format images are encoded using the three-qubit error correction method and compared to the 1/3 rate polar codes. Our results demonstrate that the three-qubit error correction code significantly outperforms advanced classical polar codes in both classical and quantum domains, achieving a maximum PSNR of 61.5 dB (SSIM = 0.9997) in HEIF and 58.3 dB (SSIM = 0.9994) in JPEG. This showcases its potential as a robust solution for quantum communication.

Wireless sensor networks and nodes are faced with severe constraints in power capacity and lifespan, especially in harsh and cold environments. Electromagnetic radiation energy harvesters serve as a promising alternative power source for sensor nodes. However, the power output from reported energy harvesters remains limited, emphasizing the critical need to reduce power consumption in sensor nodes. In this study, we propose a miniaturized low-voltage controllable metamaterial. It is intended for 2.4 GHz wireless band and is capable of operating in extremely adverse conditions, for instance, beneath the ice, while requiring only 3.3 V control voltage and consumes about 0.3 μW energy. Measurements in sub-ice environment demonstrate its outstanding reflection control characteristics, making it highly suitable as a backscatter communication node in wireless sensor networks in harsh environments.

Magnetic resonance imaging (MRI) has significantly improved the monitoring of tumor ablation by providing high-contrast soft tissue images. However, MRI-guided surgical procedures often involve extremely delicate and complex manipulations, requiring an MRI-compatible robotic system to enhance precision and reduce human error from jitter during prolonged high-precision tasks. This article introduces a novel MRI-compatible, hydraulically actuated needle insertion robot for minimally invasive stereotactic neurosurgery. The robot operates based on the principles of spherical chain mechanisms and communicating vessels, being driven by a linear piston and actuator. A double-layer spherical chain mechanism translates the linear motion of the piston into rotational motion. An MRI-compatible linear encoder is constructed using a grating ruler and optical fiber to ensure precise control. Model predictive control (MPC) enabled the robot to track a given trajectory accurately, allowing for precise braking pressure management and fast response times. Testing and analysis revealed that the robotic system achieves an average positional accuracy of 1.435 mm, with angle accuracies of 0.401°. Additionally, the precision and repeatability of the robot fall within the ideal range. The results demonstrate that the developed puncture robot is both materially and structurally compatible with the MRI environment, showcasing effective spatial positioning and trajectory planning capabilities.

In this paper, an algorithm applying adaptive wavelet neural network(AWNN) and sliding mode control(SMC) is proposed, investigated and exploited for tracking control of microelectromechanical(MEMS) gyroscope. Such an AWNN model can be regarded as a special radius basis function neural network, and utilizes Mexican hat function as activation function. Besides, Taylor expansion is used for analyzing activation radius which is considered as an adaptive variable. The parameters of MEMS gyroscope model are hard to obtain in engineering application, thus, AWNN and SMC are designed for approximating the uncertain function of MEMS gyroscope and the unknown asymmetrical dead zone in control scheme. The weights updating laws and the activation radius adaptive laws in AWNN are derived from the Lyapunov stability analysis, which result in the control error converging to the desired value and the weights and activation radius converging to its real value. Computer simulation results substantiate the theoretical analysis and further demonstrate the efficacy of such an algorithm combining with AWNN and SMC for MEMS gyroscope control.

We propose highly transparent (optical transmittance 92.7 % at 550 nm) single-layer bandpass filter operating in dual frequency bands of 26 and 38 GHz for 5G communication systems. The dual-band bandpass filter achieves not only high optical transparency but good electrical performance. The transmission performance of the filter is stable to the polarization and incidence angle of electromagnetic waves. As a proof of concept, we fabricate a filter with periodic square loops patterned on 188 μm-thick substrate, and each loop is realized with metallic mesh electrodes having a pitch of 10 μm. Measurements are conducted using two different measurement systems; a free-space measurement system and a vector network analyser-based material characterization kit. After calibrating the measurement system, the scattering parameters of the filter are measured. The measured results obtained using the two systems showed good agreement and also agree with the simulated ones. The designed filter has insertion loss of less than 3 dB in the dual passband region.

not-yet-known not-yet-known not-yet-known unknown Optimum performance of co-existing radar and communication (CRC) system is a challenging task when target and user exist within a crowded area where path-loss is dominant. Inspired by the application of intelligent reflecting surface (IRS) in reconstructing the wireless transmission environment, this paper investigates deploying IRS to the CRC system to pursue performance improvement. Particularly, we consider an IRS-assisted CRC system where the IRS not only provides an indirect communication path but inevitably introduces additional interfering paths. Our goal is to maximize the radar signal-to-interference-plus noise ratio (SINR) by jointly optimizing the transmit beamform and the phase of IRS while satisfying the user SINR, the total transmit power at the radar and base station (BS), the restriction of IRS phaseshift. An efficient alternative optimization algorithm combining the second-order cone programming (SOCP) and semidefinite programming (SDP) optimization methods is exploited to solve the complicated non-convex unit-norm problem. Simulation results reveal the advantages of deploying IRS in the CRC system and the effectiveness of our proposed algorithm.