ABSTRACTBistatic synthetic aperture radar (SAR) is a new type of SAR system which is capable of achieving multi‐angle target detection and flexible configuration. For spaceborne bistatic SAR, when operating at low frequencies, it is susceptible to the ionosphere. The ionospheric Faraday rotation effect will cause a rotation of polarisation plane of electromagnetic waves, leading to amplitude and phase errors between different polarisation channels of SAR. This effect can be compensated by estimating the Faraday rotation angle. Currently, Faraday rotation angle estimation methods for spaceborne SAR are all applicable to monostatic SAR. However, due to the different signal propagation paths between bistatic SAR and monostatic SAR, the Faraday rotation angle estimation method differs for bistatic SAR. This paper proposes a Faraday rotation angle estimation method for bistatic spaceborne SAR, which is applicable to the alternating bistatic mode. The effectiveness of the method is verified through simulation experiments and the performance of the method is analysed. Under different signal‐to‐noise ratios (SNR) and filtering window sizes, the estimation error range is 0.02°‐0.26°.

ABSTRACTThis paper presents the design and study of an extremely thin, wideband, and polarization‐insensitive metamaterial absorber (MMA) tailored for applications in the visible and infrared (IR) spectral ranges. The proposed MMA introduces a diamond‐shaped resonator setup, achieves high absorption efficiency beyond 90% across a wide wavelength range from 331.64 to 2163.1 nm. The average bandwidth of absorption is 95.64%, having the highest absorption rate of 99.039% witnessed at 511.47 nm. The unit cell of the absorber is condensed and designed to cover the visible optical range, along with the near and short infrared optical windows. Aluminium nitride (AlN) is used as the substrate, while nickel (Ni) serves as both the ground and resonator layer material, contributing to the MMA's durability and thermal stability. As the absorber is symmetric in design, it ensures polarization independence, with steady absorption performance maintained at oblique angles of incidence up to 75°. Comprehensive parametric simulations were performed, examining elements such as surface current distribution, layer thicknesses, different substrate materials and variations in permittivity and permeability, all of which demonstrate the MMA's robustness and adaptability for various applications and by covering a wide spectral range and exhibiting flexibility to polarization and incident angle variations, this absorber offers an effective platform for environmental monitoring, IR detection, and solar energy harvesting.

ABSTRACTThe basic structure of conventional Muller C element is studied and the output errors caused by node parasitic capacitors are analysed in this letter. In order to avoid such errors, an improved C element with two inverted ratio MOS transistors added is proposed to control high resistance nodes of the circuit. To reduce the power consumption of C element, two resistors are added. The proposed C element has been designed in 28 nm CMOS process and 2.5 V devices are adopted. The simulation results show that proposed C element avoids such errors and the size of the layout is 67.33 µm2.

Abstract Reverberation is a factor that degrades the performance of automatic speech recognition. Linear prediction‐based algorithms remove reverberations with iterative computations for each target environment based on physical modeling. Dereverberation autoencoders map reverberant speech to clean speech using pre‐trained deep neural networks without iterative prediction during inference. Two methods were combined to design a neural network architecture for dereverberation. To improve reverberant speech recognition performance with low computational complexity, a method is proposed to extract environmental embedding named DA‐embedding instead of applying dereverberation to the input of an acoustic model. The experimental results on the REVERB Challenge and VOiCES datasets show that DA‐embedding improves reverberant speech recognition performance with low computational complexity.

ABSTRACT Digital‐domain self‐interference cancellation (SIC) technology effectively mitigates self‐interference by reconstructing the SI signal. Due to the high computational complexity of the traditional recursive least squares (RLS) algorithm in SIC, its real‐time engineering application capability is limited. To overcome this limitation, this paper proposes the application of the DCD‐RLS algorithm to SIC. The proposed method transforms the standard normal equations of the RLS algorithm into auxiliary normal equations and incorporates a bisection search strategy, thereby significantly reducing the computational complexity while maintaining optimal convergence performance. Simulation results demonstrate that the proposed algorithm not only achieves a significant reduction in computational complexity but also retains SIC performance, thus enhancing the implementation value of in‐band full‐duplex (IBFD) communication systems.We propose an RLS‐SIC algorithm based on dichotomous coordinate descent (DCD), that converts the RLS normal equations into auxiliary equations and uses a bisection search to greatly reduce computational complexity while preserving optimal convergence. Simulations show, the method maintains self‐interference cancellation performance with much lower complexity, improving the real‐time feasibility of in‐band full‐duplex communication systems.

Abstract This paper introduces the Priority‐Aware Federated Resource Allocation (PAFRA) algorithm, a novel resource allocation strategy designed for power line communication (PLC) systems. Leveraging a federated learning framework, PAFRA optimizes the distribution of limited spectrum resources across multiple nodes within a residential environment. The algorithm integrates a priority‐based time slot allocation to effectively manage subchannel conflicts and employs a Double Deep Q‐Network for local training at each node. This training modality includes comprehensive state, action, and reward configurations to fine‐tune transmission power and subchannel selections. Extensive simulations reveal that PAFRA substantially enhances system throughput, outperforming both traditional numerical optimization methods and contemporary machine learning approaches. Specifically, at a signal‐to‐noise ratio of 30 dB, PAFRA achieves a system throughput of approximately 950 Mbps, marking a 23% improvement over existing approaches. These findings highlight PAFRA's ability to significantly enhance network efficiency while complying with regulatory emission standards, demonstrating its potential to optimize dynamic PLC systems.

ABSTRACTRadar beamforming is one of the most important technologies in the radar system, and the anti unmanned aerial vehicle (anti‐UAV) radar beamforming is usually difficult because of its complex beams. In this paper, a beamforming method based on a hybrid basic beams superposition for anti‐UAV radar and a modulation parameter decoupling method are proposed. Basic beams based on a window function and their improved versions with fast attenuation side lobes are combined by different weightings on different pointing directions to obtain the anti‐UAV radar beam. A method to generate basic beams for improving the attenuation side lobes of the original basic beams is proposed by minimizing the total power without any power loss in one specific pointing direction. To obtain the optimal weightings for different directions, the average beamforming error is minimized and then solved by a genetic algorithm. The method for decoupling the modulation parameters is also derived mathematically. Three groups of experiments with different types of beams are carried out. The experimental results show that the proposed method with hybrid beams can obtain a better beamforming effect in terms of side lobe level based on the window function under different types of anti‐UAV radar beams.

ABSTRACTDigital‐to‐analog converters (DACs) based on sigma‐delta modulation are implemented with analog components that have low accuracy requirements. As a result, they have been widely employed in high‐frequency transmitter architectures over the past decades. Time interleaving techniques allow parallel structures to operate at reduced speeds, thereby mitigating speed limitations in digital circuits. However, a common approach involves multiplexing the outputs of these parallel structures to reconstruct a single digital signal, which is subsequently converted to an analog voltage using a high‐speed, low‐resolution DAC. This work investigates the feasibility of employing a parallel array of low‐speed DACs instead. To compensate for mismatches in analog paths, a novel dynamic element matching technique is proposed. Experimental results demonstrate that the proposed approach exhibits the characteristics required for high‐frequency transmitter applications.

ABSTRACTThis paper aims at developing a level of pollution severity (LPS) classifier for insulators, using a database of corona ultraviolet (UV) images and a computational intelligence algorithm based on convolutional neural networks, considering three different levels of pollution: very weak, weak and moderate. The obtained results show that the developed classifier reached high accuracy (98.57%) and low logarithmic loss values (0.059), indicating that it has a good capability to deal with the proposed classification task.

ABSTRACTImpulsive noise exists widely in ocean environment and statistical parameters of impulsive noise are necessary for ocean signal processing and communication system. However, statistical parameters are generally assumed known or acquired through complicated calculations. In this letter, a hybrid deep neural network based parameter estimation is proposed for ocean impulsive noise. The proposed method formulates the parameter estimation as a non‐linear mapping problem to be solved by deep learning network. The network incorporates one dimensional convolutional neural network to extend noise signals into new feature space without destroying large‐amplitude characteristics. Then the refined features are input to stacked long‐short term memory modules for temporal feature exploration considering temporal correlations inherently in sequential noise signals and the parameters are output through fully connected layers. The proposed network is verified and analysed through impulsive noise datasets from acknowledged ocean impulsive models and real‐test ocean noise. Experimental results prove the advantages of proposed method in parameter estimation accuracy.