In remote sensing image segmentation, recognizing buildings is challenging when the visual evidence from pixels is weak or when buildings belong to small, spatially structured objects. To address this issue, we propose a structure-prior guided adaptive context selection network (SGACS-Net) for remote sensing semantic segmentation. The core is to use structure-prior knowledge to dynamically capture prior contextual information and higher-order object structural features, thereby improving the accuracy of remote sensing building segmentation. First, an adaptive context selection module is designed. By dynamically adjusting the spatial sensing field, this module effectively models the global long-range context information dependencies. It captures varying context information of buildings at different scales, thereby enhancing the network’s ability to extract building feature representations. Second, a structure-prior guided variable loss function is proposed. It utilizes the structural features of building points, lines, and surface to identify key regions. By leveraging advanced structure-prior knowledge, it enhances the network’s ability to express structural features. Experimental results on two datasets show that the proposed SGACS-Net outperforms other typical and state-of-the-art methods in terms of remote sensing semantic segmentation performance.

Attention-based Transformer model has achieved remarkable performance in various artificial intelligence fields, while the attention computation, being a combination of matrix multiplication and softmax function, is still sub-optimized in terms of hardware implementation. Normally, it needs 3 pass of input memory access to compute attention, and the on-chip storage requirement is coupled with the input length, both of which pose significant memory issues. Further, the computation burden is heavy for long inputs. This paper proposes an algorithm-hardware co-design for attention. On algorithm side, it uses a linear-softmax fused kernel to fuse the matrix multiplication and non-linear functions, which enables high on-chip memory source utilization. On hardware side, it shows an accelerator named BLADE with identical partial product removing, which eliminates unnecessary computation by exploiting math feature of softmax. Experiment on ViT, Swin Transformer, GPT-2 and LLaMA2 show that the proposed design achieves 10.6% - 18.7% of energy efficiency improvement compared to state-of-the-art Flash Attention implementations.

The time step functions as a crucial temporal unit for simulating neuronal dynamics within spiking neural networks, which play a significant role in neuromorphic computing systems. Efficient management of these time steps is vital to ensure model accuracy while optimizing overall system performance. As system scale increases, variations in hardware across subsystems and their asynchronous operations create challenges in achieving effective time step control. To address this issue, this paper proposes an innovative framework for managing time steps in large-scale neuromorphic systems. This framework allows subsystems to dynamically adjust their time step lengths according to computational loads and to perform look-ahead computations. Such a strategy effectively reduces the overhead related to time step synchronization, enhancing system efficiency. Additionally, the paper introduces a safeguard mechanism to ensure the system’s reliability. Experimental results indicate that the proposed framework sustains the correct long-term operation of the system and improves model execution performance by 8.88% to 27.15% when compared to existing methods.

A low-cost high-responsivity design of Si/SiGe Heterojunction Bipolar Phototransistor (HPT) built in an industrial 55nm-BiCMOS technology is integrated in a cascode configuration with a bipolar bipolar transistor (HBT) to be used for microwave-photonic communication receivers. Performances of both the cascode pair and the single Heterojunction Bipolar Phototransistor are compared. At high-frequency, the cascode circuit has better performances than the single HPT stage. At 850-nm wavelength, the pair can reach a low-frequency responsivity a low-frequency responsivity of 3.24 A/W and a bandwidth of 0.69 GHz when coupling losses are corrected.

Inverse Synthetic Aperture Radar (ISAR) is a crucial radar imaging technique that relies on the relative motion between the radar and the target to produce high-resolution images. However, traditional ISAR methods are highly sensitive to inaccuracies in estimating rotational parameters, such as roll, pitch, and yaw. Errors in these estimates can result in significant image degradation. In this paper, we present a novel approach for imaging dynamically rotating scenes using a multistatic ISAR setup, where we estimate motion parameters through a Differential Semblance Optimisation (DSO) criterion. This is done by minimising discrepancies between images formed from multiple transmitter-receiver pairs, our method delivers focused images with improved estimates of the yaw rotation parameter of the targets. We demonstrate the effectiveness of this approach through a series of numerical experiments, highlighting its robustness in both noise-free and noisy environments, and discuss its potential for enhancing ISAR imaging in complex scenarios.

Fluid antenna multiple access (FAMA) is a concept capable of massive connectivity on the same physical channel without the need of precoding or interference cancellation, by exploiting a super-high dimensional received signal in the spatial domain from fluid antenna system (FAS). This letter investigates the use of 5G New Radio (NR) Modulation Coding Scheme (MCS) for FAMA systems to improve its connectivity over block fading channels. In particular, we propose an iterative decoding receiver with iterative interference covariance estimation for the coded FAMA system. Extrinsic information transfer (EXIT) chart is used to analyze the performance of the proposed iterative receiver. Both EXIT chart analysis and numerical results show that the proposed receiver outperforms the one that uses fixed covariance in the block fading channel. Also, the results indicate that coded FAMA can serve 100 user terminals (UTs) at the rate of 0.5 bit/s/Hz under rich scattering.

Phased array antennas, with high directivity and low sidelobe levels, are extensively employed in radar and communication systems. As the array scale increases, the complexity and cost of controlling each element individually are increased significantly. To overcome this challenge and improve the efficiency, one of the effective engineering solutions is subarray partitioning. Traditional clustering algorithms, such as K-means, require a predefined number of subarrays, and limits the flexibility, especially in scenarios with complex geometries or irregular distributions. A subarray partitioning approach is presented in this paper using the Iterative Self-Organizing Data Analysis (ISODATA) algorithm, and the proposed scheme dynamically adjusts the number of subarrays based on the geometric characteristics of the array. According to numerical experiments results, good performance of sidelobe suppression, beamforming and partitioning flexibility have been achieved, and shows better characteristic compared with the K-means. This algorithm has potential application in large-scale phased arrays.

In this letter, a beam downtilt reconfigurable linear antenna array is presented for the demand of the electrical downtilt of 5G/6G macro base station antennas. The antenna is capable of fine-angle beam direction reconfiguration, allowing for improved adaptability in various environmental conditions. A hierarchical network design is employed, which incorporates essential elements such as phase shifter modules, power dividers, and couplers. These fundamental components are used to construct a five-element reconfigurable feeding network for the antenna array. The designed network ensures stable phase shifts and excellent impedance matching performance. By integrating the feeding network with the antenna elements, the system achieves electronic beam tilt adjustment across four states, enabling the linear array to tilt downward within a range of 6 - 12° around 3.5 GHz. This design provides enhanced beam control, making it suitable for next-generation macro base station applications in 5G and 6G networks.

A novel approach is proposed in this paper to reconstruct the far-field radiation pattern from the phaseless electric field of an antenna scanned on a single near-field sphere. It adopts the dipole equivalence approach to project the near-field electric field into a spherically distributed array of electric dipoles. We introduce a term representing the residue from the linearly correlated portion in the least-square problem associated with the dipole equivalence, namely the linear correlation residue (LCR). It is demonstrated that by iterative search to minimize the LCR using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), near-field phase distributions can be found efficiently from the magnitude-only near-field, and the far-field radiation pattern can be computed. Two representative case studies are given here to validate the proposed method. Results demonstrate good agreements between computations and simulations.

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.

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.

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).

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.

The detection of defects on the copper chain in the production process of tobacco cutters is crucial for ensuring product quality. Traditional defect detection methods often rely on spatial domain image analysis, which not only has a large computational load but also performs poorly in handling high-frequency noise and complex backgrounds. To address this issue, this paper proposes a novel neural network model based on frequency domain analysis, called Frequency Domain Attention Network (FDANet). This network first utilizes Discrete Cosine Transform (DCT) to transform the image from the spatial domain to the frequency domain, effectively reducing computational complexity and improving processing speed. Subsequently, through the innovative Frequency Domain Attention Module (FDAM), the network automatically identifies and enhances key discriminative features in the frequency domain, thereby strengthening the model’s ability to identify defects. Finally, the frequency domain attention map, after feature extraction and integration, is inputted into the coupling detection head to achieve high-precision defect detection. Experimental results demonstrate that FDANet performs excellently in the task of detecting copper chain defects in tobacco cutters, showing significant improvement compared to traditional methods and verifying the effectiveness and practicality of the proposed approach.