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.

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).

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.

Reconfigurable intelligent surfaces (RISs) have attracted a great deal of interest due to the potential contributions to the next-generation wireless networks. This letter proposes an enhancement to the physical layer security (PLS) of a multi-hop RIS-assisted underwater optical wireless communication (UOWC) system. Owing to the complexity of the underwater environment, a security-based adaptive RIS (SA-RIS) clustering strategy, which aims to reflect optical signals among clusters to improve the performance of the overall system, is evaluated. By combining the underwater channel model, the closed-form expressions of probability density function (PDF) and cumulative distribution function (CDF) are derived. Moreover, by increasing the numbers of RIS clusters, the performance metrics such as secrecy outage probability (SOP) and average secrecy capacity (ASC) are evaluated under different scenarios. The obtained results demonstrated that, in contrast to the case without preventing the eavesdropper, the proposed strategy in evasion scenarios could improve the SOP significantly. It can be concluded that the system secrecy performances are further improved by assigning different RIS clusters with proper channel quality.

The multi-stage noise shaping (MASH) ΣΔ ADC has a good potential to achieve high-order noise shaping (NS) and high resolution. However, it suffers from the quantization noise leakage issue caused by the mismatch between the analog NS loop filter and the digital cancellation filter, which greatly degrades the ADC performance. The sturdy MASH structure can solve the noise leakage issue, but it cannot be directly used for NS SAR ADCs due to the structural limitations. In this paper, we propose a sturdy MASH 2-0 NS SAR to solve the noise leakage issue. The 4th-order NS is achieved by only using a 2-0 topology, which is hardware efficient. Instead of eliminating the first-stage quantization error, the proposed sturdy MASH 2-0 NS SAR shapes the quantization error, achieving better robustness to PVT variables. Furthermore, owing to the enhanced residue gain-error-shaping (GES) technique and the first-stage 2nd-order NS capability, the impairments of the residue amplifier, including the gain error, noise, and settling inaccuracy, are 4th-order shaped. Our proposed NS SAR ADC with the enhanced GES technique is implemented in a 28 nm CMOS process, which achieves a SNDR of 84.8 dB and a SFDR of 99.2 dB.

A document by Matthew Chen. Click on the document to view its contents.

The kT/C cancellation technique can effectively reduce the DAC size for SAR ADCs and thus relax the burden for input drivers and ref-erence buffers. However, the prior kT/C cancellation technique suffers from a hard trade-off between the noise, amplifier bandwidth and linearity. In this work, an improved kT/C noise cancellation technique is proposed to break the trade-off. It uses presampling to hold the input signal unchanged during the noise cancelling phase, leading to sig-nificantly relaxed requirements on the bandwidth and linearity of the amplifier. The proposed technique is verified in a 14-bit 200MS/s SAR ADC with the DAC size of 128 fF. Simulation results show that this work achieves 75.6 dB SNDR and consumes 1.56 mW power.

The Local Color display technology utilizes a high refresh rate LCD open-cell without a color filter, and enables color adjustment through independent RGB backlight zones that are isolated by an Anti-Scatter Grid (ASG). Synchronized calibration between the open-cell and the backlight zones is achieved by aligning the backlight controller with the Vertical Clock Pulse (CPV) / Vertical Start Pulse (STV) signals in the open-cell gate driver, thereby determining the open-cell scan line position. After the synchronous calibration is completed, the backlight controller refreshes the corresponding RGB backlight zones whenever the open-cell scan line passes through them. The Local Color display technology can reduce the cost of open-cell by 20%, improve the resolution to 3x, improve the brightness to 8x, improve the color gamut to BT2020, and improve the grayscale to 32-bits.

Point cloud data is a large collection of high dimensional 3D points with 3D coordinates and attributes, which has been one of the mainstream representations for emerging 3D applications, such as virtual reality, autonomous vehicles and robotics. Due to the large-scale unstructured high-dimensional nature of point clouds, point cloud processing, transmitting and analysing has been challenging issues in multimedia signal processing and communication. Deep learning is a powerful tool to learn statistical knowledge from massive data. Advances in artificial intelligence, especially deep learning models are offering new opportunities for point cloud processing, compression and analysis. This special issue aims at promoting cutting-edge research on deep learning-based point cloud processing, including object detection, segmentation, registration, compression, and visual quality assessment.

Silent Speech Recognition (SSR) based on Surface Electromyography (sEMG) is a voice interaction technology proposed for scenarios requiring silent operations. In this article, we abstract the SSR task based on sEMG into a short-term image sequence classification task. We perform time-frequency domain feature extraction and data reconstruction on the muscle activity segment data. Additionally, we analyze the temporal and spatial dimensions to capture the intrinsic correlation representation of muscle activity. We propose the SVIT-SSR model based on the Vision Transformer (VIT) framework. Finally, we design experiments to identify 33 types of typical silent speech commands in the SSR dataset. The results demonstrate that the proposed model achieves an accuracy of 96.67±1.15%, outperforming similar algorithms.

We propose a two-mode OPS architecture using an optical multiport MDM DFT-based label generator and processor. By combining optical labels on two modes, we exponentially increase the number of labels. Beat noise effects significantly increase the label error rate, particularly when multimode crosstalk is maximum. This impairment can be mitigated by time shifting the optical labels of the two modes by half of the label length.

Outdoor haze images are typically degraded by noise due to the external environment and imaging equipment. The existing haze image enhancement methods ignore the interrelation between haze and noise, which cannot suppress the noise and remove the haze simultaneously . To address these intractable problems, a dual-branch architecture that combines dehazing and denoising is proposed in this paper to restore clear images. First, we adopt dark channel prior and unsupervised networks in the image dehazing branch to remove the image blur. Then, the image denoising branch removes the image noise in parallel by constructing a mean/extreme sampler and a self-supervised network. Finally, a CNN fusion strategy is presented to fuse output images from the aforementioned two branches to generate the final qualified results. Extensive experiments reveal that the proposed haze image enhancement method outperforms other state-of-the-art methods in terms of PSNR and SSIM.

Leveraging the ultra-wideband advantages of the Terahertz (THz) band, Integrated Sensing and Communication (ISAC) facilitates high-precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g., radar cross-section and Micro-Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analyzing the power angular delay profiles with and without human target, we observe the changes in quantity and power of multipath components (MPCs). Then, we propose a parameter called power control factor (PCF) to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, we extract the MPCs belonging to target channel within target-oriented power delay profile to count the power proportion of each bounce MPCs of the target-Rx link, which supports the necessity of multi-bounce (indirect) paths modeling in target channel.

In this study, a machine tool operating vibration prediction method based on multi-sensor fusion and long short-term memory (LSTM) network is proposed. Machine tool vibration has a significant impact on machining quality, workpiece surface roughness, dimensional accuracy, and tool’s wear. This study combines deep learning technology with industrial applications to achieve high-precision machine tool vibration prediction by fusing multiple sensor data. The real-time data is input into the LSTM model to predict the vibration situation at the next moment. The experimental results show that the method has strong prediction ability for the periodic vibration of the machine tool and the vibration error specific to the machining action. And it can effectively predict machine vibration and improve machining accuracy.