Internet of Things (IoT) are employed in a variety of applications, leading to a significant demand for advanced antenna systems that can perform multiple distinct tasks from a single, compact hardware platform. In response to this demand, we propose a unified synthesis framework for reconfigurable sparse arrays based on a min-max beampattern matching metric. This framework integrates null/notch constraints, common excitation magnitudes, and explicit dynamic range ratio (DRR) bounds. Our approach distinguishes itself from existing research by addressing the impact of constrained DRR on excitation magnitudes, which is crucial for mitigating mutual coupling and simplifying feed networks. To solve the resultant nonconvex and nonlinear optimization problem, we introduce a Boolean selection vector, transforming the antenna position optimization problem into an antenna selection optimization problem. We then employ alternating minimization to decouple the antenna selection vector from the excitation vector. For each subproblem related to the weight vector and the antenna selection vector, we utilize successive convex approximation to derive a convex surrogate function for the nonconvex components of the subproblems. To ensure the feasibility of the transformed problem, we further introduce slack variables. Numerical experiments demonstrate that the developed approach facilitates precise control of multiple patterns with hardware-efficient sparse configurations for dynamic electromagnetic operations.

Magnetic Particle Imaging (MPI) enables noninvasive temperature imaging without depth limitations. However, due to the lack of effective calibration strategies that can simultaneously address issues such as calibration infeasibility and environmental mismatch, its practical in vivo application remains challenging. In this work, we propose a novel in vivo temperature imaging method based on a dual-mode magnetic particle spectroscopy/magnetic particle imaging (MPS/MPI) system. First, MPS is employed to capture the differences in harmonic phase responses of magnetic nanoparticles (MNPs) under in vivo and in vitro conditions, thereby enabling the construction of calibration functions that are consistent with the in vivo environment. Second, an MLP based calibration strategy is proposed, which accounts for non-ideal deviations from the approximately linear temperature-phase relationship and integrates multi-parameter information into a unified network, thereby enabling accurate and stable temperature mapping. Comprehensive simulation, in vitro, and in vivo experiments demonstrate that, compared with conventional phantom-based temperature mapping methods, the proposed method reduces the in vivo temperature reconstruction error by approximately 17.24% and achieves an average absolute temperature error below $1.257~^{\circ } $ C. These results verify the feasibility of accurate in vivo temperature imaging using MPI and provide essential technical support for temperature-sensitive applications, including magnetic hyperthermia.

Recently, Magnetic Particle Imaging, an emerging functional imaging modality, has exhibited outstanding spatial-temporal resolution and sensitivity. The general reconstruction pipeline of Magnetic Particle Imaging involves calibrating a System Matrix and then solving an ill-posed inverse problem combined with the measured particle signals. However, the introduction of noise during the System Matrix calibration procedure is inevitable, which degrades the detailed information in the reconstructed images. Therefore, frequency selection methods based on signal-to-noise ratio are commonly adopted. However, these methods lead to a decrease in the available high-frequency components, which damages the spatial resolution. To address this problem, we propose an unsupervised memory-guided denoising framework with unpaired noisy-clean System Matrix components, called U-N2C. Specifically, we design a Pattern Memory Block to memorize System Matrix patterns, directed by a position-aware frequency index embedding. Meanwhile, we devise a Noise Memory Block to implicitly approximate noise distributions. With the guidance of our dual memory blocks, we can disentangle the noise and content of the System Matrix in the latent space. Furthermore, benefiting from the ability to model complex noise, our method can generate pseudo but high-quality noisy-clean pairs and further enhance our denoising capability. Experiments on both synthetic and real noise demonstrate that our U-N2C achieves cutting-edge performance compared to other methods. Moreover, we conduct extensive qualitative and quantitative ablation studies to verify the effectiveness of our method. Our code has been available at U-N2C.

As high-performance computing clusters (HPCC) scale up and single-node computing power improves, the bottle-neck of large-scale models training is shifting from computation to network communication. Datacenter Quantized Congestion Notification (DCQCN), as the most widely used congestion control algorithm in lossless Remote Direct Memory Access (RDMA) networks today, provides HPCC with low-latency, high-bandwidth, and high-quality network services. However, DCQCN has more than ten adjustable parameters on RDMA-enabled network inter-face cards (RNICs) and switches, and currently, relying on expert experience to adjust these parameters in engineering is inefficient. Additionally, even minor increases in single-iteration time during training can lead to significant resource wastage. Therefore, after conducting an in-depth analysis of different heuristic search algorithms and loss functions, we propose Megatuner—an offline DCQCN parameters tuner tailored for the traffic pattern of large-scale models. We conducted traffic simulations on the collective communications at the foundation of large-scale models, testing both on simulation platforms and in real environments. The results show that parameters tuned by Megatuner significantly outperform those set based on expert experience. We also addresses the issue of bandwidth degradation in real environments, ensuring long-term optimization of network performance.

RDMA is a kernel-bypass and transport-offload technology that provides high throughput and low delay for datacenter networks, and DCQCN is the default and most widely used congestion control algorithm in large-scale RDMA networks. DCQCN involves over 10 parameters at RNICs and switches, and their settings significantly affect network performance, currently relying heavily on exhaustive manual tuning. Although some automatic methods are proposed to tune a subset of DCQCN parameters, none of them comprehensively address all parameters at both RNICs and switches, resulting in compromised network performance. In this paper, we propose Paraleon, an automatic and adaptive system to tune DCQCN parameters comprehensively. We design a millisecond-level sketch-based monitoring mechanism for accurate network-wide measurement, which collects runtime metrics as feedback to guide the tuning process. We also analyze the complicated parameter impacts on network performance, and leverage an improved heuristic searching algorithm for timely performance optimization with better efficiency and convergence. We implement Paraleon and conduct extensive experiments in both NS3 simulations and a real-world testbed. The results show that Paraleon achieves$3.8 \% \sim 61.4 \%$higher performance than existing tuning schemes.

To tackle the issue of space-time adaptive processing (STAP) performance degradation caused by inaccurate estimation of the clutter covariance matrix (CCM) with limited sample support, a knowledge-aided (KA) CCM estimation algorithm based on the symmetric alternating direction method of multiplier (KA-SADMM) is proposed. The CCM estimation problem is constructed based on the knowledge of persymmetric structure, the low-rank structure, and the prior covariance matrix, and the solution to the resulting problem is derived. Moreover, the contraction property of the sequence generated by KA-SADMM with respect to the solution set of the estimation problem is analyzed. With limited training samples, the simulation results show that the proposed algorithm improves the STAP performance over other similar algorithms by at least about 0.2 dB when the prior knowledge is accurate and by at least 0.3 dB when the prior knowledge is inaccurate.

Due to the freedom degree loss resulted from the unimodular constraints, it is not easy to specify proper and feasible stopband and passband levels for frequency grids of interest in spectrally constrained sequence design problems. In an attempt to avoid this difficulty, we devise a cost function that minimizes the ratio of the maximal stopband level to the minimal passband level. Next, we introduce auxiliary variables to simplify the optimization problem via decoupling the numerator and denominator. Then, the feasible solution is obtained by approximating the nondifferentiable objective function with a smooth function. We also apply an acceleration scheme to increase the algorithm convergence speed. The effectiveness of the proposed approach is demonstrated via numerical examples.

We derive a phase retrieval algorithm using the alternating direction method of multipliers. For the cost function obtained from the maximum likelihood criterion, we introduce auxiliary amplitude and phase variables to avoid the absolute value operator and decouple the determination of the auxiliary phase variables from that of the auxiliary amplitude variables. As a result, a phase retrieval algorithm consisting only of two least-squares steps is derived. The performance of the proposed algorithm is investigated via numerical examples, as well as an application to flat-spectrum periodic unimodular sequence design.

In order to increase the star identification speed and recognition rate of star sensor, an improved rapid star identification method based on neural network (NN) is presented. The proposed method is composed of three levels, including the coarse classification of navigation stars, star pattern recognition based on NN and the final validation of recognition results. The angular distance of characteristic triangle is employed for coarse classification to active the subnets for star pattern recognition. Then the star pattern obtained by the grid method for the main star is sent to the corresponding subnets for the star pattern identification. At last, the identification results of the active subnets are validated to obtain the only recognition result. The experimental results show that, compared with traditional triangle identification method, the proposed method has higher accurate recognition rate, lower redundancy and better robustness.