This brief considers distributed Kalman filtering problem for systems with sensor faults. A trust-based classification fusion strategy is proposed to resist against sensor faults. First, the local sensors collect measurements and then update their state estimations and estimation error covariance matrices. Then, sensors exchange the information (state estimations and estimation error covariance matrices) with their neighboring sensors. After obtaining the estimation information from neighboring sensors, an iterative classification/clustering algorithm, which contains three steps ( Initialization Step , Assignment Step , and Update Step ), is proposed to classify the collected estimations into two clusters (trusted and untrusted clusters). Third, the fused states and error covariance matrices are computed by Wasserstein average algorithm. Finally, the time update is performed on the basis of fusion information. Stability and convergence of the proposed filter are analyzed. A target tracking simulation example is provided to verify the effectiveness of the proposed distributed filter in a wireless sensor network.

In this letter, a broadband self-interference (SI) suppression structure is presented for collinear dipoles. By using this structure, an average SI improvement of 21.6 dB is realized within the frequency band of 2.2 - 2.7 GHz. It has a 14.8% operating bandwidth of transmit-receive isolation (TRI) higher than 50dB. The working mechanism and bandwidth extension are discussed. Moreover, the key parameters and influence of the proposed structure to the dipoles were also studied and presented. Measured results demonstrate that this design provides wider operating bandwidth, omnidirectional radiation patterns and ideal distribution distance. It is effective and suitable for reducing the SI between collinear dipoles.

This brief considers Kalman filter for linear systems with unknown structural parameters. We design a Bayesian parameter identification algorithm based on maximum likelihood (ML) criterion and expectation maximization (EM). Under the identification of structural parameter, we perform the Kalman filter to estimate the states. The Kalman filter and the parameter identification algorithm are interactive to estimate the states and the structural parameters. More specifically, first, we use EM algorithm together with Kalman smoother to estimate the structural parameters. Then, on the basis of results provided by the first step, we employ the classical Kalman filter to predict and update the states, which formulates the final proposed Kalman filter. Performance analysis and simulation results verify the presented filter.

The number of Printed Circuit Board (PCB) layers is continually increasing with the increase in data transmission rates, and the Signal Integrity (SI) of high-speed digital systems cannot be ignored. Introducing Vertical Interconnect Accesses (VIAs) in PCBs can realize the electrical connection between the top layer and the inner layers, however, VIAs represent one of the most important reasons for discontinuity between the PCBs and package. In this paper, a new optimization scheme for a differential VIA stub is proposed, with 3D full-wave numerical simulation used for modeling and simulation. Results show that this scheme optimizes the return loss and insertion loss while making the signal eye diagram more ideal, which can improve the transmission effect of high-speed signals.

This brief considers robust Kalman filtering problem for linear discrete-time systems with convex polytopic uncertain parameters. First, we characterize the uncertain parameter matrices as a combination of several vertex matrices. Then, based on the mean square stability and H2 and variance-constrained performance criteria, we design a variance-constrained based robust Kalman filter. The parametric matrices of the filter can be directly obtained by the variance-constrained optimization and Matlab Toolbox. A classical instance is provided to verify the effectiveness of the proposed filter.

Metasurface biosensing confronts a significant challenge in simultaneously achieving broadband response, high quality-factor (Q-factor), and ultrahigh sensitivity for specific trace-analyte detection at terahertz (THz) frequencies. Recently, quasi-bound states in the continuum (QBICs) metasurfaces provided enhanced light–matter interactions and ultrahigh sensitivity in narrow resonant bands. In this work, an angle-scanning QBIC metasurface array pixelated with just 5 × 5 scaling units is proposed to achieve an ultra-broad spectrum from 1 to 2.8 THz for fingerprint bio-detection. The symmetry-protected QBIC is excited by breaking the symmetry of copper block dimer resonator structures, achieving a Q-factor of 20 and a sensitivity of 500 GHz/RIU. A spectral step of approximately 10 GHz is demonstrated in this approach, and glutamic acid and glutamine are specifically detected, with detection limits reaching 15.4 μg/cm² and 14.7 μg/cm². This design provides a novel approach for achieving ultra-wideband, specific, and highly sensitive detection. This capability offers an efficient strategy for monitoring tumor metabolic biomarkers and paves the way for applications in early diagnosis and advanced broadband THz detection.