AbstractAiming at solving the problem that the capability of direction of arrival method for estimation of nested arrays decreases sharply under unknown mutual coupling, an iterative weighted low‐rank matrix reconstruction algorithm is proposed here. First, the received data covariance matrix is extended, and a weighted low‐rank matrix recovery problem is formulated for joint estimations of the Toeplitz matrix and mutual coupling coefficients. Next, the direction of arrival from recovered Toeplitz matrix is retrieved by using the root multiple signal classification algorithm. Simulation results show that iterative weighted low‐rank matrix reconstruction algorithm can effectively avoid the influence of mutual coupling while solving the grid‐mismatch problem so that the accuracy and the resolution of estimation are improved.

AbstractThis letter addresses the output consensus problem for a class of two‐dimensional (2D) multi agent systems (MASs) described by Fornasini–Marchesini model. By transforming the 2D agent into a compact form, an adaptive variable, which adjusted by the tracking errors of itself and the neighbour agents, is designed to approximate the unknown varying coefficient. Then, based on the approximated coefficient and the iteration‐varying reference surfaces, the distributed adaptive iterative learning control strategy is obtained. The output consensus of the 2D multi agent is proved. Simulations are included to verify the effectiveness of the investigated distributed adaptive ILC for 2D MASs with random variations on initial condition and reference surface.

AbstractThe authors report a novel structure to improve the high‐frequency performance of nitrogen‐polar GaN deep‐recess high electron mobility transistors (HEMTs) wherein a timed plasma etch was used to tailor the profile of the gate dielectric. A favourable trade‐off between gate fringing capacitance and transconductance was established, resulting in an improved current gain cutoff frequency times gate length (ft·LG) figure of merit compared to standard Schottky‐gate and metal‐insulator‐semiconductor (MIS) HEMTs. The etched gate dielectric MISHEMT demonstrated a maximum ft·LG of 15.0 GHz·µm for an LG of 100 nm.

AbstractA graphene field‐effect transistor (GFET) based on chemical vapour deposited (CVD) Ge‐based graphene was reported and the low‐temperature electrical characteristics primarily investigated. The self‐alignment technique was used to fabricate a GFET to reduce parasitic effects and improve transconductance and cut‐off frequency. To further explore the electrical properties, the direct current and radio frequency characteristics of the GFET were studied over a temperature range from 4.2 to 300 K, considering the temperature‐dependent resistivity of intrinsic Ge. The direct current characteristic of the GFET for 110‐nm gate length, particularly the transconductance performance, exhibits a tiny variation of only 5% across this temperature range. However, the cut‐off frequency experiences a considerable increase, improving several tens of times when the temperature decreases to 4.2 K, with a maximum value of 3.49 GHz. This work illustrates a meaningful advancement in applying GFETs in the low‐temperature, high‐frequency domain.

AbstractMeasuring heart rate is a critical component of assessing cardiac health and detecting potential heart diseases at an early stage. Various sensors, including electrocardiogram and photoplethysmograph, are commonly employed for this purpose. However, these conventional methods necessitate direct contact with the patient's skin which may be impractical or uncomfortable in situations involving patients with skin diseases or burn injuries. To address these limitations, a concerted effort has been to develop non‐contact methods leveraging frequency‐modulated continuous‐wave multiple‐input multiple‐output radar systems. Recent studies have illustrated the sensitivity of the phase component of received signals to micro‐motion, presenting a promising avenue for effective heart rate estimation. However, existing literature predominantly focuses on single‐antenna setups, overlooking the potential benefits offered by multiple‐input multiple‐output systems, which provide diverse channels with varying precision levels. This correspondence introduces a novel phase extraction model grounded in the argument of the analytic signal derived from both in‐phase and quadrature channels. Furthermore, leveraging the normal equation, we establish the feasibility of optimizing weights assigned to individual virtual antennas to achieve a robust approximation of ground truth data.

AbstractThree‐phase ac choppers feature output voltage amplitude controllability and enable more compact system realizations compared to autotransformers. For the practical realization advantageously standard power transistor with unipolar voltage blocking capability such as MOSFETs can be employed as a naturally resulting offset voltage between the grid and the input‐stage starpoint ensures purely positive power transistor voltages. This offset voltage is, however, not strictly defined and may drift to higher voltage values, resulting in high power transistor voltage stresses and finally a potential overvoltage breakdown. Traditionally, the offset voltage drift is prevented by introducing discharge resistors across the input‐stage capacitors which, however, results in substantial ohmic losses. This paper analyzes the offset voltage formation in ac choppers and proposes a novel clamping modulation scheme which ensures a strictly defined and minimum time‐varying offset voltage without need for discharge resistors. Theoretical analyses and circuit simulations are finally experimentally verified with a 400 V (rms, line‐to‐line) 50 Hz grid connected three‐phase buck‐boost ac chopper with 3 kW rated power.

AbstractAn improved model predictive control (MPC) with Luenberger state observer is proposed in this article for LC‐coupling hybrid active power filter (LC‐HAPF). Traditionally, the implementation of MPC for LC‐HAPF requires additional sensors to measure coupling capacitor voltage, thereby increases the system cost and reduces reliability. The existence of one‐step sampling delay in practice will also affect the final compensation performance. To relax these problems, both continuous and discrete models are deduced first. Then, the Luenberger observer is utilized based on the discrete model to mitigate the steady‐state error without additional sensors. In addition, the sampling delay compensation is provided by improving the prediction horizon. Finally, the effectiveness of the improved MPC for LC‐HAPF is verified by experiment results, indicating that the proposed method has fast dynamic response and low steady‐state error.

AbstractDetecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies.

AbstractIn recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%.