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This letter presents a planar inverted-F antenna (PIFAs) system for Wireless Fidelity 6 (Wi-Fi 6) and Wi-Fi 6E applications with compact size and high isolation. To realize decoupling in a guided way, single-mode structure is proposed based on common mode and differential mode theory. For the demonstration, the mutual coupling between two back-to-back asymmetric PIFAs was reduced by etching two slots on the ground. The measured results show that the two PIFAs, respectively, achieve the impedance bandwidth of 5.14–6.03 GHz and 5.90–7.24 GHz, and the port isolation higher than 17.4 dB across 5.14–7.24 GHz frequency band. [ABSTRACT FROM AUTHOR]

In this paper, we derive an upper bound on load-threshold of coded slotted ALOHA (CSA) for slot erasure channel (SEC) and that for packet erasure channel (PEC), respectively. In SEC, by exploiting the analogy between Gaussian elimination of solving linear equations and the iterative successive interference-cancellation (SIC) decoding procedure of CSA, we derive the upper performance bound for SEC. Subsequently, in PEC, utilizing density evolution to track the average probability of packet-decoded failure within the SIC decoding process, we obtain the upper bound for PEC. Moreover, we propose a numerical search algorithm of these bounds under a given calculating precision. The numerical results indicate that the asymptotic upper limit on load-threshold of CSA over SEC is always less than that over PEC with any given erasure probability and average transmission rate. [ABSTRACT FROM AUTHOR]

A simple, high gain, and dual-polarized patch antenna array for Wi-Fi 6/6E application is proposed in this communication. Different from the conventional feeding method, the patches are driven by coupling dipoles that are printed on the same board. The dipoles are specially designed to be T-shaped to adjust the impedance matching. Broadband covering both 5 and 6 GHz bands of Wi-Fi 6/6E is achieved. The feeding circuit is easily integrated on the board with only parallel dual line utilized. A prototype is fabricated and tested to validate the concept. The measured and simulated results are in good agreement. The measured bandwidth with $S_{11} < -10$ dB for two ports are 5.12–7.50 and 5.15–7.50 GHz with the isolation higher than 30 dB. Moreover, the measured gains are higher than 9.76 and 9.61 dBi for two ports over the operating band with a peak value of 12.05 dBi. The proposed antenna array shows great potential in the application of Wi-Fi mesh. [ABSTRACT FROM AUTHOR]

In this communication, we present a longitudinally slotted substrate integrated slot (SIS) leaky-wave antenna (LWA) for point-to-point communication. To mitigate the dispersion of traditional substrate integrated waveguide (SIW) LWAs, an SIS is proposed by etching a long straight slot along the SIW. The loaded slot mitigates the dispersion of SIW due to the special electric field distribution. Anti-TE10 mode is excited inside the SIS. Then, longitudinal slots are symmetrically loaded on both sides of the long slot. The prototype of SIS LWA is validated by the simulated and experimental results. A frequency bandwidth of 10.3% is realized with the beam squinting within 7° and realized gain of 13.9 ± 0.7 dB at a fixed angle of −7°. The proposed antenna is intended for point-to-point communication. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a common and severe psychiatric illness marked by loss of interest and low energy, which result in the highest burden of disability among all mental disorders. Clinical MDD diagnosis still utilizes the phenomenological approach of syndrome-based interview, which leads to a high rate of misdiagnosis. Therefore, it is highly imperative to explore effective biomarkers to enable precise personalized diagnosis. There still exist two main challenges due to complexity of MDD and individual differences. On the one hand, discriminative features need to be investigated to better reflect the characteristics of MDD. On the other hand, the performance from shallow and static learning models is still not satisfactory. To overcome these issues, we propose a novel Multi-Stage Graph Fusion Networks (MSGFN) for major depressive disorder diagnosis. At first, functional connectivity is calculated to better characterize interactions between white matter and gray matter. Second, multi-stage features are obtained by a deep subspace learning model, and a number of graphs are constructed under the self-expression constraints at each stage. Finally, a novel graph convolutional fusion module is proposed with graph convolutional operations to integrate features and graph at each stage. Extensive experiments demonstrate the superior performance of the proposed framework. Our source code is available on: https://github.com/LIST-KONG/MSGFN-master. [ABSTRACT FROM AUTHOR]

To solve the motion planning problems of redundant manipulators disturbed by the periodic noise from device hardwares or their surroundings, a Taylor-type discrete-time circadian rhythms neural network (TD-CRNN) method is proposed, developed, and studied in this article. First, a representative bicriteria optimization scheme combining torque criterion and acceleration criterion is presented for the redundant manipulator. Second, inspired by a continuous-time circadian rhythms model, the corresponding TD-CRNN model is derived based on the Taylor discrete formulation. Third, the 0-stability, convergence, and consistency of the proposed TD-CRNN model are analyzed theoretically and proved strictly. Finally, to confirm the capacity of the resisting periodic noise in the tracking problem of manipulators, two groups of comparative simulations and experiments conducted by the proposed TD-CRNN model are performed before the conclusion is given. [ABSTRACT FROM AUTHOR]

To realize the robust control of multirotor unmanned aerial vehicle (UAV) systems, adaptive multilayer neural dynamics (AMND) controllers are proposed and analyzed. The proposed AMND controllers with the strong anti-perturbation property can drive multirotor UAVs to track time-varying tasks and deal with parameter uncertainty problems. First, the design method of the general multilayer neural dynamics (MLND) controllers is introduced and analyzed. Second, based on the design method, the attitude angles, height, and position controllers of a UAV system are designed. Third, according to the adaptive control theory, a novel AMND controller is designed, which can self-tune the parameters of the UAV. Finally, the proposed AMND method applies to a real-world hexrotor UAV system to illustrate its reliability. Mathematical analysis, computer simulations, and experiments verify the reliability, stability, and effectiveness of the proposed controllers which are used to track time-varying tasks. [ABSTRACT FROM AUTHOR]

Many scientific research and engineering problems can be converted to time-varying quadratic programming (TVQP) problems with constraints. Thus, TVQP problem solving plays an important role in practical applications. Many existing neural networks, such as the gradient neural network (GNN) or zeroing neural network (ZNN), were designed to solve TVQP problems, but the convergent rate is limited. The recent varying-parameter convergent-differential neural network (VP-CDNN) can accelerate the convergent rate, but it can only solve the equality-constrained problem. To remedy this deficiency, a novel barrier varying-parameter dynamic learning network (BVDLN) is proposed and designed, which can solve the equality-, inequality-, and bound-constrained problem. Specifically, the constrained TVQP problem is first converted into a matrix equation. Second, based on the modified Karush–Kuhn–Tucker (KKT) conditions and varying-parameter neural dynamic design method, the BVDLN model is conducted. The superiorities of the proposed BVDLN model can solve multiple-constrained TVQP problems, and the convergent rate can achieve superexponentially convergence. Comparative simulative experiments verify that the proposed BVDLN is more effective and more accurate. Finally, the proposed BVDLN is applied to solve a robot motion planning problems, which verifies the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

In this article, the repetitive motion control of redundant manipulators is investigated. First, a repetitive motion control scheme is presented, and a continuous zeroing neural network (CZNN) model is obtained for solving the scheme. Meanwhile, the development of a discrete zeroing neural network (DZNN) model is desired for convenient computational processing. Based on this, this article proposes a 6-step discretization formula, which has high precision. By using the 6-step discretization formula and the 4-step backward difference formula, a 6-step DZNN (6SDZNN) model is further proposed to handle the repetitive motion control scheme. Theoretical analyses verify the efficacy of the 6SDZNN model. Additionally, some discrete forms of conventional models are developed for comparison. Computer simulations on the basis of the 4-link redundant manipulator are carried out, verifying the theoretical analyses and showing the efficacy of the 6SDZNN model. Finally, physical experiments on the basis of the Kinova Jaco2 manipulator substantiate the practicability of the 6SDZNN model. [ABSTRACT FROM AUTHOR]

As for time-varying tracking control problems, many neural-dynamics-based control methods have been proposed because of their high efficiency. The varying-parameter convergent neural dynamics design method with the characteristic of super-exponential convergence has been applied to design controllers for unmanned aerial vehicles. Although the varying-parameter convergent neural dynamics controller has a fast convergence speed, it still needs long enough time to achieve tracking theoretically. By combining the finite-time activation function with the varying-parameter convergent neural dynamics design method, a finite-time-gain-adjustment design method is proposed and proved theoretically in this paper. This controller can make state variables of the system converge to their time-varying targets in finite time. Compared with some traditional methods, contrastive experiments and application to a multi-rotor unmanned aerial vehicle system illustrate that the proposed controller has finite convergence time, better anti-noise performance, and faster convergence speed, which enable the multi-rotor unmanned aerial vehicles to track time-varying targets more quickly and accurately, so as to achieve more complex and efficient control tasks. [ABSTRACT FROM AUTHOR]