The design and test of dual/triple‐mode bandpass filter based on temporal coupled‐mode theory

In this article, the design and test of both a K‐band dual‐mode bandpass filter (BPF) pair and a K‐band triple‐mode BPF are presented based on N‐mode temporal coupled‐mode theory (CMT). The expressions of transmission and reflection responses are analytically derived. All the parameters in the expressions have clear physical meanings and are easily optimized to reach the required filter performance. Aided by eigenmode simulations, concrete structures of the three integrated BPFs are designed and optimized to approach the calculated physical parameters. After the fabrications and measurements of the three BPFs, extended upper/lower stopband with high stopband rejections are achieved, and by increasing the number of resonant modes, improved frequency selectivity and better passband flatness are obtained. The analytical analysis well predicts the simulation and measurement results, which provides an efficient way for BPF designs. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:609–622, 2016.     

Annular ring slot antenna design with reconfigurable polarization

A microstrip‐fed conventional annular ring slot (ARS) antenna with linear polarization (LP) is initially studied. To generate two orthogonal degenerate modes for circular polarization (CP) radiations, two identical meandering perturbation slots (MPS) are symmetrically loaded into the ARS. By further incorporating a PIN diode switch across each MPS, the proposed antenna can switch between left‐hand CP (LHCP), right‐hand CP (RHCP), and LP. All three polarizations have shown good impedance bandwidth and broad CP bandwidth that can satisfy the wireless local area network (WLAN) 2.4‐GHz operating band (2400–2480 MHz). Furthermore, desirable gains of 1.8–2.0 dBi and 2.40–2.84 dBic are also exhibited at LP and LHCP/RHCP, respectively. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:110–120, 2016.     

Robust Input Covariance Constraint Control for Uncertain Polytopic Systems

In this paper, the robust input covariance constraint (ICC) control problem with polytopic uncertainty is solved using convex optimization with linear matrix inequality (LMI) approach. The ICC control problem is an optimal control problem that optimizes the output performance subjected to multiple constraints on the input covariance matrices. This control problem has significant practical implications when hard constraints need to be satisfied on control actuators. The contribution of this paper is the characterization of the control synthesis LMIs used to solve the robust ICC control problem for polytopic uncertain systems. Both continuous‐ and discrete‐time systems are considered. Parameter‐dependent and independent Lyapunov functions have been used for robust ICC controller synthesis. Numerical design examples are presented to illustrate the effectiveness of the proposed approach.     

Robust Adaptive Neural Control for a Class of Time‐Varying Delay Systems with Backlash‐like Hysteresis Input

This paper proposes a robust adaptive dynamic surface control (DSC) scheme for a class of time‐varying delay systems with backlash‐like hysteresis input. The main features of the proposed DSC method are that 1) by using a transformation function, the prescribed transient performance of the tracking error can be guaranteed; 2) by estimating the norm of the unknown weighted vector of the neural network, the computational burden can be greatly reduced; 3) by using the DSC method, the explosion of complexity problem is eliminated. It is proved that the proposed scheme guarantees all the closed‐loop signals being uniformly ultimately bounded. The simulation results show the validity of the proposed control scheme.     

Formation stabilization and resizing based on the control of inter‐agent distances

We propose a control strategy that could steer the group of mobile agents in the plane to achieve a specified formation. The control law could be implemented in a fully decentralized manner so that each agent moves on their own local reference frame. Under the acyclic minimally persistent graph topology, each agent measures the relative displacements of neighboring agents and then adjusts the distances between them to achieve the desired formation. As well as achieving a fixed formation, we could resize the formation only by changing the leader edge, which connects the leader with the first‐follower in acyclic minimally persistent graph, without changing the structures of the control law. Copyright © 2014 John Wiley & Sons, Ltd.     

Less conservative stability criteria for linear systems with interval time‐varying delays

The problem of the stability of a linear system with an interval time‐varying delay is investigated. A new Lyapunov–Krasovskii functional that fully uses information about the lower bound of the time‐varying delay is constructed to derive new stability criteria. It is proved that the proposed Lyapunov–Krasovskii functional can lead to less conservative results than some existing ones. Based on the proposed Lyapunov–Krasovskii functional, two stability conditions are developed using two different methods to estimate Lyapunov–Krasovskii functional's derivative. Two numerical examples are given to illustrate that the two stability conditions are complementary and yield a larger maximum upper bound of the time‐varying delay than some existing results. Copyright © 2013 John Wiley & Sons, Ltd.     

Performance of estimating depth in projection based stereoscopic virtual display

Accurate distance estimation is essential for effective user interaction with objects appearing in the virtual space. Research has shown that the distance estimation in the virtual space is not as accurate as that in the real world. The inaccuracy phenomenon is almost exclusively documented for estimating objects appearing straight ahead of the eye. A collection of the accuracy data for targets located in different angles of view in the visual field may be useful in aiding the design of user‐virtual object interaction. This study collected the object to object distance estimation accuracy for targets displayed stereoscopically. There are five horizontal and two vertical viewing angles and three depths, resulting in a total of 30 locations in the virtual space. The distance estimation for any of the 30 object pairs were performed with or without the aid of spatial cues. Significant factors influencing the accuracy and time of distance estimation were identified. Implications of the finding were discussed with respect to the stereoscopic display environment.     

Evaluating the Wisdom of Strangers: The Perceived Credibility of Online Consumer Reviews on Yelp

This study examined the effects of review valence, the reviewer profile, and the receiver's familiarity with the platform (user/nonuser) on the perceived credibility of a review on Yelp.com and on the receiver's attitude toward the reviewed object. The results demonstrated a difference in cue‐taking between users and nonusers. For users, there was an interaction effect of 2 profile cues (number of friends and number of reviews) on competence. Users interpreted the cues in combination, whereas non‐users were not influenced by them. The friends × reviews × platform familiarity interaction indirectly affected attitude through competence. Further, review valence was positively associated with perceived credibility and attitude. The findings support and extend the social information processing theory and cue combination literature.     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

Confirmation Bias in Online Searches: Impacts of Selective Exposure Before an Election on Political Attitude Strength and Shifts

Impacts of Internet use on political information seeking and subsequent processes have been subject to much debate. A 2‐session online field study presented online search results on political topics to examine selective exposure and its attitudinal impacts. Session 1 captured attitudes, including their accessibility. Session 2 tracked what online search results participants selected and how long they read them; participants then reported attitudes again. The study represented a 4x8x2x2 within‐subjects design: 4 topics, 8 browsing intervals each, with articles presenting opposing stances, with low versus high source credibility. Attitude‐consistent messages and messages from high‐credibility sources were preferred. Exposure to attitude‐consistent search results increased attitude accessibility and reinforced attitudes, whereas exposure to attitude‐discrepant content had opposite effects, regardless of messages' source credibility.