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In this paper, we propose a new approach to the analysis and modeling of esophageal manometry (EGM) data to assist the diagnosis of esophageal motility disorders in humans. The proposed approach combines three techniques, namely, wavelet decomposition (WD), nonlinear pulse detection technique (NPDT), and statistical pulse modeling. Specifically, WD is applied to the filtering of the EGM data, which is contaminated with electrocardiography (ECG) artifacts. A new NPDT is applied to the denoised data leading to identification and extraction of diagnostically important information, i.e., esophageal pulses from the respiration artifacts. Such information is used to generate a statistical model that can classify the EGM patterns. The proposed approach is computationally effortless, thus making it suitable for real-time application. Experimental results using measured EGM data of 20 patients, including ten abnormal cases is presented. Comparison of our results with those from existing techniques illustrates the advantages of the proposed approach in terms of accuracy and efficiency. Index Terms--Esophageal manometry, nonlinear pulse detection, statistical pulse modeling, wavelet decomposition.

In this paper, artificial neural-network approaches to electromagnetic (EM)-based modeling in both frequency and time domains and their applications to nonlinear circuit optimization are presented. Through accurate and fast EM-based neural models of passive components, we enable consideration of EM effects in high-frequency and high-speed computer-aided design, including component's geometrical/physical parameters as optimization variables. Formulations for standard frequency-domain neural modeling approach, and recent time-domain neural modeling approach based on state-space concept, are described. A new EM-based time-domain neural modeling approach combining existing knowledge in the form of equivalent circuits (ECs), with state--space equations (SSEs) and neural networks (NNs), called the EC--SSE--NN, is proposed. The EC--SSE--NN models allow EM behaviors of passive components in the circuit to interact with nonlinear behaviors of active devices, and facilitate nonlinear circuit optimization in the time domain. An automatic mechanism for EM data generation, which can lead to efficient training of neural models for EM components, is presented. Demonstration examples including EM-based frequency-domain optimization of a three-stage amplifier, time-domain circuit optimization in a multilayer printed circuit board, including geometrical/physical-oriented neural models of power-plane effects, and EM-based optimization of a high-speed interconnect circuit with embedded passive terminations and nonlinear buffers in the time domain are presented. Index Terms--Electromagnetic (EM), embedded passives, modeling, neural networks, optimization, simulation.

In this paper, we propose an efficient knowledge-based automatic model generation (KAMG) technique aimed at generating microwave neural models of the highest possible accuracy using the fewest accurate data. The technique is comprehensively derived to integrate three distinct powerful concepts, namely, automatic model generation, knowledge neural networks, and space mapping. For the first time, we simultaneously utilize two types of data generators, namely, coarse data generators that are approximate and fast (e.g., two-and-one-half-dimensional electromagnetic), and fine data generators that are accurate and slow (e.g., three-dimensional electromagnetic). Motivated by the space-mapping concept, the KAMG technique utilizes extensive coarse data, but fewest fine data to generate neural models that accurately match the fine data. Our formulation exploits a variety of knowledge neural-network architectures to facilitate reinforced neural-network learning from coarse and fine data. During neural model generation by KAMG, both coarse and fine data generators are automatically driven using adaptive sampling. The KAMG technique helps to increase the efficiency of neural model development by taking advantage of a microwave reality, i.e., availability of multiple sources of training data for most high-frequency components. The advantages of the proposed KAMG technique are demonstrated through practical microwave examples of MOSFET and embedded passive components used in multilayer printed circuit boards. Index Terms--Computer-aided design (CAD), modeling, neural networks, optimization.

Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. A trained neural network can be used for high-level design, providing fast and accurate answers to the task it has learned. Neural networks are attractive alternatives to conventional methods such as numerical modeling methods, which could be computationally expensive, or analytical methods which could be difficult to obtain for new devices, or empirical modeling solutions whose range and accuracy may be limited. This tutorial describes fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them. Neural-network structures and their training methods are described from the RF/microwave designer's perspective. Electromagnetics-based training for passive component models and physics-based training for active device models are illustrated. Circuit design and yield optimization using passive/active neural models are also presented. A multimedia slide presentation along with narrative audio clips is included in the electronic version of this paper. A hyperlink to the NeuroModeler demonstration software is provided to allow readers practice neural-network-based design concepts. Index Terms--Computer-aided design (CAD), design automation, modeling, neural networks, optimization, simulation.

For the first time, we propose a robust algorithm for automating the neural-network-based RF/microwave model development process. Starting with zero amount of training data and then proceeding with neural-network training in a stage-wise manner, the algorithm can automatically produce a neural model that meets the user-desired accuracy. In each stage, the algorithm utilizes neural-network error criteria to determine additional training/validation samples required and their location in model input space. The algorithm dynamically generates these new data samples during training, by automatic driving of simulation tools (e.g., OSA90, Ansoft-HFSS, Agilent-ADS). Initially, fewer hidden neurons are used, and the algorithm adjusts the neural-network size whenever it detects under-learning. Our technique integrates all the subtasks involved in neural modeling, thereby facilitating a more efficient and automated model development framework. It significantly reduces the intensive human effort demanded by the conventional step-by-step neural modeling approach. The algorithm inherently distinguishes nonlinear and smooth regions of model behavior and uses relatively fewer samples in smooth subregions. It automatically deals with large data errors that can occur during dynamic sampling by using Huber quasi-Newton technique. The algorithm is demonstrated through practical microwave device and circuit examples. Index Terms--Design automation, modeling, neural-network applications, optimization.

ABSTRACT The use of substrate integrated waveguides (SIW) for microwave and millimeter wave integrated components has increased dramatically over the last decade. They mimic the performance of conventional metallic waveguides and they are fabricated using printed circuit boards using the top and bottom metallization with two rows of vias forming the side walls. This creates a low profile, compact, and light weight alternative to conventional metallic waveguides, and they allow a direct interconnection with printed circuit boards and active components. This article reviews the fundamental theory, documents the research that has been performed over the past decade, and summarizes progress up to the recent state-of-the-art including novel SIW structures for passive circuits and antennas as well as new applications for reconfigurable and printed circuits using SIW technology. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:129-145, 2016. [ABSTRACT FROM AUTHOR]