Progress in neural network based techniques for signal integrity analysis–a survey

With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data

Convex searches for discrete-time Zames-Falb multipliers

In this paper we develop and analyse convex searches for Zames--Falb multipliers. We present two different approaches: Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) multipliers. The set of FIR multipliers is complete in that any IIR multipliers can be phase-substituted by an arbitrarily large order FIR multiplier. We show that searches in discrete-time for FIR multipliers are effective even for large orders. As expected, the numerical results provide the best $\ell_{2}$-stability results in the literature for slope-restricted nonlinearities. Finally, we demonstrate that the discrete-time search can provide an effective method to find suitable continuous-time multipliers.Comment: 12 page

Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process

This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the non-normalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a square-shaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models

EYE-HEIGHT/WIDTH PREDICTION USING ARTIFICIAL NEURAL NETWORKS FROM S-PARAMETERS WITH VECTOR FITTING

Artificial neural networks (ANNs) have been used to model microwave and RF devices over the years. Conventionally, S-parameters of microwave/RF designs are used as the inputs of neural network models to predict the electrical properties of the designs. However, using the S-parameters directly as inputs into the ANN results in a large number of inputs which slows down the training and configuration process. In this paper, a new method is proposed to first disassemble the S-parameters into poles and residues using vector fitting, and then the poles and residues are used as the input data during configuration and training of the neural networks. Test cases show that the ANN trained using the proposed method is able to predict the eye-heights and eye-widths of typical interconnect structures with minimal error, while showing significant speed improvement over the conventional method

The perception of Faculty of Health Sciences (FSK) students towards Projek Tunas FSK and the conventional mentor-mentee programme in FSK, Universiti Kebangsaan Malaysia

The mentor-mentee programme was started in Universiti Kebangsaan Malaysia (UKM) in the early 90’s and involved monitoring students (mentee) academic performances with the lecturer (mentor). The mentor-mentee programme in the Faculty of Health Sciences (FSK), UKM, needs improvement to stay relevant to students. Therefore, some modifications were made by implementing a new programme known as Projek Tunas FSK, which is more comprehensive and employs the Modul Mentor Berkesan© in rebranding the existing mentor-mentee programme. Our research was conducted to study the perception of FSK, UKM students on the conventional mentor-mentee programme and Projek Tunas FSK. The Year 2

A systematic review on recent advances in autonomous mobile robot navigation

Recent years have seen a dramatic rise in the popularity of autonomous mobile robots (AMRs) due to their practicality and potential uses in the modern world. Path planning is among the most important tasks in AMR navigation since it demands the robot to identify the best route based on desired performance criteria such as safety margin, shortest time, and energy consumption. The complexity of the problem can however become intractable when challenging scenarios are considered, which include navigation in a dynamic environment and solving multi-objective optimizations. Various classical and heuristic techniques have been proposed by researchers to mitigate such issues. The purpose of this paper is to provide a comprehensive and up-to-date literature review of the path planning strategies that have received a considerable attention over the past decade. A systematic analysis of the strengths, shortcomings, and scope of each method is presented. The trends as well as challenges in practical implementation of the strategies are also discussed at the end of this paper. The outcome of this survey provides useful guidance for future research into creating new strategies that can enhance the autonomy level of AMRs while preserving their robustness against unforeseen circumstances in practice

Temporal convolutional networks for transient simulation of high-speed channels

While the recurrent neural network (RNN) architecture has been the go-to model in transient modeling, recently the temporal convolutional network (TCN) has been garnering more attention as it has a longer memory than recurrent architectures with the same capacity. In this paper, we propose the use of the TCN for transient simulation of high-speed channels. The adaptive successive halving algorithm (ASH-HPO) is used to perform automated hyperparameter optimization for the TCN. It has two components, progressive sampling and successive halving. It iteratively expand the size of training dataset and eliminates a certain percentage of bad performing models. The progressive sampling component is modified to preserve the original sequencing of time series data to prevent information leakage. Also, the successive halving component is modified so that each eliminated model must be validated using at least two different validation datasets before it is being removed. The robustness of the proposed method is demonstrated using four high-speed channel examples, and the TCN is compared against existing convolutional neural network long short-term memory (CNN-LSTM) and dilated causal convolution (DCC) models. The TCN outperforms the other models consistently in all four tasks in terms of training speed, amount of training data to converge, and accuracy

Flex Sensor Compensator via Hammerstein–Wiener Modeling Approach for Improved Dynamic Goniometry and Constrained Control of a Bionic Hand

In this paper, a new control-centric approach is introduced to model the characteristics of flex sensors on a goniometric glove, which is designed to capture the user hand gesture that can be used to wirelessly control a bionic hand. The main technique employs the inverse dynamic model strategy along with a black-box identification for the compensator design, which is aimed to provide an approximate linear mapping between the raw sensor output and the dynamic finger goniometry. To smoothly recover the goniometry on the bionic hand’s side during the wireless transmission, the compensator is restructured into a Hammerstein–Wiener model, which consists of a linear dynamic system and two static nonlinearities. A series of real-time experiments involving several hand gestures have been conducted to analyze the performance of the proposed method. The associated temporal and spatial gesture data from both the glove and the bionic hand are recorded, and the performance is evaluated in terms of the integral of absolute error between the glove’s and the bionic hand’s dynamic goniometry. The proposed method is also compared with the raw sensor data, which has been preliminarily calibrated with the finger goniometry, and the Wiener model, which is based on the initial inverse dynamic design strategy. Experimental results with several trials for each gesture show that a great improvement is obtained via the Hammerstein–Wiener compensator approach where the resulting average errors are significantly smaller than the other two methods. This concludes that the proposed strategy can remarkably improve the dynamic goniometry of the glove, and thus provides a smooth human–robot collaboration with the bionic hand