IEEE Circuits and Systems Magazine - Q4 2019 - 1

Circuits
and Systems
IEEE

MAGAZINE

Volume 19, Number 4

Fourth Quarter 2019

Features

From Behavioral Design of Memristive Circuits and Systems
to Physical Implementations
Nima TaheriNejad and David Radakovits

Since Hewlett Packard (HP) announced the passive fabrication of their memristors, various memristive
technologies-as a promising emerging technology-have gained ever-increasing attention from the
researchers. Although a natural application is using them as memory units, there have been several
works in the literature showing their utilization in circuits and systems. While research on various
aspects of memristive circuit and systems has been proliferating, the majority of these works are based
on simulations at different levels of modeling abstraction. Simulation is a very helpful design tool, and
there have been several efforts in modeling memristors; however, we contend that at this point these
simulations represent the reality of the behavior of memristors, especially in a circuit or system set-up,
only to a very limited extent. We show how this negatively affects the reproduction of designed circuits
and systems in different simulation levels, and more importantly in a real-world setup with physical
implementation. Following that, we look into some considerations which can improve the reproducibility
of the circuit and systems to be designed in the future. We conclude the paper by suggesting certain
approaches to tackle these practical challenges at device level as well as circuit and system level.

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Digital Object Identifier 10.1109/MCAS.2019.2945207

FOURTH QUARTER 2019

Applications of Deep Learning to Audio Generation

Recent Development in Public Transport Network Analysis
From the Complex Network Perspective

Yuanjun Zhao, Xianjun Xia, and Roberto Togneri

In recent years, deep learning based machine learning systems have demonstrated remarkable
success for a wide range of learning tasks in multiple domains such as computer vision, speech
recognition and other pattern recognition based applications. The purpose of this article is to
contribute a timely review and introduction of state-of-the-art deep learning techniques and their
effectiveness in speech/acoustic signal processing. Thorough investigations of various deep learning architectures are provided under the categories of discriminative and generative algorithms,
including the up-to-date Generative Adversarial Networks (GANs) as an integrated model. A comprehensive overview of applications in audio generation is highlighted. Based on understandings
from these approaches, we discuss how deep learning methods can benefit the field of speech/
acoustic signal synthesis and the potential issues that need to be addressed for prospective realworld scenarios. We hope this survey provides a valuable reference for practitioners seeking to
innovate in the usage of deep learning approaches for speech/acoustic signal generation.

Tanuja Shanmukhappa, Ivan Wang-Hei Ho, Chi K. Tse, and Kin K. Leung

A graph, comprising a set of nodes connected by edges, is one of the simplest yet remarkably useful
mathematical structures for the analysis of real-world complex systems. Network theory, being an
application-based extension of graph theory, has been applied to a wide variety of real world systems
involving complex interconnection of subsystems. The application of network theory has permitted indepth understanding of connectivity, topologies, and operations of many practical networked systems
as well as the roles that various parameters play in determining the performance of such systems. In
the field of transportation networks, however, the use of graph theory has been relatively much less
explored, and this motivates us to bring together the recent development in the field of public transport
analysis from a graph theoretic perspective. In this paper, we focus on ground transportation, and in
particular the bus transport network (BTN) and metro transport network (MTN), since the two types
of networks are widely used by the public and their performances have significant impact to people's
life. In the course of our analysis, various network parameters are introduced to probe into the impact
of topologies and their relative merits and demerits in transportation. The various local and global
properties evaluated as part of the topological analysis provide a common platform to comprehend
and decipher the inherent network features that are partly encoded in their topological properties.
Overall, this paper gives a detailed exposition of recent development in the use of graph theory in
public transport network analysis, and summarizes the key results that offer important insights for
government agencies and public transport system operators to plan, design, and optimize future
public transport networks in order to achieve more efficient and robust services.
IEEE CIRCUITS AND SYSTEMS MAGAZINE

IEEE Circuits and Systems Magazine - Q4 2019

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