Li Jun Jiang

Bio: Li Jun Jiang is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Integral equation & Computational electromagnetics. The author has an hindex of 32, co-authored 418 publications receiving 3820 citations. Previous affiliations of Li Jun Jiang include IBM & University of California, Los Angeles.

Circuit Oriented Electromagnetic Modeling Using the PEEC Techniques

Abstract: This book provides intuitive solutions to electromagnetic problems by using the Partial Element Equivalent Circuit (PEEC) method. This book begins with an introduction to circuit analysis techniques, laws, and frequency and time domain analyses. The authors also treat Maxwell's equations, capacitance computations, and inductance computations through the lens of the PEEC method. Next, readers learn to build PEEC models in various forms: equivalent circuit models, non-orthogonal PEEC models, skin-effect models, PEEC models for dielectrics, incident and radiate field models, and scattering PEEC models. The book concludes by considering issues like stability and passivity, and includes five appendices some with formulas for partial elements.

A mixed-form fast multipole algorithm

Abstract: The fast multipole algorithm manifests in two very different forms at low frequencies and at mid frequencies. Each can operate in their respective regimes, but are not tenable in the other regimes. The paper reports on a way to factorize the Green's function for fast algorithm using a mixed form. The low-frequency fast multipole algorithm (LF-FMA) will be used at low frequencies or the long-wavelength regime, and the multilevel fast multipole algorithm (MLFMA) will be used for the mid frequencies or the shorter-wavelength regime. For object modeling where both long-wavelength and wave physics are important, we propose a mixed-form fast multipole algorithm (MF-FMA). This algorithm has no low frequency break down, and it can work seamlessly from static (where circuit physics is important) to dynamic (where wave physics is important).

Ultrathin Complementary Metasurface for Orbital Angular Momentum Generation at Microwave Frequencies

Abstract: Electromagnetic (EM) waves with helical wave front carry orbital angular momentum (OAM), which is associated with the azimuthal phase of the complex electric field. OAM is a new degree of freedom in EM waves and is promising for channel multiplexing in the communication system. Although the OAM-carrying EM wave attracts more and more attention, the method of OAM generation at microwave frequencies still faces challenges, such as efficiency and simulation time. In this communication, by using the circuit theory and equivalence principle, we build two simplified models, one for a single scatter and one for the whole metasurface to predict their EM responses. Both of the models significantly simplify the design procedure and reduce the simulation time. In this communication, we propose an ultrathin complementary metasurface that converts a left-handed (right-handed) circularly polarized plane wave without OAM to a right-handed (left-handed) circularly polarized wave with OAM of arbitrary orders, and a high transmission efficiency can be achieved.

Graphene based functional devices: A short review

Abstract: Graphene is an ideal 2D material system bridging electronic and photonic devices. It also breaks the fundamental speed and size limits by electronics and photonics, respectively. Graphene offers multiple functions of signal transmission, emission, modulation, and detection in a broad band, high speed, compact size, and low loss. Here, we have a brief view of graphene based functional devices at microwave, terahertz, and optical frequencies. Their fundamental physics and computational models were discussed as well.

Compact Metallic RFID Tag Antennas With a Loop-Fed Method

Abstract: Several compact, low profile and metal-attachable RFID tag antennas with a loop-fed method are proposed for UHF RFID systems. The structure of the proposed antennas comprise of two parts: (1) The radiator part consists of two shorted patches, which can be treated as two quarter-wave patch antennas or a cavity. (2) A small loop printed on the paper serves as the feeding structure. The small loop provides the needed inductance for the tag and is connected to the RFID chip. The input impedance of the antenna can be easily adjusted by changing loop dimensions. The antenna has the compact size of 80 mm × 25 mm × 3.5 mm, and the realized gain about dB. The measured results show that these antennas have good performance when attached onto metallic surfaces.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning with Python

Abstract: This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Electronic Transport In Mesoscopic Systems

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