Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method

Development fo electrochemical sensors for heavy metal ions detection in environmental samples

The work presented in this thesis was concerned with the development of single-use drop-on sensors incorporating a three-electrode configuration (graphite carbon- working electrode, carbon-counter electrode and silver/silver chloride - reference electrode) for on-site detection of toxic heavy metals in various environmental matrices. The fabricated three-electrode configuration system was coupled with square-wave anodic stripping voltammetry (SWASV) or constant current stripping chronopotentiometry (CCSCP) in order to provide a means of a relatively inexpensive on-site detector for trace levels of lead (II), copper (II) and cadmium (II). Detections and determinations of these metals were carried out on bare screen-printed carbon electrodes (SPCEs), mercury film SPCE, bismuth film SPCE and SPCEs modified with Nafion, 2,5- Dimercapto-1, 3, 4- thiadiazole (DMTD), bismuth oxide (Bi₂O₃) and polyethyleneimine (PEI) using the optimised procedures developed for measurements. With the optimised working conditions, the results obtained indicate that the screen-printed electrochemical sensors are sensitive and reproducible enough for the CCSCP and SWASV determination of lead, copper and cadmium in the microgram per litre - milligram per litre range. Limits of detection below 20 µg I¯¹ were estimated for the trace metal detection of lead, copper and cadmium on both the bismuth and mercury film electrodes. For the bare SPCE, detection limits of 35, 45 and 59 µg I¯¹ were obtained for lead, cadmium and copper detection using CCSCP. The reproducibility of the measurements, which also contributed to the interest in developing the electrochemical sensing devices for metal ions, was below 15 % for the bare SPCE, bismuth film SPCE, and mercury film SPCE. Modifications of SPCEs with an ion-exchanger (Nafion) and a complexing agent (DMTD) provided means of increasing the sensitivity of stripping response obtained at the bare SPCE. Detection limits of 20 and 22 µg I¯¹ were estimated for lead (II) measurements at the Nafion modified SPCE and at the screen-printed DMTD modified electrode, respectively. The application of the various electrodes to real samples is demonstrated and proved successful for both water and soil extracted samples including in situ measurements at a contaminated site

Diffraction modelling of mobile radio wave propagation in built-up areas

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis examines theoretical methods of modelling radio wave propagation in built-up areas, with particular application to mobile radio systems Theoretical approaches allow precise quantitative description of the environment in terms of parameters such as mean building heights and densities, in contrast to the ambiguous nature of more conventional empirical models. The models are constructed using both scalar and vector field analysis techniques. The vector analysis is accomplished using the Geometrical Theory of Diffraction to describe the detailed effects of building shape and positioning, particularly for short-range situations. Over longer ranges propagation can often be described in terms of multiple edge diffraction over building rooftops using a scalar field representation. This mechanism accounts well for measured field strength variations, but is time consuming to calculate accurately using standard methods. A rapid algorithm for calculating scalar diffraction over multiple building edges with arbitrary positioning is constructed. This model can be used for deterministic prediction of sector median field strengths including slow fading variations when appropriate building data exists. It is also applicable to terrain diffraction problems. For the case when only average building parameters are available a closed form solution to the problem of multiple diffraction over buildings of equal heights and spacings is derived. The solution is applicable to any antenna heights and so provides a rapid and efficient way to predict gross propagation characteristics. Both models are tested against measurements made in the UHF band and are found to yield good prediction accuracy.This work is funded by the Science and Engineering Research Council under award number 889088999 and by Philips Radio Communication Systems Ltd. of Cambridge under an industrial studentship

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018