Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Light-Emitting Diodes. (Monographs in Electrical and Electronic Engineering.) By A. A. Bergh and P. J. Dean. Pp. viii+591. (Clarendon: Oxford; Oxford University: London, 1976.) £22.

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Light-emitting diodes

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions.

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An Overview on Application of Machine Learning Techniques in Optical Networks

Olanzapine is an atypical antipsychotic drug used for mental disorders. There are limited studies providing sufficient pharmacokinetic data, thus the variability of concentrations of olanzapine used in Chinese paediatric patients aged 10 to 17 years remains to be evaluated.

Population pharmacokinetics and dosing optimization of olanzapine in Chinese paediatric patients: Based on the impact of sex and concomitant valproate on clearance

Proposed in this paper is a 10-Gbps uplink transmission over 25-km fiber in WDM-PON, in which band-limited RSOAs are applied to colorless ONUs and cascaded Mach-Zehnder Interferometers are used as three-tap optical equalizers in OLT.

10-Gbps WDM-PON with RSOA-based colorless ONUs and MZI-based optical equalizers

This paper presents the design and implementation of a tunable optical delay system, capable of calibrating the optical signal delay by optical measuring instruments. It is also able to measure the delay by optical communication systems, the laser linewidth, the maximum radius of passive optical networks (PONs) and so on. Structure and implementation method are detailed and results show that it is able to measure the optical signal delay within an error of only +/-0.33ns.

Design and Implementation of a Tunable Optical Delay System

We proposed a scheme of fiber Bragg grating (FBG) sensor interrogation system based on all fiber Mach-Zehnder Interferometer (MZI) edge filter and tunable-FBG. The equal power signals from FBG sensor and tunable FBG are coupled into port 1 and port 2 of MZI respectively. The experimental results show that the interrogation precision of the proposed system is 0.02 nm in a range of 1nm. Furthermore, the precision can be improved by adjusting the two interference arms of the filter.

MZI-based wavelength interrogation system with tunable-FBG

We propose an IDMA scheme combined with OFDM for downlink visible light communication. The experiment results indicate that IDMA-OFDM offers better bit error rate performance compared with OFDMA.

Min Zhang

Papers

Population pharmacokinetics and dosing optimization of olanzapine in Chinese paediatric patients: Based on the impact of sex and concomitant valproate on clearance

10-Gbps WDM-PON with RSOA-based colorless ONUs and MZI-based optical equalizers

Design and Implementation of a Tunable Optical Delay System

MZI-based wavelength interrogation system with tunable-FBG

Experimental Demonstration of IDMA-OFDM for Downlink Visible Light Communication