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

There is a need for facile yet reliable bacterial pretreatment and signal-graded strategies for current pathogens' point-of-care testing (POCT). Herein, we present a multifunction and portable 3D-printed magnetic separation device with graded lateral flow assay (LFA) for semi-automated pretreatment and rapid detection of Staphylococcus aureus (S. aureus) in food. The 3D-printed pretreatment device allows purification and enrichment of the [email protected] nanoparticles ([email protected] NPs)-antibody-S. aureus complex. The recovery rate was 81.7–96.1% in actual food samples. [email protected] NPs-mediated signal amplification immunoassay achieved graded detection of S. aureus, reducing the detection limit from 103 CFU/mL to 102 CFU/mL, corresponding to the highest safety limit value (M) and acceptable level limit value (m) of S. aureus in many countries and organizations, respectively. This study paves the way for cost-effective and sensitive POCT with multiplexing potential in applications ranging from pretreatment of samples in the complex matrix as well as foodborne pathogens graded and rapid analysis. 

A portable 3D-printed pretreatment device combined with graded lateral flow assay for detection of S. aureus

Based on rate equations for carrier density in the active region of the QDSOA, the performance of a XOR gate using a quantum-dot semiconductor optical amplifier- based Mach-Zehnder interferometer (QDSOA-MZI) is analyzed in terms of Q factor through numerical simulations. The control pulse energy, the pulse width and the carrier capture time from the wetting layer into the dots are examined, which prove to be relevant to the Q factor. Our numerical results show that at 160Gb/s a high quality output signal with a Q factor over 5.9dB can be achieved.

Performance analysis of all optical XOR gate using quantum dot semiconductor optical amplifier-based Mach-Zehnder interferometer

Ultraviolet is a vital spectrum in wireless optical communication. There are a lot of studies have been done in wireless ultraviolet communication. However, Ultraviolet communication networking mainly focuses on self-organizing networks and multi-hop networks. We proposed a new multi-access scheme of star topology network for confidential communications in semi-open spaces on the surface of ships. We verified the feasibility of the scheme through theoretical analysis and system simulation. It is proved that the scheme is suitable for ultraviolet communication and has better performance in a multi-user network. This scheme could be a sub-scheme for the next generation communication network combined with ultraviolet light communication.

A Multi-Point Access Scheme for Ultraviolet Wireless Network

Proposed in this paper is a kind of polarization-maintaining beam switch with isolator hybrid component, which can be used in insertion less (TL) and extinction ratio (ER) testing for PM isolated components and systems. Its working theory, structure and testing methods are especially presented.

Research and design of a novel polarization-maintaining beam switch with isolator hybrid component

In this paper, the effects of walk-off in NOLM-based wavelength conversion is analyzed, as well as group velocity dispersion (GVD) and self-phase modulation (SPM). Fiber Bragg gratings (FBGs) are introduced into the NOLM, serving as narrow bandpass filters. Corresponding coupled mode nonlinear Schroedinger equations (CMNSEs) and equations of the filter transmission window are given. Meanwhile, the optimal parameters of the system. Finally, time-resolved numerical analysis of our scheme is performed. And it reveals that the introduction of FBGs has improved the performance of NOLM-based wavelength conversion.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Min Zhang

Papers

A portable 3D-printed pretreatment device combined with graded lateral flow assay for detection of S. aureus

Performance analysis of all optical XOR gate using quantum dot semiconductor optical amplifier-based Mach-Zehnder interferometer

A Multi-Point Access Scheme for Ultraviolet Wireless Network

Research and design of a novel polarization-maintaining beam switch with isolator hybrid component

Improving the performance of wavelength-converter in a NOLM using fiber Bragg gratings