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.

/pdf/light-emitting-diodes-ubzde6g9qc.pdf

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.

/pdf/a-comprehensive-survey-on-machine-learning-for-networking-3t5h3kbpe8.pdf

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.

/pdf/an-overview-on-application-of-machine-learning-techniques-in-3eel2cbu3o.pdf

An Overview on Application of Machine Learning Techniques in Optical Networks

We propose an AWGR and SOA based scalable optical packet switch architecture with modular structure and highly distributed control scheme for data center networks. Numerical results show this architecture can achieve high throughput with low blocking probability and latency.

A distributed and scalable optical packet switch architecture for data centers

In this letter a fiber Bragg grating (FBG) interrogation system based on thermistance and matching-FBG of temperature sensitivity enhancement is proposed and demonstrated. According to the relationship between the center wavelength of the matching-FBG and the resistance value of the thermistance, the scheme interrogates the wavelength-shift by adjusting the matching-FBGs temperature and detecting the resistance value of the thermistance. In measure experiments, the results show that the precision of the proposed system is 0.048 nm in a range of 9.4 nm.

Wavelength-shift detection system based on matching-FBG with enhanced temperature sensitivity

With little interference from atmospheric environment, the prospect of ultraviolet (UV) communication in the solar blind region (220 nm–280 nm) is promising. Due to the large path-loss propagation and the no-line-of-sight (NLOS) attenuation, the intensities of UV beams may be severely degraded, and thereby the efficient transmission distance is heavily decreased. In this paper, we design and implement a scheme of UV communication system, with polar channel coding to enhance the efficient transmission distance. Both numerical and experimental results show that our scheme is able to reach a longer efficient transmission distance in contrast with the existing low-density parity-check (LDPC) scheme and uncoded OOK scheme, in the UV communication system.

Polar Channel Coding for the Ultraviolet Communication System Combating Path-Loss Propagation and NLOS Effects

* This work is partially supported by NSF Grant #61072008, 863 Program #2012AA011302 and Doctoral Scientific Fund Project of the ministry of Education of China#20120005110010 to M. Zhang. Abstract A variable step size Least Mean Square (LMS) algorithm based on the difference of the error signal (ESD-VSS) is proposed. The convergence and steady state behavior of the ESDVSS is analyzed and the key parameters are given. Simulations are conducted and the results show that, compared with the Modified Variable Step Size (MVSS) algorithm, modified LMS algorithm and the standard LMS algorithm, the ESD-VSS provides faster convergence speed and smaller steady state misadjustment. Index Terms LMS algorithm, variable step size, adaptive filter, MSE

/pdf/error-signal-difference-based-variable-step-size-lms-2o8fqi8oe3.pdf

Error Signal Difference Based Variable Step Size LMS Algorithm

Presented in this paper is a dual routing engine-enabled optical network platform with multi-domains and multi-layers. Structure and key technologies of the platform are outlined. Experiments on it with 2 routing strategies are conducted, the results are discussed and the network scalability is evaluated.

Min Zhang

Papers

A distributed and scalable optical packet switch architecture for data centers

Wavelength-shift detection system based on matching-FBG with enhanced temperature sensitivity

Polar Channel Coding for the Ultraviolet Communication System Combating Path-Loss Propagation and NLOS Effects

Error Signal Difference Based Variable Step Size LMS Algorithm

DREAMCSAPE: A dual-routing-engine-enabled multi-domain multi-layer optical network platform