Investigation of the M2/G2/1/ ∞, N Queue with Restricted Admission of Priority Customers and its Application to HSDPA Mobile Systems

This paper investigates a queuing system for QoS optimization of multimedia traffic consisting of aggregated streams with diverse QoS requirements transmitted to a mobile terminal over a common downlink shared channel. The queuing system, proposed for buffer management of aggregated single-user traffic in the base station of High-Speed Downlink Packet Access (HSDPA), allows for optimum loss/delay/jitter performance for end-user multimedia traffic with delay-tolerant non-real-time streams and partially loss tolerant real-time streams. In the queuing system, the real-time stream has non-preemptive priority in service but the number of the packets in the system is restricted by a constant. The non-real-time stream has no service priority but is allowed unlimited access to the system. Both types of packets arrive in the stationary Poisson flow. Service times follow general distribution depending on the packet type. Stability condition for the model is derived. Queue length distribution for both types of customers is calculated at arbitrary epochs and service completion epochs. Loss probability for priority packets is computed. Waiting time distribution in terms of Laplace-Stieltjes transform is obtained for both types of packets. Mean waiting time and jitter are computed. Numerical examples presented demonstrate the effectiveness of the queuing system for QoS optimization of buffered end-user multimedia traffic with aggregated real-time and non-real-time streams.

Parallels between machine and brain decoding

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies. © 2012 Springer-Verlag.

Ion-acoustic supersolitons in plasmas with two-temperature electrons: Boltzmann and kappa distributions

Acoustic supersolitons arise when a plasma model is able to support three consecutive local extrema of the Sagdeev pseudopotential between the undisturbed conditions and an accessible root. This leads to a characteristic electric field signature, where a simple bipolar shape is enriched by subsidiary maxima. Large-amplitude nonlinear acoustic modes are investigated, using a pseudopotential approach, for plasmas containing two-temperature electrons having Boltzmann or kappa distributions, in the presence of cold fluid ions. The existence domains for positive supersolitons are derived in a methodological way, both for structure velocities and amplitudes, in terms of plasma compositional parameters. In addition, typical pseudopotentials, soliton, and electric field profiles have been given to illustrate that positive supersolitons can be found in the whole range of electron distributions from Maxwellian to a very hard nonthermal spectrum in kappa. However, it is found that the parameter ranges that support supersolitons vary significantly over the wide range of kappa considered. VC 2013 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4818888]

Starbog: The Garden Virtuoso and the Turing Machine

This article will discuss a recent ensemble composition entitled Starbog which was toured and broadcast in Britain in 2006 . The composition of Starbog focused on developing working methods which combined computer-based techniques (using OpenMusic) with more subconscious means of generating musical ideas. The challenge in achieving this was as much aesthetic/philosophical as it was technical and the present article is intending as a ‘sounding’ which focuses on the influence OpenMusic has had on the composer’s music, rather than documenting the nature of the often simple application of algorithms.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.