52,865 research outputs found
Mixtures of Shifted Asymmetric Laplace Distributions
A mixture of shifted asymmetric Laplace distributions is introduced and used
for clustering and classification. A variant of the EM algorithm is developed
for parameter estimation by exploiting the relationship with the general
inverse Gaussian distribution. This approach is mathematically elegant and
relatively computationally straightforward. Our novel mixture modelling
approach is demonstrated on both simulated and real data to illustrate
clustering and classification applications. In these analyses, our mixture of
shifted asymmetric Laplace distributions performs favourably when compared to
the popular Gaussian approach. This work, which marks an important step in the
non-Gaussian model-based clustering and classification direction, concludes
with discussion as well as suggestions for future work
Performance Analysis of Adaptive Physical Layer Network Coding for Wireless Two-way Relaying
The analysis of modulation schemes for the physical layer network-coded two
way relaying scenario is presented which employs two phases: Multiple access
(MA) phase and Broadcast (BC) phase. It was shown by Koike-Akino et. al. that
adaptively changing the network coding map used at the relay according to the
channel conditions greatly reduces the impact of multiple access interference
which occurs at the relay during the MA phase. Depending on the signal set used
at the end nodes, deep fades occur for a finite number of channel fade states
referred as the singular fade states. The singular fade states fall into the
following two classes: The ones which are caused due to channel outage and
whose harmful effect cannot be mitigated by adaptive network coding are
referred as the \textit{non-removable singular fade states}. The ones which
occur due to the choice of the signal set and whose harmful effects can be
removed by a proper choice of the adaptive network coding map are referred as
the \textit{removable} singular fade states. In this paper, we derive an upper
bound on the average end-to-end Symbol Error Rate (SER), with and without
adaptive network coding at the relay, for a Rician fading scenario. It is shown
that without adaptive network coding, at high Signal to Noise Ratio (SNR), the
contribution to the end-to-end SER comes from the following error events which
fall as : the error events associated with the removable
singular fade states, the error events associated with the non-removable
singular fade states and the error event during the BC phase. In contrast, for
the adaptive network coding scheme, the error events associated with the
removable singular fade states contributing to the average end-to-end SER fall
as and as a result the adaptive network coding scheme
provides a coding gain over the case when adaptive network coding is not used.Comment: 10 pages, 5 figure
StackInsights: Cognitive Learning for Hybrid Cloud Readiness
Hybrid cloud is an integrated cloud computing environment utilizing a mix of
public cloud, private cloud, and on-premise traditional IT infrastructures.
Workload awareness, defined as a detailed full range understanding of each
individual workload, is essential in implementing the hybrid cloud. While it is
critical to perform an accurate analysis to determine which workloads are
appropriate for on-premise deployment versus which workloads can be migrated to
a cloud off-premise, the assessment is mainly performed by rule or policy based
approaches. In this paper, we introduce StackInsights, a novel cognitive system
to automatically analyze and predict the cloud readiness of workloads for an
enterprise. Our system harnesses the critical metrics across the entire stack:
1) infrastructure metrics, 2) data relevance metrics, and 3) application
taxonomy, to identify workloads that have characteristics of a) low sensitivity
with respect to business security, criticality and compliance, and b) low
response time requirements and access patterns. Since the capture of the data
relevance metrics involves an intrusive and in-depth scanning of the content of
storage objects, a machine learning model is applied to perform the business
relevance classification by learning from the meta level metrics harnessed
across stack. In contrast to traditional methods, StackInsights significantly
reduces the total time for hybrid cloud readiness assessment by orders of
magnitude
- …