7,505 research outputs found
Distributed Linear Precoding and User Selection in Coordinated Multicell Systems
In this manuscript we tackle the problem of semi-distributed user selection
with distributed linear precoding for sum rate maximization in multiuser
multicell systems. A set of adjacent base stations (BS) form a cluster in order
to perform coordinated transmission to cell-edge users, and coordination is
carried out through a central processing unit (CU). However, the message
exchange between BSs and the CU is limited to scheduling control signaling and
no user data or channel state information (CSI) exchange is allowed. In the
considered multicell coordinated approach, each BS has its own set of cell-edge
users and transmits only to one intended user while interference to
non-intended users at other BSs is suppressed by signal steering (precoding).
We use two distributed linear precoding schemes, Distributed Zero Forcing (DZF)
and Distributed Virtual Signal-to-Interference-plus-Noise Ratio (DVSINR).
Considering multiple users per cell and the backhaul limitations, the BSs rely
on local CSI to solve the user selection problem. First we investigate how the
signal-to-noise-ratio (SNR) regime and the number of antennas at the BSs affect
the effective channel gain (the magnitude of the channels after precoding) and
its relationship with multiuser diversity. Considering that user selection must
be based on the type of implemented precoding, we develop metrics of
compatibility (estimations of the effective channel gains) that can be computed
from local CSI at each BS and reported to the CU for scheduling decisions.
Based on such metrics, we design user selection algorithms that can find a set
of users that potentially maximizes the sum rate. Numerical results show the
effectiveness of the proposed metrics and algorithms for different
configurations of users and antennas at the base stations.Comment: 12 pages, 6 figure
Open-ended evolution to discover analogue circuits for beyond conventional applications
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
On Multi-Step Sensor Scheduling via Convex Optimization
Effective sensor scheduling requires the consideration of long-term effects
and thus optimization over long time horizons. Determining the optimal sensor
schedule, however, is equivalent to solving a binary integer program, which is
computationally demanding for long time horizons and many sensors. For linear
Gaussian systems, two efficient multi-step sensor scheduling approaches are
proposed in this paper. The first approach determines approximate but close to
optimal sensor schedules via convex optimization. The second approach combines
convex optimization with a \BB search for efficiently determining the optimal
sensor schedule.Comment: 6 pages, appeared in the proceedings of the 2nd International
Workshop on Cognitive Information Processing (CIP), Elba, Italy, June 201
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Mining discriminative subgraph patterns from graph data has attracted great
interest in recent years. It has a wide variety of applications in disease
diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the
graph representation alone. However, in many real-world applications, the side
information is available along with the graph data. For example, for
neurological disorder identification, in addition to the brain networks derived
from neuroimaging data, hundreds of clinical, immunologic, serologic and
cognitive measures may also be documented for each subject. These measures
compose multiple side views encoding a tremendous amount of supplemental
information for diagnostic purposes, yet are often ignored. In this paper, we
study the problem of discriminative subgraph selection using multiple side
views and propose a novel solution to find an optimal set of subgraph features
for graph classification by exploring a plurality of side views. We derive a
feature evaluation criterion, named gSide, to estimate the usefulness of
subgraph patterns based upon side views. Then we develop a branch-and-bound
algorithm, called gMSV, to efficiently search for optimal subgraph features by
integrating the subgraph mining process and the procedure of discriminative
feature selection. Empirical studies on graph classification tasks for
neurological disorders using brain networks demonstrate that subgraph patterns
selected by the multi-side-view guided subgraph selection approach can
effectively boost graph classification performances and are relevant to disease
diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM)
201
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
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