139,722 research outputs found
KL-Divergence Guided Two-Beam Viterbi Algorithm on Factorial HMMs
This thesis addresses the problem of the high computation complexity issue that arises when decoding hidden Markov models (HMMs) with a large number of states. A novel approach, the two-beam Viterbi, with an extra forward beam, for decoding HMMs is implemented on a system that uses factorial HMM to simultaneously recognize a pair of isolated digits on one audio channel. The two-beam Viterbi algorithm uses KL-divergence and hierarchical clustering to reduce the overall decoding complexity. This novel approach achieves 60% less computation compared to the baseline algorithm, the Viterbi beam search, while maintaining 82.5% recognition accuracy.Ope
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A niching memetic algorithm for simultaneous clustering and feature selection
Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data
Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization
Unmanned aerial vehicle (UAV) based aerial base stations (BSs) can provide
rapid communication services to ground users and are thus promising for future
communication systems. In this paper, we consider a scenario where no
functional terrestrial BSs are available and the aim is deploying multiple
aerial BSs to cover a maximum number of users within a certain target area. To
this end, we first propose a naive successive deployment method, which converts
the non-convex constraints in the involved optimization into a combination of
linear constraints through geometrical relaxation. Then we investigate a
deployment method based on K-means clustering. The method divides the target
area into K convex subareas, where within each subarea, a mixed integer
non-linear problem (MINLP) is solved. An iterative power efficient technique is
further proposed to improve coverage probability with reduced power. Finally,
we propose a robust technique for compensating the loss of coverage probability
in the existence of inaccurate user location information (ULI). Our simulation
results show that, the proposed techniques achieve an up to 30% higher coverage
probability when users are not distributed uniformly. In addition, the proposed
simultaneous deployment techniques, especially the one using iterative
algorithm improve power-efficiency by up to 15% compared to the benchmark
circle packing theory
Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning
In this paper, we propose a two-timescale delay-optimal dynamic clustering
and power allocation design for downlink network MIMO systems. The dynamic
clustering control is adaptive to the global queue state information (GQSI)
only and computed at the base station controller (BSC) over a longer time
scale. On the other hand, the power allocations of all the BSs in one cluster
are adaptive to both intra-cluster channel state information (CCSI) and
intra-cluster queue state information (CQSI), and computed at the cluster
manager (CM) over a shorter time scale. We show that the two-timescale
delay-optimal control can be formulated as an infinite-horizon average cost
Constrained Partially Observed Markov Decision Process (CPOMDP). By exploiting
the special problem structure, we shall derive an equivalent Bellman equation
in terms of Pattern Selection Q-factor to solve the CPOMDP. To address the
distributive requirement and the issue of exponential memory requirement and
computational complexity, we approximate the Pattern Selection Q-factor by the
sum of Per-cluster Potential functions and propose a novel distributive online
learning algorithm to estimate the Per-cluster Potential functions (at each CM)
as well as the Lagrange multipliers (LM) (at each BS). We show that the
proposed distributive online learning algorithm converges almost surely (with
probability 1). By exploiting the birth-death structure of the queue dynamics,
we further decompose the Per-cluster Potential function into sum of Per-cluster
Per-user Potential functions and formulate the instantaneous power allocation
as a Per-stage QSI-aware Interference Game played among all the CMs. We also
propose a QSI-aware Simultaneous Iterative Water-filling Algorithm (QSIWFA) and
show that it can achieve the Nash Equilibrium (NE)
Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure
In this paper we extend an earlier result within Dempster-Shafer theory
["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc.
Seventh Int. Conf. Information Processing and Management of Uncertainty in
Knowledge-Based Systems (IPMU'98)] where several pieces of evidence were
clustered into a fixed number of clusters using a neural structure. This was
done by minimizing a metaconflict function. We now develop a method for
simultaneous clustering and determination of number of clusters during
iteration in the neural structure. We let the output signals of neurons
represent the degree to which a pieces of evidence belong to a corresponding
cluster. From these we derive a probability distribution regarding the number
of clusters, which gradually during the iteration is transformed into a
determination of number of clusters. This gradual determination is fed back
into the neural structure at each iteration to influence the clustering
process.Comment: 6 pages, 10 figure
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