45 research outputs found
Four Dimensional Quantum Topology Changes of Spacetimes
We investigate topology changing processes in the WKB approximation of four
dimensional quantum cosmology with a negative cosmological constant. As
Riemannian manifolds which describe quantum tunnelings of spacetime we consider
constant negative curvature solutions of the Einstein equation i.e. hyperbolic
geometries. Using four dimensional polytopes, we can explicitly construct
hyperbolic manifolds with topologically non-trivial boundaries which describe
topology changes. These instanton-like solutions are constructed out of
8-cell's, 16-cell's or 24-cell's and have several points at infinity called
cusps. The hyperbolic manifolds are non-compact because of the cusps but have
finite volumes. Then we evaluate topology change amplitudes in the WKB
approximation in terms of the volumes of these manifolds. We find that the more
complicated are the topology changes, the more likely are suppressed.Comment: 26 pages, revtex, 13 figures. The calculation of volume and
grammatical errors are correcte
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
search
Blind source separation by fully nonnegative constrained iterative volume maximization
Blind source separation (BSS) has been widely discussed in many real applications. Recently, under the assumption that both of the sources and the mixing matrix are nonnegative, Wang develop an amazing BSS method by using volume maximization. However, the algorithm that they have proposed can guarantee the nonnegativities of the sources only, but cannot obtain a nonnegative mixing matrix necessarily. In this letter, by introducing additional constraints, a method for fully nonnegative constrained iterative volume maximization (FNCIVM) is proposed. The result is with more interpretation, while the algorithm is based on solving a single linear programming problem. Numerical experiments with synthetic signals and real-world images are performed, which show the effectiveness of the proposed method
Blind Source Separation by Nonnegative Matrix Factorization with Minimum-Volume Constraint
Recently, nonnegative matrix factorization (NMF) attracts more and more attentions for the promising of wide applications. A problem that still remains is that, however, the factors resulted from it may not necessarily be realistically interpretable. Some constraints are usually added to the standard NMF to generate such interpretive results. In this paper, a minimum-volume constrained NMF is proposed and an efficient multiplicative update algorithm is developed based on the natural gradient optimization. The proposed method can be applied to the blind source separation (BSS) problem, a hot topic with many potential applications, especially if the sources are mutually dependent. Simulation results of BSS for images show the superiority of the proposed method
Topology Changes by Quantum Tunneling in Four Dimensions
We investigate topology-changing processes in 4-dimensional quantum gravity
with a negative cosmological constant. By playing the ``gluing-polytope game"
in hyperbolic geometry, we explicitly construct an instanton-like solution
without singularity. Because of cusps, this solution is non-compact but has a
finite volume. Then we evaluate a topology change amplitude in the WKB
approximation in terms of the volume of this solution.Comment: 13 pages revtex.sty, 6 uuencoded figures contained,
TIT/HEP-260/COSMO-4
Environmental Sound Recognition Using Time-Frequency Intersection Patterns
Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we use a multistage perceptron neural network system for environmental sound recognition. The input data is a combination of time-variance pattern of instantaneous powers and frequency-variance pattern with instantaneous spectrum at the power peak, referred to as a time-frequency intersection pattern. Spectra of many environmental sounds change more slowly than those of speech or voice, so the intersectional time-frequency pattern will preserve the major features of environmental sounds but with drastically reduced data requirements. Two experiments were conducted using an original database and an open database created by the RWCP project. The recognition rate for 20 kinds of environmental sounds was 92%. The recognition rate of the new method was about 12% higher than methods using only an instantaneous spectrum. The results are also comparable with HMM-based methods, although those methods need to treat the time variance of an input vector series with more complicated computations
Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment
Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to a undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds-10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases