22,159 research outputs found
DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things
We propose a new energy harvesting strategy that uses a dedicated energy
source (ES) to optimally replenish energy for radio frequency (RF) energy
harvesting powered Internet of Things. Specifically, we develop a two-step dual
tunnel energy requesting (DTER) strategy that minimizes the energy consumption
on both the energy harvesting device and the ES. Besides the causality and
capacity constraints that are investigated in the existing approaches, DTER
also takes into account the overhead issue and the nonlinear charge
characteristics of an energy storage component to make the proposed strategy
practical. Both offline and online scenarios are considered in the second step
of DTER. To solve the nonlinear optimization problem of the offline scenario,
we convert the design of offline optimal energy requesting problem into a
classic shortest path problem and thus a global optimal solution can be
obtained through dynamic programming (DP) algorithms. The online suboptimal
transmission strategy is developed as well. Simulation study verifies that the
online strategy can achieve almost the same energy efficiency as the global
optimal solution in the long term
P-adic Simpson correpondence via prismatic crystals
Let be a proper smooth rigid analytic variety over a -adic field
with a good reduction over . In this paper, we
construct a Simpson functor from the category of generalised representations on
to the category of Higgs bundles on with
-actions using the methods in \cite{LZ} and \cite{DLLZ}.
For the other direction, we construct an inverse Simpson functor from the
category of Higgs bundles on with "arithmetic Sen operators" to
the category of generalised representations on by using the
prismatic theory developed in \cite{BS-a}, especially the category of
Hodge--Tate crystals on (\mathfrak X)_{\Prism}. The main ingredient is the
local computation of absolute prismatic cohomology, which is a generalisation
of our previous work in \cite{MW-b}.Comment: submitted versio
Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
An effective approach to non-parallel voice conversion (VC) is to utilize
deep neural networks (DNNs), specifically variational auto encoders (VAEs), to
model the latent structure of speech in an unsupervised manner. A previous
study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for
VC. How- ever, VAE using other types of spectral features such as mel- cepstral
coefficients (MCCs), which are related to human per- ception and have been
widely used in VC, have not been prop- erly investigated. Instead of using one
specific type of spectral feature, it is expected that VAE may benefit from
using multi- ple types of spectral features simultaneously, thereby improving
the capability of VAE for VC. To this end, we propose a novel VAE framework
(called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework
utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple
objectives in order to constrain the behavior of the learned encoder and de-
coder. Experimental results demonstrate that the proposed CD- VAE framework
outperforms the conventional VAE framework in terms of subjective tests.Comment: Accepted to ISCSLP 201
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