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 $X$ be a proper smooth rigid analytic variety over a $p$-adic field $K$ with a good reduction $\mathfrak X$ over $\mathcal O_K$. In this paper, we construct a Simpson functor from the category of generalised representations on $X_{\rm proet}$ to the category of Higgs bundles on $X_{\mathbb C_p}$ with ${\rm Gal}(\bar K/K)$-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 $\mathfrak X$ with "arithmetic Sen operators" to the category of generalised representations on $X_{\rm proet}$ 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