867 research outputs found
Recommended from our members
High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks.
Dynamic optical imaging (e.g. time delay integration imaging) is troubled by the motion blur fundamentally arising from mismatching between photo-induced charge transfer and optical image movements. Motion aberrations from the forward dynamic imaging link impede the acquiring of high-quality images. Here, we propose a high-resolution dynamic inversion imaging method based on optical flow neural learning networks. Optical flow is reconstructed via a multilayer neural learning network. The optical flow is able to construct the motion spread function that enables computational reconstruction of captured images with a single digital filter. This works construct the complete dynamic imaging link, involving the backward and forward imaging link, and demonstrates the capability of the back-ward imaging by reducing motion aberrations
Root Cross Z-Complementary Pairs with Large ZCZ Width
In this paper, we present a new family of cross -complementary pairs
(CZCPs) based on generalized Boolean functions and two roots of unity. Our key
idea is to consider an arbitrary partition of the set with
two subsets corresponding to two given roots of unity for which two truncated
sequences of new alphabet size determined by the two roots of unity are
obtained. We show that these two truncated sequences form a new -ary CZCP
with flexible sequence length and large zero-correlation zone width.
Furthermore, we derive an enumeration formula by considering the Stirling
number of the second kind for the partitions and show that the number of
constructed CZCPs increases significantly compared to the existing works.Comment: This work has been presented in 2022 IEEE International Symposium on
Information Theory (ISIT), Espoo, Finlan
A Direct and Generalized Construction of Polyphase Complementary Set with Low PMEPR and High Code-Rate for OFDM System
A major drawback of orthogonal frequency division multiplexing (OFDM) systems
is their high peak-to-mean envelope power ratio (PMEPR). The PMEPR problem can
be solved by adopting large codebooks consisting of complementary sequences
with low PMEPR. In this paper, we present a new construction of polyphase
complementary sets (CSs) using generalized Boolean functions (GBFs), which
generalizes Schmidt's construction in 2007, Paterson's construction in 2000 and
Golay complementary pairs (GCPs) given by Davis and Jedwab in 1999. Compared
with Schmidt's approach, our proposed CSs lead to lower PMEPR with higher
code-rate for sequences constructed from higher-order () GBFs. We
obtain polyphase complementary sequences with maximum PMEPR of and
where are non-negative integers that can be easily derived
from the GBF associated with the CS
Text-to-3D using Gaussian Splatting
In this paper, we present Gaussian Splatting based text-to-3D generation
(GSGEN), a novel approach for generating high-quality 3D objects. Previous
methods suffer from inaccurate geometry and limited fidelity due to the absence
of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
recent state-of-the-art representation, to address existing shortcomings by
exploiting the explicit nature that enables the incorporation of 3D prior.
Specifically, our method adopts a progressive optimization strategy, which
includes a geometry optimization stage and an appearance refinement stage. In
geometry optimization, a coarse representation is established under a 3D
geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
iterative refinement to enrich details. In this stage, we increase the number
of Gaussians by compactness-based densification to enhance continuity and
improve fidelity. With these designs, our approach can generate 3D content with
delicate details and more accurate geometry. Extensive evaluations demonstrate
the effectiveness of our method, especially for capturing high-frequency
components. Video results are provided at https://gsgen3d.github.io. Our code
is available at https://github.com/gsgen3d/gsgenComment: Project page: https://gsgen3d.github.io. Code:
https://github.com/gsgen3d/gsge
Power-Imbalanced Low-Density Signatures (LDS) From Eisenstein Numbers
As a special case of sparse code multiple access (SCMA), low-density
signatures based code-division multiple access (LDS-CDMA) was widely believed
to have worse error rate performance compared to SCMA. With the aid of
Eisenstein numbers, we present a novel class of LDS which can achieve error
rate performances comparable to that of SCMA in Rayleigh fading channels and
better performances in Gaussian channels. This is achieved by designing
power-imbalanced LDS such that variation of user powers can be seen both in
every chip window and the entire sequence window. As LDS-CDMA is more flexible
in terms of its backwards compatibility, our proposed LDS are a promising
sequence candidate for dynamic machine-type networks serving a wide range of
communication devices
- β¦