266 research outputs found
A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization
Indoor localization is getting increasing demands for various cutting-edged
technologies, like Virtual/Augmented reality and smart home. Traditional
model-based localization suffers from significant computational overhead, so
fingerprint localization is getting increasing attention, which needs lower
computation cost after the fingerprint database is built. However, the accuracy
of indoor localization is limited by the complicated indoor environment which
brings the multipath signal refraction. In this paper, we provided a scheme to
improve the accuracy of indoor fingerprint localization from the frequency
domain by predicting the channel state information (CSI) values from another
transmitting channel and spliced the multi-band information together to get
more precise localization results. We tested our proposed scheme on COST 2100
simulation data and real time orthogonal frequency division multiplexing (OFDM)
WiFi data collected from an office scenario
Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective
The realm of classical phase retrieval concerns itself with the arduous task
of recovering a signal from its Fourier magnitude measurements, which are
fraught with inherent ambiguities. A single-exposure intensity measurement is
commonly deemed insufficient for the reconstruction of the primal signal, given
that the absent phase component is imperative for the inverse transformation.
In this work, we present a novel single-shot phase retrieval paradigm from a
fractional Fourier transform (FrFT) perspective, which involves integrating the
FrFT-based physical measurement model within a self-supervised reconstruction
scheme. Specifically, the proposed FrFT-based measurement model addresses the
aliasing artifacts problem in the numerical calculation of Fresnel diffraction,
featuring adaptability to both short-distance and long-distance propagation
scenarios. Moreover, the intensity measurement in the FrFT domain proves highly
effective in alleviating the ambiguities of phase retrieval and relaxing the
previous conditions on oversampled or multiple measurements in the Fourier
domain. Furthermore, the proposed self-supervised reconstruction approach
harnesses the fast discrete algorithm of FrFT alongside untrained neural
network priors, thereby attaining preeminent results. Through numerical
simulations, we demonstrate that both amplitude and phase objects can be
effectively retrieved from a single-shot intensity measurement using the
proposed approach and provide a promising technique for support-free coherent
diffraction imaging
Low-gap zinc porphyrin as an efficient dopant for photomultiplication type photodetectors
A new zinc porphyrin, named as Por4IC, was synthesized, which through extension of conjugation and an enhancement of planarity and donor-acceptor interactions exhibits a very low band gap. The molecule was able to efficiently facilitate a photomultiplication effect in blend with P3HT which was assisted by electron trapping followed by hole tunneling injection from the Al electrode giving rise to a high external quantum efficiency of 22 182% and a specific detectivity of 4.4
7 1012 Jones at 355 nm and at -15 V bias. This work introduces porphyrin derivatives as promising dopants for photomultiplication type photodetectors. This journal i
Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving
Multi-view depth estimation has achieved impressive performance over various
benchmarks. However, almost all current multi-view systems rely on given ideal
camera poses, which are unavailable in many real-world scenarios, such as
autonomous driving. In this work, we propose a new robustness benchmark to
evaluate the depth estimation system under various noisy pose settings.
Surprisingly, we find current multi-view depth estimation methods or
single-view and multi-view fusion methods will fail when given noisy pose
settings. To address this challenge, we propose a single-view and multi-view
fused depth estimation system, which adaptively integrates high-confident
multi-view and single-view results for both robust and accurate depth
estimations. The adaptive fusion module performs fusion by dynamically
selecting high-confidence regions between two branches based on a wrapping
confidence map. Thus, the system tends to choose the more reliable branch when
facing textureless scenes, inaccurate calibration, dynamic objects, and other
degradation or challenging conditions. Our method outperforms state-of-the-art
multi-view and fusion methods under robustness testing. Furthermore, we achieve
state-of-the-art performance on challenging benchmarks (KITTI and DDAD) when
given accurate pose estimations. Project website:
https://github.com/Junda24/AFNet/.Comment: Accepted to CVPR 202
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