152 research outputs found
Waveform and Beamforming Design for Intelligent Reflecting Surface Aided Wireless Power Transfer: Single-User and Multi-User Solutions
In this paper, we study the waveform and passive beamforming design for
intelligent reflecting surface (IRS)-aided wireless power transfer (WPT).
Generalized multi-user and low complexity single-user algorithms are derived
based on alternating optimization (AO) framework to maximize the weighted sum
output DC current, subject to transmit power constraints and passive
beamforming phases unit modulus constraints. The input signal waveform and IRS
passive beamforming phase shifts are jointly designed as a function of users'
individual frequency-selective channel state information (CSI). The energy
harvester nonlinearity is explored and two IRS deployment schemes, namely
frequency selective IRS (FS-IRS) and frequency flat IRS (FF-IRS), are modeled
and analyzed. This paper highlights the fact that IRS can provide an extra
passive beamforming gain on output DC power over conventional WPT designs and
significantly influence the waveform design by leveraging the benefit of
passive beamforming, frequency diversity and energy harvester nonlinearity.
Even though FF-IRS exhibits lower output DC current than FS-IRS, it still
achieves substantially increased DC power over conventional WPT designs.
Performance evaluations confirm the significant benefits of a joint waveform
and passive beamforming design accounting for the energy harvester nonlinearity
to boost the performance of single-user and multi-user WPT system.Comment: 32 pages, 19 figures, submitted for publicatio
IRS-Aided SWIPT: Joint Waveform, Active and Passive Beamforming Design Under Nonlinear Harvester Model
The performance of Simultaneous Wireless Information and Power Transfer
(SWIPT) is mainly constrained by the received Radio-Frequency (RF) signal
strength. To tackle this problem, we introduce an Intelligent Reflecting
Surface (IRS) to compensate the propagation loss and boost the transmission
efficiency. This paper proposes a novel IRS-aided SWIPT system where a
multi-carrier multi-antenna Access Point (AP) transmits information and power
simultaneously, with the assist of an IRS, to a single-antenna User Equipment
(UE) employing practical receiving schemes. Considering harvester nonlinearity,
we characterize the achievable Rate-Energy (R-E) region through a joint
optimization of waveform, active and passive beamforming based on the Channel
State Information at the Transmitter (CSIT). This problem is solved by the
Block Coordinate Descent (BCD) method, where we obtain the active precoder in
closed form, the passive beamforming by the Successive Convex Approximation
(SCA) approach, and the waveform amplitude by the Geometric Programming (GP)
technique. To facilitate practical implementation, we also propose a
low-complexity design based on closed-form adaptive waveform schemes.
Simulation results demonstrate the proposed algorithms bring considerable R-E
gains with robustness to CSIT inaccuracy and finite IRS states, and emphasize
the importance of modeling harvester nonlinearity in the IRS-aided SWIPT
design.Comment: Source code available at
https://github.com/SnowzTail/irs-aided-swipt-joint-waveform-active-and-passive-beamforming-design-under-nonlinear-harvester-mode
Ferritin level prospectively predicts hepatocarcinogenesis in patients with chronic hepatitis B virus infection
Previous studies have detected a higher level of ferritin in patients with hepatocellular carcinoma (HCC), but a potential causal association between serum ferritin level and hepatocarcinogenesis remains to be clarified. Using a well-established prospective cohort and longitudinally collected serial blood samples, the association between baseline ferritin levels and HCC risk were evaluated in 1,152 patients infected with hepatitis B virus (HBV), a major risk factor for HCC. The association was assessed by Cox proportional hazards regression model using univariate and multivariate analyses and longitudinal analysis. It was demonstrated that HBV patients who developed HCC had a significantly higher baseline ferritin level than those who remained cancer-free (188.00 vs. 108.00 ng/ml, P\u3c0.0001). The patients with a high ferritin level (≥200 ng/ml) had 2.43-fold increased risk of HCC compared to those with lower ferritin levels [hazard ratio (HR), 2.43; 95% confidence interval, 1.63-3.63]. A significant trend of increasing HRs along with elevated ferritin levels was observed (P for trend \u3c0.0001). The association was still significant after multivariate adjustment. Incorporating ferritin into the α-fetoprotein (AFP) model significantly improved the performance of HCC prediction (the area under the curve from 0.74 to 0.77, P=0.003). Longitudinal analysis showed that the average ferritin level in HBV patients who developed HCC was persistently higher than in those who were cancer-free during follow-up. HCC risk reached a peak at approximately the fifth year after baseline ferritin detection. Moreover, stratified analyses showed that the association was noted in both males and females, and was prominent in patients with a low AFP value. In short, serum ferritin level could independently predict the risk of HBV-related HCC and may have a complementary role in AFP-based HCC diagnosis. Future studies are warranted to validate these findings and test its clinical applicability in HCC prevention and management. © 2018, Spandidos Publication
Multi-parametric quantitative microvascular imaging with optical-resolution photoacoustic microscopy in vivo
Many diseases involve either the formation of new blood vessels (e.g., tumor angiogenesis) or the damage of existing ones (e.g., diabetic retinopathy) at the microcirculation level. Optical-resolution photoacoustic microscopy (OR-PAM), capable of imaging microvessels in 3D in vivo down to individual capillaries using endogenous contrast, has the potential to reveal microvascular information critical to the diagnosis and staging of microcirculation-related diseases. In this study, we have developed a dedicated microvascular quantification (MQ) algorithm for OR-PAM to automatically quantify multiple microvascular morphological parameters in parallel, including the vessel diameter distribution, the microvessel density, the vascular tortuosity, and the fractal dimension. The algorithm has been tested on in vivo OR-PAM images of a healthy mouse, demonstrating high accuracy for microvascular segmentation and quantification. The developed MQ algorithm for OR-PAM may greatly facilitate quantitative imaging of tumor angiogenesis and many other microcirculation related diseases in vivo
The Application of JDL to Suppress Sea Clutter for Shipborne HFSWR
This paper deals with the problem of sea clutter suppression for shipborne high frequency surface wave radar (HFSWR) based on the joint domain localized (JDL) adaptive processing algorithm. The performance of the novel method is compared with 2D FFT plus digital beamforming (FFT-DBF) and orthogonal weight in different azimuths. The results based on simulated and real data show that the novel method provides higher detection performance than others
Dirac quantum spin liquid emerging in a kagome-lattice antiferromagnet
Emerging quasi-particles with Dirac dispersion in condensed matter physics
are analogous to their cousins in high-energy physics in that both of them can
be described by the Dirac equation for relativistic electrons. Recently, these
Dirac fermions have been widely found in electronic systems, such as graphene
and topological insulators. At the conceptual level, since the charge is not a
prerequisite for Dirac fermions, the emergence of Dirac fermions without charge
degree of freedom has been theoretically predicted to be realized in Dirac
quantum spin liquids. In such case, the Dirac quasiparticles are charge-neutral
and carry a spin of 1/2, known as spinons. Despite of theoretical aspirations,
spectra evidence of Dirac spinons remains elusive. Here we show that the spin
excitations of a kagome antiferromagnet,
YCu(OD)Br[Br(OD)], are conical with a spin continuum
inside, which are consistent with the convolution of two Dirac spinons. The
spinon velocity obtained from the spin excitations also quantitatively
reproduces the low-temperature specific heat of the sample. Interestingly, the
locations of the conical spin excitations differ from those calculated by the
nearest neighbor Heisenberg model, suggesting an unexpected origin of the Dirac
spinons. Our results thus provide strong spectra evidence for the Dirac
quantum-spin-liquid state emerging in this kagome-lattice antiferromagnet.Comment: 7 pages, 4 figure
Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation
The vast majority of people who suffer unexpected cardiac arrest are
performed cardiopulmonary resuscitation (CPR) by passersby in a desperate
attempt to restore life, but endeavors turn out to be fruitless on account of
disqualification. Fortunately, many pieces of research manifest that
disciplined training will help to elevate the success rate of resuscitation,
which constantly desires a seamless combination of novel techniques to yield
further advancement. To this end, we collect a custom CPR video dataset in
which trainees make efforts to behave resuscitation on mannequins independently
in adherence to approved guidelines, thereby devising an auxiliary toolbox to
assist supervision and rectification of intermediate potential issues via
modern deep learning methodologies. Our research empirically views this problem
as a temporal action segmentation (TAS) task in computer vision, which aims to
segment an untrimmed video at a frame-wise level. Here, we propose a
Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three
indispensable modules, including a textual prompt-based Video Features
Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a
regression-based Prediction Refinement Calibrator (PRC). The backbone of the
model preferentially derives from applications in three approved public
datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which
accounts for the excavation of the segmentation pipeline on the CPR dataset. In
general, we unprecedentedly probe into a feasible pipeline that genuinely
elevates the CPR instruction qualification via action segmentation in
conjunction with cutting-edge deep learning techniques. Associated experiments
advocate our implementation with multiple metrics surpassing 91.0%.Comment: Transformer for Cardiopulmonary Resuscitatio
Multi-parametric quantitative microvascular imaging with optical-resolution photoacoustic microscopy in vivo
Many diseases involve either the formation of new blood vessels (e.g., tumor angiogenesis) or the damage of existing ones (e.g., diabetic retinopathy) at the microcirculation level. Optical-resolution photoacoustic microscopy (OR-PAM), capable of imaging microvessels in 3D in vivo down to individual capillaries using endogenous contrast, has the potential to reveal microvascular information critical to the diagnosis and staging of microcirculation-related diseases. In this study, we have developed a dedicated microvascular quantification (MQ) algorithm for OR-PAM to automatically quantify multiple microvascular morphological parameters in parallel, including the vessel diameter distribution, the microvessel density, the vascular tortuosity, and the fractal dimension. The algorithm has been tested on in vivo OR-PAM images of a healthy mouse, demonstrating high accuracy for microvascular segmentation and quantification. The developed MQ algorithm for OR-PAM may greatly facilitate quantitative imaging of tumor angiogenesis and many other microcirculation related diseases in vivo
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