9 research outputs found
An Invitation to Hypercomplex Phase Retrieval: Theory and Applications
Hypercomplex signal processing (HSP) provides state-of-the-art tools to
handle multidimensional signals by harnessing intrinsic correlation of the
signal dimensions through Clifford algebra. Recently, the hypercomplex
representation of the phase retrieval (PR) problem, wherein a complex-valued
signal is estimated through its intensity-only projections, has attracted
significant interest. The hypercomplex PR (HPR) arises in many optical imaging
and computational sensing applications that usually comprise quaternion and
octonion-valued signals. Analogous to the traditional PR, measurements in HPR
may involve complex, hypercomplex, Fourier, and other sensing matrices. This
set of problems opens opportunities for developing novel HSP tools and
algorithms. This article provides a synopsis of the emerging areas and
applications of HPR with a focus on optical imaging.Comment: 10 pages, 4 figures, 2 table
Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization
Joint radar-communications (JRC) has emerged as a promising technology for
efficiently using the limited electromagnetic spectrum. In JRC applications
such as secure military receivers, often the radar and communications signals
are overlaid in the received signal. In these passive listening outposts, the
signals and channels of both radar and communications are unknown to the
receiver. The ill-posed problem of recovering all signal and channel parameters
from the overlaid signal is terms as dual-blind deconvolution (DBD). In this
work, we investigate a more challenging version of DBD with a multi-antenna
receiver. We model the radar and communications channels with a few (sparse)
continuous-valued parameters such as time delays, Doppler velocities, and
directions-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to
minimize the sum of multivariate atomic norms (SoMAN) that depends on the
unknown parameters. To this end, we devise an exact semidefinite program using
theories of positive hyperoctant trigonometric polynomials (PhTP). Our
theoretical analyses show that the minimum number of samples and antennas
required for perfect recovery is logarithmically dependent on the maximum of
the number of radar targets and communications paths rather than their sum. We
show that our approach is easily generalized to include several practical
issues such as gain/phase errors and additive noise. Numerical experiments show
the exact parameter recovery for different JRCComment: 40 pages, 6 figures. arXiv admin note: text overlap with
arXiv:2208.0438
Factor Graph Processing for Dual-Blind Deconvolution at ISAC Receiver
Integrated sensing and communications (ISAC) systems have gained significant
interest because of their ability to jointly and efficiently access, utilize,
and manage the scarce electromagnetic spectrum. The co-existence approach
toward ISAC focuses on the receiver processing of overlaid radar and
communications signals coming from independent transmitters. A specific ISAC
coexistence problem is dual-blind deconvolution (DBD), wherein the transmit
signals and channels of both radar and communications are unknown to the
receiver. Prior DBD works ignore the evolution of the signal model over time.
In this work, we consider a dynamic DBD scenario using a linear state space
model (LSSM) such that, apart from the transmit signals and channels of both
systems, the LSSM parameters are also unknown. We employ a factor graph
representation to model these unknown variables. We avoid the conventional
matrix inversion approach to estimate the unknown variables by using an
efficient expectation-maximization algorithm, where each iteration employs a
Gaussian message passing over the factor graph structure. Numerical experiments
demonstrate the accurate estimation of radar and communications channels,
including in the presence of noise.Comment: 13 pages, 4 figure
Dual-Blind Deconvolution for Overlaid Radar-Communications Systems
The increasingly crowded spectrum has spurred the design of joint
radar-communications systems that share hardware resources and efficiently use
the radio frequency spectrum. We study a general spectral coexistence scenario,
wherein the channels and transmit signals of both radar and communications
systems are unknown at the receiver. In this dual-blind deconvolution (DBD)
problem, a common receiver admits a multi-carrier wireless communications
signal that is overlaid with the radar signal reflected off multiple targets.
The communications and radar channels are represented by continuous-valued
range-time and Doppler velocities of multiple transmission paths and multiple
targets. We exploit the sparsity of both channels to solve the highly ill-posed
DBD problem by casting it into a sum of multivariate atomic norms (SoMAN)
minimization. We devise a semidefinite program to estimate the unknown target
and communications parameters using the theories of positive-hyperoctant
trigonometric polynomials (PhTP). Our theoretical analyses show that the
minimum number of samples required for near-perfect recovery is dependent on
the logarithm of the maximum of number of radar targets and communications
paths rather than their sum. We show that our SoMAN method and PhTP
formulations are also applicable to more general scenarios such as
unsynchronized transmission, the presence of noise, and multiple emitters.
Numerical experiments demonstrate great performance enhancements during
parameter recovery under different scenarios.Comment: 26 pages, 13 figures, 1 tabl
DUF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion
Compressive spectral imaging (CSI) has attracted significant attention since
it employs synthetic apertures to codify spatial and spectral information,
sensing only 2D projections of the 3D spectral image. However, these optical
architectures suffer from a trade-off between the spatial and spectral
resolution of the reconstructed image due to technology limitations. To
overcome this issue, compressive spectral image fusion (CSIF) employs the
projected measurements of two CSI architectures with different resolutions to
estimate a high-spatial high-spectral resolution. This work presents the fusion
of the compressive measurements of a low-spatial high-spectral resolution coded
aperture snapshot spectral imager (CASSI) architecture and a high-spatial
low-spectral resolution multispectral color filter array (MCFA) system. Unlike
previous CSIF works, this paper proposes joint optimization of the sensing
architectures and a reconstruction network in an end-to-end (E2E) manner. The
trainable optical parameters are the coded aperture (CA) in the CASSI and the
colored coded aperture in the MCFA system, employing a sigmoid activation
function and regularization function to encourage binary values on the
trainable variables for an implementation purpose. Additionally, an
unrolling-based network inspired by the alternating direction method of
multipliers (ADMM) optimization is formulated to address the reconstruction
step and the acquisition systems design jointly. Finally, a spatial-spectral
inspired loss function is employed at the end of each unrolling layer to
increase the convergence of the unrolling network. The proposed method
outperforms previous CSIF methods, and experimental results validate the method
with real measurements.Comment: 12 pages, 11 figure
Multi-dimensional dual-blind deconvolution approach toward joint radar-communications
We consider a joint multiple-antenna radar-communications system in a
co-existence scenario. Contrary to conventional applications, wherein at least
the radar waveform and communications channel are known or estimated \textit{a
priori}, we investigate the case when the channels and transmit signals of both
systems are unknown. In radar applications, this problem arises in multistatic
or passive systems, where transmit signal is not known. Similarly, highly
dynamic vehicular or mobile communications may render prior estimates of
wireless channel unhelpful. In particular, the radar signal reflected-off
multiple targets is overlaid with the multi-carrier communications signal. In
order to extract the unknown continuous-valued target parameters (range,
Doppler velocity, and direction-of-arrival) and communications messages, we
formulate the problem as a sparse dual-blind deconvolution and solve it using
atomic norm minimization. Numerical experiments validate our proposed approach
and show that precise estimation of continuous-valued channel parameters, radar
waveform, and communications messages is possible up to scaling ambiguities.Comment: 5 pages, 3 figure
Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries
Background
Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks.
Methods
The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned.
Results
A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31).
Conclusion
Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)