scholarly journals Sparse Signal Recovery from Modulo Observations

2020 ◽  
Author(s):  
Viraj Shah ◽  
Chinmay Hegde
Keyword(s):  
Phase Retrieval ◽  
Dynamic Range ◽  
Signal Reconstruction ◽  
Real Data ◽  
Superior Performance ◽  
Signal Recovery ◽  
Reconstruction Methods ◽  
Novel Approach ◽  
Sparsity Constraints ◽  
Improved Performance

Abstract We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.

scholarly journals Sparse signal recovery from modulo observations

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Viraj Shah ◽  
Chinmay Hegde
Keyword(s):  
Dynamic Range ◽  
Signal Reconstruction ◽  
Image Data ◽  
Signal Recovery ◽  
Reconstruction Methods ◽  
Novel Approach ◽  
Sparsity Constraints ◽  
Ill Posed ◽  
Special Case ◽  
Recovery Problem

AbstractWe consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a relatively new imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the signal recovery problem under sparsity constraints for the special case to modulo folding limited to two periods. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal. We also provide experiments validating our approach on toy signal and image data and demonstrate its promising performance.

Joint detection of germline and somatic copy number events in matched tumor–normal sample pairs

2019 ◽  
Vol 35 (23) ◽  
pp. 4955-4961
Author(s):  
Yongzhuang Liu ◽  
Jian Liu ◽  
Yadong Wang
Keyword(s):  
Copy Number ◽  
Simulated Data ◽  
Real Data ◽  
Copy Number Variations ◽  
Superior Performance ◽  
Supplementary Information ◽  
Normal Sample ◽  
Joint Detection ◽  
Novel Approach ◽  
Powerful Approach

Abstract Motivation Whole-genome sequencing (WGS) of tumor–normal sample pairs is a powerful approach for comprehensively characterizing germline copy number variations (CNVs) and somatic copy number alterations (SCNAs) in cancer research and clinical practice. Existing computational approaches for detecting copy number events cannot detect germline CNVs and SCNAs simultaneously, and yield low accuracy for SCNAs. Results In this study, we developed TumorCNV, a novel approach for jointly detecting germline CNVs and SCNAs from WGS data of the matched tumor–normal sample pair. We compared TumorCNV with existing copy number event detection approaches using the simulated data and real data for the COLO-829 melanoma cell line. The experimental results showed that TumorCNV achieved superior performance than existing approaches. Availability and implementation The software TumorCNV is implemented using a combination of Java and R, and it is freely available from the website at https://github.com/yongzhuang/TumorCNV. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Jianhong Xiang ◽  
Huihui Yue ◽  
Xiangjun Yin ◽  
Linyu Wang
Keyword(s):  
Signal Reconstruction ◽  
Real Data ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Signal Recovery ◽  
Sparse Signal Reconstruction ◽  
System Equation ◽  
Norm Minimization ◽  
Better Than

Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has attracted extensive attention in recent years. The essence of sparse signal reconstruction is how to recover the original signal accurately and effectively from an underdetermined linear system equation (ULSE). For this problem, we propose a new algorithm called regularization reweighted smoothed L0 norm minimization algorithm, which is simply called RRSL0 algorithm. Three innovations are made under the framework of this method: (1) a new smoothed function called compound inverse proportional function (CIPF) is proposed; (2) a new reweighted function is proposed; and (3) a mixed conjugate gradient (MCG) method is proposed. In this algorithm, the reweighted function and the new smoothed function are combined as the sparsity promoting objective, and the constraint condition y-Φx22 is taken as a deviation term. Both of them constitute an unconstrained optimization problem under the Tikhonov regularization criterion and the MCG method constructed is used to optimize the problem and realize high-precision reconstruction of sparse signals under noise conditions. Sparse signal recovery experiments on both the simulated and real data show the proposed RRSL0 algorithm performs better than other popular approaches and achieves state-of-the-art performances in signal and image processing.

scholarly journals A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu
Keyword(s):  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Data Sets ◽  
Binomial Thinning ◽  
Free Case ◽  
Two Parameters ◽  
Conditional Maximum ◽  
Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals Complex wavefront sensing based on coherent diffraction imaging using vortex modulation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rujia Li ◽  
Liangcai Cao
Keyword(s):  
Phase Retrieval ◽  
Dynamic Range ◽  
A Priori ◽  
Measured Intensity ◽  
Physical Constraints ◽  
Coherent Diffraction Imaging ◽  
Effective Dynamic ◽  
Ill Posed ◽  
Retrieval Problem ◽  
Diffraction Imaging

AbstractPhase retrieval seeks to reconstruct the phase from the measured intensity, which is an ill-posed problem. A phase retrieval problem can be solved with physical constraints by modulating the investigated complex wavefront. Orbital angular momentum has been recently employed as a type of reliable modulation. The topological charge l is robust during propagation when there is atmospheric turbulence. In this work, topological modulation is used to solve the phase retrieval problem. Topological modulation offers an effective dynamic range of intensity constraints for reconstruction. The maximum intensity value of the spectrum is reduced by a factor of 173 under topological modulation when l is 50. The phase is iteratively reconstructed without a priori knowledge. The stagnation problem during the iteration can be avoided using multiple topological modulations.

scholarly journals A Tip–Tilt and Piston Detection Approach for Segmented Telescopes

Photonics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Shun Qin ◽  
Wai Kin Chan
Keyword(s):  
Error Detection ◽  
Phase Retrieval ◽  
Dynamic Range ◽  
Detection Accuracy ◽  
Wavefront Sensing ◽  
Next Generation ◽  
Large Aperture ◽  
Detection Approach ◽  
Segmented Mirror ◽  
And Control

Accurate segmented mirror wavefront sensing and control is essential for next-generation large aperture telescope system design. In this paper, a direct tip–tilt and piston error detection technique based on model-based phase retrieval with multiple defocused images is proposed for segmented mirror wavefront sensing. In our technique, the tip–tilt and piston error are represented by a basis consisting of three basic plane functions with respect to the x, y, and z axis so that they can be parameterized by the coefficients of these bases; the coefficients then are solved by a non-linear optimization method with the defocus multi-images. Simulation results show that the proposed technique is capable of measuring high dynamic range wavefront error reaching 7λ, while resulting in high detection accuracy. The algorithm is demonstrated as robust to noise by introducing phase parameterization. In comparison, the proposed tip–tilt and piston error detection approach is much easier to implement than many existing methods, which usually introduce extra sensors and devices, as it is a technique based on multiple images. These characteristics make it promising for the application of wavefront sensing and control in next-generation large aperture telescopes.

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

scholarly journals Filtering-Based Regularized Sparsity Variable Step-Size Matching Pursuit and Its Applications in Vehicle Health Monitoring

2021 ◽  
Vol 11 (11) ◽  
pp. 4816
Author(s):  
Haoqiang Liu ◽  
Hongbo Zhao ◽  
Wenquan Feng
Keyword(s):  
Health Monitoring ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Superior Performance ◽  
Sparse Signal ◽  
Variable Step Size ◽  
Step Size ◽  
Signal Sampling ◽  
Reconstruction Performance ◽  
Sparsity Level

Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmission. To address issues of signal sampling and transmission, compressed sensing (CS) has served as a promising solution in vehicle health monitoring, which performs signal sampling and compression simultaneously. Signal reconstruction is regarded as the most critical part of CS, while greedy reconstruction has been a research hotspot. However, the existing approaches either require prior knowledge of the sparse signal or perform with expensive computational complexity. To exploit the structure of the sparse signal, in this paper, we introduce an initial estimation approach for signal sparsity level firstly. Then, a novel greedy reconstruction algorithm that relies on no prior information of sparsity level while maintaining a good reconstruction performance is presented. The proposed algorithm integrates strategies of regularization and variable adaptive step size and further performs filtration. To verify the efficiency of the algorithm, typical voltage disturbance signals generated by the vehicle power system are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the existing methods.

scholarly journals Adaptive aspects of impulsivity and interactions with effects of catecholaminergic agents in the 5-choice serial reaction time task: implications for ADHD

2021 ◽  
Author(s):  
Chiara Toschi ◽  
Mona El-Sayed Hervig ◽  
Parisa Moazen ◽  
Maximilian G. Parker ◽  
Jeffrey W. Dalley ◽  
...  
Keyword(s):  
Reaction Time ◽  
Reaction Time Task ◽  
Serial Reaction Time Task ◽  
Superior Performance ◽  
Serial Reaction Time ◽  
Impaired Performance ◽  
Time Task ◽  
Serial Reaction ◽  
Behavioural Profile ◽  
Improved Performance

Abstract Background Work in humans has shown that impulsivity can be advantageous in certain settings. However, evidence for so-called functional impulsivity is lacking in experimental animals. Aims This study investigated the contexts in which high impulsive (HI) rats show an advantage in performance compared with mid- (MI) and low impulsive (LI) rats. We also assessed the effects of dopaminergic and noradrenergic agents to investigate underlying neurotransmitter mechanisms. Methods We tested rats on a variable inter-trial interval (ITI) version of the 5-choice serial reaction time task (5CSRTT). Rats received systemic injections of methylphenidate (MPH, 1 mg/kg and 3 mg/kg), atomoxetine (ATO, 0.3 mg/kg and 1 mg/kg), amphetamine (AMPH, 0.2 mg/kg), the alpha-2a adrenoceptor antagonist atipamezole (ATI, 0.3 mg/kg) and the alpha-1 adrenoceptor agonist phenylephrine (PHEN, 1 mg/kg) prior to behavioural testing. Results Unlike LI rats, HI rats exhibited superior performance, earning more reinforcers, on short ITI trials, when the task required rapid responding. MPH, AMPH and ATI improved performance on short ITI trials and increased impulsivity in long ITI trials, recapitulating the behavioural profile of HI. In contrast, ATO and PHEN impaired performance on short ITI trials and decreased impulsivity, thus mimicking the behavioural profile of LI rats. The effects of ATO were greater on MI rats and LI rats. Conclusions These findings indicate that impulsivity can be advantageous when rapid focusing and actions are required, an effect that may depend on increased dopamine neurotransmission. Conversely, activation of the noradrenergic system, with ATO and PHEN, led to a general inhibition of responding.

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