Low Rate Measurements Research Articles

Operational Support Estimator Networks.

In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in sparse signals. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support estimators rely on computationally expensive iterative signal recovery techniques to achieve such non-linearity. Contrary to the convolutional layers, the proposed OSEN approach consists of operational layers that can learn such complex non-linearities without the need for deep networks. In this way, the performance of non-iterative support estimation is greatly improved. Moreover, the operational layers comprise so-called generative super neurons with non-local kernels. The kernel location for each neuron/feature map is optimized jointly for the SE task during training. We evaluate the OSENs in three different applications: i. support estimation from Compressive Sensing (CS) measurements, ii. representation-based classification, and iii. learning-aided CS reconstruction where the output of OSENs is used as prior knowledge to the CS algorithm for enhanced reconstruction. Experimental results show that the proposed approach achieves computational efficiency and outperforms competing methods, especially at low measurement rates by significant margins.

Development and Application of Integrated Injection System for Injection and Profile Control in Dagang Oilfield

Abstract Dagang Oilfield is a heterogeneous, multi-layered, and structurally complex fault block oil reservoir. During the production process, the contradictions between layers and within layers are becoming increasingly prominent. Split injection and profile control are two important development processes for ensuring production and increasing production in current oilfield development. However, during the construction process of split injection wells for profile control, there are often problems with plugging agents, which affect the effectiveness of split injection and the success rate of testing and adjustment, and even require well repair operations. This has become a key quality control production link that affects the efficiency of oil recovery construction and the improvement of quality, cost, and efficiency. The idea of developing a new type of integrated water distributor for injection and profile control has been proposed, which can achieve dual functions of layered water injection and profile control using a set of pipe columns. Through several years of technological breakthroughs, technological breakthroughs have been made in various aspects such as main structure design, anti backflow structure design, and water nozzle optimization design. Innovative proposals have been made and the integrated water distribution device structure design for injection and profile control has been successfully developed, fundamentally solving the pain points and difficulties of low measurement and adjustment rate after injection well profile control construction, significantly improving the qualification rate of measurement and adjustment, and significantly improving work efficiency and construction quality.

Research and application of the method of measuring the effective utilization coefficient of irrigation water on farmland

Abstract Under the guidance of the new water management idea of “water conservation priority, spatial balance, system management, and two-handed effort,” the National Water Conservation Action Plan released in 2019 puts forward the key task requirements of “double control of total intensity,” in which the effective utilization coefficient of irrigation water in farmland is one of the indicators of water intensity control. The effective utilization coefficient of irrigation water is one of the water intensity control indicators. At present, there are practical difficulties in measuring the effective utilization coefficient of irrigation water in farmland, such as the low measurement rate of water consumption in the current irrigation area, the high input equipment and manpower requirements, and the difficulty in accurately measuring the net irrigation water absorbed by crops. Therefore, this paper proposed the method of “field monitoring - remote sensing inversion - a comprehensive analysis of data.” The method of “field monitoring - remote sensing inversion - econometric model construction - comprehensive analysis” was proposed in this paper, which can obtain gross irrigation water utilizing field monitoring, overcome the difficulty of accurate monitoring of net irrigation water using remote sensing, and carried out a comprehensive analysis, to realize the scientific, effective and rapid calculation of effective utilization coefficient of irrigation water. This method was applied to the Meixian District, Guangdong Province, and the research results show that it not only generates reliable measurement data but also alleviates workload. Furthermore, it offers scientific measurement techniques for water use efficiency indicators, facilitating the achievement of “dual control over total volume and intensity.” This paper provides an important technical basis for the rational allocation of water consumption and scientific approval of water use plans and supports the assessment of the effectiveness of water-saving irrigation development and the reasonable evaluation of the water-saving potential of farmland irrigation, thus providing a basis for local government departments at all levels to make planning, scientific decision-making, and macro-management.

The muon beam monitor for the FAMU experiment: design, simulation, test, and operation

FAMU is an INFN-led muonic atom physics experiment based at the RIKEN-RAL muon facility at the ISIS Neutron and Muon Source (United Kingdom). The aim of FAMU is to measure the hyperfine splitting in muonic hydrogen to determine the value of the proton Zemach radius with an accuracy better than 1%. The experiment has a scintillating-fibre hodoscope for beam monitoring and data normalisation. In order to carry out muon flux estimation, low-rate measurements were performed to extract the single-muon average deposited charge. Then, detector simulation in Geant4 and FLUKA allowed a thorough understanding of the single-muon response function, which is crucial for determining the muon flux. This work presents the design features of the FAMU beam monitor, along with the simulation and absolute calibration measurements in order to enable flux determination and enable data normalisation.

Measurement-induced transitions beyond Gaussianity: A single particle description

Repeated measurements can induce entanglement phase transitions in the dynamics of quantum systems. Interacting models, both chaotic and integrable, generically show a stable volume-law entangled phase at low measurement rates that disappears for free, Gaussian fermions. Interactions break the Gaussianity of a dynamical map in its unitary part, but non-Gaussianity can be introduced through measurements as well. By comparing the entanglement and non-Gaussianity structure of different protocols, we propose a single particle indicator of the measurement-induced phase transition, and we use it to argue in favor of the stability of the transition when non-Gaussianity is purely provided by measurements. Published by the American Physical Society 2024

Single-pixel imaging based on self-supervised conditional mask classifier-free guidance

Reconstructing high-quality images at a low measurement rate is a pivotal objective of Single-Pixel Imaging (SPI). Currently, deep learning methods achieve this by optimizing the loss between the target image and the original image, thereby constraining the potential of low measurement values. We employ conditional probability to ameliorate this, introducing the classifier-free guidance model (CFG) for enhanced reconstruction. We propose a self-supervised conditional masked classifier-free guidance (SCM-CFG) for single-pixel reconstruction. At a 10% measurement rate, SCM-CFG efficiently completed the training task, achieving an average peak signal-to-noise ratio (PSNR) of 26.17 dB on the MNIST dataset. This surpasses other methods of photon imaging and computational ghost imaging. It demonstrates remarkable generalization performance. Moreover, thanks to the outstanding design of the conditional mask in this paper, it can significantly enhance the accuracy of reconstructed images through overlay. SCM-CFG achieved a notable improvement of an average of 7.3 dB in overlay processing, in contrast to only a 1 dB improvement in computational ghost imaging. Subsequent physical experiments validated the effectiveness of SCM-CFG.

A Real-Time and Robust Neural Network Model for Low-Measurement-Rate Compressed-Sensing Image Reconstruction.

Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for robust reconstruction results. This paper proposes a neural network called RootsNet, which integrates the CS mechanism into the network to prevent error propagation. So, RootsNet knows what will happen if some modules in the network go wrong. It also implements real-time and successfully reconstructed extremely low measurement rates that are impossible for traditional optimization-theory-based methods. For qualitative validation, RootsNet is implemented in two real-world measurement applications, i.e., a near-field microwave imaging system and a pipeline inspection system, where RootsNet easily saves 60% more measurement time and 95% more data compared with the state-of-the-art optimization-theory-based reconstruction methods. Without losing generality, comprehensive experiments are performed on general datasets, including evaluating the key components in RootsNet, the reconstruction uncertainty, quality, and efficiency. RootsNet has the best uncertainty performance and efficiency, and achieves the best reconstruction quality under super low-measurement rates.

Random-time quantum measurements

The analysis of a continuous measurement record $z(t)$ poses a fundamental challenge in quantum measurement theory. Different approaches were used in the past as records can, e.g., exhibit predominantly Gaussian noise, telegraph noise, or clicks at random times. The last case may appear as photon clicks in an optical spin-noise measurement at very low probe laser power. Here we show that such random-time quantum measurements can, similarly to the first two cases, be analyzed in terms of higher-order temporal correlations of the detector output $z(t)$ and be related to the Liouvillian of the measured quantum system. Our analysis in terms of up to fourth-order spectra (quantum polyspectra) shows that this type of spectra reveals the same valuable information as previously studied higher-order spectra in the case of the usual continuous quantum measurements. Surprisingly, broadband system dynamics is revealed even for deliberately low average measurement rates. Many applications are envisioned in high-resolution spectroscopy, single-photon microscopy, circuit quantum electrodynamics, quantum sensing, and quantum measurements, in general.

Compression Reconstruction Network with Coordinated Self-Attention and Adaptive Gaussian Filtering Module

Although compressed sensing theory has many advantages in image reconstruction, its reconstruction and sampling time is very long. Fast reconstruction of high-quality images at low measurement rates is the direction of the effort. Compressed sensing based on deep learning provides an effective solution for this. In this study, we propose an attention-based compression reconstruction mechanism (ACRM). The coordinated self-attention module (CSAM) is designed to be embedded in the main network consisting of convolutional blocks and utilizes the global space and channels to focus on key information and ignore irrelevant information. An adaptive Gaussian filter is proposed to solve the loss of multi-frequency components caused by global average pooling in the CSAM, effectively supplementing the network with different frequency information at different measurement rates. Finally, inspired by the basic idea of the attention mechanism, an improved loss function with attention mechanism (AMLoss) is proposed. Extensive experiments show that the ACRM outperforms most compression reconstruction algorithms at low measurement rates.

Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation.

The efforts in compressive sensing (CS) literature can be divided into two groups: finding a measurement matrix that preserves the compressed information at its maximum level, and finding a robust reconstruction algorithm. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutions are used to recover the signals. Using random matrices when handling large or multi-dimensional signals is cumbersome especially when it comes to iterative optimizations. Recent deep learning-based solutions increase reconstruction accuracy while speeding up recovery, but jointly learning the whole measurement matrix remains challenging. For this reason, state-of-the-art deep learning CS solutions such as convolutional compressive sensing network (CSNET) use block-wise CS schemes to facilitate learning. In this work, we introduce a separable multi-linear learning of the CS matrix by representing the measurement signal as the summation of the arbitrary number of tensors. As compared to block-wise CS, tensorial learning eases blocking artifacts and improves performance, especially at low measurement rates (MRs), such as [Formula: see text]. The software implementation of the proposed network is publicly shared at https://github.com/mehmetyamac/GTSNET.

Gaussian‐like measurement likelihood based particle filter for extended target tracking

AbstractExtended target tracking based on the star‐convex model is a multi‐dimensional nonlinear estimation problem. To approximate the shape of an extended target, the star‐convex model requires a higher‐order Fourier series expansion, which will increase the dimensionality of the extended state, so that the nonlinear filters under Gaussian assumptions cannot converge to the optimal state estimation. In this paper, a novel particle filter algorithm based on Gaussian‐like measurement likelihood (GL) is proposed. As latent variables with high uncertainty in the measurement model, the scattering centres are modelled as a Gaussian‐like probability distribution characterising the target's shape. Then the measurement noise is integrated into the Gaussian‐like probability distribution via Gaussian quadrature to obtain the accurate measurement likelihood. Moreover, particle swarm optimization is employed to reduce computational complexity. It enables particles to move into the vicinity of measurements, thereby increasing the diversity of the particles belonging to the target's shape. The simulation results show that the GL can improve the accuracy of shape estimation under high measurement noise and low measurement rates. Furthermore, the proposed particle filter can track the extended target effectively with an aeroplane‐like shape.

Single Pixel Imaging Based on Generative Adversarial Network Optimized With Multiple Prior Information

Reconstructing high-quality images at low measurement rate is one of the research objectives for single-pixel imaging (SPI). Deep learning based compressed reconstruction methods have been shown to avoid the huge iterative computation of traditional methods, while achieving better reconstruction results. Benefiting from improved modeling capabilities under the constant game of generation and identification, Generative Adversarial Networks (GANs) has achieved great success in image generation and reconstruction. In this paper, we proposed a GAN-based compression reconstruction network, MPIGAN. In order to obtain multiple prior information from the dataset and thus improving the accuracy of the model, multiple Autoencoders are trained as regularization terms to be added to the loss function of the generative network, and then adversarial training is performed with a multi-label classification network. Experimental results show that our scheme can significantly improve reconstruction quality at a very low measurement rate, and reconstruction results are better than the existing network.

Frequency Subsampling of Ultrasound Nondestructive Measurements: Acquisition, Reconstruction, and Performance.

In ultrasound nondestructive testing (NDT), a widespread approach is to take synthetic aperture measurements from the surface of a specimen to detect and locate defects within it. Based on these measurements, imaging is usually performed using the synthetic aperture focusing technique (SAFT). However, SAFT is suboptimal in terms of resolution and requires oversampling in the time domain to obtain a fine grid for the delay-and-sum (DAS). On the other hand, parametric reconstruction algorithms give better resolution, but their usage for imaging becomes computationally expensive due to the size of the parameter space and a large amount of measurement data in realistic 3-D scenarios when using oversampling. In the literature, the remedies to this are twofold. First, the amount of measurement data can be reduced using state-of-the-art sub-Nyquist sampling approaches to measure Fourier coefficients instead of time-domain samples. Second, parametric reconstruction algorithms mostly rely on matrix-vector operations that can be implemented efficiently by exploiting the underlying structure of the model. In this article, we propose and compare different strategies to choose the Fourier coefficients to be measured. Their asymptotic performance is compared by numerically evaluating the Cramér-Rao bound (CRB) for the localizability of the defect coordinates. These subsampling strategies are then combined with an l1 -minimization scheme to compute 3-D reconstructions from the low-rate measurements. Compared to conventional DAS, this allows us to formulate a fully physically motivated forward model matrix. To enable this, the projection operations of the forward model matrix are implemented matrix-free by exploiting the underlying two-level Toeplitz structure. Finally, we show that high-resolution reconstructions from as low as a single Fourier coefficient per A-scan are possible based on simulated data and measurements from a steel specimen.

Measurement-Induced Phase Transition in the Monitored Sachdev-Ye-Kitaev Model.

We construct Brownian Sachdev-Ye-Kitaev (SYK) chains subjected to continuous monitoring and explore possible entanglement phase transitions therein. We analytically derive the effective action in the large-N limit and show that an entanglement transition is caused by the symmetry breaking in the enlarged replica space. In the noninteracting case with SYK_{2} chains, the model features a continuous O(2) symmetry between two replicas and a transition corresponding to spontaneous breaking of that symmetry upon varying the measurement rate. In the symmetry broken phase at low measurement rate, the emergent replica criticality associated with the Goldstone mode leads to a log-scaling entanglement entropy that can be attributed to the free energy of vortices. In the symmetric phase at higher measurement rate, the entanglement entropy obeys area-law scaling. In the interacting case, the continuous O(2) symmetry is explicitly lowered to a discrete C_{4} symmetry, giving rise to volume-law entanglement entropy in the symmetry-broken phase due to the enhanced linear free energy cost of domain walls compared to vortices. The interacting transition is described by C_{4} symmetry breaking. We also verify the large-N critical exponents by numerically solving the Schwinger-Dyson equation.

Measurement transformation algorithm for extended target tracking

Extended target tracking algorithms require the estimation of the target's shape in addition to its kinematic properties. The star-convex model can describe the details of the target's shape compared with the traditional elliptical model. The shape estimation based on the star-convex model is a complex nonlinear problem, which also contains the uncertainty of the measurement distribution on the surface of the target. Therefore, we propose a novel nonlinear measurement transformation (MT) to map measurements into a high dimensional space, so that the transformed measurements have a more linear relationship with the states. The MT also extracts the uncorrelated distance and angle information of the measurement in the local polar coordinates to improve the stability and accuracy of the shape estimation. Then a linear minimum mean square error estimator is constructed using the nonlinear uncorrelated MT, and a Bayesian filter under Gaussian assumptions is derived. Moreover, we also propose a novel mathematical metric based on the star-convex model that measures the distance between the reference shape parameters and their estimated values. Simulation results demonstrate the effectiveness of the proposed algorithm in the case of high sensor noise, low measurement rate, and dense sensor clutter.

Compression of Remotely Sensed Astronomical Image Using Wavelet-Based Compressed Sensing in Deep Space Exploration

Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the wavelet domain. The algorithm starts with a traditional 2D discrete wavelet transform (DWT), which provides frequency information of an image. The wavelet coefficients are rearranged in a new structured manner determined by the parent–child relationship between the sub-bands. We design scanning modes based on the direction information of high-frequency sub-bands, and propose an optimized measurement matrix with a double allocation of measurement rate. Through a single measurement matrix, higher measurement rates can be simultaneously allocated to sparse vectors containing more information and coefficients with higher energy in sparse vectors. The double allocation strategy can achieve better image sampling. At the decoding side, orthogonal matching pursuit (OMP) and inverse discrete wavelet transform (IDWT) are used to reconstruct the image. Experimental results on simulated image and remotely sensed astronomical images show that our algorithm can achieve high-quality reconstruction with a low measurement rate.

Pseudo complementary measurement for traditional single-pixel cameras**Project supported by the National Key Research and Development Program of China (Grant No. 2018YFB0504302), the Youth Innovation Promotion Association of Chinese Academy of Sciences, and the National Natural Science Foundation of China (Grant Nos. 11701545, 11971466, and 11991021).

A traditional single-pixel camera needs a large number of measurements to reconstruct the object with compressive sensing computation. Compared with the 1/0 matrices in classical measurement, the 1/−1 matrices in the complementary measurement has better property for reconstruction computation and returns better reconstruction results. However, each row of the 1/−1 matrices needs two measurements with the traditional single-pixel camera which results into double measurements compared with the 1/0 matrices. In this paper, we consider the pseudo complementary measurement which only takes the same amount of measurements with the row number of some properly designed 1/0 matrix to compute the total luminous flux of the objective and derives the measurement data of the corresponding 1/−1 matrix in a mathematical way. The numerical simulation and experimental result show that the pseudo complementary measurement is an efficient tool for the traditional single-pixel camera imaging under low measurement rate, which can combine the advantages of the classical and complementary measurements and significantly improve the peak signal-to-noise ratio.

Learning Non-Locally Regularized Compressed Sensing Network With Half-Quadratic Splitting

Deep learning-based Compressed Sensing (CS) reconstruction attracts much attention in recent years, due to its significant superiority of reconstruction quality. Its success is mainly attributed to the employment of a large dataset for pre-training the network to learn a reconstruction mapping. In this paper, we propose a non-locally regularized compressed sensing network for reconstructing image sequences, which can achieve high reconstruction quality without pre-training. Specifically, the proposed method attempts to learn a deep network prior for the reconstruction of an individual instance under the constraint that the network output can well match the given CS measurement. The non-local prior is designed to guide the network to capture the long-range dependencies by exploiting the self-similarities among images, and it can also make the network noise-aware. In order to deal with the compound of non-local prior and deep network prior, we construct a half-quadratic splitting based optimization method for network learning, in which the two priors are decoupled into two simple sub-problems by introducing an auxiliary variable and a quadratic fidelity constraint. Extensive experimental results demonstrate that our method is competitive to the popular methods, including sparsity prior based methods and deep learning based methods, even better than them in the cases of low measurement rates.

Measurement-driven entanglement transition in hybrid quantum circuits

In this paper we continue to explore "hybrid" quantum circuit models in one-dimension with both unitary and measurement gates, focussing on the entanglement properties of wavefunction trajectories at long times, in the steady state. We simulate a large class of Clifford circuits, including models with or without randomness in the unitary gates, and with or without randomness in the locations of measurement gates, using stabilizer techniques to access the long time dynamics of systems up to 512 qubits. In all models we find a volume law entangled phase for low measurement rates, which exhibits a sub-dominant logarithmic behavior in the entanglement entropy, S_A = {\alpha} ln |A| + s|A|, with sub-system size |A|. With increasing measurement rate the volume law phase is unstable to a disentangled area law phase, passing through a single entanglement transition at a critical rate of measurement. At criticality we find a purely logarithmic entanglement entropy, S_A = {\alpha}(p_c) ln|A|, a power law decay and conformal symmetry of the mutual information, with exponential decay off criticality. Various spin-spin correlation functions also show slow decay at criticality. Critical exponents are consistent across all models, indicative of a single universality class. These results suggest the existence of an effective underlying statistical mechanical model for the entanglement transition. Beyond Clifford circuit models, numerical simulations of up to 20 qubits give consistent results.

Low-Cost Image Compressive Sensing with Multiple Measurement Rates for Object Detection.

When measurement rates grow, most Compressive Sensing (CS) methods suffer from an increase in overheads of transmission and storage of CS measurements, while reconstruction quality degrades appreciably when measurement rates reduce. To solve these problems in real scenarios such as large-scale distributed surveillance systems, we propose a low-cost image CS approach called MRCS for object detection. It predicts key objects using the proposed MYOLO3 detector, and then samples the regions of the key objects as well as other regions using multiple measurement rates to reduce the size of sampled CS measurements. It also stores and transmits half-precision CS measurements to further reduce the required transmission bandwidth and storage space. Comprehensive evaluations demonstrate that MYOLO3 is a smaller and improved object detector for resource-limited hardware devices such as surveillance cameras and aerial drones. They also suggest that MRCS significantly reduces the required transmission bandwidth and storage space by declining the size of CS measurements, e.g., mean Compression Ratios (mCR) achieves 1.43–22.92 on the VOC-pbc dataset. Notably, MRCS further reduces the size of CS measurements by half-precision representations. Subsequently, the required transmission bandwidth and storage space are reduced by one half as compared to the counterparts represented with single-precision floats. Moreover, it also substantially enhances the usability of object detection on reconstructed images with half-precision CS measurements and multiple measurement rates as compared to its counterpart, using a single low measurement rate.