Low Rate Measurements Research Articles

Convolutional Neural Networks for Noniterative Reconstruction of Compressively Sensed Images

Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this paper, we propose a data-driven noniterative algorithm to overcome the shortcomings of earlier iterative algorithms. Our solution, ReconNet , is a deep neural network, which is learned end-to-end to map block-wise compressive measurements of the scene to the desired image blocks. Reconstruction of an image becomes a simple forward pass through the network and can be done in real time. We show empirically that our algorithm yields reconstructions with higher peak signal-to-noise ratios (PSNRs) compared to iterative algorithms at low measurement rates and in presence of measurement noise. We also propose a variant of ReconNet, which uses adversarial loss in order to further improve reconstruction quality. We discuss how adding a fully connected layer to the existing ReconNet architecture allows for jointly learning the measurement matrix and the reconstruction algorithm in a single network. Experiments on real data obtained from a block compressive imager show that our networks are robust to unseen sensor noise.

Integral versions of input-to-state stability for dual-rate nonlinear sampled-data systems

This paper presents versions of integral input-to-state stability and integral input-to-integral-state stability for nonlinear sampled-data systems, under the low measurement rate constraint. In particular, we compensate the lack of measurements using an estimator approximately reconstructing the current state. Interestingly, under certain checkable conditions, we establish that a controller that semiglobally practically integral input-to-(integral-)state stabilizes an approximate discrete-time model of a single-rate nonlinear sampled-data system, also stabilizes the exact discrete-time model of the nonlinear sampled-data system in the same sense implemented in a dual-rate setting. Numerical simulations are given to illustrate the effectiveness of our results.

Performance Analysis of ToA-Based Positioning Algorithms for Static and Dynamic Targets with Low Ranging Measurements.

Indoor Positioning Systems (IPSs) for emergency responders is a challenging field attracting researchers worldwide. When compared with traditional indoor positioning solutions, the IPSs for emergency responders stand out as they have to operate in harsh and unstructured environments. From the various technologies available for the localization process, ultra-wide band (UWB) is a promising technology for such systems due to its robust signaling in harsh environments, through-wall propagation and high-resolution ranging. However, during emergency responders’ missions, the availability of UWB signals is generally low (the nodes have to be deployed as the emergency responders enter a building) and can be affected by the non-line-of-sight (NLOS) conditions. In this paper, the performance of four typical distance-based positioning algorithms (Analytical, Least Squares, Taylor Series, and Extended Kalman Filter methods) with only three ranging measurements is assessed based on a COTS UWB transceiver. These algorithms are compared based on accuracy, precision and root mean square error (RMSE). The algorithms were evaluated under two environments with different propagation conditions (an atrium and a lab), for static and mobile devices, and under the human body’s influence. A NLOS identification and error mitigation algorithm was also used to improve the ranging measurements. The results show that the Extended Kalman Filter outperforms the other algorithms in almost every scenario, but it is affected by the low measurement rate of the UWB system.

Fast reconstruction of high-qubit-number quantum states via low-rate measurements

Due to the exponential complexity of the resources required by quantum state tomography (QST), people are interested in approaches towards identifying quantum states which require less effort and time. In this paper, we provide a tailored and efficient method for reconstructing mixed quantum states up to $12$ (or even more) qubits from an incomplete set of observables subject to noises. Our method is applicable to any pure or nearly pure state $\rho$, and can be extended to many states of interest in quantum information processing, such as multi-particle entangled $W$ state, GHZ state and cluster states that are matrix product operators of low dimensions. The method applies the quantum density matrix constraints to a quantum compressive sensing optimization problem, and exploits a modified Quantum Alternating Direction Multiplier Method (Quantum-ADMM) to accelerate the convergence. Our algorithm takes $8,35$ and $226$ seconds respectively to reconstruct superposition state density matrices of $10,11,12$ qubits with acceptable fidelity, using less than $1 \%$ of measurements of expectation. To our knowledge it is the fastest realization that people can achieve using a normal desktop. We further discuss applications of this method using experimental data of mixed states obtained in an ion trap experiment of up to $8$ qubits.

SENSOR FAULT DETECTION AND SIGNAL IMPROVEMENT USING PREDICTIVE FILTERS

Navigation systems used in racing boats require sensors to be more and more sophisticated in order to obtain accurate information in real time. To meet the need for accuracy of the surface speed measurement, the mechanical sensor paddle wheel has been replaced by the ultrasonic sensor. This ultrasonic sensor measures the water speed precisely and with very good linearity. Furthermore, by its principle of operation, it measures the water flow several centimetres from the sensor, which puts it outside the boundary layer, the region close to the hull where the flow is disturbed. However, this sensor has several drawbacks: it is quite sensitive and if the flow contains too many air bubbles, the sensor picks them up, which can happen quite frequently on boat with a planing hull. Another limitation of this sensor is its low frequency measurement rate. In this paper, we explain the techniques used based on Kalman filters to address these shortcomings, firstly by identifying the inaccurate measurements caused by inadvertent dropouts, then by improving the useful sensor frequency with GNSS data fusion.

Continuous glucose monitoring system in the operating room and intensive care unit: any difference according to measurement sites?

Given the benefit of glucose control in the perioperative period, we evaluated the accuracy and performance of the continuous glucose monitoring system (CGMS) depending on different measurement sites in the operating room (OR) and in the intensive care unit (ICU). Patients over 18years of age scheduled for elective surgery and ICU admission were enrolled prospectively. Two CGMS sensors were inserted into the subcutaneous tissue of the proximal lateral thigh and the lateral abdomen. The rate of successful measurements from thigh and abdomen in the OR and in the ICU were calculated separately. Each CGMS values were compared with the time-matched arterial blood glucose measurements. CGMS values from both measurement sites were also compared. A total of 22 patients undergoing cardiac surgeries were studied. The rate of successful measurements was higher in the ICU (73.2%) than in the OR (66.0%) (P=0.01); however, that from thigh (72.9%) and from abdomen (58.7%) showed statistically significant difference only in the OR (P=0.04). The Pearson correlation coefficient of thigh and abdomen versus arterial values was 0.67 and 0.60, respectively (P<0.001). In Clarke error grid analysis, 94.6% (89.3% in the OR and 96.1% in the ICU) of values from thigh fell into clinically acceptable zones compared to 93.7% (89.0% in the OR and 95.4% in the ICU) from abdomen. There were no statistically significant differences in the accuracy according to measurement sites. The CGMS showed high measurement failure rate, especially in the OR. In the OR, the rate of successful measurement was higher from thigh than from abdomen. The CGMS showed low accuracy compared to arterial reference values. Nevertheless, there was no difference in the accuracy of the CGMS between two measurement sites. Perioperative performance of the CGMS still needs to be improved considering relatively low successful measurement rates.

Lock-in spectroscopy employing a high-speed camera and a micro-scanner for volumetric investigations of unsteady flows.

Spectroscopic methods are established tools for nonintrusive measurements of flow velocity. However, those methods are either restricted by measuring pointwise or with low measurement rates of several hertz. To investigate fast unsteady phenomena, e.g., in sprays, volumetric (3D) measurement techniques with kHz rate are required. For this purpose, a spectroscopic technique is realized with a power amplified, frequency modulated laser and an Mfps high-speed camera. This allows fast continuous planar measurements of the velocity. Volumetric data is finally obtained by slewing the laser light sheet in depth with an oscillating microelectromechanical systems (MEMS) scanner. As a result, volumetric velocity measurements are obtained for 256×128×25 voxels over 14.4 mm×7.2 mm×6.5 mm with a repetition rate of 1 kHz, which allows the investigation of unsteady phenomena in sprays such as transients and local velocity oscillations. The respective measurement capabilities are demonstrated by experiments. Hence, a significant progress regarding the data rate was achieved in spectroscopy by using the Mfps high-speed camera, which enables new application fields such as the analysis of fast unsteady phenomena.

Output feedback stabilization of nonlinear dual-rate sampled-data systems via an approximate discrete-time model

The problem of output feedback stabilization of nonlinear sampled-data control systems is considered under the low measurement rate constraint. A dual-rate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the missing output values between measured output samples. It is shown that if an observer-based output feedback controller that semiglobally practically asymptotically (SPA) stabilizes the single-rate sampled-data plant model is given, then the SPA stability property will be preserved for the closed-loop dual-rate sampled-data system, on the basis of the proposed dual-rate control scheme under standard assumptions. A numerical example is given to illustrate the design method.

Impulsive noise rejection method for compressed measurement signal in compressed sensing

Abstract The Lorentzian norm of robust statistics is often applied in the reconstruction of the sparse signal from a compressed measurement signal in an impulsive noise environment. The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments. In this article, the impulsive noise rejection for the compressed measurement signal with the design for image reconstruction is proposed. It is used as the preprocessing for any compressed sensing reconstruction given that the sparsified version of the signal is obtained by utilizing octave-tree discrete wavelet transform with db8 as the mother wavelet. The presence of impulsive noise is detected from the energy distribution of the reconstructed sparse signal. After the noise removal, the noise corrupted coefficients are estimated. The proposed method requires neither complex optimization nor complex parameter adjustments. The performance of the proposed method was evaluated on 60 images. The experimental results indicated that the proposed method effectively rejected the impulsive noise. Furthermore, at the same impulsive noise corruption level, the reconstruction with the proposed method as the preprocessing required much lower measurement rate than the model-based Lorentzian iterative hard thresholding.

Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling

Abstract The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.

TH‐C‐BRC‐04: Hierarchical Real‐Time Respiratory Motion Prediction: A Divide‐and‐Conquer Scheme for Application‐Oriented Optimality

Purpose: Motion management applications differ in their measurement rates and prediction length requirement: it is unlikely to find a Universityersally optimal predictor. This study develops a divide‐and‐conquer scheme, by partitioning the operation region and assigning proper predictor to each sub‐ region. Such hierarchical fusion architecture achieves application‐dependent optimal prediction performance. Method and Materials: We parameterize the overall operation region of real‐time prediction in terms of (measurement rate, prediction length) to parameterize the configuration and requirement of various motion management systems. The region of study is set to be [5Hz, 30Hz]*[200ms,600ms], spanning the full scope from non‐ tomographic to tomographic measurement, and for both gating and tracking controls. Four predictor candidates are examined: (a) adaptive linear (b) interactive multiple linear (MLM) (c) KDE (d) local circular motion (LCM); and it is determined that LCM is optimal for low measurement rates and short prediction lengths, while KDE is superior for medium‐to‐high measurement rate and medium‐to‐long prediction length. An operation‐ region specific hybrid model is developed and compared with the individual predictors. Prediction performance is quantified by nRMSE between predicted value and retrospective observations. Normalization with data deviation ensures meaningful population‐based statistics. Results: The proposed method outperforms all individual benchmark methods uniformly. The nRMSE was controlled to be under 0.15 for 200ms prediction and 0.4 for 600ms prediction, translating to sub‐mm prediction accuracy for typical respiratory motions. Conclusion: We have developed a hybrid predictor that adapts to specific motion management applications. This hybrid predictor combines the simplicity of the LCM model under low data rate condition, and the flexibility of the nonparametric KDE approach for long prediction lengths. The general methodology of divide‐and‐conquer extend to incorporate other predictors naturally, should they offer better performance in a sub‐region. The resulting hierarchical predictor uniformly outperforms individual methods, and provides an ideal motion management module for motion‐adaptive radiotherapy.

Input-to-state stabilization for nonlinear dual-rate sampled-data systems via approximate discrete-time model

The problem of state feedback stabilization of nonlinear sampled-data systems is considered under the “low measurement rate” constraint. A dual-rate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state. Given an approximate discrete-time model of a sampled nonlinear plant and given a family of controllers that stabilizes the plant model in input-to-state sense, we show that under some standard assumptions the closed loop dual-rate sampled data system is input-to-state stable in the semiglobal practical sense.

Preservation of input-to-state stability under sampling and emulation: multi-rate case

The stabilization problem of sampled-data non-linear systems is considered under the low measurement rate constraint. A multi-rate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state. We show that if we design a continuous-time controller for a continuous-time plant so that the closed-loop continuous-time system is input-to-state stable and then discretize the controller and implement it using sample and zero order hold devices, then input-to-state stability property will be preserved for the sampled-data multi-rate closed loop system in a practical sense.

Multirate sampling and input-to-state stable receding horizon control for nonlinear systems

In this paper, a robust receding horizon control for multirate sampled-data nonlinear systems with bounded disturbances is presented. The proposed receding horizon control is based on the solution of Bolza-type optimal control problems for the approximate discrete-time model of the nominal system. “Low measurement rate” is assumed. It is shown that the multistep receding horizon controller that stabilizes the nominal approximate discrete-time model also practically input-to-state stabilizes the exact discrete-time system with disturbances.

MULTIRATE SAMPLING AND DELAYS IN RECEDING HORIZON STABILIZATION OF NONLINEAR SYSTEMS

In this paper, a multirate version of the receding horizon algorithm for the stabilization of sampled-data nonlinear systems is presented. The computations are based on discrete-time approximate models. “Low measurement rate” is assumed, and the presence of measurement and computational delays are taken into account. It is shown that, under reasonable assumptions, the proposed algorithm gives a closed-loop system which is stable in appropriate sense.

Multirate versions of sampled-data stabilization of nonlinear systems

The problem of state feedback sampled-data stabilization of nonlinear systems is considered under the “low measurement rate” constraint and in the presence of (not necessarily small) time delay in the measurement channel. A multirate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state from the delayed measurements. For both the controller emulation approach and the approach based on approximate discrete-time model of the system, we show that under standard assumptions the closed-loop multirate sampled data system is asymptotically stable in the semiglobal practical sense. An illustrative example of sampled-data control of vertical take off and landing aircraft is discussed.

Approximating multirate estimation

A scheme of approximating high rate estimates by low rate estimates using a filter bank is derived. The dimensions of the low rate measurement models are the same as the high rate measurement model and, due to the lowpass filtering effect of the filter bank, better approximated estimates are obtained.

Reconciliation of process flow rates by matrix projection. Part I: Linear case

AbstractFlow rate measurements in a steady‐state process are reconciled by weighted least squares so that the conservation laws are obeyed. A projection matrix is constructed which can be used to decompose the linear problem into the solution of two subproblems, by first removing each balance around process units with an unmeasured component flow rate. The remaining measured flow rates are reconciled, and the unmeasured flow rates can then be obtained from the solution of the conservation equations. The basic case contains constraints which are linear in the component and the total flow rates. The method is extended to cases with bilinear constraints, involving unknown parameters such as split fractions.Chi‐square and normal statistics are used to test for overall gross measurement errors, for gross error in each node imbalance which is fully measured, and for each measurement adjustment.

Potential sources of error in the measurement of low rates of planktonic photosynthesis and excretion.

THE introduction of liquid scintillation counting techniques to the study of aquatic productivity1–3 has stimulated interest in the accurate determination of low rates of algal photosynthesis, such as occur in tropical waters or in deep living phytoplankton populations. Attention is also being directed to the measurement of soluble extracellular products of photosynthesis4. Our studies have revealed some potential sources of interference in the widely used 14C technique when used to study low rates: (1) retention of NaH14C03 by filters; (2) passive adsorption of radioactivity by filters and/or seston and (3) soluble and insoluble radioactive impurities in commercially available NaH14C03.

Measurement of Low Rates of Oxygen Consumption With a Horizontal Capillary-Differential Syringe Manometer.

SummaryA small differential syringe manometer with a 30 mm horizontal capillary is described. Estimates of accuracy and reproducibility were made by measuring the release of known volumes of nitrogen gas and by measuring the slow respiration rates that occur in unsupplemented liver homoge-nates. Absorption of oxygen at rates as low as 3 μliters/hour could be measured with a reproducibility of ±1 μliter. The small size of the apparatus together with the accuracy, reproducibility, and simplicity of measurement recommend its use over conventional vertical column differential manometers.