Guided Wavefield Reconstruction from Sparse Measurements Using Compressed Sensing

Accurate distance information is essential for remote sensing mapping and 3D information reconstruction. Dense depth reconstruction from sparse measurements in indoor and outdoor natural scenes has been extensively researched. However, in aerial scenes, dense height image reconstruction from sparse height measurements has received relatively limited attention. This paper investigates the challenge of reconstructing aerial ground height from sparse height measurements. In this paper, we comprehensively evaluate the feasibility and performance of various sparse reconstruction algorithms in aerial scenes for the first time, and explore the optimal sampling rate and pattern for preserving the image structure and details. Additionally, we propose two novel algorithms based on the normalized weighted non-local Laplacian, which can achieve state-of-the-art results without relying on color image guidance. Moreover, our algorithm is inherently robust to different sampling configurations. The solver tools relied upon also have proven hardware and software optimisation and acceleration schemes in the industry, making them well suited for deployment in real-world scenarios. We validate the effectiveness and robustness of the proposed algorithms on three datasets.

Noncontact acoustic inspection method using airborne sound waves and laser Doppler vibrometer (LDV) has been studied to detect internal defects in structures due to aging. To improve the inspection efficiency, fundamental experiments were conducted on movement measurement using a cart equipped with a sound source and LDV. The experimental results confirmed that the LDV resonated when the excitation sound pressure was increased to improve the signal-to-noise ratio. Therefore, an attempt was made to reduce the effects of LDV resonance by subtraction processing using vibration waveforms measured in the healthy part first. Next, a study was conducted on further improvements to defect detection performance by combining several noise reduction processes. As a result, it was revealed that even with movement measurements, defect with a diameter of 300 mm and a depth of 100 mm can be detected at average travel speed of 0.26 m s−1.

To reduce adverse customer selection, service firms are empowering employees to use decision latitude to decide whether to provide a service to a potential customer. Customer selection often requires complex decision making that involves both quantitative and qualitative customer information. The authors introduce service employees’ desire for decision latitude (DDL) as an individual difference construct that influences the processing of quantitative and qualitative information in customer selection decisions. Across several studies, the authors find that individual differences in DDL moderate how service personnel synthesize quantitative and qualitative information in customer selection decisions. Service employees who have relatively higher levels of DDL integrate quantitative and qualitative information such that the cues combine interactively. Thus, they may act as though they are going against organizational norms by allowing qualitative information to override organizational decision-making norms. Conversely, low DDL individuals appear to combine both quantitative and qualitative information in a linear pattern to influence customer selection. These results suggest that managers should employ DDL to match employees with customers so that an optimal fit occurs. Managers should examine procedures for how information should be used and provide employees with clearly defined guidelines for how to employ quantitative and qualitative information when making customer selection decisions.

Information fusion in data association applications

Thermal Imaging can be really valuable to military, other users of surveillance camera and physicians. Contrary to the usual sampling process where weavers want to sample at the Nyquist rate the Compressive Sensing provides a stricter sampling condition when the signal is known to be sparse or compressible. It turned out that the Nyquist-Shannon theorem is a sufficient condition but not a necessary condition for perfect reconstruction. The compressive sensing field of interest provides a stricter sampling condition when the signal is known to be sparse or compressible. This theorem still struggles with three main problems: sparse representation, measurement matrix and reconstruction algorithm. Unfortunately, thermal image camera resolution is considerably lower than of optical cameras, mostly only 160×120 or 320×240 pixels narrow view. The 360-degree infrared surveillance would be the clear choice. Day or night, the IR Revolution 360 panoramic infrared camera provides instantaneous, long range, wide area, high resolution thermal imaging. The method utilizes double sensor thermal imaging cameras. Moreover the temporal resolution has been enhanced. This paper goal is to keep evolving with compressed sensing and thermal imaging technology.

On the basis of the compressed sensing theory, this study proposed an improved wavelet sparse represented compressed sensing based image fusion algorithm. This algorithm firstly got the wavelet sparse domain linear measurement values of the original images by the dual radial sampling mode. Then a simple maximum absolute value fusion rule was adopted on the compressed sensing domain. Finally, the minimum total variation method was used to reconstruct the fused image. The experiment result shows that this algorithm has good fusion effect.

Electrocardiogram (ECG) is a vital signal which represents the state of health of astronauts in a manned spaceflight mission and hence must be continuously acquired and transmitted throughout the mission. Telemetry bandwidth is a premium resource in such applications. Traditional method of sampling at Nyquist rate is highly bandwidth inefficient. Hence it is advantageous to use Compressive sensing (CS) technique to optimize data at measurement point itself. Conventional CS techniques employ computationally intensive measurement matrices which are not hardware efficient for both acquisition as well as recovery. In this work, a hardware efficient scheme with use of a sparse binary measurement matrix is proposed. The signal is recovered from the compressive measurement using a constraint function based on inverted Laplace distribution function. Gradient decent method is used to recursively recover the original ECG signal from the compressive measurements in an efficient manner. Apart from this, a projection scheme is also proposed to minimize the recovery error. The proposed scheme was extensively evaluated with different ECG samples with different compression ratios. Finally, the proposed scheme was benchmarked with Approximated L0 norm based method and it is found to perform more efficiently in compressively sensing and recovery of ECG signals.

The purpose of this study is to evaluate a hybrid system as a decision support model to assist with the auditor's going‐concern assessment. The going‐concern assessment is often an unstructured decision that involves the use of both qualitative and quantitative information. An expert system that predicts the going‐concern decision has been developed in consultation with partners at three of the Big Five accounting firms. This system is combined with a statistical model that predicts bankruptcy, as a component of the auditor's decision, to form a hybrid system. The hybrid system, because it combines the use of quantitative and qualitative information, has the potential for better prediction accuracy than either the expert system or statistical model predicting separately. In addition, testing of the system provides some insight into the characteristics of firms that experience problems, but do not necessarily receive a going‐concern modification. Further investigation into those firms that have problems could reveal factors that may be incorporated into decision support systems for auditors, in order to improve accuracy and reliability of these decision tools. © 2001 John Wiley & Sons, Ltd.

Deterministic construction of sparse binary matrices via incremental integer optimization

In this letter, we propose a fast reconstruction algorithm for 3-D turntable microwave imaging from sparse measurements. A conventional Fourier-transform-based 3-D microwave imaging method collects data over densely azimuth-elevation samples and needs a large amount of data storage and long collection time. To reduce the cost of data acquisition, the proposed method exploits the sparsity in the image domain to achieve 3-D microwave imaging by utilizing sparse measurements. For this aim, the signal model is first represented as a tensor array, and then, a novel sparse reconstruction algorithm called 3-D-SL0 is applied to recover the 3-D scattering reflectivity, i.e., a 3-D image. Simulation results are finally shown to investigate the validity of the proposed method.

Compressive sensing (CS) is an innovative approach to simultaneously measure and compress signals such as biomedical signals that are sparse or compressible. A major effort in CS is to design a measurement matrix that can be used to encode and compress such signals. The measurement matrix structure has a direct impact on the computational and storage costs as well as the recovered signal quality. Sparse measurement matrices (i.e. with few non-zero elements) may drastically reduce these costs. We propose a permuted Kronecker-based sparse measurement matrix for sensing and data recovery in CS applications. In our study, we use three classes of sub-matrices (normalized Gaussian, Bernoulli, and BCH-based matrices) to create the proposed measurement matrix. Using ECG signals from the MIT-BIH Arrhythmia database, we show that the reconstructed signal quality is comparable to the ones achieved using well known CS methods. Our methodology results in an overall reduction in storage and computations, both during the sensing and recovery process. This approach can be generalized to other classes of eligible measurement matrices in CS.

High-speed non-contact defect imaging for a plate-like structure

The identification of defects in plate-like structures has been successfully treated using both local guided ultrasonic waves and global modal quantities. Although there are many papers on these techniques, a lack of comparability between the two methods persists. This makes it difficult for users to identify the most appropriate method for the defect in question. This paper examines the effect of different parameterizations of a circular defect in a square aluminum plate on the system response in a case study. The measured local ultrasound signal from a propagating guided wave makes up the initial data set. The first method uses the entire waveform for the objective function of the optimization problem, while the second considers the velocity of the A0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_0$$\\end{document} mode. In the third and fourth methods, the first and second global natural frequencies of the plate modeled with free-free boundary conditions are investigated. The numerical models are validated experimentally through measurements with a laser Doppler vibrometer. This results in the qualitative and quantitative evaluation of the objective functions for all parameter combinations of the defect. The recommendation to sequentially employ modal analysis and then the ultrasound procedure is made for the defect type used in this study. The data gathered on the objective functions suggests that potential joint employment of the natural frequency and ultrasound methods may increase computational efficiency. For a specific case, the methods could also complement each other in terms of challenges such as local minima.

Deconvolution processing for improved acoustic wavefield imaging

Economic modeling is well-known difficult due to the complexity of the economy caused by humans as the roles of the economic activities. Humans are irrational sometimes and they are interacting with each other. They behave differently in different time and different situation. In theoretical mathematical economics, economists try to model the economy qualitatively using abstract mathematical tools. Applied mathematical economics introduces simplifying approximations to the theoretical qualitative model and quantifies model parameters for deterministic or stochastic computation or reasoning in order to predict and control the economic processes. This paper presents a framework for macro-economic modeling based on integrating qualitative and quantitative information. The framework introduces a new concept of integrated model, and incorporates integrated model construction, applied model selection and integration, model system evolution, and model-based qualitative and quantitative reasoning in a human-machine system. It is a modeling methodology in a multidimensional model space and a qualitative and quantitative mixed information environment. Under this framework, the complex economy modeling will be systemal and insightful, and the model(s) will be more applicable.