Beamforming using locally optimum performance indicator model fitting

Data processing in high energy physics experiments is a multi-tiered process in which raw detector signals are first processed locally into physics objects, and then collated into event records which can be scrutinized by a fast online trigger system. The resulting selection of events are reconstructed and pass through a number of software filters before arriving at a final offline analysis where hard physical constants are extracted. Although sophisticated statistical data analysis techniques are routinely employed high energy physics, the use of statistical signal processing in the field is has until now been rare. Our paper will begin with an overview of a typical high energy physics data acquisition system, outlining the technologies and tradeoffs involved at each stage. We will then proceed to argue that the dominant roles of model dependence and systematic errors in final physics analyses render statistical signal processing techniques largely inapplicable at this level. We observe, however, that at the low-level pattern recognition and event reconstruction levels, statistical signal processing techniques have been making inroads in high energy physics for a number of years, and examples from the literature will be cited. The viability of the technique for second level triggers will be assessed. Parallels to other other approaches, such as neural networks, will also be drawn. It will be argued that the falling cost of computing hardware favors the growth of statistical signal processing methods in high energy physics

Partial Discharge (PD) detection is one of the important methods used to evaluate the insulation condition of in-service high voltage (HV) electrical equipment. On-site PD detection methods are preferred compared to other methods as non-intrusive sensors are easier to install and have no interruptions on the operation. An electrical PD testing utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the in-service instrument transformer was used to detect partial discharge activities. However, the limitation of this method is the measurement often lacks of accuracy due to interference in the measured PD waveforms. Therefore, such PD signals are hard to be distinguished from the stochastic noise pulses. Hence, the characteristics of PD signals and interference signals are studied in the Phase Resolved Partial Discharge (PRPD) pattern in order to identify the extent of PD occurrence in the signals. The discharge magnitude could increase significantly because of the noise signals superposed with PD signals. Therefore a method that can remove high magnitude noise will be proposed by using statistical signal processing technique.

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique real-time geochemical analysis that is being developed for portable use outside of the laboratory. In this study, statistical signal processing and classification techniques were applied to single-shot, broadband LIBS spectra, comprising measured plasma light intensities between 200 and 960 nm, for a suite of 157 garnets of different composition from 92 locations worldwide. Partial least squares discriminant analysis was applied to sets of 25 LIBS spectra for each garnet sample and used to classify the garnet samples based on composition and geographic origin. Careful consideration was given to the cross-validation procedure to ensure that the classification algorithm is robust to unseen data. The results indicate that broadband LIBS analysis can be used to discriminate garnets of different composition and has the potential to discern geographic origin.

In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.

In this paper, an image fusion technique based on statistical signal processing is studied. Without the multiscale transform (MST) for the original images, the fusion is directly made via using Expectation-Maximization (EM) algorithm. Unlike the standard EM fusion algorithm, we only use the neighboring pixels in the window to estimate the centered pixel. The improvement of the iterative procedure is helpful to obtain the final fusion result effectively. At last, some performance evaluations are suggested and applied to compare the performance of these fusion methods. Consequently, the obtained results and comparisons indicate that the method here has significant advantage. Keywords-image fusion; statistical signal processing; EM algorithm

<p>The Marsh Lane viaduct is a masonry railway bridge constructed during the 19th century nearby Leeds Central Railway Station. The bridge appears significantly damaged due to the increase of the operational train loads over the last decades and due to environmental effects. Due to this degradation, extensive repair was conducted in 2015. After this repair work, an extensive fibre optic sensor network was installed below three spans of the bridge to monitor surface strains at 68 locations on the underside of the arch spans. The paper compares data collected from two monitoring periods, 16 months apart. Combining statistical analysis and signal processing techniques, the results show that local damage, as well as change in the global dynamic behaviour of the structure over time, can be effectively detected with the use of Fibre Bragg Grating sensors.</p>

Blind signal separation is applied to suppress backscatter for a 532 nm chaotic lidar underwater rangefinder. When operating in turbid waters, chaotic lidar returns contain information for both submerged objects and an optical backscatter “clutter” component. The statistical digital signal processing technique of blind signal separation (BSS) is applied to dynamically measure and cancel out the backscatter component before computing range to the desired object. Results from simulations and laboratory experiments are presented to demonstrate the combined chaotic/BSS approach. The chaotic/BSS approach extends operating range by almost 40% and achieves centimeter-order range accuracy. Receiver operating curves are simulated for the chaotic/BSS approach, which show convergence towards the ideal classifier in several conditions of interest.

Invasive arterial blood pressure (BP) is a vital sign in hemodynamic monitoring of trauma intensive care unit (ICU) patients. Continuous BP analysis can potentially provide additional information about patient status, predict morbidity and mortality, and automatically populate electronic nurse charting systems than intermittent monitoring. Challenges to routine application in the ICU include integration of complex physiological data collection systems, artifacts, missing data, and the various clinical interventions that may temporarily corrupt the BP signal. We have developed and previously described SIMON (signal interpretation and monitoring), a physiological data collection system in the Trauma ICU at Vanderbilt University. In order to extract useful information from continuous arterial line BP monitoring, it is necessary to remove non-physiological artifacts. In this setting, potential artifacts appear to be caused by resonance, over-damping, and data transmission. We designed a simple filter to identify various sources of non-physiological artifacts using statistical signal processing techniques. We implemented the filter to arterial invasive BP signals of 1852 trauma patients throughout their length of ICU stay. After filtering, the power of BP measures to predict hospital death was enhanced. Therefore, we concluded that our strategy of removing non-physiological artifact was simple, fast and useful for an accurate assessment of BP measures in trauma patients

This paper addresses the problem of parameter estimation for tracking charged particles. The ad hoc techniques commonly used in high energy physics may not be sufficient to perform tracking in the complex background environment of future experiments, and algorithms based on modern signal processing may give interesting results. This paper describes a specific parameter estimation problem of a muon detector used in the ATLAS experiment, where the use of the estimation maximization (EM) algorithm results in improved performance, as an example of the potential benefit of using statistical signal processing techniques in high energy physics.

Based on the correlation properties of the pseudo-random code in spread spectrum receivers, a novel and unified quantizer model was established, which was suitable for both uniform and nonuniform quantization. Statistical signal processing technique and extreme value theory were employed to obtain the analytical formulas of the optimal quantization value and corresponding quantization losses. Numerical results of uniform quantization, nonuniform quantization and theoretically optimal quantization are achieved using genetic algorithm and Monte Carlo simulation, which can be used to guide the engineering design of the GNSS receivers.

To overcome the common problem, protocol complexity of conventional underwater node estimation techniques, a statistical signal processing technique was proposed as an alternative. The technique uses cross-correlation as signal processing tool and hence it is called cross-correlation based underwater node estimation technique. This technique can overcome the protocol complexities, human interaction, over cost, etc., of conventional techniques. However, to introduce the readers with different technical aspects regarding this technique, the paper is introduced. We have used mean, and standard deviation as our estimation parameter to estimate nodes. At the same time, we have illustrated a brief discussion on effect of signal length, underwater bandwidth, etc., simultaneously in this paper. The present work can boost the researches in this field.

Target selection and ranking is a critical aspect of mineral exploration that is complicated when the host geology either masks or mimics the responses from targets of interest. The approach described here uses statistical signal processing techniques, specifically matched filtering, to help recognise targets in the presence of such confusing effects.The essence of this approach is to assume that the data are composed of noise, generated by the background geology, and signals generated by targets of exploration interest. It is this recognition of the geology as a noise source, which allows traditional signal processing strategies to be applied to the problem. The critical step, absolutely necessary to success, involves pre-whitening the data so that those characteristics of the target that are as different as possible from this "geological noise" are emphasised.Matched filtering is a well-established technique in fields other than mineral exploration. Work here has shown that, in many instances, it can also be applied successfully to AEM data. It provides a useful way of highlighting targets for further investigation by simplifying the interpretation process and filtering out many complex anomalies that are not feasible targets. This paper presents some of the necessary theory and the results of a number of case studies where the technique has been applied.

Wind profile prediction at different scales plays a crucial role for efficient operation of wind turbines and wind power prediction. This problem can be approached in two different ways: one is based on statistical signal processing techniques and both linear and nonlinear models can be employed either separately or combined together for profile prediction; on the other hand, wind/atmospheric flow analysis is a classical problem in computational fluid dynamics (CFD) in applied mathematics, which employs various numerical methods and algorithms, although it is an extremely time-consuming process with high computational complexity. In this work, a new method is proposed based on synergy's between the signal processing approach and the CFD approach, by alternating the operations of a quaternion-valued least mean square (QLMS) algorithm and the large eddy simulation (LES) in CFD. As demonstrated by simulation results, the proposed method has a much lower computational complexity while maintaining a comparable prediction result.

Previous work in subsurface object discrimination using electromagnetic induction data has shown that discrimination algorithms based on statistical signal processing techniques are effective for classifying data from objects that occur in isolation. However, for multiple closely spaced subsurface objects, the raw (unprocessed) measurement is a mixture of the responses from several objects and as such cannot be used directly to determine the identity of each of the individual objects. Thus, we propose to separate individual signatures from the mixture by posing the problem as a blind source separation (BSS) problem and effecting signature separation using independent component analysis. We propose to apply BSS to separate the mixed signatures and then follow the separation process with a Bayesian classifier. This approach is evaluated using both simulated data and data from unexploded ordnance items. The results show that this approach can be used to effectively classify multiple closely spaced objects.

The results of experimental studies to detect and locate subsurface hidden objects by near-surface determination of statistical variations of the natural pulsed electromagnetic background field in the 0.1-50 kHz frequency range are presented. There are possibilities of passive electromagnetic probing by use of the necessary electronic equipment and statistical signal processing technique described. Some field results to locate subsurface pipelines and cables and other hidden objects including archeological ones are shown. The perspectives of this passive probing technique for variety of applications in urban conditions are discussed.