Optimal Passive Source Localization for Acoustic Emissions

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Hybrid Method of Video Shot Segmentation Based on YCbCr Space Color Model

In this digital world, Video analysis is the most important and useful task. Currently, tremendous tasks have been done in video analysis like compressing the videos, video retrieval process and video database indexing, etc. For all these tasks, one common step is segmenting the video shots, which are referred to as Video Shots Segmentation (VSS). Video shots segmentation is used to segment the input videos into a number of frames sequentially where the scene changes occurred, i.e. called shots. In this article, segmenting the video shots follows a hybrid procedure. Here, we have introduced the moments of colors, distance metrics and threshold techniques. All the videos follow the above mentioned steps for segmenting the video shots. But, before that, the input video is converted into a specific color model i.e. YCbCr. Then, apply the color moments to extract the feature vectors of frames, which are differentiated based on the color features of frames. In every two frames of the video, distance metrics methods are applying to compute the similarity and dissimilarity of frames. And the dissimilarity of the frames can be computed by using the threshold technique to get the shots from the video. In this paper, we are using the adaptive threshold technique to segment the videos into various shots. In this step, we will get a true number of shots. By the experimental results, this proposed methodology can be evaluated with the sequence of videos based on the performance or evaluation metrics.

Time-Variant Front-End Read-Out Electronics for High-Data-Rate Detectors

The foreseen incremental luminosity for near-future high-energy physics experiments demands evolution for the read-out electronics in terms of event data-rate. However, the filtering necessary to reject noise and meet the signal-to-noise-ratio requirements imposes a restriction on the operational speed of the conventional read-out electronics. The stringent trade-off between signal-to-noise-ratio and the event data-rate originates from the time-invariant behavior of the conventional systems. In this paper, the cases of time-variant systems are addressed, studying a benchmark with the RC-CR shaping function used in time-over-threshold methods. It was demonstrated that the time-variant systems enable a higher data-rate for the given noise performance. Moreover, taking advantage of time-variant systems, the proposed rising-edge method enables further data-rate enhancement with respect to the traditional time-over-threshold technique by reading the data from the rising edge of the analog output waveform. A comparison between the conventional time-invariant time-over-threshold technique, its time-variant equivalent and rising-edge method confirms the better performance of the latter one in terms of data-rate enhancement for a target noise performance. Moreover, design challenges for time-variant systems are briefly discussed, considering the ATLAS Monitored Drift Tube detector as a design case.

Segmentation of Online Freehand Sketching Based on Speed Feature

We present a novel method to extract speed feature points for segmenting hand-drawn strokes into geometric primitives. The method consists of three steps. Firstly, the input strokes are classified into uniform and nonuniform speed strokes, representing a stroke drawn at relatively constant or uneven speeds, respectively. Then, a sharpening filter is used to enhance the peak features of the uniform speed strokes. Finally, a three-threshold technique that uses the average speed of the pen and its upper and lower deviations is used to extract speed feature points of strokes. We integrate the proposed method into our freehand sketch recognition (FSR) system to improve its robustness to support multiprimitive strokes. Through a user study with 8 participants, we demonstrate that the proposed method achieves higher segmentation efficiency in finding speed feature points than the existing method based on a single speed threshold.

Parkinson’s Patients’ Tolerance for Risk and Willingness to Wait for Potential Benefits of Novel Neurostimulation Devices: A Patient-Centered Threshold Technique Study

Background. Parkinson’s disease (PD) is neurodegenerative, causing motor, cognitive, psychological, somatic, and autonomic symptoms. Understanding PD patients’ preferences for novel neurostimulation devices may help ensure that devices are delivered in a timely manner with the appropriate level of evidence. Our objective was to elicit preferences and willingness-to-wait for novel neurostimulation devices among PD patients to inform a model of optimal trial design. Methods. We developed and administered a survey to PD patients to quantify the maximum levels of risks that patients would accept to achieve potential benefits of a neurostimulation device. Threshold technique was used to quantify patients’ risk thresholds for new or worsening depression or anxiety, brain bleed, or death in exchange for improvements in “on-time,” motor symptoms, pain, cognition, and pill burden. The survey elicited patients’ willingness to wait to receive treatment benefit. Patients were recruited through Fox Insight, an online PD observational study. Results. A total of 2740 patients were included and a majority were White (94.6%) and had a 4-year college degree (69.8%). Risk thresholds increased as benefits increased. Threshold for depression or anxiety was substantially higher than threshold for brain bleed or death. Patient age, ambulation, and prior neurostimulation experience influenced risk tolerance. Patients were willing to wait an average of 4 to 13 years for devices that provide different levels of benefit. Conclusions. PD patients are willing to accept substantial risks to improve symptoms. Preferences are heterogeneous and depend on treatment benefit and patient characteristics. The results of this study may be useful in informing review of device applications and other regulatory decisions and will be input into a model of optimal trial design for neurostimulation devices.