Analyses of muscular activity during rhythmic behaviors provide critical data for biomechanical studies. Electrical potentials measured from muscles using electromyography (EMG) require discrimination of noise regions...
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Analyses of muscular activity during rhythmic behaviors provide critical data for biomechanical studies. Electrical potentials measured from muscles using electromyography (EMG) require discrimination of noise regions as the first step in analysis. An experienced analyst can accurately identify the onset and offset of EMG but this process takes hours to analyze a short (10-15 s) record of rhythmic EMG bursts. Existing computational techniques reduce this time but have limitations. These include a universal threshold for delimiting noise regions (i.e., a single signal value for identifying the EMG signal onset and offset), pre-processing using wide time intervals that dampen sensitivity for EMG signal characteristics, poor performance when a low frequency component (e.g., DC offset) is present, and high computational complexity leading to lack of time efficiency. We present a new statistical method and MATLAB script (EMG-Extractor) that includes an adaptive algorithm to discriminate noise regions from EMG that avoids these limitations and allows for multi-channel datasets to be processed. We evaluate the EMG-Extractor with EMG data on mammalian jaw-adductor muscles during mastication, a rhythmic behavior typified by low amplitude onsets/offsets and complex signal pattern. The EMG-Extractor consistently and accurately distinguishes noise from EMG in a manner similar to that of an experienced analyst. It outputs the raw EMG signal region in a form ready for further analysis. (C) 2016 Elsevier Ltd. All rights reserved.
In this article, we describe a low-cost indoor navigation system, based on the capabilities of modern smartphones commonly equipped with accelerometer, gyroscope, camera and Internet connection. The main claim of this...
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In this article, we describe a low-cost indoor navigation system, based on the capabilities of modern smartphones commonly equipped with accelerometer, gyroscope, camera and Internet connection. The main claim of this paper is that, relaxing the requirement of best accuracy, with an intelligent use of inertial sensors, digital maps, and ambient tagging, it is still possible to get good results. Our mobile application helps the user in retrieving directions and finding places in large indoor environments where the global positioning system (GPS) is not available, such as airports, hospitals, museums and so on. The goal is to get a system able to work without the use of any physical ad hoc infrastructure and without relying on any wearable device. We name our infrastructure-free system Roodin, and its features are as follows: user-friendly interface, quick install and calibration tool, point-of-interest search and guidance. All the features presented in this paper are designed and implemented, and the application has been evaluated with real users. A summary of user evaluation is reported in the paper.
This tutorial offers a comprehensive view of technological solutions and theoretical fundamentals of localization and tracking (LT) systems for wireless networks. We start with a brief classification of the most commo...
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This tutorial offers a comprehensive view of technological solutions and theoretical fundamentals of localization and tracking (LT) systems for wireless networks. We start with a brief classification of the most common types of LT systems, e.g. active versus passive technologies, centralized versus distributed solutions and so forth. To continue, we categorize the LT techniques based on the elementary types of position-related information, namely, connectivity, angle, distance and power-profile. The attention is then turned to the difference between active and passive LT systems, highlighting the evolution of the localization techniques. Motivated by the interests of industry and academia on distance-based active localization system, a deep review of the most common algorithms used in these systems is provided. Non-bayesian and bayesian techniques will be tackled and compared with numerical simulations. To list some of the proposed approaches, we mention the multidimensional scaling (MDS), the semidefinite programming (SDP) and the Kalman filter (KF) methods. To conclude the tutorial, we address the fundamental limits of the accuracy of range-based positioning. Based on the unifying framework proposed by Abel, we derive the closed-form expressions for the Cramer-Rao lower bound (CRLB), the Battacharyya Bound (BB), the Hammersley-Chapmann-Robbins Bound (HCRB) and the Abel Hybrid Bound (AHB) in a source localization scenario. We show a comparison of the aforementioned bounds with respect to a Maximum-Likelihood estimator and explore the difference between random and regular (equi-spaced anchors) network topologies. Finally, extensions to cooperative scenarios are also discussed in connection with the concept of information-coupling existing in multitarget networks.
This paper is devoted to the presentation of a new technique of characterization of the Intra-Pixel Sensitivity Variations (IPSVs) of astronomical detectors. The IPSV is the spatial variation of the sensitivity within...
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ISBN:
(纸本)9780819496225
This paper is devoted to the presentation of a new technique of characterization of the Intra-Pixel Sensitivity Variations (IPSVs) of astronomical detectors. The IPSV is the spatial variation of the sensitivity within a pixel and it was demonstrated that this variation can contribute to the instrument global error. Then IPSV has not to be neglected especially in the case of under-sampled instruments for high quality imaging and accurate photometry. The common approaches to measure the IPSV consist in determining the pixel response function (PRF) by scanning an optical probe through the detector. These approaches require high-aperture optics, high precision mechanical devices and are time consuming. The original approach we will present in this paper consists in projecting high-resolution periodic patterns onto the whole sensor without classic optics but using the self-imaging property (the Talbot effect) of a Continuously Self Imaging Grating (CSIG) illuminated by a plane wave. This paper describes the test bench and its design rules. The methodology of the measurement is also presented. Two measurement procedures are available: global and local. In the global procedure, the mean PRF corresponding to the whole Focal Plane Array (FPA) or a sub-area of the FPA is evaluated. The results obtained applying this procedure on e2v CCD 204 are presented and discussed in detail. In the local procedure, a CSIG is moved in front of each pixel and a pixel PRF is reconstructed by resolving the inverse problem. The local procedure is presented and validated by simulations. (Color images and figures in online version)
Atomic force microscopy (AFM) is a technique widely used to image or apply forces to surface-bound biomolecules in liquid. Traditional methods for imaging DNA and protein-DNA complexes in liquid have drawbacks: DNA co...
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Atomic force microscopy (AFM) is a technique widely used to image or apply forces to surface-bound biomolecules in liquid. Traditional methods for imaging DNA and protein-DNA complexes in liquid have drawbacks: DNA conformations with an anomalous persistence length, low SNR, and/or ionic conditions detrimental to preserving native protein-DNA interactions. Here, we introduce a minimally perturbative method for imaging surface-bound DNA that improves data quality and quantity. In comparison to prior protocols, an eight-fold larger fraction (90%) of 680-nm-long DNA molecules were quantifiable, and the technique is viable for imaging DNA of many lengths and proteins in a variety of physiological buffers. In addition to improving AFM imaging, this work also advances the reliability and accuracy of single-molecule force spectroscopy (SMFS) AFM experiments. In particular, recent improvements in time resolution and data throughput of SMFS experiments highlight the need for high-precision, automated characterization of force-induced intra- and inter-molecular bond ruptures. We describe a new algorithm to automatically identify the locations of molecular ruptures in SMFS data. In order to improve molecular energy landscape characterization, we also applied the inverse Weierstrass transform to SMFS data and removed the energy associated with the AFM force probe, yielding the molecular free-energy landscape. Combined, these improvements in AFM methodology -- minimally perturbative imaging of DNA-protein complexes, automated detection of molecular rupture events, and energy deconvolution of force probes -- advance the quality and reproducibility of biophysical insights gained from AFM-based experiments.
Reinforcement learning has emerged as a prominent technique for enhancing robot obstacle avoidance capabilities in recent years. This research provides a comprehensive overview of reinforcement learning methods, focus...
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The Thompson Sampling algorithm's use in individualized healthcare decision-making is examined in this study. Utilizing constantly changing patient data, the algorithm, which is rooted in bayesian principles, dyna...
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