scholarly journals Realization of a device for the evaluation of the muscular effort through the Electromyogram signal EMG

2021 ◽  
Vol 8 (1) ◽  
pp. 01-08
Author(s):  
S.M Debbal
Keyword(s):  
Real Time ◽  
Digital Data ◽  
Instrumentation Amplifier ◽  
Surface Electrode ◽  
Graphical Interface ◽  
Digital Conversion ◽  
Emg Signal ◽  
Muscular Effort

In this work, a device for the evaluation of the muscular effort through the electromyogram signal is produced. This device consists essentially of three parts: the sensor part, the shaping portion, the acquisition part and the software part. The sensor part allows the EMG signal to be collected by means of surface electrode. The shaping port is realized based on an instrumentation amplifier. The acquisition part concerns the analogue digital conversion and the transfer of the digital data to the pc; this is done via an arduino card, which is equipped with a microcontroller for the visualization in real time and the storage of the EMG signal on the pc on which the processing logitiels will be implemented. The signal thus processed must be displayed with the data allowing the evaluation of the effort on the monitor of the pc through a graphical interface; these are the different steps that are carried out to finalize this work.

Study of Wireless Torque Measurement System Based on CC2500

2011 ◽  
Vol 383-390 ◽  
pp. 5300-5303
Author(s):  
Wei Liu ◽  
Xiao Jie Song ◽  
Wen Gang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Digital Data ◽  
Measuring System ◽  
Instrumentation Amplifier ◽  
Torque Sensor ◽  
Torque Measurement ◽  
Analog To Digital ◽  
Wireless Transceiver ◽  
Precision Measuring ◽  
Transmission Shaft

It’s very difficult to get high precision measuring result using contact torquemeter because of very low signal-to-noise ratio. To overcome this defect, a wireless torque measuring system is designed based on CC2500. This system uses strain gauge torque sensor to measure the surface principal stress of the transmission shaft, and get the maximum shearing stress, and then the torque that the transmission shaft bears. The weak output signal of torque sensor is magnified by the instrumentation amplifier AD623, and sent to the analog-to-digital convertor. These digital data are transmited to the portable receiving terminal by the wireless transceiver chip CC2500. The dynamic wireless torque measurement is realized by this system.

A comparison of select trigger algorithms for automated global seismic phase and event detection

1998 ◽  
Vol 88 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Mitchell Withers ◽  
Richard Aster ◽  
Christopher Young ◽  
Judy Beiriger ◽  
Mark Harris ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
Spectral Characteristics ◽  
Digital Data ◽  
Robust Detection ◽  
Location System ◽  
Global Correlation ◽  
Seismic Coda ◽  
Wide Range

Abstract Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

scholarly journals DESIGN AND ANALYSIS OF DIFFERENT TECHNIQUES FOR ANALOG-TO-DIGITAL CONVERSION OF VIBRATION SIGNALS FOR WIRELESS MEASUREMENT SYSTEMS

2018 ◽  
Vol 17 (1) ◽  
pp. 39
Author(s):  
Milan Dinčić ◽  
Dragan Denić ◽  
Zoran Perić
Keyword(s):  
Digital Data ◽  
Bit Rate ◽  
Rate Reduction ◽  
Digital Conversion ◽  
Vibration Signals ◽  
Analog To Digital ◽  
Measurement Systems ◽  
Analog To Digital Conversion ◽  
Uniform Quantization ◽  
Wireless Measurement

The aim of this paper is to design, analyze and compare four different systems for ADC (analog-to-digital conversion) of vibration signals. Measurement of vibration signals is of particular importance in many areas, such as predictive maintenance or structural health monitoring. Wireless systems for vibration measurements becomes very topical, due to much easier and cheaper installation compared to wired systems. Due to the lack of transmission bandwidth and energy in wireless measurement systems, the amount of digital data being sent has to be reduced; hence, we have to apply ADC systems that can achieve the required digital signal quality, reducing the bit-rate. Four ADC systems are analyzed, for possible application in wireless measurement systems: PCM (pulse code modulation) based on uniform quantization; DPCM (differential PCM) to exploit high correlation of vibration signals; two adaptive ADC systems to cope with significant variations of characteristics of vibration signals in time - APCM (adaptive PCM) with adaptation on variance and ADPCM (adaptive DPCM), with double adaptation (both on variance and correlation). These ADC models are designed and optimized specifically for vibration signals, based on the analysis of 20 vibration signals from a referent database. An experiment is done, applying designed ADC systems for digitalization of vibration signals. APCM, DPCM and ADPCM systems allow significant bit-rate reduction compared to the PCM system, but with the increasing of complexity, hence the compromise between the bit-rate reduction and complexity is needed.

scholarly journals A State-wide Health IT Infrastructure for Population Health: Building a Community-wide Electronic Platform for Maryland’s All-Payer Global Budget

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Elham Hatef ◽  
Hadi Kharrazi ◽  
Ed VanBaak ◽  
Marc Falcone ◽  
Lindsey Ferris ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Population Health ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Smoking Status ◽  
Digital Data ◽  
Health It ◽  
Global Budget ◽  
Health Initiatives

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland’s unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH’s EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland.First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high- level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs.

Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach

2021 ◽  
Author(s):  
Aurore Lafond ◽  
Maurice Ringer ◽  
Florian Le Blay ◽  
Jiaxu Liu ◽  
Ekaterina Millan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Large Scale ◽  
Hybrid Approach ◽  
Physical Models ◽  
Training Data ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
New System

Abstract Abnormal surface pressure is typically the first indicator of a number of problematic events, including kicks, losses, washouts and stuck pipe. These events account for 60–70% of all drilling-related nonproductive time, so their early and accurate detection has the potential to save the industry billions of dollars. Detecting these events today requires an expert user watching multiple curves, which can be costly, and subject to human errors. The solution presented in this paper is aiming at augmenting traditional models with new machine learning techniques, which enable to detect these events automatically and help the monitoring of the drilling well. Today’s real-time monitoring systems employ complex physical models to estimate surface standpipe pressure while drilling. These require many inputs and are difficult to calibrate. Machine learning is an alternative method to predict pump pressure, but this alone needs significant labelled training data, which is often lacking in the drilling world. The new system combines these approaches: a machine learning framework is used to enable automated learning while the physical models work to compensate any gaps in the training data. The system uses only standard surface measurements, is fully automated, and is continuously retrained while drilling to ensure the most accurate pressure prediction. In addition, a stochastic (Bayesian) machine learning technique is used, which enables not only a prediction of the pressure, but also the uncertainty and confidence of this prediction. Last, the new system includes a data quality control workflow. It discards periods of low data quality for the pressure anomaly detection and enables to have a smarter real-time events analysis. The new system has been tested on historical wells using a new test and validation framework. The framework runs the system automatically on large volumes of both historical and simulated data, to enable cross-referencing the results with observations. In this paper, we show the results of the automated test framework as well as the capabilities of the new system in two specific case studies, one on land and another offshore. Moreover, large scale statistics enlighten the reliability and the efficiency of this new detection workflow. The new system builds on the trend in our industry to better capture and utilize digital data for optimizing drilling.

Data Science

2018 ◽  
pp. 141-151
Author(s):  
Sabitha Rajagopal
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Research And Development ◽  
Real Time ◽  
Data Science ◽  
Data Warehousing ◽  
Holistic Approach ◽  
Digital Data ◽  
Data Application ◽  
Development Analysis

Data Science employs techniques and theories to create data products. Data product is merely a data application that acquires its value from the data itself, and creates more data as a result; it's not just an application with data. Data science involves the methodical study of digital data employing techniques of observation, development, analysis, testing and validation. It tackles the real time challenges by adopting a holistic approach. It ‘creates' knowledge about large and dynamic bases, ‘develops' methods to manage data and ‘optimizes' processes to improve its performance. The goal includes vital investigation and innovation in conjunction with functional exploration intended to notify decision-making for individuals, businesses, and governments. This paper discusses the emergence of Data Science and its subsequent developments in the fields of Data Mining and Data Warehousing. The research focuses on need, challenges, impact, ethics and progress of Data Science. Finally the insights of the subsequent phases in research and development of Data Science is provided.

The Data Imperative

2020 ◽  
pp. 19-43
Author(s):  
Henri Schildt
Keyword(s):  
Real Time ◽  
Business Processes ◽  
New Technologies ◽  
Digital Data ◽  
Digital Information ◽  
Information Flows ◽  
End Point ◽  
The World ◽  
The Creation ◽  
And Control

This chapter examines digitalization as a set of new normative ideals for managing and organizing businesses, enabled by new technologies. The data imperative consists of two mutually reinforcing goals: the pursuit of omniscience—the aspiration of management to capture the world relevant to the company through digital data; and the pursuit of omnipotence—an aspiration of managers to control and optimize activities in real-time and around the world through software. The data imperative model captures a self-reinforcing cycle of four sequential steps: (1) the creation and capture of data, (2) the combination and analysis of data, (3) the redesign of business processes around smart algorithms, and (4) the ability to control the world through digital information flows. The logical end-point of the data imperative is a ‘programmable world’, a conception of society saturated with Internet-connected hardware that is able to capture processes in real time and control them in order to optimize desired outcomes.

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