35 research outputs found
Intelligent instrumentation: principles and applications
No full textWith the advent of microprocessors and digital-processing technologies as catalyst, classical sensors capable of simple signal conditioning operations have evolved rapidly to take on higher and more specialized functions including validation, compensation, and classification. This new category of sensor expands the scope of incorporating intelligence into instrumentation systems, yet with such rapid changes, there has developed no universal standard for design, definition, or requirement with which to unify intelligent instrumentation. Explaining the underlying design methodologies of intelligent instrumentation, Intelligent Instrumentation: Principles and Applications provides a comprehensive and authoritative resource on the scientific foundations from which to coordinate and advance the field. Employing a textbook-like language, this book translates methodologies to more than 80 numerical examples, and provides applications in 14 case studies for a complete and working understanding of the material. Beginning with a brief introduction to the basic concepts of process, process parameters, sensors and transducers, and classification of transducers, the book describes the performance characteristics of instrumentation and measurement systems and discusses static and dynamic characteristics, various types of sensor signals, and the concepts of signal representations, various transforms, and their operations in both static and dynamic conditions. It describes smart sensors, cogent sensors, soft sensors, self-validating sensors, VLSI sensors, temperature-compensating sensors, microcontrollers and ANN-based sensors, and indirect measurement sensors. The author examines intelligent sensor signal conditioning such as calibration, linearization, and compensation, along with a wide variety of calibration and linearization techniques using circuits, analog-to-digital converters (ADCs), microcontrollers, ANNs, and software. The final chapters highlight ANN techniques for pattern classification, recognition, prognostic diagnosis, fault detection, linearization, and calibration as well as important interfacing protocols in the wireless networking platform
Multi Channel Sensor Linearization in Field Programmable Gate Array for Real Time Applications
No full textAbstract: In industrial applications multi-channel data acquisition and logging is of prime importance for process monitoring and control. In such situations linearization of the non-linear responses obtained from multichannel data acquisition systems is of prime importance for precise monitoring and control. Although various techniques have been developed for sensor linearization, real time multi channel linearization approaches are rare. This paper describes FPGA (Field Programmable Gate Array) implementation of different linearization techniques for multi-channel nonlinear sensor characteristics. The proposed multi-channel linearization techniques are implemented in real time using -piecewise linearization (PWL), look up table (LUT) based linearization, linearization by interpolation (LI) and artificial neural network (ANN) based linearization methods. We have discussed the different aspects of these linearization techniques and the trade-off between accuracy and implementation area in FPGA. The comparative analysis was performed by using identical thermistors connected to 8 channels for linearization and the performance of each method was analyzed. The performance of different techniques of linearization is estimated on the basis of logic utilization, linearization accuracy, execution time, noise immunity and speed of operation. Fixed point arithmetic has been used for data representation in the FPGA implementatio
Robust Detection of R-Wave Using Wavelet Technique
No full textElectrocardiogram (ECG) is considered to be the
backbone of cardiology. ECG is composed of P, QRS & T waves and
information related to cardiac diseases can be extracted from the
intervals and amplitudes of these waves. The first step in extracting
ECG features starts from the accurate detection of R peaks in the
QRS complex. We have developed a robust R wave detector using
wavelets. The wavelets used for detection are Daubechies and
Symmetric. The method does not require any preprocessing therefore,
only needs the ECG correct recordings while implementing the
detection. The database has been collected from MIT-BIH arrhythmia
database and the signals from Lead-II have been analyzed. MatLab
7.0 has been used to develop the algorithm. The ECG signal under
test has been decomposed to the required level using the selected
wavelet and the selection of detail coefficient d4 has been done based
on energy, frequency and cross-correlation analysis of decomposition
structure of ECG signal. The robustness of the method is apparent
from the obtained results
Networks and GMM for Vocal/Nonvocal segmentation for Singer Identification
No full textmanab @ tezu.ernet.in Abstract — Vocal and nonvocal segmentation is an important task in singing voice signal processing. Before identifying the singer it is necessary to locate the singer’s voice in a song. Maximum of the songs start with a piece of instrumental accompaniment known as ‘prelude ’ in musical terms after which the singing voice comes into play. Therefore, it is necessary to detect the vocal region in the song in order to extract the singer’s voice characteristics and to avoid the non-vocal region which includes the instrumental accompaniment. This work thus classifies Vocal and Nonvocal region in songs using three different classifiers: Gaussian Mixture Model (GMM), Artificial Neural Network (ANN) with Feed Forward Backpropagation algorithm and Learning Vector Quantization (LVQ). Mel Frequency Cepstral Coefficient (MFCC) has been considered as the primary feature for classification. An available database MUSCONTENT is used and a newly created Database ASDB1 consisting of sixty excerpts from a wide variety of Assamese songs has been examined applying the same methods of classification. The efficacy of the classifiers has been tested and the results indicate that LVQ is a robust classifier compared to FFBP and GMM
