Auto-adaptive Signal Segmentation Using Spectral Intrinsic Decomposition

This thesis contains new FPGA implementations of adaptive signal segmentation and autoregressive modeling techniques. Both designs use Simulink-to-FPGA methodology and have been successfully implemented onto Xilinx Virtex II Pro device. The implementation of adaptive signal segmentation is based on the conventional RLSL algorithm using double-precision floating point arithmetic for internal computation and is programmable for users providing data length and order selection functions. The implemented RLSL design provides very good performance of obtaining accurate conversion factor values with a mean correlation of 99.93% and accurate boundary positions for both synthesized and biomedical signals. The implementation of autoregressive (AR) modeling is based on the Burg-lattice algorithm using fixed point arithmetic. The implemented Burg design with order of 3 provides good performance of calculating AR coefficients of input biomedical signals.

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High Level FPGA Implementation Of Adaptive Signal Segmentation And Autoregressive Modeling Techniques

10.32920/ryerson.14662368 ◽

2021 ◽

Author(s):

Beibei. Jiao

Keyword(s):

Double Precision ◽

Autoregressive Modeling ◽

Biomedical Signals ◽

Signal Segmentation ◽

Modeling Techniques ◽

Lattice Algorithm ◽

High Level ◽

Ar Modeling ◽

Adaptive Signal ◽

Point Arithmetic

This thesis contains new FPGA implementations of adaptive signal segmentation and autoregressive modeling techniques. Both designs use Simulink-to-FPGA methodology and have been successfully implemented onto Xilinx Virtex II Pro device. The implementation of adaptive signal segmentation is based on the conventional RLSL algorithm using double-precision floating point arithmetic for internal computation and is programmable for users providing data length and order selection functions. The implemented RLSL design provides very good performance of obtaining accurate conversion factor values with a mean correlation of 99.93% and accurate boundary positions for both synthesized and biomedical signals. The implementation of autoregressive (AR) modeling is based on the Burg-lattice algorithm using fixed point arithmetic. The implemented Burg design with order of 3 provides good performance of calculating AR coefficients of input biomedical signals.

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A New Adaptive Signal Segmentation Approach Based on Hiaguchi’s Fractal Dimension

Communications in Computer and Information Science - Emerging Intelligent Computing Technology and Applications ◽

10.1007/978-3-642-31837-5_22 ◽

2012 ◽

pp. 152-159 ◽

Cited By ~ 3

Author(s):

Hamed Azami ◽

Alireza Khosravi ◽

Milad Malekzadeh ◽

Saeid Sanei

Keyword(s):

Fractal Dimension ◽

Signal Segmentation ◽

Segmentation Approach ◽

Adaptive Signal

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Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

Methods of Information in Medicine ◽

10.1055/s-0038-1634962 ◽

1994 ◽

Vol 33 (01) ◽

pp. 60-63 ◽

Cited By ~ 3

Author(s):

E. J. Manders ◽

D. P. Lindstrom ◽

B. M. Dawant

Keyword(s):

Computer Architecture ◽

Real Time ◽

Data Abstraction ◽

Monitoring Strategy ◽

Intelligent Monitoring ◽

Patient Status ◽

On Line ◽

Main Components ◽

And Control ◽

Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

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