Efficient seismic response data storage and transmission using ARX model-based sensor data compression algorithm

2006 ◽  
Vol 35 (6) ◽  
pp. 781-788 ◽  
Author(s):  
Yunfeng Zhang ◽  
Jian Li
Keyword(s):  
Data Compression ◽  
Seismic Response ◽  
Data Storage ◽  
Compression Algorithm ◽  
Sensor Data ◽  
Arx Model ◽  
Response Data ◽  
Model Based

An Evaluation of Model-Based Approaches to Sensor Data Compression

2013 ◽  
Vol 25 (11) ◽  
pp. 2434-2447 ◽  
Author(s):  
Nguyen Quoc Viet Hung ◽  
Hoyoung Jeung ◽  
Karl Aberer
Keyword(s):  
Data Compression ◽  
Sensor Data ◽  
Model Based

ARX Model Identification of Shui-Kou Concrete Gravity Dam Based on Seismic Response Data

2013 ◽  
Vol 353-356 ◽  
pp. 1959-1964
Author(s):  
Wen Qiao ◽  
Guo Ming Liu ◽  
Jin Wen He
Keyword(s):  
Seismic Response ◽  
Dynamic Characteristics ◽  
Natural Frequencies ◽  
Model Identification ◽  
Modal Identification ◽  
Gravity Dam ◽  
Concrete Gravity Dam ◽  
Arx Model ◽  
Response Data ◽  
Peak Point

Research on the dynamic characteristics of gravity dam was carried out by adopting ARX model using the seismic response data of concrete gravity dam at Shui-Kou hydropower station. The applicability and effectiveness of single-output-multiple-input ARX model were deduced and verified. A corresponding computer program was developed, and performed to identify the modal parameters of the system. The identified natural frequencies and damping ratios were basically same with the results by traditional peak point pick-up method, and also close to the finite element method (FEM) results. It is indicated that the structure natural frequencies and damping ratios are determined by the characteristics of the structure, the dynamic characteristics identified by ARX model are correct, and ARX model can avoid frequency leakiness when smoothing processing and Fourier transform are conducted in solving process of the peak point pick-up method. The modal identification can be applied to other structures.

A Lossless Data Compression Algorithm for Wireless Sensor Networks Based on Linear Regression Model

2011 ◽  
Vol 403-408 ◽  
pp. 2441-2444
Author(s):  
Hong Zhi Lu ◽  
Xue Jun Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Data Compression ◽  
Linear Regression Model ◽  
Compression Algorithm ◽  
Sensor Data ◽  
Simple Linear Regression ◽  
Compression Algorithms ◽  
One Dimensional ◽  
Lossless Data Compression

According to the theory of simple linear regression model, this paper designed a lossless sensor data compression algorithm based on one-dimensional linear regression model. The algorithm computes the linear fitting values of sensor data’s differences and fitting residuals, which are input to a normal distribution entropy encoder to perform compression. Compared with two typical lossless compression algorithms, the proposed algorithm indicated better compression ratios.

scholarly journals Efficient Sensor Data Compression Algorithm for Data-Centric Storage

2010 ◽  
Vol 10 (11) ◽  
pp. 58-67
Author(s):  
Kyu-Jong Roh ◽  
Myung-Ho Yeo ◽  
Dong-Ook Seong ◽  
Kyoung-Soo Bok ◽  
Jae-Ryong Shin ◽  
...  
Keyword(s):  
Data Compression ◽  
Compression Algorithm ◽  
Sensor Data

scholarly journals Trajectory Data Compression Algorithm Based on Motion State Changing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shuo Zhang ◽  
Jian Zhang ◽  
Lin Qi
Keyword(s):  
Data Compression ◽  
Data Storage ◽  
Compression Algorithm ◽  
Moving Object ◽  
Trajectory Data ◽  
Compression Efficiency ◽  
Motion State ◽  
Object Movement ◽  
Data Points ◽  
Movement Tendency

The trajectory information generated by the moving object plays an important role in studying the object movement. In this paper, a trajectory data compression algorithm based on the motion state changing is proposed to reduce trajectory data storage space and increase compression speed, which can accurately show the motion state and trajectory characteristics. This study has certain significance for the exploration of mass traffic data and the planning of traffic network. Combining the angle threshold with the velocity threshold of a moving object, the key data points are found and the redundant information is removed. Subsequently, the compressed trajectory is obtained. The experimental results show that the new algorithm can help to improve compression efficiency. The compressed trajectory has high similarity with the original trajectory in movement tendency.

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

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