Real-time cable tension estimation from acceleration measurements using wireless sensors with packet data losses: analytics with compressive sensing and sparse component analysis

2021 ◽  
Author(s):  
Debasish Jana ◽  
Satish Nagarajaiah ◽  
Yongchao Yang ◽  
Shunlong Li
Keyword(s):  
Compressive Sensing ◽  
Real Time ◽  
Wireless Sensors ◽  
Component Analysis ◽  
Cable Tension ◽  
Sparse Component Analysis ◽  
Packet Data

Multi-harmonic sources location based on sparse component analysis and complex independent component analysis

2020 ◽  
Vol 14 (19) ◽  
pp. 4195-4206
Author(s):  
Guangui Wang ◽  
Xiaoyang Ma ◽  
Weikang Wang ◽  
Honggeng Yang ◽  
Chang Chen ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Sparse Component Analysis

scholarly journals Real-Time Principal Component Analysis

2020 ◽  
Vol 1 (2) ◽  
pp. 1-36
Author(s):  
Ranak Roy Chowdhury ◽  
Muhammad Abdullah Adnan ◽  
Rajesh K. Gupta
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis

Compressive Sensing and Morphology Singular Entropy-Based Real-Time Secondary Voltage Control of Multiarea Power Systems

2019 ◽  
Vol 15 (7) ◽  
pp. 3796-3807 ◽  
Author(s):  
Irfan Khan ◽  
Yinliang Xu ◽  
Soummya Kar ◽  
Mo-yuen Chow ◽  
Vikram Bhattacharjee
Keyword(s):  
Power Systems ◽  
Compressive Sensing ◽  
Real Time ◽  
Voltage Control ◽  
Secondary Voltage ◽  
Multiarea Power Systems

scholarly journals Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

scholarly journals Real-Time Primitives for CoAP: Extending the Use of IoT for Time Constraint Applications for Social Good

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1257
Author(s):  
Gabriel Eggly ◽  
Mariano Finochietto ◽  
Emmanouil Dimogerontakis ◽  
Rodrigo Santos ◽  
Javier Orozco ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Real Time ◽  
Processing Speed ◽  
Wireless Sensors ◽  
Communication Protocols ◽  
Social Good ◽  
Vulnerable Communities ◽  
The Social

Internet of Things (IoT) have become a hot topic since the official introduction of IPv6. Research on Wireless Sensors Networks (WSN) move towards IoT as the communication platform and support provided by the TCP/UDP/IP stack provides a wide variety of services. The communication protocols need to be designed in such a way that even simple microcontrollers with small amount of memory and processing speed can be interconnected in a network. For this different protocols have been proposed. The most extended ones, MQTT and CoAP, represent two different paradigms. In this paper, we present a CoAP extension to support soft real-time communications among sensors, actuators and users. The extension facilitates the instrumentation of applications oriented to improve the quality of life of vulnerable communities contributing to the social good.

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