Imaging of Multiples and its Application on Single Sensor Data

2017 ◽  
Author(s):  
J. Mao ◽  
S.N. Baldock ◽  
J. Sheng
Keyword(s):  
Sensor Data ◽  
Single Sensor

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

2018 ◽  
Vol 14 (04) ◽  
pp. 4
Author(s):  
Xuemei Yao ◽  
Shaobo Li ◽  
Yong Yao ◽  
Xiaoting Xie
Keyword(s):  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Monitoring And Diagnosis ◽  
Multi Sensor Data Fusion ◽  
Basic Probability ◽  
Optimal Fusion ◽  
Single Sensor ◽  
Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

Interpretation of Single-sensor Data - a Practical Perspective

2013 ◽  
Author(s):  
V. Aarre ◽  
A. Poole ◽  
B. Mitchell ◽  
S. Tan ◽  
G. Busanello
Keyword(s):  
Sensor Data ◽  
Single Sensor

De-noising Strategy on Single-source Single-sensor Data: a Case Study from North Kuwait

2018 ◽  
Author(s):  
T. Rebert ◽  
J. Rivault ◽  
G. Gigou ◽  
L. Vivin ◽  
H. Toubiana ◽  
...  
Keyword(s):  
Sensor Data ◽  
Single Source ◽  
Single Sensor

Sensor Integration and Data Fusion Theory

2010 ◽  
pp. 160-188
Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou
Keyword(s):  
Data Fusion ◽  
Error Compensation ◽  
Intelligent Manufacturing ◽  
Sensor Integration ◽  
Sensor Data ◽  
Sensor Technology ◽  
Fusion Technology ◽  
Knowledge Based ◽  
Manufacturing Intelligence ◽  
Single Sensor

In previous chapters, the engineering scientific foundations of manufacturing intelligence (such as the knowledge-based system, Multi-Agent system, data mining and knowledge discovery, and computing intelligence) have been discussed in detail. Sensor integration and data fusion is another important theory of manufacturing intelligence. With the development of integrated systems, there is an urgent requirement for improving system automaticity and intelligence. Without improvement, the complexity and scale of systems are increased. Such systems need to be more sensitive to their work environment and independent state, and obviously, single sensor technology hardly meets these requirements. Multi-sensor and data fusion technology are therefore employed in automatic and intelligent manufacturing as it is more comprehensive and accurate than traditional single sensor technology if the information redundancy and complementarity are used reasonably. In theory, the outputs of multi-sensors are mutually validated. Multi-sensor integration is a brand new concept for intelligent manufacturing, and without doubt, sensor integration-based intelligent manufacturing is the development orientation of manufacturing in the future. With reference to the information fusion problem of the multi-sensor integration system, the development state, technical background, application scope and basic meaning of the multi-sensor integration and the data fusion are first reviewed in this chapter. Secondly the classification, level, system structure and function model of the data fusion system is discussed. The theoretical method of the data fusion is then introduced, and finally, attention is paid to cutting tool condition detection, machine thermal error compensation and online detection and error compensation because those are the main applications of multi-sensor data fusion technology in intelligent manufacturing.

Design and Implementation of Engine Waveform Tester Based on Virtual Instrument

2013 ◽  
Vol 288 ◽  
pp. 223-227
Author(s):  
Jin Gang Ma ◽  
Hong Ying Song ◽  
Jiang Li Hou
Keyword(s):  
Fault Diagnosis ◽  
Virtual Instrument ◽  
Sensor Data ◽  
Waveform Analysis ◽  
Signal Acquisition ◽  
Design And Implementation ◽  
Single Sensor ◽  
Data Acquisition And Processing ◽  
And Control ◽  
Fusion Theory

Engine fault diagnosis and detection is inseparable from the analysis and examination of each sensor or actuator, and the waveform display is the most intuitive and convenient way. This system is an engine waveform tester developed based on virtual instrument and the batch estimate fusion theory is used in the process of the single sensor data acquisition and processing. It is proved by practice that this instrument can conveniently and quickly realize the functions such as signal acquisition and control, waveform analysis and processing and result expressing and output, thus providing technical support for comprehensive intelligent engine fault diagnosis technology.

Research on Equipment Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 466-467 ◽  
pp. 1222-1226
Author(s):  
Bin Ma ◽  
Lin Chong Hao ◽  
Wan Jiang Zhang ◽  
Jing Dai ◽  
Zhong Hua Han
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Diagnostic Accuracy ◽  
Evidence Theory ◽  
Experimental Results ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Sensor

In this paper, we presented an equipment fault diagnosis method based on multi-sensor data fusion, in order to solve the problems such as uncertainty, imprecision and low reliability caused by using a single sensor to diagnose the equipment faults. We used a variety of sensors to collect the data for diagnosed objects and fused the data by using D-S evidence theory, according to the change of confidence and uncertainty, diagnosed whether the faults happened. Experimental results show that, the D-S evidence theory algorithm can reduce the uncertainty of the results of fault diagnosis, improved diagnostic accuracy and reliability, and compared with the fault diagnosis using a single sensor, this method has a better effect.

Optimal Scheduling and Filtering of Distorted Multi-Sensor Data

2002 ◽  
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Error Function ◽  
Sensor Data ◽  
Practical Implementation ◽  
Signal Recovery ◽  
Fusion Procedure ◽  
Source Data ◽  
True Representation ◽  
Single Sensor ◽  
Fusion Scheme ◽  
Recovery Schemes

In this paper, a framework to schedule and filter distorted multi-sensor outputs is developed. The distortion caused by sensor nonlinearity is considered. As the distorted signal is not a true representation of the original signal, it is often necessary to develop and incorporate signal recovery schemes. Once such a scheme is developed, implementation may be straightforward if the system is employed with a single sensor. When the bandwidth of a signal of interest is very high, the use of a single sensor may not be feasible. High cost and accuracy are major concerns worth noting. It is proposed that a good practical solution to this problem is to employ an array of low bandwidth sensors. Practical Implementation of recovery schemes is very challenging and difficult in this case due to a possible overlapping of multi-source data. This sensor scheduling problem is investigated in detail and a data fusion scheme based on the optimization of weighted error function is initiated and developed for the two-sensor case. Simulation results are presented to validate the fusion procedure developed.

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