scholarly journals Multi-level sensor fusion for improved target discrimination

2002 ◽  
Author(s):  
C.L. McCullough ◽  
B.V. Dasarathy ◽  
P.C. Lindberg
Keyword(s):  
Sensor Fusion ◽  
Target Discrimination ◽  
Level Sensor ◽  
Multi Level

Multi-Level Sensor Fusion Algorithm Approach for BMD Interceptor Applications

1998 ◽  
Author(s):  
Doug Allen ◽  
Bart Smith ◽  
Norman Morris ◽  
Charles Bjork ◽  
John Rushing
Keyword(s):  
Sensor Fusion ◽  
Fusion Algorithm ◽  
Level Sensor ◽  
Multi Level

scholarly journals Camera-LiDAR Multi-Level Sensor Fusion for Target Detection at the Network Edge

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3992
Author(s):  
Javier Mendez ◽  
Miguel Molina ◽  
Noel Rodriguez ◽  
Manuel P. Cuellar ◽  
Diego P. Morales
Keyword(s):  
Sensor Fusion ◽  
Target Detection ◽  
Autonomous Vehicle ◽  
Machine Learning Algorithms ◽  
Processing Unit ◽  
Fusion Model ◽  
Detection Model ◽  
Level Sensor ◽  
Multi Level ◽  
Cloud Servers

There have been significant advances regarding target detection in the autonomous vehicle context. To develop more robust systems that can overcome weather hazards as well as sensor problems, the sensor fusion approach is taking the lead in this context. Laser Imaging Detection and Ranging (LiDAR) and camera sensors are two of the most used sensors for this task since they can accurately provide important features such as target´s depth and shape. However, most of the current state-of-the-art target detection algorithms for autonomous cars do not take into consideration the hardware limitations of the vehicle such as the reduced computing power in comparison with Cloud servers as well as the reduced latency. In this work, we propose Edge Computing Tensor Processing Unit (TPU) devices as hardware support due to their computing capabilities for machine learning algorithms as well as their reduced power consumption. We developed an accurate and small target detection model for these devices. Our proposed Multi-Level Sensor Fusion model has been optimized for the network edge, specifically for the Google Coral TPU. As a result, high accuracy results are obtained while reducing the memory consumption as well as the latency of the system using the challenging KITTI dataset.

Feature-level sensor fusion

1999 ◽  
Author(s):  
Tamar Peli ◽  
Mon Young ◽  
Robert Knox ◽  
Kenneth K. Ellis ◽  
Frederick Bennett
Keyword(s):  
Sensor Fusion ◽  
Level Sensor

scholarly journals GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8080
Author(s):  
Ahmed Shaheen ◽  
Umair bin Waheed ◽  
Michael Fehler ◽  
Lubos Sokol ◽  
Sherif Hanafy
Keyword(s):  
Deep Learning ◽  
Template Matching ◽  
Carbon Capture And Storage ◽  
Microseismic Monitoring ◽  
Seismic Events ◽  
Learning Approaches ◽  
Monitoring Networks ◽  
Level Sensor ◽  
Multi Level ◽  
Low Magnitude

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.

A Multi-Level Sensor based Spinal Cord disorder Classification Model for Patient Wellness and Remote Monitoring

2020 ◽  
pp. 1-1
Author(s):  
Sk. H Ahammad ◽  
Md. Zia Ur Rahman ◽  
L Koteswara Rao ◽  
Asiya Sulthana ◽  
Navarun Guptha ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Remote Monitoring ◽  
Classification Model ◽  
Level Sensor ◽  
Multi Level
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