A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System

2020 ◽  
Vol 67 (12) ◽  
pp. 3521-3530
Author(s):  
S. Islam ◽  
V. Shah ◽  
S.T.R. Gidde ◽  
P. Hutapea ◽  
S. H. Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensing System ◽  
Brain Deformation

scholarly journals Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

2021 ◽  
Vol 13 (3) ◽  
pp. 401
Author(s):  
Cadan Cummings ◽  
Yuxin Miao ◽  
Gabriel Dias Paiao ◽  
Shujiang Kang ◽  
Fabián G. Fernández
Keyword(s):  
Machine Learning ◽  
Chlorophyll Content ◽  
Vegetation Index ◽  
Soil Drainage ◽  
Management Information ◽  
Area Index ◽  
Sensing System ◽  
Extreme Gradient Boosting ◽  
Split Plot ◽  
N Status

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

Field-effect transistor array modified by a stationary phase to generate informative signal patterns for machine learning-assisted recognition of gas-phase chemicals

2019 ◽  
Vol 4 (2) ◽  
pp. 386-389
Author(s):  
Toshihiro Yoshizumi ◽  
Tatsuro Goda ◽  
Rui Yatabe ◽  
Akio Oki ◽  
Akira Matsumoto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stationary Phase ◽  
Gas Phase ◽  
Field Effect ◽  
Field Effect Transistor ◽  
Machine Learning Techniques ◽  
Informative Signal ◽  
Sensing System ◽  
Learning Techniques ◽  
Effect Transistor

We propose an artificial intelligence-based chemical-sensing system integrating a porous gate field-effect transistor (PGFET) array modified by gas chromatography stationary phase materials and machine-learning techniques.

scholarly journals A Wireless Intracranial Brain Deformation Sensing System for Blast-Induced Traumatic Brain Injury

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
S. Song ◽  
N. S. Race ◽  
A. Kim ◽  
T. Zhang ◽  
R. Shi ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Sensing System ◽  
Brain Deformation

Machine Learning for Molecular Perceptron: A Perception-Based Sensing System

2020 ◽  
Vol MA2020-01 (6) ◽  
pp. 632-632
Author(s):  
Yoona Yang ◽  
Zvi A Yaari ◽  
Alex Settle ◽  
Daniel A. Heller ◽  
Ming Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensing System

scholarly journals Machine Learning for Turning Optical Fiber Specklegram Sensor into a Spatially-Resolved Sensing System. Proof of Concept

2018 ◽  
Vol 36 (17) ◽  
pp. 3733-3738 ◽  
Author(s):  
Alberto Rodriguez Cuevas ◽  
Marco Fontana ◽  
Luis Rodriguez-Cobo ◽  
Mauro Lomer ◽  
Jose Miguel Lopez-Higuera
Keyword(s):  
Machine Learning ◽  
Optical Fiber ◽  
Proof Of Concept ◽  
Sensing System ◽  
Spatially Resolved

scholarly journals Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4197
Author(s):  
Aimé Lay-Ekuakille ◽  
John Djungha Okitadiowo ◽  
Moïse Avoci Ugwiri ◽  
Sabino Maggi ◽  
Rita Masciale ◽  
...  
Keyword(s):  
Machine Learning ◽  
Particle Tracking ◽  
Learning Approach ◽  
Still Images ◽  
Video Imaging ◽  
Video Signals ◽  
Sensing System ◽  
Reliable Monitoring ◽  
Machine Learning Approach ◽  
Imaging Approach

The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the deposition of materials within the bed of the channel, which might lead to critical floods. A reliable monitoring system can thus help to protect properties and, in the most critical cases, save lives. A sensing system capable of monitoring the flow conditions and the possible geo-environmental constraints within a channel can operate using still images or video imaging. The latter approach better supports the above two features, but the acquisition of still images can display a better accuracy. To increase the accuracy of the video imaging approach, we propose an improved particle tracking algorithm for flow hydrodynamics supported by a machine learning approach based on a convolutional neural network-evolutionary fuzzy integral (CNN-EFI), with a sub-comparison performed by multi-layer perceptron (MLP). Both algorithms have been applied to process the video signals captured from a CMOS camera, which monitors the water flow of a channel that collects rain water from an upstream area to discharge it into the sea. The channel plays a key role in avoiding upstream floods that might pose a serious threat to the neighboring infrastructures and population. This combined approach displays reliable results in the field of environmental and hydrodynamic safety.

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