Neural-network-enhanced small low-cost low-power sensor for atmospheric gases

2000 ◽  
Author(s):  
Shannon R. Campbell ◽  
Edgar A. Mendoza ◽  
Emile Fiesler
Keyword(s):  
Neural Network ◽  
Low Power ◽  
Low Cost ◽  
Atmospheric Gases ◽  
Power Sensor

Nonlinear modulation with low-power sensor networks using undersampling

2021 ◽  
pp. 147592172098288
Author(s):  
Peter Oppermann ◽  
Lennart Dorendorf ◽  
Marcus Rutner ◽  
Christian Renner
Keyword(s):  
Low Power ◽  
Sensor Network ◽  
Damage Identification ◽  
Low Cost ◽  
Computational Effort ◽  
Test Scenario ◽  
Efficient Manner ◽  
Power Sensor ◽  
Nonlinear Modulation ◽  
Cost Efficient

Nonlinear modulation is a promising technique for ultrasonic non-destructive damage identification. A wireless sensor network is ideally suited to monitor large structures using nonlinear modulation in a cost-efficient manner. However, existing approaches rely on high sampling rates and resource-demanding computations that are not feasible on low-cost and low-power sensor network devices. We present a new damage indicator that uses the short-time Fourier transform to derive amplitude and phase modulation with less computational effort and memory usage. Evaluation of the proposed method using real experiment data exhibits performance and reliability similar to the conventionally used modulation index. Undersampling is demonstrated, which reduces the memory demand in a test scenario by more than 100 times, and the required energy for sampling and processing more than four times. The loss of accuracy introduced by undersampling is shown to be negligible.

scholarly journals Real-Time Mask Recognition

IoT ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 688-716
Author(s):  
Rachel M. Billings ◽  
Alan J. Michaels
Keyword(s):  
Neural Network ◽  
Low Power ◽  
Real Time ◽  
Low Cost ◽  
Model Performance ◽  
Network Models ◽  
Recognition System ◽  
Neural Network Models ◽  
Still Images ◽  
Experimental Dataset

While a variety of image processing studies have been performed to quantify the potential performance of neural network-based models using high-quality still images, relatively few studies seek to apply those models to a real-time operational context. This paper seeks to extend prior work in neural-network-based mask detection algorithms to a real-time, low-power deployable context that is conducive to immediate installation and use. Particularly relevant in the COVID-19 era with varying rules on mask mandates, this work applies two neural network models to inference of mask detection in both live (mobile) and recorded scenarios. Furthermore, an experimental dataset was collected where individuals were encouraged to use presentation attacks against the algorithm to quantify how perturbations negatively impact model performance. The results from evaluation on the experimental dataset are further investigated to identify the degradation caused by poor lighting and image quality, as well as to test for biases within certain demographics such as gender and ethnicity. In aggregate, this work validates the immediate feasibility of a low-power and low-cost real-time mask recognition system.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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