A Low Complexity Radioisotope Identification System using an Integrated Multichannel Analyzer and Embedded Neural Network

2019 ◽  
Author(s):  
Samuel J. Murray ◽  
Joseph Schmitz ◽  
Sina Balkir ◽  
Michael W. Hoffman
Keyword(s):  
Neural Network ◽  
Low Complexity ◽  
Identification System ◽  
Multichannel Analyzer

scholarly journals Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1715
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Claudio Turchetti
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Network ◽  
Low Cost ◽  
Low Complexity ◽  
Human Activity Recognition ◽  
Motion Artifacts ◽  
Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

scholarly journals Multiple-Molecule Drug Design Based on Systems Biology Approaches and Deep Neural Network to Mitigate Human Skin Aging

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3178
Author(s):  
Shan-Ju Yeh ◽  
Jin-Fu Lin ◽  
Bor-Sen Chen
Keyword(s):  
Neural Network ◽  
Systems Biology ◽  
Signaling Pathways ◽  
Human Skin ◽  
Deep Neural Network ◽  
Middle Age ◽  
Skin Aging ◽  
Identification System ◽  
Potential Candidate ◽  
Systems Medicine

Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology methods and deep neural networks. We constructed the candidate genomewide genetic and epigenetic network (GWGEN) via big database mining. After doing systems modeling and applying system identification, system order detection and principle network projection methods with real time-profile microarray data, we could obtain core signaling pathways and identify essential biomarkers based on the skin aging molecular progression mechanisms. Afterwards, we trained a deep neural network of drug–target interaction in advance and applied it to predict the potential candidate drugs based on our identified biomarkers. To narrow down the candidate drugs, we designed two filters considering drug regulation ability and drug sensitivity. With the proposed systems medicine design procedure, we not only shed the light on the skin aging molecular progression mechanisms but also suggested two multiple-molecule drugs for mitigating human skin aging from young adulthood to middle age and middle age to old age, respectively.

Low-complexity nonlinear equalizer based on artificial neural network for 112 Gbit/s PAM-4 transmission using DML

2021 ◽  
Vol 67 ◽  
pp. 102724
Author(s):  
Lei Wang ◽  
Xiangye Zeng ◽  
Jingyi Wang ◽  
Dongqi Gao ◽  
Mengshuai Bai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Complexity ◽  
Artificial Neural

An Optical Fiber Sensing Network Supporting Weak Optical Signal Detection and a Neural Network Method for Information Collection

2021 ◽  
Vol 16 (2) ◽  
pp. 188-195
Author(s):  
Keyuan Liu ◽  
Haibin Li ◽  
Ya Wang
Keyword(s):  
Neural Network ◽  
Optical Fiber ◽  
Signal Detection ◽  
Mobile Phone ◽  
Optical Power ◽  
Information Collection ◽  
Identification System ◽  
Power Meter ◽  
Fiber Sensing ◽  
Optical Fiber Sensing

The weak direct current (DC) signals detected and converted by the photodetector are output to the mobile phone by voltage/frequency switching, and the signals are processed by the mobile phone APP and audio conversion module. The photodetector is equipped with the automatic switching function to design an optical power meter and detect weak signals. Meanwhile, the optical cable identification system is analyzed and combined with the optical power meter to generate an optical fiber sensing network to improve the weak alternating current (AC) signal detection. This network needs data fusion in sensor nodes’ data collection. The cluster routing protocol is introduced and combined with the back propagation neural network (BPNN) to propose a method suitable for this photoelectric transmission and improve the information fusion and accuracy. In the experiment, the optical power meter is output in gears first, and the output waveforms are normal. The photodiode’s optical power is adjusted to obtain different frequencies on the oscilloscope. In the proposed optical fiber sensing network, weak AC signals are amplified significantly, and different optical fiber lines can be distinguished in the optical cables. The proposed information collection method can reduce network communication and node energy consumption.

scholarly journals Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

2021 ◽  
Vol 2 ◽  
Author(s):  
Chengjie Li ◽  
Lidong Zhu ◽  
Zhongqiang Luo ◽  
Zhen Zhang ◽  
Yilun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Blind Source Separation ◽  
Bp Neural Network ◽  
Source Separation ◽  
Optimal Solution ◽  
Blind Signal Separation ◽  
Automatic Identification ◽  
Identification System ◽  
Data Set ◽  
Processing Power

In space-based AIS (Automatic Identification System), due to the high orbit and wide coverage of the satellite, there are many self-organizing communities within the observation range of the satellite, and the signals will inevitably conflict, which reduces the probability of ship detection. In this paper, to improve system processing power and security, according to the characteristics of neural network that can efficiently find the optimal solution of a problem, proposes a method that combines the problem of blind source separation with BP neural network, using the generated suitable data set to train the neural network, thereby automatically generating a traditional blind signal separation algorithm with a more stable separation effect. At last, through the simulation results of combining the blind source separation problem with BP neural network, the performance and stability of the space-based AIS can be effectively improved.

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