Identification of Deadliest Mosquitoes Using Wing Beats Sound Classification on Tiny Embedded System Using Machine Learning and Edge Impulse Platform

2021 ◽  
Author(s):  
Kirankumar Trivedi ◽  
Harsh Shroff
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
Sound Classification

scholarly journals Birds Sound Classification Based on Machine Learning Algorithms

2021 ◽  
pp. 1-11
Author(s):  
Aska E. Mehyadin ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar Abas Hasan ◽  
Jwan N. Saeed
Keyword(s):  
Machine Learning ◽  
Noise Suppression ◽  
Bird Species ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Learning Technology ◽  
Species Classification ◽  
Data Set ◽  
Sound Classification ◽  
Mel Frequency Cepstral Coefficient

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

scholarly journals Controlling Embedded Systems Remotely via Internet-of-Things Based on Emotional Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mohammad J. M. Zedan ◽  
Ali I. Abduljabbar ◽  
Fahad Layth Malallah ◽  
Mustafa Ghanem Saeed
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Remote Control ◽  
Embedded Systems ◽  
Embedded System ◽  
Electronic Devices ◽  
Recognition Rate ◽  
Disabled People ◽  
Research Attention ◽  
Image Preprocessing

Nowadays, much research attention is focused on human–computer interaction (HCI), specifically in terms of biosignal, which has been recently used for the remote controlling to offer benefits especially for disabled people or protecting against contagions, such as coronavirus. In this paper, a biosignal type, namely, facial emotional signal, is proposed to control electronic devices remotely via emotional vision recognition. The objective is converting only two facial emotions: a smiling or nonsmiling vision signal captured by the camera into a remote control signal. The methodology is achieved by combining machine learning (for smiling recognition) and embedded systems (for remote control IoT) fields. In terms of the smiling recognition, GENKl-4K database is exploited to train a model, which is built in the following sequenced steps: real-time video, snapshot image, preprocessing, face detection, feature extraction using HOG, and then finally SVM for the classification. The achieved recognition rate is up to 89% for the training and testing with 10-fold validation of SVM. In terms of IoT, the Arduino and MCU (Tx and Rx) nodes are exploited for transferring the resulting biosignal remotely as a server and client via the HTTP protocol. Promising experimental results are achieved by conducting experiments on 40 individuals who participated in controlling their emotional biosignals on several devices such as closing and opening a door and also turning the alarm on or off through Wi-Fi. The system implementing this research is developed in Matlab. It connects a webcam to Arduino and a MCU node as an embedded system.

scholarly journals An Evaluation and Dissemination Model for the Machine Learning Embedded System Lifecycle in Clinical Practice Settings

2021 ◽  
Author(s):  
melanie besculides ◽  
Ksenia Gorbenko ◽  
Cardinale Smith ◽  
Robert Freeman ◽  
David Reich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Embedded System ◽  
Quantitative Methods ◽  
Clinical Decision Making ◽  
Comprehensive Evaluation ◽  
Healthcare Setting ◽  
Clinical Decision ◽  
Qualitative And Quantitative Methods ◽  
Better Than

Machine learning (ML) algorithms are gaining popularity in clinical practice settings due to their ability to process information in ways that augment human reasoning. While tools that rely on output from ML algorithms in the healthcare setting are appealing for their ability to aid in clinical decision making and streamline workflows, their implementation and effectiveness are not well documented. There is an abundance of published ML literature that focuses on whether algorithms can predict an outcome or predict it better than previous algorithms, but a dearth of effort evaluating their implementation or impact on patient outcomes. While developing and validating algorithms is an important first step in research, comprehensive evaluation is needed before application of ML algorithms in new settings is considered. Evaluation should examine both the process of implementation and the outcomes using a mix of qualitative and quantitative methods. This commentary describes a model we developed to guide our institutional ML evaluation efforts.

scholarly journals Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognitionon Embedded Systems

2019 ◽  
Vol 9 (18) ◽  
pp. 3885 ◽  
Author(s):  
Bruno da Silva ◽  
Axel W. Happi ◽  
An Braeken ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Real Time ◽  
Urban Environments ◽  
Machine Learning Techniques ◽  
Embedded Devices ◽  
Sound Classification ◽  
Learning Techniques ◽  
Urban Monitoring ◽  
Constrained Devices

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing.

Towards Machine Learning Support for Embedded System Tests

2021 ◽  
Author(s):  
Stefan Scharoba ◽  
Kai-Uwe Basener ◽  
Jens Bielefeldt ◽  
Hans-Werner Wiesbrock ◽  
Michael Hubner
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
Learning Support

General Concept of the EMG Controlled Bionic Hand

2020 ◽  
Vol 8 (1) ◽  
pp. 26-34
Author(s):  
Adam Pieprzycki ◽  
Daniel Król
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Control System ◽  
Embedded System ◽  
Real Time ◽  
General Concept ◽  
System Implementation ◽  
The Real ◽  
Emg Signal ◽  
Hand Control

The article presents a general concept of a bionic hand control system using a multichannel EMG signal, being under development at present. The method of acquisition and processing of multi-channel EMG signal and feature extraction for machine learning were described. Moreover, the design of the control system implementation in the real-time embedded system was discussed.

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