scholarly journals Cyber-Physical System for Environmental Monitoring Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3655
Author(s):  
Íñigo Monedero ◽  
Julio Barbancho ◽  
Rafael Márquez ◽  
Juan F. Beltrán
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Research Area ◽  
Cyber Physical System ◽  
Dynamic Configuration ◽  
Sound System ◽  
Physical Systems ◽  
Different Types ◽  
The Internet Of Things

Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. This system is based on convolutional neural networks (CNNs) and is focused on the different types of vocalization of two species of anurans. CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate tool for the classification of environmental sounds. The classification results obtained are excellent (97.53% overall accuracy) and can be considered a very promising use of the system for classifying other biological acoustic targets as well as analyzing biodiversity indices in the natural environment. The paper concludes by observing that the execution of this type of CNN, involving low-cost and reduced computing resources, are feasible for monitoring extensive natural areas. The use of CPS enables flexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals Intelligent Cybernetic System Using Gestalt Processing

2021 ◽  
Vol 25 (6) ◽  
pp. 53-63
Author(s):  
V. M. Trembach ◽  
A. S. Aleshchenko ◽  
A. A. Mikryukov
Keyword(s):  
Internet Of Things ◽  
Physical System ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Emergent Properties ◽  
Cyber Physical System ◽  
Cognitive Approach ◽  
Physical Systems ◽  
Control Actions ◽  
The Internet Of Things

Purpose of the study. The aim of the study is to create and develop modern cyber physical systems. The evolution of cyber physical systems (CPS) is associated with the development of a cognitive approach within the framework of the application of mechanisms used by humans to solve their daily tasks. In the cognitive approach to working with cyber physical systems, gestalt is considered as one of the ways of solving modern tasks within the framework of the new Industry 4.0 technology. In the cognitive approach a simple task is considered for cyber physical systems of the Internet of Things (CPS IoT) with gestalt processing. When investigating such a task for a simple cyber physical system, it will be possible to use a gestalt with a simple structure. The complication of the task and structure of gestalt can occur with the development of CPS IoT. The article examines an intelligent cyber physical system of the Internet of Things using methods of gestalt processing of their states - a picture of the world, while solving various problems of the Internet of Things.Materials and research methods. To solve tasks within the framework of a cognitive approach to the construction and development of cyber physical systems, new methods and developments of specialists in the field of intelligent systems are required. In the context of Industry 4.0 technologies, the Internet of Things the gestalt processing of CPS IoT is considered. Within the framework of the cognitive approach sensory images, concept-representations, concept-scenarios, concept-gestalts of cyber physical systems are used to interact with the real world. It is important to use concept gestalts that can reflect CPS IoT with new emergent properties. CPS IoT gestalt refers to a certain state of the cyber physical system and its habitat, which occurs when a need arises and closes after the need is satisfied. The main task of gestalt processing for a cyber physical system is to satisfy its needs. The solution to this problem includes: the organization of the collection and the direct collection of the necessary elements for the formation of the gestalt, and later for its closure; the formation of the gestalt; the closure of the gestalt. For the accumulation of experience, its use and development, it is proposed to use machine learning methods. Machine learning results can be presented in the form of concept representations, concept scenarios.Results. The concepts-gestalts of CPS IoT, gestalt processing of CPS IoT are proposed within the framework of the cognitive approach. As the main stages of gestalt processing, the article highlights: - preparation of initial data for the formation of the need for CPS IoT: - formation of an imaginative perception - a picture of the world, including the current state of CPS IoT and necessary for the closure of the gestalt; - formation of gestalt; – formation of initial data for planning the control actions necessary for closing the CPS IoT gestalt; - implementation of control actions to close the CPS IoT gestalt; - saving the gestalt processing scenario for possible reuse in the future. These stages of gestalt processing relate to IoT CPS of any nature and are focused on any tasks of the Internet of Things. The demo example shows the use of gestalt processing for CPS IoT with a simple model without training.Conclusion. The article discusses the cognitive approach that refers to the use and development of intelligent cyber physical systems for the Internet of things and the Internet of everything. A method related to the gestalt processing of CPS IoT situations is proposed, which allows recognizing a need, and forming of a gestalt. Based on the generated CPS IoT gestalt, control actions are planned to close the CPS IoT gestalt. The implementation of the proposed approach, development and use of gestalt concepts will allow to reflect CPS IoT with new emergent properties.

scholarly journals Cyber-physical systems of the industrial Internet of things

2021 ◽  
Vol 16 (92) ◽  
pp. 72-81
Author(s):  
Emil A. Gumerov ◽  
◽  
Tamara V. Alekseeva ◽  
Keyword(s):  
Information Security ◽  
Internet Of Things ◽  
Industrial Application ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Cyber Physical System ◽  
Industrial Internet Of Things ◽  
Physical Systems ◽  
Industrial Internet ◽  
The Internet Of Things

Cyber-physical systems are a means of effectively managing industrial applications of the Internet of things. Physical layer cyber-physical system implements the control devices of the industrial Internet of things and intelligent algorithms digital system level provide management and information security applications. Effective management and information security determine the success of the industrial Internet of things, so the research topic is relevant. The purpose of this article is to develop an optimal architecture of a cyber-physical system based on the principles of data processing at the place of their occurrence and the application of a control action at the place of the problem occurrence. The authors were faced with the task of exploring all the possibilities associated with the application of the proposed principles and developing an optimal application architecture for the industrial Internet of things. In the study proposed the concept of intelligent application of industrial Internet of things, which enables processing of continuously generated data in their source and provides application control action to the location of the problem. The proposed solution: a) increases the information security of the industrial application of the Internet of things (data is not transmitted over the network) and b) prevents an attack on the software of the Industrial application of the Internet of things. The solution can be used by developers of industrial IoT systems to effectively launch and implement projects

Arrhythmia Diagnosis through ECG Signal Classification Using A Hybrid CNN-LSTM Model: Comparison with current Deep Learning Approaches (Preprint)

2020 ◽  
Author(s):  
Nicos Maglaveras ◽  
Georgios Petmezas ◽  
Vassilis Kilintzis ◽  
Leandros Stefanopoulos ◽  
Andreas Tzavelis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Model Comparison ◽  
Temporal Dynamics ◽  
Experimental Studies ◽  
Signal Classification ◽  
Ecg Signal ◽  
Learning Approaches ◽  
Different Types

BACKGROUND Electrocardiogram (ECG) recording and interpretation is the most common method used for the diagnosis of cardiac arrhythmias, nonetheless this process requires significant expertise and effort from the doctors’ perspective. Automated ECG signal classification could be a useful technique for the accurate detection and classification of several types of arrhythmias within a short timeframe. OBJECTIVE To review current approaches using state-of-the-art CNNs and deep learning methodologies in arrhythmia detection via ECG feature classification techniques and propose an optimised architecture capable of different types of arrhythmia diagnosis using publicly existing annotated arrhythmia databases from the MIT-BIH databases available at PHYSIONET (physionet.org) . METHODS A hybrid CNN-LSTM deep learning model is proposed to classify beats derived from two large ECG databases. The approach is proposed after a systematic review of current AI/DL methods applied in different types of arrhythmia diagnosis using the same public MIT-BIH databases. In the proposed architecture the CNN part carries out feature extraction and dimensionality reduction, and the LSTM part performs classification of the encoded ECG beat signals. RESULTS In experimental studies conducted with the MIT-BIH Arrhythmia and the MIT-BIH Atrial Fibrillation Databases average accuracies of 96.82% and 96.65% were noted respectively. CONCLUSIONS The proposed system can be used for arrhythmia diagnosis in clinical and mHealth applications managing a number of prevalent arrhythmias such as VT, AFIB, LBBB etc. The capability of CNNs to reduce the ECG beat signal’s size and extract its main features can be effectively combined with the LSTMs’ capability to learn the temporal dynamics of the input data for the accurate and automatic recognition of several types of cardiac arrhythmias. CLINICALTRIAL Not applicable.

scholarly journals Categorical Semantics of Cyber-Physical Systems Theory

2021 ◽  
Vol 5 (3) ◽  
pp. 1-32
Author(s):  
Georgios Bakirtzis ◽  
Cody H. Fleming ◽  
Christina Vasilakopoulou
Keyword(s):  
System Architecture ◽  
Cyber Physical Systems ◽  
Cyber Physical System ◽  
System Behavior ◽  
Modeling And Analysis ◽  
Physical Systems ◽  
System Models ◽  
Computational Dynamics ◽  
Different Types ◽  
Categorical Semantics

Cyber-physical systems require the construction and management of various models to assure their correct, safe, and secure operation. These various models are necessary because of the coupled physical and computational dynamics present in cyber-physical systems. However, to date the different model views of cyber-physical systems are largely related informally, which raises issues with the degree of formal consistency between those various models of requirements, system behavior, and system architecture. We present a category-theoretic framework to make different types of composition explicit in the modeling and analysis of cyber-physical systems, which could assist in verifying the system as a whole. This compositional framework for cyber-physical systems gives rise to unified system models, where system behavior is hierarchically decomposed and related to a system architecture using the systems-as-algebras paradigm. As part of this paradigm, we show that an algebra of (safety) contracts generalizes over the state of the art, providing more uniform mathematical tools for constraining the behavior over a richer set of composite cyber-physical system models, which has the potential of minimizing or eliminating hazardous behavior.

scholarly journals Low-Cost Automatic Weather Stations in the Internet of Things

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 146
Author(s):  
Konstantinos Ioannou ◽  
Dimitris Karampatzakis ◽  
Petros Amanatidis ◽  
Vasileios Aggelopoulos ◽  
Ilias Karmiris
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Low Cost ◽  
The Internet ◽  
Climatic Data ◽  
Time Data ◽  
New Era ◽  
Observation Systems ◽  
Weather Stations ◽  
The Internet Of Things

Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the implementation of automatic observing systems that will provide scientists with the real-time data needed to design and apply proper environmental policy. In this paper, an extended review is performed regarding the technologies currently used for the implementation of Automatic Weather Stations. Furthermore, we also present the usage of new emerging technologies such as the Internet of Things, Edge Computing, Deep Learning, LPWAN, etc. in the implementation of future AWS-based observation systems. Finally, we present a case study and results from a testbed AWS (project AgroComp) developed by our research team. The results include test measurements from low-cost sensors installed on the unit and predictions provided by Deep Learning algorithms running locally.

scholarly journals Classification of soybean tempe quality using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012022
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Traditional Food ◽  
Quality Classification ◽  
Non Destructive

Abstract Tempe is a traditional food originating from Indonesia, which is made from the fermentation process of soybean using Rhizopus mold. The purpose of this study was to classify three quality levels of soybean tempe i.e., fresh, consumable, and non-consumable using a convolutional neural network (CNN) based deep learning. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The sensitivity analysis showed the highest quality classification accuracy of soybean tempe was 100% can be achieved when using AlexNet with SGDm optimizer and learning rate of 0.0001; GoogLeNet with Adam optimizer and learning rate 0.0001, GoogLeNet with RMSProp optimizer, and learning rate 0.0001, ResNet50 with Adam optimizer and learning rate 0.00005, ResNet50 with Adam optimizer and learning rate 0.0001, and SqueezeNet with RSMProp optimizer and learning rate 0.0001. In further testing using testing-set data, the classification accuracy based on the confusion matrix reached 98.33%. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the quality of soybean tempe with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

scholarly journals Time Signature Detection: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6494
Author(s):  
Jeremiah Abimbola ◽  
Daniel Kostrzewa ◽  
Pawel Kasprowski
Keyword(s):  
Deep Learning ◽  
Research Area ◽  
Future Research ◽  
Genre Classification ◽  
Signature Detection ◽  
Learning Techniques ◽  
Input Signals ◽  
Different Types ◽  
Time Signature ◽  
Music Genre Classification

This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. However, the success of this research area could be an added advantage in a broader area of music genre classification using deep learning techniques. Suggestions for improved estimates and future research projects are also discussed.

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