scholarly journals Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT

2022 ◽  
Vol 12 (1) ◽  
pp. 785
Author(s):  
Thi Thu Nguyen ◽  
Phuc Thinh Doan ◽  
Anh-Ngoc Le ◽  
Kolla Bhanu Prakash ◽  
Subrata Chowdhury ◽  
...  
Keyword(s):  
Real Time ◽  
Emergency Departments ◽  
Mobile Application ◽  
Classification Algorithm ◽  
Acceleration Sensor ◽  
The Real ◽  
Average Accuracy ◽  
The Status ◽  
To Come ◽  
Better Than

<span>In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%).</span>

scholarly journals A Real-Time Structure of Attitude Algorithm for High Dynamic Bodies

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Xingcheng Li ◽  
Shuangbiao Zhang
Keyword(s):  
Real Time ◽  
Time Structure ◽  
Flow Diagram ◽  
The Real ◽  
Time Problem ◽  
Conventional Structure ◽  
High Dynamic ◽  
Two Stages ◽  
Better Than ◽  
Attitude Matrix

To solve the real-time problem of attitude algorithm for high dynamic bodies, a real-time structure of attitude algorithm is developed by analyzing the conventional structure that has two stages, and a flow diagram of a real-time structure for a Matlab program is provided in detail. During the update of the attitude matrix, the real-time structure saves every element of attitude matrix in minor loop in real time and updates the next attitude matrix based on the previous matrix every subsample time. Thus, the real-time structure avoids lowering updating frequency, though the multisubsample algorithms are used. Simulation and analysis show that the real-time structure of attitude algorithm is better than the conventional structure due to short update time of attitude matrix and small drifting error, and it is more appropriate for high dynamic bodies.

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

scholarly journals Pedestrian detection based on improved LeNet-5 convolutional neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261987360 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Meng-Yue Yang ◽  
Hong-Jun Zeng ◽  
Jian-Ping Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Network Model ◽  
Pedestrian Detection ◽  
The Real ◽  
Better Than

In this article, according to the real-time and accuracy requirements of advanced vehicle-assisted driving in pedestrian detection, an improved LeNet-5 convolutional neural network is proposed. Firstly, the structure of LeNet-5 network model is analyzed, and the structure and parameters of the network are improved and optimized on the basis of this network to get a new LeNet network model, and then it is used to detect pedestrians. Finally, the miss rate of the improved LeNet convolutional neural network is found to be 25% by contrast and analysis. The experiment proves that this method is better than SA-Fast R-CNN and classical LeNet-5 CNN algorithm.

The Design of Product Packaging Transmission Line Control System Based on Siemens PLC

2014 ◽  
Vol 709 ◽  
pp. 327-330
Author(s):  
Zong Yang Zhong ◽  
Hui Lai Sun
Keyword(s):  
Control System ◽  
Transmission Line ◽  
Real Time ◽  
Control Program ◽  
Main Function ◽  
Time Data ◽  
Product Packaging ◽  
The Real ◽  
Real Time Data ◽  
The Status

This paper mainly introduces a kind of product packaging transmission line control system based on Siemens S7-200 PLC. It elaborated the main function principle of the system and the implementation of the control program; SIMATIC WinCC flexible 2008 of SIEMENS was used to monitor the status of the system, and display the real-time data and alarm report. At the same time the system can also be controlled by the screen.

scholarly journals Incremental and predictive interpretation: Experimental evidence and possible accounts

2015 ◽  
Vol 25 ◽  
pp. 57 ◽  
Author(s):  
Adrian Brasoveanu ◽  
Jakub Dotlacil
Keyword(s):  
Experimental Evidence ◽  
Real Time ◽  
Formal Semantics ◽  
Main Question ◽  
The Real ◽  
To Come ◽  
The Right ◽  
Present Experimental Evidence

The main question we investigate is whether meaning representations of the kind that are pervasive in formal semantics are built up incrementally and predictively when language is used in real time, in much the same way that the real-time construction of syntactic representations has been argued to be. The interaction of presupposition resolution with conjunctions vs. conditionals with a sentence-final antecedent promises to provide us with the right kind of evidence. Consider the following 'cataphoric' example and the contrast between "and" and "if": "Tina will have coffee with Alex again and / if she had coffee with him at the local cafe". We expect the second clause to be more difficult after "and" than after "if": the conjunction "and" signals that an antecedent that could resolve the "again" presupposition is unlikely to come after this point (the second conjunct is interpreted relative to the context provided by the first conjunct), while the conditional "if" leaves open the possibility that a suitable resolution for the "again" presupposition is forthcoming (the first clause is interpreted relative to the context provided by the second clause). We present experimental evidence supporting these predictions and discuss two approaches to analyze this kind of data.

scholarly journals Implementation of IoT in Agriculture

2021 ◽  
Author(s):  
B. Mohamed Arafath Rajack ◽  
N. Subramanian ◽  
N. Arun Pragadesh ◽  
R. Suvanesh ◽  
S. Vignesh
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Mobile Application ◽  
Raspberry Pi ◽  
Modern World ◽  
The Real ◽  
Agriculture Sector ◽  
The World ◽  
Potential Pest ◽  
World Agriculture

In this modern world agriculture is one of the major booming sectors around the world. In India around 60 percent of GDP comes from agriculture sector alone. Also, around the world there are many technologies showing up in the field of agriculture. In this paper proposed a technology by means of which potential pest attack in the crops can be detected and the respective pesticide is also sprayed as well. Along with these there is a range of sensor employed in the field connected to the controller that will take the real time values from the field and can be displayed in the respective screen (monitor or mobile screen) by means of technology called IOT (Internet of Things). Raspberry-pi is used as the controller to perform IoT. system is linked with an application called “cain” Which allows us to display various values of sensors in the monitor or in mobile application.

Bayesian Spam Detection Framework on Mobile Device

2019 ◽  
Vol 12 ◽  
Author(s):  
Yitao Yang ◽  
Guozi Sun ◽  
Chengyan Qiu
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Spam Detection ◽  
Spam Filtering ◽  
Learning Mechanism ◽  
The Real ◽  
Filtering Method ◽  
Feedback Learning

In recent years, the spam message problem becomes more serious. Similar to spam mail, the spam message in phone brings a big trouble to users. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtration methods. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtering method. A Bayesian spam detection framework is designed in the paper and is deployed on Android device to test. Besides it can filtering coming messages and classify them into normal or spam in real time, it introduces feedback learning mechanism to make its result more accurate. The experiments are conducted under the real environment. The results show that the framework can meet the requirement of spam filtering.

Abstract 240: Smartphone Application to Provide Real-Time Cardiopulmonary Resuscitation Quality Feedback

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Emily Stumpf ◽  
Ravi Ambati ◽  
Raj Shekhar ◽  
Steven Staffa ◽  
David Zurakowski ◽  
...  
Keyword(s):  
Cardiopulmonary Resuscitation ◽  
Real Time ◽  
Mobile Application ◽  
Sensor Data ◽  
Smart Device ◽  
Compression Rate ◽  
Acceleration Sensor ◽  
Compression Depth ◽  
Quality Feedback ◽  
Cpr Quality

Introduction: Quality of cardiopulmonary resuscitation (CPR) contributes significantly to morbidity and mortality in both in-hospital and out-of-hospital cardiac arrest. Key parameters that determine the CPR quality are compression rate, compression depth, duration of interruptions, chest recoil factor and respiratory rate. Several studies have demonstrated that real-time audiovisual feedback improves CPR quality in both bystanders and hospital staff. This study aims to develop and validate a smart device (phones and wearable technology) application to provide real-time audiovisual and haptic feedback to optimize CPR quality, by calculating aforementioned chest compression parameters. Hypothesis: A mobile application using acceleration sensor data from smart devices can provide accurate real time CPR quality feedback. Methods: A mobile application was developed to track the compression depth, compression rate and pause duration in real time using the data captured from the on-device accelerometer. The mobile device was placed on an adult manikin’s chest along the midline close to the point of compressions. Data from the application was compared directly to data obtained from a validated clinical standard CPR quality tool. Results: CPR quality parameters were obtained from the app and the standard for 60, 10-second-long sessions. Bland-Altman plot analysis for compression depth showed agreement between the app measurements and standard within +/-3.5mm (Figure 1). The intraclass correlation for agreement in the measurement of compression count was 0.92 (95% CI: 0.88-0.95), indicative of very strong agreement. Conclusions: Smart device (phones and wearable technology) applications using acceleration sensor data can accurately provide real-time CPR quality feedback. With further development and validation they can provide a ubiquitous CPR feedback tool valuable for out of hospital arrests and in under-privileged areas worldwide.

Remote Advanced Supervision and Diagnostics of Robotized Welding Cell

2013 ◽  
Vol 198 ◽  
pp. 39-44
Author(s):  
Zbigniew Pilat ◽  
Jacek Zieliński ◽  
Marcin Słowikowski
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Global Network ◽  
Diagnostic Tools ◽  
Practical Solution ◽  
The Real ◽  
Welding Equipment ◽  
Advanced Control ◽  
The Status ◽  
Effective Exchange

Welding equipment and other components included in the robotized or automated welding station are now usually equipped with advanced control. It not only enables effective exchange of the information between components of the system, but also allows to implement a very efficient hardware and software diagnostic tools, which is not commonly utilized, especially in smaller installations. The use of communication controllers and devices connected to the global network shall enable the realization of such monitoring in remote mode and with the use of modern ICT solutions the effectiveness of such monitoring will be greatly increased. Device operators and technologists will gain the ability to monitor remotely the status of these devices as well as the processes even in the real time [. This article presents a practical solution of implementing remote monitoring to the welding installation with the use of WEB2.0 services.

scholarly journals Implementation of Low Cost SDR in HF Band for Emergency Application

2020 ◽  
Author(s):  
Madhuri Gummineni ◽  
Trinatha Rao Polipalli
Keyword(s):  
Response Time ◽  
Real Time ◽  
Communication System ◽  
Emergency Services ◽  
Low Cost ◽  
Relay Node ◽  
Reliable Communication ◽  
The Real ◽  
Different Types ◽  
To Come

Abstract When events are uncontrolled due to hurricanes, the communication will breakdown. During this strenuous situation there is a need for reliable communication system to provide alert and emergency services. There remains a particular attention towards portability, reconfigurability, and interoperability in a communication system to reduce response time during emergencies. The real-time implementation helps to come across alternatives to overcome practical difficulties and challenges. This motivates us to implement a Relay Node in real-time using SDR , hence an experiment is carried out in order to gain first hand experience between different types of architecture of SDR i.e.VUSDR, HackRF One, and USRP. The architecture of Low-cost VUSDR and comparison between different types of SDR has been presented in this paper. Future scope and applications are presented.

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