This article discusses the possibilities of measuring Particulate Matter using optical low-cost sensors. Depending on the sensor used, not only can there be problems with the positioning of the sensor with respect to the incoming sunlight, but it may also be possible to measure ultrafine particles. While PM2.5 and PM10 are commonly used to characterize air quality, in this paper we point out the need to pay more attention to smaller particles up to 0.1 µm in size (also referred to as ultrafine particulates) due to their highly negative effects on the human health. Attention is also paid to the correlation between particulate matter and meteorological factors like pressure, temperature, and humidity. However, the sudden change in Pearson’s correlation coefficient unveiled a need to look into the effect of the wind on particulate matter.
Nazirah Abd Hamid, Raja Hasyifah Raja Bongsu, Mohamad Afendee Mohamed, Ahmad Faisal Amri Abidin Bharun, Mohd Fadzil Abdul Kadir, Nurazizah Youzlan, Volodymyr Rusyn
KEYWORDS: Education and training, Random forests, Analytical research, Machine learning, Feature extraction, Biometrics, Data modeling, Image classification, Detection and tracking algorithms, Deep learning
Many people are using their mobile or smartphone to store data and allow the user to access the internet and many online services. These situations are causing an intensification of cybercrimes, thus, to improve the security of the password required, the mobile phone came out with an alternative method, by using biometrics technology. This study firstly would be focusing on the implementation of typing patterns known as keystroke technique and secondly to conduct analysis to determine whether it is suitable to be implemented as an authentication technique by training and testing the dataset with several machine learning classifiers. From the seven classification techniques, three suitable classifiers have been identified and the results show that Random Forest with False Accepted Rate (FAR) of 0.2% and False Rejection Rate (FRR) of 5.31% had the best performance It can be concluded that keystroke technique has the potential to be a good method to authenticate a user with further research.
In this paper, we studied comparative distance detection using a Laser Rangefinder equipped with color detection and image processing techniques in air and water environments. The main objective of this study is to evaluate the performance and accuracy of the distance detection methods in these two mediums. Furthermore, color detection is employed in the air environment to identify objects within the captured images, followed by image processing techniques such as edge detection and contour analysis to calculate the distances between the objects and the Laser Range Finder. In the water environment, challenges arise due to the refractive properties of water, which can distort object appearances. To overcome this, we employ an underwater Laser Range Finder system, along with color detection and image processing methods adjusted for the refractive index of water. The findings show that the distance measurement in the air has a small error value of 0.63 %. Meanwhile, measuring distances in water has a high error value of 35.18 %. These results indicate that measurements in the air perform better than measurements in water. Water’s scattering and refractive properties caused significant deviations and higher error values. The light intensity fluctuations had minimal impact on measurements in water but were significant in water.
In this paper, the circuit of a non-autonomous generator that realizes chaotic behavior is presented. This oscillator-circuit contains four resistors, one capacitor, one inductor, two diodes, one operational amplifier, one bipolar voltage source and one sinusoidal source. All nominal of components are shown. The proposed circuit was modeled by utilizing NI’s MultiSim software environment. The system’s behavior was investigated through numerical simulations, by using wellknown tools of nonlinear theory, such as phase portrait, chaotic attractor and time distributions of two chaotic systemvariables. Spectral analysis are also presented.
For demonstrate nonlinear behavior we used new one-dimensional modified logistic system. Analysis, equation and system conditions are presented. For analysis of the iteration series with different parameter r and computer modelling was used one of the modern software environment LabView. Programming code and nominal components are also presented. For visualizing and practical realization of the new modified nonlinear logistic map we used Arduino Uno board and ten light-emitting diodes (LEDs) with ten resistors for each part of segment of the range [0;1]. The Arduino was connected to a computer through the USB port and programmed using a language similar to C++. Sketch was uploaded into Arduino using program software ArduinoIDE.
In this paper, we study a three-dimensional Lorenz system that demonstrates chaotic behavior. We present the state equations and mathematical analysis of the system. We use a system-design platform, like LabView, to study and analyze main information properties, such as chaotic attractors, time series, bifurcation diagram, and dynamic behavior of the overall system. We implemented an Arduino Uno based design to display chaotic attractors of the Lorenz system. The Arduino is connected to a computer through the USB port, while graphs were uploaded using program software ArduinoIDE. Finally, the connection scheme and programming code are also presented.
A novel simple autonomous optoelectronic circuit that demonstrate chaotic behavior is presented. In this circuit a lightemitting diode is a simple optoelectronic element. The mathematical model that contain exponential nonlinearity and six terms with two parameters is described by three first-order ordinary differential equations. A great interest is the simulation that using different software environments allows to demonstration different information properties of chaotic oscillations. For modelling of information properties of the chaotic system and demonstrate results was selected one of the modern software LabView (LabView-2015 (32-bit version for Windows). Temporal dependence of the system is discussed, the chaotic attractors are found and the signal spectrum is given.
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