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With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomo...
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... proposed mobile-robot design can easily be adapted to new and future research studies. The physical appearance of the robot was evaluated, and its design was based on several criteria, including functionality, material availability, and mobility. During the analysis of different guided robots of reduced size and simple structure (as shown in Fig. 4), the work experience of the authors with mechanical structures for robots was also ...
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The majority of the more recent studies have mainly focused on how to achieve energy-efficient goals by optimizing the driving behavior for human-driven vehicles or designing trajectory planning and tacking algorithms for autonomous vehicles. However, the energy-saving advantages of autonomous vehicles have not been quantitatively and theoretically...
Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system...
Autonomous vehicles (AVs) are crucial robotic
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Citations
... Due to environmental instability, several techniques for the operation of line-following robots have been developed using intelligent control mechanisms. In such practice, the author [18] implement Convolutional neuronal networks (CNN) on autonomous vehicles, as well as a Proportional Integral Derivative algorithm (PID) used by most applications requiring movement. ...
... Once assembled, tests run was performed. The author [18] designed a CNN controller based on PID, meaning a neuronal network controller that analyzes 2D data like PID based images and video, regulating speed according to motor measurements. ...
Esta investigación documental-descriptiva pretende explorar el avance de la inteligencia artificial (IA) en los vehículos autoconducidos y su repercusión en beneficio de la sociedad. Si bien es innegable que los vehículos autoconducidos se han convertido prácticamente en una realidad tecnológica en el campo de la informática, tanto en la actualidad como en un futuro previsible. Sin embargo, es necesario abordar varios aspectos aparentemente problemáticos en términos de legislación, ya que los coches autónomos están llamados a convertirse en parte integrante de la vida cotidiana. Por ello, esta investigación examina mediante una revisión bibliográfica los importantes avances y esfuerzos dedicados al diseño de software en este ámbito de estudio centrado en el funcionamiento de los vehículos autónomos en entornos urbanos y su capacidad para sortear los obstáculos de la ciudad, así como los retos ante eventualidades imprevistas.
... Moreover, labels can be read simply, in this way information of the customer can be leaked [7]. In the study, Mixed-Integer Linear Programming has been proposed at every junction and transformed the parking idea into a linear problem and it gives a unique view to explore the solution of the problem [18]. Moreover, Machine learning and cloud computing method approaches are used for massive data execution. ...
... Work in uses Raspberry Pi processor and high resolution cameras. This proposed technology helps in maintaining the parking effectively in congested urban communities [18]. In addition, the usage of Open Computer Vision (CV) technology detects the presence of cars and their location sectors. ...
The number of vehicles on the road has been increasing rapidly, especially in metropolitan cities. It's observed finding parking space is a time consuming and hectic task for the drivers, moreover, the environment is also get affected due to the level of fuel burn in the parking lot. Therefore, a practically feasible solution is demanded to find out parking space in small spam of time to save the driver's time and to keep our cities clean. In this paper, a framework is proposed to provide parking space for vehicles by using IoT and some other technologies. Moreover, application based hardware is proposed using microcontrollers, IoT devices and web applications. The proposed design also gives multiple options for the payment to save time and deadlocks in the parking area.
... Medical professionals may now quickly diagnose and treat patients using HIoT services using mobile devices. Several studies have looked into mobile healthcare computing [8]. ...
... Using an RFID-based healthcare monitoring system is simple and cost-effective. Any sensor that can capture and transmit data about the environment or physical qualities is a WSN [8]. ...
... Linear Regression (LR) is a straightforward method that established the relationship between at least one independent variable or explanatory variable and a dependent variable in statistical machine learning. LR is the key form of regression analysis that received a lot of attention and is commonly used in practical applications [23][24][25]. ...
COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as consider other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and
... However, these methods may not able to effectively learn high-level abstract feature representations for the complex FC networks [15]. As an important feature extraction technique, deep learning models can automatically learn lower-dimensional abstract feature representations from the initial input, and have significantly promoted the performances of computer vision, natural language processing, communications and fog computing [16][17][18][19][20]. Recently, more and more works have applied deep learning methods to the FC-based brain disease classification [21][22][23]. ...
Raspberry Pi is an invaluable and popular prototyping tool in scientific research for experimenting with a wide variety of ideas, ranging from simple to complex projects. This review article explores how Raspberry Pi is used in various studies, discussing its pros and cons along with its applications in various domains such as home automation, agriculture, healthcare, industrial control, and advanced research. Our aim is to provide a useful resource for researchers, educators, students, product developers, and enthusiasts, helping them to grasp the current status and discover new research possibilities using Raspberry Pi.
In a controlled indoor environment, line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots. A line tracking robot is a self-mobile machine that can recognize and track a painted line on the floor. In general, the path is set and can be visible, such as a black line on a white surface with high contrasting colors. The robot’s path is marked by a dis-tinct line or track, which the robot follows to move. Several scientific contribu-tions from the disciplines of vision and control have been made to mobile robot vision-based navigation. Localization, automated map generation, autonomous navigation and path tracking is all becoming more frequent in vision appli-cations. A visual navigation line tracking robot should detect the line with a camera using an image processing technique. The paper focuses on combining computer vision techniques with a proportional-integral-derivative (PID) control-ler for automatic steering and speed control. A prototype line tracking robot is used to evaluate the proposed control strategy.
The novel virus, often called COVID-19, is an infection that spreads from one to another in multiple chains. The novel virus has caused a universal pandemic, and some investigations utilize diverse statistical methods to deliver models in order to analyze the current state of the pandemic and the losses incurred for other reasons depending upon place to place. The obtained statistical models depend on diverse aspects, and studies are purely based on possible preferences. In this research, a model is suggested for predicting the diverse blowout of COVID-19. Machine learning classifiers like linear regression, multilayer perceptron, and vector autoregression can be chosen to predict the likely patterns of COVID-19 effects in various parts of the world based on their climate, environment, culture, behavior, and socioeconomic factors.KeywordsLinear regression (LR)PredictionVector autoregression (VAR)Multilayer perceptron (MLP)COVID-19
Since images leveraged with pertinent information demonstrates a
promising involvement in many field of researches such as medical imaging, micro-
scopic imaging, soft computing, smart cities, network transmission, military areas,
feature extraction, object identification and tracking, remote sensing, geographical
areas, classification, and so on. Whereas, image fusion schemes yields an informative
image via integration of two or more than two partly focused images. Universal image
fusion technique design is not possible in practical. Each image fusion technique has
its own limitations as well as specific application advantages. In this research paper,
authors have analyzed five fusion techniques namely guided filtering-based image
fusion technique (IFGF), image fusion relied on image matting-based technique
(IFIM), image fusion relied on focused region detection (IFFRD), image fusion
technique centered on boundary finding (IFBF), and convolution neural network-
based fusion technique (CNN). Two performance metrics, i.e., MI and FMI, have
been considered to test the performance results. For metric MI, the best performing
method is IFBF with a value of 1.144. For FMI metric, the method with best perfor-
mance is IFBF with a value of 0.666. Lytro multi-focus color image dataset has been
used to perform this experimentation work.
