Vehicles play a vital role in modern-day intelligent transportation systems (ITS). Plate characters provide a standard means of identification for any vehicle. To serve this purpose, an automatic license plate recognition system is studied. In this paper, we intend to create an optimized algorithm for implementing the scheme. Firstly, we undertake several challenging stages. The first step is introduced as the determination of plate location. Then, in the second phase, we apply an initial improvement to decline the likely noises using the Gaussian function to provide an appropriate filter for this target. Next, the rest of the project is organized as follows, finding the edge of images, enhancing modified pictures, and selecting the exact place of the plate. Afterward, tilting and plate rotation improvement and plate characters' extraction are considered two essential steps in this regard. Eventually, the final step of this project consists of several stages, such as employing a neural network to extract the plate characters automatically.

The paper aims to design and develop an innovative solution in the Smart Building context that increases guests’ hospitality level during the COVID-19 and future pandemics in locations like hotels, conference locations, campuses, and hospitals. The solution supports features intending to control the number of occupants by online appointments, smart navigation, and queue management in the building through mobile phones and navigation to the desired location by highlighting interests and facilities. Moreover, checking the space occupancy, and automatic adjustment of the environmental features are the abilities that can be added to the proposed design in the future development. The proposed solution can address all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things (IoT) sensors. Then, storing and processing collected data in servers and finally sending the desired information to the end-users. Consequently, through the integration of multiple IoT technologies, a unique platform with minimal hardware usage and maximum adaptability for smart management of general and healthcare services in hospital buildings will be created.

Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing. Materials and Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision. Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes. Conclusion: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.

No abstract

No abstract