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The human brain needs to rely on the information obtained by the eyes to process information. To a certain extent, the eyes are the reflection of the human brain activity. Based on this, many people pursue eyeball changes to judge a person's psychological state, and eye tracking technology has also been proposed, playing an increasingly important role in different fields. The purpose is to study the human attention distribution system in the computer environment. We use the eye tracking technology of the eye tracker to record any actions of each learner during learning, use the function of the eye tracker to obtain various data, and use statistical analysis to analyze it. The experimental results show that the eyes can reflect human behavior information well. Analyzing eye movement data can deepen people's understanding of the mechanism of attention distribution and improve learning efficiency by 20%. [ABSTRACT FROM AUTHOR]

With the advent of the information age, the rapid development of computer software and hardware and related high-tech equipment has made today's interior design expression and display forms increasingly diversified, providing designers with a broad performance platform. On the basis of clarifying the ideas of the combined application of hand-drawn and computer graphics, we analyze the methods and approach of their combined application and expound on the actual application. It is hoped that the research can provide design ideas and references for the interior design industry and teaching activities. The Small Business Innovation Research (SBIR) program consists of three phases, the first of which is a feasibility study for the innovation. If the innovation is feasible, enter the second stage to finalize the innovative product, and after private and public sector investment, the third phase, commodity promotion and commercialization. The influence of intelligent equipment such as computers and hand-painted boards on the design industry is obvious. With the development of the social economy and the era of technological innovation, a social culture and a value system will be formed. Therefore, the arrival of media technology will also generate perceptions, ways of thinking, and value orientations and will affect our social interaction space, people's main living places, and indoor space. The software module in the computer provides designers with the opportunity to use computer design software to quickly present the designer's ideas on an electronic display screen. Its increasingly convenient design conditions promote the diversity and richness of design software. Combining intelligent multimedia images, with the help of compressed image noise reduction programming, SBIR technology, etc., it has created the development of an interior design. The number of students studying interior design has increased by nearly 60% every year, and the popularity of smart multimedia technology has also reached 78.9%. In the media age, people and interior designers are constantly changing their aesthetic tastes, narrative methods, expression methods, life function needs, and aspirations for a spiritual destination. Good interior design methods do not necessarily produce excellent interior design works, but good interior design works must be guaranteed by a set of practical design techniques and procedures. The article attempts to tap the multiple values of multimedia in indoor space to build a more humanized, humane, and media-inspired indoor space. [ABSTRACT FROM AUTHOR]

With the rapid development of electronic computers and information technology, face recognition is widely used in fields such as enterprises, entertainment, information security, and daily life. However, the current face recognition technology is still relatively poor in distinguishing facial features, resulting in a low accuracy of face recognition, which cannot meet increasing application requirements. For this reason, it is necessary to develop more accurate face recognition technology. Residual neural network (ResNet) neural network is a deep residual learning convolutional neural network, which can be used to solve the degeneration problem (i.e., after adding more layers to the neural network, the performance drops rapidly) of deep-learning neural networks. We aim to study the specific principles, calculation methods, and characteristics of the ResNet neural network, analyze the complexity of the classroom environment, propose the use of ResNet network for face feature extraction, and use additive angular margin loss for deep face recognition (ArcFace) as the loss function of the ResNet network to improve the ResNet network. Because the ArcFace function has the advantages of high performance, easy programming, low complexity and high training efficiency. We use this scheme to conduct field tests of face recognition on many students in the classroom. The test results show that this scheme enhances the discrimination of facial features and improves the accuracy of face recognition. In the case of different numbers of people, facial defects, and strong light exposure, the system can still detect and recognize faces stably. In the case of facial defects, the recognition accuracy rate is still 63.3%, and the recognition accuracy rate is still more than 60% under the illumination of strong light, and the recognition accuracy rate under correct conditions is more than 70%. [ABSTRACT FROM AUTHOR]

Data mining is a common basic function in humans and many other intellectual systems. The popularization of computers has created a good research environment for the advanced application of remote sensing satellites. With China's economic and population growth, the demand for food is also increasing. Obtaining the planting area is one of the most important tasks in calculating harvest costs. However, traditional cost analysis methods lack information. The traditional remote sensing image classification method is based on the pixel classification method, which cannot effectively extract the spatial texture information in an image. The pixel-based classification method also has the problem of salt and pepper phenomenon in the classification results, which generates a large number of invalid broken patches and ultimately leads to low classification accuracy. Here, we investigated the application of remote state system mapping for the extraction of the planting area. We analyzed the basic principles of remote image processing and remote image sharing methods and introduced an audio system and optical algorithm. Here, two separation methods are selected, long-distance traction measurements are performed in three training areas, and the two results are compared and analyzed. Experimental results show that the method of objective orientation increases the working speed compared with the method of manuscript translation. The benefit of speed measurement becomes more apparent in both seasons as the measuring range increases. Computer automatic classification and the method of artificial visual interpretation are tested sequentially, and the total time taken is 49 and 13.5 days, respectively. [ABSTRACT FROM AUTHOR]