Volume 14

Due to the insufficient data acquisition rate and high power consumption of sensors, this article focuses on addressing the issue of clock cycle interaction error resulting from the excessive amount of data on integrated circuit chips. Specifically, a FIFO design is proposed to achieve the transmission and transformation of data under different clock cycles. The technical challenges associated with creating an asynchronous FIFO, reducing the probability of encountering semi-stable states, and achieving delay control are analyzed in this paper. To tackle the semi-stable error, a Gray code converter and two-stage synchronizer are employed. The designed FIFO also leverages the difference and phase difference of read and write pointers to achieve high-accuracy delay control. The experiments demonstrate that the designed FIFO can successfully facilitate correct writing and reading operations. Through Modelsim simulation tests, the waveform is more precise than before, and the operation of the designed FIFO is realized.

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We notice that there are lots of researches that studies celebrity worship behaviour and celebrity endorsement. Nevertheless, few go deep into a mathematical perspective, much less try to quantify this ambiguous process due to the lack of suitable models. In this research, we pioneer a new way, using SICR epidemic model, to fully describe the process of celebrity worship in details, through which we make predictions based on the basic reproduction number whether a celebrity could expand his or her fan base continuously. Meanwhile, not only do we further diversify the function of SICR model, but we also make improvements on the model itself. At the end of research, we also provide useful suggestions for celebrities and their brokerage companies about how to keep the number of their fans increasing rather than hit a bottleneck so quickly. The fact is that celebrities should emphasize the avoidance of negative events instead of positive publicity and be attractive to more groups of people rather than stick to one specific group only.

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Matrix multiplication have become increasingly important nowa-days, which is applied in many kinds of fields. In this case, the need to improve the speed and efficiency of matrix multiplication is in-creasing. In this paper, the author analyzes some relevant theories about matrix multiplication as well as the advantage and disad-vantage of some applications that based on matrix multiplication. It turns out that matrix multiplication has a lot of room for devel-opment in the future cause the current method still has many de-fects and is not perfect. For example, it will still take plenty of time to finish the process of matrix multiplication when the matrices are large in size. However, it’s gratifying that some improvements have been achieved, which can help to optimize the efficiency. In addi-tion, some advanced methods start to appear. For instance, the method of combining matrix multiplication with AI provides a new direction for future research and development. Consequently, it is predictable that a significant achievement to optimize matrix mul-tiplication will be made in the future.

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In modern large scale ASIC designs, multiple clock systems are often involved, which can create problems with data transfer in different clock domains. A practical solution to this problem is the use of asynchronous FIFOs (First In First Out) for buffering the transfer of data from different clock domains. A high-capacity asynchronous FIFO cascaded with a synchronous FIFO is designed based on a conventional asynchronous FIFO using the Verilog language. The input data is processed across the clock domain by the asynchronous FIFO, and the data output from the asynchronous FIFO is cached and output again by the synchronous FIFO. The module increases the FIFO depth while enabling data to be transferred across the clock domain. The simulation is completed within the Modelsim software which accordingly verifies the two main roles of the FIFO in processing data, namely the cross-clock domain and the data caching function. Simulation results show that the asynchronous FIFO data is written and read correctly and that the empty/full flag signal is correct.

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Blockchain technology is a technology that inherently solves trust issues. It has the character-istics of decentralization, distributed storage, tamper resistance, security, and transparency. It ensures reliable communication between nodes that do not trust each other through consensus mechanisms, smart contracts, and other means. Blockchain stores each transaction data on each transaction node to make the data public and transparent, and generates the data into a blockchain.. As a relatively new distributed database system, blockchain has expanded to many other fields since the initial digital currency, but its development is seriously constrained by problems such as large storage overhead and low query efficiency. In order to find a suita-ble optimization method, select the representative blockchain system Bitcoin, analyze its data structure, data storage and data query processing mechanism, discuss the problems existing in the two functions of storage and query, summarize the existing relevant optimization methods, and look forward to the main research problems of the blockchain system represented by Bitcoin in the future.

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With the uncreasing reform of times and the continuous advancement of my science and technology, in recent years, my overall national strength is strengthening gradually. I have entered an information age, one digital age, the use of digital signal processing is becoming more and more frequent and important. Therefore, this paper mainly analyzes the application of DSP, the digital signal processing technology show to make the exposition. Digitalization is a major trend in today's society and the main theme of the development of the times, while digital signal processor is a component born with the times. The so-called digital signal processing is to use a universal digital signal chip to extract useful information from the signal by digital calculation. In recent years, in the wake of the constantly improving of science and technology and the continuous reform and innovation of technology in China, DSP is more and more close to people's production and life, and plays an important role in practical applications. Therefore, it is necessary to discuss and analyze its development.

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Universal Asynchronous Transceiver (UART) is a Universal Serial Data Bus, hardware communication protocol using asynchronous serial communication in a configurable manner rate. In most embedded systems, microcontrollers and computers, UART plays an important communication role as a hardware communication protocol between devices to ensure the efficient transmission of communication data. The understanding and use of the UART protocol is crucial in our design process. In order to better understand. basic principle this protocol and realize the use of URAT, this paper introduces serial communication, UART frame format explanation, the working principle of UART, simulation of simple UART3. Depending on the source clock, also known as the Baud Clock, each heterogeneous device in a complete system may be able to create a distinct baud rate and connect with other devices over a UART interface. The fundamental tenet of the UART protocol stipulates that for proper communication, the transmitter and receiver must be set at the same baud rate. It also introduces several advanced designs and applications of UART with baud rate, namely UART with automatic frequency generator and frequency divider and Determination of UART Receiver Baud Rate Tolerance.

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Memristors have many advantages, such as small size, fast speed, and low power consumption. If the cross array is used as the memristor, a structure similar to the matrix can be obtained, which can store data and do calculation. Based on the characteristics of memristor, the realization of various control functions has a wide range of application prospects and advantages. The memristor matrix supply can be used to achieve relevant control, so that the control becomes simple and efficient. At the same time, memristor has outstanding advantages in arithmetic calculation, neural network, and artificial intelligence. This article introduces memristor and shows the theory needed to apply memristor to the control of robot arm, improve MAC calculation by memristor and the application in neural network. There is no doubt that the research in this paper will contribute to the further exploration of the control method of memristor and lay a solid foundation for its practical potential application theory in the future.

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With the development of times and technology, artificial intelligence-based neural network algorithms have been widely used in scientific research and life. Among them, Convolutional neural network (CNN) is the most classic and the most representative. CNN have been widely used in images classification in recent years. This article focuses on the basic information of the convolutional neural networks and its applications especially in object detection. Also, the advantages of the CNN and the possible future improvements will be told in this article. In the end of this article some research of experiments will be shown in order to prove the accuracy of convolutional neural networks in detecting objects and visualizing results. This article gathers basic knowledge of convolutional neural networks. As a result, it may help new starters to get to know the CNN and then make contributions to the development of deep learning especially the CNN according to the advantages and future improvements mentioned in the article.

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Convolutional neural networks play a very important role in computer vision, such as image classification, image segmentation, and handwriting recognition have been widely used. In daily life, this technology is used in the photo recognition of e-commerce platforms. However, the timing of the identification process grows into a major problem. Therefore, it is particularly important to reduce the recognition time by optimizing the deep learning model. To solve this problem, two experimental methods are proposed to optimize the volume of the convolutional neural network model. The first is to reduce the size of the model by scaling down the convolutional kernel. The second is to prune the model with L-1 norm to reduce the size of the model and improve the running speed. According to the experimental results, the two experimental methods have achieved remarkable optimization effects. In the first experiment, the method of scaling down convolutional kernel has an important optimization effect for training the deep learning model of small data sets. In another experiment using L-1 pruning algorithm greatly improves the running speed of models by reducing the size of models. To sum up, the optimization method proposed above for the convolutional neural network model on the mobile end can be applied in the field that requires a large amount of image classification, such as delivery package sorting. At the same time, to better improve the performance of the model, it will become feasible to use a variety of optimization methods to tune it.

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