Proceedings of the 5th International Conference on Computing and Data Science
Alan Wang, University of Auckland
Marwan Omar, Illinois Institute of Technology
Roman Bauer, University of Surrey
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Matrix multiplication is used in machine learning to train larger, more accurate models with massive data more effectively. The distributed machine learning paradigm necessitates the distributed computing of many matrix multiplications once it is adopted. The execution time of distributed machine learning algorithms increases on dropout nodes, which are computational nodes in distributed clusters that randomly slow down computation due to a variety of factors (e.g., node failure, system failure, communication bottlenecks, etc.), becoming a significant bottleneck in distributed computing systems. It has been discovered that coded computation is less expensive than replica methods and can more effectively reduce the effects of dropped nodes. In this study, we present theoretical insights on how encoded solutions might produce significant gains over unencoded solutions.
In modern electronic systems, data transmission is an essential requirement within and between boards, or between lower and upper computers. To ensure data transmission accuracy, communication protocols are established that must be followed by all parties involved. These protocols include UART (universal asynchronous transmitter and receiver), IIC (Inter-Integrated Circuit), SPI (Serial Peripheral Interface), USB2.0/3.0(Universal Serial Bus), and Ethernet. Among these protocols, UART is the most basic one and is widely used in embedded devices due to its simple circuit structure and low cost. With the exponential growth of information technology, UART-based embedded devices can easily achieve wired and wireless communication through various communication interfaces and wireless modules. In this paper, the author presents an example of a receiving module for UART communication that converts parallel data into string data. The entire module is developed using the hardware description language Verilog HDL. Simulations are performed using ModelSim, and the results demonstrate that the simulation waveform is consistent with the expected receiving data. This approach facilitates the transformation of serial data to parallel data, improving the efficiency and accuracy of data transmission.
Recommender system (RS) has become an essential component of e-commerce, social media, and other online platforms. Collaborative filtering (CF) is one of the most commonly used techniques in RS that relies on user-item interactions to generate recommendations. However, CF suffers from the cold-start problem, sparsity, and scalability issues. To address these challenges, this work propose a hybrid system called Convolutional Matrix Factorization for Sequential Movie Recommendations (CMF-SMR), which combines matrix factorization (MF) with convolutional neural networks (CNNs). CMF-SMR leverages the non-linear feature extraction capabilities of CNNs and the representation learning abilities of deep learning to enhance the accuracy and robustness of traditional MF-based RS. Specifically, CNNs and MF were used to respectively extract features from user-item interaction data and use them as input for learning user and item representations. The learned representations are then used to predict user-item ratings. This work evaluates the performance of our proposed method on two publicly available datasets, and the experimental results demonstrate that our method outperforms several state-of-the-art techniques in terms of accuracy, scalability, and robustness. Moreover, this work conduct evaluation metrics to demonstrate the accuracy of our proposed method. Overall, our proposed CMF-SMR provides a promising solution for addressing the limitations of traditional CF-based RS and can be applied in various domains, including e-commerce, social media, and personalized content recommendation systems.
An asynchronous FIFO is a classic circuit device used for caching data and accommodating the frequency of asynchronous signals. With the continuous improvement of technology, the advantages of this device in real-time transmission, multi-core processing, and data acquisition have become more apparent. As a result, the original structure has been optimized and extended using algorithm and design techniques to achieve greater efficiency and applicability in various academic and industrial applications. This article discusses several methods for optimizing FIFO in different aspects and examines how an unlocked FIFO queue is applied in a CAN bus data acquisition system, as well as its use in data hybrid framing technology. The findings show that FIFO performs exceptionally well as a cache structure, particularly in situations involving large amounts of data acquisition and real-time procession. The various optimization techniques discussed in this article include using pipeline registers, implementing a dual-clock FIFO, and employing Gray-code-based pointers. These techniques can increase the speed and reduce the power consumption of FIFO, thereby improving its overall efficiency. In addition, the article introduces the unlocked FIFO queue, which provides more flexibility than conventional locked FIFOs. Unlocked FIFO queues allow multiple cores to access the buffer simultaneously, which makes them ideal for high-performance systems. Finally, the article explores the application of FIFO in CAN bus data acquisition systems, where it is utilized as a buffer between the bus and other devices. Additionally, the use of FIFO in data hybrid framing technology is discussed, where it is essential for maintaining the integrity of the data stream.
In this paper, an improved method of using binary pointers is proposed based on the analysis of the basic principles and structure of FIFO. The paper also presents a high-speed data acquisition system based on STM32 with an asynchronous FIFO designed to solve the insufficient data acquisition speed and high-power consumption problems of fiber-optic grating sensors in the electrical industry. The use of binary pointers significantly improves the processing speed and reduces the power consumption of the FIFO, thus improving the overall efficiency.In addition, the paper introduces the use of a field-programmable gate array (FPGA) design as the control core of a color sorter system. This overcomes the drawbacks of processor-based multi-channel color sorters, including low delay accuracy, poor consistency, and persistent signal loss. The FPGA offers superior performance due to its parallel computing capabilities and flexibility in algorithm implementation, making it ideal for high-performance applications such as color sorting systems.The color sorter system designed using FPGA technology is capable of sorting different materials based on color, shape, and size. The system's superior performance is due to the use of an asynchronous FIFO buffer that enables real-time color recognition and processing. By incorporating the FIFO buffer, the system can achieve high processing speeds, consistent color recognition, and minimal signal loss, providing a valuable solution for applications requiring high-precision color sorting.
In recent years, people's living quality has improved, so the price of cars has fallen, and the number of cars in the world has been on the rise. In cities where land is expensive, there are fewer places to park, fewer parking Spaces, and parking itself is a difficult technology to learn. Therefore, the number of accidents caused by parking increases year by year. It is urgent to solve the safety problem of parking. Although some auxiliary astern tools have emerged as The Times require, these tools still have their shortcomings. For example, the commonly used astern radar and astern image cannot see objects behind, and astern image is a wide-angle lens that makes it difficult for the driver to judge the distance between the car and obstacles. In this environment, the current situation needs to be improved. In order to improve the accuracy of obstacle judgment, this paper chooses semantic segmentation as the function. This paper chooses lightweight model to speed up the process of deliver information, such as ShuffleNetV2.
With the vigorous development of blockchain technology, its decentralization, privacy, convenience, and other characteristics make it shine in the financial field. Today, blockchain technology has gradually been widely integrated into government, the Internet of Things, supply chain management, healthcare, and other areas. It can change the traditional centralized data management model and provide distributed and decentralized data storage and management methods. This method has higher security, transparency, and reliability, which can solve the problems existing in traditional data management methods and improve the efficiency and reliability of data management. In the meantime, using new technologies also brings many practical problems to solve. These include insufficient technology maturity, privacy concerns, and volatile legal and regulatory policies. This paper delves into these real-world scenarios and summarizes some recommendations. This will give a greater understanding of the principles and characteristics of blockchain technology, promote the popularization of blockchain technology, have a more comprehensive understanding of the use of blockchain technology in different fields, and provide valuable research materials for researchers in related fields in the future.
Background: As time passes, people forget the past and everything that happened in the past fades into memory with the passage of time. Time, space and orientation are added. Objective: To recreate scenes, evoke memories and share memories through the adaptation of virtual reality technology. And, allowing for a more realistic reproduction of memories. Methodology: Use software to input text, video, audio, photos, etc., set the scene and recreate it through potential logical substitution based on the five senses (hearing, sight, touch, taste, smell). This work is not only an effective aid to the treatment of mental illnesses. Another example is the cultural heritage, which is a very effective solution to the problem of passing on a particular technique. Also, it is a new way for people to record and share their lives, to explore the past and to preserve the good things. My point in designing this project is that through this project memories can be preserved more completely and the past can be more authentically recreated.
In today's world, digital technology is rapidly evolving and the way people live their lives is changing dramatically. For example, tools such as computers, the internet and mobile phones are bringing us new experiences like never before, while digital technology is also bringing unprecedented advances and innovations to business and science. New opportunities are growing and traditional hierarchies are beginning to fall apart - and with them comes a breakdown in trust. Google, Instagram, Wechat, Xiaohongshu - these disruptive platforms,networks and technologies have changed the current status of their respective industries, making our lives easier while there are bad actors in the unseen grey areas stealing and exploiting our privacy and data. The emergence of COVID-19 in recent years has forced us to use and rely on digital devices with high frequency, especially in China, which has accelerated our transition to a digital world, with both advantages and disadvantages. This article will explore how to better protect the privacy and data of digital nomads within reasonable and lawful limits, and provide an idea as a solution. The article presents the idea of a data protection app called Data Butler, which is an application dedicated to protecting the privacy of its users. The aim is to enable users to enjoy the benefits of big data while protecting their private data. Data Butler is designed to enable users to enjoy the benefits of big data while their private data is protected.
In today`s world, more and more machine learning methods and prediction methods have been proposed. There are also lots of investors who choose the more difficult and complex models to use as technical analysis tools to predict stock prices. However, Rome wasn't built in a day. So, it`s necessary to learn the traditional models penetratingly. To have a more thorough understanding of classic models in the past, this paper provides a great way to analyze the ARIMA model. It explains the methods of fitting the ARIMA model step by step and improves the ARIMA model by adding seasonal parameters to fit the SARIMA model, enabling readers to better understand the advantages and disadvantages of this model. Although the results of the ARIMA model are unfortunately similar, and difficult to demonstrate its predictive power in images, the SARIMA model presents a trend prediction that conforms to people's imagine. As of today, the change in the price of Netflix's stock has been visible, which is different from the predicted price. Therefore, while making technical predictions, investors also need to combine fundamental analyses like the supply and demand balance, changes in interest rates, government regulations, macroeconomic indicators, and unique features of the industry in question, among others. Rather than relying solely on a model.