Proceedings of the 2023 International Conference on Mechatronics and Smart Systems
Seyed Ghaffar, Brunel University London
Alan Wang, University of Auckland
With the increasingly diverse functional requirements of contemporary electronic products, the complexity of CMOS circuits often used in chips becomes higher and the number of transistors used increases. To solve the resulting performance problems of CMOS circuits, researchers have searched for many transistor sizing technologies. This paper summarizes three methods of CMOS circuit optimization. The paper introduces these three methods in terms of principle, effect, and application scenarios, and compares them respectively. Through analysis and simulation, it can be found that the use of these methods in circuit design can effectively achieve the purpose of improving speed, reducing power consumption, and improving the overall performance of the circuit. This lays a solid foundation for finally being able to present a good product with excellent performance and enhance the market competitiveness of the product. CMOS circuits are widely used, and circuit optimization is of great importance to the overall circuit design, and better optimization methods can even promote the development of the entire electronics and chip manufacturing fields.
With Asian American population on the rise, Asian Americans have more power than ever in political elections. While qualitative methods have been used to measure this uptrend in Asian American political participation, a thorough and cohesive quantitative study on the topic has yet to be done. Therefore, we utilize machine learning and dictionary-based matching to identify the most accurate numbers for Asian American political participation, categorized by political affiliations, geography, gender, etc. We find that Asian American political participation tends to the Democratic party, is predominantly male, and is proportional to the population size of the individual’s respective state.
Medical image segmentation can provide valuable information for doctors, it has important research value in the medical field. Meanwhile, U-Net, as the fundamental networks for such tasks, brings a substantial improvement in the segmentation performance of traditional medical images. With the increasingly widespread use of U-Net, researchers have designed various U-Net variants according to different task requirements. However, most of the current summaries of U-Net variants are divided according to the direction of network applications, and the structural relationship between the variant networks and U-Net is not elaborated. Therefore, this paper classifies U-Net variants according to their network framework by elaborating the principles of U-Net structure. According to the U-Net network structure, it is divided into three main categories: backbone improvement, module addition and cross-network fusion. Further, the characteristics, advantages and disadvantages of different categories of variants are introduced, and the directions of the variants for U-Net optimization are analyzed. Finally, the article summarizes the current development direction of U-Net variants and provides an outlook on the future directions that can continue to be optimized.
The volatility of Australian companies' stock prices in 2020, caused by China's trade restrictions, poses a significant challenge for predicting financial gain or loss. This research contributes to future scholarship in predicting stock prices under specific circumstances or during special time periods. The study proposes a novel approach to stock price prediction, incorporating news sentiment analysis into a deep learning model. The research collected news items potentially affecting the stock price, incorporating them into an analysis model to generate a new feature for the Long Short-Term Memory (LSTM) model. The LSTM model used in this study was bidirectional, with two sets of gates per layer, and a three-layer model with different units. Each layer employed a dropout layer and a dense layer in the final stage. The study also utilized the feature engineering of lookback, selecting a window of time in the past to predict the next day's stock prices. Following multiple hyperparameter tunings and feature engineering adjustments, the results and graphs demonstrate a successful prediction for all three of the chosen companies, even during an unstable stock market. The overall trend lines achieve optimal predictions for the stock prices, illustrating both upward and downward trends.
Polymer modulators based on Mach-Zehnder interferometers were designed, modelled, fabricated, and tested in this project. Built on a silicon-on-insulator wafer with 220 nm active silicon width, electron beam lithography, plasma etching, metal deposition and lift-off, and polymer deposition were used in the fabrication process. The simulated waveguide group index is 4.377 at 1.55 µm, the estimated r33 of the polymer modulator is 8.46 pm/V and the Vπ is 908 V. The measured group index is 4.541, indicating the fabricated feature size is smaller than the design.
In order to solve the restrictions on athletes and the time and cost of equipment replacement caused by traditional sports equipment, which are easy to corrode, have short service life, poor mechanical properties, and are relatively heavy, etc., more excellent polymer materials and new materials are used in various aspects. technology to replace traditional materials. At the same time, the diversified characteristics of polymer materials can provide more types of choices for various sports equipment. In order to understand the application and influence of polymer composite materials in competitive sports, the application of polymer compounds is analyzed in combination with the preparation process in this paper, material performance and performance requirements of sports equipment. By comparing the performance of athletes in different eras without using the same equipment and the length of their competitive career, this paper concluded that the application of polymer composite materials in competitive sports has greatly improved the performance of athletes and reduced the risk of injury , It also increases convenience and safety for ordinary people.
The emergence of artificial intelligence (AI) technology has once again promoted the development of robots by making them more efficient, independent, and intelligent. With the progress of the times, the two promote each other and develop together in many fields, especially in the field of medical and health. This paper describes the strengths and limitations of surgical robots and intelligent medical robots as well as their possible development trend in the future from both hardware and software aspects. Conclusions can be drawn that, on the one hand, surgical robots can improve the safety and reliability of the surgery and intelligent medical robots can help achieve drug research and development, intelligent diagnosis and treatment, intelligent image recognition, etc. These can help doctors make diagnoses and treatments more accurately; on the other hand, it is difficult to install an electronic sensor on the mechanical arm at present, the current positioning technology is not perfect, and the rotation angle of the mechanical arm is limited. These limitations improve the difficulty for doctors to use surgical robots. However, with the continuous progress of robot and AI technology, the limitations of many surgical robots can be improved and more targeted AI technology can be developed and applied in specific fields.
With our growing need for highly efficient energy supplies, a demand for new devices with better energy conversion and storage capabilities arises. Asymmetric supercapacitors combining a pseudocapacitive and an EDLC electrode show great potential as a prospective energy storage device. The relatively broad voltage window and low cost of iron (II, III) oxide and the eminent properties of graphene nanosheets (high electrical conductivity, large surface area, etc.) make them ideal candidates for the electrode materials of supercapacitors. An asymmetric supercapacitor using iron (II, III) oxide as the anode and graphene nanosheets the cathode with an aqueous electrolyte of 3 M potassium hydroxide was proposed here. Iron (II, III) oxide nanoparticles were synthesized from FeCl3·6H2O using a hydrothermal approach. Graphene nanosheets were prepared from fine-grained graphite raw materials via Hummer’s method. The electrochemical performance of the device was characterized using a triple-electrode setup, with the electrodes being submerged in a medium of 6 M KOH. This specific asymmetrical supercapacitor shows promise as a practical foundation towards a future of more effective energy transformation and storage, such as line-filtering and signal selection, particularly due to the relative abundance and environmentally-friendly nature of its electrode materials.
Many super high-rise buildings emerge in modern cities with urban development, facilitating work, accommodations, etc. However, their safety risks and accidents due to the wind are urgent problems with the complex flow field in cities. The research on wind loads of super high-rise buildings is thus crucial, but most studies tend to consider only the influence of the surrounding single-scale building clusters, rarely considering multi-scale ones. In this paper, the influence of the surrounding multi-scale building clusters on the wind loads of a super high-rise building is investigated. The wind field of a super high-rise building surrounded by four different arrangements of idealized, simplified buildings is first simulated using computational fluid dynamics (CFD) methods: RANS and Hybrid LES/RANS models. It is found that surrounding tall buildings can significantly affect the pressure distribution on the windward and leeward sides of the super high-rise building, such as fluctuating, extreme, and mean wind pressure. The vortex, formed largely due to short buildings, increases the negative pressure at the back of the super high-rise building. In addition, simulations are conducted for the wind field around the CITIC Tower in Beijing CBD, and it is found that the flow field of the actual building group is more complex due to the strong interactions between buildings, and the flow near the ground is even more complex. All simulation results are validated by the wind tunnel tests. This study can provide important guidance for the wind safety design of super high-rise buildings and the future planning of urban buildings.
The lack of access to extensive and varied datasets remains one of the major issues facing the field of machine learning, despite recent advancements. This is especially true in the healthcare sector, where it can be challenging to gather and use patient data for research because it is frequently compartmentalized across many healthcare providers. By enabling secure and privacy-preserving access to distributed data, blockchain technology, and federated learning have the potential to overcome these difficulties. In this article, we'll look at how federated learning and blockchain are used in the healthcare industry and talk about their benefits and drawbacks. We will also examine the Hedera platform, which makes use of blockchain technology and a new algorithm called Gossip Degree to provide a revolutionary method of federated learning. We will also go over the potential effects of federated learning on the healthcare sector and what it means for future research.
With an increasingly strong concept from the public in protecting the ecological environment, green buildings account for more percentages in the buildings built in recent years. However, there are still many problems to be solved in the design and construction of green buildings. Therefore, this paper combines the construction and design process of the Shanghai Tower, a famous green high-rise building, and analyses many aspects, such as foundation, structural design, material selection, circulatory system, technology application, and other areas, to draw some options that can be effectively used and practiced in green buildings. For example, the selection of alternative materials and the promotion and application of energy-saving design aspects, the use of geothermal energy and rainwater resources near the building and the effectiveness that BIM can play provide inspiration for the future development of green building design and construction, as well as an important embodiment of the concept of green and sustainable development.
Recently, ferroelectric material is playing a more and more important role in the applications of semiconductor devices, especially in random access memory(RAM) devices, and transistors. Compared with traditional flash memories, FRAMs have advantages such as low operation voltage, a huge number of writes, non-volatile properties, and high write speed. However, in the early stage, the main materials used to produce FRAMs are perovskites with crystal structures. Those materials like PbTiO3/PbZr0.3Ti0.7O3 are restricted by the size and the complementary-metal-oxide-semiconductor (CMOS) technology, which is the common technology used to process semiconductor materials. Hafnium oxide material is a newly discovered material with ferroelectricity when doped with Zirconium(Zr). The Hf0.5Zr0.5O2 thin film is an ideal material for FRAMs, which has a smaller size than perovskites FRAMs and is compatible with current CMOS technology, which means lower cost and higher performance. This article aims to explain some properties of hafnium oxide materials based on different aspects, like dopants, thickness, annealing, and electrodes, and elaborate on the advantages of FRAMs made by hafnium oxide materials.
5G mobile communication network and cloud computing are the technological products and focus of today's era. Compared to 5G, 5G has seen a huge increase in peak speeds to 10-20Gbit/s, air interface latency as low as 1ms and much more. Cloud computing uploads data to the cloud so that users can access it more easily. They bring great convenience and high working efficiency to people's life. The use of cloud computing in 5G could make more efficient.5G, as a combination of new technology and cloud computing, will become a much larger market. This paper mainly describes the theoretical basis of 5G mobile communication network and cloud computing, the application of cloud computing in 5G (including automatic driving technology, surgery mobile communication network) and the current dilemma and the improvement needed. It aims to further promote the combination of 5G mobile communication network and cloud computing.
Based on the results of interviews conducted with university students in a certain university in Yunnan Province, this study identifies the problems of excessive information navigation, complex interface layout, and difficulty in locating the school system within the university management system APP. From the perspective of information architecture, this study designs a cognitive model that is suitable for the university management system APP of a certain university in Yunnan Province. By conducting tracking surveys and interviews with students from different majors, the study utilizes the affinity diagram method to construct a cognitive model for student users, which is further categorized by involving 10 participants. The data obtained after categorization is analyzed using hierarchical cluster analysis, and the information construction of the university management system APP is restructured accordingly. Through experiments, the characteristics and existing problems of the informationization construction of the university management system APP are determined, and the navigation label information of the management system is improved. The cognitive model of the university management system APP is reconstructed, resulting in the development of navigation names and classification methods for information construction that align with the cognitive models of student users. The research findings provide a basis and reference for other university management system APPs.
Facial Emotion Recognition (FER) holds great importance in the fields of computer vision and machine learning. In this study, the aim is to improve the accuracy of facial expression recognition by incorporating attention mechanisms into Convolutional Neural Networks (CNN) with FER2013 dataset, which consists of grayscale images categorized into seven expressions. The combination of proposed CNN architecture and attention mechanisms is thoroughly elucidated, emphasizing the operations and interactions of their components. Additionally, the effectiveness of the new model is evaluated through experiments, comparing its performance with existing approaches in terms of accuracy. Besides, the results demonstrate that the CNN architecture with attention mechanisms outperforms the original CNN by achieving an improved accuracy rate of 69.07%, which is higher than 68.04% accuracy rate of original CNN. Moreover, the study further discusses the confusion matrix analysis, revealing the challenges faced in recognizing specific emotions due to limited training data and vague facial features. In the future, this study suggests addressing these limitations through data augmentation and to reduce the gap between training and testing accuracy. Overall, this research highlights the potential of attention mechanisms in enhancing facial expression recognition systems, paving the way for advanced applications in various domains.
The aim of developing the technology of "image captioning," which integrates natural language and computer processing, is to automatically give descriptions for photographs by the machine itself. The work can be separated into two parts, which depends on correctly comprehending both language and images from a semantic and syntactic perspective. In light of the growing body of information on the subject, it is getting harder to stay abreast of the most recent advancements in the area of image captioning. Nevertheless, the review papers that are now available don't go into enough detail about those findings. The approaches, benchmarks, datasets, and assessment metrics currently in use for picture captioning are reviewed in this work. The majority of the field's ongoing study is concentrated on robust learning-based techniques, where deep reinforcement, adversarial learning, and attention processes all seem to be at the heart of this research area. Image captioning entails a brand-new field in research on computer vision. Generating a comprehensive natural language description for the source images is the fundamental issue of image captioning. This essay explores and evaluates earlier work on image captioning. Image captioning's application and task situations are introduced. The merits and disadvantages of each approach are explored after the analysis of the image captioning algorithms based on encoder-decoder and template structure. The assessment and baseline dataset for picture captioning are therefore shown. Ultimately, prospects for image captioning's progress are presented.
The wireless system did the huge cost-cutting in monitoring the structure so, now it can be used permanently as an integral part of the system as a smart infrastructure that will give them real-time information the structure. The wireless devices transmit the collected data about cracks, displacement, and excess vibration in slab-tracks. The train which will collect the data and train will be used as a data mule in this paper which will upload the information to a remote-control centre. The data which will be collected stored in the database and to know the status of the track a query will be fired from an application. In this paper, many design for communication systems are proposed which are efficient, with fine accuracy, and most importantly it is a low-cost system.
The lithium-ion battery has become one of the most widely used green energy sources, and the materials used in its electrodes have become a research hotspot. There are many different types of electrode materials, and negative electrode materials have developed to a higher level of perfection and maturity than positive electrode materials. Enhancing the electrochemical capabilities of positive electrode materials is therefore crucial. In addition to exploring and choosing the preparation or modification methods of various materials, this study describes the positive and negative electrode materials of lithium-ion batteries. Among the negative electrode materials, Li4Ti5O12 is beneficial to maintain the stability of the battery structure, and the chemical vapor deposition method is the best way to prepare nitrogen-doped graphene materials. Doping and coating modifications for positive electrode materials can offer a smoother mobile route for lithium ions, which can enhance the cathode material’s cycling performance. This paper’s study, summary, and outlook on electrode materials for lithium-ion batteries can aid those researchers in developing a more thorough understanding of electrode materials. Also, it can be advantageous for the growth of associated follow-up research projects and the expansion of the lithium battery market.
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.
The classic 2D SLAM are not good enough in nowadays environment. This report uses virtual machine with Ubuntu based Slam_bot package, based on the RTAB-MAP algorithm and Vision SLAM mapping to simulate the four-wheeled robot to autonomously navigate to the target point in various environments. Also, this report introduces a RGBD-SLAM based algorithm which combines the visual and depth data to process the data collect from the sensors. This robot has many sensors like, lidar sensors, RGB vision camera and odometry sensors. To see how the RTAB-MAP algorithm with RGB-D sensor replace for the 2D SALM. As results, the robot with RGB-D and RTAB-MAP algorithms have very good performance. The results show that the navigation system can complete the navigation and localization in lots of complex situations. However, some problems still exist, the speed of the robot is not fast. This may limit the application of the self-navigation robot to a certain extent, like some emergency occasion.