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.

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.

With the flourishing development of artificial intelligence and computer animation technologies, there has been an increasing intersection between these two. In the field of computer animation, the use of artificial intelligence significantly reduces the difficulties in design, production, and post-production processes, which has a massive impact on the entire field. The paper attempts to discuss the relationship between artificial intelligence and computer animation. Not only does the paper elaborate on the related applications of artificial intelligence in various subfields of computer animation, but it also analyzes existing problems and future development trends. The research indicates that AI has achieved significant breakthroughs in computer animation, such as auto-generation of animations, real-time character driving, and emotionally responsive animation creation. However, it also faces challenges like handling interactions in complex scenarios, maintaining realism, and animating high-level abstract concepts. Despite these challenges, it is believed that in the future, AI will further propel the development of computer animation, aiding creators in producing animations that are more vibrant, intricate, and personalized.

The purpose of this paper is to compare and evaluate the performance differences between flexible and rigid hand exoskeletons in terms of functional recovery and assistance in daily activities.Flexible hand exoskeletons are lightweight and soft devices with stretchable materials and flexible mechanisms designed to mimic the flexibility and versatility of natural hand movements.They typically consist of elastic materials, sensors and actuators that enable natural hand movements and provide light strength support.The main advantages of flexible hand exoskeletons are their comfort and flexibility, and their ability to provide personalized assistance for a variety of daily activities and tasks.This form of design is suitable for patients who require mild hand support and dexterity, such as individuals with mildly impaired hand motor function or who need to perform fine motor movements. In contrast, a rigid hand exoskeleton is a more rigid and stable device that uses robust materials and a rigid mechanism designed to provide a greater degree of strength support and stability.They are typically constructed of metal or composite materials, have a high degree of rigidity and stability, and provide strength support through electrical motors or hydraulic systems.The main advantage of a rigid hand exoskeleton is its higher force output and stability for tasks that require higher loads or complex movements.This form of design is suitable for patients who require greater strength support and stability, such as individuals with reduced hand muscle strength or who need to carry heavy loads

To decrease workload on lightweight devices, this project accelerates the computation of Convolution Neural Networks (CNNs) and preserves accuracy through modifying the CNNs’ training process. First, this research implements distributed computing to optimally divide the network workload onto both devices and the cloud. To reduce communication latency between devices and the cloud, this research introduces feature pruning by setting elements in the communicated feature to 0. However, naively pruning the feature causes a significant accuracy drop. To compensate for this limitation, this research applies pruning-aware training to preserve the CNNs’ task performance. This research evaluates the proposed methods on multiple datasets and CNN models, like VGG-11 and ResNet-18 with PyTorch. Empirical results demonstrate that the methods can reduce the computational latency by 50-75% with a negligible 1% accuracy loss. Specifically, this research first identifies the system bottleneck by comparing on-device, on-cloud, and communication latencies (on-device: 14.8%, on-cloud: 1.7%, communication: 83.5%). Then, this research compares multiple pruning strategies and observe the superiority of magnitude-based pruning. At 0.992 sparsity, magnitude-based pruning outperforms other strategies by 45% in accuracy. Finally, this research verifies the effectiveness of the proposed pruning-aware training method by comparing it with the baseline at various splitting points and networks. Pruning-aware training decreases the accuracy loss by up to 26% at 0.998 sparsity. In conclusion, even though distributed computing accelerates applications on lightweight devices, compressing the communication cost is crucial and challenging. This research proposed methods effectively reduce communication latency without sacrificing accuracy, conserving the effectiveness of CNN.

This paper describes the importance of robot path planning in artificial intelligence and control theory, and proposes three improvements to the A-algorithm: bi-directional A-search, improved heuristic functions and pruning strategies. The performance of the different algorithms in terms of computation time, path length and number of nodes is compared through experiments. Moreover, it is emphasised in the article that in practical applications suitable algorithms and their improvements are selected according to the characteristics of the specific problem and reasonable evaluation criteria are used to measure the performance of the algorithms.

The emergence of cryptocurrencies has revolutionized the concept of digital currencies and attracted significant attention from financial markets. Predicting the price dynamics of cryptocurrencies is crucial but challenging due to their highly volatile and non-linear nature. This study compares the performance of various models in predicting cryptocurrency prices using three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models analyzed include Moving Average (MA), Logistic Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The objective is to uncover underlying patterns in cryptocurrency price movements and identify the most accurate and reliable approach for predicting future prices. Through the analysis, it could be observed that MA, LR, and ARIMA models struggle to capture the actual trend accurately. In contrast, LSTM and CNN-LSTM models demonstrate strong fit to the actual price trend, with CNN-LSTM exhibiting a higher level of granularity in its predictions. Results suggest that deep learning architectures, particularly CNN-LSTM, show promise in capturing the complex dynamics of cryptocurrency prices. These findings contribute to the development of improved methodologies for cryptocurrency price prediction.

To be competitive in today's market, companies must constantly assess and improve their operations, and financial data analysis has emerged as a crucial tool for this purpose. Executives can utilize data analysis to gain a deeper understanding of the underlying facts in their data and make better informed decisions about their company and the market. Multiple data models in the language make it possible to have a fully functional language environment with tools for statistical analysis and visual visualization of data. With its ability to enhance the quality of work in statistical computations and graphical analysis, the R language is ideally suited to the industrial data analysis environment. In this paper, we use a literature review methodology to examine the domestic and international literature on R language big data visualization auditing, analyze the visualization application areas of R language big data auditing, discuss the benefits and challenges of big data auditing, demonstrate how R language can be adapted to real-world auditing scenarios, and offer reasonable recommendations and optimistic outlooks for the field's future.

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.

As Internet technology continues to evolve, recommender systems have become an integral part of daily life. However, traditional methods are increasingly falling short of meeting evolving user expectations. Utilizing survey data from the MovieLens dataset, a comparative approach was employed to investigate the efficacy, performance, and applicability of the UCB (Upper Confidence Bound) algorithm in addressing the multi-armed bandit problem. The study reveals that the UCB algorithm significantly impacts the cumulative regret value, indicating its robust performance in the multi-armed bandit setting. Furthermore, LinUCB—an enhanced version of the UCB algorithm—exhibits exceptional overall performance. The algorithm's efficiency is not just limited to the regret value but extends to handling high-dimensional feature spaces and delivering personalized recommendations. Unlike traditional UCB algorithms, LinUCB adapts more fluidly to high-dimensional environments by leveraging a linear model to simulate the reward function associated with each arm. This adaptability makes LinUCB particularly effective for complex, feature-rich recommendation scenarios. The performance of the UCB algorithm is also contingent upon parameter selection, making this an important factor to consider in practical implementations. Overall, both UCB and its modified version, LinUCB, present compelling solutions for the challenges faced by modern recommender systems.

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 the rapid development of social economy, the consumption of conventional energy is growing at an amazing rate. The energy shortage crisis and the environmental problems brought by conventional energy will seriously restrict social development and affect the daily lives of residents. Therefore, paying attention to the creation and use of new energy, protecting the environment, improving efficiency, controlling the emission of pollutants and realizing sustainable development have become the main research topics in the new era of the energy sector. Photovoltaic energy has the advantages of economic energy saving, green environmental protection, wide application and sustainability, and is an ideal new energy, that has been developed to the third generation. This paper mainly combs the development process of photovoltaic technology, summarizes the characteristics, advantages and disadvantages of the third generation of photovoltaic technology, analyzes the current situation and prospects of photovoltaic technology development, and analyzes the problems and challenges faced. This research finds that as the economy and technology continue to advance, photovoltaic cell technology is developing rapidly, and the application cost is constantly reduced. The photovoltaic cell industry will get more attention and better development, and its application prospect is very broad. The research of this topic is helpful in enhancing the comprehensive and objective understanding of the development of photovoltaic cell technology, and will provide a valuable reference in order to advance the photovoltaic sector in the future, which has important practical significance.

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.

Amidst the escalating complexities that define the contemporary financial market and the rapid proliferation of information, traditional methods of formulating investment decisions confront increasingly formidable challenges. In response to these intricate dynamics, the realm of financial data mining has emerged as a prominent avenue of scholarly investigation within the investment domain. This paper's fundamental objective is to conduct a comprehensive retrospective analysis of the diverse applications of financial data mining in the context of investment decision-making.This scholarly pursuit entails a meticulous synthesis of existing academic inquiries, concurrently proposing potential avenues for future advancements in this field. By undertaking this academic endeavor, the paper strives to make substantive contributions to the refinement of methodologies essential for adeptly navigating the multifaceted landscape of modern investments. As the financial landscape continues to evolve, this study aspires to offer insights that not only enhance the efficacy of investment strategies but also foster a deeper understanding of the intricate interplay between data mining techniques and decision-making processes. Through the synthesis of empirical findings and theoretical perspectives, this paper seeks to underscore the pertinence of leveraging data-driven approaches in investment practices, thereby promoting a more informed and sophisticated investment landscape.

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.

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.

With the growing global aging population, there's been an amplified societal emphasis on preserving the health of the elderly and enhancing their quality of life. In this scenario, robotic exoskeletons have emerged as a cutting-edge solution to assist the elderly and those with lower limb muscle deficiencies in Sit-to-Stand (STS) exercises. These exoskeletons adopt two main approaches: full assistance for those with entirely weakened lower limbs and partial assistance for those with some remaining muscle strength. This article introduces two modeling methods and concepts for these control strategies, aligning with the full and partial assistance directions, respectively. Both approaches hinge on the Lagrange equation as their foundational structure, integrating distinct kinematic designs to form their individual dynamic models. Based on this, the models are further adapted to address the specific risks associated with STS activities as per each strategy. Research outcomes highlight that by assessing the wearer's EMG signal, the partial assistance strategy considerably mitigates the lower limb muscle strength required for STS under conditions such as low-speed, medium-speed, sitback-like, and step-like. This not only improves balance but also augments the likelihood of successful STS execution, consequently diminishing fall incidents.

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.

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.