Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bit-Interleaved Coded Modulation Scheme Based on the Polar Code in Underwater Acoustic OFDM Communication System

A Bit-Interleaved Code Modulation (BICM) scheme based on the polar codes (Polar-BICM), suitable for the underwater acoustic (UWA) communication system utilizing orthogonal frequency division multiplexing (OFDM) is proposed in this paper. In the proposed scheme, the design and implementation of the encoder and the modulator are combined to improve the communication rate, while the traditional schemes usually consider independently. The system BER performance affected by variance interleaver types, interleaver lengths and code lengths are carefully discussed in this paper. The LDPC-BICM system and the Trellis Coded Modulation (TCM) scheme are chosen as the control schemes. Simulation and experimental results show that the BER performance of the UWA OFDM communication system based on Polar-BICM is much better than that based on the TCM scheme. Moreover, a higher coding gain than the LDPC-BICM UWA OFDM system is obtained because of the Polar Code. At the same time, the compiled code implementation of Polar Code is relatively simpler than that of the LDPC coding scheme. Therefore, the Polar-BICM system improves the transmission efficiency of the system effectively without losing reliability, which contributes a lot to the practical application of the coding and modulation combination technology in UWA communication.
Household Intelligent Medicine Box Based on STM32

Based on STM32 single-chip microcomputer, this paper realizes an intelligent medicine box system with functions such as timing and quantitative medicine taking, temperature measurement, and SOS. On the basis of the common medicine box, the system is equipped with a WIFI module, which can remotely control the medicine box on the APP side, display the temperature change trend and receive one-key call for help. The experimental results show that the system is helpful for the elderly to take medicine conveniently and quickly, and assists children to fulfill their duty of caring for the elderly.
A method for radar time-frequency image data detection based on an improved YoloV4 network

In sea surface target detection missions, due to the variable weather conditions at sea, the complex and variable surges formed in adverse weather conditions make target detection difficult, and radar sensors are widely used based on their ability to reliably detect targets in all adverse weather conditions. Sea clutter is defined as the backscattered echoes formed by radar irradiation onto the sea surface. Detection of small targets on the sea surface in the background of sea clutter is usually faced with unfavourable factors such as low signal-to-noise ratio. This thesis proposes a method for radar time-frequency image data detection based on an improved yolov4 network, which can perform the task of sea surface target detection better and can achieve an accuracy of 90.11% on the measured radar time-frequency image dataset, outperforming similar classical deep learning methods, and ablation experiments are done to demonstrate the effectiveness of the improvement.
Short-Term PV Power Prediction Based on NGO-BILSTM

The accuracy of load forecasting can be impacted by changes in external elements, such as the environment, in short-term PV power forecasts. This research suggests the NGO-BILSTM prediction model, which combines the Bi-directional Long Short-term Memory (BILSTM) network and the Northern Goshawk Optimization (NGO) algorithm, to solve this issue. First, high correlation features are selected as input data by Pearson correlation analysis, then the hyperparameters of BILSTM are optimized by the NGO algorithm. The NGO-BILSTM prediction model is then established based on the optimized parameters, and the prediction is carried out on the dataset. The experimental prediction findings demonstrate that the NGO-BILSTM model's mean absolute error, root mean square error, and linear regression coefficient index are, respectively, 1.434, 1.809, and 0.972, which are better than those of other comparable models, demonstrating the model's efficacy.
Architectures and Applications of U-net in Medical Image Segmentation: A Review

Recently, with the increasing application of deep learning in the medical field, convolutional neural networks, represented by U-Net, has been widely applied in medical image segmentation. The improved U-shaped network structure based on U-Net has gradually become a hot topic in medical image segmentation research. This article summarizes the improvement work related to U-Net from three perspectives: modifying skip connections, adding or replacing blocks and concatenating multiple neural networks. Then, taking the segmentation of retina, lungs, brain, abdomen, and other organs as examples, the characteristics and difficulties of various organ segmentation were introduced. Finally, a summary and outlook were made.
A Glimpse of the History of Analog ICs: A Tale of Amplifiers, Data Converters, and Sensor Interfaces

Probably the most distinct divide in electronic circuits is that between digital and linear (analog) circuits. Using vacuum tubes; later, transistors; and then ICs, circuits based on switching (binary and digital signals) and amplification (analog signals) have always been at the heart of electronic systems. Even though electronics are making our world more digital, the real world remains stubbornly analog. Circuits for interfacing sensors and driving actuators, amplifying (weak) analog signals, manipulating these signals through analog signal processing, and, finally, converting them into the digital domain and vice versa were, are, and will remain fundamental research and development fields in circuit design. Due to the wide scope of the field, ranging from RF circuits, power management, reference generation, filter design, and oscillators to comparators and other nonlinear circuits, just to name a few, it is clear that a short review article cannot possibly mention all topics, let alone cover them all. So, choices were made. We begin this article with amplifiers, which are one of the critical analog building blocks that often determine system performance. We briefly review the early days of IC-based amplifiers and some outstanding circuit innovations for amplifier design. Thereafter, we highlight the history and state of the art of ADCs, their architectures, and efficiency improvements over four decades. Finally, we review sensor interfaces, first with a general focus on their history and the state of the art of various sensor modalities and, second, with a special focus on biomedical interface circuits for biopotential recording in the context of neural amplifiers. With this variety of topics, we intend to highlight the importance of the transistor and analog ICs to the world as we know it today.
Latency characteristics analysis of express frames in TSN frame preemption

The frame preemption mechanism in Time-Sensitive Networking can effectively improve the transmission latency performance of express frames. However, in different network operating environments, express frame latency presents complex dynamic characteristics. In response to the complexity of express frame latency characteristics under frame preemption conditions, this article first analyzes the operation principle of the frame preemption mechanism and provides a calculation method for the latency of express frames. Then, considering the impact of link utilization and preemptable frame length on the average latency of express frames, we design a simulation analysis algorithm for the latency characteristics of express frames. Finally, using the proposed algorithm, we analyze the average latency, latency randomness, jitter, and latency boundary characteristics of express frames. The research results are valuable for engineering reference for the effective application of the TSN network frame preemption mechanism.
BabelMR: A Polyglot Framework for Serverless MapReduce

The MapReduce programming model and its open-source implementation Hadoop have democratized large-scale data processing by providing ease-of-use and scalability. Subsequently, systems such as Spark have dramatically improved efficiency. However, for a large number of users and applications, using these frameworks remains challenging, because they typically restrict them to specific programming languages or require cluster management expertise. In this paper, we present BabelMR, a data processing framework that provides the MapReduce programming model to arbitrary containerized applications to be executed on serverless cloud infrastructure. Users provide application logic in Map and Reduce functions that read and write their inputs and outputs to the ephemeral filesystem of a serverless function container. BabelMR orchestrates the data-parallel programs across stages of concurrent cloud function executions and efficiently integrates with serverless storage systems and columnar storage formats. Our evaluation shows that BabelMR reduces the entry hurdle to analyzing data in a distributed serverless environment in terms of development effort. BabelMR’s I/O and data shuffle building blocks outperform handwritten Python and C# code, and BabelMR is competitive with state-of-the-art serverless MapReduce systems.
A Cell-Free Millimeter-Wave Channel Estimation Algorithm Based on Federated Learning

Although machine learning is powerful in physical layer design like channel estimation, its centralized training approach generates significant communication overhead and privacy risks due to the need for processing large amounts of data centrally. We leverage the distributed deployment characteristics of access points (APs) in a cellular-free network architecture in a cell-free massive MIMO system and combine federal learning for channel estimation, this solution ensures accurate channel estimation, effectively addressing the potential issues mentioned above. A novel model aggregation approach is proposed in this paper, which incorporates a feedback mechanism to bring the training model closer to the convergence upper bound. Simulation results demonstrate that the proposed algorithm yields a neural network model with superior channel estimation performance and requires fewer training rounds compared to the benchmark algorithm.
Optimal Control Method of Building Construction Cost Based on Data Fusion

In the construction of prefabricated buildings, the traditional cost control method cannot control the cost scientifically, and there are phenomena such as material waste. In order to reduce the cost of prefabricated construction, based on the concept of data fusion, a construction cost control method for the whole process is studied. Using the cost control method of building prefabricated structure, on the basis of the cost comparison between prefabrication and cast-in-place, combined with value engineering theory, quota design, 4D and diagrams, the influencing factors of the cost of each construction stage are analyzed. Virtual construction technology progress management method, component production process analysis and construction simulation, integration of project cost and graphical processing of big data, and establishment of a large-scale project cost database. Combined with big data technology, use logic sequence and other information to carry out data fusion analysis to realize project cost control. The simulation results show that the proposed method can effectively control the construction cost, accurately generate progress statistics, and realize the dynamic management of project resources.
Research on Multi Maintenance Stations Scheduling Method for Passenger Elevators with Considering Priority

Aiming at the problem that the current passenger elevator maintenance scheduling mainly relies on manual work and cannot meet the large-scale maintenance scheduling which needs to consider the priority, a multi-optimization objective and multi-maintenance station maintenance scheduling method considering the priority of maintenance tasks was proposed. Firstly, the maintenance scheduling characteristics of multiple maintenance stations were analyzed, and the task of multi-maintenance station scheduling was decomposed into a single maintenance station maintenance scheduling model using K-means ++ algorithm. Then, an improved NSGA-II algorithm for single maintenance station scheduling was proposed to consider the priority of maintenance tasks, and a double-strand coding method was designed. At the same time, the crossover operator and mutation operator are improved, and finally the case analysis is carried out. The analysis results show that the proposed method can quickly realize multi-station maintenance scheduling considering priority, and the number of maintenance personnel required by different maintenance stations is different. The proposed method can provide reference for the formulation of maintenance route and the determination of maintenance personnel.
Industry 4.0 Implementation in Sri Lankan Manufacturing Firms: A Lean Perspective

Manufacturing industries require the highest quality and efficiency throughout their value chain, to compete with countries having a labor cost advantage. Today, manufacturing firms are in a fast-phased run to automate their processes and increase value chain integration through advanced technologies. Industry 4.0 has gained traction within this community, where its components like IoT, Big data, and Cloud computing are being used by manufacturing firms to optimize and increase the efficiency of their workplaces. Obtaining the proper outcomes from these advanced technologies has been an issue for most of its users. Very few studies were found in the literature, that propose ways to mitigate the issues faced by these companies in their Industry 4.0 journey. Lean concepts are a popular and proven methodology used by firms worldwide to decrease the complexity and increase the productivity of their processes. Based on a systematic literature review, the study identifies the current knowledge on mitigating the barriers faced by manufacturing firms in Industry 4.0 implementations. To address the knowledge gap identified in the literature review, the study proposes and statistically tests a framework, on how the manufacturing environment can be improved to obtain the expected outcomes of Industry 4.0 implementations, through a lean theoretical lens. Thus, the stakeholders of the company can contribute towards successful implementations of Industry 4.0 while organizational processes are being standardized and optimized to integrate these advanced technological shifts.
Research on the Relationship between Stability, Heterogeneity of Top Management Team and Enterprise Technological Innovation Performance Evidence from Chinese listed companies

In recent years, the Chinese market has been one of the most innovative markets in the world. We know that China implements an economic system in which public ownership is the mainstay and multiple ownerships develop together. The impact of differences in corporate ownership structures on corporate technological innovation capabilities has always been controversial. In fact, economic theory does not support the impact of ownership structure differences on technological innovation performance. Entrepreneurs should be the reason for the differences in technological innovation capabilities among enterprises, because in the technological innovation practices of Chinese enterprises that emphasize collective leadership, the impact of entrepreneurial factors on technological innovation performance is not very prominent. Therefore, based on the context of Chinese corporate technological innovation, we explain its relationship with corporate technological innovation performance from the perspective of corporate executive team stability and heterogeneity. We try to take an empirical approach to explore what characteristics of the top management team will be conducive to the improvement of corporate technological innovation performance, which will provide theoretical guidance for the construction of corporate top management teams that promote technological innovation.
How to Promote New Product Development through Networked and Embedded Innovation? Based on the Perspective of Digital Transformation

Although current studies indicated that embedded innovation is a crucial addition to the cooperative innovation and the process is often accompanied by digital transformation, few have declared the action path or long-term effects. The effects of networked and embedded innovation on new product development are discussed in a unified theoretical framework in this study. Drawing upon network embeddedness and resource orchestration theories, we propose that: (1) networked innovation promotes new product development performance, and this effect is fully mediated by digital transformation; (2) there exists an inverted U-shaped relationship between embedded innovation and new product development performance, where digital transformation plays the role of complementary partial mediation. Unbalanced panel data made up of 245 firms operating in China’s A-share computer communication industry from 2007 to 2020 has been used to test the theoretical model. We aim to emphasize that based on loosely coupled innovation enhancement and power aggregated conflict mitigation mechanisms, digital transformation has a mediating effect in the relationship between the two paradigms of cooperative innovation and new product development, respectively. According to the findings of our study, a future fit innovation strategy is suggested for technology firms.
The Role of Social Media (Twitter) in Analysing Home Violence: A Machine Learning Approach

Home Violence (HV) has been a persistent issue across the globe, transcending economic status and cultural boundaries. The COVID-19 pandemic has further exacerbated this problem, bringing it to the forefront of public discourse. This study aims to analyse the impact of HV by utilising Twitter data and Machine Learning (ML) techniques, categorising tweets into three groups: (i) HV Incident Tweets, (ii) HV Awareness Tweets, and (iii) HV Shelter Tweets. This categorisation provides several advantages, such as uncovering new or hidden evidence, filling information gaps, and identifying potential suspects. Over 40,000 tweets were collected using the Twitter API between April 2019 and July 2021. Data pre-processing and word embedding were performed to prepare the data for analysis. Initially, tweets were categorised into HV Positive (containing relevant information) and HV Negative (noise or unrelated content) groups. Manually labelled tweets were used for training and testing purposes. Machine learning models, including SVM, NB, Logistic Regression, Decision Tree (DT), Artificial Neural Networks (ANN), and LSTM, were employed for this task. Subsequently, HV Positive tweets were classified into the three aforementioned categories. Manually labelled tweets were again used for training and testing. Models such as Tf-IDF+SVM, Tf-IDF+DT, Tf-IDF+NB, and GloVe+LSTM were utilised. Several evaluation metrics were used to assess the performance of the models. The study’s results provide important new understandings of the prevalence, patterns, and causes of HV as they are reported on social media and how the general population reacts to these problems. The research clarifies how social media may help spread knowledge, provide assistance, and link victims to resources. These insights can be instrumental in informing policymakers, non-profit organisations, and researchers as they work to develop targeted interventions and strategies to address HV during and beyond the COVID-19 pandemic.
DrivEmo: A Novel Approach for EEG-Based Emotion Classification for Drivers

Electroencephalogram (EEG) based emotion recognition approaches have proven to be successful with the latest technologies, and therefore, driver emotion recognition is also being widely discussed for enhancing road safety. This paper reveals a unique approach to driver emotion recognition for the calm, fear, sad, and anger emotional states where calm is the desired state of mind while driving. Emotiv EPOC X 14 channel EEG headset is utilised for the EEG collection, and ten subjects are involved in the experiment. EEG preprocessing of the collected EEG data is done using the EEGLAB toolbox in Matlab. EEG feature extraction is performed using Matlab, and feature selection and classification model training is done using the Classification Learner app in Matlab. ANOVA and ReliefF are employed as the feature selection algorithms, and Support Vector Machine (SVM) and Naïve Bayes classifiers are utilised for the emotion classification. The outcomes reveal that the highest mean accuracy of 95% is achieved from the Coarse Gaussian SVM classifier, while the lowest mean accuracy of 85% is obtained from the Fine Gaussian SVM classifier detecting the calm, fear, sad, and anger emotional states. In addition, all the other trained classifier models have an accuracy between 85% and 95%. Therefore, the findings suggest that the proposed EEG-based implementation approach of an emotion classification model for drivers is highly successful and can be employed in future research in the paradigm of driver emotion recognition as well. Besides, this research presents a critical literature review concerning critical aspects of EEG-based emotion recognition research.
An Improved Gtsam-based Nonlinear Optimization Algorithm in ORBSLAM3

Autonomous driving and robotics play an important role in smart cities. SLAM technology is the core of its perception module. Simultaneous Localization and Mapping (SLAM) has become a application hotspot in the field of the urban traffic in recent years. Most SLAM algorithms are time-consuming and thus their real-time performance is difficult to be guarantee. To solve this problem, this paper proposes an improved ORBSLAM3 speeding-up algorithm based on Gtsam(Georgia Tech Smoothing and Mapping). The factor graph optimizer based on Gtsam library is used to describe the local map BA and solve the pose BA to improve computing efficiency. The Experimental results based on Euroc data set show that Gtsam improves the running speed of tracking thread by 13%-15% compared with g2o, and the rmse of absolute track error analysis decrease by 0.21.
Advances in Active Learning Kriging Surrogate Models for Reliability Assessment

Reliability assessment is an important link to ensure product quality. However, both the approximate analytical method and the simulation method have shortcomings in applicability. At present, active learning Kriging surrogate model has become a hot spot in reliability assessment methods owing to its high calculating effectiveness and accuracy. The composition and structure for the Kriging theories, the methods for samples generation, together with the theories related to active learning are described in detail. Several kinds of classical active learning Kriging algorithms are analyzed. This paper emphasizes the status of research on Kriging algorithms with active learning processes for solving small failure probability, system reliability, time-dependent reliability and hybrid variable problems. Finally, the development prospect of active learning Kriging algorithm is discussed.