Integrated Development Environment Research Articles

Enhanced Security and Efficiency in Attendance Management: A Novel RFID and Arduino Integrated System

The advent of Radio Frequency Identification (RFID) technology has ushered in a new paradigm in the domain of automated attendance systems, offering a sophisticated yet user-friendly approach to personnel management. This paper presents a comprehensive study on the design and deployment of an RFID-based attendance system powered by the versatile Arduino platform, elucidating its operational tenets, system architecture, and practical implementations. At the heart of the system lies the MFRC522 RFID reader, which synergizes with an Arduino microcontroller to facilitate the identification and logging of attendance data. The system is enhanced by the inclusion of an SD Card Module for data storage and a Real-Time Clock (RTC) Module to ensure accurate timestamping of attendance events. The seamless integration of these components results in a robust mechanism that not only simplifies the attendance tracking process but also fortifies the security framework by leveraging unique identifiers for each user. The study spans the detailed process of assembling the hardware, crafting the software in the Arduino Integrated Development Environment (IDE), and meticulously testing the integrated system to affirm its efficacy. The resulting attendance system embodies a significant stride towards refining attendance management practices, eliminating the shortcomings of manual tracking while providing a scalable and reliable solution adaptable to various organizational settings.

Interactive Abstract Interpretation with Demanded Summarization

We consider the problem of making expressive, interactive static analyzers compositional . Such a technique could help bring the power of server-based static analyses to integrated development environments (IDEs), updating their results live as the code is modified. Compositionality is key for this scenario, as it enables reuse of already-computed analysis results for unmodified code. Previous techniques for interactive static analysis either lack compositionality, cannot express arbitrary abstract domains, or are not from-scratch consistent. We present demanded summarization, the first algorithm for incremental compositional analysis in arbitrary abstract domains that guarantees from-scratch consistency. Our approach analyzes individual procedures using a recent technique for demanded analysis, computing summaries on demand for procedure calls. A dynamically updated summary dependency graph enables precise result invalidation after program edits, and the algorithm is carefully designed to guarantee from-scratch-consistent results after edits, even in the presence of recursion and in arbitrary abstract domains. We formalize our technique and prove soundness, termination, and from-scratch consistency. An experimental evaluation of a prototype implementation on synthetic and real-world program edits provides evidence for the feasibility of this theoretical framework, showing potential for major performance benefits over non-demanded compositional analyses.

Research on the System Design and Target Recognition Method of the Rebar-Tying Robot

In the construction industry, the construction process of rebar tying is highly dependent on manual operation, which leads to a wide range of work areas, high labor intensity, and limited efficiency. Therefore, robot technology for automatic rebar tying has become an inevitable trend in on-site construction. This study aims to develop a planar rebar-tying robot that can achieve autonomous navigation, precise positioning, and efficient tying on a plane rebar mesh without boundaries. Our research covers the overall design of the robot control systems, the selection of key hardware, the development of software platforms, and the optimization of core algorithms. Specifically, to address the technical challenges of accurately recognizing the tying position and status, we propose an innovative two-stage identification method that combines a depth camera and an industrial camera to obtain image information about the area to be tied. The effectiveness of the planar rebar-tying robot system, including the recognition method proposed in this study, was verified by experiments on a rebar mesh demonstration platform. The following application of our robot system in the field of the Shenyang Hunnan Science and Technology City Phase IV project achieved satisfactory performance. It is shown that this research has made a unique and significant innovation in the field of automatic rebar tying.

A Short Range Radar System using Arduino

RADAR is a detection system that uses radio waves to determine the characteristics of the detected objects such as: range, height, direction, or the speed of objects. This paper, is aimed at designing a radar system that uses an ultrasonic sensor to detect objects. The ultrasonic sensor is used to measure the distance between the radar and any object-based non-contact technology. This system is controlled through Arduino. Arduino UNO board is sufficed to control ultrasonic sensor and also to interface the sensor and display device. Whereas, the movement of the sensor is controlled by using a small servo motor. This radar is controlled using the Arduino Uno board as a microcontroller. The signal received from the sensor is processed using “Processing Development Environment Software". Ultra-sonic sensor is attached to the servo motor it rotates about 180 degree and gives visual representation on the software called processing IDE. Processing IDE gives graphical representation and it also provides angle or position of the object and distance of the objects on the PC or monitor.

Intelligent Program Correction and Evaluation System

The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder’s lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct short code blocks containing mistakes. This makes code composition easier and more time-efficient, thus improving the efficacy of large-scale development environments. Because Python is becoming more popular, the goal of our study is to enhance the efficiency of writing Python code by offering an automatic code-correcting approach. Furthermore, automatic program evaluation has been performed to assist in the debugging of small code blocks, which will ultimately be employed in the creation of real-time computer applications. The proposed technique would be useful for new learners who wish to create small Python code blocks for ease of writing and debugging on online platforms (like edX, Coursera, and Udacity). One of the major contributions of the project is to create an erroneous dataset of Python coding that contains all potential forms of probable syntax errors. The dataset induces variety through the use of multiple coding templates and is used to train deep learning models. We used the state-of-the-art text-to-text T5 transformer network model to automatically repair and evaluate the incorrect code. The outcomes of auto-correction are examined using the ROUGE and BLEU scores, as well as accuracy. The model corrects Python code with single, double, and the multiple number of errors with greater than 80% accuracy. Similarly, the performance of the basic T5 transformer network for program auto-evaluation with and without mistakes has been examined, and the model achieves greater than 65% accuracy in both cases. The proposed T5 base transformer outperforms the SOTA auto-correction models in terms of accuracy, according to a comparison study of the proposed method with the earlier techniques for auto-correcting codes.

DSP in the loop implementation of an improved sliding mode control for induction motor drive

This paper deals with the design and processor in the loop implementation of an improved sliding mode control of three-phase induction motor. Studied control strategy uses a state-space model expressed in the rotor flux frame. Proposed control strategy is designed in two steps based in cascaded method. This controller is based on new smooth functions replacing sign functions to reduce chattering phenomenon. This work integrates also a calculation of controller parameters to ensure set response times. It focuses on the study of these proposed techniques and their effect on motor waveforms. For code generation of the control algorithm, implementation and validation by processor in the loop technique we have created a platform based on MATLAB/Simulink with code composer studio as integrated development environment and the development board LAUNCHXL-F28069M from Texas instruments. Obtained results show that the proposed strategy reduces chattering using a simple algorithm.

A verifiable EVM-based cross-language smart contract implementation scheme for matrix calculation

The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses, which involve the calculation of complex functions, such as matrix operations. However, complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine (EVM)-based smart contract platforms due to their distributed security environment limitations. Existing off-chain methods often result in a significant reduction in contract execution efficiency, thus a platform software development kit interface implementation method has become a feasible way to reduce overheads, but this method cannot verify operation correctness and may leak sensitive user data. To solve the above problems, we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations, especially matrix operations, which can guarantee operation correctness and user privacy while ensuring computational efficiency. In this scheme, a verifiable interaction process is designed to verify the computation process and results, and a matrix blinding technology is introduced to protect sensitive user data in the calculation process. The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.

Cloud-Based Lightweight Modern Integrated Development Environments (IDEs) and their Future

Integrated development environments (IDEs) have been considered one of the most important tools for developing software. Integrated development environments (IDEs) act as a toolkit designed to facilitate the whole process of designing and developing any software. However, there exists a significant difference between traditional IDEs and modern IDEs. This paper highlights the difference between traditional and modern IDEs and the relevance of Cloud-based IDEs in the present and future.

DETERMINING THE WEIGHT OF OIL EXTRACTED WITH A SCREW PRESS USING A STRAIN GAUGE SENSOR, HX711 MODULE, AND ARDUINO

This research presents an automated system for quantifying oil extracted from a screw press, leveraging a strain gauge sensor and HX711 module in synergy with the Arduino platform. This innovative setup measures the target mass of extracted oil, wherein the collected data is further processed by an Arduino microcontroller. User-defined measurement parameters, such as specific oil mass, dictate when the system terminates the screw press operation. Hitting the set oil mass triggers the Arduino microcontroller to halt the press, allowing for the conservation of raw materials and energy, and consequently ensuring product quality consistency. Conversely, if the desired mass value remains unreached, the extraction continues until fulfillment of target outcomes. This approach optimizes press use, manifesting in maximum efficiency and selectivity in the production process. It demonstrates how such a control system can automate measurement and production supervision, thus fostering optimal screw press operation. The article suggests the solution for integrating Arduino, HX711 module, and strain gauge sensor to measure the weight of the oil extracted through the screw press. The strain gauge and HX711 module were programmed within the Arduino Integrated Development Environment, enabling the accurate measurement of extracted oil mass and data transference to the Arduino controller. A housing bracket 3D model for the strain gauge and HX711 module within the existing screw press was also created using 3D modelling and subsequently printed and implemented. This aids reliable sensor fitting and ensures system stability for oil mass measurement. This study demonstrates the potential for implementing this advancement within pre-existing press infrastructures with minimal changes. Our methodology offers prospective enhancements in quality and efficiency, notably within the oil production domain.

AgTC and AgETL: open-source tools to enhance data collection and management for plant science research.

Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through web-based tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences.

Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor.

The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.

Incorporating DRAGON12-light trainer board in an introductory microprocessors course

The HCS12 microcontroller and DRAGON12-Light Trainer boards are extensively utilized in microprocessor system design education. This paper details the rationale, approach, and outcomes from implementing the DRAGON12-Light Trainer board in teaching an upper-level undergraduate microprocessors course at Oakland University. The course’s primary goal is for students to acquire the skills necessary to design both hardware and software for microprocessor-based systems, with applications across various industries. The paper assesses the effectiveness of employing the Motorola HCS12 microcontroller for its real-world relevance and integrates the Freescale CodeWarrior IDE v5.1 environment for software development. This research uniquely contributes by measuring the improvement in students’ practical skills, specifically in hardware and software design using assembly and C programming, through hands-on lab assignments. It reports on the development of students’ abilities to engage in microprocessor-based system design and critically evaluates the applied aspects of pedagogy by incorporating the DRAGON12-Light Trainer in lab exercises, such as those involving ’Measuring Human Reaction Time’ and ’Servo motor interfacing.’ Quantitative results, derived from student surveys and assessments, indicate significant improvements in students’ programming competencies. The paper provides statistical results showcasing the increase in students’ self-rated confidence levels in assembly and C language programming before and after course completion. Additionally, qualitative insights are discussed, reflecting students’ experiences and the perceived applicability of the skills they acquired. These results underscore the pedagogical value of integrating practical training devices like the DRAGON12-Light Trainer board into microprocessors curricula.

How to implement and evaluate with scrum methodology in business subjects - an experience in an educational innovation Project

The following paper arises from the Educational Innovation Project PIE 15-132 "Implementation and evaluation of the scrum methodology (project based learning) in the subjects of Business Creation and Management", funded by the Vice-Rectorate for Teaching and Research Staff of the University of Malaga. The idea stems from the analysis and study of the agile scrum methodology, focused on iterations and continuous reviews. The proposal consists of transferring the application of this methodology, widely used in software development environments, to subjects linked to the business environment, given the flexibility that characterises it. Its application made it possible to obtain interesting results, both related to the objectives of the subject "Business Creation", as well as others of a transversal nature, which are in great demand in today's environments.

Development of Surface Roughness Prediction and Monitoring System in Milling Process

High surface quality is an important indicator for high-performance machining during the manufacturing process. The surface roughness generated in machining can be affected by cutting parameters and machining vibration. To achieve processing efficiency, monitoring surface quality within the desired range is important. This study aimed to develop a surface roughness prediction system for the milling process. The predictive model was established based on data collected from machining experiments with the response surface methodology. The surface roughness is related to independent variables, including cutting parameters and machining vibration, in terms of nonlinear functions by regression analysis and the neural network approach, respectively. To be implemented in a CNC milling machine for online application, a predictive model was introduced in the Virtual Machine Extension (VMX) intelligent software development platform. This model can acquire the cutting parameters from the controller via the Open Platform Communications Unified Architecture (OPCUA) interface as well as the vibration features from the sensory module. The system can calculate the roughness based on these data and issue alert when the predicted value exceeds the preset threshold or abnormal vibration is detected.

Didactic and interactive project with PIC microcontroller and CAN network for temperature and luminosity control

Didactic and interactive project with PIC microcontroller and CAN network for temperature and luminosity control

Probabilistic Model Checking GitHub Repositories for Software Project Analysis

GitHub serves as a platform for collaborative software development, where contributors engage, evolve projects, and shape the community. This study presents a novel approach to analyzing GitHub activity that departs from traditional methods. Using Discrete-Time Markov Chains and probabilistic Computation Tree Logic for model checking, we aim to uncover temporal dynamics, probabilities, and key factors influencing project behavior. By explicitly modeling state transitions, our approach provides transparency and explainability for sequential properties. The application of our method to five repositories demonstrates its feasibility and scalability and provides insights into the long-term probabilities of various activities. In particular, the analysis provides valuable perspectives for project managers to optimize team dynamics and resource allocation. The query specifications developed for model checking allow users to generate and execute queries for specific aspects, demonstrating scalability beyond the queries we present. In conclusion, our analysis provides an understanding of GitHub repository properties, branch management, and subscriber behavior. We anticipate its applicability to various open-source projects, revealing trends among contributors based on the unique characteristics of repositories.

Multi-Agent System in Web Services

The term "service-oriented architecture" (SOA) refers to a software paradigm for creating systems made up of a variety of services that interact with one another to accomplish a certain task. The communications involve more than just transmitting data back and forth; they also connect two or more services to coordinate the required operations. Cross-platform communication is necessary for the coordinating process when services are spread across several platforms. Several web service standards and specifications are used in the present SOA implementation. Based on the prototype's implementation and simulation, this proposed research study has been validated and evaluated.  The suggested cross-platform communications architecture is implemented using NetBeans, JADE, WSIG, and OWL-S. An integrated development environment for Java is offered by Oracle in the form of NetBeans. Telecom Italia's Java Agent DEvelopment Framework (JADE) is an agent software framework that is entirely built in the Java language. Web Service Integration Gateway (WSIG), which helps to facilitate JADE agent services being called by Web service clients. The core fundamental engine of the suggested framework is OWL-S. It is a web ontology language used to describe Semantic Web Services in the Semantic Web's OWL-based framework. A quantitative approach is used in this study's performance analysis and comparative investigation for evaluation and validation. The prototype's major element is the Java agent development framework (JADE), which was used to create a multi-agent system for the agent-based MOM framework that has been presented.  The creation of the multi-agent systems was facilitated by the JADE platform. JADE 3.7 was the version that was employed. Two key features of JADE are a FIPA-compliant agent platform and a package for creating Java agents. The implementation results show that the proposed agent-based MOM framework was successful communicate between multiple types of SOA application with a better performance of the average of round-trip time where the proposed framework was successful in responding to all the requests.

Exploring the Impact of Vocabulary Techniques on Code Completion: A Comparative Approach

Integrated Development Environments (IDEs) are pivotal in enhancing productivity with features like code completion in modern software development. Recent advancements in Natural Language Processing (NLP) have empowered neural language models for code completion. In this study, we present an extensive investigation of the impact of open and closed vocabulary systems on the task of code completion. Specifically, we compare open and closed vocabulary systems with various vocabulary sizes to observe their impact on code completion performance. We experiment with three different open vocabulary systems: byte pair encoding (BPE), WordPiece and Unigram to compare them with closed-vocabulary systems to analyze their modeling performance. We also conduct experiments with different context sizes to study their impact on code completion performance. We have experimented using various prominent language models, including one from recurrent neural networks and five from transformers. Our results indicate that vocabulary size significantly impacts modeling performance and can artificially boost the accuracy of code completion models, especially in the case of a closed-vocabulary system. Moreover, we find that different vocabulary systems have varying impacts on token coverage, whereas open-vocabulary systems exhibit better token coverage. Our findings offer valuable insights for building effective code completion models, aiding researchers and practitioners in this field.

ALGORITHMIC LITERACY: Generative Artificial Intelligence Technologies for Data Librarians

INTRODUCTION: Artificial intelligence (AI) is a novel type of library technology. AI technologies and the needs of data librarians are hybrid and symbiotic, because academic libraries must insert AI technologies into their information and data services. Library services need AI to interpret the context of big data.OBJECTIVES: In this context, we explore the use of the the OpenAI Codex, a deep learning model trained on Python code from repositories, to generate code scripts for data librarians. This investigation examines the practices, models, and methodologies for obtaining code script insights from complex code environments linked to AI GPT technologies.  METHODS: The proposed AI-powered method aims to assist data librarians in creating code scripts using Python libraries and plugins such as the integrated development environment PyCharm, with additional support from the Machinet AI and Bito AI plugins. The process involves collaboration between the data librarian and the AI agent, with the librarian providing a natural language description of the programming problem and the OpenAI Codex generating the solution code in Python.RESULTS: Five specific web-scraping problems are presented. The scripts demonstrate how to extract data, calculate metrics, and write the results to files.CONCLUSION: Overall, this study highlights the application of AI in assisting data librarians with code script creation for web scraping tasks. AI may be a valuable resource for data librarians dealing with big data challenges on the Web. The possibility of creating Python code with AI is of great value, as AI technologies can help data librarians work with various types of data sources. The Python code in Data Science web scraping projects uses a machine-learning model that can generate human-like code to help create and improve the library service for extracting data from a web collection. The ability of nonprogramming data librarians to use AI technologies facilitates their interactions with all types and data sources. The Python programming language has artificial intelligence modules, packages, and plugins such as the OpenAI Codex, which serialises automation and navigation in web browsers to simulate human behaviour on pages by entering passwords, selecting captcha options, collecting data, and creating different collections of datasets to be viewed.

Statistical analysis of lexemes generated in ‘C’ programming using fuzzy automation

The present work is an effort to support the typographical errors of keywords that are not supported by existing compilers and integrated development environment(IDE) in ’C’ language. The fuzzy automata modelling approximate string matching is proposed for error handling during lexical analysis. By introducing fuzziness to lexemes the typographical errors can be rectified at the time of compilation and flexibility of lexical analyser can be greatly improved. The recognition of fuzzy tokens during lexical analysis is described in order to correct errors caused by sticking key, deletion, typing and swapping key in keywords during C programming. Algorithms and pseudo code are being developed to measure the degree of membership of crisp and fuzzy lexemes. Accuracy is tested and examined once the fuzzy lexemes are trained using a neural network. The proposed method is an add on feature that can be incorporated in existing compilers and IDEs to increase their flexibility.