Back-end Components Research Articles

Website-Based Employee Attendance Information System (Case Study: PT. Excelindo Karya Abadi)

This research aims to develop a web-based employee attendance information system at PT. Excelindo Karya Abadi, overcoming inefficiencies in the manual attendance process. Using the Waterfall method, this research includes the stages of needs analysis, system design, implementation, testing, and maintenance. Data collection is carried out through interviews and direct observation so that it becomes the basis of a system that includes the function of recording attendance via selfie photos, managing employee data, and making reports. The system architecture is designed with front-end and back-end components, using technologies such as HTML, CSS, JavaScript, PHP, and MySQL. Testing involves black box techniques to ensure functionality and user feedback for system improvement. The implemented system demonstrated significant improvements in the accuracy and efficiency of attendance tracking, reducing the potential for data manipulation and errors. The transition to a web-based system allows for greater accessibility and integration with an organization's existing systems, thereby contributing to increased operational efficiency. The findings show that digital attendance systems can simplify administrative processes substantially, offer reliable solutions for employee attendance management, and align with technological advances to support company growth.

Phenomena observed in electron EBRT using a pulse-by-pulse radioluminescence dosimetry system with cloud-based analytics

The pulse-by-pulse characteristics of electron beams from a clinical linear accelerator (linac) at high dose rates are crucial for ensuring precise dose delivery in radiation therapy. In this research, we employ a radioluminescence scintillator made from Ge-doped silica optical fiber to analyse electron beams at standard therapy dose rates 600 cGy/min with energy of 15 MeV. The scintillator is combined with a photomultiplier tube (PMT) and a photon-counting setup to achieve microsecond (μs) gating times, providing high temporal resolution. For sub-microsecond (sub-μs) temporal resolution, we utilize a multi-pixel photon counter and a rapid digital oscilloscope. Our study examines the peak time and the time it takes for pulses to return to baseline, uncovering the effects of afterglow at elevated dose rates and its potential implications for dosimetry precision. Our results indicate that monitor chambers, which have lower temporal resolution than the radioluminescence system outlined in this study, may struggle to accurately track high-rate dose delivery, emphasizing the necessity for advanced monitoring methods in FLASH radiotherapy. The dosimetric data was further analysed for deeper analytics on a cloud-based system. The dosimetric data underwent advanced analysis on a cloud-based platform, employing a prototype analytics software system comprised of Dosi-Client and Dosi-Cloud Backend components for real-time data acquisition and processing. This innovative approach revealed significant insights into radiation dosimetry, including the detection of anomalies through a reconstruction convolutional autoencoder, enhancing the precision and reliability of dosimetric measurements.

Plant Disease Detection App Using Convolutional Neural Network

Crop diseases pose significant challenges to the agricultural sector, resulting in substantial production losses and economic setbacks. The efficacy of conventional disease diagnosis techniques, such optical examination of plant leaves, is constrained. It is imperative to improve agricultural disease detection, monitoring, and prediction in order to address this problem. This work offers a mobile-based system that uses the plant-DOC Dataset to automate the diagnosis of plant leaf diseases. The system is powered by machine learning (ML) and computer vision. Deep learning algorithms, in particular Convolutional Neural Networks (CNNs), are used in the suggested model. CNNs are excellent at identifying diseases in plants and crops. Finding the plant's sickness is the aim of this application. Additionally, image processing can be used to detect the disease type, the appropriate pesticide, and to alert farmers so that prompt action can be performed. The whole process involves gathering plant images, preprocessing them by resizing, normalizing, and augmenting, segmenting if necessary, extracting features, selecting, and training a CNN model, evaluating its performance, and using it to detect plant diseases. In this paper, we have successfully developed both the frontend and backend components for integration into our proposed model, accompanied by corresponding screenshots showcased within the document. Key Words: plant leaf diseases, agriculture, mobile app, computer vision, machine learning, deep learning, Convolutional Neural Networks.

Campus Management System

The Campus Management System (CMS) represents a pivotal advancement in educational technology, offering an integrated software solution tailored to the complex needs of modern educational institutions. This comprehensive system is designed to revolutionize administrative tasks, enhance efficiency, and foster a more organized campus environment. Through seamless automation of various processes, CMS empowers administrators, faculty, and students to navigate campus operations with ease. Utilizing Full Stack Development, CMS ensures seamless integration of front-end and back-end components, resulting in a user-friendly interface accessible to all stakeholders. This approach streamlines administrative tasks, simplifying processes such as student enrolment, course management, and resource allocation. Furthermore, CMS harnesses the power of Machine Learning (ML) algorithms to optimize key processes. These ML algorithms enable intelligent automation of tasks such as resume evaluation, eligibility criteria assessment, and course recommendations based on student performance. By analyzing vast amounts of data, CMS empowers institutions to make data-driven decisions, enhancing the overall educational experience for students. The integration of technology within educational institutions through CMS represents a significant step forward in campus management. By leveraging Full Stack Development and Machine Learning algorithms, CMS offers a robust solution to the challenges faced by modern educational institutions, ultimately paving the way for a more efficient, effective, and student-centric campus environment.

“QUICK” - Designing and Developing a Blog Application Using The MERN Stack

Abstract: In today's digital age, blogging has emerged as a prevalent means of sharing ideas, experiences, and knowledge across the internet. I made “QUICK” using MERN (MongoDB, Express, React JS and Node JS. My Application not only provides the creation, editing, and deletion of blogs by authors but also build up user experience through advanced features such as categorization, filtering, and search functions. The main focus of this research is to explore how the MERN stack give to the seamless integration of front-end and back-end components. By leveraging React.js for the front end and Express.js and Node.js for the server-side logic, QUICK ensures a responsive and interactive user interface. Moreover, the implementation of features such as category-based organization, filtering options, and robust search, allowing readers to easily discover relevant content based on their preferences. Furthermore, it empowers authors with exclusive rights to edit and delete their own blogs.

PDF-Chat SaaS Platform Using MERN Stack

With the rise of PDF-Driven Question Answering Software as a Service (SaaS) platforms in recent years, the field of document processing technology has experienced a paradigm shift. Examining these systems difficulties in detail, this review article concentrates on how they include Next.js, React, Prisma, TRPC, and Tailwind CSS into the MERN (MongoDB, Express.js, React, Node.js) stack. With a careful analysis of the architecture, functions, and ramifications, this study seeks to offer a thorough grasp of the technological developments behind this groundbreaking methodology. The research holds significance due to its intersectionality, as it seamlessly integrates document processing, user interaction, and data administration. The foundation of these systems is the use of Prisma for effective database administration, Next.js and React for creating dynamic and responsive user interfaces, TRPC for reliable API connectivity, and Tailwind CSS for contemporary and beautiful styling. A comprehensive strategy like this tackles the difficulties in processing PDF documents for user identification, question-answering, and facilitating seamless communication between front-end and back-end components. The research attempts to significantly advance our understanding of PDF-driven question-answering SaaS systems by doing this thorough review. Giving insights into the opportunities and complexities of technology, it is a useful tool for practitioners, academics, and developers. These results highlight how important MERN stack technologies are for developing reliable, scalable, and user-focused solutions for the changing document-centric application market. Key Word: PDF-Driven; MERN; TRPC; API.

Cotton Disease Prediction System

The Cotton Disease Prediction System represents a groundbreaking approach in agricultural management, addressing the persistent threats posed by diseases to this pivotal global cash crop. With the imperative need for timely disease detection to safeguard yield and quality, this research introduces a novel methodology leveraging transfer learning with Convolutional Neural Networks (CNNs). By analyzing high-resolution images of cotton leaves, the system utilizes a user-friendly web interface developed using HTML and CSS in Visual Studio Code. The backend functionality, orchestrated through Jupyter Notebook, enables real-time processing and predictions. Through rigorous testing, the system demonstrates remarkable robustness and accuracy, offering farmers a proactive tool for disease management. By seamlessly integrating frontend and backend components, this system not only enhances efficiency but also empowers farmers with actionable insights, heralding a new era in precision agriculture.

LoRaWAN end device disaggregation and decomposition by means of lightweight virtualization

The LoRaWAN infrastructure has been designed to allow easy implementation of backends in mixed IoT scenario with both on premise and cloud servers. Some very popular providers offer the LoRaWAN backend in the form of SaaS, or the user can install containerized versions of the backends into his/her own servers. In this paper, the containerization of LoRaWAN end node is discussed, addressing different architectures for disaggregating the tasks and decomposing the stack levels from the hardware. Such an analysis leads to the introduction of a new fully virtual end node, that paves the way to innovative use of the LoRaWAN backend. The proposed container-based architecture is then applied to some general IoT use cases, which have been setup using a commercial, industrial-grade, framework for the experimental phase. The experimental evaluation, based on purposely defined metrics, takes into account the latency in transferring data among the different stack level and backend components. The focus is on comparing the performance of the proposed architecture in the diverse use cases, and on giving a clear overview of advantages and disadvantages of the possible choices. For instance, noticeable differences emerged when comparing containerization running directly on Operating System vs. containerization running inside virtual machine. Finally, a discussion about the use of the proposed solution for increasing LoRaWAN capabilities in terms of flexibility and performance (e.g., by means of multiple end nodes leveraging a single radio for redundancy purpose) is carried out.

Transpiler-Based Architecture Design Model for Back-End Layers in Software Development

The utilization of software architectures and designs is widespread in software development, offering conceptual frameworks to address recurring challenges. A transpiler is a tool that automatically converts source code from one high-level programming language to another, ensuring algorithmic equivalence. This study introduces an innovative software architecture design model that integrates transpilers into the back-end layer, enabling the automatic transformation of business logic and back-end components from a single source code (the coding artifact) into diverse equivalent versions using distinct programming languages (the automatically produced code). This work encompasses both abstract and detailed design aspects, covering the proposal, automated processes, layered design, development environment, nest implementations, and cross-cutting components. In addition, it defines the main target audiences, discusses pros and cons, examines their relationships with prevalent design paradigms, addresses considerations about compatibility and debugging, and emphasizes the pivotal role of the transpiler. An empirical experiment involving the practical application of this model was conducted by implementing a collaborative to-do list application. This paper comprehensively outlines the relevant methodological approach, strategic planning, precise execution, observed outcomes, and insightful reflections while underscoring the the model’s pragmatic viability and highlighting its relevance across various software development contexts. Our contribution aims to enrich the field of software architecture design by introducing a new way of designing multi-programming-language software.

АНАЛІЗ ПРОФЕСІЙНОЇ ДІЯЛЬНОСТІ ФАХІВЦЯ З РОЗРОБКИ КОРИСТУВАЛЬНИЦЬКИХ ІНТЕРФЕЙСІВ

The article is dedicated to the challenging problem of improving the quality of professional education of future bachelors in the field of software engineering, in particular, defining qualification requirements, updating the goals and content of educational programs for their education by analyzing the professional activities of a user interface development specialist. As technology advances and integrates into our daily lives, the demand for such specialists has grown significantly. At the same time, the field of information technology is constantly evolving, creating a need for more specialized skills that can only be acquired through a professional education. During the analysis of the professional activity of a specialist in the development of user interfaces, the subjects, means, process, technologies, conditions and products at each stage of creating user interfaces of various software are considered. The user interface is defined as the means by which users interact with digital devices or software. The article considers the job responsibilities of a UI designer, UX designer, front-end developer, graphic designer and user experience researcher – specialists who develop user interfaces. The process of developing user interfaces is analyzed at the following stages: determining the needs, desires and behavioral patterns of users; defining the problem to be solved; generating ideas for solving the problem; prototyping; testing (in accordance with the concept of «Design Thinking»); development of the software part of the user interface (writing program code, front-end and back-end development, integration of front-end and back-end components into a single system, testing and quality control, cross-platform support, ensuring accessibility for people with disabilities). The qualification requirements for a specialist at each stage of user interface development are determined to improve the quality of professional training of future bachelors in the field of software engineering, to update the goals and content of educational programs for their training. Key words: professional activity, user interface development, UI design, UX design, design-thinking, qualification requirements.

Implementation of a workbench platform for the management of smart contracts in BlockChain nodes on Azure Cloud

Objectives: Analyze, design and build backend components (web API, XML web service and database) and frontend (website) with Microsoft technology that allow the integration of a functional workbench platform to automate the launch of compiled smart contracts in blockchain nodes hosted in Azure Cloud so that they can be used by companies in general. Methodology: The SCRUM methodology was applied for the agile development of the technological products of the backend and frontend, because it adapted very well to the nature of this research, since in each iteration there was always an update of the tools, programming components, testing and configuring services to achieve goals. Contribution: It is shown that the development platform integrated by the solidity language, Azure cloud blockchain, the Visual Studio .Net IDE and the SQL Server database manager allowed to design and build a blockchain workbench platform easily exploitable by companies to validate their processes and generate transparency in the handling of their information.

A Cloud Infrastructure for Large Scale Health Monitoring in Older Adult Care Facilities

Technology development in the sub-field of older adult care has always been on the back-burner compared to other healthcare areas. But with increasing life expectancy, this is poised to change. With the increasing older adult population, the current older adult care facilities and personnel are struggling to keep up with demand. To address this matter, we proposed a distributed system infrastructure that will enable large-scale monitoring of vital signals and early detection of emergency situations in nursing homes and assisted living communities in this study. The system is mainly comprised of node devices and cloud infrastructure. The node device collects data from multiple users and forwards this data to the cloud service. Communication between the vital-sensing devices on the users and the node device is accomplished using Bluetooth Low-Energy (BLE). The Node device is implemented on a Raspberry Pi Model 3b+ which has built-in BLE capability. A cloud server consists of back-end and front-end components. The back-end handles all the data processing and logical decisions that drive the front-end. The front-end is the interface provided to the end-users, which can monitor all patients in nursing homes at the same time. We examined our system in terms of scalability, real-time operation, cost, and usability and then found that our system provides not only a smart remote monitoring solution that can provide a better aging experience for older adults and their families, but also increase automation in nursing homes leading to a reduction in running cost and an increase in capacity.

Night Motion Detection and Alert System

Closed-Circuit TV (CCTV) system is one of the technologies in the surveillance field to solve the problem of detection and monitoring by providing extra features such as email alert or motion detection. However, detecting and alerting the admin on CCTV system may complicate due to the complexity to integrate the main program with an external Application Programming Interface (API). In this study, pixel processing algorithm is applied due to its efficiency and SMS alert is added as an alternative solution for users who opted out email alert system or have no Internet connection. A CCTV system with SMS alert (CMDSA) was developed using evolutionary prototyping methodology. The system interface was implemented using Microsoft Visual Studio while the backend components, which are database and coding, were implemented on SQLite database and C# programming language, respectively. The main modules of CMDSA are motion detection, capturing and saving video, image processing and Short Message Service (SMS) alert functions. Subsequently, the system is able to reduce the processing time making the detection process become faster, reduce the space and memory used to run the program and alerting the system admin instantly.

Infrastructure Optimization in Kubernetes Cluster

Kubernetes is a compact, extensible, open-source stage for overseeing containerized responsibilities and administrations, that works with both decisive setup and robotization. Kubernetes is like VMs, however having loosened up isolation properties to share the Operating System (OS) among the applications. The container conversely with VM, has its own document framework, a portion of Central Processing Unit(CPU), memory, process space, and much more. Kubernetes cluster is a bunch of node machines for running containerized applications. Each cluster contains a control plane and at least one node. Infrastructure Optimization is the process of analyzing and arranging the portion of cloud resources that power applications and workloads to augment the presentation and limit squander due to over-provisioning. In the paper, a “Movie Review System” web application is designed using GoLang for backend components and HTML, CSS, and JS for frontend components. Using AWS, an EC2 instance is created and the web application is deployed onto EC2 and hosted in the instance server. The web application is also deployed on Kubernetes locally using the MiniKube tool. A performance analysis is performed for both the deployments on considering common performance metrics for both AWS EC2 / Virtual Machine (VM) and Kubernetes.

Dynamic Service Entity Placement for Latency Sensitive Applications in Transportation Systems

With the development of applications on end devices, such as cell phones and tablets, more and more passengers would like to have entertainment on these end devices when they are cruising on vehicles. Due to the limited computation ability of the end devices, some of these applications have back-end components on the edge clouds, which are realized by Service Entities (SEs). In this work, we propose a system named DSEP to Dynamically determine the SEPlacement, such that the maximum latency experienced by the passengers can be minimized. To this end, we first train two sequential neural networks to predict the position of each individual vehicle, and propose an efficient algorithm based on optimization relaxation and Lagrange decomposition to determine the SE placement. Through extensive real-data driven simulations, we find that with the two sequential neural networks proposed in this paper, there are less than 1 percent errors on estimating where the passengers will access the edge cloud system. When the computation resources in the edge cloud are limited, DSEP can reduce the response latency by up to 43 percent compared with the nearest placement scheme. Even averaging the performance improvement over all simulation settings, DSEP can reduce the response latency by 16 percent.

Cross-Mixing Hybrid Beamformer for Wideband Apertures

Existing beamforming arrays suffer from the size and cost of the RF front-end and digital back-end components. In this paper, a novel hybrid beamforming configuration for wideband receivers is introduced. The design replaces phase shifters and local oscillators (LO) with cross mixing antenna elements to maximize diversity gain. In this paper, an analytical model of the cross mixing beamformer (CMB) is first presented. Simulations are carried out showing that a maximum diversity gain can be achieved with the CMB approach. Two prototypes were implemented using $2\times 1$ and $4\times 2$ element arrays and tested at 2.31 GHz. Measured results show that CMB achieves coherent signal combining and can preserve the phase delay information needed for hybrid beamforming setups.

Incipient fault diagnosis method for DC–DC converters based on sensitive fault features

DC–DC converters play an important role in electrical systems. The fault state of a DC–DC converter has a major impact on the operation of the back-end components and the entire electrical system. Therefore, the fault diagnosis is very necessary when the fault state of DC–DC is in the early stage. It can decrease the likelihood of happening the serious faults that may result in the enormous economic loss. To effectively diagnose the incipient fault in DC–DC converters, an incipient fault diagnosis method based on sensitive fault features is proposed. Firstly, for each type of the incipient fault, this study obtains the statistical features in time domain and the wavelet analysis local energy values in the frequency domain of the output. Then to further improve the incipient fault diagnosis accuracy, this study removes the redundant fault features based on overlap calculation, the unique fault features for each type of incipient fault will be selected. Finally, these sensitive fault features are used to build support vector data description models, which can diagnose the incipient faults. Simulation and hardware experimental results validate the practicability and effectiveness of the proposed method.

Next Steps in the Evolution of the Scratchpads Virtual Research Platform: A framework for the evaluation of data storage alternatives for Scratchpads

The Scratchpads virtual research platform is widely used by more than a thousand communities to share and manage biodiversity data. It is an open source project that can be run by any individual, but most sites are hosted on a dedicated service run by the Natural History Museum London. After 12 years of use and several major upgrades, the size and complexity of the Scratchpads codebase makes it increasingly difficult to maintain. The underlying content management system (Drupal 7) reaches its end of support in November 2022, providing a further justification to migrate Scratchpad sites to a new, more sustainable platform, at the same time allowing us to improve standards compliance and add functionality for users. Building on the work initiated at the Biodiversity_Next symposium SI46 Biodiversity Virtual Research Environments: past, present & possible futures, we identified the need to decouple the front and backend components of the architecture. This allows for a simpler migration path for our existing sites, allowing us to migrate the Scratchpad data to alternative backend platforms while retaining the familiar front end experience for users. This decoupling approach is more sustainable, affording potential for deep collaboration on the backend and its associated API (application programming interface). Analysis of current Scratchpad user activities shows that the data requirements of Scratchpads sites vary significantly. To support this variation, a successor platform must incorporate a well-defined API that can give an individual site flexibility in the choice of a backend. In this presentation we will update the community on our efforts to evaluate alternative backend platforms (both domain-specific and generalist), considering an assessment derived from the Technology Readiness Level (TRLs) of these systems, and their functionality fit with current Scratchpad features. We will discuss our evaluation approach and the results of this study, taking into account the diverse nature of these systems and their fit to our criteria. This approach may be useful to others when addressing similar challenges for evaluating choices on platform migration, and may help to identify gaps or overlapping features in the virtual research software ecosystem. These gaps and overlaps can then be addressed in collaboration with other platform developers. Finally, we will present our preliminary recommendations and invite further input from developers and stakeholders.

Online Anomaly Detection in DC/DC Converters by Statistical Feature Estimation Using GPR and GA

DC/DC converters play an important role in electrical systems. The anomalous state of a dc/dc converter has a major impact on the operation of the back-end components and the entire electrical system. To effectively recognize an anomalous state, particularly for dc/dc converters with unknown circuit structures, an online anomaly detection method that involves statistical feature estimation using Gaussian process regression (GPR) and a genetic algorithm (GA) is proposed. In the proposed method, the normal output range is built upon the dc/dc normal output signal using GPR, and seven statistical features are considered as the detection indexes. In the detection process, the output signal of the dc/dc converter is acquired, and the corresponding extreme values of statistical features are calculated by using GA, which can effectively reduce the operation time and the calculation consumption. The working state of the dc/dc converter is distinguished according to these features. Simulation and hardware experimental results validate the practicability and effectiveness of the proposed method.

Standardized Heat Spreader Design for Passive Cooling of Interconnects in the BEOL of ICs

The back-end components of integrated circuits (ICs) are susceptible to self-heating deteriorating effects due to the microscale interconnects carrying large current densities, and thus require innovative passive cooling strategies. This article investigates the cooling effect of the high conductivity metallization (MET) fill that is used in conjunction with the chemical mechanical planarization (CMP) process. Thermal simulation models are developed for a three-level back end of line (BEOL) stack and then validated experimentally using high-resolution thermal imaging. A coupled approach is used to optimize the numerical model and fully characterize the BEOL embedded passive cooling solution. Results indicate that stricter thermal constraints are present for devices higher in the BEOL stack and exhibit the highest cooling potential from the use of CMP fill in the underlying MET layers. As a thermal improvement strategy for such thermally critical interconnects, a CMP fill-based cooling solution is presented by modifying the fill pattern in order to maximize heat dissipation within the BEOL. The resulting heat spreader (HS) design achieved up to 30% reduction in temperature for 10- $\mu \text{m}$ -wide interconnects and can reach 43% for narrower interconnects. The gained cooling makes it possible to extend the activation limits of interconnects by 15% and 25%, respectively. The experimentally validated HS simulation model is used to conduct parametric analysis for a range of interconnect dimensions with an eye on standardizing the HS design within the BEOL stage of ICs.