Front-end Components Research Articles

Design and realization of a new structure of a complementary split ring coupled resonator filter for applications in communication systems

With the rapid growth of terrestrial and mobile telecommunications, companies in this sector need highly selective equipment, lightweight with low manufacturing costs, hence the interest in miniaturizing front-end electronic components such as filters, antennas, mixers etc. In this paper, a novel structure of a passband and highly selective filter is proposed and discussed. The design involving two complementary split-ring resonator filter (CSRRF) configurations in microstrip technology operating at 6 GHz is investigated. To achieve the goal of this research the “N + 2” cross-coupling matrix method is used. The step-shaped CSRR filter that yields better electrical performance, smaller size, and low weight is realized. Its frequency responses simulated with HFSS software are compared with experimental measurements. An excellent agreement is observed in terms of bandwidth and center frequency.

Hydra: computer vision for data quality monitoring

Hydra, initially developed for Hall-D in 2019, is a system that utilizes computer vision to perform near real time data quality monitoring. Since then, it has been deployed across all experimental halls at Jefferson Lab, with the CLAS12 collaboration in Hall-B being the first outside of GlueX to fully utilize Hydra. The system comprises back end processes that manage the models, their inferences, and the data flow. The front-end components, accessible via web pages, allow detector experts and shift crews to view and interact with the system.

Upgraded front ends for SLS 2.0 with next-generation high-power diaphragms and slits.

The upgrade of the Swiss Light Source, called SLS 2.0, necessitates comprehensive updates to all 18 user front ends. This upgrade is driven by the increased power of the synchrotron beam, reduced floor space, changing source points, new safety regulations and enhanced beam properties, including a brightness increase by up to a factor of 40. While some existing front-end components are being thoroughly refurbished and upgraded for safety reasons, other components, especially those designed to tailor the new synchrotron beam, are being completely rebuilt. These new designs feature innovative and enhanced cooling systems to manage the high-power load and meet new requirements such as mechanical stability and compact footprints.

Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy Experimentation

When personal information is shared across the Internet, we have limited confidence that the designated second party will safeguard it as we would prefer. Privacy policies offer insight into the best practices and intent of the organization, yet most are written so loosely that sharing with undefined third parties is to be anticipated. Tracking these sharing behaviors and identifying the source of unwanted content is exceedingly difficult when personal information is shared with multiple such second parties. This paper formulates a model for realistic fake identities, constructs a robust fake identity generator, and outlines management methods targeted towards online transactions (email, phone, text) that pass both cursory machine and human examination for use in personal privacy experimentation. This fake ID generator, combined with a custom account signup engine, are the core front-end components of our larger Use and Abuse of Personal Information system that performs one-time transactions that, similar to a cryptographic one-time pad, ensure that we can attribute the sharing back to the single one-time transaction and/or specific second party. The flexibility and richness of the fake IDs also serve as a foundational set of control variables for a wide range of social science research questions revolving around personal information. Collectively, these fake identity models address multiple inter-disciplinary areas of common interest and serve as a foundation for eliciting and quantifying personal information-sharing behaviors.

Reflectivity measurement of a silicon carbide mirror sample for ITER divertor vacuum ultraviolet spectrometer.

The first mirror is the front-end optic component that reflects light emitted from the plasma to the diagnostic system in fusion plasmas. Silicon carbide (SiC), known for its relatively high mechanical strength and radiation tolerance, has been selected as the substrate material for the first mirror in the ITER divertor vacuum ultraviolet (VUV) spectrometer. To measure the reflectivity of the ellipse cylindrical SiC mirror to be manufactured, a device for reflectivity measurement in the VUV wavelength range was developed. First, the reflectivity of a sample SiC mirror (15mm diameter × 10mm thick) was measured across the ITER-required incidence angles, and the results are reported in this study. A hollow cathode lamp with helium gas was used as the VUV light source in the wavelength range of 23-60nm, and a dedicated VUV spectrometer to select specific wavelengths was developed. The spectrometer utilized laminar-type replica diffraction gratings (Shimadzu 30-006) and two back-illuminated charge-coupled devices (BI-CCD, Andor DO 940P-BEN) for the grating and detector, respectively. A cropping technique with aperture was employed to precisely localize the VUV light's reflection onto the SiC mirror surface. The experimentally measured reflectivity values of SiC at the required incidence angles of VUV light were compared with theoretically calculated reflectivity curves. The oxidation layer (SiO2) formed on the SiC surface and the incidence angle of VUV light to the BI-CCD chip (E2V) would be the factors affecting the accuracy of the reflectivity.

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.

On the design of vehicle front-ends for pedestrian safety with the advanced Pedestrian Legform Impactor (aPLI)

The latest Euro NCAP Vulnerable Road Users Assessment Protocol (v11.3) introduces the advanced Pedestrian Legform Impactor (aPLI) to reduce the risk of lower extremity injury to pedestrians in a vehicle collision. The addition of femur injury criteria and the added simplified upper body mass, compared to its predecessor –the FlexPLI–, allows a more comprehensive assessment of passive pedestrian safety. However, this presents new challenges for vehicle manufacturers as these additions must be accounted for. We perform four sensitivity analyses at two test positions (centre and at the headlight) on two vehicles (compact car and SUV). Thereby, we investigate five design parameters that represent the main front-end components and identify that the vehicle type determines the sensitive design parameters significantly. Furthermore, we show that the influence of the lower stiffener and energy-absorbing foam diminishes compared to older legform impactors, while that of the bonnet increases.

Polymeric piezoelectric accelerometers with high sensitivity, broad bandwidth, and low noise density for organic electronics and wearable microsystems

Piezoelectric accelerometers excel in vibration sensing. In the emerging trend of fully organic electronic microsystems, polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals, such as vocal vibrations in wearable speaking assistants for those with speaking difficulties. However, high-performance polymeric piezoelectric accelerometers suitable for such applications are rare. Piezoelectric organic compounds such as PVDF have inferior properties to their inorganic counterparts such as PZT. Consequently, most existing polymeric piezoelectric accelerometers have very unbalanced performance metrics. They often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers, leading to increased noise density and limited application potentials. In this study, a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is introduced. This new design aims to simultaneously achieve high sensitivity, broad bandwidth, and low noise. Five samples were manufactured and characterized, demonstrating an average sensitivity of 29.45 pC/g within a ± 10 g input range, a 5% flat band of 160 Hz, and an in-band noise density of 1.4 µg/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{Hz}}$$\\end{document}. These results surpass those of many PZT-based piezoelectric accelerometers, showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems.

Courier Service System

The demand for efficient courier services is growing rapidly, and traditional systems often struggle to meet these demands. This project introduces an Enhanced Courier Service System Application, utilizing cutting-edge technologies such as ReactJS for the frontend, Java Spring Boot for the backend, and MongoDB for the database. The primary objective is to develop a robust and scalable courier service system that offers enhanced features and functionalities to meet the evolving needs of users. The system architecture facilitates seamless communication between frontend, backend, and database components, ensuring smooth data flow and efficient processing. ReactJS for the frontend enhances user experience, while Java Spring Boot provides scalability, security, and performance. MongoDB, a NoSQL database, offers flexibility and scalability for large data storage and retrieval. The modular design allows for easy maintenance and future enhancements, adapting to changing requirements and technologies. By incorporating industry-standard practices like UML diagrams, data flow diagrams, and sample test cases, the project aims to deliver a high-quality, reliable, and user-friendly courier service system application.

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.

A 3.2-3.8 GHz low-noise amplifier for 5G/6G satellite-cellular convergence applications

The rapid evolution of wireless communication systems towards 5G standards has imposed stringent requirements on the performance of radio frequency front-end components. Among these, the Low-Noise Amplifier (LNA) plays a pivotal role in determining the overall noise figure and sensitivity of the receiver chain. This paper presents a comprehensive design and analysis of a 3.2-3.8 GHz LNA tailored for 5G applications, employing a 0.3 μmm gate length Gallium Arsenide (GaAs) pseudomorphic high electron transistor (pHEMT) technology process. The proposed LNA design focuses on achieving a low noise figure (NF), high gain, and robust linearity to accommodate the dense signal environment and wide bandwidth of 5G networks. The design leverages advanced matching networks and feedback topologies to enhance stability and reduce the noise contribution from the active devices. Simulation results predict a noise figure of 1.3-1.4 dB, a gain of 20-21 dB across the band of interest, and an input-referred third-order intercept point (IIP3) of 18 dBm. The LNA demonstrates excellent performance in a 5G testbed, showing a significant improvement in the signal-to-noise ratio and the potential to enhance 5G receiver sensitivity. The research substantiates the LNA’s viability for integration into 5G base stations and user equipment, underscoring its potential to contribute to the efficient and reliable operation of next-generation wireless networks. This LNA can be used for 5G New Release (NR) band of n77 and n78 (3.5-3.7 GHz)

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.

Design and Implementation of a Tailored Dealer Management System (DMS) for the Automotive Industry

This paper presents a thorough investigation into the design and implementation of a Dealer Management System tailored for the automotive industry. The DMS is intended to streamline after-sales processes such as inventory and expense management, service order creation, and financial data monitoring. The system integrates with a variety of other platforms, including ERP, CRM, and payment systems, which improves collaboration between dealers and distributors. The architecture of DMS, known as Copilot Next, is detailed, with a focus on the backend, frontend, and integration components. Sentinet and Dell Boomi are integration tools that allow for seamless connectivity with other systems. The findings show that the DMS effectively meets the needs of multiple vehicle brands, improves part ordering accuracy with AI tools, and ensures data consistency across systems. This study offers useful insights into the design and implementation of a DMS that can significantly improve dealership operations and customer service in the automotive industry.

鲜食玉米联合收获机分禾前端研究与试验

Aiming at the problem of high cob loss in non-opposed row harvesting of 4YZ-6 fresh corn harvester, a front-end part adapted to the grain splitter of this model was designed. The structure of the front-end part was elaborated, the sliding conditions of the stalks under the action of the grain splitter were studied, the forces between the stalks and the front-end part were theoretically analyzed, and the operating effect of the front-end part was verified through field tests. The tests show that the expected operating effect is optimal when the inclination of the grain separator is 27.7°, the clamping angle of the grain splitter is 31°, the stubble height is 270 mm, and the front-end guards half width is 3.8 mm. The verification test for this parameter combination yielded a breakage rate of 8.71% and a loss rate of 9.47%, which were basically consistent with the expected values. The design of this front-end component of the grain splitter provides an effective means to reduce the loss of ears in the harvesting process of fresh corn.

“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.

Design of Calibration System for Multi-Channel Thermostatic Metal Bath

Abstract The use of the thermostatic metal bath is becoming more and more extensive and the requirements for its precision and reliability are also increasing. To meet the needs of the metal bath calibration, a 12-channel thermostatic metal bath temperature field calibration system based on a four-wire PT100 has been designed. The system design includes a front-end temperature measurement component, which consists of a four-wire PT100 and a thermostatic block, and a signal processing component, which consists of a bidirectional constant current source excitation unit, a signal conditioning unit and a high-precision acquisition unit. The STM32f407 is used as the main control chip, and the analog channel selector is used for 12-channel selection. The constant current source is used for signal excitation, the proportional method is used to measure the PT100 resistance, and an acquisition circuit with a high-precision 32-bit ADS1263 analog-to-digital converter is used to amplify, filter and convert the analog signal. After piecewise linear fitting and system calibration, the temperature measurement accuracy can reach 0.4 mK, which meets the calibration requirements of the thermostatic metal bath.

Towards absolutely calibrated ECE Michelson measurements in EC heated plasmas at W7-X

A Michelson Interferometer is in use at Wendelstein 7-X (W7-X) to probe the Electron Cyclotron Emission (ECE) spectrum [1], [2], [3]. During the past operational campaign (OP2.1), 2nd and 3rd harmonic ECE power density spectra have been routinely recorded in the presence of X2and O2-mode Electron Cyclotron Resonance Heating (ECRH). However, combination of the particular notch filter arrangement and high transmission line losses have thus far prevented overall calibration using a hot source cold source exposure at the input antenna. As an alternative, the response of the individual components is measured and summed. While reasonable numbers on electron temperature are obtained in X2-mode polarisation, interaction between front-end components is neglected and large error bars must be assumed. But the information on the individual components, together with synthetic modelling and data from experiment (OP2.1), has been used to design a new front-end with improved S/N. This optimisation is discussed in this paper with focus on notch filter selection, a new transmission line (Tx-line) and a novel combined quasi-optical taper / polarizer tuner.

Traditional art design expression based on embedded system development.

This article describes constructing an embedded system for a painting art and style presentation platform, achieving the automatic integration of digital painting art with traditional art design. The frontend components are designed using the Bootstrap framework, with Django as the web development framework and TensorFlow architecture integrated into the code. Furthermore, the Inception module and residual connections are introduced to optimize the visual geometry group (VGG) network for recognizing and analyzing image texture features. Compared to other models, experimental results indicate that the proposed model demonstrates a 2.6% increase in image style classification accuracy, reaching 87.34% and 95.33% in architectural and landscape image classification, respectively. The system's operational outcomes reveal that the proposed platform alleviates the burden on the logical function modules of the system, enhances scalability, and promotes the automated fusion of digital painting art with traditional art design expression.

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.