Implementation Of Architecture Research Articles

Implementation of Information System Architecture Using TOGAF and Ward Peppard Analysis for High School

Implementation of Information System Architecture Using TOGAF and Ward Peppard Analysis for High School

Design and implementation of novel 6G cellular communication architecture using 3D modelling in real environments

<p>Academics and businesses alike are presently fixated on the Sixth Generation (6G) network, which is seen as the telecom industry’s next major game-changer. The 6G architecture has not been finalized and is not yet being used in commercial settings. Research and development for 6G is still in its early stages. Several important features and technologies have surfaced as possible 6G system underpinnings, while development is still in its early phases. To facilitate the next generation of 3D modeling applications, this article suggests a new 6G cellular communication architecture. Aware of 6G’s revolutionary potential, we investigate its fundamental features to build a collaborative, real-time 3D modeling environment with unmatched capabilities. The goal of this project is to design the architecture for the next generation of communications systems. This will incorporate elements from two existing designs: the Decoupled RAN design, which improves security and smooth data sorting, and the high-level design, which incorporates numerous protocols inside the antenna for stringent protection. Lastly, we delve into the possible sector-specific disruptions caused by this design and examine its wider implications for the future of 3D modeling. We hope that by introducing this new 6G architecture, we may inspire more study and development towards a day when 6G technology completely changes the game when it comes to 3D modeling.</p>

Test Power Reduction through Reordering Algorithm Implementation and Advancements in BIST Architecture

Due to the reduction in transistor size, test engineers face a severe issue in power minimization. VLSI circuit's power dissipation has become a vital concern due to the switching activity of the Circuit Under Test (CUT). Switching activity is a significant factor in determining power consumption for CMOS VLSI circuits. In this paper, we formulate a distance estimation technique and reordering using the Tversky index. Here, the distance between the test vectors is calculated using the Tversky index with the formation of an adjacency matrix, and reordering is performed. The experimental results of the proposed method have been compared to different distance measures and existing techniques. It shows that the switching activity is reduced from 52.5% to 21.5%, and the average power reduction is from 42% to 14.6% when the distance is estimated on reordering using the Tversky index measure. Thus, the Tversky index distance measure performs better than the Hamming, gray, and Linear feedback shift register (LFSR) methods. Switching activity estimation for complex ISCAS circuits has been performed and compared with existing methods for superior performance. We have also developed a Built in Self-Test (BIST) for the C17 benchmark circuit, which uses the Tversky counter as a test pattern generator using the TANNER EDA tool. Thus, an efficient deterministic BIST for the C17 benchmark circuit was designed to detect all the stuck-at faults, and the same procedure can be used to create BIST for all CUT and precomputed test sets for real-time field testing.

Enhanced Traffic Light Guidance for Safe and Energy-Efficient Driving: A Study on Multiple Traffic Light Advisor (MTLA) and 5G Integration

This paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain.

Design and implementation of power and area efficient architectures of circular symmetry 2-D FIR filters using CSOA-based CSD

Design and implementation of power and area efficient architectures of circular symmetry 2-D FIR filters using CSOA-based CSD

Medicare: Doctor Appointment Website and Hosting Using Cloud Services

The "Doctor Appointment Website Hosting with Cloud Services" project aims to revolutionize the healthcare appointment scheduling process by leveraging modern web technologies and cloud infrastructure. The web application provides a user-friendly platform for patients and healthcare providers to efficiently manage appointments, access relevant information, and communicate seamlessly. By integrating AWS cloud services, the platform ensures scalability, reliability, and accessibility, accommodating growing user bases and maintaining high availability. Additionally, the project incorporates telemedicine capabilities, enabling virtual appointments and improving accessibility to healthcare services. Through the implementation of innovative features and robust system architecture, the project addresses key challenges in traditional appointment management systems and enhances the overall patient experience. Future scope includes the integration of advanced functionalities such as health monitoring, AI-powered chatbots, and predictive analytics, further advancing the platform's capabilities and fostering a patient-centric approach to healthcare delivery. Overall, the project represents a significant step towards modernizing healthcare appointment scheduling and optimizing clinical workflows in a cloud-based environment. Keywords: - Doctor Appointment, Website Hosting, Cloud Services, Appointment Management, JavaScript.

An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress.

Design and implementation of a looking-forward Lawful Interception architecture for future mobile communication systems

Law Enforcement Agencies (LEAs) heavily rely on Lawful Interception (LI) tools to investigate criminal and terrorist activities. The growing frequency of cybercrime, terrorism-related offenses, and illegal trades in the European Union (EU) has driven LEAs to explore novel LI techniques that align with the developing 5G and Beyond 5G network architectures. Moreover, the emergence of extremely dynamic and distributed networks, the increased usage of end-to-end encryption applications, and privacy protections present limitations for traditional LI approaches. In order to provide a technological solution capable of extending the 3GPP LI standard, this paper presents a novel LI framework designed on top of the standardized 3GPP LI architecture, leveraging an inspection-friendly end-to-end cryptography mechanism (e.g., a Key Escrow algorithm) at the application layer. Moreover, the proposed Lawful Interception (LI) framework enables authorized LEAs to decrypt intercepted end-to-end encrypted data within the core network. Firstly, a security proof validates the security of the proposed LI framework under two attack scenarios. Subsequently, a proof-of-concept workstation implementation that emulates a 5G network for end-to-end data exchange and cloud-based deployment validates the suggested LI framework by affirming the LEA capabilities in decrypting intercepted data. Additionally, the system performance has been studied through experimental tests, ensuring the scalability of the conceived solution and revealing the possibility of intercepting data with mainly real-time latency without affecting the Quality of Service (QoS) experienced by the user.

VLSI Architectures and Hardware Implementation of Ultra Low-Latency and Area-Efficient Pietra-Ricci Index Detector for Spectrum Sensing

VLSI Architectures and Hardware Implementation of Ultra Low-Latency and Area-Efficient Pietra-Ricci Index Detector for Spectrum Sensing

Reference architecture design and evaluation for digitalization of underground mining

Digitalization of underground mining operations requires interaction and interoperability between multiple stakeholders and their technical systems. A reference architecture (RA) facilitating design of multi-stakeholder digital solutions for underground mining would be valuable design knowledge for mining industry digitalization activities and would facilitate the design of concrete architectures to the underground mining context. The main contribution of this work is RA design, which is missing in the current research literature. The RA has empirical foundation on the implementation architectures of two prototypes, which were demonstrated and evaluated in an underground mining environment. The prototypes were evaluated based on several design science research evaluation criteria (feasibility, efficiency, robustness, operability) for fulfilling architectural concerns of related stakeholders. Digital Twin-based modeling, MQTT-based messaging, high accuracy location monitoring of vehicles with Lidars, reliable communication with 5G SA network, and utilization of safety zones and safety messaging for enabling emergency stops were identified as important aspects for RA design. Digital Twin (DT)-based data models focusing on the modeling of mining site, moving vehicles, communication data, and related equipment for the underground mining context may be considered as an additional contribution of our study.

Examining the Benefits and Drawbacks of Open Network Architectures in E-Commerce

The advent of digital commerce has revolutionized the way businesses operate and interact with customers. One of the key factors contributing to this transformation is the emergence of open network architectures, which facilitate seamless communication and data exchange between various entities within the e-commerce ecosystem. This research aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of digital commerce. And to investigate how open network architectures promote accessibility and interoperability among diverse systems, enabling seamless integration and collaboration within the e-commerce landscape. Explore the potential of open networks to foster innovation and scalability, empowering businesses to rapidly adapt to changing market demands and technological advancements. Evaluate the economic advantages of open network architectures, including reduced infrastructure costs, improved resource utilization, and streamlined operational processes. Examine the potential vulnerabilities and risks associated with open network architectures, such as data breaches, cyber threats, and privacy issues, and propose strategies to mitigate these challenges. Investigate the role of governance frameworks and industry standards in facilitating the effective implementation and management of open network architectures within the e-commerce domain. Identify and analyze the potential barriers to the widespread adoption for overcoming these challenges. This paper aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of e-commerce. Through real-world case studies, theoretical frameworks, and a multi-faceted approach encompassing technical, business, and strategic perspectives, we strive to equip e-commerce stakeholders with the insights necessary to navigate this complex landscape and make informed decisions that align with their organizational goals and market dynamics.

Real Time Chat Application using React and Firebase

Abstract: This paper presents the development of a real-time chat application leveraging modern web technologies, including React for front-end development, Tailwind CSS for styling, and Firebase for backend services. The chat application addresses the need for seamless communication among users in various contexts. We discuss the architectural design, implementation details, and performance evaluation of the application. Our findings demonstrate the effectiveness and scalability of the proposed solution, paving the way for future enhancements and applications in real-time communication systems.

A Fair Crowd-Sourced Automotive Data Monetization Approach Using Substrate Hybrid Consensus Blockchain

This work presents a private consortium blockchain-based automotive data monetization architecture implementation using the Substrate blockchain framework. Architecture is decentralized where crowd-sourced data from vehicles are collectively auctioned ensuring data privacy and security. Smart Contracts and OffChain worker interactions built along with the blockchain make it interoperable with external systems to send or receive data. The work is deployed in a Kubernetes cloud platform and evaluated on different parameters like throughput, hybrid consensus algorithms AuRa and BABE, along with GRANDPA performance in terms of forks and scalability for increasing node participants. The hybrid consensus algorithms are studied in depth to understand the difference and performance in the separation of block creation by AuRa and BABE followed by chain finalization through the GRANDPA protocol.

EXAMINING ARCHITECTURAL PROJECT APPLICATIONS WITH PHOTOGRAMMETRIC POINT CLOUD: IĞDIR UNIVERSITY MOSQUE

In accordance with the developments in today's technology, the use of unmanned aerial vehicles in architectural documentation and 3D modeling studies is becoming increasingly widespread. When the studies in the literature are examined, it is seen that they focus on the documentation and 3D modeling of historical buildings. In this study, unlike the research in the literature, the usability of UAV photogrammetry in architectural project implementation and monitoring is investigated. In line with this aim, 3D point clouds and 3D models were generated using UAV photos in the study. For the identified reference points, measurements obtained from architectural project drawings, measurements taken on-site using the measurement technique with a laser meter, and finally the comparison of the measurements corresponding to these reference points on the 3D model produced from UAV photos were conducted. Based on the obtained data, when the detailed measurements taken with a laser meter are compared with the detailed measurements taken from the 3D model obtained with UAV, the consistency level is observed to be an average of 98% and above. Thus, the accuracy of the measurements obtained using UAV photogrammetry has been proven. Additionally, it was observed that measurements taken with a laser meter from the same reference points on the architectural project were different. The reason for this is the modification of the forms of details in the implementation phase of the architectural project. In this regard, it is thought that obtaining precise measurements on photos taken with UAV photogrammetry will provide an important groundwork for future studies on architectural project implementation and monitoring.

Balancing Protection and Quality in Big Data Analytics Pipelines.

Existing data engine implementations do not properly manage the conflict between the need of protecting and sharing data, which is hampering the spread of big data applications and limiting their impact. These two requirements have often been studied and defined independently, leading to a conceptual and technological misalignment. This article presents the architecture and technical implementation of a data engine addressing this conflict by integrating a new governance solution based on access control within a big data analytics pipeline. Our data engine enriches traditional components for data governance with an access control system that enforces access to data in a big data environment based on data transformations. Data are then used along the pipeline only after sanitization, protecting sensitive attributes before their usage, in an effort to facilitate the balance between protection and quality. The solution was tested in a real-world smart city scenario using the data of the Oslo city transportation system. Specifically, we compared the different predictive models trained with the data views obtained by applying the secure transformations required by different user roles to the same data set. The results show that the predictive models, built on data manipulated according to access control policies, are still effective.

Designing Integrated IT Architecture for Health Monitoring Internet of Things: Findings Exploratory Study

IT integration with healthcare, mainly through Internet of Things-based health monitoring systems, is crucial to improving healthcare management in the digital age. However, challenges remain in the design of an integrated IT architecture that can support the sustainability and effectiveness of IoT health monitoring systems, which still need to be addressed. The shortcomings in the literature related to the application of a holistic IT architecture framework to address these challenges indicate a knowledge gap that needs to be filled. Through the application of the TOGAF methodology, this research seeks to design and analyze an integrated IT architecture for IoT-based health monitoring systems in Indonesia, taking a qualitative approach through case studies, in-depth interviews, and document analysis. The main findings show that the application of the TOGAF framework successfully addresses the challenges of interoperability, data security, and system scalability by effectively integrating IoT technologies in the healthcare environment and considering the local social and infrastructural context. The implementation of the IT architecture developed based on the TOGAF methodology demonstrated improved coordination between IoT devices and backend systems, facilitated secure and real-time data flow, and accommodated the scalability and sustainability needs of the system. The findings have significant implications in supporting the development of more efficient and effective health monitoring systems, offering strategic guidance for system developers, policymakers, and IT practitioners within the healthcare sector.

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

Communication systems at millimeter-wave (mm-wave) frequencies with high propagation losses use radio frequency (RF) budget analysis. RF system gains and losses ensure the receiver can recover the broadcast signal. Modern communication systems use compressive sensing (CS) and discrete wavelet transform (DWT). Hardware implementation is hard. Fieldprogrammable gate arrays (FPGA) adaptability, configurability, and processing speed make them popular. More mm-wave transceivers use FPGAs and advanced signal processing. FPGA-based mm-wave transceivers use compressed sensing and dual-tree complex wavelet transform (DTCWT). RF budget analysis recovers receiver signals. Energy and data efficiency transceivers have baseband processors, transmitters, and receivers. RF-to-mm-wave transmitter. Receiver demodulation and baseband conversion. CS and DTCWT processing modules boost baseband signal processing 5 Gbps Xilinx virtex-6 FPGAs. The system retrieves the signal while conserving power, according to simulations and testing. This study found that FPGA-based mm-wave transceivers can use advanced signal processing in future high-speed communication systems.

Transforming Poverty Alleviation Policies: Adapting the Regional Innovation Architecture Approach

Poverty, defined as the inability of individuals to fulfill their basic needs, poses a significant challenge to achieving community welfare in many regions. Addressing this challenge requires a holistic and integrated approach that considers Indonesian society's diverse cultural and socio-economic contexts. Adapting innovative architecture is crucial in confronting Indonesia's various cultural and socio-economic landscapes. This approach provides new alternatives for regional development strategies and opens up new opportunities for transforming and adapting innovation to tackle poverty holistically and sustainably. This research aims to restructure innovation architecture and examine its potential to transform regions in poverty alleviation efforts. The study employs a qualitative research method with a literature review analysis. It explores novel approaches for developing a solution-oriented and applicable analysis and recommendations for communities. Additionally, the research analyzes several regions that have successfully implemented regional innovation and reduced local poverty levels. Data is collected through a literature search strongly relevant to the research objectives. The findings indicate that innovation architecture can drive regional transformation in poverty alleviation through 1) Enhancing collaboration and synergy among stakeholders, 2) Creating a conducive innovation ecosystem, and 3) Developing contextual and sustainable innovative solutions. These findings demonstrate the substantial potential of innovation architecture in transforming regions to achieve poverty alleviation. This aligns with existing theories emphasizing stakeholder collaboration and synergy as key factors in creating effective, innovative solutions. Innovation architecture can serve as an effective tool in transforming regions towards poverty alleviation. Proper and sustainable implementation of innovation architecture can assist regions in crafting contextual, innovative solutions and enhancing community welfare.

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.