System Architecture Research Articles

Smart Attendance Management System with QR Code Authentication

Abstract: This paper presents the development of a Smart Attendance Management System (SAMS) designed to automate and streamline the process of tracking attendance in educational institutions and workplaces. The proposed system utilizes advanced technologies such as facial recognition and QR code to accurately and efficiently monitor attendance. By eliminating the need for manual entry, the system reduces human error, saves time, and enhances data accuracy. The SAMS also integrates with databases and analytics tools to provide real-time reporting and insights, helping institutions make data-driven decisions. The study outlines the system's architecture, implementation, and potential impact, demonstrating its ability to enhance operational efficiency and improve user experience in diverse environments

PulseGuard: Health Monitoring and Analysis System

Abstract: The use of wearable devices such as smart watches has led to a major change in the way a person is able to manage their health. In this paper, health monitoring system construction based on a smartwatch is designed which is capable of Health parameters like heartbeat monitoring, counting steps and their physical activity levels at continuous intervals. The system is built on modern web technologies and cloud services for data processing in a real-time manner, thus ensuring that alert messages are delivered immediately in case health parameters go beyond the normal ranges. The goal is to improve personal health management by providing expansion, dependability and availability to the users in areas where there is poor or no health care provision. The paper discusses system architecture, implementation, and the prospect of the system in enhancing preventive medicine and its practice for better health.

Automated Fingerprint Identification System

Abstract: AFIS is regarded as one of the greatest advancements in biometric systems as it enables fast and efficient identification of an individual by their distinct fingerprint designs. This system employs the use of digital imaging, finger-power techniques and other technologies in the storage and comparison of fingerprints. With AFIS, fingerprint images are all digitized making it easier to carry out crime investigations, border control and general identification of persons. The strength of AFIS systems is evidenced by the increased accuracy levels along with the capacity to accommodate numerous records and still allow for fast searches by fingerprinting matching to existing images for law enforcement on file. Some problems nevertheless persist which include the quality of fingerprints and environmental interferences, however machine learning and artificial intelligence improvements are still being incorporated into the systems to make them work even better. This paper investigates the system architecture and components, operational aspects, and prospects for development of the AFIS technology, which is essential in contemporary informational security

Diversities and similarities exhibited by multi-planetary systems and their architectures. I. Orbital spacings

The rich diversity of multi-planetary systems and their architectures is greatly contrasted by the uniformity exhibited within many of these systems. Previous studies have shown that compact Kepler systems tend to exhibit a peas-in-a-pod architecture: Planets in the same system tend to have similar sizes and masses and be regularly spaced in orbits with low eccentricities and small mutual inclinations. This work extends on previous research and examines a larger and more diverse sample comprising all the systems with a minimum of three confirmed planets, resulting in 282 systems and a total of 991 planets. We investigated the system architectures, focusing on the orbital spacings between adjacent planets as well as their relationships with the planets' sizes and masses. We also quantified the similarities of the sizes, masses, and spacings of planets within each system, conducting both intra- and inter-system analyses. Our results corroborate previous research showing that planets orbiting the same star tend to be regularly spaced and that pairs of adjacent planets with radii $< 1\, R_ predominantly have orbital period ratios (PRs) smaller than two. In contrast to other studies, we identified a significant similarity of adjacent orbital spacings not only at PRs < 4 but also at 1.17 < PRs < 2662 . For the systems with transiting planets, we additionally found that the reported correlation between the orbital PRs and the average sizes of adjacent planets disappears when planet pairs with $R < 1\, R_ are excluded. Furthermore, we examined the data for possible correlations between the intra-system dispersions of the orbital spacings and those of the planetary radii and masses. Our findings indicate that these dispersions are uncorrelated for the systems in which all the pairs of adjacent planets have PRs < 6 and even for the compact systems where all PRs < 2 . Notably, planets in the same system can be similarly spaced even if they do not have similar masses or sizes.

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Numerous devices, including sensors, RF ID, and other types of smart devices, have been developed as a result of the Artificial Intelligence (AI), and Internet of Things (IoT) revolution. Urban areas can become smart by monitoring and collecting data about their surroundings through the deployment of technologies with powerful computational capabilities and those that are converted into intelligent things. Waste management is among the most significant issues in smart cities, a rise in metropolitan regions and faster increases in population are the main reasons. When it comes to gathering data about waste management, intelligent services can serve as the front line. Waste management with IoT support is a common example of a service offered by smart cities. Various duties, like gathering, processing, and use of waste in appropriate facilities, are included in waste management. The present study proposed an updated waste management system architecture design after reviewing existing artificial intelligence and IoT-based waste management systems and automation in smart cities. The proposed system architecture deals with the automation of municipality trash in smarter urban areas, using IoT technology and sending notification messages based on sensor data relating to the dustbin state, such as full or empty. The notifications are sent simultaneously to the municipality office and the waste carrier vehicle driver, so that waste can be emptied on time. The proposed system architecture represents a scalable and adaptable model for municipalities that aim to transform their waste collection processes and play a key step in minimizing municipality waste in smart cities. By deploying this proposed system architecture with smart sensors and IoT devices, municipalities can monitor waste levels to ensure that bins are emptied when it is necessary. This reduces the frequency of waste collection, lowers fuel consumption, and minimizes operational costs. The Route optimization algorithms further enhance efficiency by determining the most efficient paths for waste collection trucks, so they can reduce travel time and fuel emissions.

Team Tracker: A Project Management Tool

This paper introduces "Team Tracker," a project management tool developed using Asana and Next.js. The tool integrates key functionalities such as audio and video calling, real-time task tracking, and an embedded browser, creating an all-in-one solution for remote team collaboration. The objective of Team Tracker is to streamline workflow by eliminating the need for multiple platforms, thus improving team efficiency and communication. A comparative analysis with existing tools like Jira and Slack demonstrates how Team Tracker addresses the gaps in current project management solutions by offering enhanced real-time communication and task management capabilities. The system's architecture, challenges encountered during development, expected outcomes, and potential future enhancements are also discussed. Team Tracker is poised to provide a comprehensive, scalable solution for modern project management needs. Keywords: project management, Asana, Next.js, real-time communication, task tracking, Team Tracker.

IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Block Chain-Based Approach towards a Trustworthy Cloud Computing

Cloud computing remains one of the most significant advancements in resource provision to organizations in the latest decades since they initiate conveniences on demand access. The adoption of the IoT has brought about a new era of collaborative computing by employing a pool of linked smart sensors and devices that will produce and analyze great amounts of data, thus creating new related problems in terms of size and security, hence increasing even more the importance of traditional security measures. Therefore, in this paper, we synthesize a new SSCA that combines IoT and cryptographic mechanisms, to build highly available and secure cloud systems, which makes these systems multi-users and fully usable by many users at the same time. The design resolves to a distributed structure where sever al cloud nodes will address user s requests proficiently and it uses Multicast and Broadcast Rekeying Algorithm (MBRA) to maintain the privacy and the confidentiality of the users’ detail, employing a cryptosystem that integrates MBRA, Post Quantum Cryptography (PQC) and block chain. The architecture using IoT devices collects data from the distributed sensing resources through the data storage layer; it enforces the security integrity of the information collected by employing the MBRA-PQC encryption algorithms; the blockchain provides the security for the confidential data by storing it in unalterable and dispersed registers. The proposed approach is then demonstrated with different data sets and its performance is measured based on the response time, the throughput and scalability, security and reliability. The outcome of the expressions reveals the efficiency of the proposed SSCA compared to MHE-IS-CPMT as it reduced by 1. 67 seconds and 0. 97 seconds at 250 and 1000 devices respectively. Similarly, the result as AUC value for SSCA showed a better improvement than the MHE-IS-CPMT, EAM, SCSS, and SHCEF models with improvements in percent at 25-user level: 6. 30%, 6. 90%, 7. 60%, and 7. 30%, respectively as well as; at 50-user level: 5. 20%, 9. 30%, 11. 50%.

Advancing Sustainable Mobility: A Data Acquisition System for Light Vehicles and Active Mobility

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road.

Green Edge: Harnessing situational awareness for sustainable systems

Green computing and Building Management Systems (BMS)are closely related through their shared goal of improving energy efficiency and reducing environmental impact. Field controllers that connect with the sensors and actuators at the field level act as an interface between field devices and enterprise applications. Data transfer, protocol conversion, data processing, computing, and control actuators based on predefined logic are the functions of the field controllers. Fast response time is one of the crucial parameters of these field controllers hence reducing the load on these field controllers by enabling Edge computing functionality is very much required. Maharudrayya Nandimath and others [16] in their “Edge Computing in IBMS (Integrated Building Management Systems). (2024 International Conference on Computer Communication and Informatics (iccci. in)) the paper suggested many of the measures to reduce the load on these field controllers. Building Management Systems are essential for optimizing the operation of modern buildings, ensuring comfort, safety, and energy efficiency. By integrating advanced technologies such as Situation Awareness (SA) and Artificial Intelligence (AI), BMS can be significantly enhanced to provide real-time monitoring, predictive maintenance, and intelligent decision-making. This paper explores the potential of combining SA and AI with BMS, leveraging insights from recent research on industrial processes, IoT cloud-edge continuum, and smart cities. The proposed system architecture includes feedback loops and contextual awareness, aiming to improve building operations and reduce energy consumption, thereby supporting global sustainability goals.

A Scalable, Secure, and Efficient Framework for Sharing Electronic Health Records Using Permissioned Blockchain Technology

This paper presents a scalable, secure blockchain-based healthcare system architecture that efficiently manages large patient datasets. DHTs and Skip Lists enable efficient data access, while DPoS and PBFT facilitate parallel transaction processing. Adaptive filters, Radix Trees extended by Merkle Trees, and an immutable blockchain ledger secured by Tendermint consensus ensure data integrity and protection against evolving threats. Threshold Cryptography secures consensus participant selection, and Bulletproofs verify transactions, complying with healthcare regulations. ChaCha20, a symmetric stream cipher, encrypts sensitive data, enhancing performance across devices. ABAC manages access rights, ensuring fine-grained control over data accessibility. This architecture offers a comprehensive, efficient, and secure solution for healthcare data management in blockchain environments.

Principle and Application for Rumination Computing Algorithms

To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

Two-level integrated verification evaluation method based on comprehensive weighted assessment in multiple scenarios

The complexity and diversity brought by the distributed architecture of the new generation electric information collection system have deepened the difficulty of constructing evaluation verification index systems and quality evaluation models. Moreover, the presence of differentiated components has made fair and scientific verification challenging. Therefore, leveraging graph neural networks and siamese networks, a integrated construction quality evaluation system based on comprehensive weighted assessment in multiple scenarios was developed. Firstly, a graph neural network was constructed based on terminal data of the branches electricity usage information collection system and the link topology structure. Subsequently, this network was deployed on the headquarters side to directly acquire terminal data and generate mirror network input from the branches data, enabling real-time acquisition of various system operational indicators. Finally, the similarity between the headquarters and branches data was calculated using siamese networks to compute accuracy compensation weights for checking and evaluating various indicators, thereby obtaining comprehensive weighted quality evaluation indicators of the branches new generation electric information collection system. We use three types of services including electricity data collection, load forecasting, and task scheduling as experimental scenarios. The results showed that the multidimensional comprehensive weighted quality assessment combined with accuracy compensation obtained from the siamese network resulted in business construction quality assessment values of 97.92%, 95.95%, and 99.96% in branch. This value is approximately equal to the quality evaluation value of manual work, so the method can effectively verify the construction quality of new systems in the branch.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Research on Techniques for Enhancing the Speed of Low-Power Operational Amplifier

In the context of low utilization, this paper explores various techniques for enhancing the speed of operational amplifier (OA). The main text delves into three methods to gain equilibrium: fully differential operational amplifier (FDA), twostage operational transconductance amplifier (OTA), and a novel high-speed calculation system architecture employing a “rhombus crystal tube.” The FDA improves speed by minimizing noise and enhancing bandwidth and output voltage swing, without increasing power consumption. The design of the two-stage OTA combines the characteristics of a differential amplifier and a telescopic amplifier, optimizing gain and slew rate through Miller compensation and noise gain manipulation, thereby achieving high-speed performance. A novel high-speed operational amplifier structure employs diamond transistors to supply substantial current for capacitor charging, enhancing the slew rate and overall speed. Throughout the text, these methods are presented as a means to jointly promote high speed, low power consumption, and the handling of a significant number of transistors. To further increase the speed, the size of the microcrystalline tube is crucial. The research direction is outlined, and while the design equipment is in the foreground, there remains room for progress, especially in applications for large-scale electrical models. Future research aims to enhance the power efficiency of circuits, making them more practical and efficient. This study provides valuable insights into balancing power consumption and speed in advanced electronic devices.

Application of Multi-Input Multi-Output Brain-Computer Interface in Cognitive Assessment for Alzheimer’s Disease

This research aims to augment human cognitive abilities, particularly memory function, through the development of innovative prosthetics. The hippocampus, a crucial brain region involved in memory encoding and recall, has been the focal point of numerous studies seeking to develop prosthetics that can restore or enhance memory function in individuals with impairments such as Alzheimer’s disease. This review examines two studies that delve into the development and application of hippocampal neural prosthetics, highlighting the potential of these prosthetics to revolutionize the treatment of memory impairments. One study utilized static neural stimulation patterns to enhance memory for specific information content, while another developed a nonlinear Multiple Input Multiple Output (MIMO) dynamic model to predict and stimulate neural activity patterns during memory encoding. Both studies demonstrated significant improvements in memory performance. Additionally, this paper discusses the clinical significance and application prospects of hippocampal neural prosthetics, including the potential for implantable prosthetics and the widespread applicability of MIMO stimulation. Furthermore, the paper explores advanced modeling and classification techniques for neural ensembles and visual memory decoding, including Volterra-type hierarchical modeling, optimal multi-unit stimulation patterns, and a sparse classification model for decoding visual memories. Finally, the paper presents the implementation and evaluation of neural prosthesis systems, including an application-specific integrated circuit (ASIC) design for a hippocampal prosthesis and a high-performance, scalable system architecture for real-time estimation of neural activity. These advancements hold promise for future research and applications in neural prosthetics for memory enhancement.

Concept of a medical and public health cancer genomics platform

Abstract Background This study aims to produce the concept of an open-access medical and public health cancer genomics platform aimed at aggregating and sharing documentation of ongoing initiatives and data resulting from the CAN.HEAL project, available for EU researchers, healthcare professionals, policy makers, citizens and patients. Methods The context (reference sector, stakeholders and research evidences), feasibility (privacy management and data processing), and platform’s end users were analysed; the CAN.HEAL project deliverables were listed and clustered; aspects related to the definition of the overall platform architecture and modules integration were studied. Results The project deliverables were catalogued into 8 categories which could be the platform’s cloud interface main labels: results from events, policy papers, newsletters, mapping results and reports, datasets, recommendations and guidelines, use cases, courses and training activities. To define the overall system architecture, the following key activities should be implemented: content management system choice, hosting configuration and setup of the development domain, content management system installation, development of HTML web pages, data entry, data visualisation and graphic layout, and test and debug. To the modules integration purposes the following items should be addressed: creation of a server analytics account; social network integration; activation of additional system for sending newsletters. Conclusions Cloud-based data platform allows to securely manage, integrate, analyse and share large datasets. Establishing an infrastructure to help researchers access, store and analyse large amounts of biological data is of paramount importance both to enable advances in research and support health policy making processes. The CAN.HEAL platform could help aligning clinical and population-based interventions for integrating the genome of Europe biobanking initiative into public health genomics for cancer. Key messages • Connecting healthcare data can support the challenge of accessing the relevant information needed for the policy and decision making processes to effectively promote population health and wellbeing. • The set-up of a medical and public health cancer genomics platform promotes the effective translation of genome-based knowledge and technologies into public policy and health services.

Co-simulation Architecture and Platform Establishment Method for Cloud-Based Predictive Cruise Control System

Co-simulation Architecture and Platform Establishment Method for Cloud-Based Predictive Cruise Control System

The New Method for Ground Insulation Monitoring of the Secondary Circuit in Substations

When two-or-more-point grounding faults occur in the secondary circuit of a substation, they will lead to large data measurement errors in the protection, measurement, and control devices, which can easily cause protection malfunction. At present, there is a lack of an online panoramic monitoring system for the secondary circuits of substations, and it is difficult to accurately identify the faults in these secondary circuits. In order to solve this problem, this paper proposes a cloud–edge collaboration framework for the power Internet of Things (IoT), which can accurately identify faults by monitoring the secondary circuit of power grid substations with panoramic online status, reducing maintenance costs and improving overall operational efficiency. Firstly, the collection requirements of current transformer (CT) and potential transformer (PT) fault characteristics in the secondary circuit of the substation are analyzed. The platform architecture of the secondary loop panoramic monitoring system of the cloud-side cooperative substation is proposed, along with the algorithm and data wireless acquisition scheme of the edge computing, cloud computing, and cloud-side system. Furthermore, the edge computing platform and the current and voltage wireless sensors are developed. The maximum error in the accuracy test of the voltage sensor is 1.9180%, while the maximum error in the accuracy test of the current sensor is 1.3406%.

Design of a Traditional Village Cultural Heritage Preservation and Management System Based on Multi-Agent Networks

Preserving cultural heritage is essential for maintaining cultural diversity and promoting sustainable development. This study investigates the application of multi-agent networks in the protection and management of traditional village cultural heritage, capitalizing on their inherent distributed, collaborative, and adaptable characteristics. By integrating advanced technologies such as big data, data mining, and machine learning, the research meticulously develops a comprehensive system architecture and functional modules aimed at revitalizing traditional village spaces. The findings demonstrate significant enhancements in protection efficiency, while simultaneously fostering cultural inheritance and innovation within village communities. Through these innovative approaches, this study provides valuable insights into the preservation of traditional village cultural heritage and contributes to the broader objectives of cultural diversity conservation.

Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning

This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.