Multi-user Applications Research Articles

W-band photonic-aided reconfigurable multichannel vector mm-wave transmission enabled by the digital subcarrier multiplexing technique.

As multimedia service demands rise, radio-over-fiber (RoF) systems necessitate a flexible wireless resource allocation in multi-user scenarios. Optical wavelength division multiplexing (WDM) can meet multi-user application scenarios but has the problems of high hardware cost and complexity. In this Letter, we propose a photonic-aided reconfigurable multichannel vector millimeter-wave (mm-wave) transmission scheme in the RoF system enabled by the digital subcarrier multiplexing (DSCM) technique. The scheme can flexibly adjust the number and frequency spacing of subcarriers as well as the bandwidth and modulation format allocated to each subcarrier, and it can be applied to multi-user scenarios without increasing the cost and complexity of hardware implementation. The off-line experiments verify the system transmission performance with different numbers, bandwidth, and frequency spacing of subcarriers, fully verifying the feasibility of the scheme. Furthermore, the Letter delves into the effects of the subcarrier number, bandwidth, and frequency spacing on overall system performance, highlighting the potential of this scheme for flexible and cost-effective wireless resource allocation in RoF systems.

Secure and portable health monitoring system for cyber physical systems in internet of things

The incorporation of Internet of Things (IoT) technology into healthcare has introduced vulnerabilities within medical devices, thereby posing noteworthy risks to both patient safety and the inclusive integrity of healthcare systems. This contribution addresses the security concern of health care monitoring system. The STM32BL-475E IoT node along with its sensors provides a greener way to monitor health remotely with data privacy, along with this many such applications can be containerized to enable multiuser application in virtual set up. This paper proposes a methodology of dynamic key generation highlighting the potential for transformative healthcare advancements. The proposed work provides an improved avalanche effect of 51.6%, minimal RAM utilization of 3.49% and a randomness of 2^128 times which is very unpredictable to detect. This paper aims to integrate these technologies to create a resilient health monitoring framework with greater data indemnity. The integration of artificial intelligence with the proposed design is reserved as future work.

On multi-use applications unlocking behind-the-meter flexibility for grid-serving services

This paper introduces multi-use applications to unlock behind-the-meter flexibility by integrating grid-serving capabilities with user-oriented control strategies. We deploy a hierarchical flexibility coordination process in a co-simulation research environment and propose a control strategy prioritizing self-consumption optimization with a secondary focus on grid-serving applications. This approach enhances the viability of behind-the-meter technologies, particularly benefiting battery energy storage systems with high investment costs. It also facilitates active grid operation amid the growing presence of distributed energy resources. The research’s key contribution lies in designing an information architecture to efficiently utilize behind-the-meter flexibility for grid-serving processes. The paper demonstrates the feasibility of a multi-use operation, combining user-oriented optimization and grid-serving flexibility provision for local grid congestion management. The implemented control algorithm in a home energy management system achieves grid-serving objectives while maintaining behind-the-meter asset performance. Co-simulation results show that in less than 2.5% of simulated time steps, transformer utilization exceeds operational limits in both scenarios, indicating the viability of multi-use operation for leveraging new value streams and managing grid congestion simultaneously.

SISTEM INFORMASI AKUNTANSI MANAJEMEN KEUANGAN BERBASIS MULTI USER PADA KOPERASI AROFAH KENDAL

Currently, the majority of the Arofah Kendal Cooperative in financial management is still in the form of files, causing accumulation, damage, difficulty finding and even loss of financial data which has resulted in the Arofah Kendal Cooperative experiencing difficulties in presenting reports. The Arofah Kendal Cooperative always experiences an increase in transactions for the number of deposits and loans. The customer financial service system is centralized on one computer, so services must take turns, which takes a long time and is less effective. Implementing a Multi-User-Based Financial Management Accounting Information System, the Arofah Kendal Cooperative is able to overcome problems related to processing, financial filing and reporting. With a multi-user application, the database of the Arofah Kendal Cooperative is connected to each other to make it easier for each user to access the system according to authority simultaneously for data collection, financial transactions that can present up to date financial information according to the needs of cooperative management which provides effectiveness and efficiency in financial management, improves employee performance and maximizing customer service satisfaction for the Arofah Kendal Cooperative.

Generation of a time–bin Greenberger–Horne–Zeilinger state with an optical switch

Multipartite entanglement is a critical resource in quantum information processing that exhibits much richer phenomenon and stronger correlations than in bipartite systems. This advantage is also reflected in its multi-user applications. Although many demonstrations have used photonic polarization qubits, polarization-mode dispersion confines the transmission of photonic polarization qubits through an optical fiber. Consequently, time–bin qubits have a particularly important role to play in quantum communication systems. Here, we generate a three-photon time–bin Greenberger–Horne–Zeilinger (GHZ) state using a 2 × 2 optical switch as a time-dependent beam splitter to entangle time–bin Bell states from a spontaneous parametric down-conversion source and a weak coherent pulse. To characterize the three-photon time–bin GHZ state, we performed measurement estimation, showed a violation of the Mermin inequality, and used quantum state tomography to fully reconstruct a density matrix, which shows a state fidelity exceeding 70%. We expect that our three-photon time–bin GHZ state can be used for long-distance multi-user quantum communication.

Analisis Perbandingan Aplikasi Akuntansi Berbasis Mobile Untuk UMKM

This study aims to provide recommendations for applications suitable for MSME. This study provides more detailed information about accounting-related mobile applications by analyzing application reviews independently through trials of mobile financial reporting applications. Also, it reviews users depending on ratings on Play Store or in the app store. The advantages and disadvantages of the application This is very critical to understand so that micro, small, and medium entrepreneurs can benefit. It is hoped that input will be obtained to develop MSMEs. This exploratory research uses a systematic method by looking for applications according to the keywords, for example, accounting applications, mobile applications, financial reports, and application recording finance, then grouping those apps by rating and the number of downloads to get some applications. The results of the study show that of the six applications reviewed based on independent reviews and user reviews, there are two main recommended applications: Cashbook: Cash Management App and Buku Warung Application for MSMEs. The reason behind the recommendation of these two mobile-friendly financial reporting applications is that the first. The Cashbook application: The cash Management App has advantages over other mobile-based financial reporting applications. This application is a multi-user application with reasonable access restrictions so that users of this application use the feature. Members do not need to worry about reporting errors. This advantage is why the Cashbook Application: Cash Management App is recommended for MSMEs who need structured and continuous recording without worrying about errors in the resulting reports. Furthermore, the Buku Warung Application for MSMEs is also a multi-user application that serves various alternative types of businesses as well as recording such as additional income features, including selling credit, electricity tokens, E-Wallet, data packages, Electricity Bills, BPJS, PDAM, Game Vouchers, Postpaid Credit, Credit Installments, and Internet and Cable TV. The QRIS facility has also been integrated with financial institutions. Thus, this increases the sense of security for application users in transactions.

Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar

With the popularity of virtual reality (VR) games and devices, demand is increasing for estimating and displaying user motion in VR applications. Most pose estimation methods for VR avatars exploit inverse kinematics (IK) and online motion capture methods. In contrast to existing approaches, we aim for a stable process with less computation, usable in a small space. Therefore, our strategy has minimum latency for VR device users, from high-performance to low-performance, in multi-user applications over the network. In this study, we estimate the upper body pose of a VR user in real time using a deep learning method. We propose a novel method inspired by a classical regression model and trained with 3D motion capture data. Thus, our design uses a convolutional neural network (CNN)-based architecture from the joint information of motion capture data and modifies the network input and output to obtain input from a head and both hands. After feeding the model with properly normalized inputs, a head-mounted display (HMD), and two controllers, we render the user’s corresponding avatar in VR applications. We used our proposed pose estimation method to build single-user and multi-user applications, measure their performance, conduct a user study, and compare the results with previous methods for VR avatars.

Optical encryption for multi-user based on computational ghost imaging with Hadamard modulation

In multi-user applications, one of the thorny problems for a cryptographic algorithm is how to distribute the keys and reduce the amount of transmitted data without crosstalk and security risks. Based on Hadamard modulated Computational Ghost Imaging (CGI), we propose an optical encryption scheme for multi-user, in which a common ciphertext is public for all users and one user owns a unique key to index the row sequence of Hadamard basis for decryption. Therefore, the channel resources are saved because the ciphertext does not need to be transmitted. Compared with the conventional CGI-based random intensity modulation encryption, the scheme not only reduces the amount of transmitted key data but also enhances the quality of decrypted images, which benefits from the order of Hadamard basis rather than its distribution being taken as the key. Theoretical analysis and numerical simulations are conducted to prove the feasibility and property of the scheme.

Load distribution strategy of decentralized databases

Load management in databases typically involves segmentation strategies that determine the distribution and placement of information within data storage systems. This article explores a critical aspect of data segmentation and the challenges associated with selecting the right strategy. Some of the discussed strategies, such as range-based strategy and hashing strategy, come with their limitations and risks that can complicate scalability and data migration operations. The article also highlights issues related to choosing a specific data segmentation pattern in databases, including data movement complexity, uneven load distribution, transactional problems, and data integrity preservation. Choosing the right segmentation strategy is crucial to ensure the efficiency and productivity of a data storage system, and it should be a thoughtful choice tailored to the specific requirements of the system. The article discusses important data segmentation strategies in storage systems, which play a key role in the distribution and management of large datasets. The authors explore three main strategies: the search strategy, the range strategy, and the hashing strategy. Each of these strategies has its unique advantages and characteristics. The search strategy provides maximum control over data placement and the ability to use virtual segments for data balancing. This strategy is particularly useful for multi-user applications but may introduce additional overhead during segment retrieval. The range strategy groups related data into one segment and works well with queries for ranges of values. However, it can lead to uneven load distribution. The hashing strategy helps evenly distribute the load and avoid highly active segments but may introduce additional computational overhead when calculating the hash. The article also discusses data scaling and relocation operations for each of these strategies. For example, the search strategy allows for data scaling and relocation at the user level without system downtime. This article helps readers better understand the differences between data segmentation strategies and choose the most suitable one for their specific use case.

Multi-user myoelectric pattern recognition for shoulder motion intent detection

Myoelectric interfaces have a large range of applications in rehabilitation and prosthetic fields. The electromyographic signal (EMG) generates different patterns of activations according to a specific motion activity allowing the extraction of task-discriminant features, and leading to an efficient external device control [1]. The application of such methodologies in the motion intention detection (MID) is challenging, since this requires to predict the subjects’ intentions while attempting to generate a certain movement [2]. Despite good results have been obtained for the intra-subject scenario [3], the inter- subject generalization still remains an issue. The aim of this work was to investigate myoelectric interfaces for multi-user application on data relative to the shoulder MID. The dataset was composed by eight subjects performing six shoulder movements [4]. The signals were segmented into 150 ms windows with an overlap of 75 ms and four time-domain features were extracted. The feature matrix was split into a calibration and testing subsets. The spectral regression (SR) was used to reduce the dimension of the feature space and with the canonical correlation analysis (CCA) the reduced feature set was projected into a new space, where data related to different subjects are maximally correlated. The obtained projection coefficients were then used on the testing subset. The SVM classifier was trained on the projected calibration subset of a user A and the projected testing subset of a user B was used for testing. In this way, the classifier was trained on data belonging to a certain subject, and then tested on unseen data of a different person. Outcomes demonstrated that the presented approach boosted the classification performance, even if only one trial was used for the training session, with respect to the case in which neither the SR nor the CCA were applied. Indeed, the F1-score increased from 28% up to 53%. It can be also appreciated that increasing the number of repetitions for the calibration subset, the F1-score was further improved, up to 70% already with two repetitions. The accuracy presented a similar behaviour (Fig. 1). The coupling between SR and CCA proved to be reliable for the multi-user oriented shoulder MID. In MID problems, the movement recognition is performed relying on features extracted from a very short time period and on a transient epoch of the EMG signal, before the movement was fully achieved. For this challenging task, the present architecture proved to be reliable in an inter-subject scenario. Indeed, it provided high classification performances training on one subject and then testing on an unseen one, hence supporting the use of CCA for the development of subject-independent myoelectric interfaces.

English Language Intelligent Translation System Based on 3D Visualization Technology

In the process of imparting knowledge, faculty should give more emphasis to the acquisition of students’ direct experience. Vivid three-dimensional visualization resources should be used for classroom teaching to help students better understand and grasp the visualization of knowledge. Based on this, this paper aims to study the English language intelligent translation system based on 3D visualization technology. In recent years, 3D application technology is transforming into multiuser applications supporting network environments. This transformation also promotes the application and development of 3D GIS technology. Transformer is a universal and efficient feature extractor. This paper proposes a design scheme for constructing 3D interactive visualization resources in teaching scenes. It is designed for specific disciplines and produces visual resources. Then, it analyzes and evaluates the actual effect of the application in teaching, to provide ideas for English translation education experts and teaching staff. It also builds a translation system based on B/S architecture, connects the server model and the client, and realizes the visualization and interaction of translation functions. In the experiments, 5% and 20% of the parallel corpus with a total of 12 M sentence pairs were used for pretraining of the translation model. Then, the number of monolingual corpus is gradually increased to train the model. Experiments show that when using the same amount of monolingual corpus, the translation model can achieve a more obvious effect on the pretrained model.

A Novel Distributed Multi-Agent Reinforcement Learning Algorithm Against Jamming Attacks

In a multi-user wireless network under jamming attacks, self-interested users trying to learn their best anti-jamming strategy encounter a major problem, which is interference. An interesting solution is for users to learn anti-jamming techniques cooperatively, which can be achieved using a distributed learning algorithm. In this context, works proposing distributed learning algorithms to overcome jamming in multi-user applications, rely on the availability of a safe communication link to exchange information between users. However, if this communication link wireless, assuming that this link is safe against jamming attacks would not be accurate. Consequently, we propose a novel distributed multi-agent reinforcement learning algorithm for anti-jamming, namely Cross-Check Q-learning, where users build estimates of each other’s decision-making policies and adapt to them, therefore eliminating their need to communicate. When applied against both sweeping and smart jammers, our algorithm provides users with a better understanding of their environment and helps them learn the attacker’s policy and effectively avoid mutual interference. The proposed method’s transmission rates and interference levels is compared with the standard Q-learning, the collaborative multi-agent algorithm, and random policy. Simulation results show that our algorithm improves the users’ transmission rates, eliminates mutual interference, and has the highest convergence speed under the two considered jamming attacks.

Lattice-based public key searchable encryption with fine-grained access control for edge computing

As a bridge between cloud computing platforms and the Internet of Things (IoT) devices, edge computing provides various on-demand data services to reduce latency and network congestion. To ensure data security in edge computing, sensitive data should be encrypted before being outsourced to edge servers. Public key encryption with keyword search (PEKS) can provide search service for encrypted data. Nevertheless, most existing PEKS schemes are susceptible to quantum attacks because their security assumptions are based on traditional hardness assumptions. Moreover, many lattice-based PEKS schemes only apply to the single-user scenario, limiting the range of applications. In this paper, we present an efficient lattice-based public key searchable encryption with fine-grained access control for edge computing. Our scheme achieves post-quantum security and highly flexible access control policies for multi-user applications, by utilizing the learning with errors (LWE) assumption and subset predicate encryption. The security proof illustrates that our proposed scheme is secure under chosen plaintext attacks and chosen keyword attacks. Finally, extensive experiments on real-world datasets illustrate that our encryption algorithm is faster than existing approaches.

Legal Issues for Artificial Floating Islands

Developments of multi-use floating islands are accelerated by an increase in offshore activities and pressing needs to create extra space in coastal regions for the surging population. The Horizon 2020 EU funded research project Space@Sea developed a modular floating island concept for offshore multi-use applications and examined current legal issues and barriers concerning its development. In this paper a floating island is defined to be “an artificially created floater, or set of connected floaters, moored to the seabed of which the topside can be used for activities similar to activities on land”. This paper aims to delve into governance issues like the assignment of authority on a multi-use floating island and the shift from regulating offshore living for working purposes to living purposes, as well as maritime law and property law (using the Netherlands as case study). Within the Exclusive Economic Zone (EEZ) a coastal state can establish, locate, and govern the floating island. For the high seas the International Maritime Organization (IMO) is the most probable organization to initiate a discussion on settlement of floating islands. For each activity on the floating island, limiting criteria regarding motions as well as safety rules and regulations need to be established. Industry and housing will have completely different requirements regarding safety which will not be possible to fit in a one size fits all regulation. This case study shows that current Dutch property law prohibit the division of ownership of buildings built on floating objects. They can only be owned together as one object. For urban expansion, future inhabitants will want to have the possibility to buy property, requiring an amendment of current property law. It is to be expected that other countries will have similar or additional governance challenges. The Space@Sea project has developed a technical solution for floating islands which have identified barriers for multi-use in the rules and regulations described in this paper. The paper urges the responsible institutions and stakeholders to take on their roles in overcoming these barriers.

A low-cost multi-user augmented reality application for data visualization

A low-cost multi-user augmented reality application for data visualization

Implementation of a fiber-based resonant beam system for multiuser optical wireless information and power transfer

A fiber-based resonant beam system is proposed for a simultaneous optical wireless information and power transfer (OWIPT) system. A fiber Bragg grating and an optical circulator are used to achieve simultaneous wireless optical communication and power transmission. In the receiver subsystem, a communication and energy harvesting circuit is employed to separate alternating current (AC) and direct current (DC) outputs. The AC output is used for information transmission, and the DC output is used for power transfer. We conducted experiments to test the built-in safety mechanism to verify whether the resonant beam stops immediately if any obstacle obstructs the line of sight of the system. The proposed architecture can be constructed for indoor and outdoor multiuser applications. The results demonstrated that the proposed fiber-based resonant beam system can be implemented successfully for simultaneous OWIPT.

Power allocation scheme for maximizing spectral efficiency and energy efficiency tradeoff for uplink NOMA systems in B5G/6G

Non-orthogonal multiple access (NOMA) is one of the key potential technologies for beyond 5G, i.e., 6G, networks. However, power allocation for spectral efficiency (SE) and energy efficiency (EE) tradeoff for uplink NOMA systems is still an open problem. In this study, we investigate the power allocation problem to maximize SE–EE tradeoff for systems with multi-cluster multi-user applications. Firstly, the power allocation optimization problem is formulated with the constraints of power budget and the minimum rate required by each user. Then, the optimization problem is decomposed into a group of sub-problems, which aim to maximize the tradeoff of SE–EE for each cluster. Finally, these sub-problems are simplified and solved by using bisection method and monotonicity of function. Simulation results show that the proposed scheme achieves much higher SE–EE tradeoff than the existing schemes.

Attribute-Based Identity Authentication Scheme Based on Linear Codes

In the multi-user application environment, the simple use of user private key to achieve identity authentication is too single and can not carry out fine-grained access control to users. And there is the problem of user key management, which is easy to produce system bottleneck. In order to solve the above problems, a traceable and revocable attribute-based authentication scheme is constructed based on linear codes, which divides user access rights in a fine-grained manner. The method of direct revocation is adopted in the scheme, and the cost of revocation is lower. When a key abuse occurs or the system suffers a denial of service attack, the true identity of the malicious user can be tracked based on the signature. Finally, the MBDH problem proves that the scheme meets unforgeability, and the performance comparison and numerical analysis show that the scheme has a shorter key length and less computational overhead.

A NOVEL PRIVACY PRESERVING BASED ENSEMBLE DEEP LEARNING FRAMEWORK FOR LARGE DIMENSIONAL MEDICAL DATASETS

With the rapid growth of data size, computing power and high dimensionality, it is essential to implement a novel privacy preserving model in deep learning framework. However, most of the traditional machine learning applications are integrated in deep learning framework on large databases, it is essential to secure the large sensitive patterns that are generated by the machine learning approaches before uploading to the cloud storage. As a result, how to design and implement a novel privacy preserving deep learning model (PPDLM) over high dimensional cloud data becomes a challenging task. Traditional privacy preserving deep learning frameworks are depends on data transformation approaches rather than the cryptographic approach due to high computational memory and time in cloud computing. In the real time multi-user applications, multiple datasets are distributed across the multiple users for privacy preserving. As the data size of multi-user applications increases, traditional PPDLM models require high computation memory and time for preserving the machine learning patterns. To overcome these problems, a novel data partitioning based privacy Preservation deep learning model is implemented on high dimensional datasets. Experimental results proved that the present system has high computational accuracy with privacy in the patterns compared to the existing models.

Comparison of the NFC authentication method with the methods available in Android

Most multi-user applications need an access system so that users can log in from different devices. To identify a user, at least one authentication method must be used. The article presents a comparison of the authentication method using NFC technology with other methods that are available for Android devices and differ in both implementation and specific usage.