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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Chen, Tianye Wang, Gang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

This paper analyzes the performance gain of rigid body localization (RBL) using time-of-arrival (TOA) and angle-of-arrival (AOA) measurements, when introducing non-regenerative relays having unknown positions. Although the introduction of the relays can generate more measurements, more unknown parameters are also introduces since the relay positions are not known. Hence, it is not clear whether the additional non-regenerative relays can improve the localization performance of the rigid body. To answer this question, we conduct the Cramer-Rao lower bound (CRLB) analysis and show theoretically that the answer to this question is affirmative. Simulation results confirm the correctness of the theoretical result. © 2024 IEEE.

关键词： Cramer-Rao bounds

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of Information Technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of Technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Real-Time Spectrum Sensing for TDD-LTE Signals Using Deep Learning 16

Real-Time Spectrum Sensing for TDD-LTE Signals Using Deep Le...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Xu, Xiang Jin, Ming Fang, Xiangqian Jiang, Tao Yu, Yanghang Li, Yaming Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

Existing spectrum sensing techniques for Time Division Duplex-Long Term Evolution (TDD-LTE) signals primarily focus on large time-scale signals. However, the spectrum holes that exist in the small time-scale special subframes of TDD-LTE are often ignored. This paper proposes a method for spectrum sensing within TDD-LTE special subframes by applying Parallel Convolutional Neural Networks (ParallelCNN). The effective feature extraction capability of ParallelCNN enhances the spectrum sensing performance. Simulation results demonstrate superior sensing performance under various signal-to-noise ratio (SNR) conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) Algorithm for Customer Credit Prediction 6

Implementation of Adaptive Neuro-Fuzzy Inference System (ANF...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Edy Victor Haryanto, S. Sembiring, Nita Sari Br Sinaga, Mikha Dayan Sianturi, Noprita Elisabeth Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia

ISBN: (纸本)9798331539603

Credit policies in the sales process are one of the supplier's most powerful tools for controlling demand, increasing sales and promoting commodities. Any supply chain member wishing to buy will receive full or partial trade credit based on strong decision-making rights. The problem that often occurs is the large number of bad loans that can disrupt the economic cycle. This problem has also resulted in many finance companies experiencing difficulties in processing customer loans. This problem can be prevented by applying artificial intelligence to predict which customers will take credit. Predictions will be made using the ANFIS method. Adaptive neuro-fuzzy inference system (ANFIS) methods deal with uncertainty in predictions and have been widely applied in various research contexts including medical systems, image processing, electrical systems, and so on. We chose this methodology because it has mostly proven to perform well for modeling complex data sets. The study used 200 customer data taken from a finance company, the data collected is customer data that can pay on time and customer data that cannot pay on time. The main novelty of this research lies in the application of a hybrid model that combines neural networks and fuzzy inference systems to predict credit risk. Most existing models use standard techniques such as logistic regression or decision tree-based models. By using ANFIS, which combines the ability of neural networks to handle non-linear relationships and fuzzy logic to model uncertainty, this research brings a new, more complex and more accurate approach to credit assessment. The results obtained from this study were as much as 80.5% of the data according to or valid with the predictions made using the ANFIS method. This proves that the model applied to the ANFIS method can be used to predict customer credit in the future. © 2024 IEEE.

关键词： Fuzzy systems

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A Sub-nanosecond Delay Floating-voltage Level Shifter for SiC MOSFETs Gate Driver Application 6

A Sub-nanosecond Delay Floating-voltage Level Shifter for Si...

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6th International Conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Wan, Yue Xia, Yinshui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350353563

In order to meet the requirements for the design of SiC gate driver in terms of speed and dv/dt immunity, a sub-nanosecond delay level shifter based on a high-voltage (HV) digital level shifter circuit is proposed. It can decrease the response time compared with the existing level-shifting circuitry, enabling its application in high-voltage scenarios such as closed-loop drive circuits for silicon carbide. The edge detection circuit and simple pull-up branches are utilized to reduce the transmission delay and power consumption. The circuit is simulated at a frequency of 10MHz in a 018um BCD process with Virtuoso, and the results demonstrate that the average transmission delay is about 380ps and the charging response time of the slow path is reduced to 3.3ns. © 2024 IEEE.

关键词： Delay circuits

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Proposal and Evaluation of Virtual Node Placement Algorithm in KVS (Key Value Store)

Proposal and Evaluation of Virtual Node Placement Algorithm ...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Kikuma, Kazuhiro Ueda, Katsumi Nihon University Faculty of Engineering Depertment of Computer Science Fukushima Japan

ISBN: (纸本)9798350324136

Distributed Key Value Stores (Distributed KVS) are used in large-scale cloud systems to manage huge amounts of data and to provide the required availability and fragmentation tolerance. In a KVS consisting of many physical nodes, the performance of each physical node is uniform at the beginning of the configuration, but during long-term operation of a cloud system causes variations in the performance of the physical nodes. Therefore, it was necessary to consider a load balancing placement scheme that takes heterogeneous performance into account. In previous studies, a vnode placement method has been proposed to create multiple virtual nodes (vnodes) on a physical node and then equalize the load when the performance of the physical node is mixed between 1x and 2x. However, the proposed method is limited to the case where the performance of physical nodes is mixed between 1x and 2x, and the performance of physical nodes that are added year by year cannot be limited to 1x and 2x. To solve this problem, this paper proposes a method of allocating physical nodes and vnodes when multiple physical nodes have multiple performances. A simulator is also created to evaluate the performance of the proposed method and to compare it with previous methods. © 2024 IEEE.

关键词： Apache Cassandra KVS (Key Value Store) Virtual Node

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Deep Reinforcement Learning based Adaptive Discontinuous Reception Method for Cognitive Radios 16

Deep Reinforcement Learning based Adaptive Discontinuous Rec...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Sun, Weijie Jin, Ming Jiang, Tao Li, Zhaoteng Chen, Guo Li, Wenjuan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (纸本)9798331506322

Discontinuous reception (DRX) is one of the important power-saving mechanisms for 5G/B5G cognitive radio systems. Conventional DRX methods improve the power-saving performance at the cost of data transmission delay. To address this issue, a deep reinforcement learning based DRX method is proposed. An adaptive time-slot DRX structure is propsed and the DRX mechanism is modeled as a Markov decision process. The state set, action space and reward expressions are jointly designed for the DRX mechanism. A DRX decision strategy is obtained by deep reinforcement learning, which adaptively adjusts the window length of wake-up period. Simulation results are provided to demonstrate that the proposed DRX method improves the power-saving performance while the average transmission delay of data packages closes to the lower bound. © 2024 IEEE.

关键词： Deep reinforcement learning

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Robust Covert and Secure Transmission for ISAC System With Transceiver Hardware Impairments 16

Robust Covert and Secure Transmission for ISAC System With T...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Li, Yaming Jin, Ming Jiang, Tao Chen, Chaodi Yin, Lei Zhang, Huifang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

In integrated sensing and communication (ISAC) systems, communication signals can easily be wiretapped by targets (potential eavesdroppers) or detected by wardens. Different from previous works that address a single security problem, we investigate a covert and secure beamforming design in the ISAC system to address this issue. Meanwhile, we also consider the hardware impairments (HWI) of transceiver, the angle and distance estimation errors of the eavesdroppers, and the imperfect warden channel. We formulate a secure beamforming optimization problem under sensing integrated sidelobe to mainlobe ratio (ISMR), communication rate, and covert communication constraints. To solve the non-convex optimization problem, we adopt semidefinite relaxation (SDR) and S-procedure techniques to transform it into a convex problem and solve it. The simulation results demonstrate the effectiveness of the designed scheme and reveal a trade-off between the communication and sensing performance of the system. © 2024 IEEE.

关键词： Convex optimization

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Few-Shot Aviation Materials Surface Defection Classification: From Non-Standardized Image Acquisitions 4

Few-Shot Aviation Materials Surface Defection Classification...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Wu, Zewei Peng, Chengbin Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350364798

Defect classification in the aviation industry plays a crucial role in manufacturing. However, in non-standardized captured scenes, the object to be inspected and the background are often complex and variable, e.g., lighting, angle, occlusion, etc., which can affect the core features of the image data. Therefore, achieving accurate classification using a limited number of defect samples from products photographed in non-standardized environments has emerged as a challenging yet promising research focus. To address this issue, this study proposes a learning approach that can be trained using standardized industrial defect datasets to detect defects in few-shot, non-standardized industrial defect production. Specifically, this approach initially utilizes the knowledge acquired from a standardized pre-training dataset to crop the data within the query set. Subsequently, an embedding extraction is employed to obtain feature embedding and classification results. Our practice demonstrates a performance improvement of roughly 5% on non-standard datasets compared to few-shot classification methods. © 2024 IEEE.

关键词： Zero-shot learning

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