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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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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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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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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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Optimizing Diabetes Prediction using Machine Learning with Data Deviation 6

Optimizing Diabetes Prediction using Machine Learning with D...

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

作者： Triandi, Budi Tanti, Lili Puspasari, Ratih Elhiyas, Mas Ayoe Marwan Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia

ISBN: (纸本)9798331539603

Diabetes mellitus is a chronic disease that affects global health with an increasing prevalence rate. Early prediction can provide opportunities for earlier intervention and better management of health conditions. This study explores various classification techniques to analyze data sets that include potential risk factors such as family history, body mass index (BMI), blood pressure, and blood sugar levels. This approach uses several machine learning algorithms such as Decision Trees, Gradient Boosting, support vector machines (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Random Forest (RF) to identify patterns related to the development of diabetes. In this study, we have carried out a comparison of several machine learning (ML) algorithms, aiming to classify the "No Diabetes" or "Diabetes" class by considering several parameters such as, Age, gender, polyuria, polydipsia, sudden_weight_reduction, weakness, polyphagia, genital_thrush., visual_blurry, itching, irritability, delayed_healing, partial_paresis, muscle_stiffness, alopecia, obesity and classes collected in one dataset. The ML model is optimized by normalizing standard deviation data and 20-fold cross validation to get the best model and to determine the log loss or accuracy matrix and its performance. The results show that the model built using Gradient Boosting and SVM gave the best accuracy results on the entire test set, the Decision Trees, Random Forest and k-NN models also gave good results, while Naïve Bayes proved to be the least good in terms of accuracy results. © 2024 IEEE.

关键词： Adversarial machine learning

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Closed form Solution of Moving Source Localization Using TDOA and FDOA Measurements Under Sensor Location Errors 16

Closed form Solution of Moving Source Localization Using TDO...

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

作者： Sheng, Menglei Li, Youming Zhou, Yapeng Cai, Wanyuan Qi, Qinke Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

The moving source localization is very sensitive to the knowledge of sensor locations. In this paper, we propose a two-stage closed-form method for locating a moving source by using Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurements in the presence of random errors in sensor locations. In the first stage, we establish a series of pseudo-linear equations by introducing the auxiliary variables and obtain an initial location estimation based on the bias reduced criteria and the Lagrange multiplier theory. In the second stage,we propose a modified weighted least squares method to obtain the final estimation by exploring the relationship between the auxiliary variables and the moving source location and velocity. The proposed method is in closed form and therefore computationally efficient as well as free from the divergence problem unlike iterative techniques. Simulations show the better performance of the proposed method. © 2024 IEEE.

关键词： Iterative methods

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Overview of Emerging Biological Computational Framework for Olfactory Studies Utilizing Contextual Technobiomedical engineering 2

Overview of Emerging Biological Computational Framework for ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Chaudhri, Shiv Nath Faculty of Engineering & Technology Department of Computer Science and Design Wardha India

ISBN: (纸本)9798331528713

Organoid intelligence (OI), the next paradigm of intelligence, draws inspiration from the biological learning of organs. On the other hand, artificial intelligence (AI) draws its inspiration only from the cognitive processes of the brain. While taking ethical and technological limitations into account, biological learning helps to conceptualize, accept, and apply them. Just as AI helped to make supercomputers possible, OI may help create biocomputers. Even though AI has increased the capabilities of technology to unimaginable heights, it still appears immature and is steadily moving toward cognitive computing to push the envelope even farther. In contrast to AI, OI is a new multidisciplinary computer area that attempts to be more approachable. OI has the ability to produce novel biocomputers, such as organoids, that use parts derived from live organisms to perform computational tasks. Organoids, such as human nasal organoids, are microscopic tissue cultures made from stem cells that mimic some of the functions of real organs. Although they have been used for many different purposes, including drug discovery and illness modeling, scientists are only now beginning to explore their potential for biological information processing. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Biological Information Processing Biological Learning Olfactory Organoids Organoid Intelligence (OI)

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An Efficient Closed-Form Method For Underwater Source Localization With Isogradient Sound Speed Profile 16

An Efficient Closed-Form Method For Underwater Source Locali...

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

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

ISBN: (纸本)9798331506322

In this paper, we address the underwater localization problem with isogradient sound speed profile (SSP) using time-of-arrival (TOA) measurements. The sound speed is related to the unknown source depth in the isogradient SSP model, which is highly nonlinear. Owing to this nature of the isogradient SSP model, the maximum likelihood (ML) problem for estimating the source position is non-convex such that local convergence may occur without sufficiently good initialization. To address this difficulty, we first approximate the isogradient SSP model, and then propose a closed-form two-step weighted least squares (TSWLS) method for estimating the source position based on the approximate model. The solution method is accurate since the approximate model takes the source depth into consideration, and can be used to solve the ML problem as an initialization. Moreover, the proposed TSWLS solution is simple and computationally attractive since it is in closed form. Simulation results show that the proposed TSWLS solution is sufficiently accurate and can be used as the initial estimate for solving the non-convex ML problem, such that the mean square error (MSE) of the ML solution can reach the Cramér-Rao lower bound (CRLB). Index Terms-Underwater localization, time-of-arrival, isogradient sound speed profile, two-step weighted least squares. © 2024 IEEE.

关键词： Mean square error

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UNRAVELING THE EFFECTIVENESS OF THE K-NN ALGORITHM FOR IMBALANCED DATA IN RESEARCH TOPIC RECOMMENDATIONS

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2024年第1期59卷 271-282页

作者： Muludi, Kurnia Aisyah, Nonik Nama, Gigih Forda Lumbanraja, Favorisen R. Informatics & Business Institute Darmajaya Bandar Lampung35142 Indonesia Computer Science Department Faculty of Science The University of Lampung Bandar Lampung Indonesia Informatics Engineering Department Faculty of Science The University of Lampung Bandar Lampung Indonesia

This study addresses the challenge of selecting research topics for undergraduate students, focusing on computer science, by evaluating a recommendation model based on the k-Nearest Neighbor algorithm (kNN). The objective is to enhance the accuracy of research topic recommendations in the presence of imbalanced data. The methodology involves data cleaning, transformation, and correlation-based feature selection, with a particular focus on addressing missing values and optimizing the feature set. The Synthetic Minority Over-Sampling Technique (SMOTE) is employed to balance the dataset. The model development includes a comprehensive analysis of various k values, leading to the identification of k=3 with Manhattan distance as the optimal configuration, achieving an accuracy of 82%. The experiment explores different training data proportions, revealing that a 90:10 ratio yields the highest accuracy. This study incorporates a Grid Search technique for hyperparameter tuning, highlighting the importance of selecting appropriate distance metrics and nearest neighbor values. The system implementation is presented with user and administrator interfaces. Findings indicate varying model performance across research categories, emphasizing the need for category-specific evaluation metrics. The study concludes with a discussion on the significance of the results, which contribute to the improvement of research topic recommendation systems for students through the integration of advanced machine learning techniques. © 2024 by the authors.

关键词： Recommender systems

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