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June 2024, Volume 33, Number 2 [DOI: 10.13164/re.2024-2]

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Y. Xiong, M. X. Luo [references] [full-text] [DOI: 10.13164/re.2024.0223] [Download Citations]
Searchable Encryption Scheme for Large Data Sets in Cloud Storage Environment

Cloud storage has become essential in managing and retrieving extensive volumes of data, providing economical alternatives and adaptability for effective storage environment. However, in light of the rapid expansion of comprehensive datasets in cloud storage, the preservation of security has emerged as a matter of utmost importance for large data sets. Encryption has become a crucial mechanism for protecting confidential large data sets from unauthorized individuals. Encryption is necessary for safeguarding sensitive data by transforming it into indecipherable code so prevent unauthorized entry, and the encryption and decryption process is done at the end-user and cloud server. In the present situation, searchable symmetric encryption assumes a pivotal function by facilitating safe data retrieval while concurrently upholding the principle of secrecy. This research presents the Searchable Encryption Scheme in Cloud Storage Environment (SES-CSE), which offers a resilient solution for tackling the obstacles related to data security and retrieval efficiency for large data sets. The SES-CSE framework effectively incorporates encryption techniques inside a robust search engine, establishing a reliable framework for large data sets protection with Okapi BM25. The approach exhibits significant performance benefits, as shown by an encryption time of 14.85 ms, decryption time of 10.06 ms, memory consumption of 77.87 MB, and search times of 13.5 ms. The SES-CSE model demonstrates remarkable retrieval accuracies of 98.41%, 98.57%, and 97.51% throughout the training, testing, and validation phases. The results underscore the usefulness and security of SES-CSE as a solution for cloud storage, improving both the secrecy of data and the efficiency of retrieval in large-scale settings.

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Keywords: Cloud computing, searchable encryption, data sets, security

R. H. Xiang, S. S. Li, J. L. Pan [references] [full-text] [DOI: 10.13164/re.2024.0236] [Download Citations]
A Novel IoT Intrusion Detection Model Using 2dCNN-BiLSTM

With the continuous advancement of Internet of Things (IoT) intelligence, IoT security issues have become more and more prominent in recent years. The research on IoT security has become a hot spot. A lightweight IoT intrusion detection model fusing a convolutional neural network, bidirectional long short-term memory network is proposed. It aims to improve processed data security and attack detection accuracy. First, sampling is performed by a hybrid sampling algorithm fusing SMOTE and ENN. Its aim is to minimize the impact of imbalanced-data and ensure data quantity in the process. Then, the data features are extracted by 2-dimensional convolutional neural network (2dCNN), and the effect of useless information is reduced by mean pooling and maximum pooling, so it can be adapted to the demanding resource environment of the IoT. On this basis, long-range dependent temporal features are extracted using bidirectional long short-term memory (BiLSTM), which aims to fully extract data features to improve detection accuracy in the limited resource environment. Finally, the algorithm is validated on the UNSW_NB15 dataset, and the results of the experiments reaches 93.5% at Accuracy, 86.4% at Precision, 85.3% at Recall and 85.8% at F1-Score. According to the results, the proposed algorithm can generate higher-quality samples, achieve higher detection rate with faster inference time and spend lower memory costs.

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Keywords: Internet of Things (IoT), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), intrusion detection

Z. C. Wang, Z. B. Wang, M. Gao, H. Liu, S. Fang [references] [full-text] [DOI: 10.13164/re.2024.0246] [Download Citations]
Balanced Linear-Phase Bandpass Filter Equalized with Negative Group Delay Circuit

A novel balanced linear-phase bandpass filter is proposed to achieve differential-mode linear-phase filtering and common-mode suppression characteristics. The balanced linear-phase bandpass filter consists of a proposed compact balanced bandpass filter and negative group delay circuits, in which the circuits are loaded on the ports of the filter as branches. The linear-phase performance is achieved through negative compensation of group delay fluctuations using negative group delay circuit equalization. In order to verify the design method, a 3-order balanced linear-phase bandpass filter is designed, simulated, manufactured, and measured. The results show that the group delay fluctuation of the balanced bandpass filter has been reduced by 89.6 % from 1.110 to 0.115 ns. The minimum common-mode suppression within the passband is 41.4 dB. The proposed balanced bandpass filter has an excellent differential-mode linear-phase transmission and common-mode suppression performances.

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Keywords: Balanced bandpass filter, linear-phase, negative group delay, common-mode suppression

Y. Wang, H. Tian, Y. Ji, M. Liu [references] [full-text] [DOI: 10.13164/re.2024.0253] [Download Citations]
Full-automatic Segmentation Algorithm of Brain Tumor Based on RFE-UNet and Hybrid Focal Loss Function

Semantic segmentation of glioma and its subregions plays a critical role in the entirely clinical workflow of brain cancer diagnosis, monitoring, and treatment planning. Recently, automatic tumor segmentation has attracted a lot of attention, especially supervised learning methods based on neural networks, and the popular “U-shaped” network architecture has achieved state-of-the-art performance in many fields of medical image segmentation. Despite the success of these models, the commonly used small convolution kernel can only extract local features, and more global contextual features cannot be learned, resulting in the disappointed performance of modeling long-range information. At the same time, due to the difficulty of obtaining medical image data, and the imbalance of tumor data in which tumor usually occupies a relatively small volume compared with the background, the adverse influence on the training of the model occurs. In this paper, a novel segmentation framework including TensorMixup data augmentation, improved Receptive Field Expansion UNet (RFE-UNet) and hybrid loss function is designed. Specifically, the TensorMixup algorithm in the data preprocessing phase is used to provide more high-quality training data. In the training phase, both a RFE-UNet network and a hybrid loss function are proposed respectively. RFE-UNet network adds Receptive field expansion module based on Dilated convolution in the first three stages of skip connection, which is used to learn more local and global features. In addition, hybrid loss function is mainly composed of focal loss and focal Tversky loss,focal loss increasing the weight of fewer samples and focal Tversky loss focusing on learning the characteristics of samples with incorrect predictions,which is adopted to alleviate data imbalance. The experimental results on the BraTs2019 dataset show that the average Dice value of the proposed algorithm in the intact tumor, tumor core, and enhanced tumor region can reach 91.55%, 89.23%, and 84.16% respectively, which proves the feasibility and effectiveness of using the proposed architecture.

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Keywords: Segmentation, brain tumor, magnetic resonance imaging, dilated convolutions, three-dimensional CNN

S.Yesil, A. O. Yilmaz [references] [full-text] [DOI: 10.13164/re.2024.0265] [Download Citations]
Identification of the Linear Systems of the Wiener Hammerstein RF Power Amplifier Model Using DFT Analysis

This paper presents a novel method for identification of the sub-system parameters of a Wiener-Hammerstein Nonlinear (WHNL) system that is used for modeling RF Power Amplifier characteristics. The proposed method first isolates the overall linear system from the memoryless nonlinearity by exploiting the Bussgang decomposition method. Then, Discrete Fourier Transform (DFT) analysis is used for the estimation of the inner linear system. Finally, the outer linear system parameters are updated based on the inner system estimation. The estimated systems are then used to model the target system for an In-Band-Full-Duplex (IBFD) scenario. Performance of Self-Interferene Cancellation (SIC) has been evaluated under the existence of Signal-of-Interest (SoI). Error Vector Magnitude (EVM) metric of the SoI is used to compare with a Half-Duplex (HD) receiver under various inner linear system parameters. SIC performance has been examined with respect to the changing power levels of the SoI and self-interference signal for various delay and gain values of a practical two-tap inner linear system. The benefit of modeling the inner linear system has been revealed by comparing the SIC performance with Hammerstein nonlinear model. The performance has also been compared to well known black box models such as Generalized Memory Polynomial (GMP) and Artificial Neural Networks (ANN).

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Keywords: Wiener-Hammerstein nonlinear system, digital self-interference cancellation, in-band full-duplex communications

A. K. Chaudhary, M. Manohar [references] [full-text] [DOI: 10.13164/re.2024.0274] [Download Citations]
A Quadruple Band-Notched SWB MIMO Antenna with Enhanced Isolation Using Wiggly Line

A novel quadruple band-notched spatial diversity/MIMO antenna for super wideband (SWB) application is investigated. The proposed antenna comprises two identical tapered semicircular radiators with two microstrip feedlines and a common slotted ground plane (CSGP), contributing a wide impedance bandwidth from 1.88-30 GHz. Further, a wiggly-line-decoupling-structure (WLDS) is introduced among the radiating ports to maximize the average isolation, more than 24 dB. The first band-notched functionality at 2.4 GHz is produced by etching a meandering slot on the CSGP, while the remaining three notch bands at 3.5, 5.5, and 7.5 GHz are obtained by implanting open-ended-semicircular (OES), complementary-split-ring-resonator (CSRR), and elliptical-split-ring-resonator (ESRR) slots in each radiating patch. The designed and fabricated results for the two and four elements are analyzed, which exhibit wideband characteristics, stable radiation pattern, higher efficiency (above 85%), and reasonably high peak gain within the working frequency, excluding the quadruple notched bands. Moreover, other essential parameters such as ECC, DG, CCL, and TARC have also been analyzed, showing the antenna's usefulness for radar imaging, cognitive radio, military, and long-range RF applications.

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Keywords: Diversity antenna, quadruple band-notch, SWB, tapered semicircular patch, WLDS

Y. Guo, D. Zhou, Y. Ding [references] [full-text] [DOI: 10.13164/re.2024.0282] [Download Citations]
Interrupted Sampling Repeater Jamming Suppression with Pulse Doppler Radar Using Linear Interpulse Frequency Coding

Interrupted sampling repeater jamming (ISRJ) is an advanced coherent jamming, and the suppression of this jamming has become a critical problem for modern radar electronic countermeasure. In this paper, we propose a countermeasure based on linear interpulse frequency-coding linear frequency modulation (LIFC-LFM) signal. The LIFC refers to the linear encoding of the frequency of each pulse transmitted by the radar system, which can change the distribution of the false targets formed by ISRJ in the range-Doppler (RD) spectrum. In this context, we design the frequency coding value to effectively separate the true and false targets in the RD spectrum. Furthermore, we propose a fast-time phase compensation method to separate the true and false targets in the Doppler dimension. Finally, ISRJ can be suppressed by oblique projection processing. Simulation examples demonstrate that the proposed method has an excellent and robust ISRJ suppression effect for direct forwarding ISRJ, repeated forwarding ISRJ, and frequency shifting ISRJ. Meanwhile, the signal-to-noise ratio loss caused by the jamming suppression is small.

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Keywords: Pulse-Doppler radar, deception jamming suppression, interrupted sampling repeater jamming, interpulse frequency coding, oblique projection processing, ambiguity function

S. B. Harisha, E. Mallikarjun, M. Amit [references] [full-text] [DOI: 10.13164/re.2024.0299] [Download Citations]
Reconstruction of Mixed Boundary Objects and Classification Using Deep Learning and Linear Sampling Method

The linear sampling method is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. Nevertheless, there are many challenges that this method depends on the frequency of operation and it is unable to produce satisfactory results for objects with complex shapes. This paper proposes a hybrid model, which combines conventional linear sampling method and deep learning for the reconstruction of mixed boundary objects. In this approach, the initial approximation of mixed boundary objects derived from linear sampling method serves as the training data for the U-Net based convolutional neural network. The network then learns to correlate this approximation with the corresponding ground truth profiles. Along with the reconstruction of mixed boundary objects, they are also classified as dielectric or conductor, and count of each object type are measured. Furthermore, the low-frequency and high-frequency characteristics of the linear sampling method are analyzed, and its limitations are overcome by combining it with a deep learning approach. The effectiveness of the proposed model is validated using several examples of synthetic and experimental data. The results demonstrate that the proposed method outperforms the conventional Linear sampling method in terms of accuracy.

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Keywords: Deep learning, linear sampling method, mixed boundary objects, microwave imaging

B.-H. Luu, S.-C. Lam, N.-H. Nguyen, T.-M. Hoang [references] [full-text] [DOI: 10.13164/re.2024.0312] [Download Citations]
Performance of the User in the TDD NOMA Cellular Networks Enabling FFR

Improving the user performance and spectrum efficiency are urgent problems for 5G and beyond 5G (B5G) cellular networks to support high Quality of Services such as enhanced mobile broadband, ultra-reliable, and low latency communications. Together with Fractional Frequency Reuse (FFR), Time Division Duplex (TDD) and Non-Orthogonal Multi-Access (NOMA) are promising the potential solutions for these problems. While the related researches focus on the single or combination two of three techniques, this paper proposes a system that combination of all three techniques to improve the data rate on the uplink sub-band. Specifically, each couple of Cell-Center User (CCU) and Cell-Edge User (CEU) in a given cell, that is defined by the FFR technique, is allowed to transmit on the same sub-band by the meaning of power-domain NOMA technique. In addition, the TDD technique allow the sharing sub-band between the user and Base Station (BS). The analytical results in Nakagami-m fading and regular path loss model shows that achievable total data rate on the shared sub-band in the proposed system model is 18.2% and 125% higher than that in the regular one with TDD and NOMA, respectively. The data rate improvement of the proposed system model proves the feasibility of co-exits of these techniques in the B5G systems.

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Keywords: Fractional frequency reuse, time division technique, non-orthogonal multiplexing access, Poisson point process

C. H. Yao, Y. Li, Y. F. Chen, K. X. Cheng [references] [full-text] [DOI: 10.13164/re.2024.0322] [Download Citations]
An Intelligent Denoising Method for Jamming Pattern Recognition under Noisy Conditions

Accurate identification of jamming patterns is a crucial decision-making basis for anti-jamming in wireless communication systems. Current works still face challenges in fully considering the substantial influence of environmental noise on identification performance. To address the issue, this paper proposes an automatic threshold denoising-based deep learning model. The proposed method aims to mitigate the impact of noise on recognition performance within the feature space. Considering the challenges posed by non-linear transformations in deep denoising, a shallow denoising approach based on deep learning is proposed. By constructing a dataset of 12 jamming patterns under noisy conditions, the proposed method exhibits excellent recognition performance and maintains a low computational cost.

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Keywords: Jamming pattern recognition, automatic threshold denoising, shallow layer denoising, convolutional neural network

X. Du, G. H. Wei, D. L. Wu, X. D. Pan [references] [full-text] [DOI: 10.13164/re.2024.0329] [Download Citations]
Test Evaluation Method for Second-order Intermodulation False Alarm Interference

Aiming at the quantitative evaluation requirements of radar second-order intermodulation false alarm (SIFA) effect, a radar SIFA effect model is established from the field-circuit coupling mechanism, and the parameter test method of the model is given. Taking a certain type of radar as the test object, the SIFA effect test is carried out by using the method of electromagnetic injection equivalent substitution irradiation. The results show that the tested radar will produce a SIFA signal higher than the selected sensitive level when the frequency difference of dual-frequency electromagnetic interference (EMI) is within 3 MHz and the frequency offset is within ± 200 MHz. Using the model parameters of the SIFA interference effect measured in the experiment, it is assumed that they do not change with the interference field strength. Combined with the SIFA interference field strength of the tested radar and the single frequency blocking critical interference field strength, the effect model can evaluate the degree of radar SIFA interference.

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Keywords: Electromagnetic interference (EMI), second-order intermodulation false alarm (SIFA), radar, parameter test method of effect model