T. C. Jermin Jeaunita, T. Ramesh, C. V. S. R. Manjushree, P. T. Shantala [references] [full-text] [DOI: 10.13164/re.2025.0381] [Download Citations]
A Decentralized and Efficient Crowdfunding Framework for Secure Transactions and User Engagement

Crowdfunding has become essential for financing entrepreneurial projects, innovative projects, and social initiatives. However, existing platforms face critical challenges, including a lack of transparency, low user engagement, data privacy concerns, and ineffective personalization of user experiences. To address these limitations, this study introduces a novel decentralized crowdfunding framework that integrates Federated Learning (FL), blockchain technology, and Q-learning to enhance security, transparency, and user engagement. The framework leverages FL to collaboratively train models across distributed datasets while ensuring privacy preservation by eliminating the need to share raw user data. Blockchain technology is utilized to ensure tamper-proof transaction records and automate trustless interactions through smart contracts, effectively preventing fraud while increasing transparency. Additionally, a Q-learning-based incentive mechanism is incorporated to predict and stimulate user engagement, ensuring dynamic long-term engagement. The experimental evaluation illustrates that the designed framework attains state-of-the-art performance with an accuracy rate of 99.39%, surpassing existing methodologies. The results demonstrate the effectiveness of the framework in providing a secure, decentralized, and highly personalized crowdfunding system, raising trust and engagement among stakeholders and resolving long-standing issues in crowdfunding platforms.

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Keywords: Bidirectional recurrent neural network, blockchain, crowdfunding, Federated Learning (FL), MetaMask wallet, Q-learning, smart contract

Rahman M., Wang, W., Wang J., Wang Y. [references] [full-text] [DOI: 10.13164/re.2025.0393] [Download Citations]
Fast Brain Tumor Segmentation with Model Optimization

Segmenting brain tumors is important for effective diagnosis and treatment planning. Conventional 3D segmentation models achieve high accuracy but are computationally intensive, often limiting real-time applicability. In this study, pseudo-3D convolutions, which consist of spatial and depthwise convolutions, are used in place of traditional 3D convolutions. Adaptive Dilated Multi-Fiber (DMF) units dynamically extract multi-scale features and parallel Multi-Fiber (MF) units combine them with weighted sum. Efficient Channel Attention (ECA) and Cross Attention improve feature selection and fusion in decoder and encoder. Structured pruning reduces superfluous parameters and Quantization Aware Training (QAT) increases the speed of inference with the model converted to INT8 precision. Combination of Dice Loss and Boundary Loss enhances the precision of tumor boundaries. The framework has been evaluated on the BraTS 2021 data validation set and achieved high Dice scores of Whole Tumor 91.85%, Tumor Core 88.52%, and Enhancing Tumor 85.55%, with Hausdorff95 values of 2.58 mm, 3.53 mm, and 3.65 mm. Our proposed model requires only 3.57M parameters and 21.26 GFLOPs, achieving an inference time of 0.016 seconds per 3D volume while maintaining precision alongside efficiency to clinical application.

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Keywords: Brain tumor segmentation, pseudo-3D convolutions, adaptive dilation, computational efficiency, inference time

Y. F. Shen, Q. Gao [references] [full-text] [DOI: 10.13164/re.2025.0407] [Download Citations]
DS-YOLO: A SAR Ship Detection Model for Dense Small Targets

Detecting dense small targets in Synthetic Aperture Radar (SAR) images has always been a challenge in ship target detection. To address this issue, this paper proposes a ship target detection model for SAR images, named DS-YOLO, which is based on the YOLO11 network architecture. The model introduces Space-to-Depth Convolution (SPDConv) module to enhance the detection capability of small targets. Additionally, a new module, Cross Stage Partial-Partial Pyramid Attention (CSP-PPA), is incorporated to improve the model's ability to extract features at multiple scales and suppress confusing backgrounds. The loss function is optimized using a bounding box loss based on Adaptive Weighted Normalized Wasserstein distance (AWNWD), enhancing the model's adaptability to images of varying quality. Finally, experiments were conducted on the standard datasets HRSID and SAR-Ships dataset to validate the robustness and reliability of the DS-YOLO model. The experimental results show that, compared to YOLO11n, DS-YOLO achieved an mAP0.5:0.95 of 68.6% on the SAR-Ships dataset and 69.9% on the HRSID, representing improvements of 1.6% and 0.8%, respectively. Additionally, on these small-target datasets, DS-YOLO achieved an mAP0.5:0.95 of 50.8% and 60.4%, representing improvements of 4.2% and 1.2%, respectively, demonstrating higher detection accuracy.

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Keywords: DS-YOLO, SAR image, ship detection, Space-to-Depth Convolution (SPDConv), Cross Stage Partial-Partial Pyramid Attention (CSP-PPA), Adaptive Weighted Normalized Wasserstein Distance (AWNWD)

D. Zhang, K. Zhu, S. Qi [references] [full-text] [DOI: 10.13164/re.2025.0422] [Download Citations]
A Novel High-Gain Circularly Polarized Filtenna Based on Coaxial Structure

A novel omnidirectional slot array filtenna with a circularly polarized (CP) radiation beam is presented in this paper. This filtenna utilizes the coaxial cylinder structure, and the antenna’s filtering function is primarily achieved through the direct synthesis method of a tubular bandpass filter. Four perpendicular slot pairs form the basic omnidirectional CP radiation element, which is cut into the sleeve and added to the outer conductor of the coaxial cylinder to implement the compact performance. To simplify the design, this work utilizes the high and low impedance conversion of the inner conductor, as well as isolation through dielectric materials. This filtenna achieves high gain by forming an array along the z-axis direction and placing the antenna elements reasonably for radiation in phase. A prototype was designed and fabricated to validate its practicality. The results indicate a fractional impedance bandwidth (S11 < -10 dB) of 5.8%, from 8.87 GHz to 9.39 GHz, and a 3 dB axial-ratio (AR) bandwidth of 6.1%, from 8.85 GHz to 9.40 GHz. The realized gain of the antenna is consistently higher than 5.29 dBi over the operating bandwidth, and its out-of-roundness is less than 1.5 dB in the radiation direction.

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Keywords: Circular polarized (CP) filtenna, filtering antenna, axial ratio (AR), omnidirectional antenna, tubular bandpass filter

Y. Choi, G. Kim, B. Kim, S. Kim [references] [full-text] [DOI: 10.13164/re.2025.0429] [Download Citations]
An Enhanced Noise Removal-based SAR Image Recognition Using DnCNN and Wavelet Transform

This paper presents an enhanced method for noise removal and target detection in Synthetic Aperture Radar (SAR) images using a Denoising Convolutional Neural Network (DnCNN) combined with wavelet trans¬form. Unlike conventional method, the proposed frame¬work focuses on remove the Speckle Noise through residu¬al learning and wavelet transform. The DnCNN architecture, consisting of 29 layers, efficiently removes noise while preserving high-frequency image features. The integration of wavelet transform further enhances noise removal and feature preservation. Experimental results demonstrate that the recognition rate of the proposed method improves by about 94% compared to original method under 10 dB Speckle Noise conditions. This method outperforms conventional algorithm in SAR image pro¬cessing, making it highly suitable for applications in noisy environments.

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13. DOSOVITSKIY, A., BEYER, L., KOLESNIKOV, A., et al. An image is worth 1616 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations (ICLR). Virtual (Online), 2021. DOI: 10.48550/arXiv.2010.11929

Keywords: Navy SAR, noise, Convolutional Neural Network (CNN), Denoising Convolutional Neural Network (DnCNN), wavelet transform

X. L. Kong, D. X. Zhao, N. Wang, D. Z. Xu [references] [full-text] [DOI: 10.13164/re.2025.0438] [Download Citations]
Performance Evaluation for Unknown Deterministic Target Signals Detection via Detection Information

The detection method, such as Newman-Pearson (NP) method, can provide an accurate prediction for the performance of target detection under all signal-to-noise ratio regions. However, the performance limits of radar signal detection have not been extensively studied yet. In this paper, we propose a novel detection method for unknown deterministic signals in radar system, which utilizes mutual information to characterize the uncertainty of the existence state of signal. The a posteriori probability density function of existence state of signal can be directly obtained via the Bayesian framework, ensuring that there is no loss of information regarding to the target's existence state during the mutual information computation process. Numerical simulations show that the proposed method exhibits superior detection performance compared to the NP detection method.

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Keywords: Energy detection, detection information, information theory, probability density function, unknown deterministic signals

M. V. Ta, K. K. Phuong, N. D. Trong, N. T. Linh, T. T. T. Huong, N. T. Hung, L. D. Manh [references] [full-text] [DOI: 10.13164/re.2025.0445] [Download Citations]
A Novel Design of a Low-loss and Low-cost Ku-Band Bandpass Filter for VSAT Applications

This paper proposes a novel method to design low-loss and low-cost Ku-band bandpass filters for VSAT applications based on substrate-integrated-waveguide technology. Narrow bandpass filters employed high-order resonant mode TE301 exhibited high selectivity. However, its bandwidth is not enough for VSAT applications. In this paper, we proposed a method to widen the bandwidth of narrow-band filters to meet the bandwidth requirement of VSAT applications. This approach maintains high selectivity while still achieves low insertion loss. The proposed filter was fabricated using a low-cost material. Measurement shows a good agreement with simulated results. Mid-band measured insertion loss and return loss were -1.8 dB and -19.4 dB, respectively. Such low losses were obtained owing to taking advantage of a high-quality factor of high-order mode TE301 of oversized rectangular cavities

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Keywords: High/low order mode coupling, substrate integrated waveguide, fractional bandwidth (FBW)

H. Jia, F. Wan, X. Cheng, V. Mordachev, E. Sinkevich, J. Rossignol, X. Chen, B. Ravelo [references] [full-text] [DOI: 10.13164/re.2025.0452] [Download Citations]
Characterization of Four-Layer Microwave Magnetic Probe Design for Integrated Circuit Emission Measurement

With the increase of microwave circuit and system integration design density, the test method to assess the electromagnetic compatibility (EMC) undesirable effect remains a challenging task. To tackle this issue for example with radiated emission analysis, a relevant EMC measurement notably for integrated circuits (IC) and printed circuit board (PCB) is necessary. A four-layer magnetic (H) near-field (NF) probe in miniature technology is designed, fabricated and tested. The H-NF probe works in the challenging frequency band up to 20 GHz. The proposed probe has the advantages of miniaturization, high sensitivity, high flatness, and high electric field suppression. The designed and fabricated H-NF probe characterization is validated with respect to IEC-61967 EMC standard. The device under test (DUT) IC radiation was tested and characterized. Experimental results have shown that the H-NF probe can be used for measuring IC EMC radiation emission.

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Keywords: Electromagnetic compatibility (EMC), near-field (NF) analysis, four-layer technology, magnetic NF microwave probe, design method, EMC characterization.

J. Wu, X. Wang, Y. Zhou, B. Chen, W. Li, L. Zhang [references] [full-text] [DOI: 10.13164/re.2025.0461] [Download Citations]
Intra-pulse Modulation Recognition of LPI Radar Signals Based on Improved MobileNet

Addressing the challenge of feature extraction for Low Probability of Intercept (LPI) radar signals under low signal-to-noise ratio conditions, this study introduces a new method for intra-pulse modulation recognition of LPI radar signals based on an enhanced MobileNet architecture. Initially, a Time-Frequency Image (TFI) preprocessing technique suitable for LPI radar signals is proposed, which significantly improves the recognition accuracy of subsequent networks for intra-pulse modulation of LPI radar signals. Subsequently, the MobileNet network is modified by integrating Hybrid Dilation Convolution (HDC) and Efficient Channel Attention (ECA) modules, resulting in the development of an improved MobileNet. This enhanced network expands the receptive field of feature maps and improves the network's ability to capture channel and positional information. Additionally, a label smoothing strategy is utilized to optimize the network training process, reducing overfitting and enhancing sample clustering performance. Simulation experiments indicate that this method not only yields a high recognition accuracy rate but also outperforms existing comparative networks with fewer parameters.

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2. LIU, Z., WANG, J., WU, T., et al. A method for LPI radar signals recognition based on complex convolutional neural network. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2023, vol. 37, no. 1, p. 1–13. DOI: 10.1002/jnm.3155
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4. CHEN, B., WANG, X., ZHU, D., et al. LPI radar signals modulation recognition in complex multipath environment based on improved ResNeSt. IEEE Transactions on Aerospace and Electronic Systems, 2024, vol. 60, no. 6, p. 8887–8900. DOI: 10.1109/TAES.2024.3436634
5. ZHANG, X., LIU, Y., YAN, X., et al. Frequency modulation recognition for radar signal based on Resnet-LSTM network. Artificial Intelligence Technology Research, 2024, vol. 2, no. 3, p. 100–102. DOI: 10.18686/aitr.v2i3.4426
6. QUAN, D., TANG, Z., WANG, X., et al. LPI radar signal recognition based on dual-channel CNN and feature fusion. Symmetry, 2022, vol. 14, no. 3, p. 1–13. DOI: 10.3390/sym14030570
7. WAN, C., SI, W., DENG, Z. Research on modulation recognition method of multi‐component radar signals based on deep convolution neural network. IET Radar, Sonar & Navigation, 2023, vol. 17, no. 9, p. 1313–1326. DOI: 10.1049/rsn2.12421
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Keywords: Low Probability of Intercept (LPI) radar signals, intra-pulse modulation recognition, Time-Frequency Analysis (TFA) technology, attention mechanism, lightweight convolutional neural network

Z. Zhang, Q. Liu, Y. Wang [references] [full-text] [DOI: 10.13164/re.2025.0471] [Download Citations]
LVRT Strategy Considering Reactive Power Support and Fluctuating Power Suppression for Photovoltaic Application

Addressing the insufficient negative sequence dynamic reactive current support and power doubling oscillations present in conventional low-voltage ride through control mechanisms for photovoltaic inverters, this study designs a q-axis command for both positive and negative sequence currents in accordance with recent regulatory requirements for photovoltaic grid integration technologies. The d-axis commands for positive and negative sequence currents are computed to effectively attenuate second harmonic fluctuations in active power output. The proposed approach establishes equilibrium between inverter current carrying capacity and oscillation mitigation, thereby concurrently enhancing dynamic reactive current support for both sequence components while diminishing power doubling fluctuations. The short-circuit current characteristics of photovoltaic installations are examined utilizing this enhanced low-voltage ride through control methodology. Comparative analysis between the suggested approach and current low-voltage ride through control techniques was conducted via simulation models, confirming both the efficiency of the proposed method and the accuracy of analytical expressions for short-circuit current.

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Keywords: Photovoltaic, low voltage ride through (LVRT), reactive power support, fluctuating power suppression, efficiency

B. B. Shabarinath, P. Muralidhar [references] [full-text] [DOI: 10.13164/re.2025.0482] [Download Citations]
A Streaming Dataflow Accelerator for Sparse SVM Kernel Computation in Hyperspectral Image Classification

Hyperspectral images (HSI) provide extensive spectral information but their high dimensionality and redundancy create substantial challenges for computation and storage while increasing energy demands. The proposed solution combines sparse dictionary learning with Field Programmable gate Array (FPGA)-accelerated Sparse matrix vector multiplication (SpMV) operations and Support Vector Machine (SVM) training to tackle these issues. Spatial patches and spectral blocks partition HSI to enable the extraction of compact discriminative sparse features through the use of a learned sub-dictionary. In contrast to deep learning frameworks which demand large training datasets and generate significant computational overhead, the SVM-based approach achieves efficient real-time training and adaptation. The FPGA accelerator executes intensive SpMV operations through dynamic load balancing. We tested our approach with four varied HSI datasets gathered from aerial and UAV systems as well as terrestrial platforms on the PYNQ-Z2 board. Our design reaches classification accuracies between 98.65% and 99.95% across datasets including Indian Pines,AVRIS-NG, Cubert-UAV, Cubert-Terrestrial with per-pixel classification times below 7 us and inference times up to 36x faster than optimized software baselines which remain under typical sensor acquisition times. The strategy requires less than 0.24 W of on-chip power at maximum load which makes it ideal for deployment on satellites or UAVs. The proposed method outperforms existing FPGA-based SVM architectures in classification accuracy and throughput while enabling on-device incremental learning which makes it ideal for analyzing hyperspectral images in real-time.

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Keywords: Hyperspectral image classification, support vector machine, sparse dictionary learning, sparse matrix-vector multiplication, load balancing

Q. Fang, Y. Zhao, J. Wang, L. Zhang [references] [full-text] [DOI: 10.13164/re.2025.0494] [Download Citations]
Self-Supervised Learning Driven Cross-Domain Feature Fusion Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification faces significant challenges due to the high cost of acquiring labeled samples. To mitigate this, we propose SSCF-Net, a novel self-supervised learning driven cross-domain feature fusion Network. SSCF-Net uniquely leverages readily available labeled natural images (source domain) to aid HSI (target domain) classification by transfer learning. Specifically, we employ rotation-based self-supervision in the source domain to learn transferable features, which are then transferred to the HSI domain. Within SSCF-Net, we effectively fuse local and global features: local features are extracted by a jointly trained module combining VGG and two-dimensional long short-term memory networks (TD-LSTM) networks, while global features capturing long-range dependencies are learned via a Transformer model. Crucially, in the HSI domain, we further employ contrastive learning as a self-supervised strategy to maximally utilize the limited labeled data. Extensive experiments on three benchmark HSI datasets (Salinas, Indian Pines, WHU-Hi-LongKou) demonstrate that SSCF-Net significantly outperforms existing methods, validating its effectiveness in addressing the label scarcity problem. The code is available at https://github.com/6pangbo/SSCF-Net.

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Keywords: Hyperspectral image classification, self-supervised learning, transfer learning, feature fusion

R. Olivera, J. Flores, R. Olivera, J. Perez, J. Munoz [references] [full-text] [DOI: 10.13164/re.2025.0509] [Download Citations]
Heuristic Approach to Indoor Localization Using LoRa RSSI Measurements

This research presents a heuristic approach for indoor localization using standard LoRa modules operating at 915MHz. To overcome the challenges presented by signal attenuation, multipath propagation, and environmental variability. The proposed method combines RSSI based distance estimation with a path loss exponent tuned empirically for different environments. A trilateration algorithm based on OLS is employed to estimate target positions, and performance is enhanced using filtering techniques such as Median Filter(MF) and Moving Average Filter (MAF). Additionally, two receiver geometries were analyzed to assess the robustness of the proposed method under different geometric configurations. To complement the OLS estimator, a Weighted Least Squares (WLS) method was also implemented usinga Gauss–Newton optimization approach. While WLS shows promising results, further refinement of the covariance matrix Q is identified as a direction for future work. These findings underscore the potential of the approach as a low cost, scalable solution for precise indoor localization in complex environments. Experimental evaluations conducted in various laboratory environments demonstrated that the optimized parameters yield a substantial reduction in positioning error. Performance was quantified using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) metrics, with MSE values as low as 0.2491 m in some settings, across all scenarios without filters, and achieving 0.07 m, with appropriate window size for MF and MAF. A brief review of results shows that an MF and MAF with window size

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Keywords: Indoor Location, LoRa, Heuristic Approach, Median Filter

Y. V. Pershin, D. C. Nguyen [references] [full-text] [DOI: 10.13164/re.2025.0526] [Download Citations]
Accurate Modeling of Continuous-time SAT Solvers in SPICE

Recently, there has been an increasing interest in employing dynamical systems as solvers of NP-complete problems. In this paper, we present accurate implementations of two continuous-time dynamical solvers, known in the literature as analog SAT and digital memcomputing, using advanced numerical integration algorithms of SPICE circuit simulators. For this purpose, we have developed Python scripts that convert Boolean satisfiability (SAT) problems into electronic circuits representing the analog SAT and digital memcomputing dynamical systems. Our Python scripts process conjunctive normal form (CNF) files and create netlists that can be directly imported into LTspice. We explore the SPICE implementations of analog SAT and digital memcomputing solvers by applying these to a selected set of problems and present some interesting and potentially useful findings related to digital memcomputing and analog SAT. In this work, we also introduce networks of continuous-time solvers with potential applications extending beyond the solution of Boolean satisfiability problems.

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Keywords: SPICE, nonlinear dynamical systems, computing technology, boolean satisfiability problem, 3-SAT

B. Choudhury, A. Nag, D. Rabha, S. Nandi [references] [full-text] [DOI: 10.13164/re.2025.0541] [Download Citations]
Lightweight Multi Party Authorisation for IoT Device Access Using Bilinear Pairing and Shamir's Secret Sharing

With the advancement of new hardware and software technologies, the Internet of Things (IoT) has become ubiquitous in our day-to-day life. Along with many diversified applications, IoT has made inroads into several sensitive areas like Healthcare, Industries (IIoT), Smart Cities, Realtime Systems and so on. With the exploding application of IoT, there is an exponential increase in the requirement for security and keeping in mind the constrained nature of IoT devices and networks, customized lightweight protocols and measures have been proposed in the literature. Multi-party authorisation is one of the key aspects of IoT security. Access to sensitive IoT devices should be allowed only after authorisation from trusted entities. In this work, we have proposed a novel Lightweight Multi Party Authorisation for IoT Device Access with key establishment using Bilinear Pairing and multi party authorisation through Shamir's Secret Sharing. All communications are protected by lightweight XOR-based encryption with pairwise session keys. Further, threshold based Shamir's Secret Sharing facilitates the provision of dynamic authorisation policy set by the Admin according to application requirement. A prototype is developed using Raspberry Pi3, DHT11 sensor and an Android Application and tested for satisfactory performance. The scheme is formally verified on AVISPA and an informal security analysis is performed to assess its resistance to various attacks. A feature based comparison of the proposed scheme with other state of the are works established the unique advantages of the system. The proposed scheme has potential applications including, but not limited to, IoMT, IIoT and Smarthome.

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Keywords: Bilinear pairing, Shamir's Secret Sharing, Internet of Things, multi party authorisation, lightweight, IoT security

S. C. Lam, N. H. Nguyen, T. M. Hoang, K. Sandrasegaran [references] [full-text] [DOI: 10.13164/re.2025.0554] [Download Citations]
Power Control for Cell-Edge User in NOMA Ultra Dense Networks

Adjusting user transmission power is an effective approach to managing Inter-Cell Interference (ICI) in Ultra-Dense Networks (UDNs). Thus, users can proactively adjust their transmission power to minimize total power consumption while maintaining the required quality-of-service. Most recent research on power control mechanisms focuses on designing policies that increase the transmission power for all active users, including both near users with good received signal qualities and far users with poor ones. However, since near users-referred to as Cell-Center Users (CCUs)-can already achieve the desired service quality, this paper applies the power control mechanism to far users, known as Cell-Edge Users (CEUs). The uplink coverage probabilities of near and far users are derived under the stretched path loss model and Rayleigh fading for systems with and without the power-domain Non-Orthogonal Multiple Access (NOMA) technique. The analysis shows that the proposed mechanism can significantly reduce transmission power by up to 25% in conventional systems and up to 36.6% in NOMA systems. Moreover, the NOMA system model with the proposed power control mechanism can also improve the ergodic capacity by up to 72.48%.

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Keywords: Power control, 5G, B5G, ultra dense networks, coverage probability, non-orthogonal multiple access

D. Ju, R. Zhang, X. Wang, Z. Liu, B. Huang, H. Yu [references] [full-text] [DOI: 10.13164/re.2025.0564] [Download Citations]
Ball Mill Grinding Condition Classification Method Based on Triaxial Vibration Spectrograms and Deep Attention Networks

Accurate detection of mill conditions during the cement grinding process directly impacts the quality of particle size distribution and energy consumption per ton of cement. This paper proposes a mill condition classification method based on three-axis wireless vibration sensing and deep feature learning to address issues such as distortion of mill condition characterization caused by power grid disturbances in traditional electrical power methods and sound transmission attenuation in mill sound methods. First, three-axis wireless vibration sensors were installed on the mill shell to collect three-dimensional vibration signals. After filtering and outlier removal, the Fast Fourier Transform (FFT) was applied to generate frequency-domain energy distribution images, creating a vibration spectrum dataset with physical interpretability. Next, a deep dilated separable convolution and multi-head attention fusion network model is proposed. In this model, dilated convolution captures multi-scale frequency domain features using adjustable dilation rates, and the multi-head attention mechanism dynamically adjusts the weight distribution of key frequency bands, enabling adaptive extraction of global frequency domain correlations and local resonance features. Experimental results show that the use of three-dimensional vibration signals to characterize mill conditions improves accuracy by 10% compared to one-dimensional signals. Classification accuracy increased by 6.7% compared to traditional convolutional neural networks, and by 7.6% and 5.5% compared to linear models and machine learning methods, respectively.

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Keywords: Cement grinding, mill grinding conditions, vibration signal, frequency domain features