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An Ultra-Wideband Quad-Mode SIW Cavity-Backed Slot Antenna for NextG Communication and Sensing Arrays

IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3605-3615页

作者： Muppala, Aditya Varma Sarabandi, Kamal University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States

This paper presents a novel Substrate Integrated Waveguide (SIW) Cavity-Backed Slot Antenna (CBSA) to enable the next generation of communication and sensing infrastructure. The antenna achieves a bandwidth of 40%, with a low profile (c), high front to back ratio, good polarization purity, high isolation, and scalability to large active arrays such as phased arrays and Reconfigurable Intelligent Surfaces (RIS). To achieve wideband operation, four resonant characteristic modes are excited in a compact T-shaped cavity with a rectangular slot aperture. The interaction of these four modes is shown to create fictitious shorts along the length of the slot that result in multiple resonances. Characteristic Mode Analysis (CMA) and optimization are used to place 4 such fictitious short modes at the right frequencies in order to cover a fractional bandwidth of 40%, which is shown to be close to the theoretical bandwidth limit of a single slot with broadside radiation. The antenna is integrated into a 2×1 back-to-back array with a novel ultra-wideband out-of-phase power divider to achieve a symmetric beam pattern. This unit cell is used to demonstrate a high isolation, full-duplex, broadband antenna system. Next, the antenna is integrated into a 1×6 electronically scanned array to demonstrate 1-D beam-steering over the entire band. Finally, 2-D scalability is investigated by prototyping a 2×2 triangular lattice array. All the antennas presented are fabricated in a standard 4-layer printed circuit board to cover the 20 - 30 GHz band. © 1963-2012 IEEE.

关键词： Slot antennas

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A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 657-675页

作者： Dhawan, Sanjeev Singh, Kulvinder Batra, Amit Choi, Anthony Choi, Ethan Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Electrical and Computer Engineering Mercer University MaconGA United States

The increasing number of electronic transactions on the Internet has given rise to the design of recommendation systems. The main objective of these systems is to give recommendations to the users about the items (i.e., movies, music, books, etc.) they are likely to purchase in near future. These recommendations are on the foundation of the response provided by the other individuals about these products in the terms of ratings, which are in the form of a five-star rating system. Plenty of algorithms have been proposed in the past for building recommender systems. The most popular of these algorithms comprise content-centered and collaborative filtering recommendation methodologies. Recently, matrix factorization has appeared as a technique in recommender systems. Several algorithms centered on matrix factorization have been suggested in the recent past. In this paper, an improved alternating least squares algorithm has been proposed for building more efficient and accurate recommender systems which improves the performance of matrix factorization. The performance of the model has been evaluated using metrics like root-mean-square-error and execution time. However, in general, the time complexity of both algorithms is O(n^3), where n is the number of users or items. This is because both IALS and ALS involve iteratively solving a system of linear equations, and the time complexity of solving a system of linear equations is O(n^3). However, IALS can achieve significant performance improvements over ALS, particularly for large datasets, due to its incorporation of stochastic gradient descent and parallelization techniques. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Medicare fraud detection: a comparative study on the effectiveness of one-class and binary classification models

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International Journal of computers and Applications 2025年第3期47卷 323-328页

作者： Leevy, Joffrey L. Hancock, John Khoshgoftaar, Taghi M. Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL United States

Research into Medicare fraud detection that utilizes machine learning methodologies is of great national interest due to the significant fiscal ramifications of this type of fraud. Our big data analysis pertains to three Medicare datasets: Part B, Part D, and DMEPOS. In this paper, One-Class Classification (OCC) and binary classification approaches are evaluated with six different classifiers. We note that when a practitioner is faced with a high cost or unreasonable wait associated with acquiring a second label for binary classification, OCC may intuitively seem like the better alternative. The evaluative metrics used in our study are Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision-Recall Curve (AUPRC). As a result of our investigation, binary classification was found to be more effective than OCC in detecting Medicare fraud. Additionally, we determined that gradient-boosting algorithms, specifically CatBoost and XGBoost, yield the most robust performance. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Contrastive Learning

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Optimized Distribution Systems Reconfiguration Considering Protection Devices Constraints

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IEEE Transactions on Power Delivery 2025年第3期40卷 1267-1278页

作者： Kahnamouei, Ali Shakeri Lotfifard, Saeed Rostami, Mohammadali Washington State University School of Electrical Engineering and Computer Science PullmanWA99164 United States

This paper presents a method for the optimized reconfiguration of radial distribution systems that explicitly considers the protection systems constraints. A fully automated method based on graph analysis is proposed to automatically identify the primary-backup relation between each pair of protection devices in different network topologies. Accordingly, protection systems constraints are derived automatically and incorporated into the optimization problem. The linearized and convexified nonlinear power flow equations, along with protection system coordination constraints, are solved using the second-order cone programming (SOCP) that assures the feasibility of the results while accelerating the convergence rate. The effectiveness of the proposed methodology is demonstrated through the simulation of the 33-node IEEE test system. © 1986-2012 IEEE.

关键词： Second-order cone programming

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More than just a name?From magnetic to optical skyrmions and the topology of light

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Light(science & Applications) 2025年第1期14卷 26-29页

作者： Jian Chen Andrew Forbes Cheng-Wei Qiu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore School of Physics University of the WitwatersrandJohannesburgSouth Africa

Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying textures and topological invariants(skyrmion numbers),depending on how to construct the skyrmion vector when projecting from real to parameter *** demonstrate the fragility of optical skyrmions under a ubiquitous scenario-simple reflection off an optical *** topology is not without benefit,but it must not be assumed.

关键词： numbers space. topology

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Unsupervised anomaly detection and localization via bidirectional knowledge distillation

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Neural Computing and Applications 2024年第29期36卷 18499-18514页

作者： Wang, Xiaoming Wang, Yongxiong Pan, Zhiqun Wang, Guangpeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model provides evidence for anomaly detection and localization. However, the teacher model is pretrained for classification, while the anomaly scores in the distillation-based anomaly detection method are indirectly derived from the classification scores. The mismatch between the two tasks can hinder the optimization of the model. To tackle this issue, we propose an innovative bidirectional knowledge distillation model. In this approach, forward knowledge distillation is pivotal in bolstering the model’s capacity for generalization. Simultaneously, backward knowledge distillation promotes diversity in representing anomalies. This reciprocal knowledge exchange effectively wards off potential performance declines due to target inconsistency. Through bidirectional knowledge distillation, we establish a more encompassing and resilient framework for knowledge transfer. Additionally, we introduce a novel data augmentation strategy to simulate anomalies and effectively eliminate unnecessary noise. In experiments on the MVTec AD, the proposed model achieves competitive results compared to state-of-the-art methods, 97.47% on image-level AUC, 98.23% on pixel-level AUC, and 94.77% on instance-level PRO. These results demonstrate the practicality of our approach in anomaly detection and localization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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A Review of Anonymization Algorithms and Methods in Big Data

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Annals of Data science 2025年第1期12卷 253-279页

作者： Shamsinejad, Elham Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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An End-to-End Two-Branch Network Towards Robust Video Fingerprinting

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2371-2384页

作者： Xu, Yingying Zhou, Yuanding Li, Xinran Zhao, Gejian Qin, Chuan University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

With the increasing number of edited videos, many robust video fingerprinting schemes have been proposed to solve the problem of video content authentication. However, most of them either deal with the temporal and spatial features symmetrically or insufficiently consider the temporal information. In this work, an end-to-end two-branch network toward robust video fingerprinting (RVFNet) is proposed, where the two branches focus on the temporal and spatial information, respectively. The temporal branch aims to comprehensively capture complex motion patterns by combining subtle motion changes with the overall motion trend. The spatial branch exploits the pixel-level information obtained by multiple receptive fields while preserving significant structural features. Deep metric learning is employed in the training process, and we adopt hard triplet loss to constrain the generation of fingerprints. Furthermore, we construct a large-scale and complex dataset for the robust video fingerprinting task based on multiple video content-preserving manipulations in actual scenarios. The size of our dataset exceeds most datasets adopted in the current robust video fingerprinting schemes. Based on the proposed dataset, experimental results demonstrate that our scheme achieves outstanding performance improvements compared with the state of the art. © 2020 IEEE.

关键词： Convolution

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Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

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Annals of Data science 2025年第1期12卷 223-252页

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle of K-anonymity and propels the field forward by critically evaluating various anonymization methods and algorithms, benchmarking their performance with respect to time and space complexities. A distinctive formula for optimized cluster determination in the K-means algorithm is presented, along with a novel tuple expiration time strategy for the efficient purging of clusters. The integration of these components into Spark’s RDD and MLlib modules results in a significant decrease in execution time and data loss rates, even with increasing data volumes. The paper’s notable contributions are its methodological advancements that offer a robust, scalable solution for data anonymization, safeguarding user privacy without sacrificing data utility or processing efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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Improving Machine Translation Formality with Large Language Models

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computers, Materials & Continua 2025年第2期82卷 2061-2075页

作者： Murun Yang Fuxue Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Electrical Engineering Yingkou Institute of TechnologyYingkou115014China

Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.

关键词： Neural machine translation formality large language model text style transfer style evaluation reinforcement learning

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