A cross-layer approach to message authentication based on sparse representation for wireless body area networks by Ning Wang, Weiwei Li, Ting Jiang

“…Furthermore, the computational complexity of the proposed algorithm and the efficiency of the cross-layer approach are analyzed, respectively. …”
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A Simplified Multiband Sampling and Detection Method Based on MWC Structure for Mm Wave Communications in 5G Wireless Networks by Min Jia, Xue Wang, Xuemai Gu, Qing Guo

“…In the reconstruction stage, the proposed method simplifies the computational complexity by exploiting simple linear operations instead of CS recovery algorithms and provides more stable performance of signal recovery. …”
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Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review by Avantika Rattan, Abhishek Rudra Pal, Muralimohan Gurusamy

“…The promise of quantum computing to handle the massive data and computational complexity that classical methods struggle with necessitates new studies in quantum machine learning (QML) for autonomous vehicles.…”
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Effective Processing of Radar Data for Bridge Damage Detection by Tomasz Owerko, Przemysław Kuras

“…The method’s low computational complexity allows for implementation in real-time solutions. …”
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TIE‐GCM ROPE ‐ Dimensionality Reduction: Part I by Piyush M. Mehta, Richard J. Licata

“…However, the computational complexity of such models have primarily kept them being used operationally. …”
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Image Super-Resolution Reconstruction Based on the Lightweight Hybrid Attention Network by Chu Yuezhong, Wang Kang, Zhang Xuefeng, Liu Heng

“…In order to solve the problem that the current image super-resolution model has too many parameters and high computational complexity, this paper proposes a lightweight hybrid attention network (LHAN). …”
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A-priori multi-objective optimization for the short-term dispatch of distributed energy resources by G. Carpinelli, A.R. Di Fazio, S. Perna, A. Russo, M. Russo

“…Effective linear power flow equations are included into both the objective functions and the inequality constraints of the MOO model, thus yielding benefits in terms of reduced model dimension and computational complexity. The weighted sum (WS) method with the a-priori assignment of weights is used to transform the MOO into a single-objective optimization (SOO) that directly provides the final solution on the Pareto front. …”
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A randomized block policy gradient algorithm with differential privacy in Content Centric Networks by Lin Wang, Xingang Xu, Xuhui Zhao, Baozhu Li, Ruijuan Zheng, Qingtao Wu

“…In order to reduce computational complexity when processing high-dimensional data, we randomly select a block coordinate to update the gradients at each round. …”
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TAS Scheduling With Grouping Flows by Hironao Abe, Yuhei Kawakami, Hideo Kawata, Takashi Nakanishi, Tomoaki Yoshida

“…This paper proposes a method to reduce the computational complexity of TAS schedules for the problem that the computation time increases exponentially as the amount of scheduled traffic (ST) flows increases. …”
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Application Study of Sigmoid Regularization Method in Coke Quality Prediction by Shaohong Yan, Hailong Zhao, Liangxu Liu, Qiaozhi Sang, Peng Chen, Jie Li

“…A regularized network training method based on Sigmoid function is designed considering that redundancy of network structure may lead to the learning of undesired noise, in which weights having little impact on performance and leading to overfitting are removed in terms of computational complexity and training errors. The cascade forward neural network with validation is found to be the most suitable one for coke quality prediction, with errors around 5%, followed by feedforward neural network structure and radial basis neural networks. …”
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Measurement and Statistical Analysis of Distinguishable Multipaths in Underground Tunnels by Junchang Sun, Shuai Ma, Hui Zhou, Chun Du, Shiyin Li

“…The commonly used method is the support vector machine (SVM) method with high computational complexity. To tackle this problem, this paper adopts the SVM classifier based on fewer selected features of the normalized power delay profile (PDP). …”
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Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output syst... by Xiaoping Zhou, Haichao Liu, Bin Wang, Qian Zhang, Yang Wang

“…This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.…”
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Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand by Yongjian Yang, Xintao Wang, Yuanbo Xu, Qiuyang Huang

“…Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. …”
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Binocular stereo vision-based relative positioning algorithm for drone swarm by Qing Cheng, Yazhe Wang

“…Abstract To address the challenges of high computational complexity and poor real-time performance in binocular vision-based Unmanned Aerial Vehicle (UAV) formation flight, this paper introduces a UAV localization algorithm based on a lightweight object detection model. …”
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Block-Based Mode Decomposition in Few-Mode Fibers by Chenyu Wang, Jianyong Zhang, Baorui Yan, Shuchao Mi, Guofang Fan, Muguang Wang, Peiying Zhang

“…A block-based mode decomposition (BMD) algorithm is proposed in this paper, which reduces computational complexity and enhances noise resistance. The BMD uses randomly selected sample blocks of the beam images to restore mode coefficients instead of all pixels in the beam images. …”
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A lightweight power quality disturbance recognition model based on CNN and Transformer by ZHANG Bide, QIU Jie, LOU Guangxin, ZHOU Can, LUO Qingqing, LI Tianqian

“…A lightweight power quality disturbances (PQDs) recognition model that integrates convolutional neural network (CNN) and Transformer (CaT) is proposed to address the high number of parameters and computational complexity in existing deep learning-based models. …”
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Intelligent methods for natural data analysis: application to space weather by O.V. Mandrikova

“…But these methods have high computational complexity, failing to provide accurate estimates when the signal-to-noise ratio is low. …”
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DBnet: A Lightweight Dual-Backbone Target Detection Model Based on Side-Scan Sonar Images by Quanhong Ma, Shaohua Jin, Gang Bian, Yang Cui, Guoqing Liu

“…Due to the large number of parameters and high computational complexity of current target detection models, it is challenging to perform fast and accurate target detection in side-scan sonar images under the existing technical conditions, especially in environments with limited computational resources. …”
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Fast binary logistic regression by Nurdan Ayse Saran, Fatih Nar

“…Furthermore, to address the common problem of collinear features, we apply singular value decomposition (SVD), resulting in a low-rank representation commonly used to reduce computational complexity while preserving essential features and mitigating noise. …”
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FDK-Type Algorithms with No Backprojection Weight for Circular and Helical Scan CT by A. V. Narasimhadhan, Kasi Rajgopal

“…s algorithm in terms of computational complexity.…”
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