Performance Statistics of Autoregressive Short and Ultrashort Signal Detectors by V. M. Kutuzov, V. P. Ipatov, S. S. Sokolov

“…The paper presents original decision functions derived from the transformation of power spectral density estimates of the Burg method. …”
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Research on the Inversion Method of Dust Content on Mining Area Plant Canopies Based on UAV-Borne VNIR Hyperspectral Data by Yibo Zhao, Shaogang Lei, Xiaotong Han, Yufan Xu, Jianzhu Li, Yating Duan, Shengya Sun

“…The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. …”
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A Novel Spectrum Sensing Method for Multiple Unknown Signal Sources Using Frequency Domain Energy Detection and DBSCAN by Rui Gao, Guanghui Yan, Ruiting Niu, Wenwen Chang, Tianfeng Yan, Chunyang Tang

“…The FDED algorithm detects signal occupancy in sub-channels, while FDNE calculates an adaptive threshold based on noise variance estimates. …”
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Deep Learning-Based Atmospheric Visibility Detection by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin, Fan Que

“…This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. …”
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A Human-Centric, Uncertainty-Aware Event-Fused AI Network for Robust Face Recognition in Adverse Conditions by Akmalbek Abdusalomov, Sabina Umirzakova, Elbek Boymatov, Dilnoza Zaripova, Shukhrat Kamalov, Zavqiddin Temirov, Wonjun Jeong, Hyoungsun Choi, Taeg Keun Whangbo

“…Face recognition systems often falter when deployed in uncontrolled settings, grappling with low light, unexpected occlusions, motion blur, and the degradation of sensor signals. Most contemporary algorithms chase raw accuracy yet overlook the pragmatic need for uncertainty estimation and multispectral reasoning rolled into a single framework. …”
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Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period by A. A. Monakov

“…The algorithm uses the scale invariant Lamperti transform. …”
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Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles by Guo Ma, Cong Li, Hui Jing, Bing Kuang, Ming Li, Xiang Wang, Guangyu Jia

“…The experimental results show that the algorithm achieves superior state estimation performance on several typical visual–inertial datasets, demonstrating excellent accuracy and robustness.…”
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A Weak Signal Detection Method for Bearing Based on QGA and Stochastic Resonance by GE Jiang hua, WANG Yan, WANG Ya ping, SUN Yong guo, Xu Di

“…Firstly, the large parameter signal is scale transformed and the noise intensity is estimated according to the input signal to realize the initialization of the parameters. …”
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Structural Optimization-Based Enhancement of the Dynamic Performance for Horizontal Axis Wind Turbine Blade by Ahmed Zarzoor, Alaa Jaber, Ahmed Shandookh

“…It employs a complex optimization framework that combines aerodynamics and structural analysis via MATLAB and a genetic algorithm. The investigation maximizes the length of the chord, the angle of twist, and the selection of airfoils while maintaining constraints. …”
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Privacy-Aware Detection for Large Language Models Using a Hybrid BiLSTM-HMM Approach by Maryam Abbasalizadeh, Sashank Narain

“…Large Language Models (LLMs) have transformed natural language processing, enabling applications such as conversational agents and machine translation. …”
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An Interactive Human-in-the-Loop Framework for Skeleton-Based Posture Recognition in Model Education by Jing Shen, Ling Chen, Xiaotong He, Chuanlin Zuo, Xiangjun Li, Lin Dong

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Joint user activity and signal detection for massive multiple-input multiple-output by Xiaoqun SONG, Ming JIN, Zhongjie JIA

“…In uplink grant-free massive multiple-input multiple-output (mMIMO) systems, the performance of available methods for joint user activity and signal detection deteriorates when the correlation of receiving antennas or the number of active devices increases.Moreover, the available methods require the knowledge of noise power, which is often practically unknown.To address the above issues, combining approximate message passing with unitary transformation and expectation maximization algorithm to jointly implement user activity and signal detection was proposed.Different from the conventional approximate message passing algorithm, the proposed one assumes that the noise power was unknown.Firstly, by exploiting the approximate message passing algorithm with unitary transform, the distribution of transmitted symbols together with the distribution of noise power was obtained.Secondly, expectation maximization algorithm was applied to estimate the user activity.Finally, the signal detection was implemented by deriving the posterior distribution of the decoupled signal belongs.Simulation results show that the proposed method is better than the traditional method in joint user activity and signal detection.…”
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The multivariate shared truncated normal frailty model with application to medical data by Diego I. Gallardo, Yolanda M. Gómez, John L. Santibañez, Osvaldo Venegas, Marcelo Bourguignon

“…In addition, the n-th derivative of the Laplace transform can be expressed recursively. Parameter estimation is performed by a classical approach through the EM algorithm. …”
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Challenges and Opportunities of Assessing Efficiency of Digital Technology Introduction in Construction Projects by T. V. Vashchenko

“…The authors propose optimum indicators of assessment in view of investors interested both in increasing their capitals and in development of construction field and describe possible algorithms and methods of estimating investment-construction projects, where new technologies could be used.…”
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DRHT: A Hybrid Mathematical Model for Accurate Ultrasound Probe Calibration and Efficient 3D Reconstruction by Xuquan Ji, Yonghong Zhang, Huaqing Shang, Lei Hu, Xiaozhi Qi, Wenyong Liu

“…The homography transformation, which can be estimated using the singular value decomposition method, reduces the dimensionality of the calibration data and could significantly accelerate the computation of image points in ultrasonic three-dimensional reconstruction. …”
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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

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Experimental study and techno-economic evaluation of an active fault detection kit in the prospect of future zero energy building installations by Christos Pechlivanis, Nick Rigogiannis, Andreas Tichalas, Faidra Kotarela, Nick Papanikolaou

“…Its operation is based on harmonic voltage injection, in series with the electrical installation, through a low-power H-bridge inverter and a current transformer, along with the corresponding harmonic current measurement, to estimate the impedance and effectively detect faulty conditions; the fast and robust Goertzel algorithm is utilized. …”
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Angular Super-Resolution of Forward-Looking Scanning Radar via Grid-Updating Split SPICE-TV by Ruitao Li, Jiawei Luo, Yin Zhang, Yongchao Zhang, Lu Jiao, Deqing Mao, Yulin Huang, Jianyu Yang

“…However, it is unable to preserve the target profile, and the estimator is constrained by high computational complexity and memory consumption. …”
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Statistical models for urban growth forecasting: With application to the Baltimore–Washington area by Carlo Grillenzoni

“…In this paper, we consider space–time autoregressive (STAR) models that can be applied to the timelapse video of land transformations available on Internet. The corresponding datasets are in the form of big 3D arrays and require fast algorithms of parameter estimation and forecasting. …”
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Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review by Muaaz Bin Kaleem, Wei He, Heng Li

“…Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. …”
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