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Journal articles on the topic 'Improved adaptive multi threshold'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Huang, Hui Xian, Juan Gong, and Te Zhang. "Method of Adaptive Wavelet Thresholding Used in Image Denoising." Advanced Materials Research 204-210 (February 2011): 1184–87. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1184.

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According to multi-resolution analysis of wavelet threshold denoising principle, this paper presented two improved algorithms of continuity and adaptive threshold based on hard thresholding. The soft thresholding (hyperbolic thresholding) was used in the intervals after setting two thresholds, and the isolated points were removed according to the adjacent correlation coefficient during the processing. As a result, the hard thresholding’s shortcomings were reduced. The simulation results show that improved algorithms have both better visual effect and PSNR than the traditional approaches.

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Chen, Li Na, and Chun Yu Miao. "Spectrum Detection for Cognitive Radio Based on Auto-Adaptive Threshold." Advanced Materials Research 187 (February 2011): 614–20. http://dx.doi.org/10.4028/www.scientific.net/amr.187.614.

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This paper proposed a cooperative spectrum sensing scheme based on auto-adaptive threshold in multi-user cognitive radio network, which can improve detection performance of cognitive user. According to closed-form expressions of detection probability, we compared the noncooperative scheme with the multi-user cooperative scheme, and compared the auto-adaptive threshold with fixed threshold in terms of detection probability through imitation. Result shown that the detection probability of multi-user cooperative detection is improved greatly compared to traditional noncooperative detection, and a

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3

Wang, Shuhai, Yijia Zhou, Fenglin Bai, Junnan Li, and Hongzhe Li. "Improved ViBe algorithm based on multi-frame combined with adaptive threshold." Journal of Physics: Conference Series 2303, no. 1 (2022): 012021. http://dx.doi.org/10.1088/1742-6596/2303/1/012021.

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Abstract For moving human target detection under video surveillance, the traditional vibe algorithm is often disturbed by environmental changes, resulting in “ghosts”, incomplete detection results and “holes” in the human body. An improved vibe algorithm based on multi frame combined with adaptive threshold is proposed. Firstly, the sample set of vibe algorithm is expanded to 24 fields to reduce the possibility of pixel misclassification; Secondly, the historical pixel queue is introduced, and the initialization background model without ghost is obtained according to the change of foreground a

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Ondra, Eka Putra, Sumijan Sumijan, and Tajuddin Muhammad. "Improved adaptive multi-threshold method for automatic identification of rhinosinusitis in paranasal sinus images." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 119–29. https://doi.org/10.11591/ijai.v14.i1.pp119-129.

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Rhinosinusitis, characterized by inflammation of the mucosa or mucous membrane within the paranasal sinuses, anatomical cavities situated in the facial bones, is the focus of this investigation. This study employs computed tomography (CT)-scan images comprising sagittal slices of the paranasal sinuses, acquired through a CT device featuring a Philips Ingenuity CT model MRC880 tube type, identified by tube serial number 163889, with a pixel value resolution of 0.24 mm. The primary objective of this research is to automatically identify and delineate rhizosinusitis-affected areas. This involves

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Li, Yihui, Manling Ge, Shiying Zhang, and Kaiwei Wang. "Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank." Sensors 24, no. 3 (2024): 1031. http://dx.doi.org/10.3390/s24031031.

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In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and th

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6

Zhang, Ming Jun, and Xing Qi Yuan. "Study on Filtering Algorithm of Image Using Matlab." Applied Mechanics and Materials 239-240 (December 2012): 1173–78. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1173.

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To increase signal to noise ratio (SNR) and to stress on expectation characters, an improved adaptive minutia preserving smoothing algorithm is proposed using Matlab based on multi-scale and multidirectional masks. This algorithm keeps the mask’s good performance in preserving details. It divides image into sub-images according to the statistics from image gradation-gradient histogram, and the adaptive threshold value generate according to the gradient information of the whole and the local image. This method deals with the difficulty of choosing threshold and improves the automation of image

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Chen, Jian, Shaohua Zheng, Lun Yu, and Lin Pan. "Improved algorithm for adaptive median filter with multi-threshold based on directional information." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 27, no. 2 (2013): 156–61. http://dx.doi.org/10.3724/sp.j.1187.2013.00156.

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Hu, Xiaolong, Hongbing Ji, and Long Liu. "Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold." Sensors 19, no. 5 (2019): 1120. http://dx.doi.org/10.3390/s19051120.

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Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of new

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9

Dong, Yuxue, Mengxia Li, and Mengxiang Zhou. "Multi-Threshold Image Segmentation Based on the Improved Dragonfly Algorithm." Mathematics 12, no. 6 (2024): 854. http://dx.doi.org/10.3390/math12060854.

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In view of the problems that the dragonfly algorithm has, such as that it easily falls into the local optimal solution and the optimization accuracy is low, an improved Dragonfly Algorithm (IDA) is proposed and applied to Otsu multi-threshold image segmentation. Firstly, an elite-opposition-based learning optimization is utilized to enhance the diversity of the initial population of dragonflies, laying the foundation for subsequent algorithm iterations. Secondly, an enhanced sine cosine strategy is introduced to prevent the algorithm from falling into local optima, thereby improving its abilit

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10

Qian, Hong, Shuyin Sun, Xuehua Wang, and Zhenpeng Li. "Study of Condenser Spatial State Model Based on Dynamic Thresholding of Environmentally Adaptive Multi-Dimension Eigenvalues." E3S Web of Conferences 598 (2024): 02006. http://dx.doi.org/10.1051/e3sconf/202459802006.

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This study proposes an approach that takes into account the impact of marine environmental impacts on the multi-dimension eigenvalues and analyzes the condenser status based on multi-dimension eigenvalues dynamic threshold. Firstly, we get the multi-dimensional eigenvalues of the condenser Based on thermodynamic theory analysis. Then, utilizing seawater-temperature and seawater-level as environmental parameters, the partitioning of the condenser marine environmental region is optimized based on the CalinskiHarabasz index. Subsequently, the multi-dimension eigenvalues are used to calculate to g

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11

Wang, Weiqing, Zhonggang Xiong, and Zhong Liu. "ORB Feature Uniform Distribution Algorithm Integrating Quadtree and Adaptive Thresholding." Journal of Physics: Conference Series 3024, no. 1 (2025): 012016. https://doi.org/10.1088/1742-6596/3024/1/012016.

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Abstract To enhance the uniform distribution characteristic of ORB extraction and leverage the global information of the image for improved positioning accuracy in visual SLAM, a novel ORB feature point extraction method combining quadtree and adaptive threshold was introduced. This method involves building a multi-resolution image pyramid and dividing the images into 30 pixels × 30 pixels grids to determine the number of feature points needed for each layer. The adaptive threshold for detecting feature points is derived from the image’s gray mean and variance. Feature points and their descrip

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12

Wang, Wenqing, Yuan Yan, Rundong Zhang, Zhen Wang, Yongqing Fan, and Chunjie Yang. "Multi-Attribute Fusion Algorithm Based on Improved Evidence Theory and Clustering." Sensors 19, no. 19 (2019): 4146. http://dx.doi.org/10.3390/s19194146.

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In most of the application scenarios of industrial control systems, the switching threshold of a device, such as a street light system, is typically set to a fixed value. To meet the requirements for a smart city, it is necessary to set a threshold that is adaptive to different conditions by fusing the multi-attribute observations of the sensors. This paper proposes a multi-attribute fusion algorithm based on fuzzy clustering and improved evidence theory. All of the observations are clustered by fuzzy clustering, where a proper clustering method is chosen, and the improved evidence theory is u

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13

Putra, Ondra Eka, Sumijan Sumijan, and Muhammad Tajuddin. "Improved adaptive multi-threshold method for automatic identification of rhinosinusitis in paranasal sinus images." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 119. http://dx.doi.org/10.11591/ijai.v14.i1.pp119-129.

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Rhinosinusitis, characterized by inflammation of the mucosa or mucous membrane within the paranasal sinuses, anatomical cavities situated in the facial bones, is the focus of this investigation. This study employs computed tomography (CT)-scan images comprising sagittal slices of the paranasal sinuses, acquired through a CT device featuring a Philips Ingenuity CT model MRC880 tube type, identified by tube serial number 163889, with a pixel value resolution of 0.24 mm. The primary objective of this research is to automatically identify and delineate rhizosinusitis-affected areas. This involves

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14

Shaofeng, Wu, Tong Yifei, Liu Jiafeng, Tan Qingmeng, and Li Dongbo. "The Chromatic Aberration 2-D Entropy Threshold Segmentation Method Based on Adaptive Step-Length Firefly Algorithm." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 2 (2019): 130–37. http://dx.doi.org/10.2174/2352096511666180508151015.

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Background: To effectively solve the segmentation problem with multi-target complex image, the chromatic aberration 2-D entropy threshold segmentation method based on Adaptive Step- Length Firefly Algorithm (ASLFA) is proposed in this paper. Methods: Firstly, the significance of image entropy value is analyzed and the threshold segmentation is proposed with maximum entropy principle. Then, in order to solve the problem of large amount and longtime of calculation in the threshold segmentation process, the improved firefly algorithm (FA) is proposed replacing the fixed step-length with adaptive

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15

Xiao, Baosen, Jingbo Xia, Xiaolu Li, and Qinquan Gao. "An Improved Vehicle Detection Algorithm Based on Multi-Intermediate State Machine." Mathematical Problems in Engineering 2021 (April 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/5540837.

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The vehicle detection algorithm is an important part of the intelligent transportation system. The accuracy of the algorithm will determine whether accurate vehicle information can be obtained. The system contains several functional modules, including signal amplification, wireless communication, A/D converter, and sensor set/reset functions. To detect all the intersection vehicles, a number of magnetoresistive sensors are connected to the computer system through the wireless communication module, and then, the detected vehicle information will be transferred back to the master host computer.

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16

Zhao, Lin, Hong-Yi Dai, Lin Lang, and Ming Zhang. "An Adaptive Filtering Method for Cooperative Localization in Leader–Follower AUVs." Sensors 22, no. 13 (2022): 5016. http://dx.doi.org/10.3390/s22135016.

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In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative l

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17

Liu, Liqun, and Jiuyuan Huo. "Apple Image Recognition Multi-Objective Method Based on the Adaptive Harmony Search Algorithm with Simulation and Creation." Information 9, no. 7 (2018): 180. http://dx.doi.org/10.3390/info9070180.

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Aiming at the low recognition effect of apple images captured in a natural scene, and the problem that the OTSU algorithm has a single threshold, lack of adaptability, easily caused noise interference, and over-segmentation, an apple image recognition multi-objective method based on the adaptive harmony search algorithm with simulation and creation is proposed in this paper. The new adaptive harmony search algorithm with simulation and creation expands the search space to maintain the diversity of the solution and accelerates the convergence of the algorithm. In the search process, the harmony

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18

Elnur Ahmadzade, Bahram Ismayılov, Elnur Ahmadzade, Bahram Ismayılov. "WAVELET SIGNAL DENOISING: IMPROVED MULTI-SCALE PRINCIPAL ANALYSIS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 49, no. 02 (2025): 179–89. https://doi.org/10.36962/pahtei49022025-179.

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This study presents a fault diagnosis method and system designed to overcome the challenges associated with noise and randomness in process data using an improved wavelet threshold denoising technique. Modern industrial systems have become increasingly complex, necessitating accurate and timely fault detection to ensure production stability and safety. While Principal Component Analysis (PCA) has been a popular multivariate statistical technique for handling high-dimensional, noisy, and highly correlated data, its single-scale limitation restricts its effectiveness in analyzing multi-scale pro

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19

Li, Yazhou, Wei Dai, Tingting Huang, Meihua Shi, and Weifang Zhang. "Adaptive Early Warning Method Based on Similar Proportion and Probability Model." Applied Sciences 10, no. 12 (2020): 4278. http://dx.doi.org/10.3390/app10124278.

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This paper presents a multi-state adaptive early warning method for mechanical equipment and proposes an adaptive dynamic update model of the equipment alarm threshold based on a similar proportion and state probability model. Based on the similarity of historical equipment, the initial thresholds of different health states of equipment can be determined. The equipment status is divided into four categories and analyzed, which can better represent its status and provide more detailed and reasonable guidance. The obtained dynamic alarm lines at all levels can regulate the operation range of equ

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20

Liao, Bin, Hui Ying Sun, and Jun Gang Xu. "Adaptive Corner Detection Based on Direct Curvature Scale Space." Applied Mechanics and Materials 391 (September 2013): 488–92. http://dx.doi.org/10.4028/www.scientific.net/amm.391.488.

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Corner detection based on global and local curvature properties is an advanced method for detecting corners in images, which is a fundamental composition of many algorithms. However, we find that it is time-consuming for real-time applications and might detect wrong corners or lose some important corners. To alleviate these problems, we propose an improved curvature product corner detector with dynamic region of support based on Direct Curvature Scale Space (DCSS). Firstly, we use direct curvature scale space to reduce the complexity of computation instead of curvature scale space. Secondly, m

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21

Shen, Yue, and Yang Yu. "Bearing Acoustic Emission Signal Processing based on Improved SGMD." Frontiers in Computing and Intelligent Systems 11, no. 3 (2025): 41–47. https://doi.org/10.54097/4xsx0n69.

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To solve the problems of poor adaptability to fixed thresholds and obvious endpoint effect in traditional Symplectic Geometric Mode Decomposition (SGMD), an improved SGMD (ISGMD) algorithm is proposed to improve the acoustic emission signal processing performance in bearing fault diagnosis. Firstly, a dynamic threshold adjustment model was constructed by fusing the signal Lyapunov exponent and the fractal dimension to realize the adaptive decomposition of signals of different complexity. Secondly, combined with the improved mirror extension and cosine smoothing technology, the endpoint effect

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22

Wang, Xinqing, Xia Hua, Feng Xiao, Yuyang Li, Xiaodong Hu, and Pengyu Sun. "Multi-Object Detection in Traffic Scenes Based on Improved SSD." Electronics 7, no. 11 (2018): 302. http://dx.doi.org/10.3390/electronics7110302.

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In order to solve the problem that, in complex and wide traffic scenes, the accuracy and speed of multi-object detection can hardly be balanced by the existing object detection algorithms that are based on deep learning and big data, we improve the object detection framework SSD (Single Shot Multi-box Detector) and propose a new detection framework AP-SSD (Adaptive Perceive). We design a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor and then we train and screen the optimal feature extraction convolution kernel to replace the low-level convolution k

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23

Zhang, Jianhua, Qiang Zhu, Fei Song, Lingchao Zhang, Juan Wang, and Changjun Liu. "Multi-Scale Edge Detection of Crack in Extra-High Arch Dam Based on Orthogonal Wavelet Construction." Traitement du Signal 39, no. 3 (2022): 977–89. http://dx.doi.org/10.18280/ts.390325.

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This paper conducts a research on the wavelet construction and application of image edge detection. Taking the image edge detection algorithm based on wavelet modulus maxima as the research subject, this paper discusses the problem of dislocation phenomenon, threshold selection, multi-scale edge fusion and evaluation criterion in the algorithm, and proposes an improved self-adaptive hierarchical threshold algorithm based on information amount and vanishing moment. From the angle of wavelet symmetry, filter composition and vanishing moment, the influence of wavelet property on image edge detect

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24

Liu, Ran, Xiangqian Li, Jinping Sun, and Tao Shan. "Multi-Information-Assisted Joint Detection and Tracking of Ground Moving Target for Airborne Radar." Remote Sensing 17, no. 12 (2025): 2093. https://doi.org/10.3390/rs17122093.

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Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, e

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Zhou, Ziqi, and Mao Pang. "Stereo Matching Algorithm of Multi-Feature Fusion Based on Improved Census Transform." Electronics 12, no. 22 (2023): 4594. http://dx.doi.org/10.3390/electronics12224594.

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This article proposes an improved stereo matching algorithm in order to address the issue that the conventional Census transform is overly dependent on the center pixel of the window, which makes the algorithm susceptible to noise interference and results in low matching accuracy in regions with weak texture and complex texture. In the cost calculation stage, the noise threshold is set utilizing the absolute difference detection approach, and pixels that exceed the threshold are replaced with the mean gray values of the neighboring pixels in the 3 × 3 window. This stage also includes the intro

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26

Liu, Ming, and Chao Xu. "An Ameliorative Defogging Algorithm Based on the MSR." Applied Mechanics and Materials 556-562 (May 2014): 3483–86. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3483.

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In view of the existing problems of traditional defogging algorithms,such as color distortion and halo phenomenon in MSR algorithm (multi-scale Retinex) , we improve it by using adaptive filter and different weighting factor.In other word,when gray levels of pixels within a certain threshold, use homomorphic filtering; otherwise use different state filtering device. Experiments shown that image details are enhanced, and contrast is enhanced, visual effect is improved.

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27

Jiang, Wuhua, Chuanzheng Song, Hai Wang, Ming Yu, and Yajie Yan. "Obstacle Detection by Autonomous Vehicles: An Adaptive Neighborhood Search Radius Clustering Approach." Machines 11, no. 1 (2023): 54. http://dx.doi.org/10.3390/machines11010054.

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For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing; however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation of point clouds, resulting in false detection or missed detection of obstacles. In order to solve these problems, a new obstacle detection method was required. Firstly, we applied a distance-based filter and a ground segmentation algorithm, to pre-process the original 3D point cloud. Secondly, we p

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28

Jiang, Yuanyuan, Dong Zhang, Wenchang Zhu, and Li Wang. "Multi-Level Thresholding Image Segmentation Based on Improved Slime Mould Algorithm and Symmetric Cross-Entropy." Entropy 25, no. 1 (2023): 178. http://dx.doi.org/10.3390/e25010178.

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Multi-level thresholding image segmentation divides an image into multiple regions of interest and is a key step in image processing and image analysis. Aiming toward the problems of the low segmentation accuracy and slow convergence speed of traditional multi-level threshold image segmentation methods, in this paper, we present multi-level thresholding image segmentation based on an improved slime mould algorithm (ISMA) and symmetric cross-entropy for global optimization and image segmentation tasks. First, elite opposition-based learning (EOBL) was used to improve the quality and diversity o

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29

Xiang, Liang, Xiajie Zhao, Jianfeng Wang, and Bin Wang. "An Enhanced Human Evolutionary Optimization Algorithm for Global Optimization and Multi-Threshold Image Segmentation." Biomimetics 10, no. 5 (2025): 282. https://doi.org/10.3390/biomimetics10050282.

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Thresholding image segmentation aims to divide an image into a number of regions with different feature attributes in order to facilitate the extraction of image features in the context of image detection and pattern recognition. However, existing threshold image-segmentation methods suffer from the problem of easily falling into locally optimal thresholds, resulting in poor image segmentation. In order to improve the image-segmentation performance, this study proposes an enhanced Human Evolutionary Optimization Algorithm (HEOA), known as CLNBHEOA, which incorporates Otsu’s method as an object

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Wang, Hongxia, Xiao Jin, Yukun Du, Nan Zhang, and Hongxia Hao. "Adaptive Hard Parameter Sharing Method Based on Multi-Task Deep Learning." Mathematics 11, no. 22 (2023): 4639. http://dx.doi.org/10.3390/math11224639.

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Multi-task learning (MTL) improves the performance achieved on each task by exploiting the relevant information between tasks. At present, most of the mainstream deep MTL models are based on hard parameter sharing mechanisms, which can reduce the risk of model overfitting. However, negative knowledge transfer may occur, which hinders the performance improvement achieved for each task. In this paper, for situations when multiple tasks are jointly trained, we propose the adaptive hard parameter sharing method. On the basis of the adaptive hard parameter sharing method, the number of nodes in the

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31

Jiao, Xinyu, Diange Yang, Kun Jiang, Chunlei Yu, Tuopu Wen, and Ruidong Yan. "Real-time lane detection and tracking for autonomous vehicle applications." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 9 (2019): 2301–11. http://dx.doi.org/10.1177/0954407019866989.

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This article proposes an improved lane detection and tracking method for autonomous vehicle applications. In real applications, when the pose and position of the camera are changed, parameters and thresholds in the algorithms need fine adjustment. In order to improve adaptability to different perspective conditions, a width-adaptive lane detection method is proposed. As a useful reference to reduce noises, vanishing point is widely applied in lane detection studies. However, vanishing point detection based on original image consumes many calculation resources. In order to improve the calculati

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32

Wang, Shouhua, Shuaihu Wang, and Xiyan Sun. "A Multi-Scale Anti-Multipath Algorithm for GNSS-RTK Monitoring Application." Sensors 23, no. 20 (2023): 8396. http://dx.doi.org/10.3390/s23208396.

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During short baseline measurements in the Real-Time Kinematic Global Navigation Satellite System (GNSS-RTK), multipath error has a significant impact on the quality of observed data. Aiming at the characteristics of multipath error in GNSS-RTK measurements, a novel method that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and adaptive wavelet packet threshold denoising (AWPTD) is proposed to reduce the effects of multipath error in GNSS-RTK measurements through modal function decomposition, effective coefficient sieving, and adaptive thresholdi

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Tian, Xuehong, Xin Huang, Haitao Liu, and Qingqun Mai. "Adaptive Fuzzy Event-Triggered Cooperative Control for Multi-Robot Systems: A Predefined-Time Strategy." Sensors 23, no. 18 (2023): 7950. http://dx.doi.org/10.3390/s23187950.

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A predefined-time adaptive fuzzy cooperative controller with event triggering is proposed for multi-robot systems that takes into account external disturbances, input saturation, and model uncertainties in this paper. First, based on the asymmetric tan-type barrier Lyapunov function, a predefined-time controller is proposed to acquire a quick response and more precise convergence time under the directed communication topology. Second, predefined-time fuzzy logic systems are developed to approximate external disturbances and model uncertainties. Third, a dynamic relative threshold event-trigger

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Yu, Tianyi, Houming Wang, Shunming Li, et al. "A Novel Improved Threshold Adaptive Forgetting Variable Step Size Blind Separation Model for Weak Signal Detection." Shock and Vibration 2022 (September 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/7608911.

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The online blind source separation (BSS) is seriously disturbed by strong noise when extracting weak signals and has the defects that it cannot both give consideration to convergence speed and steady-state error. In order to solve the abovementioned problems, a novel improved threshold adaptive forgetting variable step size blind separation model (ITAFBS) for weak signal detection is proposed. Firstly, an improved lifting wavelet transform (ILWT) is proposed to reduce the noise of weak signals. In ILWT, a threshold function containing an adjustment factor is proposed to reduce the constant dev

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35

Niu, Ji Gao, Feng Lai Pei, Su Zhou, and Tong Zhang. "Multi-Objective Optimization Study of Energy Management Strategy for Extended-Range Electric Vehicle." Advanced Materials Research 694-697 (May 2013): 2704–9. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2704.

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A new method based on genetic-particle swarm hybrid algorithm was presented for parameter optimization of energy management strategy for extended-range electric vehicle (E-REV). Taking a logic threshold control strategy of an E-REV as example, for the aims of minimizing fuel consumption and emissions, a constrained nonlinear programming parameter optimization model was established. Based on this model, genetic algorithm (GA) and particle swarm optimization (PSO) were improved respectively. Further, a genetic-particle swarm hybrid algorithm was put forward and applied to the multi-objective opt

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Sun, Qiuqin, Fei Lin, Weitao Yan, Feng Wang, She Chen, and Lipeng Zhong. "Estimation of the Hydrophobicity of a Composite Insulator Based on an Improved Probabilistic Neural Network." Energies 11, no. 9 (2018): 2459. http://dx.doi.org/10.3390/en11092459.

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The estimation of hydrophobicity for composite insulators is of great importance for the purpose of predicting the surface degradation. The hydrophobic image is firstly decomposed by the 2-level wavelet, along with the multi-Retinex algorithm in this paper. The processed low frequency sub-band and high frequency sub-band images are then reconstructed. The 3 × 3 Sobel operator is performed to measure the basic spatial gradient in four directions, including the horizontal direction, the diagonal direction, and then the vertical direction. The shape factor, the area ratio of the largest water dro

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Xie, Jilong, Shanshan Lv, Xihai Zhang, Weixian Song, Xinyi Liu, and Yinghui Lu. "Exploring Nutrient Deficiencies in Lettuce Crops: Utilizing Advanced Multidimensional Image Analysis for Precision Diagnosis." Sensors 25, no. 7 (2025): 1957. https://doi.org/10.3390/s25071957.

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In agricultural production, lettuce growth, yield, and quality are impacted by nutrient deficiencies caused by both environmental and human factors. Traditional nutrient detection methods face challenges such as long processing times, potential sample damage, and low automation, limiting their effectiveness in diagnosing and managing crop nutrition. To address these issues, this study developed a lettuce nutrient deficiency detection system using multi-dimensional image analysis and Field-Programmable Gate Arrays (FPGA). The system first applied a dynamic window histogram median filtering algo

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Gao, Xingyue, Huanli Lou, and Qingyuan Miao. "An RFID multi-Tag anti-collision algorithm based on Successive Interference Cancellation with power adaptive regulation." Journal of Physics: Conference Series 2246, no. 1 (2022): 012079. http://dx.doi.org/10.1088/1742-6596/2246/1/012079.

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Abstract Radio Frequency Identification (RFID) is a key technology supporting the Internet of Things (IoT). The simultaneous identification of multiple Tag signals by a Reader will generate the Tag collision problem, which will significantly reduce the identification efficiency of the system and prolong the identification time in the scenario of large-scale Tags. In order to solve this problem, this paper researches RFID Tag collision prevention algorithm, and establishes a multi-Tag collision prevention algorithm model by analyzing the Aloha Tag collision prevention algorithm, which can accur

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Shivaprakasha, K. S., Muralidhar Kulkarni, and Nishant Joshi. "Improved network survivability using multi-threshold adaptive range clustering (M-TRAC) algorithm for energy balancing in wireless sensor networks." Journal of High Speed Networks 19, no. 2 (2013): 99–113. http://dx.doi.org/10.3233/jhs-130466.

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Han, Jun-Xian, Xiao-Yan Jiang, Long-Xue Cheng, Jian-Fang Xue, Kai-Xin Cheng, and Bo Liu. "Multi Agent System Control and Scheduling Optimization Method Under Adaptive Event Triggering." Journal of Computers 36, no. 2 (2025): 233–48. https://doi.org/10.63367/199115992025043602016.

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In the process of intelligent equipment manufacturing, AGV is an important link to realize material flow. With the increase of manufacturing scale and disturbance events, the main research direction of multi-agent systems is to realize the optimization of computing resources and the stability of control system in frequent information interaction and complex associated scenes. Therefore, in this paper, for the collaborative control of multi-agent AGV vehicle system, considering that AGV will be affected by signal interference and network packet loss during driving, as well as the dynamic time-v

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Fan, Qigao, Hai Zhang, Peng Pan, et al. "Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System." Sensors 19, no. 2 (2019): 294. http://dx.doi.org/10.3390/s19020294.

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Pedestrian dead reckoning (PDR) systems based on a microelectromechanical-inertial measurement unit (MEMS-IMU) providing advantages of full autonomy and strong anti-jamming performance are becoming a feasible choice for pedestrian indoor positioning. In order to realize the accurate positioning of pedestrians in a closed environment, an improved pedestrian dead reckoning algorithm, mainly including improved step estimation and heading estimation, is proposed in this paper. Firstly, the original signal is preprocessed using the wavelet denoising algorithm. Then, the multi-threshold method is pr

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Li, Xin, Yu Jiang, Jian Jun Song, and Wei Li Cui. "WSN Image Watermarking Algorithm Based on Multi-Wavelet." Applied Mechanics and Materials 44-47 (December 2010): 3912–16. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3912.

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A WSN image watermarking algorithm based on multi-wavelet was proposed. When the instability of image transmission in wireless sensor networks was considered, the proposed algorithm can increase the capacity of the image watermark. In this paper, the wavelet transform coefficients were analyzed by the noise visibility function. And the bits were embedded into the coefficients which have more ability to the tolerance of the noise according to the designated threshold. We achieved the adaptive adjustment in the watermarking strength combined with the JND in the wavelet domain. The experimental r

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Zheng, Zhen, Bingting Zha, Yu Zhou, Jinbo Huang, Youshi Xuchen, and He Zhang. "Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information." Remote Sensing 14, no. 2 (2022): 367. http://dx.doi.org/10.3390/rs14020367.

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This paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise filtering algorithm has difficulty quickly completing the single-stage adaptive filtering of multi-scale noise. The feature information from each point of the point cloud is obtained based on the efficient k-dimensional (k-d) tree data structure and amended normal vector estimation methods, and the adaptive threshold is used to divide the point cloud into large-scale noise, a feature-rich regio

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Srinivasa Rao Yalavarthy. "Efficient Link Handling for Enhanced Quality of Service in eMLSR Devices Under OBSS Scenarios." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1418–25. https://doi.org/10.30574/wjaets.2025.15.3.1082.

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This article proposes an innovative approach to optimize Enhanced Multi-Link Single Radio (eMLSR) operations in IEEE 802.11be networks under Overlapping Basic Service Set (OBSS) interference conditions. While Wi-Fi 7's Multi-Link Operation (MLO) offers unprecedented performance potential, simultaneous utilization of 5 GHz and 6 GHz links introduces significant cost and implementation challenges due to required isolation mechanisms. To address these constraints, eMLSR enables dynamic link switching, but the associated transition latency can degrade network performance when switches occur too fr

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Nazir, Hafiza Mamona, Ijaz Hussain, Ishfaq Ahmad, Muhammad Faisal, and Ibrahim M. Almanjahie. "An improved framework to predict river flow time series data." PeerJ 7 (July 1, 2019): e7183. http://dx.doi.org/10.7717/peerj.7183.

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Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using

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Zhang, Zheng, Haobo Yang, Xusheng Bai, Shuo Zhang, and Chaobin Xu. "The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm." Applied Sciences 15, no. 7 (2025): 3700. https://doi.org/10.3390/app15073700.

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In the field of mobile robot path planning, traditional genetic algorithms face issues such as slow convergence, lack of dynamic adaptability, and uncertain mutation directionality. To address these issues, a dichotomy-based multi-step method was used during the population initialization phase to enhance both the quality and diversity of the population. The selection strategy was improved for tournament selection, which reduces the monopolistic dominance of exceptional individuals and preserves population diversity through multiple rounds of grouping competitions. The crossover strategy adopte

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Chen, Peiyan, Ying Fu, Jinrong Hu, Bing He, Xi Wu, and Jiliu Zhou. "An Adaptive Remote Sensing Image-Matching Network Based on Cross Attention and Deformable Convolution." Electronics 12, no. 13 (2023): 2889. http://dx.doi.org/10.3390/electronics12132889.

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There are significant background changes and complex spatial correspondences between multi-modal remote sensing images, and it is difficult for existing methods to extract common features between images effectively, leading to poor matching results. In order to improve the matching effect, features with high robustness are extracted; this paper proposes a multi-temporal remote sensing matching algorithm CMRM (CNN multi-modal remote sensing matching) based on deformable convolution and cross-attention. First, based on the VGG16 backbone network, Deformable VGG16 (DeVgg) is constructed by introd

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He, Yecong, and Min Tan. "Design of Indoor Temperature Monitoring and Energy Saving Control Technology Based on Wireless Sensor." International Journal of Online Engineering (iJOE) 13, no. 07 (2017): 100. http://dx.doi.org/10.3991/ijoe.v13i07.7289.

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<p><span style="font-family: 宋体;">Considering the indoor temperature monitoring and energy saving control technology, based on the traditional low energy adaptive clustering hierarchy (LEACH), a multi-hop clustering routing algorithm is proposed. By adding a threshold in LEACH, the algorithm makes the nodes with high residual energy and high nodes become cluster heads. The results show that the improved algorithm can effectively prolong the life cycle of wireless sensor networks. Based on above findings, it is concluded that the proposed algorithm can save the system energy and imp

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Saraereh, Omar A., Imran Khan, Qais Alsafasfeh, Salem Alemaishat, and Sunghwan Kim. "Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems." Symmetry 11, no. 5 (2019): 713. http://dx.doi.org/10.3390/sym11050713.

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Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive M

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Yan, Jiangqiao, Hongqi Wang, Menglong Yan, Wenhui Diao, Xian Sun, and Hao Li. "IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery." Remote Sensing 11, no. 3 (2019): 286. http://dx.doi.org/10.3390/rs11030286.

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Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance

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