Bias-field digitized counterdiabatic quantum optimization by Alejandro Gomez Cadavid, Archismita Dalal, Anton Simen, Enrique Solano, Narendra N. Hegade

“…We introduce a method for solving combinatorial optimization problems on digital quantum computers, where we incorporate auxiliary counterdiabatic (CD) terms into the adiabatic Hamiltonian, while integrating bias terms derived from an iterative digitized counterdiabatic quantum algorithm. …”
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Mine underground object detection algorithm based on TTFNet and anchor-free by Song Zhen, Qing Xuwen, Zhou Meng, Men Yuting

“…To solve the problem of poor object detection effect caused by uneven light and high noise in underground mines, this study proposes a TTFNet (training-time-friendly network)-based object detection algorithm for underground mines. First, CenterNet and TTFNet algorithms are introduced, then pooling is introduced into CSPNet basic structure to design a lightweight feature extraction network, at the same time optimizing the feature fusion way in the original algorithm, optimizing residual shrinkage network structure, and introducing it into object detection task. …”
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Observation optimization for marine target tracking by airborne and maritime unmanned platforms cooperation using adaptive enhanced dung beetle optimization by Qiuyang Dai, Faxing Lu, Haoran Shi, Junfei Xu, Jianjing Qu

“…Existing research predominantly focuses on homogeneous platform cooperative observation optimization under random error influences, while neglecting the collaborative optimization challenges of heterogeneous platforms affected by systematic errors. …”
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Hybrid Denoising Algorithm for Architectural Point Clouds Acquired with SLAM Systems by Antonella Ambrosino, Alessandro Di Benedetto, Margherita Fiani

“…This work presents the implementation of a denoising algorithm for point clouds acquired with LiDAR SLAM systems, aimed at optimizing data processing and the reconstruction of surveyed object geometries for graphical rendering and modeling. …”
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N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning by Juliana Manrique-Cordoba, Miguel Ángel de la Casa-Lillo, José María Sabater-Navarro

“…The results show that incorporating additional dimensions significantly enhances trajectory simplification while preserving key information. Additionally, the study highlights the importance of selecting appropriate encoding parameters to achieve optimal resolution. …”
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Comparisons of performances of structural variants detection algorithms in solitary or combination strategy. by De-Min Duan, Chinyi Cheng, Yu-Shu Huang, An-Ko Chung, Pin-Xuan Chen, Yu-An Chen, Jacob Shujui Hsu, Pei-Lung Chen

“…Numerous algorithms for short-read SV detection exist, but none are universally optimal, each having limitations for specific SV sizes and types. …”
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DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS by Daniel KWEGYIR, Francis Effah BOAFO, Daniel OPOKU, Emmanuel Asuming FRIMPONG

“…This approach aims to minimize energy consumption while maintaining thermal comfort. The algorithm’s performance was evaluated using real-world data from the Spanish Transmission Service Operators (TSO). …”
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Optimization of costs in the enterprise. Effective strategies for business by N. Yu. Pracheva

“…«The process of cost optimization cannot be started for a while and then stopped. …”
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Game and Application Purchasing Patterns on Steam using K-Means Algorithm by Salman Fauzan Fahri Aulia, Yana Aditia Gerhana, Eva Nurlatifah

“…The elbow method determines the optimal number of clusters, resulting in three clusters from the k-means algorithm. …”
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Development and Evaluation of a Multi-Robot Path Planning Graph Algorithm by Fatma A. S. Alwafi, Xu Xu, Reza Saatchi, Lyuba Alboul

“…The algorithm is suitable for identifying optimal and complete collision-free paths. …”
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Precise Retrieval of Sentinel-1 Data by Minimizing the Redundancy With Greedy Algorithm by Kaiwen Yang, Lei Zhang, Jicang Wu, Jinsong Qian

“…Aiming to address this issue, we present here an optimized retrieval method grounded in a greedy algorithm, which can substantially reduce redundant data by approximately 20–65% while ensuring comprehensive data coverage over the areas of interest. …”
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Grouped Byzantine fault tolerant consensus algorithm based on aggregated signatures by Yong Wang, Qiancheng Wan, Yifan Wu, Lijie Chen

“…Experimental results show that the GABFT algorithm significantly improves system throughput and scalability while reducing latency and communication overhead, making it well-suited for large-scale networks.…”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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Key frame extraction algorithm for surveillance videos using an evolutionary approach by Manjusha Rajan, Latha Parameswaran

“…Existing methods include the Adaptive Key Frame Extraction Algorithm, which reduces redundancy while ensuring maximum content coverage; the Optimal Key Frame Extraction Algorithm, which utilizes a Genetic Algorithm (GA) to select key frames optimally; and the Rapid Key Frame Extraction Algorithm, which employs clustering techniques to identify typical key frames. …”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses by Zahra Heydari, Ashlynn S. Stillwell

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Algorithm and mathematical model for geometric positioning of segments on aspherical composite mirror by B. Conquet, L. F. Zambrano, N. K. Artyukhina, R. V. Fiodоrtsev, A. R. Silie

“…This approach allows: to expand the spectral operating range from 0.2 to 11.0 μm and to increase the diameter of the entrance pupil of the receiving optical system, while maintaining the optimal value of the exponent mS– mass per unit area.Two variants of adjusting the position of mirror segments are considered when forming an aspherical surface of the second order, with respect to the base surface of the nearest sphere, including geometrical and opto-technical positioning.The purpose of the research was to develop an algorithm for solving the problem of geometric positioning of hexagonal segments of a mirror telescope, constructing an optimal circuit for traversing elements when aligning to the nearest radius to an aspherical surface, and also to program the output calculation parameters to verify the adequacy of the results obtained.Various methods for forming arrays from regular hexagonal segments with equal air gaps between them are considered. …”
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Insulator Defect Detection Algorithm Based on Improved YOLOv11n by Junmei Zhao, Shangxiao Miao, Rui Kang, Longkun Cao, Liping Zhang, Yifeng Ren

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Pulmonary Nodules Detection Algorithm Combining Multi-view and Attention Mechanism by LIU Yu-bo, LIU Guo-zhu, SHI Cao, XU Can-hui

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Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm by Hongxin Teng, Yudi Wang, Wentao Li, Tao Chen, Qinghua Liu

“…The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis.…”
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