Experimental Demonstration of 16-QAM DD-SEFDM With Cascaded BPSK Iterative Detection by Jun Huang, Qi Sui, Zhaohui Li, Fei Ji

“…To simplify the complexity of a spectrally efficient frequency-division multiplexing (SEFDM) system, cascaded binary-phase-shift-keying iterative detection (CBID) is proposed for square <inline-formula> <tex-math notation="LaTeX">$M$</tex-math></inline-formula>-ary quadrature-amplitude-modulation (M-QAM) SEFDM as the first decoding stage, in conjunction with a fixed sphere decoder (FSD). …”
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Automatic detection of foreign object intrusion along railway tracks based on MACENet. by Xichun Chen, Yu Tian, Ming Li, Bin Lv, Shuo Zhang, Zixian Qu, Jianqing Wu, Shiya Cheng

“…Ensuring high accuracy and efficiency in foreign object intrusion detection along railway lines is critical for guaranteeing railway operational safety under limited resource conditions. …”
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Redundancy and conflict detection method for label-based data flow control policy by Rongna XIE, Xiaonan FAN, Suzhe LI, Yuxin HUANG, Guozhen SHI

“…To address the challenge of redundancy and conflict detection in the label-based data flow control mechanism, a label description method based on atomic operations has been proposed.When the label is changed, there is unavoidable redundancy or conflict between the new label and the existing label.How to carry out redundancy and conflict detection is an urgent problem in the label-based data flow control mechanism.To address the above problem, a label description method was proposed based on atomic operation.The object label was generated by the logical combination of multiple atomic tags, and the atomic tag was used to describe the minimum security requirement.The above label description method realized the simplicity and richness of label description.To enhance the detection efficiency and reduce the difficulty of redundancy and conflict detection, a method based on the correlation of sets in labels was introduced.Moreover, based on the detection results of atomic tags and their logical relationships, redundancy and conflict detection of object labels was carried out, further improving the overall detection efficiency.Redundancy and conflict detection of atomic tags was based on the relationships between the operations contained in different atomic tags.If different atomic tags contained the same operation, the detection was performed by analyzing the relationship between subject attributes, environmental attributes, and rule types in the atomic tags.On the other hand, if different atomic tags contained different operations without any relationship between them, there was no redundancy or conflict.If there was a partial order relationship between the operations in the atomic tags, the detection was performed by analyzing the partial order relationship of different operations, and the relationship between subject attribute, environment attribute, and rule types in different atomic tags.The performance of the redundancy and conflict detection algorithm proposed is analyzed theoretically and experimentally, and the influence of the number and complexity of atomic tags on the detection performance is verified through experiments.…”
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An Anomaly Detection Method for Industrial System Cybersecurity Based on GGL-WAVE-CNN by Bing Zou, Ke jun Zhang, Xin Ying Yu, Yu han Jin, Jun Wang, Ling yu Liu

“…Current approaches often struggle to handle complex, unknown topological time series data, thereby necessitating improved anomaly detection accuracy. …”
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Integrating ANN and ANFIS for effective fault detection and location in modern power grid by Yadav Goutam Kumar, Kirar Mukesh Kumar, Gupta S.C., Rajender Jatoth

“…The increasing complexity and demand for reliability in modern power systems necessitate advanced techniques for fault detection, classification, and location. …”
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Intelligent Casting Quality Inspection Method Integrating Anomaly Detection and Semantic Segmentation by Min-Chieh Chen, Shih-Yu Yen, Yue-Feng Lin, Ming-Yi Tsai, Ting-Hsueh Chuang

“…Customized optical path design is often required, especially when conducting internal and external defect inspections, which increases overall operational complexity and reduces inspection efficiency. We developed an automated optical inspection (AOI) system to address these challenges. …”
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Effects of Ageing and Sex on Complexity in the Human Sleep EEG: A Comparison of Three Symbolic Dynamic Analysis Methods by Pinar Deniz Tosun, Derk-Jan Dijk, Raphaelle Winsky-Sommerer, Daniel Abasolo

“…All three SDA techniques distinguished the vigilance states (i.e., wakefulness, REM sleep, NREM sleep, and its sub-stages: stage 1, stage 2, and slow wave sleep). Complexity of the sleep EEG increased with ageing. Sex on the other hand did not affect the complexity values assessed with any of these three SDA methods, even though FFT detected sex differences. …”
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Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD by Chao Chen, Zhuo Chen, Hao Li, Yawen Wang, Guangzhou Lei, Lingling Wu

“…Given the high computational complexity and poor real-time performance of current photovoltaic cell surface defect detection methods, this study proposes a lightweight model, YOLOv8-FSD, based on YOLOv8. …”
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ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection by Zezhong Ding, Yanfang Li, Bin Hu, Zhiwei Chen, Houzhen Jia, Yali Shi, Xingmin Zhang, Xuesong Zhu, Wenjie Feng, Chunwang Dong

“…To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. …”
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DART-scale Impacts into a Complex Low-strength Target: Insights into Asteroid Dimorphos’s Interior from Simulations by Cem Berk Senel, Robert Luther, Özgür Karatekin, Gareth S. Collins, Steven Goderis, Philippe Claeys

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Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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Multi-Scale Construction Site Fire Detection Algorithm with Integrated Attention Mechanism by Haipeng Sun, Tao Yao

“…To address the issues of large target-scale variations and frequent false detections in construction site fire monitoring, we propose a fire detection algorithm based on an improved YOLOv8 model, achieving real-time and efficient detection of fires on construction sites. …”
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Quantitative Detection of Water Content of Winter Jujubes Based on Spectral Morphological Features by Yabei Di, Huaping Luo, Hongyang Liu, Huaiyu Liu, Lei Kang, Yuesen Tong

“…The spectral information extracted from hyperspectral images is characterized by redundancy and complexity, while the spectral morphological features extracted from the spectral information help to simplify the data and provide rich information about the material composition. …”
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Forecast of the morphology and occurrence conditions of complex chromite ore bodies (on example of Labogeyskoe-2 site, Polar Urals) by T. P. Morozova, E. V. Karelina, V. E. Markov

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Dynamic Aerial Small Target Detection Algorithm Based on Compound Zoom Scaling by Jiang Yuan, Zhu Gaofeng, Zhu Fenghua, Xiong Gang

“…Experiments conducted on the VisDrone2019 UAV aerial ima-gery dataset demonstrate that the proposed algorithm improves mAP by 2.1%, reduces FLOPs by 32.5%, and decreases computational complexity, resulting in superior detection performance.…”
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Towards real-time interest point detection and description for mobile and robotic devices by Patrick Rowsome, Muhammad Adil Raja, R. Muhammad Atif Azad

“…This paper demonstrates how techniques, developed for other CNN use cases, can be integrated into interest point detection and description systems to compress their network size and reduce the computational complexity; this reduces the barrier to their uptake in computationally challenged environments. …”
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Anomaly detection and removal strategies for in-line permittivity sensor signal used in bioprocesses by Emils Bolmanis, Emils Bolmanis, Emils Bolmanis, Selina Uhlendorff, Miriam Pein-Hackelbusch, Vytautas Galvanauskas, Oskars Grigs

“…Trivial approaches, such as moving average filtering, do not adequately capture the complexity of the problem. However, our method provides a structured solution through three consecutive steps: 1) Signal preprocessing to reduce noise and eliminate context dependency; 2) Anomaly detection using threshold-based identification; 3) Validation and removal of identified anomalies.Results and discussionWe demonstrate that our approach effectively detects and removes anomalies by compensating signal shift value, while remaining computationally efficient and practical for real-time use. …”
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Vehicle detection in drone aerial views based on lightweight OSD-YOLOv10 by Yang Zhang, Xiaobing Chen, Su Sun, Hongfeng You, Yuanyuan Wang, Jianchu Lin, Jiacheng Wang

“…Compared to other YOLO series and lightweight models, OSD-YOLOv10 exhibits superior detection accuracy and lower computational complexity, achieving an optimal balance between high accuracy and low resource consumption. …”
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Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks by Hang Wei, Zulin Wang, Yuanhan Ni

“…Post-training quantization (PTQ) provides an efficient method for pre-training neural networks to effectively reduce memory and computational resources without retraining. …”
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