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A Benchmarking on Optofluidic Microplastic Pattern Recognition: A Systematic Comparison Between Statistical detection Models and ML-Based algorithms

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IEEE ACCESS 2024年 12卷 20091-20102页

作者： Sasso, Daniele Merone, Mario Nicolai, Eleonora Vollero, Luca Sabatini, Anna Univ Campus Biomed Roma Dept Engn Res Unit Comp Syst & Bioinformat I-00128 Rome Italy Univ Roma Tor Vergata Dept Expt Med I-00133 Rome Italy

Microplastics, small particles of plastic found in the environment, have become an increasingly worrying topic in recent years. This paper compares a statistical detection model to classifiers from various supervised learning paradigms in order to detect microplastics. The objective of this paper is to present a benchmark for detecting microplastics using statistical and machine learning models. The main goal is to assess and compare their performance when the defined parameters deviate from the optimal solution of the respective model. Results are presented in terms of probability error, comparing the performance of the machine learning techniques to the statistical model. The study considers a range of signal-to-noise ratios and a priori event probabilities, focusing on the classifiers' ability to handle amplitude variability and threshold variation. Results show that as the number of analyzed particles in the flow increases, the detection performance improves, with Support Vector Machine, Linear Discriminant Analysis and Naive Bayes standing out from the other methods.

关键词： Plastics Particle measurements Biological system modeling Atmospheric measurements Water pollution Symbols Support vector machines detection algorithms Microplastics embedded system detection algorithm

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Evaluation and comparison of RFI detection algorithms

Evaluation and comparison of RFI detection algorithms

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Microwave Radiometry and Remote Sensing of the Environment, MICRORAD

作者： Janne Lahtinen Josu Uusitalo Teemu Ruokokoski Jukka Ruoskanen Harp Technologies Ltd Espoo Finland Finnish Defence Research Agency Riihimäki Finland

ISBN: (纸本)9781509029525

Anthropogenic Radio Frequency Interference (RFI) is an increasing problem in microwave remote sensing radiometry. Therefore, there is a growing interest on methods to detect and filter RFI. In this study, the performance of several different detection algorithms has been studied and compared to detect Continuous Wave (CW), QPSK modulated, and pulse modulated (with 0.1%, 1%, and 10% duty cycles) RFI. The mission scenario corresponds a spaceborne, polar-orbiting, conically scanning microwave radiometer. However, the qualitative results (e.g., the relative performances of algorithms) are applicable to other scenarios as well. It has been shown that RFI detection thresholds in 1 K range can be achieved in this scenario if complementary RFI detection algorithms can be incorporated in the system, e.g., in a digital RFI processor. Even 0.1 K level can be achieved for pulsed RFI with low duty cycles. Kurtosis and spectral kurtosis are effective in detecting pulsed RFI but not optimal in detecting constant envelope signals (such as CW or QPSK). Spectral Density Estimation and polarimetry, on the other hand, have a performance that is independent on the modulation or duty cycle of the RFI - they are sensitive to the average power.

关键词： Bandwidth Microwave radiometry detection algorithms Signal processing algorithms Receivers Microwave filters

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Efficiency of Gas detection algorithms Using Fluctuation Enhanced Sensing

Efficiency of Gas Detection Algorithms Using Fluctuation Enh...

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International Conference on Noise and Fluctuations

作者： Lentka, L. Smulko, J. Ionescu, R. Gdansk Univ Technol Fac ETI PL-80233 Gdansk Poland Rovira & Virgily Univ Dept Elect Tarragona Spain

ISBN: (纸本)9781467383356

Efficiency of various gas detection algorithms by applying fluctuation enhanced sensing method was discussed. We have analyzed resistance noise observed in resistive WO3-nanowires gas sensing layers. Power spectral densities of the recorded noise were used as the input data vectors for two algorithms: the principal component analysis (PCA) and the support vector machine (SVM). The data were used to determine gas concentration by regression methods. Additionally, the SVM algorithm used the slope of 1/f noise estimated for consecutive low frequency bands only to reduce the volume of computation by limiting the input data vector size. The results show that the SVM method gives the best results when the input vector is a power spectral density.

关键词： Gas sensors detection algorithms Fluctuaction Enhanced Sensing Principal Component Analysis Support Vector Machine

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Geometric Congestion detection algorithms in the Speed-Flow and Flow-Density Spaces 18

Geometric Congestion Detection Algorithms in the Speed-Flow ...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Moylan, Emily Rey, David Waller, S. Travis Univ New S Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia

ISBN: (纸本)9781467365963

Many ITS applications rely on known attributes of the traffic conditions. One useful property is congestion state which allows for differential behaviour in the system when demand is below, at or above capacity. Congestion detection in certain common data types such as loop detectors is frequently and idiosyncratically addressed by many researchers and practitioners. A set of flexible, objective and robust methods would facilitate the comparison of congestion state across datasets, locations and times of day to better model the response of the system to ITS interventions. This work develops geometric congestion detection algorithms for use in speed-flow and flow-density space. The methods are applicable to any dataset comprised of vehicle flows and speeds (such as loop detector data). The speed-flow space algorithm attempts to identify clusters in speed-flow space based on effective capacity and a cut-off free flow speed. The flow-density diagram builds on the theory supporting the triangular fundamental diagram and classifies congestion based on a density cut-off. Both methods incorporate time-of-day selection. The methods are successful in identifying clearly congested or uncongested observations along a test corridor. In conjunction, the two methods are able to distinguish two regions of ambiguity associated with the transition from uncongested to congested and vice versa. The combination of the two methods offers a promising approach for quickly and robustly classifying observations from a variety of location-typologies into two, three or four traffic states depending on the application.

关键词： intelligent transportation systems road traffic ITS applications flow-density diagram flow-density spaces geometric congestion detection algorithms speed-flow spaces traffic conditions triangular fundamental diagram Approximation algorithms Australia detection algorithms Detectors Mathematical model Monitoring Robustness

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Anomaly detection algorithms for the Sleeping Cell detection in LTE Networks 81

Anomaly Detection Algorithms for the Sleeping Cell Detection...

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81st IEEE Vehicular Technology Conference (VTC Spring)

作者： Chernov, Sergey Cochez, Michael Ristaniemi, Tapani Univ Jyvaskyla Dept Math Informat Technol SF-40351 Jyvaskyla Finland

ISBN: (纸本)9781479980888

The Sleeping Cell problem is a particular type of cell degradation in Long-Term Evolution (LTE) networks. In practice such cell outage leads to the lack of network service and sometimes it can be revealed only after multiple user complains by an operator. In this study a cell becomes sleeping because of a Random Access Channel (RACH) failure, which may happen due to software or hardware problems. For the detection of malfunctioning cells, we introduce a data mining based framework. In its core is the analysis of event sequences reported by a User Equipment (UE) to a serving Base Station (BS). The crucial element of the developed framework is an anomaly detection algorithm. We compare performances of distance, centroid distance and probabilistic based methods, using Receiver Operating Characteristic (ROC) and Precision-Recall curves. Moreover, the theoretical comparison of the methods' computational efficiencies is provided. The sleeping cell detection framework is verified by means of a dynamic LTE system simulator, using Minimization of Drive Testing (MDT) functionality. It is shown that the sleeping cell can be pinpointed.

关键词： Sleep Local thermodynamic equilibrium Long Term Evolution detection algorithms Cells anomaly detection data mining

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Context Exploitation in Intelligence, Surveillance, and Reconnaissance for detection algorithms 15

Context Exploitation in Intelligence, Surveillance, and Reco...

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IEE* Win* App* Com* Vis* Wor*

作者： Tucker, Jonathan D. Stanfill, S. Robert Lockheed Martin Missiles & Fire Control Appl Res Orlando FL 32819 USA

ISBN: (纸本)9780769554693

Intelligence, Surveillance, and Reconnaissance (ISR) missions involve complex analysis of sensor data that can benefit from the exploitation of geographically aligned context. In this paper we discuss our approach to utilizing geo-registered imagery and context for the purpose of aiding ISR applications. Specifically this includes rendering context masks on imagery, increasing the speed at which detection algorithms process data, providing a way to intelligently control detection density for given ground areas, identifying difficult traffic terrain, and peak suppression refinement.

关键词： feature extraction geophysical image processing image registration rendering (computer graphics) ISR applications ISR missions context exploitation context masks rendering detection algorithms detection density geo-registered imagery geographically aligned context ground areas intelligence-surveillance-reconnaissance missions peak suppression refinement sensor data complex analysis traffic terrain Buildings Context detection algorithms Detectors Process control Rail transportation Rendering (computer graphics) detection algorithms Rail transportation grinding area Process control Reconnaissance image registration Buildings Feature extraction Rendering (computer graphics) Sensor Data

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A Helmet detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency detection algorithms

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Internet of Things Assisted Plant Disease detection and Crop Management Using Deep Learning for Sustainable Agriculture

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IEEE ACCESS 2025年 13卷 3512-3520页

作者： Al-Shahari, Eman A. Aldehim, Ghadah Aljebreen, Mohammed Saad Alqurni, Jehad Salama, Ahmed S. Abdelbagi, Sitelbanat King Khalid Univ Coll Sci & Arts Mahayil Dept Biol Mahayil 62529 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Community Coll Dept Comp Sci Riyadh 11437 Saudi Arabia Imam Abdulrahman bin Faisal Univ Coll Educ Dept Educ Technol Dammam 31441 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia

The integration of Internet of Things (IoT) technology with deep learning (DL) algorithms has revolutionized plant disease detection and crop management and paved the way for sustainable agricultural practices. Real-time information on soil moisture, plant health, and environmental conditions can be collected by deploying a network of connected devices and sensors in agricultural fields. DL algorithms, specifically convolutional neural networks (CNN), analyze this massive dataset, facilitating timely and accurate recognition of plant diseases. This early detection allows farmers to implement targeted interventions, like adjustment to irrigation or precision application of pesticides, maximizing crop yield, and minimizing resource wastage. Therefore, this article develops an automated Plant Disease detection and Crop Management using a spotted hyena optimizer with deep learning (APDDCM-SHODL) technique for Sustainable Agriculture. The APDDCM-SHODL approach aims to detect the existence of plant diseases and improve crop productivity in the IoT infrastructure. To achieve this, the APDDCM-SHODL method primarily employs the Vector Median Filter (VMF) technique. In addition, the Densely Connected Networks (DenseNet201) model is deployed for feature extraction. In addition, the SHO technique is exploited for optimum hyperparameter tuning of the DenseNet201 model. Furthermore, the classification algorithm is implemented by using the recurrent spiking neural network (RSNN) model. A brief set of experiments has been made to determine the experimental validation of the APDDCM-SHODL model. The comprehensive results inferred that the APDDCM-SHODL method reaches remarkable performance over other existing methods with the highest accuracy of 98.60%.

关键词： Feature extraction Crops Vectors Plant diseases Agriculture Internet of Things Tuning detection algorithms Hyperparameter optimization Sustainable development Deep learning Plant disease detection hyperparameter tuning sustainable agriculture deep learning

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Privacy-Preserving Multilayer Community detection via Federated Learning

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第2期12卷 832-846页

作者： Ma, Shi-Yao Xu, Xiao-Ke Xiao, Jing Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Beijing Normal Univ Computat Commun Res Ctr Beijing 100875 Peoples R China Beijing Normal Univ Sch Journalism & Commun Beijing 100875 Peoples R China Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China

Existing frameworks of privacy-preserving multilayer community detection have room for improving detection performance and reducing communication overhead. To address these issues, we propose a novel privacy-preserving multilayer community detection framework based on federated learning which is called federated multilayer community detection (FMCD). First, we propose a novel aggregation strategy by utilizing the network average degree of local networks to aggregate the parameters uploaded by clients in the step of aggregation, which can improve the performance of community detection. Second, we design a training procedure to complete multilayer community detection in multiorganizations, which can reduce communication overhead by transmitting merged community information instead of the global parameter. Finally, experiment results on synthetic and real networks with different criteria illustrate that FMCD can achieve significant performance gains, compared with state-of-the-art algorithms.

关键词： Nonhomogeneous media detection algorithms Organizations Periodic structures Data models Training Servers Privacy Federated learning Computational modeling Community detection federated learning (FL) multilayer network privacy protection

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An Empirical Study of Ground Segmentation for 3-D Object detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 3071-3083页

作者： Yang, Hongcheng Liang, Dingkang Liu, Zhe Li, Jingyu Zou, Zhikang Ye, Xiaoqing Bai, Xiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Baidu Inc Beijing 100085 Peoples R China

The ratio of foreground and background points directly impacts the accuracy and speed of the lidar-based 3D object detection methods. However, existing methods generally ignore the impact of ground points. Although some traditional ground segmentation algorithms are available to remove ground point clouds, they usually suffer from over-segmentation, which leads to a sub-optimal and even worse performance for the downstream 3D detection task. We conduct an in-depth analysis and attribute this phenomenon to the reason that some crucial foreground points attached to the ground (e.g., the wheels of Cars, or the feet of Pedestrians) are directly removed due to over-segmentation. To this end, we propose a new Attached Point Restoring (APR) module to recover these discarded foreground points. Experimental results demonstrate the effectiveness and generalization of APR by integrating it into various ground segmentation algorithms to boost the performance or the running time of 3D detection on KITTI and Waymo datasets. Finally, we hope this paper can serve as a new guide to inspire future research in this field. Code is available at https://***/yhc2021/GPR.

关键词： Three-dimensional displays Point cloud compression Feature extraction Object detection Accuracy Detectors detection algorithms Convolution Solid modeling Mathematical models Point cloud 3D object detection ground segmentation

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