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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Xinran Li Liang Chen Wenhong Liu College of Automation and Electrical Engineering Shenyang Ligong University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In order to solve the problem of dangerous accidents caused by unsafe behaviors in university laboratories, We propose an improved method based on the RTFM model. This method combines Dilated convolution and multi-head attention mechanism to form a new feature aggregation module and improve the sensitivity to abnormal features. In addition, the Gated Recurrent Unit (GRU) is introduced to capture the timing information in the video, effectively solving the problem of false detection when RTFM detects anomalies. Experimental results show that compared with the original model, the method in this paper improves AUC by 4%. Effectively improves the accuracy of identifying abnormal behaviors of laboratory personnel.

关键词： Representation learning Accuracy Sensitivity Data analysis Laboratories signal processing algorithms Logic gates Timing Personnel Chemicals

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Optimal Feature Manipulation Attacks Against Linear Regression

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IEEE TRANSACTIONS ON signal processing 2021年 69卷 5580-5594页

作者： Li, Fuwei Lai, Lifeng Cui, Shuguang Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Future Network Intelligence Inst Shenzhen 518172 Peoples R China

In this paper, we investigate how to manipulate the coefficients obtained via linear regression by adding carefully designed poisoning data points to the dataset or modifying the original data points. Given the energy budget, we first provide the closed-form solution of the optimal poisoning data point when our target is modifying one designated regression coefficient. We then extend the analysis to a more challenging scenario where the attacker aims to change one particular regression coefficient while making others to be changed as small as possible. For this scenario, we introduce a semidefinite relaxation method to design the best attack scheme. Finally, we study a more powerful adversary who can perform a rank-one modification on the feature matrix. We propose an alternating optimization method to find the optimal rank-one modification matrix. Numerical examples are provided to illustrate the analytical results obtained in this paper.

关键词： Linear regression Robustness Optimization methods Training data Tuning Support vector machines signal processing algorithms Linear regression adversarial robustness poisoning attack non-convex optimization

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Simulation Study of Submarine Evading Anti-Submarine Helicopter Search Based on Improved Particle Swarm Optimization Algorithm

Simulation Study of Submarine Evading Anti-Submarine Helicop...

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6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Jiaming Zhang Jian Liu Navy Submarine Academy Qingdao China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

It is a typical path optimization problem for submarines to avoid dipping sonar and sonobuoy joint search in a certain sea area by pure maneuvering. Particle swarm optimization ( PSO ) and improved particle swarm optimization ( IPSO ) based on parallel computing are used to solve this problem by using nonlinear inertia weight and improved learning factor. The objective function adopts the principle of distance maximization and the principle of minimum and maximum respectively. The simulation results show that the parallel improved particle swarm optimization algorithm can quickly calculate and obtain a better feasible solution, and the submarine can meet the objective function through maneuver avoidance to meet the submarine defense needs.

关键词： Simulation signal processing algorithms Sonar Parallel processing signal processing Linear programming Trajectory Particle swarm optimization Underwater vehicles Optimization

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Smart Phone-Assisted Rapid Aggregate Size Analysis Technology

Smart Phone-Assisted Rapid Aggregate Size Analysis Technolog...

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International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Yiran Wang Jun Wei Kai Zhu Wenlong Zhou Jinming Chen Jiguang Han School of Automation and Electrical Engineering University of Jinan Jinan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Particle size distribution or aggregate gradation is the key to ensure the quality of concrete and optimize the structure design. In this paper, a method of measuring aggregate particle size distribution based on smartphone image is proposed. Firstly, the image contrast is enhanced by gray-scale and luminance transform, and the noise is removed by adaptive median filtering algorithm, and the edge of particles is preserved. Then the watershed algorithm is used to segment the image on the basis of clustering, and the two-dimensional shape and size parameters are extracted from the segmentation results. Compared with the screening method and the manual measurement method, the cumulative error rate of this method is controlled within 5%, which verifies its reliability. This method is suitable for rapid assessment of aggregate particle size distribution and provides effective technical support for quality control of concrete engineering.

关键词： Image segmentation Atmospheric measurements Aggregates signal processing algorithms Clustering algorithms Manuals Biomedical measurement Filtering algorithms Size measurement Particle measurements

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A Real-Time Detection Algorithm for Electrocardiogram and Arterial Blood Pressure signals in Intra-Aortic Balloon Pump

A Real-Time Detection Algorithm for Electrocardiogram and Ar...

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International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Shiqi Yu Lepeng Zeng Jian Wu Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate School Shenzhen China ShenZhen Lifetech Cardio MedicalElectronics Co. Ltd. Shenzhen China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

This paper proposes an algorithm combining a dynamic adaptive thresholding R-wave detection strategy and a differential feature-based blood pressure waveform recognition technique to realize rapid and real-time electrocardiogram (ECG) and arterial blood pressure (ABP) signal detection. The aim is to address the demand for accuracy and real-time performance of the intra-aortic balloon pump (IABP) system in heart failure treatment as an adjunct to heart failure. The proposed algorithm was validated in two public databases: the MIT-BIH arrhythmia database (MITDB) and the polysomnography database (SLPDB). The results showed that the sensitivity and positive predictive value of the ECG R-wave detection were 97.51 % and 99.73%, respectively, while the accuracy of the blood pressure feature point detection could reach 99.18%. Furthermore, the real-time performance of the algorithm was also evaluated, and the algorithm's detection average lead time was +0.46 milliseconds. In summary, this algorithm provides effective help for IABP timing technology and shows good application potential in the field of mechanical circulation-assisted therapy of cardiovascular diseases.

关键词： Accuracy Databases signal processing algorithms Medical treatment Electrocardiography Prediction algorithms Real-time systems Blood pressure Timing Cardiovascular diseases

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Advancements in Phishing Website Detection: A Comprehensive Analysis of Machine Learning and Deep Learning Models

Advancements in Phishing Website Detection: A Comprehensive ...

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IEEE signal processing and Communications Applications (SIU)

作者： Soudabeh Mousavi Mahdi Bahaghighat Figen Ozen Computer Engineering Department Imam Khomeini International University Qazvin Iran Halic University İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Today, machine learning (ML) and deep learning (DL) have become potent tools for detecting phishing websites. This paper has employed a bunch of ML algorithms such as Random Forest, Extra Tree, XGBoost, LGBM, and XGBoost-bagging, along with some proposed neural network architectures found on Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for identifying website phishing. Implementing a comprehensive approach, we devised our model based on 56 proposed features extracted from each target website using Feature Importance. Then, we conducted five strategic steps to enhance the performance of ML and DL models. Through these steps, we improved DL model accuracy remarkably, notably elevating LSTM accuracy from the lowest accuracy of $96.18 \%$ to $98.78 \%$ with an AUC-ROC LSTM of about $\mathbf{9 9 . 8 5 \%}$.

关键词： Deep learning Accuracy Phishing Neural networks signal processing algorithms Feature extraction Recording

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Research on Path Planning of Unmanned Mine Truck in Open-pit Mine Based on Vehicle-road Coordination

Research on Path Planning of Unmanned Mine Truck in Open-pit...

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Lincong Zhang Tingjin Chen Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

To facilitate the implementation of unmanned trucks in open-pit mines, this paper explores the essential technologies for path planning for unmanned truck. It proposes a novel research approach, advocating for the organic integration of vehicle-road cooperation and path planning for unmanned truck. And this paper proposes a R-PSO algorithm based on return value for the sudden accident in the open-pit mine area. The simulation results show that the algorithm has better optimization ability, faster convergence speed and shorter time consumption than the traditional algorithm.

关键词： Simulation Roads signal processing algorithms signal processing Path planning Optimization Accidents Convergence

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Research on dynamic motion pattern recognition and analysis algorithm based on artificial intelligence

Research on dynamic motion pattern recognition and analysis ...

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signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Kequan Chen Zhanjiang University of Science and Technology Zhanjiang China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper focuses on the dynamic motion pattern recognition and analysis algorithm based on artificial intelligence, and proposes an innovative motion pattern recognition method, which combines fast Fourier transform (FFT) and fusion random forest model. Firstly, the motion signal is efficiently transformed from the time domain into the frequency domain through the application of the fast Fourier transform, enabling the extraction of key frequency characteristics, thereby enhancing the recognition capability of the motion pattern. The accuracy and stability of the model are subsequently enhanced by integrating the outputs from multiple decision trees, leveraging the power of the fusion random forest algorithm. In the random forest decision algorithm, the integrated learning strategy is introduced, which realizes the deficiency of single decision tree and further improves the classification performance of motion patterns. The experimental outcomes demonstrate that this method exhibits outstanding performance across various motion pattern recognition tasks, coupled with remarkable real-time capabilities and precision. This research provides a new idea and method for the field of motion pattern recognition, which has important theoretical value and practical application prospect.

关键词： Accuracy Fast Fourier transforms Heuristic algorithms Dynamics signal processing algorithms Pattern recognition Classification algorithms Decision trees Artificial intelligence Random forests

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Orbital Angular Momentum Modes Filtering for Multiple Modes Vortex Wave Multiplexing

Orbital Angular Momentum Modes Filtering for Multiple Modes ...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Yuqian Liu Qiang Feng Huiliang Liu Song Jin Xiao Tao Zhongyue Liu Yinqiao Li Yaxing Cai Institute of Telecommunication and Navigation Satellites China Academy of Space Technology Beijing China School of Electronic Engineering Xidian University Xi'an China China Academy of Space Technology Xi'an Xi'an China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

In this paper, an orbital angular momentum (OAM) modes filtering method for multiple modes multiplexing is proposed. The whole aperture sample reception (WASR) method is used first for multiple OAM modes reception based on the fast Fourier transform (FFT). Then the chirp z-transform (CZT) algorithm is used for multiple OAM modes filtering. For demonstrating the effectiveness of the method, a classic uniform circular array antenna is designed to generate multiple combination OAM mode simultaneously. And different filtering results are shown through applying the CZT algorithm.

关键词： Multiplexing Orbital calculations Filtering Fast Fourier transforms signal processing algorithms Digital signal processing Microwave filters

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Sparse Features for Multi-Exposure Fusion

Sparse Features for Multi-Exposure Fusion

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European signal processing Conference (EUSIPCO)

作者： Zeynep Ovgu Yayci Mehmet Turkan Department of Aerospace Engineering Izmir University of Economics Izmir Turkiye Department of Electrical and Electronics Engineering Izmir University of Economics Izmir Turkiye

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

High dynamic range (HDR) capture and display devices can be used to approximately mimic the human perception of gamut of colors and fine details. However, the relative high-cost of these devices may currently make them be not affordable for many consumers. Multi-exposure image fusion (MEF) offers a cost-effective software-based solution to this problem. By fusing low dynamic range (LDR) images with different exposure levels, MEF aims to create HDR-like images for LDR display devices, that are high in quality but low in cost. This study proposes a novel MEF weight-map extraction method using sparse signal representations and k-means clustering. A preprocessing stage extracts initial masks from over- and under-exposed images to be used for weight map extraction and the proposed clustering model allows the overall algorithm to have good fusion performance regardless of the number of input images contained in the input exposure sequence. After a final multi-scale pyramidal fusion, the resulting HDR-like images show not only visually pleasing but also statistically significant results when compared to state-of-the-art methods in the literature.

关键词： Image quality Visualization Image color analysis signal processing algorithms Clustering algorithms signal processing Feature extraction High dynamic range signal representation Image fusion

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