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检索条件"主题词=Signal Processing Algorithms"

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FMCW Height Measurement System for Complex Urban Environments

FMCW Height Measurement System for Complex Urban Environment...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Xiaole Bian Jun Zhang Yan Zhou School of Electronic Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

A Frequency Modulated Continuous Wave (FMCW) height measurement system designed for complex urban environments is presented, employing a single-antenna structure, multi-threshold OS-CFAR algorithm, and weighted smoothing filtering techniques. The system enhances anti-interference capability and measurement accuracy. The single-antenna structure optimizes signal transmission and reception processes, achieving system miniaturization and high adaptability. The multi-threshold OS-CFAR algorithm utilizes segmented detection and adaptive parameter adjustment to effectively suppress the effects of multipath interference and Rayleigh fading, improving false target suppression rates by approximately 25%. Weighted smoothing filtering reduces Doppler-induced errors by integrating multiple measurement results, limiting the measurement error to within 0.5 m. Experimental results confirm the system's high stability and accuracy in complex environments, providing reliable technical support for height measurement in low-altitude navigation and obstacle detection.

关键词： Weight measurement Smoothing methods Accuracy Urban areas signal processing algorithms Interference Rayleigh channels Height measurement Frequency measurement Reliability

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Enhancing Customer Retention Strategies: Predicting Churn Rate in Telecom Sectors Using Machine Learning Ensemble Techniques

Enhancing Customer Retention Strategies: Predicting Churn Ra...

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International Conference on Communications and signal processing

作者： Chinmay Chourdia Stuti Roy Apoorva Vikrant Kulkarni Bhuvaneswari Arunagiri Symbiosis Centre for Information Technology Symbiosis International (Deemed University) Pune India Anna University & Adhiparasakthi Engineering College India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In the competitive telecommunications industry, clients risk revenue and marketing costs. This study focuses on churn using ensemble methods such as RandomForest, ExtraTrees, LightGBM, and XGBoost. Evaluating eight algorithms including Decision Tree and Gradient Boosting, AdaBoost emerges as the best performer with the highest F1 score, ensuring a balanced trade-off between accuracy and recall He advises telecom companies on flying routes improving the storage of strategic importance, using machine learning. The findings provide a strategy for telecommunication companies to improve customer retention strategies in a dynamic market.

关键词： Industries Machine learning algorithms signal processing algorithms Companies Forestry signal processing Predictive models

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Review of Research on Fatigue Driving Detection Methods

Review of Research on Fatigue Driving Detection Methods

引用

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

作者： Yingna Qin Ning Chen School of Electronics andInformation Xi'an Polytechnic University Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Fatigue driving is one of the main causes of road traffic accidents, posing a serious threat to traffic safety. Fatigue detection is crucial for drivers. This article introduces fatigue driving and its characteristic manifestations, discusses the two categories of current fatigue driving detection methods: subjective detection and objective detection. The principles of various detection methods are analyzed, and the latest research progress is summarized. Furthermore, an overview of classic datasets is provided, and the issues that current fatigue driving detection methods need to address, as well as future development directions, are identified.

关键词： Costs Accuracy Software algorithms signal processing algorithms Fatigue Feature extraction Software Safety Vehicles Accidents

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Continual Adaptation for Unsupervised Time Series Anomaly Detection

Continual Adaptation for Unsupervised Time Series Anomaly De...

引用

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

作者： Ting Yu Bo Ding Xu Wang State Key Laboratory of Complex & Critical Software Environment College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The application of time series anomaly detection has been widespread across various industrial scenarios. Although current algorithms for time series anomaly detection have shown promising results, they primarily focus on the characteristics of the data distribution within the current training set. This often leads to a lack of adaptability to changes in data distribution while retaining the ability to accommodate new data and preserve existing knowledge. Given the dynamic nature of real-world industrial production systems, the issue of continuous updating of time series anomaly detection algorithms warrants significant attention. However, existing time series anomaly detection algorithms predominantly rely on unsupervised methods, thereby resulting in sparse availability of relevant update strategies. Recognizing this challenge, this paper proposes an unsupervised anomaly detection algorithm update mechanism that incorporates continual learning methods. This approach aims to address the issue in time series anomaly detection algorithms stemming from the absence of label guidance, which leads to a lack of continuous updating capability in models. The method employs a strategy of retaining key information about the past data's feature distribution to ensure memory of past information, thus fulfilling the need for updating and adapting to new data features with minimal storage overhead. Compared to other potential improvement algorithms for continual updating, our approach achieves comparable performance with significantly reduced overhead. In particular, it addresses the issue in the current domain of unsupervised time series anomaly detection, where the lack of labeled data results in a deficiency of continuous updating methods.

关键词： Continuing education Training Production systems Heuristic algorithms Time series analysis Fitting signal processing algorithms Gaussian distribution Recording Anomaly detection

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Unlocking the Potential of Raw Images for Object Detection with YOLOv8 and BOT-SORT Techniques

Unlocking the Potential of Raw Images for Object Detection w...

引用

Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Bolai Jin Faculty of Engineering University of Sydney Sydney Australia

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

The basic use case of raw image for object detection and tracking in autonomous driving with the help of YOLOv8,also it uses the BOT-SORT algorithm be discussed in this paper. Converting raw sensor data to processed images using Image signal processing (ISP) is a computationally intensive and expensive task that traditionally has been in the image processing pipelines of many applications. In this paper, we examine these costs and the processing pipeline itself and propose a solution that eliminates them by consuming raw images directly. We perform our experiments on the RobotCar dataset, which suggest that raw images achieve competitive precision, recall and F1 scores to their preprocessed counterparts but in a fraction of the time. This work validates that raw images are a feasible and advantageous way to perform real-time object detection and tracking, contributing as possible alternative for coachbuilders. This work verifies by experiments the efficiency of raw images applying YOLOv8 and BOT-SORT in-depth, which significantly reduces cost and time over other related researches as a primary contribution. Our work on the findings studied indicate that raw images lend a direction toward efficacy and performance for improving autonomous driving system.

关键词： Costs Pipelines signal processing algorithms Systems architecture Object detection signal processing Streaming media Routing Real-time systems Autonomous vehicles

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A Lifted Bregman Strategy for Training Unfolded Proximal Neural Network Gaussian Denoisers

A Lifted Bregman Strategy for Training Unfolded Proximal Neu...

引用

IEEE Workshop on Machine Learning for signal processing

作者： Xiaoyu Wang Martin Benning Audrey Repetti School of Mathematical and Computer Sciences Heriot-Watt University Department of Computer Science University College London

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. They consist in designing a neural network for a specific task by unrolling a proximal algorithm for a fixed number of iterations, where linearities can be learned from prior training procedure. PNNs have shown to be more robust than traditional deep learning approaches while reaching at least as good performances, in particular in computational imaging. However, training PNNs still depends on the efficiency of available training algorithms. In this work, we propose a lifted training formulation based on Bregman distances for unfolded PNNs. Leveraging the deterministic mini-batch block-coordinate forward-backward method, we design a bespoke computational strategy beyond traditional back-propagation methods for solving the resulting learning problem efficiently. We assess the behaviour of the proposed training approach for PNNs through numerical simulations on image denoising, considering a denoising PNN whose structure is based on dual proximal-gradient iterations.

关键词： Training Deep learning Neural networks Noise reduction signal processing algorithms signal processing Network architecture Numerical simulation Standards Optimization

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An Adaptive Extended Kalman Filter Based on Variational Bayesian and Sage-Husa Prediction algorithms

An Adaptive Extended Kalman Filter Based on Variational Baye...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Lian Ma Ran Cao Chun Long Huang Harbin Engineering University Harbin China Key Laboratory of Ocean Observation Technology MNR Tianjin China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

An adaptive extended Kalman filter based on variational Bayesian and Sage-Husa prediction algorithms is proposed to address the problems of motion model mismatch and high-level environmental noise interference in target tracking. It combines variational Bayesian inference and Sage-Husa Kalman filtering. This algorithm can adaptively modify the present motion model and uncertain measurement variance caused by stochastic ocean environmental noise. Through simulation results, it has been verified that the tracking accuracy and robustness of this method are superior to traditional Kalman filter tracking estimation methods.

关键词： Adaptation models Target tracking Working environment noise Simulation signal processing algorithms Stochastic processes Prediction algorithms Inference algorithms Bayes methods Kalman filters

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Pneumonia Classification using deep learning: a comparative study

Pneumonia Classification using deep learning: a comparative ...

引用

International Conference on Image and signal processing and their Applications (ISPA)

作者： Mohamed Lakhdar Tiar Nadjiba Terki Juan Jose Dominguez-Jimenez IL3CUB Laboratory University of Mohamed Khider Biskra Algeria Electrical Engineering Department University of Mohamed Khider Biskra Algeria Dept. Computer Languages and Systems University of Cadiz Escuela Superior de Ingenieria Cadiz Spain

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Pneumonia is an infectious disease of the lungs, caused by viruses, bacteria or fungi. Pneumonia is distinguished by acute inflammation of the lung tissue, causing the consolidation of the terminal bronchioles and alveoli. According to the WHO (the World Health Organization), this disease causes about 4 million deaths. Among the methods of diagnosing pneumonia uses a chest X-ray. This is widely used to visualize pulmonary abnormalities. This work aims at the detection and characterization of pneumonia by the development of computer-assisted diagnosis systems (DAOC). We use deep learning algorithms and chest X-ray images from a pneumonia database to examine the classification of pneumonia. To improve the accuracy, we compare several deep learning models. This research contributes to meeting the growing demand for medical personnel by addressing the global incidence of chest disorders.

关键词： Deep learning Visualization Pneumonia Lung signal processing algorithms Medical services signal processing

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An Attention Auxiliary Network-Based Method for WiFi Fingerprint Indoor Localization

An Attention Auxiliary Network-Based Method for WiFi Fingerp...

引用

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

作者： Qiao Fu Wen Liu Yuanzhi Chen Zhongliang Deng School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Communication University of China Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The fingerprint location system based on WiFi RSSI is often affected by APs changes, which makes it difficult to obtain positioning signals with high location distinction and long-term stability. However, existing methods for reducing cross-domain fingerprint distribution discrepancy don't effectively introduce specific features of different time domains and cannot achieve long-term accurate localization. In this paper, we proposes a WiFi fingerprint indoor positioning algorithm based on attention auxiliary network(AAN). Specifically, we first extract the specific feature in different time domains and transfer heterogeneous fingerprint to homogeneous fingerprint. Then, we increase the weight of important features based on the attention mechanism. Finally, we extract hierarchical features to improve the location distinction of fingerprints. Experimental results show that AAN achieves state-of-the-art localization accuracy and is robust to long-term changing environments.

关键词： Location awareness Dimensionality reduction Accuracy Attention mechanisms signal processing algorithms Fingerprint recognition signal processing Feature extraction Time-domain analysis Wireless fidelity

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Multi-Rate Vibration signal Analysis for Enhanced Data-Driven Monitoring of Bearing Faults in Induction Machines

Multi-Rate Vibration Signal Analysis for Enhanced Data-Drive...

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International Conference on Electrical Machines, ICEM

作者： Nada El Bouharrouti Semen Koveshnikov Tomas Alberto Garcia-Calva Milla Vehviläinen Karolina Kudelina Usman Muhammad Naseer Toomas Vaimann Anouar Belahcen Dept. Electr. Eng. and Automation Aalto University Espoo Finland Research Group HSPDigital Universidad de Valladolid Valladolid Spain VTT Technical Research Centre of Finland Ltd. Espoo Finland Dept. Elecr. Power Eng. and Mechatronics Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

In the realm of data-driven condition monitoring for induction machines, the widespread adoption of machine learning has been notable, particularly for identifying bearing faults from noisy vibration signals in electrical setups. However, the quality of the signals used to train these machine learning algorithms is important to ensure good classification performances. This paper investigates whether optimizing the Fast Fourier Transform of a vibration signal through multi-rate resampling and windowing techniques enhances the quality of the statistical features used for data-driven condition monitoring of ball bearings in induction motors. To do so, the features obtained under different frequency resolutions have been studied along with the performances of two classes of machine learning algorithms trained with them: support vector machines and random forests. The results show that for a given frequency resolution, higher sampling frequencies generally lead to improved machine learning performance. However, they also highlight the trade-off between high-frequency resolution in the Fast Fourier Transform and the number of samples available in the dataset for training. Finally, the use of the Hanning windowing technique was found not to significantly improve the quality of features for machine learning performance.

关键词： Vibrations Support vector machines Condition monitoring Machine learning algorithms Fast Fourier transforms Ball bearings signal processing algorithms Feature extraction signal resolution Monitoring

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