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Asia-Pacific Conference on Microwave

作者： Joo-Eun Park Geon-Haeng Lee Yuna Park Sang-Hoon Jeon Joon-Young Park Jong-Ryul Yang Dept. Electrical and Electronics Eng. Konkuk University Seoul Republic of Korea

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

An optimization algorithm is proposed to select the mode decomposition level of intrinsic mode functions (IMFs) that contain vital signals in bio-radar sensors. The phase spectrum of each IMF is analyzed to determine if the phase variation has the range of 2π, allowing for the estimation of frequency components related to vital signals. The respiration and heart rate in the baseband signals of the bio-radar can be obtained through IMF extraction using empirical mode decomposition, but the extraction level can vary depending on the surroundings and measurement conditions. The proposed method accurately extracts the IMFs at levels that contain the respiration and heart rate signals, and it helps in rejecting respiration harmonics located around the heart rate signal. Demonstrations using an in-house 5.8-GHz bio-radar sensor with the proposed method showed mean error rates of 4.2% and 2.1% in respiration and heart rate detection, respectively.

关键词： signal processing algorithms Heart rate detection Microwave theory and techniques Harmonic analysis Frequency estimation Biosensors Data mining signal detection Optimization signal to noise ratio

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Non-Stationary Blind Source Separation and Advanced signal processing Techniques for Effective Fetal ECG Extraction

Non-Stationary Blind Source Separation and Advanced Signal P...

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International Conference on Circuits, Communication, Control and Computing

作者： Shashidhar V Reddy Varun M V V Hemanth Jayashree R Dept. of ECE PES University Bangalore India Dept. of CSE (AIML) PES University Bangalore India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Fetal electrocardiogram (FECG) extraction from maternal electrocardiogram (MECG) signals presents a major obstacle in biomedical signal processing. In this paper Fast Independent Subspace Analysis (FastISA), Fast Independent Component Analysis (FastICA) and Join Approximate Diagonalization of Eigen Matrices(JADE) is used as for Blind Source Separation (BSS) with stern pre-processing steps like band pass filtering, wavelet denoising, orthogonalization and Wiener deconvolution. To evaluate the quality of separation Amari Index has been implemented achieving 0.19 for synthetic data using fastISA and 0.33 for real-world using JADE algorithm providing reliable operation for different situations as FECG extraction is crucial for its insights on fetal hearts electrical activity with non-invasive means to monitor and diagnose early during pregnancy. A predictive model based on Random Forest and a Neural Network along with R Peak Detection based on Pan-Tompkins algorithm has been implemented to predict pregnancy states and physiological metric for analyzing Mothers Health condition. Integration of predictive model and signal processing techniques like FastISA and Pan-Tompkins algorithm provides a wider application of this approach in biomedical engineering for improved healthcare and diagnostics.

关键词： Pregnancy Filtering signal processing algorithms Electrocardiography Predictive models Prediction algorithms Wavelet analysis Blind source separation Data mining Biomedical monitoring

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Implementation of a Computational Intelligence System to Identify Respiratory Distress from Digital signal processing of Voice

Implementation of a Computational Intelligence System to Ide...

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Biomedical Engineering and Bioengineering (CI-IB&BI), IEEE International Congress of

作者： Sara Fernandez Karen Ramos David Ramirez Julián Villamarin Danilo Hernane Spatti Facultad de Ingeniería Mecánica Electrónica y Biomédica Antonio Nariño University Popayán Cauca Colombia Institute of Mathematical and Computer Sciences University of São Paulo São Carlos - SP Brazil

ISBN: (数字)9798331532352

ISBN: (纸本)9798331532369

This paper presents a computational intelligence system that allows us to identify people with respiratory distress in an automatic and non-invasive way. This is accomplished through the digital signal processing techniques of voice, integrating the calculation of acoustic, spectral, temporal, and sta-tistical parameters and neural networks. The computational method is implemented in a client-server architecture that allows recording two sustained vowel sounds (vowel “a” and “o”) of a spanish-speaking population group aged between 18 and 49 years old. Results show that the vowel “a” is more efficient in women with a hit rate of 97.62% for detecting respiratory distress. In contrast, the vowel “o” is more efficient in men, reaching a hit rate of 96.77%. This system is expected to contribute to the development of novel support tools that could be used in the medical field to facilitate the diagnosis of respiratory diseases and prevent their spread.

关键词： Pathology Protocols Reviews Pulmonary diseases signal processing algorithms Digital signal processing Computer architecture Recording Computational intelligence Biomedical engineering

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Network Traffic Co-Movement Assessment via Oriented Basis signal processing and Ensemble Decision Trees

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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International Conference on Intelligent Transportation

作者： Mohammad Reza Mohebbi Mohammad Javad Hassan Zada Majid Rostami-Shahrbabaki Mario Döller Iuliia Yamnenko Josef Ressel Center Vision2Move project University of Applied Sciences Kufstein Tirol Austria Department of Computer Science University of Passau Germany Chair of Traffic Engineering and Control Technical University of Munich Germany Department of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost.

关键词： Wavelet transforms Radio frequency Accuracy Machine learning algorithms Urban areas signal processing algorithms Detectors Prediction algorithms Decision trees Random forests

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DOA Estimation by Jointly Exploiting L1-SVD and Enhanced Spatial Smoothing in Coherent Environment

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Jingchao Li, Muheng Bai, Longxin Qiao, Liyan Harbin Inst Technol HIT Dept Automat Test & Control Harbin 150080 Peoples R China

As a sparse-based direction of arrival (DOA) estimation algorithm, the L1-singular value decomposition (SVD) algorithm is widely used to measure the orientation of targets. In real measurements, the coherent environment that often arises due to multipath propagation leads to the deterioration of the noise immunity and estimation accuracy of the L1-SVD algorithm. Although the decoherence of L1-SVD can be enhanced by introducing spatial smoothing after SVD, which is called SS-L1-SVD, the algorithm does not fully utilize the available information in the observed data. In this article, we propose a new method called L1-enhanced spatial smoothing decomposition (ESSD). ESSD combines spatial smoothing with matrix decomposition by utilizing the relationship among the covariance matrix and the left singular matrix and the singular value matrix. ESSD not only improves the decoherence ability of the algorithm but also makes full use of the information in the observed data and reduces the computational complexity, which makes the algorithm more practical than the traditional algorithms in real measurements. In order to further verify the performance of the new algorithm, we not only performed simulation experiments but also designed a physical experimental platform that can be used for DOA estimation and constructed a real coherent environment caused by multipath propagation and performed physical experiments. The results of simulation and physical experiments show that the L1-ESSD algorithm reduces the error by about 1 degrees and the computation time by about 8 s compared with the conventional L1-SVD algorithm.

关键词： Estimation Direction-of-arrival estimation signal processing algorithms Smoothing methods Noise Antenna arrays Accuracy Vectors Classification algorithms Array signal processing Coherent environment direction of arrival (DOA) enhanced spatial smoothing decomposition (ESSD) L1-singular value decomposition (SVD) multipath effect

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Deep Learning-Based Noise Reduction Techniques in Electronic signal processing

Deep Learning-Based Noise Reduction Techniques in Electronic...

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International Conference on Science Technology Engineering and Management (ICONSTEM)

作者： Lalit Johari P Ramya M. Suganya Hirald Dwaraka Praveena P. S. LathaMageshwari M Rajendiran Department of Computer Applications IFTM University Moradabad Uttar Pradesh India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India Department of Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidya Nikethan Engineering College) Tirupati Andhra Pradesh India Department of Physics (S&H) R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This paper explores the operation of deep literacy- grounded noise reduction ways in electronic signal processing. Noise hindrance poses a significant challenge in colorful signal processing operations, impacting the delicacy and trustability of data analysis and interpretation. Traditional noise reduction styles frequently calculate on heuristic approaches or signal processing algorithms, which may have limited effectiveness, especially in complex and dynamic surroundings. In discrepancy, deep literacy algorithms, similar as convolutional neural networks(CNNs) and intermittent neural networks(RNNs), offer promising results for noise reduction by learning complex patterns and connections directly from data. This paper determines the state- of- the- art deep literacy- grounded noise reduction ways, including denoising autoencoders, generative inimical networks(GANs), and intermittent neural network- grounded models. Case studies and experimental results demonstrate the effectiveness and performance of these ways in colorful signal processing tasks, similar as audio denoising, image restoration, and biomedical signal processing. The integration of deep literacy- grounded noise reduction styles into electronic signal processing systems shows significant eventuality for perfecting signal quality and enhancing the delicacy of data analysis in different operations.

关键词： Data analysis Heuristic algorithms Noise reduction Noise Neural networks signal processing algorithms signal processing

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Study on local discharge type identification based on acoustic signal processing and PCA-BP method

Study on local discharge type identification based on acoust...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Qi Yang Deng Song Liuqing Yang Xianyin Mao Huarong Zeng Yao Zhong Meihua Mountain Freezing Climate Disaster and Equipment Safety Observation and Research Station of Guizhou Province Guiyang China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang 550002 Guizhou China Guiyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Acoustic detection technology is a technology capable of detecting and pinpointing the origins of abnormal acoustic signals stemming from localized discharges in power equipment. Nevertheless, the current iteration of this technology primarily locates the sources but struggles to elucidate their fundamental origins. As such, a comprehensive composition analysis of these detected acoustic signals is imperative. To tackle this challenge, this paper introduces a methodology for identifying distinct types of localized discharge, alongside the construction of an experimental apparatus simulating various local discharge scenarios. The apparatus employs an acoustic sensor array to capture these sounds. Considering the inherent noise within the experimental setting, we apply a noise reduction strategy to purify the acquired acoustic signals before proceeding with their extraction and identification. We then convert the signals from the time domain to the frequency domain using FFT and wavelet transforms, subsequently extracting their characteristic features via the Principal Component Analysis (PCA) algorithm. Finally, the Back Propagation (BP) neural network ingests these condensed feature vectors, enabling the accurate classification of various local discharge types.

关键词： Accuracy Fault detection Neural networks Noise reduction signal processing algorithms Fault location Feature extraction Discharges (electric) Acoustics Principal component analysis

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Evaluation of the Versal Intelligent Engines for Digital signal processing Basic Core Units

Evaluation of the Versal Intelligent Engines for Digital Sig...

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Conference on Design of Circuits and Integrated Circuits (DCIS)

作者： Fernando Flores María Dolores Valdés Peña José Manuel Villapún Sánchez Jesús Manuel Costa Pazo Camilo Quintáns Graña Digital hardware for airborne systems area Indra Vigo Spain Dept. of Electronics Technology University of Vigo Vigo Spain Signal processing for naval systems area Indra Vigo Spain

ISBN: (数字)9798350364392

ISBN: (纸本)9798350364408

In this work, the novel Intelligent Engines of the Versal Adaptive Compute Acceleration Platform (ACAP) from AMD (formerly Xilinx) are evaluated for Digital signal processing (DSP) applications. Different configurations of Finite Impulse Response (FIR) filters and Fast Fourier Transforms (FFTs) serve as benchmarks. The Intelligent Engines comprise the new DSP blocks generation (DSP58) and the AI Engines (AIEs). As FIR filters and FFTs are core units in almost all DSP algorithms, this benchmark is of great value for understanding the best way to implement DSP algorithms on these new devices. A comparison of several implementations using the Programmable Logic (PL) and DSP blocks of Zynq Ultrascale+ and Versal families, along with the scalar and vector units of the Versal’s AIEs, is provided. Throughput, latency, power consumption and hardware cost are measured as key performance indicators for the algorithms under test. Results highlight the Versal architectural advantages in optimizing compute-intensive applications and its potential in terms of throughput, power and resources efficiency. The outcomes also provide valuable insights into the optimal configurations for DSP algorithms, depending on the used data types, filter taps, FFT length, as well as the latency, throughput, power and resources constraints of the target application.

关键词： Power measurement Power demand Finite impulse response filters Fast Fourier transforms signal processing algorithms Digital signal processing Benchmark testing Throughput Vectors Engines

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Hardware Efficient FIR Filter for signal processing Applications

Hardware Efficient FIR Filter for Signal Processing Applicat...

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International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Pa Saisivakumaran S Surendhar V Saai Miruthunjayan K. Mariammal Department of Electronics Engineering Madras Institute of Technology Campus Anna University Chennai India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

This work produced a power and area efficient architecture of FIR filter where the existing VLSI signal processing algorithms were utilized. The algorithms included in the reference paper [1] was a novel matrix CSE technique and CSD representation of filter coefficients. These techniques were used in reducing the no of nonzero terms present in filter coefficients. Seismic signal data set were converted into signal, plotted in time domain and fed into the implemented FIR filter, and conventional fourth order FIR filter. It was found that conventional filter could not process the non-periodic signal as that of implemented FIR filter. The On chip power for the implemented FIR architecture was reduced by 12.5% and the Worst Hold Slack was reduced by 13.4%. Further the number of adders used were reduced by reworking the existing fourth order FIR filter of the base paper (taken as reference). This was considered as fine improvement in the implementation as the seismic signal processing is done in real time.

关键词： Finite impulse response filters Cutoff frequency Low-pass filters signal processing algorithms Computer architecture signal processing Filtering algorithms

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Research on signal processing and Sensor Fusion Technology in Electrical Engineering Automation

Research on Signal Processing and Sensor Fusion Technology i...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Rong Chen Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This study introduces an electrical measurement system grounded in multi-sensor fusion. Through the incorporation of information fusion techniques and parallel computing strategies, the system achieves reconstruction and optimization. The system is designed with a dynamic clustering algorithm that effectively merges data from various sensors, thereby enhancing both the precision and real-time performance of the measurements. To validate the system’s effectiveness, this paper conducts simulations and carries out detailed data analysis of the simulation outcomes. The findings demonstrate that the proposed sensor fusion approach substantially increases the measurement accuracy of the electrical measurement system while maintaining robust stability in challenging environments. Furthermore, the system’s parallel computing capabilities notably boost processing efficiency, reducing the time required for data handling. These outcomes indicate that the signal processing and sensor fusion technologies explored in this research hold significant potential for applications in electrical engineering automation, offering a solid theoretical foundation and technical support for future engineering implementations.

关键词： Electrical engineering Automation Heuristic algorithms Computational modeling Electric variables measurement signal processing algorithms Clustering algorithms Sensor fusion signal processing Parallel processing

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