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检索条件"主题词=Signal Processing Algorithms"

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Optimized Front-End Circuit Design for High-Frequency signal Down-Conversion

Optimized Front-End Circuit Design for High-Frequency Signal...

引用

IEEE International Conference on Advanced Scientific Computing (ICASC)

作者： Alexandru Oprea Gergo Kovacs Raluca Nelega Bogdan Belean Emanuel Puschita Center for Advanced Research and Technologies for Alternative Energy @ National Institute for Research and Development of Isotopic and Molecular Technologies Cluj-Napoca Romania Communications Department @ Technical University of Cluj-Napoca & Center for Advanced Research and Technologies for Alternative Energy @ National Institute for Research and Development of Isotopic and Molecular Technologies Cluj-Napoca Romania

ISBN: (数字)9798350356526

ISBN: (纸本)9798350356533

This paper examines the critical role of a Front-End circuit in the down-conversion of high-frequency signals, addressing challenges posed by noise, intermodulation, inadequate filtering, and component-induced distortions. It emphasizes the theoretical foundations and simulations of RF components to model signals, evaluate different Front-End configurations, optimize performance, and mitigate signal loss. Experimental results validate the proposed Front-End design methodology, demonstrating significant improvements in isolating signals of interest despite interference. This highlights its potential for real-world applications in advanced telecommunications and radio signal processing.

关键词： Scientific computing Noise Refining signal processing algorithms Distortion Solids Robustness Telecommunications Integrated circuit modeling signal analysis

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Prediction of Autism Spectrum Disorder using Voting Classifier based Ensemble Techniques

Prediction of Autism Spectrum Disorder using Voting Classifi...

引用

Artificial Intelligence and signal processing (AISP), INDIA, International Conference on

作者： M. Sucharitha Raavi Haprishwa Kurri Srujana School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India School of Computer Science VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Autism, often referred to as autism spectrum disorder is a neurodevelopmental behavioral disease that includes challenges like social interactions, lack of focus, communication skills, repetitive behaviors, and child impairment. Screening for autism traits can be both costly and time-intensive. With technological advancements in developing countries, early pre-diction of autism can become more feasible. Detection of ASD in medicine can be efficient using fields like machine learning and pattern recognition. So, we developed a system to predict autism spectrum disorder based on the voting and stacking classifiers of ensemble learning technique which involves training of various algorithms like KNN, SVM, XGB, Logistic Regression, Extra Trees, Decision Tree, Random Forest, AdaBoost and Gradient Boosting.

关键词： Training Support vector machines Autism Logistic regression Accuracy Stacking signal processing algorithms Vectors Random forests Tuning

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A Semantic Feature-Driven Multiscale Grid Dehazing Method

A Semantic Feature-Driven Multiscale Grid Dehazing Method

引用

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

作者： Hao Zhang Hongmei Jin Zhanli Li Ning He Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the problems of incomplete dehazing and detail distortion in the dehazing process of existing algorithms, an end-to-end dehazing method SemanticGridDehazeNet is proposed, which is semantic feature-driven and combines multi-scale feature fusion. The method consists of three modules: preprocessing, backbone, and post-processing. First, preprocessing is performed using a newly designed residual dense fusion block (RDFB) and convolutional layer. The Backbone module is based on the extended GridN et, which contains three processing scales, and semantic features extracted based on VGG16 Net are added in front of each scale to enhance learning. Each scale contains five RDFB blocks to fuse the feature map to the last column. Then an RDFB post-processing module improves the output quality. The main innovations include: (1) the introduction of the RDFB fusion module to focus on haze features, dynamically adjust the level of attention paid to channel or pixel locations to more effectively learn and utilize key information about the haze distribution in the input data; (2) Semantic features are integrated into the image dehazing process by incorporating semantic features extracted from VGG16 Net before each processing scale. This enhances multi-scale learning and enables more accurate focus on haze features, improving dehazing effectiveness. Experimental results demonstrate superior performance in target enhancement, robustness, and generalization. The method effectively removes residual hzae and addresses detail distortion issues.

关键词： Measurement Technological innovation Image color analysis Fuses Semantics signal processing algorithms Feature extraction Distortion Robustness Real-time systems

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Distributed Diffusion-Based Manifold Particle Filters for Orientation Estimation

Distributed Diffusion-Based Manifold Particle Filters for Or...

引用

European signal processing Conference (EUSIPCO)

作者： Claudio J. Bordin Marcelo G.S. Bruno Universidade Federal do ABC Santo André SP Brazil Instituto Tecnológico de Aeronáutica São José dos Campos SP Brazil

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Determining orientation is a fundamental problem in fields such as robotics, computer vision, and autonomous driving. In three-dimensional space, orientations can be represented in many forms, including unit quaternions or points of the Special Orthogonal Group (SO(3)). These representations present challenges due to their non-Euclidean nature. This paper addresses the problem of computing a joint Bayesian estimate of a rotation given the observations of a set of receivers that consist of corrupted and transformed versions of the true orientation. We evaluate the performance of the described algorithms via numerical simulations in terms of accuracy and communication requirements.

关键词： Manifolds Quaternions signal processing algorithms Europe Estimation Receivers Filtering algorithms Numerical simulation Particle filters Numerical models

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Wireless Interference Classification in Satellite Communications with the Standardized Variable Distances Learning Algorithm and Adaptive Spectral Domain Segmentation

Wireless Interference Classification in Satellite Communicat...

引用

Electronics, Communication and signal processing (ICECSP), International Conference on

作者： Gianmarco Baldini European Commission Joint Research Centre Ispra Italy

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Satellite communications has evolved significantly in recent years with the deployment of new communication satellites both in the traditional geostationary orbit (GSO) and non-GSO (NGSO) satellites. Wireless interference to satellite communication links either intentional (jamming) or unintentional (due to network engineering errors or equipment malfunction) can cause service degradation or denial of service. In parallel, machine learning and deep learning is increasingly used in the wireless communication domain and more specifically the satellite communication domain for a variety of tasks. On the other side, deep learning algorithm require significant computational resources and time for inference, which may be an issue for a fast detection and classification of wireless interferences. Then, this paper proposes the application of computing efficient distance-based machine learning (DML) algorithms to this problem. In particular, the Standardized Variable Distances (SVD) DML algorithm is used with an improvement based on the specific aspects of the wireless interference context. Instead of applying SVD directly to the original time domain representation of the satellite signal, this paper proposes a spectral domain approach where the characteristics of the wireless interference seen as outliers of the main communication signal are exploited to adaptively reduce the input data to the SVD algorithm for enhanced classification performance and robustness to noise. The concept is to use an outlier identification algorithm to identify the most discriminating segments in the spectral domain and then reassemble them as input to the SVD algorithm. The approach is applied to a recently published data set where three types of interference are generated for four different modulation schemes. The results show that the proposed approach is able to outperform other machine learning algorithms and the baseline where the entire spectral domain is used in input to the SVD algorith

关键词： Wireless communication Deep learning Machine learning algorithms Satellites signal processing algorithms Interference Inference algorithms Classification algorithms Satellite communications Spectral analysis

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Human gait identification using UWB radar micro-Doppler signature

Human gait identification using UWB radar micro-Doppler sign...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Maowen Yin Charalambos Hadjipanayi Alan Bannon Timothy Constandinou Department of Electrical and Electronic Engineering and UK Dementia Research Institute (Care Research and Technology Centre) at Imperial College London United Kingdom

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study introduces the application of impulse radio ultra-wideband radar (IR-UWB) for person identification using the micro-Doppler signature of human gait. A signal processing framework is developed to extract the features from the spectrogram, including the physically interpretable features and the features derived through Principal Component Analysis (PCA). In addition, a cross-entropy-based approach was employed to quantify uncertainties in the radar classification model, enhancing reliability in the classifications. The efficacy of this method was evaluated using a dataset comprising radar data from 14 individuals, each recorded while walking for a duration of 10 minutes. Employing a Support Vector Machine (SVM) classifier, our approach achieved a remarkable accuracy rate of 97.1%. These results demonstrate the capability of the IR-UWB radar and our proposed algorithm to discern variations in gait patterns, thereby effectively identifying individuals. This indicates its potential as a valuable addition to smart home systems, significantly benefiting the development of home care systems.

关键词： Support vector machines Uncertainty Surveillance signal processing algorithms Smart homes Feature extraction Ultra wideband radar Wearable sensors Spectrogram Principal component analysis

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CGM-Freq: A Python Library for Frequency Domain Analysis of Continuous Glucose Monitoring Data

CGM-Freq: A Python Library for Frequency Domain Analysis of ...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Elizabeth Healey Isaac Kohane Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

In recent years, the number of patients using continuous glucose monitoring (CGM) has increased. In addition to helping patients manage their disease, CGM produces time series data that can be used for integration in control algorithms, predictive models, and for retrospective analyses. Through feature extraction, many digital biomarkers can be derived from CGM. In this work, we provide a tool to extract features derived from the frequency domain. We first introduce a novel open-source Python library, CGM-Freq, for the analysis of CGM data in the frequency domain. We then test the library on real data. This work provides an open-source tool to further investigate the frequency domain of CGM signals.

关键词： Continuous glucose monitoring Frequency-domain analysis Time series analysis signal processing algorithms signal processing Predictive models Feature extraction Prediction algorithms Libraries Diseases

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Automatic Grayscale Mapping for Mammograms Enhancement

Automatic Grayscale Mapping for Mammograms Enhancement

引用

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

作者： Jingwei Wang Junfeng Wang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Mammography is an effective method for the early diagnosis of breast cancer, so improving the quality of mammograms is critical for accurate diagnosis. This can be achieved through grayscale mapping and high-pass filtering. However, conventional grayscale mapping requires engineers to manually adjust the equipment parameters, which, once set, remain unchanged. This process is not only time-consuming but also fails to consistently ensure image quality. In this paper, we propose an automatic grayscale mapping algorithm. Our method first extracts the grayscale demarcation ${\bm{x}}1$ between the background and the breast, then identifies the grayscale demarcation ${\bm{x}}2$ between the breast edges and the breast tissue. Subsequently, a non-linear grayscale mapping based on ${\bm{x}}1$ and ${\bm{x}}2$ is conducted to enhance the details of the breast tissue while maintaining the integrity of the breast edges and reducing the background grayscale. After these steps, high-frequency details of the image are further enhanced through high-pass filtering. Experimental results demonstrate the effectiveness and superiority of our method.

关键词： Image quality Filtering Image edge detection signal processing algorithms Breast tissue Gray-scale signal processing Mammography Breast cancer Informatics

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Comparative Study of Narrowband and Ultra-Wideband Systems for Device-Free Localization

Comparative Study of Narrowband and Ultra-Wideband Systems f...

引用

IEEE Microwave Theory and Techniques in Wireless Communications (MTTW)

作者： Marco Cimdins Sven Ole Schmidt Horst Hellbrück Technische Hochschule Lübeck Lübeck Germany

ISBN: (数字)9798331533175

ISBN: (纸本)9798331533182

Setting up a device-free localization (DFL) system requires an appropriate selection of radio technologies and algorithms. The selection has impacts on the complexity of both, signal processing, and hardware infrastructure, as well as the maintenance effort. The radio technology has an impact on the system’s infrastructure as some localization algorithms demand extensive manual calibration, leading to increased maintenance efforts. To address these challenges, we developed and compared two DFL systems: one based on narrowband (NB) radio technology with received signal strength (RSS) measurements, and another with an ultra-wideband (UWB) radio chip with channel impulse response (CIR) measurements that enables utilization of multipath propagation. We evaluated both systems using a fingerprinting method and a radio tomographic imaging algorithm across various configurations. Our experimental evaluation demonstrates the differences in radio technologies and localization accuracy, and maintenance requirements, therefore provides help for developers of localization systems.

关键词： Location awareness Wireless communication Semiconductor device measurement signal processing algorithms Tomography signal processing Maintenance Channel impulse response Ultra wideband technology Narrowband

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Blind Identification of Overlapping Communities from Nodal Observations

Blind Identification of Overlapping Communities from Nodal O...

引用

European signal processing Conference (EUSIPCO)

作者： Ruben Wijnands Geert Leus Borbála Hunyadi Department of Microelectronics Delft University of Technology Delft The Netherlands

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Identifying overlapping communities from data is crucial for grasping the complex structure and dynamics of networks, amongst others in fields such as computational neuroscience. Research using fMRI has demonstrated that brain regions can change their functional network membership over time using temporal independent component analysis (tICA). However, reproducibility of such overlapping communities remains a challenge. Recently, several alternative approaches have been proposed to identify such overlapping communities. While results are promising, less is known about the model and assumptions that underlie these approaches. This paper shows that the bilinear model, combined with the assumption of quasistationary and uncorrelated sources, underlies novel methods for identifying overlapping brain networks. Furthermore, we propose a new algorithm, and through simulations, we investigate the robustness of our algorithm and several existing methods to solve the problem in noisy conditions with few available data samples. We conclude that quasi-stationary blind source separation-based techniques can have a promising advantage over tICA in terms of identifiability of overlapping communities and thus have the potential to contribute towards greater reproducibility of results.

关键词： Neuroimaging signal processing algorithms Independent component analysis Grasping signal processing Brain modeling Reproducibility of results Robustness Noise measurement signal to noise ratio

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