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A STABLE 2-STAGE RECURSIVE-LIKE ADAPTIVE NOISE CANCELLER

PROCEEDINGS OF THE IEEE 1981年第4期69卷 487-488页

作者： GARDNER, WA Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Davis CA USA

An approach to the realization of a recursive-like adaptive noise canceller that employs only nonrecursive adaptive filters is presented. This approach is an alternative to prior art that has the advantage of guarante... 详细信息

关键词： Noise cancellation Finite impulse response filter Adaptive filters image processing Nonlinear filters Distortion signal processing Least mean square algorithms Art

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A SOFT-CONSTRAINED RECURSIVE-LIKE ADAPTIVE NOISE CANCELLER

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PROCEEDINGS OF THE IEEE 1981年第4期69卷 488-489页

作者： GARDNER, WA Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Davis CA USA

The adaptive adjustment algorithm for a prior art recursive-like noise canceller must incorporate a constraint that prevents convergence to the useless trivial solution. This constraint can also prevent convergence to the best attainable solution. An alternative to the prior-art hard-constrained recursive-like canceller, that "softens" the constraint by making it adaptive, is proposed.

关键词： Noise cancellation Art Distortion Matched filters Roentgenium Equations Poles and zeros System identification signal to noise ratio Predistortion

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Kalman filtering approach to multispectral/hyperspectral image classification

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1999年第1期35卷 319-330页

作者： Chang, CI Brumbley, C IEEE Remote Sensing Signal and Image Processing Laboratory Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County

Linear unmixing is a widely used remote sensing image processing technique for subpixel classification and detection where a scene pixel is generally modeled by a linear mixture of spectral signatures of materials present within the pixel. tin approach, called linear unmixing Kalman filtering (LUKF), is presented which incorporates the concept of linear unmixing into Kalman filtering so as to achieve signature abundance estimation, subpixel detection and classification for remotely sensed images. Zn this case, the linear mixture model used in linear unmixing is implemented as the measurement equation in Kalman filtering. The state equation which is required for Kalman filtering but absent in linear unmixing is then used to model the signature abundance. By utilizing these two equations the proposed LUKF not only can detect abrupt change in various signature abundances within pixels, but also can detect and classify desired target signatures. The performance of effectiveness and robustness of the LUKF is demonstrated through simulated data and real scene images, Satellite Pour l'Observation de la Terra (SPOT) and Hyperspectral Digital imagery Collection (HYDICE) data.

关键词： .Baltimore County abundance image classification Equation image processing unmixing Kalman filtering SUBPIXEL spectral signatures Linear Mixture Model HYDICE LUKF

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THE SPECTRAL CORRELATION THEORY OF CYCLOSTATIONARY TIME-SERIES

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signal processing 1986年第1期11卷 13-36页

作者： GARDNER, WA Signal Image and Processing Laboratory Department of Electrical and Computer Engineering University of California at Davis Davis CA 95616 U.S.A.

A spectral correlation theory for cyclostationary time-series is introduced. It is established that a time-series is cyclostationary if and only if there exists a quadratic time-invariant transformation that generates spectral lines, and this is so if and only if the time-series exhibits spectral correlation. Fundamental properties of a characterizing spectral correlation function are developed. These include the effects of periodic modulation and periodically time-variant linear filtering. Relationships between the spectral correlation function and the radar ambiguity function and the Wigner-Ville distribution are explained. The spectral correlation properties of Rice's representation for bandpass time-series are derived. A generalization of the Wiener relation from the spectral density function to the spectral correlation function is developed, and generalizations of the aliasing formula for periodic time-sampling, and the frequency conversion formula for amplitude modulation, from the spectral density function to the spectral correlation function are developed. Zusammenfassung Vorgetellt wird eine Theorie der spektralen Korrelation zyklisch-stationärer Zeitreihen. Es wird gezeigt, daβ eine Zeitreihe dann und nur dann zyklisch-stationär ist, wenn eine zeitinvariant quadratische Transformation existiert, die ein Linienspektrum erzeugt; und dies ist dann und nur dann so, wenn die Zeitreihe eine spektrale Korrelation aufweist. Die grundlegenden Eigenschaften der zugehörigen spektralen Korrelationsfunktion werden entwickelt. Diese beinhalten die Auswirkung periodischer Abtastung, einer Frequenzwandlung sowie periodisch zeitveräanderlicher linearer Filterung. Die Beziehungen zwischen der spektralen Korrelationsfunktion, der Mehrdeutigkeitsfunktion für Radar und der Zeit-Frequenzverteilung der Leistungsdichte Wigner-Ville werden erklärt. Ebenso werden die Eigens haften der spektralen Korrelation für bandbegrenzte Zeitreihen in der Darstellung von Rice hergeleitet. Eine

关键词： Cyclostationary processes time-series analysis spectral correlation periodic phenomena time-frequency signal representation

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CORRELATION ESTIMATION AND TIME-SERIES MODELING FOR NONSTATIONARY PROCESSES

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signal processing 1988年第1期15卷 31-41页

作者： GARDNER, WA Signal and Image Processing Laboratory Department of Electrical Engineering and Computer Science University of California Davis CA 95616 U.S.A.

Various types of statistical signal processing require the estimation of time-variant correlation functions and the fitting of time-variant models for nonstationary processes. In this paper, the situations for which nonstationary probabilistic correlation functions can be accurately estimated from a single sample path of a stochastic process are delineated. These include only (i) known form of nonstationarity, (ii) periodic or almost periodic nonstationarity, and (iii) slowly fluctuating nonstationarity. Known methods of estimation for each of these situations are reviewed within the unifying framework of orthogonal series expansions. The fact that estimators based on orthogonal series expansions for nonstationary correlation functions other than (i)–(iii) cannot be guaranteed to be accurate is established. Ramifications for time-variant autoregressive model fitting are discussed. Zusammenfassung Verschiedene Aufgabenbereiche der signalverarbeitung erfordern die Schätzung zeitvarianter Korrelationsfunktionen und die zeitvariante Modellierung nichtstationärer Prozesse. Im folgenden werden die Situationen dargelegt, für die nicht-stationäre Korrelationsfunktionen aus einem einzigen Repräsentanten eines Prozesses geschätzt werden können. Erfaßt werden können nur Fälle mit (i) bekannter Form der Instationarität, (ii) periodischer oder fast-periodischer Instationarität und (iii) langsam veränderlicher Instationarität. Die bekannten Schätzverfahren für jede dieser Gegebenheiten werden in einem einheitlichen Rahmen, dem der Orthogonalreihen-Entwicklung, zusammengestellt. Die Grenzen der Nutzung von Reihenentwicklungen bei der Schätzung nicht-stationärer Korrelationsfunktionen werden aufgezeigt. Variationsmöglichkeiten für die zeitvariante AR-Modellierung werden diskutiert.

关键词： Nonstationary correlation correlation estimation time-series modeling nonstationary time-series

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Multi-modal signal integration for enhanced sleep stage classification: Leveraging EOG and 2-channel EEG data with advanced feature extraction

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Artificial Intelligence in Medicine 2025年 166卷 103152-103152页

作者： Samaee, Mahdi Yazdi, Mehran Massicotte, Daniel Signal and Image Processing Laboratory School of Electrical and Computer Engineering Shiraz University Shiraz Iran Laboratory of Signal and System Integration Department of Electrical and Computer Engineering Université du Québec à Trois-Rivières Trois-Rivières Canada

This paper introduces an innovative approach to sleep stage classification, leveraging a multi-modal signal integration framework encompassing Electrooculography (EOG) and two-channel electroencephalography (EEG) data. We explore the utility of various feature extraction techniques, including Short-Time Fourier Transform (STFT), Wavelet Transform, and raw signal processing, alongside the utilization of neural networks as feature extractors. This unique combination allows us to harness the benefits of traditional feature extraction methods while capitalizing on the power of neural networks to enhance classification performance. Our comprehensive classifier evaluation encompasses a range of models, including Long Short-Term Memory (LSTM) networks and XGBoost. Remarkably, our results reveal exceptional performance with the XGBoost classifier, achieving an overall accuracy of 84.57 % and a macro-F1 score of 78.21 % on the Sleep-EDF expanded dataset, and an overall accuracy of 86.02 % and a macro-F1 score of 81.96 % on the ISRUC-Sleep dataset. Class-specific accuracies highlight its proficiency, particularly in detecting wake and N2 stages, solidifying its superiority among the classifiers tested. This amalgamation of feature sets, complemented by Principal Component Analysis (PCA) for dimensionality reduction, underscores its significance in yielding top-tier classification outcomes. The integration of traditional feature extraction methods with neural networks as feature extractors creates a robust and comprehensive system for sleep stage classification, offering the advantages of both approaches to enhance the accuracy and reliability of the results. © 2025

关键词： Discrete wavelet transforms

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Efficient RNN Models for IOT Intrusion Detection System

Efficient RNN Models for IOT Intrusion Detection System

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Jablaoui, Rahma Liouane, Noureddine University of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9798350361025

Due to the growing number of network devices, traffics and services, designing robust Intrusion Detection System (IDS) become a crucial need in the face of complex and various network attacks as a protective measure from hackers and cybercriminals. However, the traditional Machine Learning (ML) approach shows success in many research topics but with the increase in the amount of data and the diversification of network threats methods, it seems to lack reliability and accuracy. Therefore, considering a large amount of real-world cyber traffic, Deep Learning (DL) may be able to extract big data features more effectively. In this paper, we suggest an intrusion detection system for the Internet of Things (IoT) network based on Deep Learning to recognize various assault types for both binary and multiclass classification using two variants of Recurrent Neural Network (RNN) models long short-term memory (LSTM) and Bidirectional LSTM (BiLSTM). We have experimented the models with CSE-CIC-IDS2018, which is the newest comprehensive network traffic dataset. Accuracy, precision, recall, and F1 score are a few performance criteria where the suggested approach clearly excels. After comparison, we can infer that Bi-directional LSTM outperforms LSTM and other existing efforts in the literature. The accuracy of the experimental results was high, coming in at 98.62%. © 2024 IEEE.

关键词： Computer crime

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Ideal Behavior of Vernier and Flash TDCs Implemented in a Spartan-6 FPGA

Ideal Behavior of Vernier and Flash TDCs Implemented in a Sp...

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2023 International Conference on Advances in Electronics, Control and Communication Systems, ICAECCS 2023

作者： Chouial, Wafia Maamoun, Mountassar Signal Processing and Image Laboratory University of Blida 1 Blida Algeria

ISBN: (纸本)9781665463102

In this paper, we will illustrate the working principle of the most popular time-to-digital converter (TDC) architectures in both application specific integrated circuits (ASIC) and field programmable gate array (FPGA) applications - Vernier and tapped delay line (TDL) designs are used as a time interpolation method to yield a sub-clock period resolution. In order to understand their mechanism, Vernier and TDL-derived configurations are described first. Then, we propose a simulation work relying on fixing the finite propagation speed of each delay element of the delay chain to a delay equal to one or two clock cycles of the system clock period. That aims to assure the uniformity of the TDC binning and to eliminate the effect of process, voltage, and temperature (PVT) variations, which results in an ideal behavior of TDC that will be reflected in an ideal thermometer code. We have designed, implemented, and tested ideal TDCs in low-cost Spartan-6 FPGA, as well as by using the Xilinx ISIM tool. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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An effective deep CNN-LSTM based intrusion detection system for network security

An effective deep CNN-LSTM based intrusion detection system ...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Jablaoui, Rahma Liouane, Noureddine University of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9798350361025

Deep learning (DL) models have become prevalent and consistently exhibit outstanding performance across diverse domains, particularly in information security. As a subset of machine learning (ML), DL has proven adept at handling complex, unstructured, and large-scale data, making it well-suited to tackle the evolving challenges of cybersecurity. By leveraging DL techniques, we can develop robust intrusion detection systems (IDS) to counter a wide range of network attacks, serving as a crucial defense against hackers and cybercriminals. The integration of DL into IDS holds immense potential for significantly enhancing network security and mitigating cyber risks in various real-world scenarios. This work proposes an IDS architecture that integrates Convolutional Neural Network (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal feature extraction and sequential analysis to predict and classify malicious cyberattacks in IoT network traffic. Our experiments with the CSE-CIC-IDS2018 dataset, the latest comprehensive network traffic dataset, demonstrate the superiority of our approach in terms of accuracy, precision, recall, and F1 score. Our model achieves an unprecedented accuracy of over 99% in both binary and multiclass classification, surpassing existing efforts in the literature. © 2024 IEEE.

关键词： Long short-term memory

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Characterization of anomaly detection in hyperspectral imagery

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Sensor Review 2006年第2期26卷 137-146页

作者： Chang, Chein-I Hsueh, Mingkai Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD United States

Purpose - The paper aims to characterize anomaly detection in hyperspectral imagery. Design/methodology/approach - This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image-based computer simulations and real image experiments to substantiate the work proposed in this paper. Findings - This paper developed an ACAD and custom-designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are - first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation. Originality/value - This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image-based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

关键词： image processing

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