Gotowe bibliografie tematyczne / Signal detection

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Gotowa bibliografia na temat „Signal detection”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Signal detection"

Gudiškis, Andrius. "HEART BEAT DETECTION IN NOISY ECG SIGNALS USING STATISTICAL ANALYSIS OF THE AUTOMATICALLY DETECTED ANNOTATIONS / ŠIRDIES DŪŽIŲ NUSTATYMAS IŠ IŠKRAIPYTŲ EKG SIGNALŲ ATLIEKANT AUTOMATIŠKAI APTIKTŲ ATSKAITŲ STATISTINĘ ANALIZĘ." Mokslas – Lietuvos ateitis 7, no. 3 (2015): 300–303. http://dx.doi.org/10.3846/mla.2015.787.

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This paper proposes an algorithm to reduce the noise distortion influence in heartbeat annotation detection in electrocardiogram (ECG) signals. Boundary estimation module is based on energy detector. Heartbeat detection is usually performed by QRS detectors that are able to find QRS regions in a ECG signal that are a direct representation of a heartbeat. However, QRS performs as intended only in cases where ECG signals have high signal to noise ratio, when there are more noticeable signal distortion detectors accuracy decreases. Proposed algorithm uses additional data, taken from arterial bloo

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Thompson, William Forde, and Max Coltheart. "The role of signal detection and amplification in the induction of emotion by music." Behavioral and Brain Sciences 31, no. 5 (2008): 597–98. http://dx.doi.org/10.1017/s0140525x08005529.

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AbstractWe propose that the six mechanisms identified by Juslin & Västfjäll (J&V) fall into two categories: signal detection and amplification. Signal detection mechanisms are unmediated and induce emotion by directly detecting emotive signals in music. Amplifiers act in conjunction with signal detection mechanisms. We also draw attention to theoretical and empirical challenges associated with the proposed mechanisms.

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Park, Do-Hyun, Min-Wook Jeon, Da-Min Shin, and Hyoung-Nam Kim. "LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function." Sensors 23, no. 20 (2023): 8564. http://dx.doi.org/10.3390/s23208564.

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In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an

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Liu, Shuai, Xiang Chen, Ying Li, and Xiaochun Cheng. "Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis." Symmetry 11, no. 12 (2019): 1471. http://dx.doi.org/10.3390/sym11121471.

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When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the cor

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Khudov, Hennadii, Serhii Yarosh, Oleksandr Kostyria, et al. "Improving a method for non-coherent processing of signals by a network of two small-sized radars for detecting a stealth unmanned aerial vehicle." Eastern-European Journal of Enterprise Technologies 1, no. 9 (127) (2024): 6–13. http://dx.doi.org/10.15587/1729-4061.2024.298598.

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The object of this study is the process of detecting stealth unmanned aerial vehicles by a network of two small-sized radars with incoherent signal processing. The main hypothesis of the study assumed that combining two small-sized radars into a network could improve the quality of detection of stealth unmanned aerial vehicles with incoherent signal processing. The improved method for detecting a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing, unlike the known ones, provides for the following: – synchronous inspection of the airspace by

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Wang, Liwei, Senxiang Lu, Xiaoyuan Liu, and Jinhai Liu. "Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features." Machines 10, no. 12 (2022): 1111. http://dx.doi.org/10.3390/machines10121111.

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Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect detection and inversion. Current methods, based on machine learning and deep learning have been able to classify signals with significant differences. However, the ultrasonic internal detection signal is interspersed with a large number of anomalous signals of an unknown origin and is affected by the time shift of echo features and noise interference, which leads to the low recognition accuracy of the ultrasonic internal detection signal, at this stage. To address the above problems, this paper propo

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Mao, Hongcun, Yuling Feng, Xiaoqian Wang, Chao Gao, Changhao Lin, and Zhihai Yao. "A New Chaotic Weak Signal Detection Method Based on a Simplified Fractional-Order Genesio–Tesi Chaotic System." Fractal and Fractional 9, no. 2 (2025): 74. https://doi.org/10.3390/fractalfract9020074.

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The detection of weak signals is a well-established application in chaos theory. This theory leverages the inherent robustness of chaotic systems, enabling them to resist noise and thus serve as effective tools for identifying weak signals. However, challenges remain in selecting appropriate chaotic systems and in their practical implementation—areas that are still under-explored. In this paper, we analyze a simplified fractional-order Genesio–Tesi chaotic system, which exhibits a unique chaos-divergence characteristic. Based on this characteristic, we propose a new detection method that uses

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Cai, Ming Shan. "Weak Signal Detection Principle Based on Chaotic Duffing Oscillator and its Simulation Method." Advanced Materials Research 108-111 (May 2010): 834–37. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.834.

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Based on Matlab as the software platform, chaos detection principle and methods used for weak signals detection are studied. The model used for simulation is established, then the simulation results of weak periodic signal detection in strong noise atmosphere are given and the steps for detecting weak signals with chaos method are listed. Furthermore, the influence of sampling period on system’s performance is studied. Simulation results show that the chao detection approach proposed in this paper can detect the signal even if it is small to 10-10v, and even when it is in the environment with

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Kumar, Anoop, and Henna Khan. "Signal Detection and their Assessment in Pharmacovigilance." Open Pharmaceutical Sciences Journal 2, no. 1 (2015): 66–73. http://dx.doi.org/10.2174/1874844901502010066.

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Signal detection and its assessment is the most important aspect in pharmacovigilance which plays a key role in ensuring that patients receive safe drugs. For detection of adverse drug reactions, clinical trials usually provide limited information as they are conducted under strictly controlled conditions. Some of the adverse drug reactions can be detected only after long term use in larger population and in specific patient groups due to specific concomitant medications or disease. The detection of unknown and unexpected safety signals as early as possible from post marketing data is one of t

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Schiano, J. L., A. J. Blauch, and M. D. Ginsberg. "Optimization of NQR Pulse Parameters using Feedback Control." Zeitschrift für Naturforschung A 55, no. 1-2 (2000): 67–73. http://dx.doi.org/10.1515/zna-2000-1-213.

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A new method for increasing the probability of detecting nuclear resonance signals is demon-strated experimentally. It is well known that the detection of signals with a low signal to noise ratio (SNR) results in missed detections of false alarms. In situations where the noise is correlated or where limited data is averaging, it may not be possible to achieve a desired SNR through averaging alone. We present an alternative approach in which a feedback algorithm automatically adjusts pulse parameters so that the SNR and probability of correct detection are increased. Experimental results are pr

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Rozprawy doktorskie na temat "Signal detection"

Park, Subok. "Signal detection with random backgrounds and random signals." Diss., The University of Arizona, 2004. http://hdl.handle.net/10150/280729.

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In this dissertation we explore theoretical and computational methods to investigate Bayesian ideal observers for performing signal-detection tasks. Object models are used to take into account object variability in image backgrounds and signals for the detection tasks. In particular, lumpy backgrounds (LBs) and Gaussian signals are used for various paradigms of signal-detection tasks. Simplified pinhole imaging systems in nuclear medicine are simulated for this work. Markov-chain Monte Carlo (MCMC) methods that estimate the ideal observer test statistic, the likelihood ratio, for signal-known-

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Lie, Celia, and n/a. "Punishment and human signal detection." University of Otago. Department of Psychology, 2007. http://adt.otago.ac.nz./public/adt-NZDU20071004.134135.

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Detection and choice research have largely focused on the effects of relative reinforcer frequencies or magnitudes. The effects of punishment have received much less attention. This thesis investigated the effects of punishment on human signal-detection performance using a number of different procedures. These included punisher frequency and magnitude variations, different types of punishers (point loss & time-outs), variations in stimulus disparity, and different detection tasks (judgments of stimulus arrays containing either more blue or red objects, or judgments of statements that were eith

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Huang, Wensheng. "Wavelet Transform Adaptive Signal Detection." NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19991104-151423.

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<p>Wavelet Transform Adaptive Signal Detection is a signal detection method that uses the Wavelet Transform Adaptive Filter (WTAF). The WTAF is the application of adaptive filtering on the subband signals obtained by wavelet decomposition and reconstruction. The WTAF is an adaptive filtering technique that leads to good convergence and low computational complexity. It can effectively adapt to non-stationary signals, and thus could find practical use for transient signals. Different architectures for implementing the WTAF were proposed and studied in this dissertation. In terms of the type of t

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Krause, Michael. "Signal Detection for Overloaded Receivers." Thesis, University of Canterbury. Department of Electrical and Computer Engineering, 2009. http://hdl.handle.net/10092/2959.

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In this work wireless communication systems with multiple co-channel signals present at the receiver are considered. One of the major challenges in the development of such systems is the computational complexity required for the detection of the transmitted signals. This thesis addresses this problem and develops reduced complexity algorithms for the detection of multiple co-channel signals in receivers with multiple antennas. The signals are transmitted from either a single user employing multiple transmit antennas, from multiple users or in the most general case by a mixture of the two. The

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Arslanian, A. S. "Spectral techniques for signal detection." Thesis, University of Strathclyde, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372465.

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Shikhaliev, Azer P. "Techniques for Adaptive Signal Detection." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610123085674943.

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Gallas, Brandon Dominic. "Signal detection in lumpy backgrounds." Diss., The University of Arizona, 2001. http://hdl.handle.net/10150/290090.

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In this dissertation we explore signal detection with model and human observers in the setting of nuclear medicine. Regarding the model observer, the main focus is on the linear observer that maximizes detectability, which we call the Hotelling observer. In particular, we outline two methods for realizing an estimate of this observer. The first uses a Fourier representation. The second uses a representation with a small number of channels chosen for optimal performance. The work employs statistically defined lumpy backgrounds to test the methods and results. These backgrounds are more complica

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Zhang, Hongbin. "Signal detection in medical imaging." Diss., The University of Arizona, 2001. http://hdl.handle.net/10150/290512.

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The goal of this research is to develop computational methods for predicting how a given medical imaging system and reconstruction algorithm will perform when mathematical observers for tumor detection use the resulting images. Here the mathematical observer is the ideal observer, which sets an upper limit to the performance as measured by the Bayesian risk or receiver operating characteristic analysis. This dissertation concentrates on constructing the ideal observer in complex detection problems and estimating its performance. Thus the methods reported in this dissertation can be used to app

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Mabrouk, Mohamed Hussein Emam Mabrouk. "Signal Processing of UWB Radar Signals for Human Detection Behind Walls." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31945.

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Non-contact life detection is a significant component of both civilian and military rescue applications. As a consequence, this interest has resulted in a very active area of research. The primary goal of this research is reliable detection of a human breathing signal. Additional goals of this research are to carry out detection under realistic conditions, to distinguish between two targets, to determine human breathing rate and estimate the posture. Range gating and Singular Value Decomposition (SVD) have been used to remove clutter in order to detect human breathing under realistic condition

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Pike, Cameron M. "Multipath signal detection using the bispectrum." Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1183467926.

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Książki na temat "Signal detection"

Tuzlukov, Vyacheslav P. Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8.

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Tuzlukov, V. P. Signal Detection Theory. Birkhäuser Boston, 2001.

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Barkat, Mourad. Signal detection and estimation. Artech House, 1991.

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Song, Iickho. Advanced Theory of Signal Detection: Weak Signal Detection in Generalized Observations. Springer Berlin Heidelberg, 2002.

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Jinsoo, Bae, and Ki Sun Yong 1968-, eds. Advanced theory of signal detection: Weak signal detection in generalized observations. Springer, 2002.

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Song, Iickho, Jinsoo Bae, and Sun Yong Kim. Advanced Theory of Signal Detection. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04859-7.

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Hart, G. F. Wind propeller signal detection improvements. Tennessee Applied Physics, Inc., 1992.

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D, Whalen Anthony, and Whalen Anthony D, eds. Detection of signals in noise. 2nd ed. Academic Press, 1995.

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Kassam, Saleem A. Signal Detection in Non-Gaussian Noise. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3834-8.

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Weber, Charles L. Elements of Detection and Signal Design. Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4774-6.

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Części książek na temat "Signal detection"

Elsner, James B., and Anastasios A. Tsonis. "Signal Detection." In Singular Spectrum Analysis. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2514-8_7.

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Nahler, Gerhard. "signal detection." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1283.

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Evans, David C. "Signal Detection." In Bottlenecks. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2580-6_8.

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Robinson, Michael. "Detection." In Topological Signal Processing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36104-3_4.

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Tuzlukov, Vyacheslav P. "Detection Performances." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_7.

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Tuzlukov, Vyacheslav P. "Introduction." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_1.

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Tuzlukov, Vyacheslav P. "Classical Signal Detection Theory." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_2.

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Tuzlukov, Vyacheslav P. "Modern Signal Detection Theory." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_3.

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Tuzlukov, Vyacheslav P. "Generalized Approach." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_4.

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Tuzlukov, Vyacheslav P. "Signals with Stochastic Parameters." In Signal Detection Theory. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_5.

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Streszczenia konferencji na temat "Signal detection"

Liang, Yuxuan, Yibing Li, and Tao Jiang. "Lie Group-Based Detector for Signal Detection." In 2024 IEEE INC-USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2024. http://dx.doi.org/10.23919/inc-usnc-ursi61303.2024.10632326.

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Garvanov, Ivan, Magdalena Garvanova, Georgi Tsonkov, Desislava Garvanova, Maria Mindova, and Daniela Borissova. "Signal Detection in Impulse Interference." In 2024 International Conference Automatics and Informatics (ICAI). IEEE, 2024. https://doi.org/10.1109/icai63388.2024.10851678.

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Baghbani, S., A. Sarlak, and S. J. Rupitsch. "B7.1 - Enhanced Signal Detection for Chlorophyll a Fluorescence Signal." In SMSI 2025. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2025. https://doi.org/10.5162/smsi2025/b7.1.

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Mosayebi, Reza, and Lutz Lampe. "Anomaly Detection in Optical Fiber: A Change-Point Detection Perspective." In Signal Processing in Photonic Communications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/sppcom.2024.spth2g.4.

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We present a change-point detection algorithm for optical fibers. Utilizing SNR, our approach swiftly identifies soft anomalies, aiding early failure detection. This proactive identification can mitigate connectivity disruptions, an important step toward enhancing network reliability.

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Jones, Marcus, Sneha Mathew, and Kirill Afonin. "Fluorescence correlation as an output signal for programmable biosensing." In Frontiers in Biological Detection: From Nanosensors to Systems XVII, edited by Benjamin L. Miller, Sharon M. Weiss, and Amos Danielli. SPIE, 2025. https://doi.org/10.1117/12.3056792.

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Mitrevski, Jovan. "Low Energy LArTPC Signal Detection Using Anomaly Detection." In Low Energy LArTPC Signal Detection Using Anomaly Detection. US DOE, 2023. http://dx.doi.org/10.2172/2204657.

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Kolodiy, Zenoviy, and Andriy Kolodiy. "Detection of Informational Signal Among Noisy Signals." In 2023 International Conference on Noise and Fluctuations (ICNF). IEEE, 2023. http://dx.doi.org/10.1109/icnf57520.2023.10472749.

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Plazenet, Thibaud, Thierry Boileau, Cyrille Caironi, and Babak Nahid-Mobarakeh. "Signal processing tools for non-stationary signals detection." In 2018 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2018. http://dx.doi.org/10.1109/icit.2018.8352466.

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Lei, Zhongding, and Francois Chin. "WiMax signal detection." In MILCOM 2008 - 2008 IEEE Military Communications Conference (MILCOM). IEEE, 2008. http://dx.doi.org/10.1109/milcom.2008.4753616.

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Katz, A., X. J. Lu, E. G. Kanterikis, Yao Li, Yan Zhang, and N. P. Caviris. "Real-time optoelectronic Gabor detection of transient signals." In OSA Annual Meeting. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.ml6.

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Detection of transient signals in a noisy environment is an important topic in radar, sonar, and communications. An optoelectronic system for detection of transient signals has been constructed. The detection scheme is based on the Gabor representation of a signal, which can be used to represent transient signals of unknown shape and arrival time. The transient signal and Gabor window function are written to the laser beam profile via either transparency or spatial light modulator. The Gabor coefficients are detected by a 2-D CCD array. The use of a liquid crystal television, allowing for real

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Raporty organizacyjne na temat "Signal detection"

Broder, Bruce, and Stuart Schwartz. Quickest Detection Procedures and Transient Signal Detection. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230068.

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Hughes, Timothy M. A Signal Energy Detection Implementation. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada372823.

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Baker, C. R., M. R. Frey, and A. F. Gualtierotti. Some Results on Nongaussian Signal Detection. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada207255.

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Rao, C. R. Some Recent Results in Signal Detection. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177197.

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TEXAS UNIV AT AUSTIN APPLIED RESEARCH LABS. Continuation of Signal Detection Using Polyspectra. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada280176.

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Schlesinger, M. E., and T. P. Barnett. On greenhouse gas signal detection strategies. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6282370.

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Forrest, Robert. Convolutional Neural Networks for Signal Detection. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1813655.

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Zhang, Xin Zhu. Spatial CUSUM for Signal Region Detection. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1317.

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VALLEY, MICHAEL T., BRUCE D. HANSCHE, THOMAS L. PAEZ, ANGEL URBINA, and DENNIS M. ASHBAUGH. Advanced Signal Processing for Thermal Flaw Detection. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/787641.

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Forrest, R. N. Active Sonar Detection and Signal Excess Fluctuations. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada200932.

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