Relevant bibliographies by topics / Signal detection

Academic literature on the topic 'Signal detection'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Signal detection"

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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 (July 13, 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 blood pressure signal which was recorded in parallel to ECG signal, and uses it to support the QRS detection process in distorted signal areas. Proposed algorithm performs as well as classical QRS detectors in cases where signal to noise ratio is high, compared to the heartbeat annotations provided by experts. In signals with considerably lower signal to noise ratio proposed algorithm improved the detection accuracy to up to 6%. Širdies ritmas yra vienas svarbiausių ir daugiausia informacijos apie pacientų būklę teikiančių fiziologinių parametrų. Širdies ritmas nustatomas iš elektrokardiogramos (EKG), atliekant QRS regionų, kurie yra interpretuojami kaip širdies dūžio ãtskaitos, paiešką. QRS regionų aptikimas yra klasikinis uždavinys, nagrinėjamas jau keletą dešimtmečių, todėl širdies dūžių nustatymo iš EKG signalų metodų yra labai daug. Deja, šie metodai tikslūs ir patikimi tik esant dideliam signalo ir triukšmo santykiui. Kai EKG signalai labai iškraipomi, QRS aptiktuvai ne visada gali atskirti QRS regioną, o kartais jį randa ten, kur iš tikro jo būti neturėtų. Straipsnyje siūlomas algoritmas, kurį taikant sumažinama triukšmo įtaka nustatant iš EKG signalų QRS regionus. Tam naudojamas QRS aptiktuvas, kartu prognozuojantis širdies dūžio atskaitą. Remiamasi arterinio kraujo spaudimo signalo duomenimis, renkama atskaitų statistika ir atliekama jos analizė.
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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 (October 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 (October 18, 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 effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.
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Egberts, Toine C. G. "Signal Detection." Drug Safety 30, no. 7 (2007): 607–9. http://dx.doi.org/10.2165/00002018-200730070-00006.

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Cheng, Yu-Chung Norman, and E. Mark Haacke. "Signal Detection." Current Protocols in Magnetic Resonance Imaging 00, no. 1 (March 2001): B2.1.1—B2.1.10. http://dx.doi.org/10.1002/0471142719.mib0201s00.

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Cheng, Yu-Chung Norman, and E. Mark Haacke. "Signal Detection." Current Protocols in Magnetic Resonance Imaging 13, no. 1 (April 2005): B2.1.1—B2.1.10. http://dx.doi.org/10.1002/0471142719.mib0201s13.

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Kumar, Anoop, and Henna Khan. "Signal Detection and their Assessment in Pharmacovigilance." Open Pharmaceutical Sciences Journal 2, no. 1 (December 17, 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 the major challenge of pharmacovigilance. The current method of detecting a signal is predominantly based on spontaneous reporting, which is mainly helpful in detecting type B adverse effects and unusual type A adverse effects. Other sources of signals detection are prescription event monitoring, case control surveillance and follow up studies. Signal assessment is mainly performed by using Upsala Monitoring scale & Naranjo scale of probability to analyze the cause and effect analysis. Signal detection and their assessment is very vital and complex process. Thus, the main objective of this review is to provide a summary of the most common methods of signal detection and their assessment used in pharmacovigilance to confirm the safety of a drug. Recent developments, challenges, & future needs have also been discussed.
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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 (December 3, 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 core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction.
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Khudov, Hennadii, Serhii Yarosh, Oleksandr Kostyria, Oleksandr Oleksenko, Mykola Khomik, Andrii Zvonko, Bohdan Lisohorskyi, Petro Mynko, Serhii Sukonko, and Taras Kravets. "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) (February 28, 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 small-sized radars; – sounding signal emission by each small-sized radar; – reception of echo signals from a stealth unmanned aerial vehicle by two small-sized radars; – coordinated filtering of incoming echo signals (separation of echo signals); – quadratic detection of signals at the outputs of matched filters; – summation of the detected signals at the outputs of the matched filters; – summation of the outputs of adders of two small-sized radars. The scheme of a stealth unmanned aerial vehicle detector is presented, optimal according to the Neumann-Pearson criterion, with incoherent signal processing. The quality of detection of a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing was evaluated. It was found that with incoherent processing, the gain in the value of the conditional probability of correct detection is on average from 19 % to 26 %, depending on the value of the signal-to-noise ratio. The gain in the value of the conditional probability of correct detection is greater at low values of the signal-to-noise ratio. At the same time, the gain in signal-to-noise value is more significant with coherent signal processing than with non-coherent signal processing by a network of two small-sized radars
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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 (November 23, 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 proposes a two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES). In the first stage, a normal signal classification method, based on the envelope feature extraction and fusion is proposed to solve the problem of the low ultrasonic signal classification accuracy under the conditions of the echo feature time shift and noise interference. In the second stage, an abnormal signal detection method, based on the local similarity feature extraction and enhancement is proposed to solve the problem of detecting abnormal signals in ultrasound internal detection data. The experimental results show that the accuracy of the two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES) in this paper is 97.43%, and the abnormal signal detection accuracy and recall rate are as high as 99.7% and 97.81%.
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Dissertations / Theses on the topic "Signal detection"

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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-exactly (SKE) tasks, where signals are nonrandom, are employed. MCMC methods are extended to signal-known-statistically (SKS) tasks, where signals are random. Psychophysical studies for the SKE and SKS tasks using non-Gaussian and Gaussian distributed LBs are conducted. The performance of the Bayesian ideal observer, the human observer, and the channelized-Hotelling observer for the SKE and SKS tasks is compared. Human efficiencies for both the SKE tasks and SKS tasks are estimated. Also human efficiencies for non-Gaussian and Gaussian-distributed LBs are compared for the SKE tasks. Finally, the theory of the channelized-ideal observer (CIO) is introduced to approximate the performance of the ideal observer by the performance of the CIO in cases where the channel outputs of backgrounds and signals are non-Gaussian distributed. Computational approaches to estimate the CIO are investigated.
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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 either true or false). It examined whether punishers have similar, but opposite, effects to reinforcers on detection performance, and whether the effects of punishment were successfully captured by existing models of punishment and choice. Experiment 1 varied the relative frequency or magnitude of time-out punishers for errors using the blue/red task. Participants were systematically biased away from the response alternative associated with the higher rate or magnitude of time-out punishers in two of three procedures. Experiment 2 varied the relative frequency of point-loss punishers using the blue/red task and the true/false task. Participants were systematically biased away from the alternative associated with the higher rate of point-loss punishers for the true/false task. Experiment 3 examined the effects of punishment on response bias from a psychophysical perspective. Previous detection research which varied stimulus discriminability while holding reinforcers ratios constant and unequal (Johnstone & Alsop, 2000; McCarthy & Davison, 1984) found that a criterion location measure (e.g., c, Green & Swets, 1966) was a better descriptor of isobias functions compared to a likelihood ratio measure (e.g., log β[G], Green & Swets, 1966). Experiment 3 varied stimulus discriminability while holding punisher ratios constant and unequal. Like previous research, isobias functions were consistent with a criterion location measure. Experiments 4, 5, 6, and 7 examined contemporary models of choice and punishment. Experiments 4, 5, and 6 varied the relative reinforcer ratio in detection tasks, both with and without the inclusion of an equal rate of punishment. Experiment 7 held the reinforcer ratio constant and unequal, and varied the durations of time-out punishers. Increases in preference (for the richer alternative) from reinforcer-only conditions to reinforcer + punisher conditions would support a subtractive model of punishment, while decreases in preference would support an additive model of punishment. Experiment 4 was a between-groups study using time-out punishers. It supported the predictions of an additive model. Experiment 5 used three different procedures in a preliminary within-subjects design, evaluating which procedure was best suited for a larger within-subjects experiment (Experiment 6). In Experiment 6, participants sat four reinforcer-only and four reinforcer + punisher conditions where reinforcers were point-gains and punishers were point-losses. The results from Experiment 6 were mixed - some participants showed increased preference while others showed little change or a slight decrease. This appeared related to the order in which participants received the reinforcer-only and reinforcer + punisher conditions. Experiment 7 also found no consistent change in preference with increases in time-out durations. Instead, there was a slow increase in bias on the richer alternative across the eight sessions. Overall, punishers had similar, but opposite, effects to reinforcers in detection procedures (Experiments 1, 2, & 3). These effects were successfully captured by Davison and Tustin�s (1978) model of detection. The later experiments did not provide support for a subtractive model punishment model of choice, which had provided the best descriptor in corresponding concurrent-schedule research. Instead, Experiment 4 supported an additive model, and Experiments 5, 6, and 7 provided no evidence for either model - limitations and implications of these studies are discussed. However, the present thesis illustrates that the signal detection procedure is promising for studying the combined effects of reinforcement and punishment, and may offer a worthwhile complement to standard concurrent-schedule choice procedures.
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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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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 the wavelet transform being used, we presented the DWT based WTAF and the wavelet tree based WTAF. In terms of the position of the adaptive filter in the signal paths of the system, we presented the Before-Reconstruction WTAF, in which the adaptive filter is placed before the reconstruction filter; and the After-Reconstruction WTAF, in which the adaptive filter is placed after the reconstruction filter. This could also be considered as implementing the adaptive filtering in different domains, with the Before-Reconstruction structure corresponding to adaptive filtering in the scale-domain, and the After-Reconstruction structure corresponding to adaptive filtering in the time-domain. In terms of the type of the error signal used in the WTAF, we presented the output error based WTAF and the subband error based WTAF. In the output error based WTAF, the output error signal is used as input to the LMS algorithm. In the subband error based WTAF, the error signal in each subband is used as input to the LMS algorithm. The algorithms for the WTAF were also generalized in this work. In order to speed up the calculation, we developed the block LMS based WTAF, which modifies the weights of the adaptive filter block-by-block instead of sample-by-sample. Experimental studies were performed to study the performance of different implementation schemes for the WTAF. Simulations were performed on different WTAF algorithms with a sinusoidal input and with a pulse input. The speed and stability properties of each structure were studied experimentally and theoretically. It was found that different WTAF structures had different tradeoffs in terms of stability, performance, computational complexity, and convergence speed. The WTAF algorithms were applied to an online measurement system for fabric compressional behavior and they showed encouraging results. A 3-stage DWT based WTAF and a block WTAF based on a 3-stage DWT was employed to process the noisy force-displacement signal acquired from the online measurement system. The signal-to-noise ratio was greatly increased by applying these WTAFs, which makes a lower sampling rate a possibility. The reduction of the required time for data sampling and processing greatly improves the system speed to meet faster testing requirements. The WTAF algorithm could also be used in other applications requiring fast processing, such as in the real-time applications in communications, measurement, and control.

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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 receiver is assumed to be overloaded in that the number of transmitted signals exceeds the number of receive antennas. Joint Maximum Likelihood (JML) is the optimum detection algorithm which has exponential complexity in the number of signals. As a result, detection of the signals of interest at the receiver is challenging and infeasible in most practical systems. The thesis presents a framework for the detection of multiple co-channel signals in overloaded receivers. It proposes receiver structures and two list-based signal detection algorithms that allow for complexity reduction compared to the optimum detector while being able to maintain near optimum performance. Complexity savings are achieved by first employing a linear preprocessor at the receiver to reduce the effect of Co-Channel Interference (CCI) and second, by using a detection algorithm that searches only over a subspace of the transmitted symbols. Both algorithms use iterative processing to extract ordered lists of the most likely transmit symbols. Soft information can be obtained from the detector output list and can then be used by error control decoders. The first algorithm named Parallel Detection with Interference Estimation (PD-IE) considers the Additive White Gaussian Noise (AWGN) channel. It relies on a spatially reduced search over subsets of the transmitted symbols in combination with CCI estimation. Computational complexity under overload is lower than that of JML. Performance results show that PD-IE achieves near optimum performance in receivers with Uniform Circular Array (UCA) and Uniform Linear Array (ULA) antenna geometries. The second algorithm is referred to as List Group Search (LGS) detection. It is applied to overloaded receivers that operate in frequency-flat multipath fading channels. The List Group Search (LGS) detection algorithm forms multiple groups of the transmitted symbols over which an exhaustive search is performed. Simulation results show that LGS detection provides good complexity-performance tradeoffs under overload. A union bound for group-wise and list-based group-wise symbol detectors is also derived. It provides an approximation to the error performance of such detectors without the need for simulation. Moreover, the bound can be used to determine some detection parameters and tradeoffs. Results show that the bound is tight in the high Signal to Interference and Noise Ratio (SINR) region.
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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 complicated than correlated Gaussian noise and are meant to complicate the signal-detection task by involving random structure. Regarding the human observer, we present a literature review of psychophysical models, including results based on these models. We then examine one current front runner--a channelized-Hotelling observer with channels modeling visual-response functions---for two experiments involving the lumpy backgrounds.
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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 approximate the ideal observer in real medical images. We define our detection problem as a two-hypothesis detection task where a known signal is superimposed on a random background with complicated distributions and embedded in independent Poisson noise. The first challenge of this detection problem is that the distribution of the random background is usually unknown and difficult to estimate. The second challenge is that the calculation of the ideal observer is computationally intensive for non stylized problems. In order to solve these two problems, our work relies on multiresolution analysis of images. The multiresolution analysis is achieved by decomposing an image into a set of spatial frequency bandpass images so each bandpass image represents information about a particular fitness of detail or scale. Connected with this method, we will use three types of image representation by invertible linear transforms. They are the orthogonal wavelet transform, pyramid transform and independent component analysis. Based on the findings from human and mammalian vision, we can model textures by using marginal densities of a set of spatial frequency bandpass images. In order to estimate the distribution of an ensemble of images given the empirical marginal distributions of filter responses, we can use the maximum entropy principle and get a unique solution. We find that the ideal observer calculates a posterior mean of the ratio of conditional density functions, or the posterior mean of the ratio of two prior density functions, both of which are high dimensional integrals and have no analytic solution usually. But there are two ways to approximate the ideal observer. The first one is a classic decision process; that is, we construct a classifier following feature extraction steps. We use the integrand of the posterior mean as features, which are calculated at the estimated background close to the posterior mode. The classifier combines these features to approximate the integral (or the ideal observer). Finally, if we know both the conditional density function and the prior density function then we can also approximate the high dimensional integral by Monte Carlo integration methods. Since the calculation of the posterior mean is usually a very high dimensional integration problem, we must construct a Markov chain, which can explore the posterior distribution efficiently. We will give two proposal functions. The first proposal function is the likelihood function of random backgrounds. The second method makes use of the multiresolution representation of the image by decomposing the image into a set of spatial frequency bands. Sampling one pixel in each band equivalently updates a cluster of pixels in the neighborhood of the pixel location in the original image.
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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 conditions. However, the information of the target range or what principal component contains target information may be unknown. DFT and Short Time Fourier Transform (STFT) algorithms have been used to detect the human breathing and discriminate between two targets. However, the algorithms result in many false alarms because they detect breathing when no target exists. The unsatisfactory performance of the DFT-based estimators in human breathing rate estimation is due to the fact that the second harmonic of the breathing signal has higher magnitude than the first harmonic. Human posture estimation has been performed by measuring the distance of the chest displacements from the ground. This requires multiple UWB receivers and a more complex system. In this thesis, monostatic UWB radar is used. Initially, the SVD method was combined with the skewness test to detect targets, discriminate between two targets, and reduce false alarms. Then, a novel human breathing rate estimation algorithm was proposed using zero-crossing method. Subsequently, a novel method was proposed to distinguish between human postures based on the ratios between different human breathing frequency harmonics magnitudes. It was noted that the ratios depend on the abdomen displacements and higher harmonic ratios were observed when the human target was sitting or standing. The theoretical analysis shows that the distribution of the skewness values of the correlator output of the target and the clutter signals in a single range-bin do not overlap. The experimental results on human breathing detection, breathing rate, and human posture estimation show that the proposed methods improve performance in human breathing detection and rate estimation.
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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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Books on the topic "Signal detection"

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

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

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

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

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Song, Iickho, Jinsoo Bae, and Sun Yong Kim. Advanced Theory of Signal Detection. Berlin, Heidelberg: 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. Fayetteville, Tenn: Tennessee Applied Physics, Inc., 1992.

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

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1945-, Papantoni-Kazakos P., ed. Detection and estimation. New York: Computer Science Press, 1990.

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1925-, Thomas John Bowman, ed. Signal detection in non-Gaussian noise. New York: Springer-Verlag, 1988.

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Book chapters on the topic "Signal detection"

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Elsner, James B., and Anastasios A. Tsonis. "Signal Detection." In Singular Spectrum Analysis, 89–112. Boston, MA: 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, 169. Vienna: 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, 85–94. Berkeley, CA: 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, 85–131. Berlin, Heidelberg: 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, 541–630. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_7.

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Mohanty, Nirode. "Detection of Signals." In Signal Processing, 457–648. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-011-7044-4_4.

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Tuzlukov, Vyacheslav P. "Introduction." In Signal Detection Theory, 1–9. Boston, MA: 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, 11–37. Boston, MA: 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, 38–224. Boston, MA: 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, 225–93. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0187-8_4.

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Conference papers on the topic "Signal detection"

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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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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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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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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. Washington, D.C.: 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-time detection of signals is investigated. Experimental results for exponentially decaying signals, are presented which clearly indicate the signal frequency and arrival time.
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Hao, Long, Dan Liu, Fei Liu, QingXin Wang, Lin Liang, and GuangHua Xu. "Research on the Weak Signal Detection of Bearing Fault Based on Duffing Oscillator." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86892.

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In this paper, chaotic system is applied to identify and extract the weak signals of bearing early fault which are often submerged in strong background noise. Chaotic system is an effective method in weak signal detection because of its properties of noise immunity and sensitivity to the weak periodic signal. However, chaotic system is not completely immune to noise in critical chaotic state. Aiming at this problem, four indicators are used to evaluate the detection performance of Duffing oscillators. Then, the influence of Duffing oscillator parameters on the four indicators is studied in detail and a new method is proposed to improve the detection performance of Duffing oscillator. The simulation and experimental results show that the proposed method can accurately obtain the characteristic signals of early bearing fault in a lower signal-to-noise ratio (SNR) situation.
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Nair, Arya Sukumaran, Peter Hoffrogge, Peter Czurratis, Christian Hollerith, Alexander Roch, Alireza Haghighat, Klaus Pressel, Frank Zudock, Mario Wolf, and Elfgard Kühnicke. "1D-ResNet Framework for Ultrasound Signal Classification." In ISTFA 2022. ASM International, 2022. http://dx.doi.org/10.31399/asm.cp.istfa2022p0021.

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Abstract Minor flaws are becoming extremely relevant as the complexity of the semiconductor package evolves. Scanning acoustic microscopy is one analytical tool for detecting flaws in such a complex package. Minor changes in the reflected signal that could indicate a fault can be lost during image reconstruction, despite the high sensitivity. Because of recent AI (Artificial Intelligence) advancements, more emphasis is being placed on developing AI-based algorithms for high precision-automated signal interpretation for failure detection. This paper presents a new deep learning model for classifying ultrasound signals based on the ResNet architecture with 1D convolution layers. The developed model was validated on two test case scenarios. One use case was the detection of voids in the die attach, the other the detection of cracks below bumps in Flip-chip samples. The model was trained to classify signals into different classes. Even with a small dataset, experiment results confirmed that the model predicts with a 98 percent accuracy. This type of signal-based model could be extremely useful in situations where obtaining large amounts of labeled image data is difficult. Through this work we propose an intelligent signal classification methodology to automate high volume failure analysis in semiconductor devices.
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Chuang, C. H., and Y. L. Lo. "Heterodyne Detection Signal Analysis in Apertureless Scanning Near-Field Optical Microscopy." In ASME 2008 First International Conference on Micro/Nanoscale Heat Transfer. ASMEDC, 2008. http://dx.doi.org/10.1115/mnht2008-52186.

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Apertureless scattering near-field optical microscopy (A-SNOM) is generally performed using a heterodyne detection technique since it provides a higher signal-to-noise (S/N) ratio than homodyne detection. Accordingly, this study constructs a robust interference-based model of the detection signal which takes account of both the tip enhancement phenomena and the tip reflective background electric field to analyze the amplitude and phase of heterodyne detection signals at different harmonics of the tip vibration frequency. The analytical results indicate that the high-order harmonic tip scattering noise decays more rapidly with a high-order Bessel function for small phase modulation depths than the near-field interaction signal. It is also shown that the signal contrast improves as the wavelength of the illuminating light source is increased or the incident angle is reduced. As compared with homodyne technique, it can be found the signal contrast is much improved in visible region in heterodyne technique. The results presented in this study provide an improved understanding of the complex signal detected in the heterodyne A-SNOM technique and suggest potential means of improving its S/N ratio such that the signal contrast of heterodyne A-SNOM can be improved.
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Beck, M., M. E. Anderson, and M. G. Raymer. "Imaging through Scattering Media Using Pulsed Homodyne Detection." In Advances in Optical Imaging and Photon Migration. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/aoipm.1994.ci.257.

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In this paper we demonstrate high resolution imaging of a target embedded in a scattering medium using a pulsed laser source. The imaging is performed by interfering the light emerging from the scattering medium with a 200 fs duration pulsed local oscillator beam, and detecting the interfered light with a balanced homodyne detector. We show that the data collected by our detector can be analyzed in two ways. One method strongly suppresses the detection of scattered light emerging from the medium, leaving almost exclusively the ballistic (unscattered) component of the signal. Using the other data analysis technique, both the scattered and ballistic components of the signal are readily detected. This shows that despite the fact that coherent (homodyne) detection is used, the system can be optimized for the detection of either coherent (unscattered) or incoherent (scattered) light.
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Peng, Chubing, M. Mansuripur, Kenichi Nagata, and Takeo Ohta. "Edge detection readout signal and cross-talk in phase-change optical data storage." In Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/ods.1998.tub.3.

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Conventionally, readout signal is obtained by differential detection in magneto-optical storage or by direct integration of the reflected light in phase-change optical storage. Mark edges are usually determined by slicing the level detection signal at the standard level, suffering from intersymbol interference when reading densely recorded short marks. Edge detection is a direct optical detection for mark edges. The readout signal is the difference signal from a split detector. Theoretically, edge detection has advantages over conventional level detection, such as high contrast and ability to identify edges of densely spaced marks. These features need to be confirmed experimentally. In magneto-optical storage [1], edge-shift of short marks using edge detection was found to be lower than that using differential level detection [2]. But in other aspects, such as signal and noise levels, edge detection was inferior to differential level detection [2, 3]. In phase-change optical storage [4], theoretical analysis indicates that edge detection has a potential superiority over conventional detection (hereafter referred to as sum detection). Experimentally, edge detection noise level has been confirmed to be lower than sum detection, especially at low and high spatial frequencies. In this work we present results for edge detection readout signal, carrier-to-noise ratio (CNR), and cross-talk characteristics in the scheme of land-groove as well as comparison with sum detection for phase-change optical storage.
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Reports on the topic "Signal detection"

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

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Hughes, Timothy M. A Signal Energy Detection Implementation. Fort Belvoir, VA: Defense Technical Information Center, December 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. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada207255.

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Rao, C. R. Some Recent Results in Signal Detection. Fort Belvoir, VA: Defense Technical Information Center, September 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. Fort Belvoir, VA: Defense Technical Information Center, June 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), February 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), November 2020. http://dx.doi.org/10.2172/1813655.

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Zhang, Xin Zhu. Spatial CUSUM for Signal Region Detection. Ames (Iowa): Iowa State University, January 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), September 2001. http://dx.doi.org/10.2172/787641.

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

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