Relevant bibliographies by topics / Signal

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Signal'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Signal"

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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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Filonenko, Sergey, Tatiana Nimchenko, and Alexandr Kosmach. "MODEL OF ACOUSTIC EMISSION SIGNAL AT THE PREVAILING MECHANISM OF COMPOSITE MATERIAL MECHANICAL DESTRUCTION." Aviation 14, no. 4 (December 31, 2010): 95–103. http://dx.doi.org/10.3846/aviation.2010.15.

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A model of acoustic emission signal formation at the prevailing mechanism of the destruction of composite materials is considered. The results of acoustic emission signal modelling are presented, taking into account the variable velocity of loading change. Acoustic emission signal experimental research results corresponding to theoretical research results are considered in this paper. It is shown that irregularity of the trailing edge of the acoustic emission signal is influenced by the change in the rate of the destruction process in composites. Santrauka Išnagrinetas akustines emisijos signalo modelis su vyraujančiu kompozitiniu medžiagu irimo mechanizmu. Pateikti akustines emisijos signalu modeliavimo rezultatai, ivertinant skirtinga apkrovos pasikeitimo greiti. Taip pat pateikti eksperimentinio akustines emisijos signalu tyrimo rezultatai, kurie sutampa su teoriniais tyrimais. Parodyta, kad akustines emisijos signalo galinio fronto netolygumas atsiranda tuomet, kai kinta kompozitines medžiagos irimo greitis.
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Shellenberger, Richard O., and Paul Lewis. "Signal Control by Six Signals." Psychological Reports 63, no. 1 (August 1988): 311–18. http://dx.doi.org/10.2466/pr0.1988.63.1.311.

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In previous signal-control experiments, several types of stimuli elicited pecking when paired with peck-contingent grain. Here, we compared the effectiveness of an auditory stimulus and five visual stimuli. For 12 pigeons, the first keypeck to follow the offset of a 4-sec. signal was reinforced with grain. We examined the following signals: a tone, a white keylight, a dark keylight, a keylight that changed from white to red, houselight onset, and houselight offset. All signals acquired strong control over responding. According to one measure, percent of signals with a peck, houselight offset showed less control than the others; according to another measure, pecking rate, the white keylight showed greater control than the others. In this experiment, we found that a wide variety of stimuli can elicit strong pecking in the signal-control procedure. The present findings increase the chances that in past conditioning experiments, some keypecks thought to be due to contingencies of reinforcement were in fact elicited.
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Hughes, Melissa. "Deception with honest signals: signal residuals and signal function in snapping shrimp." Behavioral Ecology 11, no. 6 (November 2000): 614–23. http://dx.doi.org/10.1093/beheco/11.6.614.

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Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.

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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.
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Minasian, R. A. "Photonic signal processing of microwave signals." IEEE Transactions on Microwave Theory and Techniques 54, no. 2 (February 2006): 832–46. http://dx.doi.org/10.1109/tmtt.2005.863060.

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Milligan, Graeme. "All the right signals Signal transduction." Trends in Biochemical Sciences 22, no. 10 (October 1997): 410. http://dx.doi.org/10.1016/s0968-0004(97)82532-7.

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Lessard, Charles S. "Signal Processing of Random Physiological Signals." Synthesis Lectures on Biomedical Engineering 1, no. 1 (January 2006): 1–232. http://dx.doi.org/10.2200/s00012ed1v01y200602bme001.

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Birdsall, Theodore G., Kurt Metzger, and Matthew A. Dzieciuch. "Signals, signal processing, and general results." Journal of the Acoustical Society of America 96, no. 4 (October 1994): 2343–52. http://dx.doi.org/10.1121/1.410106.

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Shinpaugh, K. A., R. L. Simpson, A. L. Wicks, S. M. Ha, and J. L. Fleming. "Signal-processing techniques for low signal-to-noise ratio laser Doppler velocimetry signals." Experiments in Fluids 12-12, no. 4-5 (March 1992): 319–28. http://dx.doi.org/10.1007/bf00187310.

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Dissertations / Theses on the topic "Signal"

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Saruwatari, Hiroshi. "BLIND SIGNAL SEPARATION OF AUDIO SIGNALS." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10406.

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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals." Doctoral thesis, Umeå universitet, Strålningsvetenskaper, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.

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Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustained contraction is performed the motor units involved in the contraction will repeatedly produce APs, which result in AP trains. A surface electromyogram (EMG) signal consists of the superposition of many AP trains generated by a large number of active motor units. The aim of this dissertation was to introduce and evaluate new methods for analysis of surface EMG signals. An important aspect is to consider where to place the electrodes during the recording so that the electrodes are not located over the zone where the neuromuscular junctions are located. A method that could estimate the location of this zone was presented in one study. The mean frequency of the EMG signal is often used to estimate muscle fatigue. For signals with low signal-to-noise ratio it is important to limit the integration intervals in the mean frequency calculations. Therefore, a method that improved the maximum frequency estimation was introduced and evaluated in comparison with existing methods. The main methodological work in this dissertation was concentrated on finding single motor unit AP trains from EMG signals recorded with several channels. In two studies single motor unit AP trains were enhanced by using filters that maximised the kurtosis of the output. The first of these studies used a spatial filter, and in the second study the technique was expanded to include filtration in time. The introduction of time filtration resulted in improved performance, and when the method was evaluated in comparison with other methods that use spatial and/or temporal filtration, it gave the best performance among them. In the last study of this dissertation this technique was used to compare AP firing rates and conduction velocities in fibromyalgia patients as compared with a control group of healthy subjects. In conclusion, this dissertation has resulted in new methods that improve the analysis of EMG signals, and as a consequence the methods can simplify physiological research projects.
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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals /." Umeå : Department of Radiation Sciences, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.

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Palekar, Trishul Ajit. "Signal optimization at isolated intersections using pre-signals." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4279.

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This research proposes a new signal operation strategy aimed at efficient utilization of green time by cutting down on the start up and response loss times. The idea is to have a "pre-signal" on each main approach a few hundred feet upstream of the intersection in addition to the main intersection signal, which is coordinated with the pre-signal. The offset between the main and pre-signal ensures that the majority of start up losses does not occur at the main signal. The benefits of the system under various traffic conditions were evaluated based on analysis of the queue discharge process and Corridor Simulation (CORSIM) study. The proposed measure should reduce the travel time and total control delay for the signalized network. To attain the objective the following two studies were undertaken: 1. Development of a queue discharge model to investigate the expected benefits of the system. 2. Simulation of the system: In the second part of the research, the proposed strategy was tested using CORSIM to evaluate its performance vis-à-vis the baseline case. The queue discharge model (QDM) was found to be linear in nature in contrast to prior expectations. The model was used to quantify the benefits obtained from the pre-signal system. The result of this analysis indicated that the proposed strategy would yield significant travel time savings and reductions in total control delay. In addition to the QDM analysis, CORSIM simulations were used to code various hypothetical scenarios to test the concept under various constraints and limitations. As per expectations, it was found that the system was beneficial for high demand levels and longer offsets. The upper limit on offsets was determined by visual observation of platoon dispersion and therefore the maximum offset distance was restricted to 450 feet. For scenarios where split phasing was used, the break even point in terms of demand level was found to be 2500 vph on a three lane approach, whereas that for a lag-lag type of phasing strategy was found to be 1800 vph, also on a three lane approach.
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Chan, M. K. "Adaptive signal processing algorithms for non-Gaussian signals." Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269023.

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Bland, Denise. "Alias-free signal processing of nonuniformly sampled signals." Thesis, University of Westminster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322992.

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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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Li, Jian. "Array signal processing for polarized signals and signals with known waveforms /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487687485808063.

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Hannaske, Roland. "Fast Digitizing and Digital Signal Processing of Detector Signals." Forschungszentrum Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:d120-qucosa-27888.

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A fast-digitizer data acquisition system recently installed at the neutron time-of-flight experiment nELBE, which is located at the superconducting electron accelerator ELBE of Forschungszentrum Dresden-Rossendorf, is tested with two different detector types. Preamplifier signals from a high-purity germanium detector are digitized, stored and finally processed. For a precise determination of the energy of the detected radiation, the moving-window deconvolution algorithm is used to compensate the ballistic deficit and different shaping algorithms are applied. The energy resolution is determined in an experiment with γ-rays from a 22Na source and is compared to the energy resolution achieved with analogously processed signals. On the other hand, signals from the photomultipliers of barium fluoride and plastic scintillation detectors are digitized. These signals have risetimes of a few nanoseconds only. The moment of interaction of the radiation with the detector is determined by methods of digital signal processing. Therefore, different timing algorithms are implemented and tested with data from an experiment at nELBE. The time resolutions achieved with these algorithms are compared to each other as well as to reference values coming from analog signal processing. In addition to these experiments, some properties of the digitizing hardware are measured and a program for the analysis of stored, digitized data is developed. The analysis of the signals shows that the energy resolution achieved with the 10-bit digitizer system used here is not competitive to a 14-bit peak-sensing ADC, although the ballistic deficit can be fully corrected. However, digital methods give better result in sub-ns timing than analog signal processing.
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Case, David Robert. "Real-time signal processing of multi-path video signals." Thesis, University of Salford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334170.

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Books on the topic "Signal"

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Lessard, Charles S. Signal Processing of Random Physiological Signals. Cham: Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3.

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Signal processing: Signals, filtering, and detection. New York: Van Nostrand Reinhold, 1987.

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von, Heijne Gunnar, ed. Signal peptidases. Austin: R.G. Landes Co., 1994.

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(Firm), Lotus, ed. Signal seminar guide: Lotus Signal. San Mateo, Calif. (1900 S. Norfolk St., San Mateo 94403): Lotus, 1985.

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Jean-Louis, Lacoume, Durrani Tariq S, Stora Raymond 1930-, Université scientifique et médicale de Grenoble., and NATO Advanced Study Institute, eds. Traitement du signal =: Signal processing. Amsterdam: North-Holland, 1987.

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DeFelice, Cynthia C. Signal. New York: Scholastic, 2011.

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Böhme, Klaus-Richard. Signal. Stockholm: Bokförlaget DN, 2005.

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Dimoski, Slave Ǵorǵo. Signal. Skopje: Tri, 2002.

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DeFelice, Cynthia C. Signal. New York: Farrar, Straus and Giroux, 2009.

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Palme, Klaus, ed. Signals and Signal Transduction Pathways in Plants. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0239-1.

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Book chapters on the topic "Signal"

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Mueller, Werner A., Monika Hassel, and Maura Grealy. "Controlling Signals, Signal Propagation and Signal Transduction." In Development and Reproduction in Humans and Animal Model Species, 323–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43784-1_11.

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Mazor, Stanley, and Patricia Langstraat. "Signals & Signal Assignments." In A Guide to VHDL, 105–35. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-2114-0_5.

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Wang, K. C. "Signals and Signal Processing." In Systems Programming in Unix/Linux, 205–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92429-8_6.

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Wang, K. C. "Signals and Signal Processing." In Design and Implementation of the MTX Operating System, 257–71. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17575-1_9.

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Mazor, Stanley, and Patricia Langstraat. "Signals & Signal Assignments." In A Guide to VHDL, 109–39. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3216-3_5.

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Barksdale, William J. "Communications Signals and Signal Processing." In Practical Computer Data Communications, 23–71. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4684-5164-1_3.

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Schwaderer, W. David. "OCP Signals and Signal Groupings." In Introduction to Open Core Protocol, 29–36. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-0103-2_4.

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Cao, Jiannong, and Yanni Yang. "Wireless Signals and Signal Processing." In Wireless Networks, 13–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08345-7_2.

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Nahler, Gerhard. "signal." In Dictionary of Pharmaceutical Medicine, 168–69. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1282.

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Weik, Martin H. "signal." In Computer Science and Communications Dictionary, 1576–77. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17334.

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Conference papers on the topic "Signal"

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Rutenbar, Rob A., David Harame, Kurt Johnson, Paul Kempf, Teresa Meng, Reza Rofougaran, and James Spoto. "Mixed signals on mixed-signal." In the 40th conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/775832.775904.

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Blundell, V., T. Clarke, and D. Williams. "Synthetic signals for signal processing." In Sensor Signal Processing for Defence (SSPD 2010). IET, 2010. http://dx.doi.org/10.1049/ic.2010.0229.

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Assaleh, Khaled T., and Richard J. Mammone. "Signal-adaptive decomposition of multicomponent signals." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Franklin T. Luk. SPIE, 1993. http://dx.doi.org/10.1117/12.160441.

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Minasian, R. A., E. H. W. Chan, and Xiaoke Yi. "Photonic signal processing of microwave signals." In 35th Australian Conference on Optical Fibre Technology (ACOFT 2010). IEEE, 2010. http://dx.doi.org/10.1109/acoft.2010.5929926.

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Ashtari, Ali, Gabriel Thomas, Hector Garces, and Benjamin C. Flores. "Radar signal design using chaotic signals." In 2007 International Waveform Diversity and Design Conference. IEEE, 2007. http://dx.doi.org/10.1109/wddc.2007.4339442.

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Saeidi, Rahim, Pejman Mowlaee, Tomi Kinnunen, Zheng-Hua Tan, Mads Graesboll Christensen, Soren Holdt Jensen, and Pasi Franti. "Signal-to-Signal Ratio Independent Speaker Identification for Co-channel Speech Signals." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.1131.

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Tian, Shuxia, Zhenmao Chen, Dongli Zhang, and Qiang Geng. "Signal Processing Schemes for Defect Identification and Sizing From Signal of Eddy Current Testing in Nuclear Power Plant." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-29825.

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In this paper, signal processing schemes developed for upgrading the Eddy Current Testing (ECT) system utilized for In Service Inspection (ISI) of Steam Generator (SG) tubes of a nuclear power plant are presented. Schemes for the wobbling noise recognition, signal indication extraction, signal classification, and the separation of mixed signals are proposed. The schemes are applied to both signals measured in a SG mock-up and signals from short tube test-pieces measured in a laboratory environment. It is demonstrated that the methods work well even for mixed signals in the support plate region of SG tubes. As it is possible to adjust the position of a signal point based on the signature of the support plates, noise signals from a welding line or a bending zone can be recognized based on the information of design and manufactory. The mixed signals of defects and support structures are processed by using a similarity analysis strategy and the identification of defect signals is performed by means of both a neural network approach and a statistic method. Satisfactory results are obtained for the processing of measured signals.
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Pang, Lihui, Yilong Tang, Yulang Liu, Bin Yang, Wenwei Zhang, and Qingyi Tan. "A signel-channel blind signal separation method based on signal type difference for time-frequency overlapped signal." In 2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2023. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00185.

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He, Daqian, Dahai Zhang, Congying Wang, and Xirui Peng. "A Broadband Array Signal Processing Method Based on Joint Sparsity of Signal Spatial Domain." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95530.

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Abstract Broadband underwater acoustic signal direction of arrival (DOA) estimation method is an important part of underwater array signal processing. The commonly used array signal DOA estimation algorithms due to the restriction of algorithm principles, are unable to process broadband array signal effectively, at the case of the arriving signals have strong correlation, small sampling snapshots or small arrival angle. Therefore, we need a new efficient algorithm to meet the increasing demand of broadband under water acoustic signal processing method. This paper makes use of the broadband acoustic signal similarity of joint sparsity in signal spatial domain received by underwater sonar arrays, establishes the whole space grid covering all broadband frequency domain slices. On the basis, the global sparsity of each frequency domain slice is combined with sparse element extraction class algorithm. By integrating the energy of signal on each slice, the spatial sparsity of each slice is obtained, from which we can get the directions of the arriving broadband wave signals. Through the simulation analysis and experimental verification on lake, we can be see that: The SDJS algorithm improves the performance and signal processing capability of the algorithm compared with the traditional algorithms. Therefore SDJS algorithm has a widely range of research value and application space.
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Klamecki, Barney E. "End Milling Process Vibration Signal Enhancement by Stochastic Resonance." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1798.

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Abstract The possible enhancement of vibration signals by the addition of a random signal component (stochastic resonance) was investigated. Vibration measurements during end milling were made using an accelerometer mounted on the workpiece. A large number of tests were run with varying spindle speed, table speed, axial depth of cut, radial depth of cut and tool condition. Signal frequency spectra were dominated by the cutting edge passing frequency and so individual cutting edge generated events could not be identified easily in the signal spectra. Enhancement of spectra peaks associated with individual cutting edge events by adding a random signal component to accelerometer signals was demonstrated in numerical experiments using actual accelerometer signals. The effects of initial spectrum peak magnitude, measurement system threshold level and added noise level on signal enhancement were determined.
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Reports on the topic "Signal"

1

Easton, Jr., R. Signal processing. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/5071979.

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2

Miller, Jr, and Willard. Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada206662.

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3

Cromwell, R. Signal Processing Studies Program Optical Signal Amplification. Volume 2. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada188054.

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4

Roehrig, H., and M. Browne. Signal Processing Studies Program Optical Signal Amplification. Volume 1. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada188055.

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5

Andrews, Michael, and David James. SBNR (Signed Binary Number Representations) Digital Signal Processor Architecture. Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada184603.

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6

Basu, Sankar. Multidimensional Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada200954.

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7

Thomas, J. B., and K. Steiglitz. Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada203744.

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Armenta, Mikaela, Laura Epifanovskaya, Joshua Letchford, Kiran Lakkaraju, Jonathan Whetzel, Bethany Goldblum, and Jake Tibbetts. SIGNAL Game Manual. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1643226.

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Baraniuk, Richard G. Compressive Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada530830.

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King-Smith, Deen, Anthony Martone, and Marc Ressler. Reflected Signal Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada513228.

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