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Опубліковано: 11 вересня 2023

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Статті в журналах з теми "CONTROL SIGNAL"

1

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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2

Logan, Gordon D., Russell J. Schachar, and Rosemary Tannock. "Impulsivity and Inhibitory Control." Psychological Science 8, no. 1 (January 1997): 60–64. http://dx.doi.org/10.1111/j.1467-9280.1997.tb00545.x.

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We report an experiment testing the hypothesis that impulsive behavior reflects a deficit in the ability to inhibit prepotent responses Specifically, we examined whether impulsive people respond more slowly to signals to inhibit (stop signals) than non-impulsive people In this experiment, 136 undergraduate students completed an impulsivity questionnaire and then participated in a stop-signal experiment, in which they performed a choice reaction time (go) task and were asked to inhibit their responses to the go task when they heard a stop signal The delay between the go signal and the stop signal was determined by a tracking procedure designed to allow subjects to inhibit on 50% of the stop-signal trials Reaction time to the go signal did not vary with impulsivity, but estimated stop-signal reaction time was longer in more impulsive subjects, consistent with the hypothesis and consistent with results from populations with pathological problems with impulse control
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3

Lacefield, Soni, and Nicholas Ingolia. "Signal Transduction: External Signals Influence Spore-Number Control." Current Biology 16, no. 4 (February 2006): R125—R127. http://dx.doi.org/10.1016/j.cub.2006.02.005.

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4

Mulung, Bibi Rawiyah, and Andino Maseleno. "Proposed SMART Traffic Control Signal in Brunei Darussalam." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 277. http://dx.doi.org/10.11591/tijee.v15i2.1540.

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This paper presents proposed SMART (Systematic Monitoring of Arterial Road Traffic Signals) traffic control signal in Brunei Darussalam. Traffic congestion due to stops and delays at traffic light signals has much been complained about in Brunei Darussalam as well as across the world during the recent years. There are primarily two types of traffic signal controls in Brunei Darussalam. The most common one is the fixed or pre-timed signal operation traffic light and the other one is the actuated signal operation traffic light. Although the actuated signal control is more efficient than the fixed or pre-fixed signal control in the sense that it provides fewer stops and delays to traffic on the major arteries, the best option for Brunei Darussalam would be to introduce smart traffic control signal. This type of traffic signal uses artificial intelligence to take the appropriate action by adjusting the times in real time to minimise the delay in the intersection while also coordinating with intersections in the neighbourhood. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay, and level of service. In Brunei Darussalam, where we have numerous intersections where several arterial roads are linked to one another, The SMART signal traffic control method should be implemented.
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5

Polushin, P. A., O. R. Nikitin, and I. R. Dubov. "Quasioptimal control in diversed signal transmission." IOP Conference Series: Materials Science and Engineering 1227, no. 1 (February 1, 2022): 012003. http://dx.doi.org/10.1088/1757-899x/1227/1/012003.

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Abstract To increase the noise immunity of signal transmission, diversity methods are now widely used, consisting in obtaining and combining several copies of the transmitted signal. In this case, it is possible to perform a combination either before the detection procedure or after it. If you do not take into account the possible use of non-linear types of modulation, then the pre-detector combination always has advantages over the post-detector combination. However, taking into account the nonlinear properties of the transmitted signals, new possibilities appear for increasing the noise immunity in combination and simplifying the processing. In the case of using analog signals, in particular frequency modulation, at certain points in time, the pre-detection combination can lose to the post-detection combination. At the same time, by combining pre-detector and post-detector combining circuits, it is possible to lower the threshold level during demodulation and increase noise immunity. In the case of using digital modes of modulation, it is possible to process only the signals after demodulation without reducing the noise immunity and to eliminate the need for preliminary phasing of the diversity signals before detection.
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6

Schlessinger, J. "SIGNAL TRANSDUCTION: Autoinhibition Control." Science 300, no. 5620 (May 2, 2003): 750–52. http://dx.doi.org/10.1126/science.1082024.

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7

Azhar, Al-Mudhaffar, and Berg Svante. "Signal Control of Roundabouts." Procedia - Social and Behavioral Sciences 16 (2011): 729–38. http://dx.doi.org/10.1016/j.sbspro.2011.04.492.

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8

Zhao, Lin. "Numerical Control Lathe Cutting Force Signal On-Line Monitoring Design." Applied Mechanics and Materials 711 (December 2014): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amm.711.329.

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Анотація:
The main research direction of Numerical control lathe cutting force signal on-line monitoring is to process real-time monitoring, using the sensor, charge amplifier, video acquisition card and computer to collect data and signal. Signal acquisition makes use of the piezoelectric sensor signals and send them to the computer in order to acquire the real-time data and display the dynamic signal so that monitor the process. Signal processing is the course that data will be collected for subsequent processing and analyzing. It includes display, filtering, correlation analysis, spectral analysis, etc. We can conclude the signal’s characteristics after the time domain and frequency domain analysis of signals.
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9

Huang, Kun, Wei Cai Peng, Zhi Xiong Huang, Min Xian Shi, and Guang Bing Wan. "Research of the Piezoelectric Composite Active Control." Advanced Materials Research 1030-1032 (September 2014): 1513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1513.

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Instantaneous signal and alternating signals are as driven signals, using PID control, change control coefficient P's size, The stopping time of impact force under instantaneous signal with the piezoelectric ceramic content increases, the stopping time is reduced; after applying active control, the stopping is shorter than that is not applied. After applying the alternating signal to force the cantilever beam, the driving effect of the ceramic sheet is located in a cantilever position A, B, C, the effect of C under controlling is at best. The effect of two driven ceramic sheets is better than that of three driven ceramic sheets, the best control position is B+C.
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10

Pan, Fuquan, Lixia Zhang, Changxi Ma, Haiyuan Li, Jinshun Yang, Tao Liu, Fengyuan Wang, and Shushan Chai. "Impact of Vehicular Countdown Signals on Driving Psychologies and Behaviors: Taking China as an Example." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5838520.

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Countdown signal control is a relatively new control mode that can inform a driver in advance about the remaining time to pass through intersections or the time needed to wait for other drivers and pedestrians. At present, few countries apply vehicular countdown signals. However, in China, some cities have applied vehicular countdown signals for years, though it is unclear how and how much such signals influence driving psychologies and behaviors compared with non-countdown signal controls. The present work aims to clarify the impact of vehicular countdown signals on driving psychologies and behaviors on the cognitive level. A questionnaire survey with 32 questions about driving psychologies and behaviors was designed, and an online survey was conducted. A total of 1051 valid questionnaires were received. The survey data were analyzed, and the main results indicate that most of the surveyed drivers prefer countdown signal controls and think that such controls can improve not only traffic safety but also traffic operational efficiency. The surveyed drivers also think that countdown signal controls have an impact on driving psychologies and behaviors and the survey results have demonstrated that the driving behaviors of female drivers surveyed are not conservative under the clear conditions of green countdown signal control. Further studies and methods concerning the effects of countdown signals on driving psychologies and behaviors are discussed.
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Дисертації з теми "CONTROL SIGNAL"

1

Renfrew, David T. "TRAFFIC SIGNAL CONTROL WITH ANT COLONY OPTIMIZATION." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/190.

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Traffic signal control is an effective way to improve the efficiency of traffic networks and reduce users’ delays. Ant Colony Optimization (ACO) is a metaheuristic based on the behavior of ant colonies searching for food. ACO has successfully been used to solve many NP-hard combinatorial optimization problems and its stochastic and decentralized nature fits well with traffic flow networks. This thesis investigates the application of ACO to minimize user delay at traffic intersections. Computer simulation results show that this new approach outperforms conventional fully actuated control under the condition of high traffic demand.
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2

Zhang, Yihua. "An Evaluation of Transit signal Priority and SCOOT Adaptive Signal control." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/33051.

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Cities worldwide are faced with the challenge of improving transit service in urban areas using lower cost means. Transit signal priority is considered to be one of the most effective ways to improve the service of transit vehicles. Transit signal priority has become a very popular topic in transportation in the past 20 to 30 years and it has been implemented in many places around the world. In this thesis, transit signal priority strategies are categorized and an extensive literature review on past research on transit signal priority is conducted. Then a case study on Columbia Pike in Arlington (including 21 signalized intersections) is conducted to assess the impacts of integrating transit signal priority and SCOOT adaptive signal control. At the end of this thesis, an isolated intersection is designed to analyze the sensitivity of major parameters on performance of the network and transit vehicles.

The results of this study indicate that the prioritized vehicles usually benefit from any priority scheme considered. During the peak period, the simulations clearly indicate that these benefits are typically obtained at the expense of the general traffic. While buses experience reductions in delay, stops, fuel consumption, and emissions, the opposite typically occurs for the general traffic. Furthermore, since usually there are significantly more cars than buses, the negative impacts experienced by the general traffic during this period outweigh in most cases the benefits to the transit vehicles, thus yielding overall negative impacts for the various priority schemes considered. For the off-peak period, there are no apparent negative impacts, as there is more spare capacity to accommodate approaching transit vehicles at signalized intersections without significantly disrupting traffic operations.

It is also shown in this study that it is generally difficult to improve the system-wide performance by using transit priority when the signal is already optimized according to generally accepted traffic flow criteria. In this study it is also observed that the system-wide performance decreases rapidly when transit dwell time gets longer.
Master of Science

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3

Al-Mudhaffar, Azhar. "Impacts of Traffic Signal Control Strategies." Doctoral thesis, Stockholm : Division of transports and logistics, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4268.

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4

Niittymäki, Jarkko. "Fuzzy traffic signal control principles and applications /." Espoo, Finland : Helsinki University of Technology, 2002. http://lib.hut.fi/Diss/2002/isbn9512257017/isbn9512257017.pdf.

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Dissertation for the degree of Doctor of Science in Technology--Helsinki University of Technology, Espoo, 2002.
"ISSN 0781-5816." Includes bibliographical references (p. 65-71). Available online as a PDF file via the World Wide Web.
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5

Yung, Sheung Kai. "Signal processing in local sensor validation." Thesis, University of Oxford, 1992. https://ora.ox.ac.uk/objects/uuid:974f513e-a556-4503-bae8-91460f10d3e3.

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Sensor integrity plays a crucial role in automatic control and system monitoring, both in achieving performance and guaranteeing safety. Conventional approaches in sensor failure detection demand precise process models and abundant central computing power. This thesis describes the development and the evaluation of a novel local sensor validation scheme which is independent of the underlying process and is applicable to a wide variety of sensors. A signal-based in-situ sensor validation scheme is proposed. Typical sensor failures are classified according to their signal patterns. To avoid the ambiguity between genuine failures and legitimate measurand variations, a pair of decomposition filters are designed to partition the sensor output; and attention is focused on characteristics beyond the measurement signal bandwidth, which is the only essential process-related variable required. In addition, the application of decimating filters is explored, both as a relief to the analog anti-aliasing filter and as an enhancement in signal discretization. An expression is derived relating the oversampling rate and the attainable improvement in signal resolution. Based on a period of failure-free observation, a whitening filter is identified by modelling the decomposed sensor signal as a stochastic time-series. Significant progress is achieved by a deliberate injection of bandlimited random noise to ensure signal stationarity and to avoid inadmissible leakage of measurement signal into the innovation sequence. The adopted failure detection strategy is primarily innovation-based. Pertinent sensor signal information is extracted recursively by a collection of efficient and robust signal processing algorithms. Its validity is continuously monitored by statistical tests on which a series of precursory failure alarms are formulated. Any aberration detected is then diagnosed under the supervision of a simple rule-based system. The practicality, efficacy and flexibility of the proposed scheme are successfully demonstrated by a bench-top thermocouple experiment and extensive synthetic simulations.
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6

Nunes, Catarina Sofia da Costa. "Advanced signal processing and control in anaesthesia." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251478.

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7

Coeymans-Avaria, Juan Enrique. "Traffic signal systems in a developing country." Thesis, University of Southampton, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305939.

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8

Cadet, Gerard Nivard. "Traffic signal control - a neural network approach." FIU Digital Commons, 1996. http://digitalcommons.fiu.edu/etd/1963.

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Artificial Neural Networks (ANNs) have been proven to be an important development in a variety of problem solving areas. Increasing research activity in ANN applications has been accompanied by equally rapid growth in the commercial mainstream use of ANNs. However, there is relatively little research of practical application of ANNs taking place in the field of transportation engineering. The central idea of this thesis is to use Artificial Neural Network Software Autonet in connection with Highway Capacity Software to estimate delay. Currently existing signal control system are briefly discussed and their short coming presented. As a relative new mathematical model, Neural Network offers an attractive alternative and hold considerable potential for use in traffic signal control. It is more adaptive to the change in traffic patterns that take place at isolated intersections. ANN also provides the traffic engineer more flexibility in term of optimizing different measures of effectiveness. This thesis focuses on a better quality signal control system for traffic engineering using Artificial Neural Networks. An analysis in terms of mean, variance and standard deviation of the traffic data is also presented.
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9

Allee, Susan J. "Neuroendocrine control of a dynamic communication signal." FIU Digital Commons, 2007. http://digitalcommons.fiu.edu/etd/1093.

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Анотація:
Multiple physiological systems regulate the electric communication signal of the weakly electric gymnotiform fish, Brachyhypopomuspinnicaudatus. Fish were injected with neuroendocrine probes which identified pharmacologically relevant serotonin (5-HT) receptors similar to the mammalian 5-HT1AR and 5-HT2AR. Peptide hormones of the hypothalamic-pituitary-adrenal/interrenal axis also augment the electric waveform. These results indicate that the central serotonergic system interacts with the hypothalamic-pituitaryinterrenal system to regulate communication signals in this species. The same neuroendocrine probes were tested in females before and after introducing androgens to examine the relationship between sex steroid hormones, the serotonergic system, melanocortin peptides, and EOD modulations. Androgens caused an increase in female B. pinnicaudatus responsiveness to other pharmacological challenges, particularly to the melanocortin peptide adrenocorticotropic hormone (ACTH). A forced social challenge paradigm was administered to determine if androgens are responsible for controlling the signal modulations these fish exhibit when they encounter conspecifics. Males and females responded similarly to this social challenge construct, however introducing androgens caused implanted females to produce more exaggerated responses. These results confirm that androgens enhance an individual's capacity to produce an exaggerated response to challenge, however another unidentified factor appears to regulate sex-specific behaviors in this species. These results suggest that the rapid electric waveform modulations B. pinnicaudatus produces in response to conspecifics are situation-specific and controlled by activation of different serotonin receptor types and the subsequent effect on release of pituitary hormones.
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10

Wang, Qichao. "Street Traffic Signal Optimal Control for NEMA Controllers." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/101552.

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This dissertation aims to reduce urban traffic congestion with street traffic signal control. The traffic signal controllers in the U.S. follow the National Electrical Manufacturing Association Standards (NEMA Standards). In a NEMA controller, the control parameters for a coordinated control are cycle, green splits, and offset. This dissertation proposed a virtual phase-link concept and developed a macroscopic model to describe the dynamics of a traffic network. The coordinated optimal splits control problem was solved using model predictive control. The outputs of the solution are the green splits that can be used in NEMA controllers. I compared the proposed method with a state-of-the-practice signal timing software under coordinated-actuated control settings. It was found that the proposed method significantly outperformed the benchmarking method. I compared the proposed NEMA-based virtual phase-link model and a Max Pressure controller model using Vissim. It was found that the virtual phase-link method outperformed two control strategies and performed close, but not as good as, the Max Pressure control strategy. The disadvantage of the virtual phase-link method stemmed from the waste of green time during a fixed control cycle length and the delay which comes from the slowing down of platoon during a road link to allow vehicles to switch lanes. Compared to the Max Pressure control strategy, the virtual phase-link method can be implemented by any traffic controller that follows the NEMA standards. The real-time requirement of the virtual phase-link method is not as strict as the Max Pressure control strategy. I introduced the offsets optimization into the virtual phase-link method. I modeled the traffic arrival pattern based on the optimization results from the virtual phase-link control method. I then derived a phase delay function based on the traffic arrival pattern. The phase delay function is a function of the offset between two consecutive intersections. This phase delay function was then used for offsets optimization along an arterial. I tested the offsets optimization method against a base case using microscopic simulations. It was found that the proposed offset optimization method can significantly reduce vehicle delays.
Doctor of Philosophy
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Книги з теми "CONTROL SIGNAL"

1

Godfrey, Keith, and Peter Jones, eds. Signal Processing for Control. Berlin/Heidelberg: Springer-Verlag, 1986. http://dx.doi.org/10.1007/bfb0008182.

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2

Keith, Godfrey, and Jones P. Dr, eds. Signal processing for control. Berlin: Springer-Verlag, 1986.

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3

Herbert, Hanselmann, ed. Digital signal processors in control. New York: IEEE, 1994.

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4

B, Ottersten, Söderström Torsten, and Wahlberg B, eds. Statistical signal processing and control. Oxford: Pergamon, 1994.

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5

Signal processing for active control. San Diego, Calif: Academic Press, 2001.

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6

1934-, Åström Karl J., Goodwin Graham C. 1945-, and Kumar P. R, eds. Adaptive control, filtering, and signal processing. New York: Springer-Verlag, 1995.

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7

Åström, K. J., G. C. Goodwin, and P. R. Kumar, eds. Adaptive Control, Filtering, and Signal Processing. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4419-8568-2.

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8

Paulraj, Arogyaswami, Vwani Roychowdhury, and Charles D. Schaper, eds. Communications, Computation, Control, and Signal Processing. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6281-8.

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9

Nekoogar, Farzad. Digital control using digital signal processing. Upper Saddler River, NJ: Prentice Hall, 1999.

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10

Gordon, Robert L. Traffic signal retiming practices in the United States. Washington, D.C: Transportation Research Board, 2010.

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Частини книг з теми "CONTROL SIGNAL"

1

Salter, R. J. "Signal control strategies." In Highway Traffic Analysis and Design, 286–91. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13423-6_32.

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Salter, R. J. "Signal control strategies." In Highway Traffic Analysis and Design, 277–79. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-20014-6_32.

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3

Abramovici, Alex, and Jake Chapsky. "Multiple Signal Paths." In Feedback Control Systems, 99–118. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4345-9_7.

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4

Albertos, Pedro, and Iven Mareels. "Signal Analysis." In Feedback and Control for Everyone, 87–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-03446-6_4.

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5

Francis, Bruce. "Snippets of H ∞ Control Theory." In Signal Processing, 79–98. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4684-7095-6_4.

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6

Cai, Guowei, Ben M. Chen, and Tong Heng Lee. "Measurement Signal Enhancement." In Advances in Industrial Control, 83–96. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-635-1_5.

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Emelyanov, Stanislav V., and Sergey K. Korovin. "Signal Differentiation." In Control of Complex and Uncertain Systems, 251–86. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0771-2_10.

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Lovely, D. F. "Signals and Signal Processing for Myoelectric Control." In Powered Upper Limb Prostheses, 35–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18812-1_3.

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9

Williamson, Darrell. "Signal Processing." In Advanced Textbooks in Control and Signal Processing, 213–325. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0541-1_4.

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Levine, William S. "Signal Processing for Control." In Handbook of Signal Processing Systems, 261–79. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6859-2_9.

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Тези доповідей конференцій з теми "CONTROL SIGNAL"

1

Li, Nan, and Guangzhou Zhao. "Adaptive signal control for urban traffic network gridlock." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737520.

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2

McDonnell, Mark D. "Signal Estimation Via Averaging of Coarsely Quantised Signals." In 2007 Information, Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/idc.2007.374533.

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3

Ferreira, Jose Gregorio, and Tadeusz Sobczyk. "Multicriteria diagnosis of synchronous machines — Rotor-mounted sensing system. Rotor signal collector construction." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915182.

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Ghorbanian, Parham, Subramanian Ramakrishnan, Adam J. Simon, and Hashem Ashrafiuon. "Stochastic Dynamic Modeling of the Human Brain EEG Signal." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3881.

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The occurrence and risk of recurrence of brain related injuries and diseases are difficult to characterize due to various factors including inter-individual variability. A useful approach is to analyze the brain electroencephalogram (EEG) for differences in brain frequency bands in the signals obtained from potentially injured and healthy normal subjects. However, significant shortcomings include: (1) contrary to empirical evidence, current spectral signal analysis based methods often assume that the EEG signal is linear and stationary; (2) nonlinear time series analysis methods are mostly numerical and do not possess any predictive features. In this work, we develop models based on stochastic differential equations that can output signals with similar frequency and magnitude characteristics of the brain EEG. Initially, a coupled linear oscillator model with a large number of degrees of freedom is developed and shown to capture the characteristics of the EEG signal in the major brain frequency bands. Then, a nonlinear stochastic model based on the Duffing oscillator with far fewer degrees of freedom is developed and shown to produce outputs that can closely match the EEG signal. It is shown that such a compact nonlinear model can provide better insight into EEG dynamics through only few parameters, which is a step towards developing a framework with predictive capabilities for addressing brain injuries.
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Wu, Jia, Abdeljalil Abbas-Turki, Aurelien Correia, and Abdellah El Moudni. "Discrete Intersection Signal Control." In 2007 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2007. http://dx.doi.org/10.1109/soli.2007.4383891.

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Biryukova, O. V., and I. V. Koretskaya. "Signal Recording System Control." In 2021 Systems of Signals Generating and Processing in the Field of on Board Communications. IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf51389.2021.9415997.

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Asada, H. Harry. "Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4246.

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A cell’s behavior in response to stimuli is governed by a signaling network, called cue-signal-response. Endothelial Cells (ECs), for example, migrate towards the source of chemo-attractants by detecting cues (chemo-attractants and their concentration gradient), feeding them into an intra-cellular signaling network (coded internal state), and producing a response (migration). It is known that the cue-signal-response process is a nonlinear, dynamical system with high dimensionality and stochasticity. This paper presents a system dynamics approach to modeling the cue-signal-response process for the purpose of manipulating and guiding the cell behavior through feedback control. A Hammerstein type model is constructed by representing the entire process in two stages. One is the cue-to-signal process represented as a nonlinear feedforward map, and the other is the signal-to-response process as a stochastic linear dynamical system, which contains feedback loops and auto-regressive dynamics. Analysis of the signaling space based on Singular-Value Decomposition yields a set of reduced order synthetic signals, which are used as inputs to the dynamical system. A prediction-error method is used for identifying the model from experimental data, and an optimal system order is determined based on Akaike’s Information Criterion. The resultant low order model is capable of predicting the expected response to cues, and is directly usable for feedback control. The method is applied to an in vitro angiogenic process using microfluidic devices.
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Kannan, Govind, Ali A. Milani, and Issa Panahi. "Active noise control of noisy periodic signals using signal separation." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517935.

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Li, Ming, Pengfei Zhang, and Meisong Luan. "Design of High-Speed PPM Signal Synchronization Based on FPGA." In 2018 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO). IEEE, 2018. http://dx.doi.org/10.1109/controlo.2018.8439785.

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Stanek, Kyle, Nathan Barnhart, and Yong Zhu. "Control of a Robotic Prosthetic Hand Using an EMG Signal Based Counter." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86032.

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This research is intended to create a prototype to generate controllable finger movement of a robotic prosthetic hand using Electromyography (EMG) signals. The instrumentation used in this project includes a Bitalino bio-signal sensor kit, skin electrodes, Arduino Uno microcontroller and a prosthetic hand. The Bitalino’s primary function is to serve as a means to obtain the EMG signal. The Arduino Uno’s function is to implement the control algorithm and actuate the robotic hand to move as intended. Using an EMG signal based counter, the method of control deemed fairly reliable since there was proportional control over the hand but it was based on the duration of the muscle in tension rather than how tense the muscle was. The overall control of the hand was generally responsive to the biological signal.
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Звіти організацій з теми "CONTROL SIGNAL"

1

Lafferriere, Gerardo. Traffic Signal Consensus Control. Transportation Research and Education Center (TREC), 2019. http://dx.doi.org/10.15760/trec.213.

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2

Lafferriere, Gerardo. Traffic Signal Consensus Control. Transportation Research and Education Center (TREC), 2019. http://dx.doi.org/10.15760/trec.221.

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3

Ramamoorthy, P. A. Intelligent Signal Processing for Active Control. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada252232.

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4

Chory, Joanne. Signal Transduction Pathways of Chloroplast Quality Control. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1416021.

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Pinto, J. G. Signal Processing Device to Control Microwave Output. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada216931.

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Willsky, Alan S. Challenges to Control in Signal Processing and Communications. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada459630.

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7

Benekohal, Rahim, Hongjae Jeon, Jesus Osorio, and Behnoush Garshasebi. Evaluation of Adaptive Signal Control Technology—Final Report. Illinois Center for Transportation, June 2019. http://dx.doi.org/10.36501/0197-9191/19-007.

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Kothuri, Sirishi, Andrew Kading, Andrew Schrope, Kelly White, Edward Smaglik, and William Gil. Addressing Bicycle-Vehicle Conflicts with Alternate Signal Control Strategies. Transportation Research and Education Center, April 2018. http://dx.doi.org/10.15760/trec.194.

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Pinsky, Mark A. Markov Processes Applied to Control, Replacement, and Signal Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada190563.

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10

Cinlar, E. Markov Processes Applied to Control, Replacement, and Signal Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160212.

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