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Статті в журналах з теми "Contrôle EMG"
Abdullah, Saad, Muhammad A. Khan, Mauro Serpelloni, and Emilio Sardini. "Hybrid EEG-EMG Based Brain Computer Interface (BCI) System For Real-Time Robotic Arm Control." Advanced Materials Letters 10, no. 1 (December 10, 2018): 35–40. http://dx.doi.org/10.5185/amlett.2019.2171.
Повний текст джерелаBortel, Radoslav, and Pavel Sovka. "EEG–EMG coherence enhancement." Signal Processing 86, no. 7 (July 2006): 1737–51. http://dx.doi.org/10.1016/j.sigpro.2005.09.011.
Повний текст джерелаGodener, Armelle, and Marianela Fornerino. "La métamorphose du contrôle de gestion." L'Expansion Management Review N° 119, no. 4 (2005): 54. http://dx.doi.org/10.3917/emr.119.0054.
Повний текст джерелаMoinard, Christian. "Les habits trop étroits du contrôle de gestion." L'Expansion Management Review N° 142, no. 3 (2011): 26. http://dx.doi.org/10.3917/emr.142.0026.
Повний текст джерелаPulcrano, Jim. "Réseautez et prenez le contrôle de votre réputation !" L'Expansion Management Review N° 146, no. 3 (2012): 110. http://dx.doi.org/10.3917/emr.146.0110.
Повний текст джерелаCorrêa, João Carlos Ferrari, Rúben De Faria Negrão Filho, and Fausto Bérzin. "Estudo EMG e eletrogoniométrico na instabilidade patelofemoral." ConScientiae Saúde 3 (January 9, 2008): 37–47. http://dx.doi.org/10.5585/conssaude.v3i0.314.
Повний текст джерелаGOPURA, R. A. R. C., and Kazuo KIGUCHI. "1P1-E13 EMG-Based Control of a 6DOF Upper-Limb Exoskeleton Robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _1P1—E13_1—_1P1—E13_3. http://dx.doi.org/10.1299/jsmermd.2009._1p1-e13_1.
Повний текст джерелаPiercey, Stephen J. "Modern Analytical Facilities 2. A Review of Quality Assurance and Quality Control (QA/QC) Procedures for Lithogeochemical Data." Geoscience Canada 41, no. 1 (March 4, 2014): 75. http://dx.doi.org/10.12789/geocanj.2014.41.035.
Повний текст джерелаIssa, Mohamed F., Gergely Tuboly, György Kozmann, and Zoltan Juhasz. "Automatic ECG Artefact Removal from EEG Signals." Measurement Science Review 19, no. 3 (June 1, 2019): 101–8. http://dx.doi.org/10.2478/msr-2019-0016.
Повний текст джерелаRoussel, Cyril. "Irak et Kurdistan d’Irak : la problématique de la frontière interne et les enjeux du contrôle territorial." Égypte/Monde arabe, no. 18 (June 15, 2018): 65–82. http://dx.doi.org/10.4000/ema.4071.
Повний текст джерелаДисертації з теми "Contrôle EMG"
Grisetto, Fanny. "Impulsivity is not just disinhibition : investigating the effects of impulsivity on the adaptation of cognitive control mechanisms." Thesis, Lille 3, 2020. http://www.theses.fr/2020LIL3H031.
Повний текст джерелаImpulsivity is a behavioral tendency frequently observed in the general population butat different degrees. Interestingly, higher impulsivity increases the probability to develop and to be diagnosed with a psychiatric disorder, such as substance use or personality disorders. To gain a better understanding on the emergence of such psychiatric disorders, my PhD project focused on the role of cognitive control in impulsive manifestations. Indeed, cognitive control is a set of basic executive functions ensuring adaptive behaviors to an ever-changing and complex environment. More particularly, during my PhD research, I investigated the flexible adaptation between reactive and proactive control mechanisms in impulsive individuals, mainly from the general population but also from an alcohol-dependent population.The first three studies of my thesis revealed that high impulsivity was characterizedby a less-proactive cognitive control system, and associated with a weaker adaptation ofcognitive control mechanisms both to external demands and internal constraints. Morespecifically, I observed that high impulsive individuals less exert proactive control whileit should be favored given contextual or individual characteristics. In the fourth study inwhich EEG signals were recorded, we were interested in the brain activity that is typicallyobserved during errors (i.e., the ERN/Ne), which is thought to signal the need for control.A reduction in this brain activity was observed in high aggressive individuals, but notin high impulsive individuals. This finding suggest that the emergence of maladaptivebehaviors may be explained, to a certain extent, by the reduced alarm signal. Finally, somepreliminary results suggest a link between a peripheral index of physiological adaptation(i.e., HRV) and the capacity to adapt control mechanisms. These findings open newavenues for therapeutic interventions in the reduction in maladaptive behaviors.Overall, findings from the current thesis suggest that impulsivity in the general population is associated with a less proactive and a less flexible cognitive control system, potentially leading to inappropriate behaviors when the control mechanisms at play are maladapted
Durandau, Guillaume. "Traitement des signaux EMG et son application pour commander un exosquelette." Mémoire, Université de Sherbrooke, 2015. http://hdl.handle.net/11143/6987.
Повний текст джерелаAyachi, Fouaz Sofiane. "Étude du recrutement des unités motrices par analyse du signal EMG de surface." Compiègne, 2011. http://www.theses.fr/2011COMP1998.
Повний текст джерелаThe central nervous system control the movement through the activation of the motors units (MUs), the smallest muscle functional structure. The MU produce electrical activity that can be detected by the technique of surface electromyography (sEMG). The stochastic nature of EMGs signal is mainly due to the superposition of trains of MU action potentials ( MUAPT) (spatial recruitment), the MUAPT are characterized by their discharge frequency (temporal recruitment) and the shape of the action potential (PA), which depends on some factors methodological and intrinsic to the muscle. The aim of this thesis is to study the possibilities and limitations of using the shape analysis of the EMGs signal’s probability density function (DP) as an indicator on MU recruitment strategies and motor control. This analysis seems relevant since the EMGs signal is the sum of random processes, the MUAPT. The contribution of this thesis is divided into two parts : the proposal of a complete model generation inspired by recent work from the literature. This model takes into consideration, for the EMGs signal generation, many physiological, anatomical and nervous parameters, as well as the force generation. Such consideration allows for greater realism in the simulation. The second part concerns several studies, simulation and experimental analysis of EMGs monopolar signals detected on the biceps brachii during isometric contractions isotonic (constant force) / anisotonique (graduated force). The aim is to extract information on the pattern of MU recruitment from these signals. In this context, we tested two approaches based on the shape analysis of the EMGs signal’s DP which are the Higher Order Statistics (HOS), and a recent algorithm, the Core Shape Modeling (CSM). The results indicate a high sensitivity of the proposed descriptors for separating classes of signals (force, sync level of the discharge), the filtering effect of adipose tissue and non propagating component. The efficiency of the classification depends the other hand of the anatomy and the number of MU which composed the muscle. For neuronal factors, both recruitment strategies tested give similar trends with one of them is physiologically more realistic. In addition, analysis of shape (SOS), in some cases, gives us information about muscle anatomy of the concerned muscle, in terms of MU position relative to the electrode. Concerning performance of classification, the algorithm CSM gives a result relatively better than SOS approach, either in simulation or experimentation. To summarize, this thesis is listed as an exploratory process of the shape analysis potential of the EMGs signal’s DP in order to extract the information on the muscular activation’s modalities. A lot of efforts are still required in accordance with the perspectives offered
Crepin, Roxane. "Système de détection de mouvements complexes de la main à partir des signaux EMG, pour le contrôle d'une prothèse myoélectrique." Master's thesis, Université Laval, 2018. http://hdl.handle.net/20.500.11794/33033.
Повний текст джерелаTechnological advances in biomedical engineering worldwide enable the development of automated and patient-friendly systems, aiming at providing the severely disabled a better comfort of life. Intelligent prostheses based on myoelectric activity allow amputees to intuitively interact with their environment and perform daily life activities. Electrodes placed on the surface of the skin, and dedicated embedded electronics allow to collect muscle signals and translate them into commands to drive a prosthesis actuators. Increasing performance while decreasing the cost of surface electromyography (sEMG) prostheses is an important milestone in rehabilitation engineering. The prosthetic hands that are currently available to patients worldwide would benefit from more effective and intuitive control. This memoir presents a real-time approach to classify finger motions based on sEMG signals. A multichannel signal acquisition platform of our design is used to record forearm sEMG signals from 7 channels. sEMG pattern classification is performed in real time, using a Linear Discriminant Analysis (LDA) approach. Thirteen hand motions can be successfully identified with an accuracy of up to 95.8% and of 92.7% on average for 8 participants, with an updated prediction every 192 ms. The approach wanted to be adapted to create an embedded system opening great opportunities for the development of lightweight, inexpensive and more intuitive electromyographic hand prostheses.
Entakli, Jonathan. "Implication des projections spinales de l'aire motrice supplémentaire lors d'un contrôle précis de force : étude par TMS et EEG." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4121/document.
Повний текст джерелаHuman dexterity is a highly developed function based on the ability to independently and precisely control forces and movements of the fingers related to the constraints of the task. Hand muscles for finger movements are steered by the lateral corticospinal (CS) system. The main source of this CS system is the primary motor area (M1), which has direct CS projections on motoneurons innervating hand muscles. Recently, CS projections from non-primary motor area have also been found, especially from the supplementary motor area (SMA). However, the functionality of this CS tract in human manual force control is little studied. The aim of this thesis was to study the implication of the CS projections from SMA in precision manual force control, using electroencephalography (EEG) and transcranial magnetic stimulation (TMS).Altogether, the results obtained in our different studies show the important implication of SMA in dexterity. It appears that this area can act in parallel with M1, directly influencing excitability of spinal motoneurons. We conclude that M1 and SMA both have direct and efficient influence on force production during fine manual motor tasks
Li, Zhan. "Nouvelle modalité de contrôle en boucle fermée de l'activation musculaire et prédiction en ligne du couple musculaire sous SEF." Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20095/document.
Повний текст джерелаFunctional electrical stimulation (FES) is one of existing rehabilitationtechniques to restore lost motor functions for motor-impaired subjects. Thestimulator generates electrical pulses to drive artificial contractions of theparalysed muscles, through activating intact motor units. Currently open-loopFES system is the most frequently used. The data acquired from the open-loop FESwould help researchers to make off-line analysis for evaluating performance ofFES systems. However, it should go through a trial and error manner, which isfar from facilitating a implementation of real-time closed-loop FES system.In this thesis, we propose and develop a method for real-time EMG-feedback torqueprediction and muscle activation control toward new modality in FES.The evoked electromyography (eEMG) which can reflect electrical muscleactivities under FES, is involved in both offline and real-time FES-inducedtorque estimation and muscle control systems. FES-induced joint torque can beestimated/predicted with eEMG by employing both Kalman filter and NonlinearAuto-Regressive with Exogenous (NARX) type recurrent neural network (RNN). Theforgetting factor of Kalman filter should be properly selected in advance andalso with proper computational settings. It is a limitation for some casesespecially when we do not have prior knowledge of new subject regarding expectedmuscle response intensity induced by FES. The proposed NARX-RNN does not sufferfrom such computational setting problems and also shows better estimation/prediction performances than that of Kalman filter.Evoked EMG based torque estimator is exploited from off-line situation toonline real-time system. Recursive Kalman filter and NARX-RNN are implementedfor real-time torque estimation/prediction with evoked EMG. The performance wasverified both in able-bodied and spinal cord injured subjects. Furthermore, real-time EMG-feedback muscle activation control in FES system is developed togetherwith wireless Vivaltis stimulator for specifying directly muscle activationinstead of conventionally specifying stimulation pattern.Toward natural multiple muscles control with multi-channel FES, muscle synergyconcept was introduced for inverse estimation of muscle activations from desiredjoint moment. The averaged synergy ratio was applied for muscle activationestimation with leave-one-out cross validation manner, which resulted in 9.3%estimation error over all the subjects. This result supports the common musclesynergy-based neuroprosthetics control concept. By combining this inverse estimation of muscle activations together with real-time EMG-feedback muscle activation control, it would open a new modality toward muscle synergy-basedmulti-muscle activation control in FES
Maheu, Véronique. "Développement des critères d'apprentissage pour le contrôle d'un bras robot manipulateur à 7 DDL par le traitement des signaux EMG chez les blessés médullaires." Mémoire, École de technologie supérieure, 2011. http://espace.etsmtl.ca/865/1/MAHEU_V%C3%A9ronique.pdf.
Повний текст джерелаZhang, Xiang Qin. "Estimation du couple généré par un muscle sous SEF à la base de l’EMG évoquée pour le suivi de la fatigue et le contrôle du couple en boucle fermée." Thesis, Montpellier 2, 2011. http://www.theses.fr/2011MON20191/document.
Повний текст джерелаFunctional electrical stimulation (FES) has the potential to provide active improvement to spinal cord injured (SCI) patients in terms of mobility, stability and side-effect prevention. In the domain of lower limb FES system, elicited muscle force must be provided appropriately to perform intended movement and the torque generation by FES should be accurate not to lose the posture balance. However, muscle state changes such as muscle fatigue is a major cause which degrades its performance. In addition, most of the complete SCI patients don't have sensory feedback to detect the fatigue and in-vivo joint torque sensor is not available yet. Conventional FES control systems are either in open-loop or not robust to muscle state changes. This thesis aims at a development of joint torque prediction and feedback control in order to enhance the FES performance in terms of accuracy, robustness, and safety to the patients.In order to predict FES-induced joint torque, evoked-Electromyography (eEMG) has been applied to correlate muscle electrical activity and mechanical activity. Although muscle fatigue represents time-variant, subject-specific and protocol-specific characteristics, the proposed Kalman filter-based adaptive identification was able to predict the time-variant torque systematically. The robustness of the torque prediction has been investigated in a fatigue tracking task in experiment with SCI subjects. The results demonstrated good tracking performance for muscle variations and against some disturbances.Based on accurate predictive performance of the proposed method, a new control strategy, EMG-Feedback Predictive Control (EFPC), was proposed to adaptively control stimulation pattern compensating to time-varying muscle state changes. In addition, this control strategy was able to explicitly avoid overstimulation to the patients, and conveniently generate appropriate stimulation pattern for desired torque trajectory
Ramdani, Beauvir Céline. "Effets de la vigilance sur le contrôle de l'erreur chez l'homme : études comportementales et électrophysiologiques." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM5019/document.
Повний текст джерелаTo study the impact of a vigilance decrease on error monitoring mechanisms in healthy participants, electromyogram and electroencephalogram were recorded during a choice reaction time task. The aim of experiment one was to decipher which indices of error monitoring at the behavioral and electrophysiological levels, were altered by sleep deprivation. In experiments two and three, decreases in vigilance were obtained through pharmacological treatments. We attempted to selectively inhibit one arousal system (either by acting on the histaminergic or on the dopaminergic pathway), so as to determine whether indices of error monitoring would be affected in the same way than after extended wakefulness. Proactive (implemented before an error execution) and reactive modes (implemented after an error execution) of error monitoring were distinguished. Within each mode, we further distinguished on-line (implemented within-trial) and off-line (between-trials) processes.Proactive off-line monitoring was unaffected by the decrease in vigilance, whether this caused by extended wakefulness, histaminergic depeltion or dopaminergic depletion). Sleep deprivation affected proactive on-line and off-line monitoring and reactive control. Histaminergic depletion affected only reactive control and reactive control seemed insensitive to dopaminergic depletion.As sleep deprivation, both histaminergic and dopaminergic depletion induced decrease in vigilance. However, effects of sleep deprivation on error monitoring were entirely reproduced neither by histaminergic nor by dopaminergic depletion, suggesting specific influences of the corresponding systems on error monitoring
Rubiano, Fonseca Astrid. "Smart control of a soft robotic hand prosthesis." Thesis, Paris 10, 2016. http://www.theses.fr/2016PA100189/document.
Повний текст джерелаThe target of this thesis disertation is to develop a new Smart control of a soft robotic hand prosthesis for the soft robotic hand prosthesis called ProMain Hand, which is characterized by:(i) flexible interaction with grasped object, (ii) and friendly-intuitive interaction between human and robot hand. Flexible interaction results from the synergies between rigid bodies and soft bodies, and actuation mechanism. The ProMain hand has three fingers, each one is equipped with three phalanges: proximal, medial and distal. The proximal and medial are built with rigid bodies,and the distal is fabricated using a deformable material. The soft distal phalange has a new smart force sensor, which was created with the aim to detect contact and force in the fingertip, facilitating the control of the hand. The friendly intuitive human-hand interaction is developed to facilitate the hand utilization. The human-hand interaction is driven by a controller that uses the superficial electromyographic signals measured in the forearm employing a wearable device. The wearable device called MyoArmband is placed around the forearm near the elbow joint. Based on the signals transmitted by the wearable device, the beginning of the movement is automatically detected, analyzing entropy behavior of the EMG signals through artificial intelligence. Then, three selected grasping gesture are recognized with the following methodology: (i) learning patients entropy patterns from electromyographic signals captured during the execution of selected grasping gesture, (ii) performing a support vector machine classifier, using raw entropy data extracted in real time from electromyographic signals
Книги з теми "Contrôle EMG"
Fund, International Monetary. Credibility, capital controls, and the EMS. Washington, D. C: International Monetary Fund, 1989.
Знайти повний текст джерелаGibson, Heather D. Between "new" and "old" EMS: Fiscal policy and capital controls in Southern Europe. Canterbury: University of Kent at Canterbury, 1991.
Знайти повний текст джерелаEichengreen, Barry. Is there a safe passage to EMU?: Evidence on capital controls and a proposal. London: Centre for Economic Policy Research, 1994.
Знайти повний текст джерелаCônsoli, Fernando L. Egg Parasitoids in Agroecosystems with Emphasis on Trichogramma. Dordrecht: Springer Science+Business Media B.V., 2010.
Знайти повний текст джерелаFleischer, Shelby. Sequential sampling plans for estimating gypsy moth egg mass density. Radnor, PA]: USDA, Forest Service, Northeastern Area, Appalachian Integrated Pest Management, 1992.
Знайти повний текст джерелаEgg donation: The reasons and the risks. New York: Rosen Pub., 2010.
Знайти повний текст джерелаNelms, R. M. Design of power electronics for TVC & EMA systems: Final report. [Washington, DC: National Aeronautics and Space Administration, 1994.
Знайти повний текст джерелаGaier, James R. EMI shields made from intercalated graphite composites. [Washington, DC]: National Aeronautics and Space Administration, 1995.
Знайти повний текст джерелаThe ISO 14000 EMS audit handbook. Boca Raton, FL: St. Lucie Press, 1997.
Знайти повний текст джерелаAlan, Knight, ed. ISO 14000 implementation: Upgrading your EMS effectively. New York: McGraw-Hill, 1999.
Знайти повний текст джерелаЧастини книг з теми "Contrôle EMG"
Santana, Aashish, and Chenguang Yang. "Robotic Control Using Physiological EMG and EEG Signals." In Advances in Autonomous Robotics, 449–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32527-4_53.
Повний текст джерелаKorczyński, R., S. Kasicki, and U. Borecka. "EMG and Hippocampal EEG Activities during Spontaneous and Elicited Movements in the Rat." In Motor Control, 75–78. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-7508-5_13.
Повний текст джерелаArchambeault, Bruce R. "Introduction to EMI/EMC Design for Printed Circuit Boards." In PCB Design for Real-World EMI Control, 1–7. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3640-3_1.
Повний текст джерелаZhang, Hua, Yuting Zhang, Chengbo Huang, Yanxing Yuan, and Lili Cheng. "Basic Knowledge of EMC and Methods of EMI Control." In Space Science and Technologies, 11–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4782-9_2.
Повний текст джерелаLi, Guanglin, Oluwarotimi Williams Samuel, Chuang Lin, Mojisola Grace Asogbon, Peng Fang, and Paul Oluwagbengba Idowu. "Realizing Efficient EMG-Based Prosthetic Control Strategy." In Advances in Experimental Medicine and Biology, 149–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2050-7_6.
Повний текст джерелаEdlinger, Guenter, Christoph Kapeller, Arnau Espinosa, Sergi Torrellas, Felip Miralles, and Christoph Guger. "Multi-modal Computer Interaction for Communication and Control Using EEG, EMG, EOG and Motion Sensors." In Universal Access in Human-Computer Interaction. Design Methods, Tools, and Interaction Techniques for eInclusion, 633–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39188-0_68.
Повний текст джерелаArchambeault, Bruce R. "EMC Fundamentals." In PCB Design for Real-World EMI Control, 9–23. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3640-3_2.
Повний текст джерелаMüller, Philipp Niklas, Philipp Achenbach, André Mihca Kleebe, Jan Ulrich Schmitt, Ute Lehmann, Thomas Tregel, and Stefan Göbel. "Flex Your Muscles: EMG-Based Serious Game Controls." In Serious Games, 230–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61814-8_18.
Повний текст джерелаViolette, J. L. Norman, Donald R. J. White, and Michael F. Violette. "EMI Control in Components." In Electromagnetic Compatibility Handbook, 503–44. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-017-7144-3_16.
Повний текст джерелаPatel, Govind Singh, Dhiraj Gupta, Baibaswata Mohapatra, and Sunil Kumar Chaudhary. "EMG-Based Robot Control Human Interfaces for Hospitals." In Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare, 137–55. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003227595-9.
Повний текст джерелаТези доповідей конференцій з теми "Contrôle EMG"
Ura, Kazuhide, Teruyoshi Sadahiro, Masami Iwase, and Shoshiro Hatakeyama. "Zero-phase tracking human interface using EMG signals and EMD." In Control (MSC). IEEE, 2011. http://dx.doi.org/10.1109/cca.2011.6044351.
Повний текст джерелаWang, Leilei, Shuo Du, Huan Liu, Jinxu Yu, Shengcui Cheng, and Ping Xie. "A virtual rehabilitation system based on EEG-EMG feedback control." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243542.
Повний текст джерелаSa-e, Sakariya, Chris T. Freeman, and Kai Yang. "Model-Based Control of FES Embedding Simultaneous Volitional EMG Measurement." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516718.
Повний текст джерелаKiguchi, Kazuo, and Yoshiaki Hayashi. "Motion Estimation Based on EMG and EEG Signals to Control Wearable Robots." In 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/smc.2013.718.
Повний текст джерелаJunfeng Gao, Pan Lin, Yong Yang, and Pei Wang. "Online EMG artifacts removal from EEG based on blind source separation." In 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010). IEEE, 2010. http://dx.doi.org/10.1109/car.2010.5456848.
Повний текст джерелаNoor, Azizah S., Suprijanto, and Miranti Indar Mandasari. "EMG Signal Enhancement from ECG Artifact Contamination for Assessment of Swallowing Function." In 2019 6th International Conference on Instrumentation, Control, and Automation (ICA). IEEE, 2019. http://dx.doi.org/10.1109/ica.2019.8916733.
Повний текст джерелаXinyi Yong, Rabab K. Ward, and Gary E. Birch. "Facial EMG contamination of EEG signals: Characteristics and effects of spatial filtering." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537319.
Повний текст джерела"Control of EMI interference for linear ECG recording." In Proceedings of the International Conference on Electromagnetic Interference and Compatibility'99. IEEE, 1999. http://dx.doi.org/10.1109/icemic.1999.871668.
Повний текст джерелаRafiee, J., M. P. Schoen, N. Prause, A. Urfer, and M. A. Rafiee. "A comparison of forearm EMG and psychophysical EEG signals using statistical signal processing." In 2009 2nd International Conference on Computer, Control and Communication (IC$). IEEE, 2009. http://dx.doi.org/10.1109/ic4.2009.4909196.
Повний текст джерелаChand, Rakesh, Pawan Tripathi, Abhishek Mathur, and K. C. Ray. "FPGA implementation of fast FIR low pass filter for EMG removal from ECG signal." In 2010 International Conference on Power, Control and Embedded Systems (ICPCES). IEEE, 2010. http://dx.doi.org/10.1109/icpces.2010.5698652.
Повний текст джерелаЗвіти організацій з теми "Contrôle EMG"
Smith, J. R., and R. Gough. Electromechanical Battery (EMB) and EMB Power Control System Final Report CRADA No. TC-723-94. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/1438807.
Повний текст джерелаRomberger, J. Chapter 19: HVAC Controls (DDC/EMS/BAS) Evaluation Protocol. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1164874.
Повний текст джерелаBrunner, Amy, and Jason Holliday. Abiotic stress networks converging on FT2 to control growth in Populus. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1484373.
Повний текст джерелаZhu, Yijun. Identification of Small Molecules Targeting the Posttranscriptional Control of ERG Expression. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada575222.
Повний текст джерелаCapela dos Santos, Denise. A política de controlo de doenças transmissíveis em Portugal. Universidade Autónoma de Lisboa, 2016. http://dx.doi.org/10.26619/ual-cee/wp012016.
Повний текст джерелаJenkins, J. Lee, Edbert B. Hsu, Anna Russell, Allen Zhang, Lisa M. Wilson, and Eric B. Bass. Infection Prevention and Control for the Emergency Medical Services and 911 Workforce. Agency for Healthcare Research and Quality (AHRQ), November 2022. http://dx.doi.org/10.23970/ahrqepctb42.
Повний текст джерелаYao, Z. S., Y. Z. Li, and J. E. Mungall. Transport and deposition of sulphide liquid - vectors to ore accumulations. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328979.
Повний текст джерелаReaves, Jimmy L., and Ralph H. Crawford. In vitro colony interactions among species of Trichoderma with inference toward biological control. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1994. http://dx.doi.org/10.2737/pnw-rp-474.
Повний текст джерелаEl Halawani, Mohamed, and Israel Rozenboim. Environmental factors affecting the decline in reproductive efficiency of turkey hens: Mediation by vasoactive intestinal peptide. United States Department of Agriculture, January 2007. http://dx.doi.org/10.32747/2007.7696508.bard.
Повний текст джерелаSteigerwalt, Ryan. Quality Control Methodologies for Advanced EMI Sensor Data Acquisition and Anomaly Classification - Former Southwestern Proving Ground, Arkansas. Fort Belvoir, VA: Defense Technical Information Center, July 2015. http://dx.doi.org/10.21236/ada626409.
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