Gotowa bibliografia na temat „Movement-based signal”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Movement-based signal”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Movement-based signal"
Liang, Sensong, Jiansheng Peng i Yong Xu. "Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology". Journal of Healthcare Engineering 2021 (25.08.2021): 1–11. http://dx.doi.org/10.1155/2021/1745292.
Pełny tekst źródłaPeters, Richard A. "Environmental motion delays the detection of movement-based signals". Biology Letters 4, nr 1 (30.10.2007): 2–5. http://dx.doi.org/10.1098/rsbl.2007.0422.
Pełny tekst źródłaTd New, Shaun, i Richard A Peters. "A framework for quantifying properties of 3-dimensional movement-based signals". Current Zoology 56, nr 3 (1.06.2010): 327–36. http://dx.doi.org/10.1093/czoolo/56.3.327.
Pełny tekst źródłaBan, Dahee, Syed Shahid i Sungoh Kwon. "Movement Noise Cancellation in Second Derivative of Photoplethysmography Signals with Wavelet Transform and Diversity Combining". Applied Sciences 8, nr 9 (1.09.2018): 1531. http://dx.doi.org/10.3390/app8091531.
Pełny tekst źródłaRahul, Yumlembam, i Rupam Kumar Sharma. "EEG Signal-Based Movement Control for Mobile Robots". Current Science 116, nr 12 (25.06.2019): 1993. http://dx.doi.org/10.18520/cs/v116/i12/1993-2000.
Pełny tekst źródłaTurnip, Arjon, Grace Gita Redhyka, Hilman S. Alam i Iwan R. Setiawan. "An Experiment of Spike Detection Based Mental Task with Ayes Movement Stimuli". Applied Mechanics and Materials 780 (lipiec 2015): 87–96. http://dx.doi.org/10.4028/www.scientific.net/amm.780.87.
Pełny tekst źródłaRahim, Md, i Jungpil Shin. "Hand Movement Activity-Based Character Input System on a Virtual Keyboard". Electronics 9, nr 5 (8.05.2020): 774. http://dx.doi.org/10.3390/electronics9050774.
Pełny tekst źródłaSuberbiola, Aaron, Ekaitz Zulueta, Jose Manuel Lopez-Guede, Ismael Etxeberria-Agiriano i Manuel Graña. "Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction". International Journal of Neural Systems 25, nr 03 (8.04.2015): 1550009. http://dx.doi.org/10.1142/s0129065715500094.
Pełny tekst źródłaSilaban, Freddy Artadima, Setiyo Budiyanto i Wahyu Kusuma Raharja. "Stepper motor movement design based on FPGA". International Journal of Electrical and Computer Engineering (IJECE) 10, nr 1 (1.02.2020): 151. http://dx.doi.org/10.11591/ijece.v10i1.pp151-159.
Pełny tekst źródłaWang, Nian Nian, Ying Zhi Wang, Li Fu Zhu, Ze Xiang Tan, Di Wang, Yue Sun, Ming Yue Li i Guo Zhong Liu. "The Design of Control System of Cursor Movement Based EEG". Applied Mechanics and Materials 665 (październik 2014): 635–39. http://dx.doi.org/10.4028/www.scientific.net/amm.665.635.
Pełny tekst źródłaRozprawy doktorskie na temat "Movement-based signal"
How, Martin John, i martin how@anu edu au. "The fiddler crab claw-waving display: an analysis of the structure and function of a movement-based visual signal". The Australian National University. Research School of Biological Sciences, 2004. http://thesis.anu.edu.au./public/adt-ANU20081001.111333.
Pełny tekst źródłaHow, Martin J. "The fiddler crab claw-waving display : an analysis of the structure and function of a movement-based visual signal /". View thesis entry in Australian Digital Theses Program, 2007. http://thesis.anu.edu.au/public/adt-ANU20081001.111333/index.html.
Pełny tekst źródłaMileros, Martin D. "A Real-Time Classification approach of a Human Brain-Computer Interface based on Movement Related Electroencephalogram". Thesis, Linköping University, Department of Mechanical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2824.
Pełny tekst źródłaA Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time.
Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network.
A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.
Monma, Yumi. "Algoritmo rápido para segmentação de vídeos utilizando agrupamento de clusters". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/116648.
Pełny tekst źródłaThis work presents a very fast algorithm to segmentation of moving parts in a video, based on detection of surfaces of the scene with closed contours. The input video is preprocessed with an edge detection algorithm based on level lines to produce the objects. The detected objects are clustered using a combination of mean shift clustering and ensemble clustering. In order decrease even more the computation time required, two methods can be used combined: object filtering by size and selecting only a few frames of the video. Since the detected objects are coherent in time, frame skipping does not affect the final result. Depending on the application the detected clusters can be refined using post processing steps.
Chen, Ching-Hao, i 陳竫昊. "Identification of drowsiness by detecting eye movement based on mindwave EEG signal". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/5sxs6b.
Pełny tekst źródła國立臺北科技大學
自動化科技研究所
103
In this study, a drowsiness identification system using mindwave EEG signal is proposed. With the noninvasive mindwave headset developed by NeuroSky, the time domain signal of the mindwave is used to recognize eye movement and the user's fatigue level. First, the EEG raw signal is transformed by the wavelet transformation. Second, the eigenvalues are computed based on the Daubechies wavelet. Third, the support vector machine and the back propagation neural network are studied to identify the status of eye movement using the eigenvalues. Finally, the fuzzy logic is used to obtain the fatigue level, according to the frequency of the eye movement and the time of closing eyes.
How, Martin John. "The fiddler crab claw-waving display: an analysis of the structure and function of a movement-based visual signal". Phd thesis, 2007. http://hdl.handle.net/1885/49333.
Pełny tekst źródłaJiang, Wei-Ling, i 江偉凌. "Design of eye movement detection system based on electrooculography signals and their human-computer interaction applications". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/35340531493212140290.
Pełny tekst źródła國立交通大學
影像與生醫光電研究所
100
In assistive research area, human-computer interface (HCI) technology is used to help disable people by conveying their intention and thinking to the outside world. Many HCI systems based on eye movement have been proposed to assistive disable people. However, due to the complexity of algorithm and difficulty of hardware implementation, there are rare general purpose designs considering the practicality and stability in real-life. Therefore, to solve these limitations and problems, a HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides the eye state detection including fixation, saccade and blink. Moreover, in saccade detection, this algorithm can distinguish ten kind of saccade movements (i.e., up, down, left, right, much left, much right, up-left, down-left, up-right and down-right). In addition, we development a HCI system based on eye movement classification algorithm. This system provides an eye-dialing interface that can be facilitated to improve the life of disable people. The significant results are achieved that proved the performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye movement features, is potential to be performed in real-life applications.
Feng, Shu-wei, i 馮書瑋. "Based on Artificial Neural Network Learning Approach for the Brain Wave Signals with Eye Movement Commands to Control Embedded Mobile Robots". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/xxgnzx.
Pełny tekst źródłaLi, Jia-ching, i 李嘉清. "The Brain Wave Signals with Eye Movement Command based on Back-Propagation Artificial Neural Network Approach for Real-time Simulation System". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/11864652067073076867.
Pełny tekst źródła國立高雄第一科技大學
系統資訊與控制研究所
101
The brainwave application is far and wide. During this decade, there are more and more progresses and achievements shown in the industry. However, We hope to probe into auxiliary equipment, which is portable and compatible with certain operating system. The computer interface is specifically designed for those patients, who have problems for manipulating computers with their hands and communicating with others. Therefore, We attempt to develop a highly stable and transmitting interface that integrates the neuroscience, signal processing, and control theory. The EEG headset catches the brainwave signals of the users with their motor areas of brain. The brainwave computer interface based on back-propagation neural network(BPN) to select the behaviours of eyes movements such as up, down, left, and right motions. The figuring out the characteristic signals of eye movement, is up to 70%. The interface of brain computer combines with simulation system. In order to prove the BPN model can certainly classify the signals of eye movement. Moreover, the brain waves can be replaced by the mouse, keyboard, and the application of simulation system. In the future, the improved BPN will search for more signals of eye movement and raise its successful rate and stability.
Książki na temat "Movement-based signal"
Fletcher, Nicholas. Movement disorders. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780198569381.003.0926.
Pełny tekst źródłaRoze, Emmanuel, i Frédéric Sedel. Gangliosidoses (GM1 and GM2). Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0050.
Pełny tekst źródłaDouglas, Kenneth. Bioprinting. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190943547.001.0001.
Pełny tekst źródłavan Craenenbroeck, Jeroen, i Tanja Temmerman, red. The Oxford Handbook of Ellipsis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198712398.001.0001.
Pełny tekst źródłaHedberg Olenina, Ana. Psychomotor Aesthetics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190051259.001.0001.
Pełny tekst źródłaCzęści książek na temat "Movement-based signal"
Sayeed, Saadman, Farjana Sultana, Partha Chakraborty i Mohammad Abu Yousuf. "Assessment of Eyeball Movement and Head Movement Detection Based on Reading". W Recent Trends in Signal and Image Processing, 95–103. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6966-5_10.
Pełny tekst źródłaChangJie, Hu. "Based on Difference Signal Movement Examination Shadow Suppression Algorithm". W Communications in Computer and Information Science, 461–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24097-3_69.
Pełny tekst źródłaArun Ganesh, K., N. Sivakumaran i S. Kumaravel. "Lower Limb Amputees Rehabilitation: IOT-Based Real-Time Human Movement". W Advances in Automation, Signal Processing, Instrumentation, and Control, 3017–33. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_283.
Pełny tekst źródłaLu, Lixin, Dongcai Wu, Guiqin Li i Peter Mitrouchev. "Signal Denoising Algorithm of Massage Chair Movement Based on iForest-EEMD". W Lecture Notes in Electrical Engineering, 79–84. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0572-8_11.
Pełny tekst źródłaLiu, Jingjing, Jia Zeng, Zhiyong Wang i Honghai Liu. "Modeling and Recognition of Movement-Inducing Fatigue State Based on ECG Signal". W Intelligent Robotics and Applications, 677–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13822-5_61.
Pełny tekst źródłaYang, Wenjin, Jianning Su, Kai Qiu, Xinxin Zhang i Shutao Zhang. "Research on Evaluation of Product Image Design Elements Based on Eye Movement Signal". W Engineering Psychology and Cognitive Ergonomics, 214–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22507-0_17.
Pełny tekst źródłaShi, Xin, Xiao-yong Rui, Li-hua Li, Yi-jun Guo i Zhi-qiang Zhao. "The Application and Research of Filtering Algorithm of the Acceleration Signal of Human Movement Based on Mathematical Morphology-Median Filtering Algorithm". W Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015, 137–46. Paris: Atlantis Press, 2016. http://dx.doi.org/10.2991/978-94-6239-180-2_14.
Pełny tekst źródłaAhlawat, Vivek, Yogendra Narayan i Divesh Kumar. "DWT-Based Hand Movement Identification of EMG Signals Using SVM". W Proceedings of International Conference on Communication and Artificial Intelligence, 495–505. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6546-9_47.
Pełny tekst źródłaEl Raheb, Katerina, i Yannis Ioannidis. "A Labanotation Based Ontology for Representing Dance Movement". W Gesture and Sign Language in Human-Computer Interaction and Embodied Communication, 106–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34182-3_10.
Pełny tekst źródłade Oliveira de Souza, João Olegário, José Vicente Canto dos Santos, Rodrigo Marques de Figueiredo i Gustavo Pessin. "Real-Time Hand Prosthesis Biomimetic Movement Based on Electromyography Sensory Signals Treatment and Sensors Fusion". W Artificial Neural Networks and Machine Learning – ICANN 2018, 147–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_15.
Pełny tekst źródłaStreszczenia konferencji na temat "Movement-based signal"
Sivasangari., A., D. Deepa., T. Anandhi., Anitha Ponraj i M. S. Roobini. "Eyeball based Cursor Movement Control". W 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182296.
Pełny tekst źródłaHaris, Mohd, Pavan Chakraborty i B. Venkata Rao. "EMG signal based finger movement recognition for prosthetic hand control". W 2015 Communication, Control and Intelligent Systems (CCIS). IEEE, 2015. http://dx.doi.org/10.1109/ccintels.2015.7437907.
Pełny tekst źródłaAydemir, Onder, i Temel Kayikcioglu. "Classification of electroencephalogram signals based on cursor movement imagery". W 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830365.
Pełny tekst źródłaKhasnobish, Anwesha, Kingshuk Chakravarty, Debatri Chatterjee i Aniruddha Sinha. "Wavelet based head movement artifact removal from electrooculography signals". W 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952303.
Pełny tekst źródłaHuang, Kai. "Traffic Agent Movement Prediction Using ResNet-based Model". W 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2021. http://dx.doi.org/10.1109/icsp51882.2021.9408922.
Pełny tekst źródłaFahmy, G., O. M. Fahmy i M. F. Fahmy. "Fast Enhanced DWT based Video Micro Movement Magnification". W 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2019. http://dx.doi.org/10.1109/isspit47144.2019.9001874.
Pełny tekst źródłaDeniz, Ozan, Mehmetcan Fal i Cengiz Acarturk. "Density based eye movement event detection algorithm (in English)". W 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7129859.
Pełny tekst źródłaAtmaji, Catur, Agfianto Eko Putra i Arrijal Hanif. "Sliding window method for eye movement detection based on electrooculogram signal". W 2018 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2018. http://dx.doi.org/10.1109/icoiact.2018.8350779.
Pełny tekst źródłaLingling Chen, Peng Yang, Linan Zu i Xiaoyun Xu. "Electromyogram signal analysis and movement recognition based on wavelet packet transform". W 2009 International Conference on Information and Automation (ICIA). IEEE, 2009. http://dx.doi.org/10.1109/icinfa.2009.5205151.
Pełny tekst źródłaGuo, Shuxiang, Songyuan Zhang, Zhibin Song, Muye Pang i Yuta Nakatsuka. "Preliminary study on upper limb movement identification based on sEMG signal". W 2012 ICME International Conference on Complex Medical Engineering (CME). IEEE, 2012. http://dx.doi.org/10.1109/iccme.2012.6275645.
Pełny tekst źródłaRaporty organizacyjne na temat "Movement-based signal"
Sadot, Einat, Christopher Staiger i Mohamad Abu-Abied. Studies of Novel Cytoskeletal Regulatory Proteins that are Involved in Abiotic Stress Signaling. United States Department of Agriculture, wrzesień 2011. http://dx.doi.org/10.32747/2011.7592652.bard.
Pełny tekst źródła