Relevant bibliographies by topics / Featureless extraction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Featureless extraction'

Author: Grafiati
Published: 28 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Featureless extraction"

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Jing Wei, Too, Abdul Rahim Bin Abdullah, Norhashimah Binti Mohd Saad, Nursabillilah Binti Mohd Ali, and Tengku Nor Shuhada Binti Tengku Zawawi. "Featureless EMG pattern recognition based on convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1291. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1291-1297.

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In this paper, the performance of featureless EMG pattern recognition in classifying hand and wrist movements are presented. The time-frequency distribution (TFD), spectrogram is employed to transform the raw EMG signals into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the images without the need of manual feature extraction. The performance of CNN with different number of convolutional layers is examined. The proposed CNN models are evaluated using the EMG data from 10 intact and 11 amputee subjects through the publicly access NinaPro database. Our results show that CNN classifier offered the best mean classification accuracy of 88.04% in recognizing hand and wrist movements.
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Kusumastuti, Adhi, Samsudin Anis, and Gunawan Muhammad Najibulloh. "Taylor-Couette Column for Emulsion Liquid Membrane System: Characterisation Study." Jurnal Bahan Alam Terbarukan 8, no. 1 (July 24, 2019): 22–27. http://dx.doi.org/10.15294/jbat.v8i1.20162.

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Study on the application of Taylor-Couette column for emulsion liquid membrane system has been done. To optimise extraction process under TCC, a research to investigate effect of viscosity and cylinders rotation is of important. Fluid viscosity was examined by varying volume ratio of kerosene to water. TCC was characterised to determine flow regimes, shear stress, and energy loss distribution. Volume ratio of oil to water was varied at 1:1, 1:3, 1:5, and 1:6 while inner and outer cylinders speed were maintained constant at 300 and 200 rpm, respectively. Investigation on the effect of volume ratio of oil to water towards flow regime ended to same flow regime of Featureless Turbulent. There was degradation of wall shear stress from 8.57x10-2 Pa to 7.42x10-2 Pa.
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Verstraete, David, Andrés Ferrada, Enrique López Droguett, Viviana Meruane, and Mohammad Modarres. "Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings." Shock and Vibration 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/5067651.

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Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Time-frequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.
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Oh, Junghyun, Changwan Han, and Seunghwan Lee. "Condition-Invariant Robot Localization Using Global Sequence Alignment of Deep Features." Sensors 21, no. 12 (June 15, 2021): 4103. http://dx.doi.org/10.3390/s21124103.

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Localization is one of the essential process in robotics, as it plays an important role in autonomous navigation, simultaneous localization, and mapping for mobile robots. As robots perform large-scale and long-term operations, identifying the same locations in a changing environment has become an important problem. In this paper, we describe a robust visual localization system under severe appearance changes. First, a robust feature extraction method based on a deep variational autoencoder is described to calculate the similarity between images. Then, a global sequence alignment is proposed to find the actual trajectory of the robot. To align sequences, local fragments are detected from the similarity matrix and connected using a rectangle chaining algorithm considering the robot’s motion constraint. Since the chained fragments provide reliable clues to find the global path, false matches on featureless structures or partial failures during the alignment could be recovered and perform accurate robot localization in changing environments. The presented experimental results demonstrated the benefits of the proposed method, which outperformed existing algorithms in long-term conditions.
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Herraiz, Álvaro Huerta, Arturo Martínez-Rodrigo, Vicente Bertomeu-González, Aurelio Quesada, José J. Rieta, and Raúl Alcaraz. "A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices." Entropy 22, no. 7 (July 1, 2020): 733. http://dx.doi.org/10.3390/e22070733.

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Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.
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Duflot, Lesley-Ann, Rafael Reisenhofer, Brahim Tamadazte, Nicolas Andreff, and Alexandre Krupa. "Wavelet and shearlet-based image representations for visual servoing." International Journal of Robotics Research 38, no. 4 (May 8, 2018): 422–50. http://dx.doi.org/10.1177/0278364918769739.

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A visual servoing scheme consists of a closed-loop control approach that uses visual information feedback to control the motion of a robotic system. Probably the most popular visual servoing method is image-based visual servoing (IBVS). This kind of method uses geometric visual features extracted from the image to design the control law. However, extracting, matching, and tracking geometric visual features over time significantly limits the versatility of visual servoing controllers in various industrial and medical applications, in particular for “low-structured” medical images, e.g. ultrasounds and optical coherence tomography modalities. To overcome the limits of conventional IBVS, one can consider novel visual servoing paradigms known as “ direct” or “ featureless” approaches. This paper deals with the development of a new generation of direct visual servoing methods in which the signal control inputs are the coefficients of a multiscale image representation. In particular, we consider the use of multiscale image representations that are based on discrete wavelet and shearlet transforms. Up to now, one of the main obstacles in the investigation of multiscale image representations for visual servoing schemes was the issue of obtaining an analytical formulation of the interaction matrix that links the variation of wavelet and shearlet coefficients to the spatial velocity of the camera and the robot. In this paper, we derive four direct visual servoing controllers: two that are based on subsampled respectively non-subsampled wavelet coefficients and two that are based on the coefficients of subsampled respectively non-subsampled discrete shearlet transforms. All proposed controllers were tested in both simulation and experimental scenarios (using a six-degree-of-freedom Cartesian robot in an eye-in-hand configuration). The objective of this paper is to provide an analysis of the respective strengths and weaknesses of wavelet- and shearlet-based visual servoing controllers.
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Moore, Paul A., David Edwards, Ana Jurcak-Detter, and Sara Lahman. "Spatial, but not temporal, aspects of orientation are controlled by the fine-scale distribution of chemical cues in turbulent odor plumes." Journal of Experimental Biology 224, no. 7 (April 1, 2021). http://dx.doi.org/10.1242/jeb.240457.

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ABSTRACT Orientation within turbulent odor plumes occurs across a vast range of spatial and temporal scales. From salmon homing across featureless oceans to microbes forming reproductive spores, the extraction of spatial and temporal information from chemical cues is a common sensory phenomenon. Yet, given the difficulty of quantifying chemical cues at the spatial and temporal scales used by organisms, discovering what aspects of chemical cues control orientation behavior has remained elusive. In this study, we placed electrochemical sensors on the carapace of orienting crayfish and measured, with fast temporal rates and small spatial scales, the concentration fluctuations arriving at the olfactory appendages during orientation. Our results show that the spatial aspects of orientation (turning and heading angles) are controlled by the temporal aspects of odor cues.
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Kita, Yusuke, and Junji Sugiyama. "Wood identification of two anatomically similar Cupressaceae species based on two-dimensional microfibril angle mapping." Holzforschung, November 11, 2020. http://dx.doi.org/10.1515/hf-2020-0079.

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AbstractIdentifying two anatomically similar species of Cupressaceae, Chamaecyparis obtusa and Thujopsis spp., is important to better understand the culture of wood use in Japan. However, the conventional method, which involves observing their cross-field pitting, cannot identify them in many cases. This study solves the above problem by introducing an anatomical criterion based on the micro fibril angle (MFA). MFA values were obtained through two-dimensional MFA images using the uniaxial optical anisotropy of cellulose microfibrils. A combination of the preprocessed MFA images and a convolutional neural network (CNN) yielded an accuracy nearly of 90% in classifying these species in cases of present and old wood specimens. Our feature extraction and classification techniques provide a new way for describing the anatomical features of wood and identifying featureless softwoods. Using the model interpretation-related methodologies of the CNN, distinct features of the two wood species were partly explained by MFA anisotropy in the S2 wall induced by the existence of pits.
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Dissertations / Theses on the topic "Featureless extraction"

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Lukáč, Peter. "Verifikace osob podle hlasu bez extrakce příznaků." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445531.

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Verifikácia osôb je oblasť, ktorá sa stále modernizuje, zlepšuje a snaží sa vyhovieť požiadavkám, ktoré sa na ňu kladú vo oblastiach využitia ako sú autorizačné systmémy, forenzné analýzy, atď. Vylepšenia sa uskutočňujú vďaka pokrom v hlbokom učení, tvorením nových trénovacích a testovacích dátovych sad a rôznych súťaží vo verifikácií osôb a workshopov. V tejto práci preskúmame modely pre verifikáciu osôb bez extrakcie príznakov. Používanie nespracovaných zvukových stôp ako vstupy modelov zjednodušuje spracovávanie vstpu a teda znižujú sa výpočetné a pamäťové požiadavky a redukuje sa počet hyperparametrov potrebných pre tvorbu príznakov z nahrávok, ktoré ovplivňujú výsledky. Momentálne modely bez extrakcie príznakov nedosahujú výsledky modelov s extrakciou príznakov. Na základných modeloch budeme experimentovať s modernými technikamy a budeme sa snažiť zlepšiť presnosť modelov. Experimenty s modernými technikamy značne zlepšili výsledky základných modelov ale stále sme nedosiahli výsledky vylepšeného modelu s extrakciou príznakov. Zlepšenie je ale dostatočné nato aby sme vytovrili fúziu so s týmto modelom. Záverom diskutujeme dosiahnuté výsledky a navrhujeme zlepšenia na základe týchto výsledkov.
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Book chapters on the topic "Featureless extraction"

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Li, Wen, and Dezhen Song. "Featureless Motion Vector-Based Simultaneous Localization, Planar Surface Extraction, and Moving Obstacle Tracking." In Springer Tracts in Advanced Robotics, 245–61. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16595-0_15.

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Conference papers on the topic "Featureless extraction"

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Pintea, S. L., P. S. Mettes, J. C. van Gemert, and A. W. M. Smeulders. "Featureless: Bypassing feature extraction in action categorization." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532346.

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Li, Wen, and Dezhen Song. "Toward featureless visual navigation: Simultaneous localization and planar surface extraction using motion vectors in video streams." In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. http://dx.doi.org/10.1109/icra.2014.6906583.

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