Search results for: 3D body movement signals

Authors: Bola M. Tunde-Awe

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The study investigated the influence of teacher’s non-verbal communication on secondary school students’ learning outcomes in English language. The study was a survey research. Participants were three hundred Senior Secondary School II students randomly selected from ten schools in Akoko South West Local Government Area of Ondo State, Nigeria. The instrument used for data collection was a questionnaire containing twenty items on a four-point Likert scale which measured teacher’s use of three types of non-verbal communication modes: body movement, eye contact and spatial distance. The data collected was analysed using simple percentage. Findings revealed that teacher’s use of these non-verbal communication modes enhanced learners’ learning outcomes in English language: a total of 271 (90.33%) participants affirmed that teacher’s body language influenced their learning of English; 224 (74.66%) maintained the same stand for eye contact; while 202 (67.33%) affirmed that teacher’s spatial distance had positive influence. Consequent upon these findings, it was recommended that teachers of English language should constantly utilize non-verbal communication in their instructional delivery. Also, non-verbal communication modes should be included in teacher education programme to equip prospective pre-service teachers with the art of non-verbal communication.

Keywords: non-verbal communication, body language, eye contact, spatial distance, learning outcomes

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Authors: Pawel Twardowski, Maciej Tabaszewski, Jakub Czyżycki

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The main goal of the work was to use machine learning to predict cutting-edge wear. The research was carried out while milling hardened steel with sintered carbide cutters at various cutting speeds. During the tests, cutting-edge wear was measured, and vibration acceleration signals were also measured. Appropriate measures were determined from the vibration signals and served as input data in the machine-learning process. Two approaches were used in this work. The first one involved a two-state classification of the cutting edge - suitable and unfit for further work. In the second approach, prediction of the cutting-edge state based on vibration signals was used. The obtained research results show that the appropriate use of machine learning algorithms gives excellent results related to monitoring cutting edge during the process.

Keywords: milling of hardened steel, tool wear, vibrations, machine learning

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Authors: Xiang Jianhong, Wang Cong, Wang Linyu

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With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%.

Keywords: telemedicine, fetal ECG, compressed sensing, joint sparse reconstruction, block sparse signal

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Authors: Hyung-Jin Kim, Dae-Wan Kim, Moo-Yeon Lee

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The objective of this study is to experimentally investigate the heat transfer characteristics of 200 W class woofer speaker units with the input voice signals. The temperature and heat transfer characteristics of the 200 W class woofer speaker unit were experimentally tested with the several input voice signals such as 1500 Hz, 2500 Hz, and 5000 Hz respectively. From the experiments, it can be observed that the temperature of the woofer speaker unit including the voice-coil part increases with a decrease in input voice signals. Also, the temperature difference in measured points of the voice coil is increased with decrease of the input voice signals. In addition, the heat transfer characteristics of the woofer speaker in case of the input voice signal of 1500 Hz is 40% higher than that of the woofer speaker in case of the input voice signal of 5000 Hz at the measuring time of 200 seconds. It can be concluded from the experiments that initially the temperature of the voice signal increases rapidly with time, after a certain period of time it increases exponentially. Also during this time dependent temperature change, it can be observed that high voice signal is stable than low voice signal.

Keywords: heat transfer, temperature, voice coil, woofer speaker

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Authors: Dora Bianchi, Anthony Schinelli, Laura Maria Fatta, Antonia Lonigro, Fabio Lucidi, Fiorenzo Laghi

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Purpose: The role of body image in adolescent binge eating is widely confirmed, albeit the various facets of this relationship are still mostly unexplored. Within the multidimensional body image framework, this study hypothesized the indirect effects of three body image coping strategies (positive rational acceptance, appearance fixing, avoidance) in the expected relationship between the perceived impact of body image on individuals’ quality of life and binge eating symptoms. Methods: Participants were 715 adolescents aged 15-21 years (49.1% girls) recruited in Italian schools. An anonymous self-report online survey was administered. A multiple mediation model was tested. Results: A more positive perceived impact of body image on quality of life was a negative predictor of adolescents’ binge eating, controlling for individual levels of body satisfaction. Three indirect effects were found in this relationship: on one hand, the positive body image impact reduced binge eating via increasing positive rational acceptance (M1), and via reducing avoidance (M2); on the contrary, the positive body image impact also enhanced binge eating via increasing appearance fixing (M3). Conclusions: The body image impact on quality of life can be alternatively protective—when adaptive coping is solicited, and maladaptive strategies are reduced—or a risk factor, which may increase binge eating by soliciting appearance fixing.

Keywords: binge eating, body image satisfaction, quality of life, coping strategies, adolescents

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

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SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

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Authors: Jing Wu, Wei Lv, Yibing Li, Yuanfan You

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The separation of speech signals has become a research hotspot in the field of signal processing in recent years. It has many applications and influences in teleconferencing, hearing aids, speech recognition of machines and so on. The sounds received are usually noisy. The issue of identifying the sounds of interest and obtaining clear sounds in such an environment becomes a problem worth exploring, that is, the problem of blind source separation. This paper focuses on the under-determined blind source separation (UBSS). Sparse component analysis is generally used for the problem of under-determined blind source separation. The method is mainly divided into two parts. Firstly, the clustering algorithm is used to estimate the mixing matrix according to the observed signals. Then the signal is separated based on the known mixing matrix. In this paper, the problem of mixing matrix estimation is studied. This paper proposes an improved algorithm to estimate the mixing matrix for speech signals in the UBSS model. The traditional potential algorithm is not accurate for the mixing matrix estimation, especially for low signal-to noise ratio (SNR).In response to this problem, this paper considers the idea of an improved potential function method to estimate the mixing matrix. The algorithm not only avoids the inuence of insufficient prior information in traditional clustering algorithm, but also improves the estimation accuracy of mixing matrix. This paper takes the mixing of four speech signals into two channels as an example. The results of simulations show that the approach in this paper not only improves the accuracy of estimation, but also applies to any mixing matrix.

Keywords: DBSCAN, potential function, speech signal, the UBSS model

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Authors: A. Hadj Taieb, H. Ennouri

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The current study aimed to investigate the effect that optical illusion garments have on a woman’s self-perception of her own body shape. First, we created different optical illusion garment by using color contrasts. Second, a short survey based on visual perception is addressed to women in order to compare the different optical illusion garments to determine if they met the established 'ideal' body shape. A ‘visual analysis method’ was used to investigate the clothing models with optical illusions. The theories in relation with the optical illusion were used through this method. The effects of the optical illusion of color contrast on body shape in the fashion sector were tried to be revealed.

Keywords: optical illusion, color contrasts, body image perception, self-esteem

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Authors: Y. Kumru, K. Enhos, H. Köymen

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In this paper, we report the experimental results on using complementary Golay coded signals at 7.5 MHz to detect breast microcalcifications of 50 µm size. Simulations using complementary Golay coded signals show perfect consistence with the experimental results, confirming the improved signal to noise ratio for complementary Golay coded signals. For improving the success on detecting the microcalcifications, orthogonal complementary Golay sequences having cross-correlation for minimum interference are used as coded signals and compared to tone burst pulse of equal energy in terms of resolution under weak signal conditions. The measurements are conducted using an experimental ultrasound research scanner, Digital Phased Array System (DiPhAS) having 256 channels, a phased array transducer with 7.5 MHz center frequency and the results obtained through experiments are validated by Field-II simulation software. In addition, to investigate the superiority of coded signals in terms of resolution, multipurpose tissue equivalent phantom containing series of monofilament nylon targets, 240 µm in diameter, and cyst-like objects with attenuation of 0.5 dB/[MHz x cm] is used in the experiments. We obtained ultrasound images of monofilament nylon targets for the evaluation of resolution. Simulation and experimental results show that it is possible to differentiate closely positioned small targets with increased success by using coded excitation in very weak signal conditions.

Keywords: coded excitation, complementary golay codes, DiPhAS, medical ultrasound

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Authors: S. N. Ekbote, G. K. Padam, Manju Arora

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Electron spin resonance (ESR) spectroscopic investigations of (Bi, Pb)₂Sr₂Ca₂Cu₃O₁₀₋ₓ (Bi-2223) bulk samples were carried out in both the normal and superconducting states. A broad asymmetric resonance signal with side signals is obtained in the normal state, and all of them disappear in the superconducting state. The temperature and angular orientation effects on these signals suggest that the broad asymmetric signal arises from electron spin resonance of conduction electrons (CESR) and the side signals from exchange interactions as Platzman-Wolff type spin waves. The disappearance of CESR and spin waves in a superconducting state demonstrates the role of exchange interactions in Cooper pair formation.

Keywords: Bi-2223 superconductor, CESR, ESR, exchange interactions, spin waves

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Authors: Hamidreza Namazi

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Fractal analysis is assessing fractal characteristics of data. It consists of several methods to assign fractal characteristics to a dataset which may be a theoretical dataset or a pattern or signal extracted from phenomena including natural geometric objects, sound, market fluctuations, heart rates, digital images, molecular motion, networks, etc. Fractal analysis is now widely used in all areas of science. An important limitation of fractal analysis is that arriving at an empirically determined fractal dimension does not necessarily prove that a pattern is fractal; rather, other essential characteristics have to be considered. For this purpose a Visual C++ based software called FRATSAN (FRActal Time Series ANalyser) was developed which extract information from signals through three measures. These measures are Fractal Dimensions, Jeffrey’s Measure and Hurst Exponent. After computing these measures, the software plots the graphs for each measure. Besides computing three measures the software can classify whether the signal is fractal or no. In fact, the software uses a dynamic method of analysis for all the measures. A sliding window is selected with a value equal to 10% of the total number of data entries. This sliding window is moved one data entry at a time to obtain all the measures. This makes the computation very sensitive to slight changes in data, thereby giving the user an acute analysis of the data. In order to test the performance of this software a set of EEG signals was given as input and the results were computed and plotted. This software is useful not only for fundamental fractal analysis of signals but can be used for other purposes. For instance by analyzing the Hurst exponent plot of a given EEG signal in patients with epilepsy the onset of seizure can be predicted by noticing the sudden changes in the plot.

Keywords: EEG signals, fractal analysis, fractal dimension, hurst exponent, Jeffrey’s measure

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Authors: Khalida S. Rijab, Mohammed E. Safi, Ayad A. Ibrahim

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When electrical electrodes are mounted on the skin surface of the muscle, a signal is detected when a skeletal muscle undergoes contraction; the signal is known as surface electromyographic signal (EMG). This signal has a noise-like interference pattern resulting from the temporal and spatial summation of action potentials (AP) of all active motor units (MU) near electrode detection. By appropriate processing (Decomposition), the surface EMG signal may be used to give an estimate of motor unit action potential. In this work, a denoising technique is applied to the MUAP signals extracted from the spatial filter (IB2). A set of signals from a non-invasive two-dimensional grid of 16 electrodes from different types of subjects, muscles, and sex are recorded. These signals will acquire noise during recording and detection. A digital fourth order band- pass Butterworth filter is used for denoising, with a tuned band-pass frequency of suitable choice of cutoff frequencies is investigated, with the aim of obtaining a suitable band pass frequency. Results show an improvement of (1-3 dB) in the signal to noise ratio (SNR) have been achieved, relative to the raw spatial filter output signals for all cases that were under investigation. Furthermore, the research’s goal included also estimation and reconstruction of the mean shape of the MUAP.

Keywords: EMG, Motor Unit, Digital Filter, Denoising

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Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

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Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

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Authors: Jianfeng Hu

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Personal authentication based on electroencephalography (EEG) signals is one of the important field for the biometric technology. More and more researchers have used EEG signals as data source for biometric. However, there are some disadvantages for biometrics based on EEG signals. The proposed method employs entropy measures for feature extraction from EEG signals. Four type of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE) and spectral entropy (PE), were deployed as feature set. In a silhouettes calculation, the distance from each data point in a cluster to all another point within the same cluster and to all other data points in the closest cluster are determined. Thus silhouettes provide a measure of how well a data point was classified when it was assigned to a cluster and the separation between them. This feature renders silhouettes potentially well suited for assessing cluster quality in personal authentication methods. In this study, “silhouettes scores” was used for assessing the cluster quality of k-means clustering algorithm is well suited for comparing the performance of each EEG dataset. The main goals of this study are: (1) to represent each target as a tuple of multiple feature sets, (2) to assign a suitable measure to each feature set, (3) to combine different feature sets, (4) to determine the optimal feature weighting. Using precision/recall evaluations, the effectiveness of feature weighting in clustering was analyzed. EEG data from 22 subjects were collected. Results showed that: (1) It is possible to use fewer electrodes (3-4) for personal authentication. (2) There was the difference between each electrode for personal authentication (p<0.01). (3) There is no significant difference for authentication performance among feature sets (except feature PE). Conclusion: The combination of k-means clustering algorithm and silhouette approach proved to be an accurate method for personal authentication based on EEG signals.

Keywords: personal authentication, K-mean clustering, electroencephalogram, EEG, silhouettes

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Authors: A.L. Tamm, N. Šott, J. Jürimäe, E. Lätt, A. Orav, Ü. Parm

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Coffee is one of the most frequently consumed beverages in the world but still its effects on human organism are not completely understood. Coffee has also been used as a method for weight loss, but its effectiveness has not been proved. There is also not similar comprehension in classifying overweight in choosing between body mass index (BMI) and fat percentage (fat%). The aim of the study was to determine associations between coffee consumption and body composition. Secondly, to detect which measure (BMI or fat%) is more accurate to use describing overweight. Altogether 103 persons enrolled the study and divided into three groups: coffee non-consumers (n=39), average coffee drinkers, who consumed 1 to 4 cups (1 cup = ca 200ml) of coffee per day (n=40) and excessive coffee consumers, who drank at least five cups of coffee per day (n=24). Body mass (medical electronic scale, A&D Instruments, Abingdon, UK) and height (Martin metal anthropometer to the nearest 0.1 cm) were measured and BMI calculated (kg/m2). Participants´ body composition was detected with dual energy X-ray absorptiometry (DXA, Hologic) and general data (history of chronic diseases included) and information about coffee consumption, and physical activity level was collected with questionnaires. Results of the study showed that excessive coffee consumption was associated with increased fat-free mass. It could be foremost due to greater physical activity level in school time or greater (not significant) male proportion in excessive coffee consumers group. For estimating the overweight the fat% in comparison to BMI recommended, as it gives more accurate results evaluating chronical disease risks. In conclusion coffee consumption probably does not affect body composition and for estimating the body composition fat% seems to be more accurate compared with BMI.

Keywords: body composition, body fat percentage, body mass index, coffee consumption

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, artificial neural network, kinect, stereotypical motor movements

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

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Authors: M. Bolacalı, Y. Öztürk, O. Yılmaz, M. Küçük, M. A. Karslı

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The aim of the study was to determine various body measurements from the birth to the 30-day age of Boer x Hair goats F1 crossbred kids and pure Hair goat kids raised in Van in Eastern Anatolia region, and reveal factors such as the effects of year, dame body weight, genotype, dame age, birth type and sex on this parameter. 49 kids born in 2012 and 76 kids born in 2014 were utilized in the study. In the statistical analysis of various body measurements data was performed using the General Lineer Model procedure in SPSS software. Duncan's multiple range test was used for multiple comparisons. Boer x Hair goats F1 crossbred kids and pure Hair goat kids from various body measurements cidago height, body length, chest length, chest depth, chest circumference, circumference of leg, cannon bone circumference, chest width were determinated in general respectively 29.90 and 27.88 cm; 29.49 and 27.93 cm; 17.28 and 16.68 cm; 13.34 and 12.82 cm; 31.74 and 29.85 cm; 28.43 and 23.95 cm; 5.41 and 5.15 cm; 8.71 and 7.63 cm at birth, respectively; 35.01 and 32.98 cm; 35.20 and 33.30 cm; 18.82 and 18.17 cm; 15.64 and 14.83 cm; 39.08 and 37.30 cm; 34.29 and 29.25 cm; 5.80 and 5.42 cm; 9.87 and 8.85 cm at 30 days age, respectively. Among factors affecting cidago height in this study, the effect of dame body weight and sex were not significant, but genotype, dame age and birth type were significant (P < 0,05 and P < 0,01) at birth; dame body weight effect of the cidago height was not significant, but the effect of genotype, birth type, of dame age and sex were significant (P < 0.05, P < 0.05 and P<0.001) at 30-day age. The effect of genotype and sex of body length were not significant, but dam age, dame body weight and birth type were significant (P < 0.05, P < 0.05 and P<0.001, respectively) at birth; the effect of sex to body length was not significant, but genotype, dame age, dame body weight and birth type were significant (P < 0.01, P < 0.05, P < 0.05 and P < 0.001, respectively) at 30-day age. While circumference of leg was insignificant the effect of dame age and sex, genotype, dame body weight and type of the birth were significant (P < 0.001, P < 0.05 and P < 0.001) at birth; the circumstance of leg at 30-day age was found to be important the effect of examined other factors except for sex (P < 0.05 and P < 0.001). The obtained results, when considered in terms of a variety of body sizes, from birth to 30-day age growth period, showed that the kids of Boer x Hair Goat F1 hybrids have higher values than the kids of Hair Goats.

Keywords: Boer x hair goat F1 crossbred, hair goat, body measurements, cidago height

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Authors: A. Grilec, S. Petrak, M. Mahnic Naglic

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Technological development and the globalization in production and sales of clothing in the last decade have significantly influenced the changes in consumer relationship with the industrial-fashioned apparel and in the way of clothing purchasing. The Internet sale of clothing is in a constant and significant increase in the global market, but the possibilities offered by modern computing technologies in the customization segment are not yet fully involved, especially according to the individual customer requirements and body sizes. Considering the growing trend of online shopping, the main goal of this paper is to investigate the differences in customer perceptions towards online apparel shopping and particularly to discover the main differences in perceptions between customers regarding three different body sizes. In order to complete the research goal, the quantitative study on the sample of 85 Croatian consumers was conducted in 2017 in Zagreb, Croatia. Respondents were asked to indicate their level of agreement according to a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). To analyze attitudes of respondents, simple and descriptive statistics were used. The main findings highlight the differences in respondent perception of 3D body scanning, using 3D body scanning in Internet shopping, online apparel shopping habits regarding their body sizes.

Keywords: consumer behavior, Internet, 3D body scanning, body types

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Authors: Luisa Villani

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The proposal aims to express concerns about the connection between mind, body, society, and environment in the Flyers’ dance, a very well-known rotatory dance in Mexico, to create meanings and to make the apprehension of the world possible. The interaction among the brain, mind, body, and environment, and the intersubjective relation among them, means the world creates and recreates a social interaction. The purpose of this methodology, based on the embodied cognition theory, which was named “A Performance-Embodied Theory” is to find the principles and patterns that organize the culture and the rules of the apprehension of the environment by Totonac people while the dance is being performed. The analysis started by questioning how anthropologists can interpret how Totonacs transform their unconscious knowledge into conscious knowledge and how the scheme formation of imagination and their collective imagery is understood in the context of public-facing rituals, such as Flyers’ dance. The problem is that most of the time, researchers interpret elements in a separate way and not as a complex ritual dancing whole, which is the original contribution of this study. This theory, which accepts the fact that people are body-mind agents, wants to interpret the dance as a whole, where the different elements are joined to an integral interpretation. To understand incorporation, data was recollected in prolonged periods of fieldwork, with participant observation and linguistic and extralinguistic data analysis. Laban’s notation for the description and analysis of gestures and movements in the space was first used, but it was later transformed and gone beyond this method, which is still a linear and compositional one. Performance in a ritual is the actualization of a potential complex of meanings or cognitive domains among many others in a culture: one potential dimension becomes probable and then real because of the activation of specific meanings in a context. It can only be thought what language permits thinking, and the lexicon that is used depends on the individual culture. Only some parts of this knowledge can be activated at once, and these parts of knowledge are connected. Only in this way, the world can be understood. It can be recognized that as languages geometrize the physical world thanks to the body, also ritual does. In conclusion, the ritual behaves as an embodied grammar or a text in movement, which, depending on the ritual phases and the words and sentences pronounced in the ritual, activates bits of encyclopedic knowledge that people have about the world. Gestures are not given by the performer but emerge from the intentional perception in which gestures are “understood” by the audio-spectator in an inter-corporeal way. The impact of this study regards the possibility not only to disseminate knowledge effectively but also to generate a balance between different parts of the world where knowledge is shared, rather than being received by academic institutions alone. This knowledge can be exchanged, so indigenous communities and academies could be together as part of the activation and the sharing of this knowledge with the world.

Keywords: dance, flyers, performance, embodied, cognition

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Authors: Maitreyee Datta

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In Anātmalaksana Sutra, Buddha talks about the importance of anattā (no-self). This notion of no-self is a critical response towards the Brahmanical tradition of classical India in which self has been taken to be eternal. Though self is taken to be eternal, ‘I’ refer to Person who is the self as determined by non-eternal body. Buddha raises questions regarding the possibility of the association between eternal self and non-eternal body. According to him, such an association is not possible. Thus, instead of an eternal self and its association with the non-eternal body, he speaks about association among five different non-eternal parts (skandhas). He holds that ‘I’ refers to Person, but this Person is not eternal self as determined by the non-eternal body. It is the combination of five different skandhas each of which is non-eternal. So according to Buddha, there is no eternal self which in association with non-eternal body is referred to as ‘I,’ but ‘I’ is a convenient designator which designates the combination of five non-eternal skandhas. If ‘I’ is taken to refer the combination of five non-eternal skandhas, then the problematic of the association between eternal self (attā) and non-eternal body will not be there. The realization that ‘I’ does not refer to any eternal self as determined by non-eternal body, but instead refer to the combination of five non-eternal skandhas leads to the cessation of suffering (duhkkha). The root of suffering lies in craving for something or the other. Thus, as soon as one realizes that the person is not constituted of any eternal self but is constituted of non-eternal skandhas, his desire to acquire and possess will be stopped. Thus, in the whole conceptual framework of Buddhist philosophy, anattā occupies a pivotal role the realization of which is admitted to be the cause of the cessation of suffering. In the present paper, an effort will be made to analyse this notion of anattā to show how the realization of the truth that person is a combination of five skandhas each of which is non-eternal helps an individual to get rid of the bondage. If eternal self is to be admitted, then there always remains the problem of connecting the eternal self with the non-eternal body, because this connection only gives rise to the notion of person in such framework.

Keywords: anatta, atta, duhkkha, skandha

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Authors: Juzhong Tan, William Kerr

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Roasting is one critical procedure in chocolate processing, where special favors are developed, moisture content is decreased, and better processing properties are developed. Therefore, determination of roasting degree of cocoa bean is important for chocolate manufacturers to ensure the quality of chocolate products, and it also decides the commercial value of cocoa beans collected from cocoa farmers. The roasting degree of cocoa beans currently relies on human specialists, who sometimes are biased, and chemical analysis, which take long time and are inaccessible to many manufacturers and farmers. In this study, a self-made electronic nose system consists of gas sensors (TGS 800 and 2000 series) was used to detecting the gas generated by cocoa beans with a different roasting degree (0min, 20min, 30min, and 40min) and the signals collected by gas sensors were used to train a three-layers ANN. Chemical analysis of the graded beans was operated by traditional GC-MS system and the contents of volatile chemical compounds were used to train another ANN as a reference to electronic nosed signals trained ANN. Both trained ANN were used to predict cocoa beans with a different roasting degree for validation. The best accuracy of grading achieved by electronic nose signals trained ANN (using signals from TGS 813 826 820 880 830 2620 2602 2610) turned out to be 96.7%, however, the GC trained ANN got the accuracy of 83.8%.

Keywords: artificial neutron network, cocoa bean, electronic nose, roasting

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Authors: Ankita Wadhawan, Parteek Kumar, Umesh Kumar

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Gesture is any body movement that expresses some attitude or any sentiment. Gestures as a sign language are used by deaf people for conveying messages which helps in eliminating the communication barrier between deaf people and normal persons. Nowadays, everybody is using mobile phone and computer as a very important gadget in their life. But there are some physically challenged people who are blind/deaf and the use of mobile phone or computer like device is very difficult for them. So, there is an immense need of a system which works on body gesture or sign language as input. In this research, Microsoft Kinect Sensor, SDK V2 and Hidden Markov Toolkit (HTK) are used to recognize the object, motion of object and human body joints through Touch less NUI (Natural User Interface) in real-time. The depth data collected from Microsoft Kinect has been used to recognize gestures of Indian Sign Language (ISL). The recorded clips are analyzed using depth, IR and skeletal data at different angles and positions. The proposed system has an average accuracy of 85%. The developed Touch less NUI provides an interface to recognize gestures and controls the cursor and click operation in computer just by waving hand gesture. This research will help deaf people to make use of mobile phones, computers and socialize among other persons in the society.

Keywords: gesture recognition, Indian sign language, Microsoft Kinect, natural user interface, sign language

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Authors: G. Espriú-Pérez, F. A. Vargas-Oviedo, I. Zenteno-Aguirrezábal, M. D. Moya-Bencomo

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One of the injuries with a higher incidence among university-level athletes in the North of Mexico is presented in the knee. This injury generates absenteeism in training and competitions for at least 8 weeks. There is no active quantitative methodology, or protocol, that directly contributes to the clinical evaluation performed by the medical personnel at the prevalence of knee injuries. The main objective is to contribute with a quantitative tool that allows further development of preventive and corrective measures to these injuries. The study analyzed 55 athletes for 6 weeks, belonging to the disciplines of basketball, volleyball, soccer and swimming. Using a motion capture system (Nexus®, Vicon®), a three-dimensional analysis was developed that allows the measurement of the range of movement of the joint. To focus on the performance of the lower limb, eleven different movements were chosen from the Functional Performance Test, Functional Movement Screen, and the Cincinnati Jump Test. The research identifies the profile of the natural movement of a healthy knee, with the use of medical guidance, and its differences between each sport. The data recovered by the single-leg crossover hop managed to differentiate the type of knee movement among athletes. A maximum difference of 60° of offset was found in the adduction movement between male and female athletes of the same discipline. The research also seeks to serve as a guideline for the implementation of protocols that help identify the recovery level of such injuries.

Keywords: Cincinnati jump test, functional movement screen, functional performance test, knee, motion capture system

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)

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Authors: Lauren Currell, Gemma Hurst

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Introduction: The present study experimentally investigated the impact of attractive Instagram images on female’s body image. It also examined whether physical appearance perfectionism and cognitive coping predicted body image following upward comparisons to idealised bodies on Instagram. Methods: One-hundred and fifty-eight females (mean age 24.35 years) were randomly assigned to an experimental (where they compared their bodies to those of Instagram models) or control condition (where they critiqued landscape painting). All participants completed measures on physical appearance perfectionism, cognitive coping, and pre- and post-measures of body image. Results: Comparing one’s body to idealised bodies on Instagram resulted in increased appearance and weight dissatisfaction and decreased confidence, compared to the control condition. Physical appearance perfectionism and cognitive coping both predicted body image outcomes for the experimental condition. Discussion: Clinical implications, such as the prevention and treatment of body dissatisfaction, are discussed. Strengths and limitations of the current study are also noted, and suggestions for future research are provided.

Keywords: perfectionism, cognitive coping, body image, social media

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Authors: Akshay B. Pawar, Rohit Y. Parasnis

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Heart Rate Variability (HRV) is a widely used indicator of the regulation between the autonomic nervous system (ANS) and the cardiovascular system. Besides being non-invasive, it also has the potential to predict mortality in cases involving critical injuries. The gold standard method for determining HRV is based on the analysis of RR interval time series extracted from ECG signals. However, because it is much more convenient to obtain photoplethysmogramic (PPG) signals as compared to ECG signals (which require the attachment of several electrodes to the body), many researchers have used pulse cycle intervals instead of RR intervals to estimate HRV. They have also compared this method with the gold standard technique. Though most of their observations indicate a strong correlation between the two methods, recent studies show that in healthy subjects, except for a few parameters, the pulse-based method cannot be a surrogate for the standard RR interval- based method. Moreover, the former tends to overestimate short-term variability in heart rate. This calls for improvements in or alternatives to the pulse-cycle interval method. In this study, besides the systolic peak-peak interval method (PP method) that has been studied several times, four recent PPG-based techniques, namely the first derivative peak-peak interval method (P1D method), the second derivative peak-peak interval method (P2D method), the valley-valley interval method (VV method) and the tangent-intersection interval method (TI method) were compared with the gold standard technique. ECG and PPG signals were obtained from 10 young and healthy adults (consisting of both males and females) seated in the armchair position. In order to de-noise these signals and eliminate baseline drift, they were passed through certain digital filters. After filtering, the following HRV parameters were computed from PPG using each of the five methods and also from ECG using the gold standard method: time domain parameters (SDNN, pNN50 and RMSSD), frequency domain parameters (Very low-frequency power (VLF), Low-frequency power (LF), High-frequency power (HF) and Total power or “TP”). Besides, Poincaré plots were also plotted and their SD1/SD2 ratios determined. The resulting sets of parameters were compared with those yielded by the standard method using measures of statistical correlation (correlation coefficient) as well as statistical agreement (Bland-Altman plots). From the viewpoint of correlation, our results show that the best PPG-based methods for the determination of most parameters and Poincaré plots are the P2D method (shows more than 93% correlation with the standard method) and the PP method (mean correlation: 88%) whereas the TI, VV and P1D methods perform poorly (<70% correlation in most cases). However, our evaluation of statistical agreement using Bland-Altman plots shows that none of the five techniques agrees satisfactorily well with the gold standard method as far as time-domain parameters are concerned. In conclusion, excellent statistical correlation implies that certain PPG-based methods provide a good amount of information on the pattern of heart rate variation, whereas poor statistical agreement implies that PPG cannot completely replace ECG in the determination of HRV.

Keywords: photoplethysmography, heart rate variability, correlation coefficient, Bland-Altman plot

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Authors: H. Bentarzi, A. Zitouni

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The reliability of the power grid depends on the successful operation of thousands of protective relays. The failure of one relay to operate as intended may lead the entire power grid to blackout. In fact, major power system failures during transient disturbances may be caused by unnecessary protective relay tripping rather than by the failure of a relay to operate. Adequate relay testing provides a first defense against false trips of the relay and hence improves power grid stability and prevents catastrophic bulk power system failures. The goal of this research project is to design and enhance the relay tester using a technology such as Field Programmable Gate Array (FPGA) card NI 7851. A PC based tester framework has been developed using Simulink power system model for generating signals under different conditions (faults or transient disturbances) and LabVIEW for developing the graphical user interface and configuring the FPGA. Besides, the interface system has been developed for outputting and amplifying the signals without distortion. These signals should be like the generated ones by the real power system and large enough for testing the relay’s functionality. The signals generated that have been displayed on the scope are satisfactory. Furthermore, the proposed testing system can be used for improving the performance of protective relay.

Keywords: amplifier class D, field-programmable gate array (FPGA), protective relay, tester

Procedia PDF Downloads 216

Authors: Said Benaissa, David Plets, Emmeric Tanghe, Jens Trogh, Luc Martens, Leen Vandaele, Annelies Van Nuffel, Frank A. M. Tuyttens, Bart Sonck, Wout Joseph

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The herd monitoring and managing - in particular the detection of ‘attention animals’ that require care, treatment or assistance is crucial for effective reproduction status, health, and overall well-being of dairy cows. In large sized farms, traditional methods based on direct observation or analysis of video recordings become labour-intensive and time-consuming. Thus, automatic monitoring systems using sensors have become increasingly important to continuously and accurately track the health status of dairy cows. Wireless sensor networks (WSNs) and internet-of-things (IoT) can be effectively used in health tracking of dairy cows to facilitate herd management and enhance the cow welfare. Since on-cow measuring devices are energy-constrained, a proper characterization of the off-body wireless channel between the on-cow sensor nodes and the back-end base station is required for a power-optimized deployment of these networks in barns. The aim of this study was to characterize the off-body wireless channel in indoor (barns) environment at 868 MHz using LoRa nodes. LoRa is an emerging wireless technology mainly targeted at WSNs and IoT networks. Both large scale fading (i.e., path loss) and temporal fading were investigated. The obtained path loss values as a function of the transmitter-receiver separation were well fitted by a lognormal path loss model. The path loss showed an additional increase of 4 dB when the wireless node was actually worn by the cow. The temporal fading due to movement of other cows was well described by Rician distributions with a K-factor of 8.5 dB. Based on this characterization, network planning and energy consumption optimization of the on-body wireless nodes could be performed, which enables the deployment of reliable dairy cow monitoring systems.

Keywords: channel, channel modelling, cow monitoring, dairy cows, health monitoring, IoT, LoRa, off-body propagation, PLF, propagation

Procedia PDF Downloads 318

Authors: Jon Gunnar Bernburg

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The present study addresses a topic that is fundamental to social movement theory, namely, the contingent link between movement support and movement participation. Usually, only a small fraction of those who agree with the cause of a social movement is mobilized into participating in it (a pattern sometimes referred to as 'the collective action problem'). However, historical moments sometimes emerge when many supporters become mobilized to participate in the movement, greatly enhancing the chance of movement success. By studying a case in point, this paper addresses the limited work on how support and participation are related at such critical moments. Specifically, the paper examines the association between supporting and participating in a huge 'pro-democracy' protest in Iceland in April 2016, in the wake of the global Panama Papers scandal. Organized via social media by only a handful of activists, but supported by a majority of Icelanders, the protest attracted about a fourth of the urban population, leading to a snap election and government change. Surveying Iceland’s urban population, this paper tests hypotheses about the processes mobilizing supporters to participate in the protest. The findings reveal how variables derived from the theories of biographical availability (males vs. females, working class vs. professionals), critical mass (expectations, prior protest success), and social embeddedness (close ties with protesters) moderate the association between protest support and participation. The study helps to account for one of the largest protests in Iceland’s history while contributing to the theory about how historical contexts shape the behavior of movement supporters.

Keywords: Iceland, crisis, protest support vs. participation, theories of mass mobilization

Procedia PDF Downloads 235