Search results for: physiological signals

Authors: Kamila Duś-Szachniewicz, Kinga M. Walaszek, Paweł Skiba, Paweł Kołodziej, Piotr Ziółkowski

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Cell adhesion is of fundamental importance in the cell communication, signaling, and motility, and its dysfunction occurs prevalently during cancer progression. The knowledge of the molecular and cellular processes involved in abnormalities in cancer cells adhesion has greatly increased, and it has been focused mainly on cellular adhesion molecules (CAMs) and tumor microenvironment. Unfortunately, most of the data regarding CAMs expression relates to study on cells maintained in standard oxygen condition of 21%, while the emerging evidence suggests that culturing cells in ambient air is far from physiological. In fact, oxygen in human tissues ranges from 1 to 11%. The aim of this study was to compare the effects of physiological lymph node normoxia (5% O2), and hyperoxia (21% O2) on the expression of cellular adhesion molecules of primary diffuse large B-cell lymphoma cells (DLBCL) isolated from 10 lymphoma patients. Quantitative RT-PCR and immunohistochemistry were used to confirm the differential expression of several CAMs, including ICAM, CD83, CD81, CD44, depending on the level of oxygen. Our findings also suggest that DLBCL cells maintained at ambient O2 (21%) exhibit reduced growth rate and migration ability compared to the cells growing in normoxia conditions. Taking into account all the observations, we emphasize the need to identify the optimal human cell culture conditions mimicking the physiological aspects of tumor growth and differentiation.

Keywords: adhesion molecules, diffuse large B-cell lymphoma, physiological normoxia, quantitative RT-PCR

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

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Authors: Raheleh Khademian, Roghayeh Aminian

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For the study of salinity stress on Vicia villosa and calcium effect for modulation of that, an experiment was conducted under hydroponic condition, and some important morphological and physiological characteristics were evaluated. This experiment was conducted as a factorial based on randomized complete design with three replications. The treatments include salinity stress in three levels (0, 50, and 100 mM NaCl) and calcium in two levels (content in Hoagland solution and double content). The results showed that all morphological and physiological traits include root and shoot length, root and shoot wet and dry weight, leaf area, leaf chlorophyll content, RWC, CMS, and biological yield was significantly different from the control and is affected by the salinity stress severely. But, calcium effect on them was not significant despite of decreasing salinity effect.

Keywords: Vicia villossa, salinity stress, calcium, hydroponic

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Authors: A. G. Miah, U. Salma, K. Schellander

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Relaxin was first described in 1926 by Frederick Hisaw. Previously it was considered as only the hormone of pregnant mammals due to its important roles in pregnancy and parturition. From the last decade, the physiological role of relaxin in male reproduction has been given experimental attention, and the results have made it clear that relaxin can no longer be considered strictly as only the hormone of female reproduction. The accessory glands (specially, the prostate glands) of the male reproductive system are the source of seminal relaxin, which is secreted into the seminal plasma and saturated with spermatozoa just after ejaculation. Several studies have reported that relaxin has important roles in improving motility in human sperm. Thereafter, the growing interest on relaxin has intensified efforts to investigate the role of relaxin in other sperm physiological phenomena like, capacitation, acrosome reaction, and their mediating factors associated with successful fertilization. Therefore, this review aims to provide up-to-date information about the physiological roles of relaxin in sperm motility, capacitation, acrosome reaction, and their mediating factors, such as, utilization of glucose, cholesterol efflux, Ca2+-influx, intracellular cAMP and protein tyrosine phosphorylation. Some studies have shown relaxin to increase the percentage of progressive motility and induce capacitation and acrosome reaction through increasing the utilization of glucose and mediating the cholesterol efflux, Ca2+-influx, intracellular cAMP and protein tyrosine phosphorylation. Thus, the review suggests that the supplementation of relaxin into the capacitating medium may contribute the possible beneficial roles in fresh and cryopreserved spermatozoal prefertilization events.

Keywords: relaxin, physiological roles, prefertilizing activities, spermatozoa

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

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Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

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Authors: Aqoul H. H. Alanezy, Ali M. Abdelsalam, Nouby M. Ghazaly

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In recent years, progress has been made in increasing the renewable energy share in the power sector particularly in the wind. The experimental study conducted in this paper aims to investigate the effects of number of blades and inflow wind speed on vibration signals of a vertical axis Savonius type wind turbine. The operation of the model of Savonius type wind turbine is conducted to compare two, three and four blades wind turbines to show vibration amplitudes related with wind speed. It is found that the increase of the number of blades leads to decrease of the vibration magnitude. Furthermore, inflow wind speed has reduced effect on the vibration level for higher number of blades.

Keywords: Savonius type wind turbine, number of blades, renewable energy, vibration signals

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Authors: Sun H. Hwang, Young E. Lee, Yunhan Ga, Gilwon Yoon

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EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering.

Keywords: EEG, focus, mental state, outlier, signal processing

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Authors: Kalpana Singh, Nikita Katiyar

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This research investigates the impact of Raga Bhairavi, a prominent musical scale in Indian classical music, on the mood and basic physiological parameters of research scholars at the University of Lucknow - India. The study focuses on the potential therapeutic effects of listening to Raga Bhairavi during morning hours. A controlled experimental design is employed, utilizing self-reporting tools for mood assessment and monitoring physiological indicators such as heart rate, oxygen saturation levels, body temperature and blood pressure. The hypothesis posits that exposure to Raga Bhairavi will lead to positive mood modulation and a reduction in physiological stress markers among research scholars. Data collection involves pre and post-exposure measurements, providing insights into the immediate and cumulative effects of the musical intervention. The study aims to contribute valuable information to the growing field of music therapy, offering a potential avenue for enhancing the well-being and productivity of individuals engaged in intense cognitive activities. Results may have implications for the integration of music-based interventions in academic and research environments, fostering a conducive atmosphere for intellectual pursuits.

Keywords: bio-musicology, classical music, mood assessment, music therapy, physiology, Raga Bhairavi

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Authors: Chenlu Zhang, Wanni Zhang, Florian Schaule

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Indoor environment in office buildings directly affects comfort, productivity, health, and well-being of building occupants. Wireless environmental sensor networks have been deployed in many modern offices to monitor and control the indoor environments. However, indoor environmental variables are not strong enough predictors of comfort and productivity levels of every occupant due to personal differences, both physiologically and psychologically. This study proposes human-centric sensor networks that integrate wireless environmental sensors, body area network sensors and participatory sensing technologies to collect data from both environment and human and support building operations. The sensor networks have been tested in one small-size and one medium-size office rooms with 22 participants for five months. Indoor environmental data (e.g., air temperature and relative humidity), physiological data (e.g., skin temperature and Galvani skin response), and physiological responses (e.g., comfort and self-reported productivity levels) were obtained from each participant and his/her workplace. The data results show that: (1) participants have different physiological and physiological responses in the same environmental conditions; (2) physiological variables are more effective predictors of comfort and productivity levels than environmental variables. These results indicate that the human-centric sensor networks can support human-centric building control and improve comfort and productivity in offices.

Keywords: body area network, comfort and productivity, human-centric sensors, internet of things, participatory sensing

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Authors: Jan Doutreloigne

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The main goal of this paper is to develop a switching amplifier with optimized power efficiency for analog signals with a very high crest factor such as audio or DSL signals. Theoretical calculations show that a switching amplifier architecture based on multi-level pulse width modulation outperforms all other types of linear or switching amplifiers in that respect. Simulations on a 2 W multi-level switching audio amplifier, designed in a 50 V 0.35 mm IC technology, confirm its superior performance in terms of power efficiency. A real silicon implementation of this audio amplifier design is currently underway to provide experimental validation.

Keywords: audio amplifier, multi-level switching amplifier, power efficiency, pulse width modulation, PWM, self-oscillating amplifier

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Authors: Virgil-Florin Duma, Dorin Demian

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Optical choppers are among the most common optomechatronic devices, utilized in numerous applications, from radiometry to telescopes and biomedical imaging. The classical configuration has a rotational disk with windows with linear margins. This research points out the laser signals that can be obtained with these classical choppers, as well as with another, novel, patented configuration, of eclipse choppers (i.e., with rotational disks with windows with non-linear margins, oriented outwards or inwards). Approximately triangular laser signals can be obtained with eclipse choppers, in contrast to the approximately sinusoidal – with classical devices. The main topic of this work refers to another, novel device, of choppers with shafts of different shapes and with slits of various profiles (patent pending). A significant improvement which can be obtained (with regard to disk choppers) refers to the chop frequencies of the laser signals. Thus, while 1 kHz is their typical limit for disk choppers, with choppers with shafts, a more than 20 times increase in the chop frequency can be obtained with choppers with shafts. Their transmission functions are also discussed, for different types of laser beams. Acknowledgments: This research is supported by the Romanian National Authority for Scientific Research, through the project PN-III-P2-2.1-BG-2016-0297.

Keywords: laser signals, laser systems, optical choppers, optomechatronics, transfer functions, eclipse choppers, choppers with shafts

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Authors: Carolina Gouveia, José Vieira, Pedro Pinho

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The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully.

Keywords: bio-signals, DC component, Doppler effect, ellipse fitting, radar, SDR

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Chante J. De Klerk

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The potential of music to facilitate superior performance during high to elite level gymnastics conditioning instigated this research. A team of seven gymnasts completed a fixed conditioning programme eight times, alternating the two variable conditions. Four sessions of each condition were conducted: without music (session 1), with music (session 2), without music (3), with music (4), without music (5), and so forth. Quantitative data were collected in both conditions through physiological monitoring of the gymnasts, and administration of the Situational Motivation Scale (SIMS). Statistical analysis of the physiological data made it possible to quantify the presence as well as the magnitude of the musical intervention’s impact on various aspects of the gymnasts' physiological functioning during conditioning. The SIMS questionnaire results were used to evaluate if their motivation towards conditioning was altered by the intervention. Thematic analysis of qualitative data collected through semi-structured interviews revealed themes reflecting the gymnasts’ sentiments towards the data collection process. Gymnast-specific descriptions and experiences of the team as a whole were integrated with the quantitative data to facilitate greater dimension in establishing the impact of the intervention. The results showed positive physiological, motivational, and emotional effects. In the presence of music, superior sympathetic nervous activation, and energy efficiency, with more economic breathing, dominated the physiological data. Fatigue and arousal levels (emotional and physiological) were also conducive to improved conditioning outcomes compared to conventional conditioning (without music). Greater levels of positive affect and motivation emerged in analysis of both the SIMS and interview data sets. Overall, the intervention was found to promote psychophysiological coherence during the physical activity. In conclusion, a strategically constructed musical intervention, designed to accompany a gymnastics conditioning session for high to elite level gymnasts, has ergogenic potential.

Keywords: arousal, fatigue, gymnastics conditioning, motivation, musical intervention, psychophysiological coherence

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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: 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: 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: 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: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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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: Mikko Salminen, Simo Järvelä, Niklas Ravaja

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One of the central models of emotional intelligence (EI) is that of Mayer and Salovey’s, which includes ability to monitor own feelings and emotions and those of others, ability to discriminate different emotions, and to use this information to guide thinking and actions. There is vast amount of previous research where positive links between EI and, for example, leadership successfulness, work outcomes, work wellbeing and organizational climate have been reported. EI has also a role in the effectiveness of work teams, and the effects of EI are especially prominent in jobs requiring emotional labor. Thus, also the organizational context must be taken into account when considering the effects of EI on work outcomes. Based on previous research, it is suggested that EI can also protect managers from the negative consequences of stress. Stress may have many detrimental effects on the manager’s performance in essential work tasks. Previous studies have highlighted the effects of stress on, not only health, but also, for example, on cognitive tasks such as decision-making, which is important in managerial work. The motivation for the current study came from the notion that, unfortunately, many stressed individuals may not be aware of the circumstance; periods of stress-induced physiological arousal may be prolonged if there is not enough time for recovery. To tackle this problem, physiological stress levels of managers were collected using recording of heart rate variability (HRV). The goal was to use this data to provide the managers with feedback on their stress levels. The managers could access this feedback using a www-based learning environment. In the learning environment, in addition to the feedback on stress level and other collected data, also developmental tasks were provided. For example, those with high stress levels were sent instructions for mindfulness exercises. The current study focuses on the relation between the measured physiological stress levels and EI of the managers. In a pilot study, 33 managers from various fields wore the Firstbeat Bodyguard HRV measurement devices for three consecutive days and nights. From the collected HRV data periods (minutes) of stress and recovery were detected using dedicated software. The effects of EI on HRV-calculated stress indexes were studied using Linear Mixed Models procedure in SPSS. There was a statistically significant effect of total EI, defined as an average score of Schutte’s emotional intelligence test, on the percentage of stress minutes during the whole measurement period (p=.025). More stress minutes were detected on those managers who had lower emotional intelligence. It is suggested, that high EI provided managers with better tools to cope with stress. Managing of own emotions helps the manager in controlling possible negative emotions evoked by, e.g., critical feedback or increasing workload. High EI managers may also be more competent in detecting emotions of others, which would lead to smoother interactions and less conflicts. Given the recent trend to different quantified-self applications, it is suggested that monitoring of bio-signals would prove to be a fruitful direction to further develop new tools for managerial and leadership coaching.

Keywords: emotional intelligence, leadership, heart rate variability, personality, stress

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Authors: Rashi Jain

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The Wireless Body Area Networks (WBANs) is an emerging interdisciplinary area where small sensors are placed on/within the human body. These sensors monitor the physiological activities and vital statistics of the body. The data from these sensors is aggregated and communicated to a remote doctor for immediate attention or to a database for records. On 6 Feb 2012, the IEEE 802.15.6 task group approved the standard for Body Area Network (BAN) technologies. The standard proposes the physical and MAC layer for the WBANs. The work provides an introduction to WBANs and overview of the physical and MAC layers of the standard. The physical layer specifications have been covered. A comparison of different protocols used at MAC layer is drawn. An introduction to the network layer and security aspects of the WBANs is made. The WBANs suffer certain limitations such as regulation of frequency bands, minimizing the effect of transmission and reception of electromagnetic signals on the human body, maintaining the energy efficiency among others. This has slowed down their implementation.

Keywords: vehicular networks, sensors, MicroController 8085, LTE

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Authors: Juzhong Tan, William Kerr

Abstract:

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

Procedia PDF Downloads 234

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

Abstract:

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)

Procedia PDF Downloads 259

Authors: H. Bentarzi, A. Zitouni

Abstract:

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: Bao Li, Aike Qiao, Gaoyang Li, Youjun Liu

Abstract:

Background: Lumped parameter model (LPM) is a common numerical model for hemodynamic calculation. LPM uses circuit elements to simulate the human blood circulatory system. Physiological indicators and characteristics can be acquired through the model. However, due to the different physiological indicators of each individual, parameters in LPM should be personalized in order for convincing calculated results, which can reflect the individual physiological information. This study aimed to develop an automatic and effective optimization method to personalize the parameters in LPM of the blood circulatory system, which is of great significance to the numerical simulation of individual hemodynamics. Methods: A closed-loop LPM of the human blood circulatory system that is applicable for most persons were established based on the anatomical structures and physiological parameters. The patient-specific physiological data of 5 volunteers were non-invasively collected as personalized objectives of individual LPM. In this study, the blood pressure and flow rate of heart, brain, and limbs were the main concerns. The collected systolic blood pressure, diastolic blood pressure, cardiac output, and heart rate were set as objective data, and the waveforms of carotid artery flow and ankle pressure were set as objective waveforms. Aiming at the collected data and waveforms, sensitivity analysis of each parameter in LPM was conducted to determine the sensitive parameters that have an obvious influence on the objectives. Simulated annealing was adopted to iteratively optimize the sensitive parameters, and the objective function during optimization was the root mean square error between the collected waveforms and data and simulated waveforms and data. Each parameter in LPM was optimized 500 times. Results: In this study, the sensitive parameters in LPM were optimized according to the collected data of 5 individuals. Results show a slight error between collected and simulated data. The average relative root mean square error of all optimization objectives of 5 samples were 2.21%, 3.59%, 4.75%, 4.24%, and 3.56%, respectively. Conclusions: Slight error demonstrated good effects of optimization. The individual modeling algorithm developed in this study can effectively achieve the individualization of LPM for the blood circulatory system. LPM with individual parameters can output the individual physiological indicators after optimization, which are applicable for the numerical simulation of patient-specific hemodynamics.

Keywords: blood circulatory system, individual physiological indicators, lumped parameter model, optimization algorithm

Procedia PDF Downloads 137