Search results for: physiological signals

Authors: Dhivya Balakrishnan, Dhamodharan Shanmugam

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Multilevel converters offer high power capability, associated with lower output harmonics and lower commutation losses. Their main disadvantage is their complexity requiring a great number of power devices and passive components, and a rather complex control circuitry. This paper proposes a single-phase seven-level inverter for grid connected PV systems, With a novel pulse width-modulated (PWM) control scheme. Three reference signals that are identical to each other with an offset that is equivalent to the amplitude of the triangular carrier signal were used to generate the PWM signals. The inverter is capable of producing seven levels of output-voltage levels from the dc supply voltage. This paper proposes a new multilevel inverter topology using an H-bridge output stage with two bidirectional auxiliary switches. The new topology produces a significant reduction in the number of power devices and capacitors required to implement a multilevel output using the asymmetric cascade configuration.

Keywords: asymmetric cascade configuration, H-Bridge, multilevel inverter, Pulse Width Modulation (PWM)

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Authors: Bulcha Belay Etana, Benny Malengier, Debelo Oljira, Janarthanan Krishnamoorthy, Lieva Vanlangenhove

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Electromyography (EMG) is a technique used to measure the electrical activity of muscles. EMG can be used to assess muscle function in a variety of settings, including clinical, research, and sports medicine. The aim of this study was to develop a wearable textile sensor for EMG monitoring. The sensor was designed to be soft, stretchable, and washable, making it suitable for long-term use. The sensor was fabricated using a conductive thread material that was embroidered onto a fabric substrate. The sensor was then connected to a microcontroller unit (MCU) and a Wi-Fi-enabled module. The MCU was programmed to acquire the EMG signal and transmit it wirelessly to the Wi-Fi-enabled module. The Wi-Fi-enabled module then sent the signal to a server, where it could be accessed by a computer or smartphone. The sensor was able to successfully acquire and transmit EMG signals from a variety of muscles. The signal quality was comparable to that of commercial EMG sensors. The development of this sensor has the potential to improve the way EMG is used in a variety of settings. The sensor is soft, stretchable, and washable, making it suitable for long-term use. This makes it ideal for use in clinical settings, where patients may need to wear the sensor for extended periods of time. The sensor is also small and lightweight, making it ideal for use in sports medicine and research settings. The data for this study was collected from a group of healthy volunteers. The volunteers were asked to perform a series of muscle contractions while the EMG signal was recorded. The data was then analyzed to assess the performance of the sensor. The EMG signals were analyzed using a variety of methods, including time-domain analysis and frequency-domain analysis. The time-domain analysis was used to extract features such as the root mean square (RMS) and average rectified value (ARV). The frequency-domain analysis was used to extract features such as the power spectrum. The question addressed by this study was whether a wearable textile sensor could be developed that is soft, stretchable, and washable and that can successfully acquire and transmit EMG signals. The results of this study demonstrate that a wearable textile sensor can be developed that meets the requirements of being soft, stretchable, washable, and capable of acquiring and transmitting EMG signals. This sensor has the potential to improve the way EMG is used in a variety of settings.

Keywords: EMG, electrode position, smart wearable, textile sensor, IoT, IoT-integrated textile sensor

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Authors: P. Moskal, T. Bednarski, P. Bialas, E. Czerwinski, A. Gajos, A. Gruntowski, D. Kaminska, L. Kaplon, G. Korcyl, P. Kowalski, T. Kozik, W. Krzemien, E. Kubicz, Sz. Niedzwiecki, M. Palka, L. Raczynski, Z. Rudy, P. Salabura, N. G. Sharma, M. Silarski, A. Slomski, J. Smyrski, A. Strzelecki, A. Wieczorek, W. Wislicki, M. Zielinski, N. Zon

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A new concept and results of the performance tests of the TOF-PET detection system developed at the Jagiellonian University will be presented. The novelty of the concept lies in employing long strips of polymer scintillators instead of crystals as detectors of annihilation quanta, and in using predominantly the timing of signals instead of their amplitudes for the reconstruction of Lines-of-Response. The diagnostic chamber consists of plastic scintillator strips readout by pairs of photo multipliers arranged axially around a cylindrical surface. To take advantage of the superior timing properties of plastic scintillators the signals are probed in the voltage domain with the accuracy of 20 ps by a newly developed electronics, and the data are collected by the novel trigger-less and reconfigurable data acquisition system. The hit-position and hit-time are reconstructed by the dedicated reconstruction methods based on the compressing sensing theory and the library of synchronized model signals. The solutions are subject to twelve patent applications. So far a time-of-flight resolution of ~120 ps (sigma) was achieved for a double-strip prototype with 30 cm field-of-view (FOV). It is by more than a factor of two better than TOF resolution achievable in current TOF-PET modalities and at the same time the FOV of 30 cm long prototype is significantly larger with respect to typical commercial PET devices. The Jagiellonian PET (J-PET) detector with plastic scintillators arranged axially possesses also another advantage. Its diagnostic chamber is free of any electronic devices and magnetic materials thus giving unique possibilities of combining J-PET with CT and J-PET with MRI for scanning the same part of a patient at the same time with both methods.

Keywords: PET-CT, PET-MRI, TOF-PET, scintillator

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Authors: M. Barański

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This paper presents a several diagnostic methods designed to electrical machines especially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives. Those methods are preferred by the author in periodic diagnostic of electrical machines. The special attention should be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methods were created in Institute of Electrical Drives and Machines Komel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.

Keywords: electrical vehicle, generator, main insulation, permanent magnet, thermography, turn-to-traction drive, turn insulation, vibrations

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Authors: Fatma Ahmed, Heba Mostafa, Bassem Ramdan, Azza El-Soussi

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Background: World Health Organization stated that by 2020 cardiac disease will be the number one cause of death worldwide and estimates that 25 million people per year will suffer from heart disease. Cardiac surgery is considered an effective treatment for severe forms of cardiovascular diseases that cannot be treated by medical treatment or cardiac interventions. In spite of the benefits of cardiac surgery, it is considered a major stressful experience for patients who are candidate for surgery. Prehabilitation can decrease incidences of postoperative complications as it prepares patients for surgical stress through enhancing their defenses to meet the demands of surgery. When patients anticipate the postoperative sequence of events, they will prepare themselves to act certain behaviors, identify their roles and actively participate in their own recovery, therefore, anxiety levels are decreased and functional capacity is enhanced. Prehabilitation programs can comprise interventions that include physical exercise, psychological prehabilitation, nutritional optimization and risk factor modification. Physical exercises are associated with improvements in the functioning of the various physiological systems, reflected in increased functional capacity, improved cardiac and respiratory functions and make patients fit for surgical intervention. Prehabilitation programs should also prepare patients psychologically in order to cope with stress, anxiety and depression associated with postoperative pain, fatigue, limited ability to perform the usual activities of daily living through acting in a healthy manner. Notwithstanding the benefits of psychological preparations, there are limited studies which investigated the effect of psychological prehabilitation to confirm its effect on psychological, quality of life and physiological outcomes of patients who had undergone cardiac surgery. Aim of the study: The study aims to determine the effect of prehabilitation interventions on outcomes of patients undergoing cardiac surgeries. Methods: Quasi experimental study design was used to conduct this study. Sixty eligible and consenting patients were recruited and divided into two groups: control and intervention group (30 participants in each). One tool namely emotional, physiological, clinical, cognitive and functional capacity outcomes of prehabilitation intervention assessment tool was utilized to collect the data of this study. Results: Data analysis showed significant improvement in patients' emotional state, physiological and clinical outcomes (P < 0.000) with the use of prehabilitation interventions. Conclusions: Cardiac prehabilitation in the form of providing information about surgery, circulation exercise, deep breathing exercise, incentive spirometer training and nutritional education implemented daily by patients scheduled for elective open heart surgery one week before surgery have been shown to improve patients' emotional state, physiological and clinical outcomes.

Keywords: emotional recovery, clinical recovery, coronary artery bypass grafting patients, prehabilitation

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Authors: H. M. Fayzan Shakir, Khadija Zubair, Tingkai Zhao

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Electromagnetic (EM) waves are being used widely now a days. Cell phone signals, WIFI signals, wireless telecommunications etc everything uses EM waves which then create EM pollution. EM pollution can cause serious effects on both human health and nearby electronic devices. EM waves have electric and magnetic components that disturb the flow of charged particles in both human nervous system and electronic devices. The shielding of both humans and electronic devices are a prime concern today. EM waves can cause headaches, anxiety, suicide and depression, nausea, fatigue and loss of libido in humans and malfunctioning in electronic devices. Polyaniline (PANI) and polypyrrole (PPY) were successfully synthesized using chemical polymerizing using ammonium persulfate and DBSNa as oxidant respectively. Barium ferrites (BaFe) were also prepared using co-precipitation method and calcinated at 10500C for 8h. Nanocomposite thin films with various combinations and compositions of Polyvinylchloride, PANI, PPY and BaFe were prepared. X-ray diffraction technique was first used to confirm the successful fabrication of all nano fillers and particle size analyzer to measure the exact size and scanning electron microscopy is used for the shape. According to Electromagnetic Interference theory, electrical conductivity is the prime property required for the Electromagnetic Interference shielding. 4-probe technique is then used to evaluate DC conductivity of all samples. Samples with high concentration of PPY and PANI exhibit remarkable increased electrical conductivity due to fabrication of interconnected network structure inside the Polyvinylchloride matrix that is also confirmed by SEM analysis. Less than 1% transmission was observed in whole NIR region (700 nm – 2500 nm). Also, less than -80 dB Electromagnetic Interference shielding effectiveness was observed in microwave region (0.1 GHz to 20 GHz).

Keywords: nanocomposites, polymers, EMI shielding, thermal imaging

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Authors: Farid Shekari

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Salicylic Acid (SA) is a plant hormone that improves some physiological responses of plants under stress conditions. Seeds of two desi type chickpea cultivars, viz., Kaka and Pirooz, primed with 250, 500, 750, and 1000 μM of SA and a group of seeds without any treating (as control) were evaluated under rain fed conditions. Seed priming in both cultivars led to higher efficiency compare to non-primed treatments. In general, seed priming with 500 and 750 μM of SA had appropriate effects; however the cultivars responses were different in this regard. Kaka showed better performance both in primed and non-primed seed than Pirooz. Results of this study revealed that not only yield quantity but also yield quality, as seed protein amounts, could positively affect by SA treatments. It seems that SA by enhancing of soluble sugars and proline amounts enhanced total water potential (ψ) and RWC. The increment in RWC led to rose of chlorophyll content of plants chlorophyll stability. In general, SA increased water use efficiency, both in biologic and seed yield base, and drought tolerance of chickpea plants. HI was a little decreased in SA treatments and it shows that SA more effective in biomass production than seed yield.

Keywords: chlorophyll, harvest index, proline, seed protein, soluble sugar, water use efficiency, yield component

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Authors: Getaye Fisseha Gelaw

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The purpose of this study was to investigate the key physical and physiological characteristics of 16 elite male Ethiopian national team distance runners, who have an average age of 28.1±4.3 years, a height of 175.0 ±5.6 cm, a weight of 59.1 ±3.9 kg, a BMI of 19.6 ±1.5, and training age of 10.1 ±5.1 yrs. The average weekly distance is 196.3±13.8 km, the average 10,000m time is 27:14±0.5 min sec, the average half marathon time is 59:30±0.6 min sec, the average marathon time is 2hr 03min 39sec±0.02. In addition, the average Cooper test (12-minute run test) is 4525.4±139.7 meters, and the average VO2 max is 90.8±3.1ml/kg/m. All athletes have a high profile and compete on the international label, and according to the World Athletics athletes' ranking system in 2021, 56.3% of the 16 participants were platinum label status, while the remaining 43.7 % were gold label status-completed an incremental treadmill test for the assessment of VO2peak, submaximal running, lactate threshold and test during which they ran continuously at 21 km/h. The laboratory determined VO2peak was 91.4 ± 1.7 mL/kg/min with anaerobic threshold of 74.2±1.6 mL/min/Kg and VO2max 81%. The speed at the AT is 15.9 ±0.6 Kmh and the altitude is 4,0%. The respiratory compensation RC point was reached at 88.7±1.1 mL/min/Kg and 97% of VO2 max. On RCP, the speed is 17.6 ±0.4 km/h and the altitude/slope are 5.5% percent, and the speed at Maximum effort is 19.5 ±1.5 and the elevation is 6.0%. The data also suggest that Ethiopian distance top athletes have considerably higher VO2 max values than those found in earlier research.

Keywords: long-distance running, Ethiopians, VO2 max, world athletics, anthropometric

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Authors: N. Isnaini, S. Wahjuningsih

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Fig fruit is the fruit of a tropical plant with content of flavanoids, vitamins A, C, and E which are antioxidants that effectively prevent and neutralize free radicals. This study was conducted to evaluate the supplementation of fig fruit extract in a physiological NaCl-based diluent on sperm motility and viability of native chicken semen after cooling. Semen was collected from 4 male mature chocks using massage method. Fresh semen evaluated for colour, pH, volume, concentration, mass motility, individual motility, life sperm and sperm abnormality. Semen was diluted with physiological NaCl-based extender supplemented with different levels of fig fruit extract (0, 10, 20 and 30 %) v/v with the ratio of 1 semen: 4 diluter. Semen used had mass motility of 2+ and motility of 70%. Immediately after dilution semen was stored in 3-5 °C and sperm motility and viability percentage were observed at 0, 12 and 24 h. The obtained data were analyze with Analysis of Variant (ANOVA) and Least Significant Difference were determined. The experiment was designed using completely random design (4 treatments and 10 replications). The results showed that the level of fig fruit extract had very significant effect (P < 0,01) on sperm motility and viability percentage in 0, 12 and 24 h of cooling. It can be concluded that the best fig fruit extract level for resulting optimal sperm motility and viability was 10%.

Keywords: chock, antioxidant, fig fruit extract, sperm

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Authors: Pogula Rakesh, T. Kishore Kumar

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Speech enhancement is a long standing problem with numerous applications like teleconferencing, VoIP, hearing aids, and speech recognition. The motivation behind this research work is to obtain a clean speech signal of higher quality by applying the optimal noise cancellation technique. Real-time adaptive filtering algorithms seem to be the best candidate among all categories of the speech enhancement methods. In this paper, we propose a speech enhancement method based on Recursive Least Squares (RLS) adaptive filter of speech signals. Experiments were performed on noisy data which was prepared by adding AWGN, Babble and Pink noise to clean speech samples at -5dB, 0dB, 5dB, and 10dB SNR levels. We then compare the noise cancellation performance of proposed RLS algorithm with existing NLMS algorithm in terms of Mean Squared Error (MSE), Signal to Noise ratio (SNR), and SNR loss. Based on the performance evaluation, the proposed RLS algorithm was found to be a better optimal noise cancellation technique for speech signals.

Keywords: adaptive filter, adaptive noise canceller, mean squared error, noise reduction, NLMS, RLS, SNR, SNR loss

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Authors: Edwin Forero-Garcia, Jaime Vitola, Brayan Cardenas, Johan Casagua

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This research article presents the integration of electronic and computer systems, which developed an ultrasonic signal processing system that performs the capture, adaptation, and analog-digital conversion to later carry out its processing and visualization. The capture and adaptation of the signal were carried out from the design and implementation of an analog electronic system distributed in stages: 1. Coupling of impedances; 2. Analog filter; 3. Signal amplifier. After the signal conditioning was carried out, the ultrasonic information was digitized using a digital microcontroller to carry out its respective processing. The digital processing of the signals was carried out in MATLAB software for the elaboration of A-Scan, B and D-Scan types of ultrasonic images. Then, advanced processing was performed using the SAFT technique to improve the resolution of the Scan-B-type images. Thus, the information from the ultrasonic images was displayed in a user interface developed in .Net with Visual Studio. For the validation of the system, ultrasonic signals were acquired, and in this way, the non-invasive inspection of the structures was carried out and thus able to identify the existing pathologies in them.

Keywords: acquisition, signal processing, ultrasound, SAFT, HMI

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Authors: Mohamed Aymen Slim, Afef Abdelkrim, Mohamed Benrejeb

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The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches.

Keywords: Electro Myo Graphic (EMG) signals, experimental approach, handwriting process, Radial Basis Functions (RBF) neural networks, velocity modeling

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Authors: Kaori Nakamura, Shun Su, Yusak Sulio, Daisuke Fukuda

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Active modes of transportation, such as walking and cycling, are crucial in promoting healthy and sustainable urban environments. Encouraging the use of these modes requires a well-designed road environment that ensures safety and comfort. Understanding what constitutes a safe environment for these users is essential. While previous research mainly focuses on subjective safety or the likelihood of collisions, there needs to be more analysis of the real-time experiences of travelers and dynamic transitions of their discomfort perceptions. Post-ride surveys or pre-ride impressions, the typical evaluation methods in past studies, may not accurately capture the immediate reactions and discomforts experienced during their rides. Though past experimental studies may also use physiological and behavioral data to evaluate road designs, they evaluated road design by comparing time-average physiological and behavioral data across different designs. This study aims to investigate the effects of the dynamic riding environmental changes experienced by cyclists during their rides on their dynamic physiological and behavioral responses and then explore how these conditions contribute to cyclists' overall subjective safety and comfort. We conducted an experiment with 24 participants who cycled approximately 500 meters in a virtual reality (VR) environment designed to mimic a typical road environment of Japanese local towns where lanes for cyclists and regular cars are adjacent in limited road spaces. Participants experienced six road designs varying in width, separation type, and bike lane color. We measured their physiological data, such as heart rate and skin conductance, and behavioral data, including steering, acceleration, and coordination of bicycles. Questionnaires for eliciting subjective impressions were conducted before and after each ride. The data analysis results indicate that wider paths (i.e., 1.5m and 2m width) are preferred, enhancing perceived safety and reducing stress, as supported by lower heart rates and skin conductance levels over narrower ones (1m width). Designs with clear divisions from car lanes may enhance perceived safety and reduce stress. The analysis of the physiological data also supports these arguments, showing that lower heart rates and skin conductance levels are found in wider, clearly marked paths. Further, the drift-diffusion decision model was performed to reveal whether different road environment designs may impact dynamic decision-making processes and physiological attributes. Designing a 1.5m wide bike lane with clear divisions from car lanes showed the highest level of clarity and safety in decision-making parameters. In contrast, designs without clear separations from car lanes resulted in less favorable decision-making outcomes. These results coincide with previous primary research indicating a preference for bike lane widths more significant than 1.5m. In conclusion, the analysis using the drift-diffusion decision model showed that decision-making ease slightly differs from subjective safety perceptions, providing a comprehensive understanding of how different road designs impact users. This study offers a solid foundation for assessing the perceptions of active mode users and highlights the importance of considering both real-time physiological and subjective data in designing road environments that encourage active transportation modes.

Keywords: active transport modes, cognitive and decision-making modeling, road environment designs, virtual reality experiment

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Authors: Renu Yadav, Sanjeev Kumar

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In northern India, chickpea (Cicer arietinum L.) come across with terminal high-temperature stress during reproductive stage which leads to reduced yield. Hence, stable production of chickpea will depend on the development of new methods like ‘priming’ which allow improved adaptation to the drought and heat stress. In the present experiment, 11-day chickpea seedling was primed with mild drought stress and put on recovery stage by irrigating and finally 30-day seedlings were exposed to heat stress 38°C (4 hours), 35°C (8 hours) and 32°C (12 hours). To study the effect of combinatorial stress, heat and drought stress was applied simultaneously. Analyses of various physiological parameters like membrane damage assay, photosynthetic pigments, antioxidative enzyme, total sugars were estimated at all stages. To study the effect of heat stress on the metabolites of the plants, GC-MS and HPLC were performed, while at transcriptional level Real-Time PCR of predicted heat stress-related genes was done. It was concluded that the heat stress significantly affected the chickpea plant at physiological and molecular level in all the five varieties. Results also show less damaging effect in primed plants by increasing the activity of antioxidative enzymes and increased expression of heat shock proteins and heat shock factors.

Keywords: chickpea, combinatorial stress, heat stress, oxidative stress, priming, RT-PCR

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Maxime Farin, Emmanuel Moulin, Lynda Chehami, Farouk Benmeddour, Pierre Campistron

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Assessing corrosion is crucial in the petrochemical and marine industry. Usual ultrasonic methods based on guided waves to detect corrosion can inspect large areas but lack precision. We propose a complementary and sensitive ultrasonic method (~ 10 MHz) based on coda wave interferometry to detect and quantify corrosion at the surface of a steel sample. The method relies on a single piezoelectric transducer, exciting the sample and measuring the scattered coda signals at different instants in time. A laboratory experiment is conducted with a steel sample immersed in salted water for 60~h with parallel coda and temperature measurements to correct coda dependence to temperature variations. Micrometric changes to the sample surface caused by corrosion are detected in the late coda signals, allowing precise corrosion detection. Moreover, a good correlation is found between a parameter quantifying the temperature-corrected stretching of the coda over time with respect to a reference without corrosion and the corrosion surface over the sample recorded with a camera.

Keywords: coda wave interferometry, nondestructive evaluation, corrosion, ultrasonics

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Authors: Ali Mashinchian Moradi, Mahmood Sinaei

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Study on the biomarkers to assess health status of marine ecosystems has an important value in biomonitoring of marine environment. Accordingly, accumulation of polycyclic aromatic hydrocarbons in sediment, water and tissues (liver and gill) of mudskipper (Boleophthalmus dussmieri) and some physiological responses like lysosomal membrane change in haemocytes and the Glutathione-S Transferase (GST) activity in the liver were measured in mudskippers. Samples were collected from five sites along the noth western cost of the Persian Gulf. PAHs concentration was measured by HPLC method. The activity of GST enzyme was analysed by spectrophotometric method. Total PAH concentration in coastal seawater, sediments, liver and gill tissues ranged between 0.80-18.34 ug/L, 113.550-3384.34 ng/g dw, 3.99-46.64 ng/g dw and 3.11-17.This study showed that PAH concentrations in this region are not higher than available standards. The findings revile that lysosomal membrane destabilization and liver GST activities are highly sensitive to PAHs in mudskipper, B. dussumieri. Sediment PAH concentrations were strongly correlated with biomarkers, indicating PAHs were biologically available to fish. Thus, mudskipper perceived to be good sentinel organism for PAH pollution biomonitoring.

Keywords: PAHs, biomarker, mudskipper, Persian Gulf

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Authors: Ujala, Diksha Sharma, Mahinder Partap, Ashish R. Warghat, Bhavya Bhargava

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In soil-less agriculture, hydroponic is considered a potential farming system for the production of uniform quality plant material in significantly less time. Therefore, for the first time, the current investigation corroborates the effect of different cultivation conditions (open-field, poly-house, and hydroponic) on morpho-physiological traits, phenolic content, and essential oil components analysis in three flower color variants (yellow, scarlet red, and orange) of Tagetes patula. The results revealed that the maximum plant height, number of secondary branches, number of flowers, photosynthesis, stomatal conductance, and transpiration rate were observed under the hydroponic system as compared to other conditions. However, the maximum content of gallic acid (0.82 mg/g DW), syringic acid (3.98 mg/g DW), epicatechin (0.48 mg/g DW), p-coumaric acid (7.28 mg/g DW), protocatechuic acid (0.59 mg/g DW), ferulic acid (2.58 mg/g DW), and luteolin (8.24 mg/g DW) were quantified maximally under open-field conditions. However, under hydroponic conditions, the higher content of vanillic acid (0.43 mg/g DW), caffeic acid (0.49 mg/g DW), and quercetin (0.92 mg/g DW) were quantified. Moreover, a total of nineteen volatile components were identified in the essential oil of different flower color variants of T. patula cultivated under different conditions. The major reported volatile components in essential oil were (-)-caryophyllene oxide, trans-β-caryophyllene, trans-geraniol, 3 methyl-benzyl alcohol, and 2,2’:5’,2”-terthiophene. It has also been observed that the volatile component percentage range in all variants was observed in open-field (70.85 % to 90.54 %), poly-house (59.03 % to 77.93 %), and hydroponic (68.78 % to 89.41 %). In conclusion, the research highlighted that morpho-physiological performance with flower production was enhanced in the hydroponic system. However, phenolic content and volatile components were maximally observed in open-field conditions. However, significant results have been reported under hydroponic conditions in all studied parameters, so it could be a potential strategy for quality biomass production in T. patula.

Keywords: Tagetes patula, cultivation conditions, hydroponic, morpho-physiology

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Toufik Bensana, Slimane Mekhilef, Kamel Tadjine

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Vibration analysis has been frequently applied in the condition monitoring and fault diagnosis of rolling element bearings. Unfortunately, the vibration signals collected from a faulty bearing are generally non stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, condition monitoring, local mean decomposition, rolling element bearing, vibration analysis

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Authors: Vanja Misic, Mohamed El-Mogy, Yousef Haj-Ahmad

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Endonuclease G (EndoG) is a well conserved mitochondrio-nuclear nuclease with dual lethal and vital roles in the cell. The aim of our study was to examine whether EndoG exerts its nuclease activity on exogenous DNA substrates such as plasmid DNA (pDNA), considering their importance in gene therapy applications. The effects of EndoG knockdown on pDNA stability and levels of encoded reporter gene expression were evaluated in the cervical carcinoma HeLa cells. Transfection of pDNA vectors encoding short-hairpin RNAs (shRNAs) reduced levels of EndoG mRNA and nuclease activity in HeLa cells. In physiological circumstances, EndoG knockdown did not have an effect on the stability of pDNA or the levels of encoded transgene expression as measured over a four day time-course. However, when endogenous expression of EndoG was induced by an extrinsic stimulus, targeting of EndoG by shRNA improved the perceived stability and transgene expression of pDNA vectors. Therefore, EndoG is not a mediator of exogenous DNA clearance, but in non-physiological circumstances it may non-specifically cleave intracellular DNA regardless of its origin. These findings make it unlikely that targeting of EndoG is a viable strategy for improving the duration and level of transgene expression from non-viral DNA vectors in gene therapy efforts.

Keywords: EndoG, silencing, exogenous DNA stability, HeLa cells

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Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina, Evgenii N. Kojaev

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In review the generalized data about biological activity of anthraquinone-containing plants and specimens on their basis is presented. Data of traditional medicine, results of bioscreening and clinical researches of specimens are analyzed.

Keywords: anthraquinones, physiologically active substances, phytopreparation, Ramon

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Authors: Sanat K. Dash, Rama S. Verma, Sarit K. Das

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A logarithmic microfluidic shear device was designed and fabricated for cellular mechanotransduction studies. The device contains four cell culture chambers in which flow was modulated to achieve a logarithmic increment. Resistance values were optimized to make the device compact. The network of resistances was developed according to a unique combination of series and parallel resistances as found via optimization. Simulation results done in Ansys 16.1 matched the analytical calculations and showed the shear stress distribution at different inlet flow rates. Fabrication of the device was carried out using conventional photolithography and PDMS soft lithography. Flow profile was validated taking DI water as working fluid and measuring the volume collected at all four outlets. Volumes collected at the outlets were in accordance with the simulation results at inlet flow rates ranging from 1 ml/min to 0.1 ml/min. The device can exert fluid shear stresses ranging four orders of magnitude on the culture chamber walls which will cover shear stress values from interstitial flow to blood flow. This will allow studying cell behavior in the long physiological range of shear stress in a single run reducing number of experiments.

Keywords: microfluidics, mechanotransduction, fluid shear stress, physiological shear

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Authors: Shih-Ming Wang, Chun-Kai Yeh, Ming-Hwang Shie, Tai-Wei Lin, Chieh-Fu Chang

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This paper introduces a FPGA-based baseband signals generator (BSG) of X-band transmitter developed by National Space Organization (NSPO), Taiwan, for earth observation. In order to gain more flexibility for various applications, a number of modulation schemes, QPSK, DeQPSK and 8PSK 4D-TCM are included. For micro satellite scenario, the maximum symbol rate is up to 150Mbsps, and the EVM is as low as 1.9%. For CubeSat scenario, the maximum symbol rate is up to 60Mbsps, and the EVM is less than 1.7%. The maximum data rates are 412.5Mbps and 165Mbps, respectively. Besides, triple modular redundancy (TMR) scheme is implemented in order to reduce single event effect (SEE) induced by radiation. Finally, the theoretical error performance is provided based on comprehensive analysis, especially when BER is lower and much lower than 10⁻⁶ due to low error bit requirement of modern high-resolution earth remote-sensing instruments.

Keywords: X-band transmitter, FPGA (Field-Programmable Gate Array), CubeSat, micro satellite

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Authors: H. Necef, A. Benayad

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Fungi are becoming more and more important in our life. Therefore, as a start for the symposium on filamentous fungi in biotechnology a short survey of the role of fungi in biotechnology. Salin soils occupy about 7% of land area; they are characterized by unsuitable physical conditions for the growth of living organisms. However, researches showed that some microorganisms especially fungi are able to grow and adapt to such extreme conditions; it is due to their ability to develop different physiological mechanisms in their adaptation. This is the first study on the physiological and biological characteristics of El-Beida marsh. Nine soil samples were taken at different points in two steps, the first was in winter (low temperature), and the second was in summer (high temperature). The physicochemical analyses of the soil were conducted, then the isolation process was applied using two methods, direct method and dilution method (10-1, 10-2, 10-3, 10-4). Different species of fungi were identified belong to 21 genera in addition to 3 yeast species, Aspergillus showed the highest proportion by 43%, then Penicillium by 20% then Alternaria by 7%, in addition to various genera in different proportions. As for the sampling periods, it was observed that the spread of fungi in winter was higher than in summer with the proportion 75.47% and 24.53% respectively. Some halotolerant fungi have a biotechnological importance especially if the salinity of the medium is necessary for the fermentation, and if the halotolerance genes of the fungus will define, this will open the research to study and improve this property for the industrial important micro-organisms.

Keywords: salinity, identification, aspergillus oryzae, halotolerance, fungi

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Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

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Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

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Authors: Petra Hypšová, Martin Seitl

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One of the challenges of forensic psychology is to detect deception during a face-to-face interview. In addition to the classical approaches of monitoring the utterance and its components, detection is also sought by observing behavioral and physiological changes that occur as a result of the increased emotional and cognitive load caused by the production of distorted information. Typical are changes in facial temperature, eye movements and their fixation, pupil dilation, emotional micro-expression, heart rate and its variability. Expanding technological capabilities have opened the space to detect these psychophysiological changes and behavioral manifestations through non-contact technologies that do not interfere with face-to-face interaction. Non-contact deception detection methodology is still in development, and there is a lack of studies that combine multiple non-contact technologies to investigate their accuracy, as well as studies that show how different types of lies produced by different interviewers affect physiological and behavioral changes. The main objective of this study is to apply a specific non-contact technology for deception detection. The next objective is to investigate scenarios in which non-contact deception detection is possible. A series of psychophysiological experiments using infrared thermal imaging, eye tracking and behavioral analysis with FaceReader 9.0 software was used to achieve our goals. In the laboratory experiment, 16 adults (12 women, 4 men) between 18 and 35 years of age (SD = 4.42) were instructed to produce alternating prepared and spontaneous truths and lies. The baseline of each proband was also measured, and its results were compared to the experimental conditions. Because the personality of the examiner (particularly gender and facial appearance) to whom the subject is lying can influence physiological and behavioral changes, the experiment included four different interviewers. The interviewer was represented by a photograph of a face that met the required parameters in terms of gender and facial appearance (i.e., interviewer likability/antipathy) to follow standardized procedures. The subject provided all information to the simulated interviewer. During follow-up analyzes, facial temperature (main ROIs: forehead, cheeks, the tip of the nose, chin, and corners of the eyes), heart rate, emotional expression, intensity and fixation of eye movements and pupil dilation were observed. The results showed that the variables studied varied with respect to the production of prepared truths and lies versus the production of spontaneous truths and lies, as well as the variability of the simulated interviewer. The results also supported the assumption of variability in physiological and behavioural values during the subject's resting state, the so-called baseline, and the production of prepared and spontaneous truths and lies. A series of psychophysiological experiments provided evidence of variability in the areas of interest in the production of truths and lies to different interviewers. The combination of technologies used also led to a comprehensive assessment of the physiological and behavioral changes associated with false and true statements. The study presented here opens the space for further research in the field of lie detection with non-contact technologies.

Keywords: emotional expression decoding, eye-tracking, functional infrared thermal imaging, non-contact deception detection, psychophysiological experiment

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Authors: Azizah M. Nahari, Peter Doerner

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Phosphate (Pi) is one of the essential macronutrients in plant growth and development, and it plays a central role in metabolic processes in plants, particularly photosynthesis and respiration. Limitation of crop productivity by Pi is widespread and is likely to increase in the future. Applications of Pi fertilizers have improved soil Pi fertility and crop production; however, they have also caused environmental damage. Therefore, in order to reduce dependence on unsustainable Pi fertilizers, a better understanding of phosphate use efficiency (PUE) is required for engineering nutrient-efficient crop plants. Enhanced Pi efficiency can be achieved by improved productivity per unit Pi taken up. We aim to identify, by using association mapping, general features of the most important loci that contribute to increased PUE to allow us to delineate the physiological pathways involved in defining this trait in the model plant Arabidopsis. As PUE is in part determined by the efficiency of uptake, we designed a hydroponic system to avoid confounding effects due to differences in root system architecture leading to differences in Pi uptake. In this system, 18 parental lines and 217 lines of the MAGIC population (a Multiparent Advanced Generation Inter-Cross) grown in high and low Pi availability conditions. The results showed revealed a large variation of PUE in the parental lines, indicating that the MAGIC population was well suited to identify PUE loci and pathways. 2 of 18 parental lines had the highest PUE in low Pi while some lines responded strongly and increased PUE with increased Pi. Having examined the 217 MAGIC population, considerable variance in PUE was found. A general feature was the trend of most lines to exhibit higher PUE when grown in low Pi conditions. Association mapping is currently in progress, but initial observations indicate that a wide variety of physiological processes are involved in influencing PUE in Arabidopsis. The combination of hydroponic growth methods and genome-wide association mapping is a powerful tool to identify the physiological pathways underpinning complex quantitative traits in plants.

Keywords: hydroponic system growth, phosphate use efficiency (PUE), Genome-wide association mapping, MAGIC population

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Authors: M. Kharrat, G. Moreau, Z. Aboura

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The latest advances in the weaving industry, combined with increasingly sophisticated means of materials processing, have made it possible to produce complex 3D composite structures. Mainly used in aeronautics, composite materials with 3D architecture offer better mechanical properties than 2D reinforced composites. Nevertheless, these materials require a good understanding of their behavior. Because of the complexity of such materials, the damage mechanisms are multiple, and the scenario of their appearance and evolution depends on the nature of the exerted solicitations. The AE technique is a well-established tool for discriminating between the damage mechanisms. Suitable sensors are used during the mechanical test to monitor the structural health of the material. Relevant AE-features are then extracted from the recorded signals, followed by a data analysis using pattern recognition techniques. In order to better understand the damage scenarios of interlock composite materials, a multi-instrumentation was set-up in this work for tracking damage initiation and development, especially in the vicinity of the first significant damage, called macro-damage. The deployed instrumentation includes video-microscopy, Digital Image Correlation, Acoustic Emission (AE) and micro-tomography. In this study, a multi-variable AE data analysis approach was developed for the discrimination between the different signal classes representing the different emission sources during testing. An unsupervised classification technique was adopted to perform AE data clustering without a priori knowledge. The multi-instrumentation and the clustered data served to label the different signal families and to build a learning database. This latter is useful to construct a supervised classifier that can be used for automatic recognition of the AE signals. Several materials with different ingredients were tested under various solicitations in order to feed and enrich the learning database. The methodology presented in this work was useful to refine the damage threshold for the new generation materials. The damage mechanisms around this threshold were highlighted. The obtained signal classes were assigned to the different mechanisms. The isolation of a 'noise' class makes it possible to discriminate between the signals emitted by damages without resorting to spatial filtering or increasing the AE detection threshold. The approach was validated on different material configurations. For the same material and the same type of solicitation, the identified classes are reproducible and little disturbed. The supervised classifier constructed based on the learning database was able to predict the labels of the classified signals.

Keywords: acoustic emission, classifier, damage mechanisms, first damage threshold, interlock composite materials, pattern recognition

Procedia PDF Downloads 135