Search results for: Haar Wavelet

Authors: M. Madheswaran, A. R. Prashant, S. Ramakrishna, V. Ramesh Naidu, P. Govindan, P. Aravindakshan

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Shock Response spectrum (SRS) tests are one of the tests that are conducted on some critical systems of spacecraft as part of environmental testing. The SRS tests are conducted to simulate the pyro shocks that occur during launch phases as well as during deployment of spacecraft appendages. Some of the methods to carryout SRS tests are pyro technique method, impact hammer method, drop shock method and using electro dynamic shakers. The pyro technique, impact hammer and drop shock methods are open loop tests, whereas SRS testing using electrodynamic shaker is a controlled closed loop test. SRS testing using electrodynamic shaker offers various advantages such as simple test set up, better controllability and repeatability. However, it is important to devise a a proper test methodology so that safety of the electro dynamic shaker and that of test specimen are not compromised. This paper discusses the challenges that are involved in conducting SRS tests, shaker validation and the necessary precautions to be considered. Approach involved in choosing various test parameters like synthesis waveform, spectrum convergence level, etc., are discussed. A case study of SRS test conducted on an optical payload of Indian Geo stationary spacecraft is presented.

Keywords: maxi-max spectrum, SRS (shock response spectrum), SDOf (single degree of freedom), wavelet synthesis

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Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Reza Rezaeipour Honarmandzad

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This paper introduces an aircraft cable fault detection and location method in light of TFDR keeping in mind the end goal to recognize the intermittent faults adequately and to adapt to the serial and after-connector issues being hard to be distinguished in time domain reflection. In this strategy, the correlation function of reflected and reference signal is used to recognize and find the airplane fault as per the qualities of reflected and reference signal in time-frequency domain, so the hit rate of distinguishing and finding intermittent faults can be enhanced adequately. In the work process, the reflected signal is interfered by the noise and false caution happens frequently, so the threshold de-noising technique in light of wavelet decomposition is used to diminish the noise interference and lessen the shortcoming alert rate. At that point the time-frequency cross connection capacity of the reference signal and the reflected signal based on Wigner-Ville appropriation is figured so as to find the issue position. Finally, LabVIEW is connected to execute operation and control interface, the primary capacity of which is to connect and control MATLAB and LABSQL. Using the solid computing capacity and the bottomless capacity library of MATLAB, the signal processing turn to be effortlessly acknowledged, in addition LabVIEW help the framework to be more dependable and upgraded effectively.

Keywords: aircraft cable, fault location, TFDR, LabVIEW

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Authors: Jyoti Rani, Ashima Anand, Shivendra Shivani

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In recent years, physiological signals obtained in telemedicine have been stored independently from patient information. In addition, people have increasingly turned to mobile devices for information on health-related topics. Major authentication and security issues may arise from this storing, degrading the reliability of diagnostics. This study introduces an approach to reversible watermarking, which ensures security by utilizing the electrocardiogram (ECG) signal as a carrier for embedding patient information. In the proposed work, Pan-Tompkins++ is employed to convert the 1D ECG signal into a 2D signal. The frequency subbands of a signal are extracted using RDWT(Redundant discrete wavelet transform), and then one of the subbands is subjected to MSVD (Multiresolution singular valued decomposition for masking. Finally, the encrypted watermark is embedded within the signal. The experimental results show that the watermarked signal obtained is indistinguishable from the original signals, ensuring the preservation of all diagnostic information. In addition, the DnCNN (Denoising convolutional neural network) concept is used to denoise the retrieved watermark for improved accuracy. The proposed ECG signal-based watermarking method is supported by experimental results and evaluations of its effectiveness. The results of the robustness tests demonstrate that the watermark is susceptible to the most prevalent watermarking attacks.

Keywords: ECG, VMD, watermarking, PanTompkins++, RDWT, DnCNN, MSVD, chaotic encryption, attacks

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

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Authors: Saurabh Farkya, Govinda Surampudi

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Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

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Authors: Jarrod Haar, David Brougham, Azka Ghafoor

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Māori are the indigenous people of Aotearoa (New Zealand) and have a unique perspective called Te Ao Māori (the Māori worldview) and important cultural values around utu (reciprocation), collectivism, long-term orientation, and whanaungatanga (networking, relationships). The present research conducts two studies to better understand how Māori businesses might have similarities and differences to New Zealand businesses. In study 1, we conducted 50 interviews with 25 Māori business owners and 25 New Zealand (non-Māori) owners. For the indigenous population, we used a kaupapa Māori research approach using Māori protocols. This ensured the research is culturally safe. Interviews were conducted around semi-structured questions tapping into the existing business challenges, the role of innovation, and business values and approaches. Transcripts were analyzed using interpretative phenomenological analytic techniques. We identified several themes shared across all business owners: (1) the critical challenge around staff attraction and retention; (2) cost pressures including inflation; (3) and a focus on human resource (HR) practices to address issues including retention. Amongst the Māori businesses, the analysis also identified (4) a unique cultural approach to business relationships. Specifically, amongst the indigenous businesses we find a strong Te Ao Māori perspective amongst Māori business towards innovation. Analysis within this group only identified, within the following sub-themes: (a) whanaungatanga, around the development of strong relationships as a way to aid recruitment and retention, and business fluctuations; (b) mātauranga (knowledge) whereby Māori businesses seek to access advanced knowledge via universities; (c) taking a long-term orientation to business relationships – including with universities. The findings suggest people practices might be a way that firms address workforce retention issues, and we also acknowledge that Māori businesses might also leverage cultural practices to achieve better gains. Thus, in study 2, we survey 606 New Zealand private sector firms including 85 who self-identify as Māori Firms. We test the benefits of high-performance work-systems (HPWS), which represent bundle of human-resource practices designed to bolster workforce productivity through enhancing knowledge, skills, abilities, and commitment of the workforce. We test these on workforce retention and include Māori firm status and cultural capital (reflecting workforce knowledge around Māori cultural values) as moderators. Overall, we find all firms achieve superior workforce retention when they have high levels of HPWS, but Māori firms with high cultural capital are better able to leverage these HR practices to achieve superior workforce retention. In summary, the present study highlights how indigenous businesses in New Zealand might achieve superior performance by leveraging their unique cultural values. The study provides unique insights into established literatures around retention and HR practices and highlights the lessons around indigenous cultural values that appear to aid businesses.

Keywords: Māori business, cultural values, employee retention, human resource practices

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Authors: Mahya Naghipoor

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Purpose: Non-small cell lung cancer (NSCLC) is the most common lung cancer type. Epidermal growth factor receptor (EGFR) mutation is the main reason which causes NSCLC. Computed tomography (CT) is used for diagnosis and prognosis of lung cancers because of low price and little invasion. Semantic analyses of qualitative CT features are based on visual evaluation by radiologist. However, the naked eye ability may not assess all image features. On the other hand, radiomics provides the opportunity of quantitative analyses for CT images features. The aim of this review study was comparing accuracy of semantic and radiomics features in prognosis of EGFR mutation in NSCLC. Methods: For this purpose, the keywords including: non-small cell lung cancer, epidermal growth factor receptor mutation, semantic, radiomics, feature, receiver operating characteristics curve (ROC) and area under curve (AUC) were searched in PubMed and Google Scholar. Totally 29 papers were reviewed and the AUC of ROC analyses for semantic and radiomics features were compared. Results: The results showed that the reported AUC amounts for semantic features (ground glass opacity, shape, margins, lesion density and presence or absence of air bronchogram, emphysema and pleural effusion) were %41-%79. For radiomics features (kurtosis, skewness, entropy, texture, standard deviation (SD) and wavelet) the AUC values were found %50-%86. Conclusions: In conclusion, the accuracy of radiomics analysis is a little higher than semantic in prognosis of EGFR mutation in NSCLC.

Keywords: lung cancer, radiomics, computer tomography, mutation

Procedia PDF Downloads 126

Authors: Arvind Srivastav, Tarun Kanti Bhattacharyya

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The north-eastern, Himalayan, and Eastern Ghats Belt of India comprise of earthquake-prone, remote, and hilly terrains. Earthquakes have caused enormous damages in these regions in the past. A wireless sensor network based earthquake early warning system (EEWS) is being developed to mitigate the damages caused by earthquakes. It consists of sensor nodes, distributed over the region, that perform majority voting of the output of the seismic sensors in the vicinity, and relay a message to a base station to alert the residents when an earthquake is detected. At the heart of the EEWS is a low-power two-stage seismic sensor that continuously tracks seismic events from incoming three-axis accelerometer signal at the first-stage, and, in the presence of a seismic event, triggers the second-stage P-wave detector that detects the onset of P-wave in an earthquake event. The parameters of the P-wave detector have been optimized for minimizing detection time and maximizing the accuracy of detection.Working of the sensor scheme has been verified with seven earthquakes data retrieved from IRIS. In all test cases, the scheme detected the onset of P-wave accurately. Also, it has been established that the P-wave onset detection time reduces linearly with the sampling rate. It has been verified with test data; the detection time for data sampled at 10Hz was around 2 seconds which reduced to 0.3 second for the data sampled at 100Hz.

Keywords: earthquake early warning system, EEWS, STA/LTA, polarization, wavelet, event detector, P-wave detector

Procedia PDF Downloads 155

Authors: M. A. Alqahtani, D. P. Coleman, N. D. Pugh, L. D. M. Nokes

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In diagnostic ultrasound, the echo graphic B-scan texture is an important area of investigation since it can be analyzed to characterize the histological state of internal tissues. An important factor requiring consideration when evaluating ultrasonic tissue texture is the depth. The effect of attenuation with depth of ultrasound, the size of the region of interest, gain, and dynamic range are important variables to consider as they can influence the analysis of texture features. These sources of variability have to be considered carefully when evaluating image texture as different settings might influence the resultant image. The aim of this study is to investigate the effect of depth on the texture features in-vivo using a 3D ultrasound probe. The left leg medial head of the gastrocnemius muscle of 10 healthy subjects were scanned. Two regions A and B were defined at different depth within the gastrocnemius muscle boundary. The size of both ROI’s was 280*20 pixels and the distance between region A and B was kept constant at 5 mm. Texture parameters include gray level, variance, skewness, kurtosis, co-occurrence matrix; run length matrix, gradient, autoregressive (AR) model and wavelet transform were extracted from the images. The paired t –test was used to test the depth effect for the normally distributed data and the Wilcoxon–Mann-Whitney test was used for the non-normally distributed data. The gray level, variance, and run length matrix were significantly lowered when the depth increased. The other texture parameters showed similar values at different depth. All the texture parameters showed no significant difference between depths A and B (p > 0.05) except for gray level, variance and run length matrix (p < 0.05). This indicates that gray level, variance, and run length matrix are depth dependent.

Keywords: ultrasound image, texture parameters, computational biology, biomedical engineering

Procedia PDF Downloads 261

Authors: Samim Khair Mohammad, Takeshi Tsuji, Chanmaly Chhun

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The Afghan Tajik (foreland) basin, located in the depression zone between mountain axes, is under compression and deformation during the collision of India with the Eurasian plate. The southern part of the Afghan Tajik basin in the Northern part of Afghanistan has not been well studied and explored, but considered for the significant potential for oil and gas resources. The Afghan Tajik basin depositional environments (< 8km) resulted from mixing terrestrial and marine systems, which has potential prospects of Jurrasic (deep) and Tertiary (shallow) petroleum systems. We used 2D regional seismic profiles with a total length of 674.8 km (or over an area of 2500 km²) in the southern part of the basin. To characterize hydrocarbon systems and structures in this study area, we applied advanced seismic attributes such as spectral decomposition (10 - 60Hz) based on time-frequency analysis with continuous wavelet transform. The spectral decomposition results yield the (averaging 20 - 30Hz group) spectral amplitude anomaly. Based on this anomaly result, seismic, and structural interpretation, the potential hydrocarbon accumulations were inferred around the main thrust folds in the tertiary (Paleogene+Neogene) petroleum systems, which appeared to be accumulated around the central study area. Furthermore, it seems that hydrocarbons dominantly migrated along the main thrusts and then concentrated around anticline fold systems which could be sealed by mudstone/carbonate rocks.

Keywords: The Afghan Tajik basin, seismic lines, spectral decomposition, thrust folds, hydrocarbon reservoirs

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Authors: Ben Hadj Hassen Sameh, Gaillard Corentin, Andrew Parker, Kristine Krug

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Perceptual decisions about sequences of sensory stimuli often show serial dependence. The behavioural choice on one trial is often affected by the choice on previous trials. We investigated whether the neuronal signals in extrastriate visual area V5/MT on preceding trials might influence choice on the current trial and thereby reveal the neuronal mechanisms of sequential choice effects. We analysed data from 30 single neurons recorded from V5/MT in three Rhesus monkeys making sequential choices about the direction of rotation of a three-dimensional cylinder. We focused exclusively on the responses of neurons that showed significant choice-related firing (mean choice probability =0.73) while the monkey viewed perceptually ambiguous stimuli. Application of a wavelet transform to the choice-related firing revealed differences in the frequency band of neuronal activity that depended on whether the previous trial resulted in a correct choice for an unambiguous stimulus that was in the neuron’s preferred direction (low alpha and high beta and gamma) or non-preferred direction (high alpha and low beta and gamma). To probe this in further detail, we applied a regularized linear decoder to predict the choice for an ambiguous trial by referencing the neuronal activity of the preceding unambiguous trial. Neuronal activity on a previous trial provided a significant prediction of the current choice (61% correc, 95%Cl~52%t), even when limiting analysis to preceding trials that were correct and rewarded. These findings provide a potential neuronal signature of sequential choice effects in the primate visual cortex.

Keywords: perception, decision making, attention, decoding, visual system

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Authors: Jarrod Haar, David Brougham

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The present study explores the links between supporting employee well-being and the potential benefits to employee performance. We focus on employee innovative work behaviors (IWBs), which have three stages: (1) development, (2) adoption, and (3) implementation of new ideas and work methods. We explore the role of organizational support focusing on employee well-being via High-Performance Work Systems (HPWS). HPWS are HR practices that are designed to enhance employees’ skills, commitment, and ultimately, productivity. HPWS influence employee performance through building their skills, knowledge, and abilities and there is meta-analytic support for firm-level HPWS influencing firm performance, but less attention towards employee outcomes, especially innovation. We explore HPWS-wellbeing being offered (e.g., EAPs, well-being App, etc.) to capture organizational commitment to employee well-being. Under social exchange theory, workers should reciprocate their firm's offering of HPWS-wellbeing with greater efforts towards IWBs. Further, we explore playful work design as a mediator, which represents employees proactively creating work conditions that foster enjoyment/challenge but don’t require any design change to the job itself. We suggest HPWS-wellbeing can encourage employees to become more playful, and ultimately more innovative. Finally, beyond direct effects, we examine whether these relations are similar by gender and ultimately test a moderated mediation model. Using N=1135 New Zealand employees, we established measures with confirmatory factor analysis (CFA), and all measures had good psychometric properties (α>.80). We controlled for age, tenure, education, and hours worked and analyzed data using the PROCESS macro (version 4.2) specifically model 8 (moderated mediation). We analyzed overall IWB, and then again across the three stages. Overall, we find HPWS-wellbeing is significantly related to overall IWBs and the three stages (development, adoption, and implementation) individually. Similarly, HPWS-wellbeing shapes playful work design and playful work design predicts overall IWBs and the three stages individually. It only partially mediates the effects of HPWS-wellbeing, which retains a significant indirect effect. Moderation effects are supported, with males reporting a more significant effect from HPWS-wellbeing on playful work design but not IWB (or any of the three stages) than females. Females report higher playful work design when HPWS-wellbeing is low, but the effects are reversed when HPWS-wellbeing is high (males higher). Thus, males respond stronger under social exchange theory from HPWS-wellbeing, at least towards expressing playful work design. Finally, evidence of moderated mediation effects is found on overall IWBs and the three stages. Males report a significant indirect effect from HPWS-wellbeing on IWB (through playful work design), while female employees report no significant indirect effect. The benefits of playful work design fully account for their IWBs. The models account for small amounts of variance towards playful work design (12%) but larger for IWBs (26%). The study highlights a gap in the literature on HPWS-wellbeing and provides empirical evidence of their importance towards worker innovation. Further, gendered effects suggest these benefits might not be equal. The findings provide useful insights for organizations around how providing HR practices that support employee well-being are important, although how they work for different genders needs further exploration.

Keywords: human resource practices, wellbeing, innovation, playful work design

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Thilini M. Yatanwala

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CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos.

Keywords: CCTV surveillance, human detection and identification, image processing, inter-process communication, security, suspicious detection

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Authors: M. S. Jouini, A. Al-Sumaiti, M. Tembely, K. Rahimov

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Characterizing rock properties of carbonate reservoirs is highly challenging because of rock heterogeneities revealed at several length scales. In the last two decades, the Digital Rock Physics (DRP) approach was implemented successfully in sandstone rocks reservoirs in order to understand rock properties behaviour at the pore scale. This approach uses 3D X-ray Microtomography images to characterize pore network and also simulate rock properties from these images. Even though, DRP is able to predict realistic rock properties results in sandstone reservoirs it is still suffering from a lack of clear workflow in carbonate rocks. The main challenge is the integration of properties simulated at different scales in order to obtain the effective rock property of core plugs. In this paper, we propose several approaches to characterize absolute permeability in some carbonate core plugs samples using multi-scale numerical simulation workflow. In this study, we propose a procedure to simulate porosity and absolute permeability of a carbonate rock sample using textures of Micro-Computed Tomography images. First, we discretize X-Ray Micro-CT image into a regular grid. Then, we use a textural parametric model to classify each cell of the grid using supervised classification. The main parameters are first and second order statistics such as mean, variance, range and autocorrelations computed from sub-bands obtained after wavelet decomposition. Furthermore, we fill permeability property in each cell using two strategies based on numerical simulation values obtained locally on subsets. Finally, we simulate numerically the effective permeability using Darcy’s law simulator. Results obtained for studied carbonate sample shows good agreement with the experimental property.

Keywords: multiscale modeling, permeability, texture, micro-tomography images

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Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

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The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

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Authors: Kevin Wang, Jian Ge, Yinan Zhao, Kevin Willis

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Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars).

Keywords: algorithms, astronomy data analysis, deep learning, exoplanet detection methods, small planets, habitable planets, transit photometry

Procedia PDF Downloads 179

Authors: Allan F. F. Alves, Fernando A. Bacchim Neto, Guilherme Giacomini, Marcela de Oliveira, Ana L. M. Pavan, Maria E. D. Rosa, Diana R. Pina

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Stroke is a worldwide concern, only in Brazil it accounts for 10% of all registered deaths. There are 2 stroke types, ischemic (87%) and hemorrhagic (13%). Early diagnosis is essential to avoid irreversible cerebral damage. Non-enhanced computed tomography (NECT) is one of the main diagnostic techniques used due to its wide availability and rapid diagnosis. Detection depends on the size and severity of lesions and the time spent between the first symptoms and examination. The Alberta Stroke Program Early CT Score (ASPECTS) is a subjective method that increases the detection rate. The aim of this work was to implement an image segmentation system to enhance ischemic stroke and to quantify the area of ischemic and hemorrhagic stroke lesions in CT scans. We evaluated 10 patients with NECT examinations diagnosed with ischemic stroke. Analyzes were performed in two axial slices, one at the level of the thalamus and basal ganglion and one adjacent to the top edge of the ganglionic structures with window width between 80 and 100 Hounsfield Units. We used different image processing techniques such as morphological filters, discrete wavelet transform and Fuzzy C-means clustering. Subjective analyzes were performed by a neuroradiologist according to the ASPECTS scale to quantify ischemic areas in the middle cerebral artery region. These subjective analysis results were compared with objective analyzes performed by the computational algorithm. Preliminary results indicate that the morphological filters actually improve the ischemic areas for subjective evaluations. The comparison in area of the ischemic region contoured by the neuroradiologist and the defined area by computational algorithm showed no deviations greater than 12% in any of the 10 examination tests. Although there is a tendency that the areas contoured by the neuroradiologist are smaller than those obtained by the algorithm. These results show the importance of a computer aided diagnosis software to assist neuroradiology decisions, especially in critical situations as the choice of treatment for ischemic stroke.

Keywords: ischemic stroke, image processing, CT scans, Fuzzy C-means

Procedia PDF Downloads 339

Authors: Shankha Sanyal, Archi Banerjee, Sayan Biswas, Sourya Sengupta, Sayan Nag, Ranjan Sengupta, Dipak Ghosh

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The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity.

Keywords: AFA, DFA, EEG, gestalt in music, Hurst exponent

Procedia PDF Downloads 301

Authors: Zhihui Liu, Dongmei Hao, Qian Qiu, Yang An, Lin Yang, Song Zhang, Yimin Yang, Xuwen Li, Dingchang Zheng

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Premature birth, defined as birth before 37 completed weeks of gestation is a leading cause of neonatal morbidity and mortality and has long-term adverse consequences for health. It has recently been reported that the worldwide preterm birth rate is around 10%. The existing measurement techniques for diagnosing preterm delivery include tocodynamometer, ultrasound and fetal fibronectin. However, they are subjective, or suffer from high measurement variability and inaccurate diagnosis and prediction of preterm labor. Electrohysterography (EHG) method based on recording of uterine electrical activity by electrodes attached to maternal abdomen, is a promising method to assess uterine activity and diagnose preterm labor. The purpose of this study is to analyze the difference of EHG signal features between term labor and preterm labor. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Among them, EHG signals from 38 term labor and 38 preterm labor were preprocessed with band-pass Butterworth filters of 0.08–4Hz. Then, EHG signal features were extracted, which comprised classical time domain description including root mean square and zero-crossing number, spectral parameters including peak frequency, mean frequency and median frequency, wavelet packet coefficients, autoregression (AR) model coefficients, and nonlinear measures including maximal Lyapunov exponent, sample entropy and correlation dimension. Their statistical significance for recognition of two groups of recordings was provided. The results showed that mean frequency of preterm labor was significantly smaller than term labor (p < 0.05). 5 coefficients of AR model showed significant difference between term labor and preterm labor. The maximal Lyapunov exponent of early preterm (time of recording < the 26th week of gestation) was significantly smaller than early term. The sample entropy of late preterm (time of recording > the 26th week of gestation) was significantly smaller than late term. There was no significant difference for other features between the term labor and preterm labor groups. Any future work regarding classification should therefore focus on using multiple techniques, with the mean frequency, AR coefficients, maximal Lyapunov exponent and the sample entropy being among the prime candidates. Even if these methods are not yet useful for clinical practice, they do bring the most promising indicators for the preterm labor.

Keywords: electrohysterogram, feature, preterm labor, term labor

Procedia PDF Downloads 528

Authors: Jihede Haj Messaoud, Hamdi Omar, Hela Fakhfakh Ben Jemia, Chokri Yaich

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The Upper Lutetian alternating marl–limestone succession of Reineche Member was deposited over a warm shallow carbonate platform that permits Nummulites proliferation. High-resolution studies of 30 meters thick Nummulites-bearing Reineche Member, cropping out in Central Tunisia (Jebel Siouf), have been undertaken, regarding pronounced cyclical sedimentary sequences, in order to investigate the periodicity of cycles and their related orbital-scale oceanic and climatic changes. The palaeoenvironmental and palaeoclimatic data are preserved in several proxies obtainable through high-resolution sampling and laboratories measurement and analysis as magnetic susceptibility (MS) and carbonates contents in conjunction with a wireline logging tools. The time series analysis of proxies permits to establish cyclicity orders present in the studied intervals which could be linked to the orbital cycles. MS records provide high-resolution proxies for relative sea level change in Late Lutetian strata. The spectral analysis of MS fluctuations confirmed the orbital forcing by the presence of the complete suite of orbital frequencies in the precession of 23 ka, the obliquity of 41 ka, and notably the two modes of eccentricity of 100 and 405 ka. Regarding the two periodic sedimentary cycles detected by wavelet analysis of proxy fluctuations which coincide with the long-term 405 ka eccentricity cycle, the Reineche Member spanned 0,8 Myr. Wireline logging tools as gamma ray and sonic were used as a proxies to decipher cyclicity and trends in sedimentation and contribute to identifying and correlate units. There are used to constraint the highest frequency cyclicity modulated by a long term wavelength cycling apparently controlled by clay content. Interpreted as a result of variations in carbonate productivity, it has been suggested that the marl-limestone couplets, represent the sedimentary response to the orbital forcing. The calculation of cycle durations through Reineche Member, is used as a geochronometer and permit the astronomical calibration of the geologic time scale. Furthermore, MS coupled with carbonate contents, and fossil occurrences provide strong evidence for combined detrital inputs and marine surface carbonate productivity cycles. These two synchronous processes were driven by the precession index and ‘fingerprinted’ in the basic marl–limestone couplets, modulated by orbital eccentricity.

Keywords: magnetic susceptibility, cyclostratigraphy, orbital forcing, spectral analysis, Lutetian

Procedia PDF Downloads 272

Authors: John Christian Lequiron, Gerry Bagtasa, Olivia Cabrera, Leoncio Amadore, Tolentino Moya

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Changes in air surface temperature play an important role in the Philippine’s economy, industry, health, and food production. While increasing global mean temperature in the recent several decades has prompted a number of climate change and variability studies in the Philippines, most studies still focus on rainfall and tropical cyclones. This study aims to investigate the trend and variability of observed air surface temperature and determine its major influencing factor/s in the Philippines. A non-parametric Mann-Kendall trend test was applied to monthly mean temperature of 17 synoptic stations covering 56 years from 1960 to 2015 and a mean change of 0.58 °C or a positive trend of 0.0105 °C/year (p < 0.05) was found. In addition, wavelet decomposition was used to determine the frequency of temperature variability show a 12-month, 30-80-month and more than 120-month cycles. This indicates strong annual variations, interannual variations that coincide with ENSO events, and interdecadal variations that are attributed to PDO and CO2 concentrations. Air surface temperature was also correlated with smoothed sunspot number and galactic cosmic rays, the results show a low to no effect. The influence of ENSO teleconnection on temperature, wind pattern, cloud cover, and outgoing longwave radiation on different ENSO phases had significant effects on regional temperature variability. Particularly, an anomalous anticyclonic (cyclonic) flow east of the Philippines during the peak and decay phase of El Niño (La Niña) events leads to the advection of warm southeasterly (cold northeasterly) air mass over the country. Furthermore, an apparent increasing cloud cover trend is observed over the West Philippine Sea including portions of the Philippines, and this is believed to lessen the effect of the increasing air surface temperature. However, relative humidity was also found to be increasing especially on the central part of the country, which results in a high positive trend of heat index, exacerbating the effects on human discomfort. Finally, an assessment of gridded temperature datasets was done to look at the viability of using three high-resolution datasets in future climate analysis and model calibration and verification. Several error statistics (i.e. Pearson correlation, Bias, MAE, and RMSE) were used for this validation. Results show that gridded temperature datasets generally follows the observed surface temperature change and anomalies. In addition, it is more representative of regional temperature rather than a substitute to station-observed air temperature.

Keywords: air surface temperature, carbon dioxide, ENSO, galactic cosmic rays, smoothed sunspot number

Procedia PDF Downloads 283

Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

Procedia PDF Downloads 131

Authors: Ibtissem Hosni, Lilia Bennaceur Farah, Saber Mohamed Naceur

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In order to assess the water availability in the soil, it is crucial to have information about soil distributed moisture content; this parameter helps to understand the effect of humidity on the exchange between soil, plant cover and atmosphere in addition to fully understanding the surface processes and the hydrological cycle. On the other hand, aerodynamic roughness length is a surface parameter that scales the vertical profile of the horizontal component of the wind speed and characterizes the surface ability to absorb the momentum of the airflow. In numerous applications of the surface hydrology and meteorology, aerodynamic roughness length is an important parameter for estimating momentum, heat and mass exchange between the soil surface and atmosphere. It is important on this side, to consider the atmosphere factors impact in general, and the natural erosion in particular, in the process of soil evolution and its characterization and prediction of its physical parameters. The study of the induced movements by the wind over soil vegetated surface, either spaced plants or plant cover, is motivated by significant research efforts in agronomy and biology. The known major problem in this side concerns crop damage by wind, which presents a booming field of research. Obviously, most models of soil surface require information about the aerodynamic roughness length and its temporal and spatial variability. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. We have introduced multi-layer aspect of the humidity of the soil surface, to take into account a volume component in the problem of backscattering radar signal. As humidity increases, the dielectric constant of the soil-water mixture increases and this change is detected by microwave sensors. Nevertheless, many existing models in the field of radar imagery, cannot be applied directly on areas covered with vegetation due to the vegetation backscattering. Thus, the radar response corresponds to the combined signature of the vegetation layer and the layer of soil surface. Therefore, the key issue of the numerical estimation of soil moisture is to separate the two contributions and calculate both scattering behaviors of the two layers by defining the scattering of the vegetation and the soil blow. This paper presents a synergistic methodology, and it is for estimating roughness and soil moisture from C-band radar measurements. The methodology adequately represents a microwave/optical model which has been used to calculate the scattering behavior of the aerodynamic vegetation-covered area by defining the scattering of the vegetation and the soil below.

Keywords: aerodynamic, bi-dimensional, vegetation, synergistic

Procedia PDF Downloads 241

Authors: A. Abdouni, C. Thieulin, M. Djaghloul, R. Vargiolu, H. Zahouani

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A decline in the main sensory modalities (vision, hearing, taste, and smell) is well reported to occur with advancing age, it is expected a similar change to occur with touch sensation and perception. In this study, we have focused on the touch sensations highlighting ageing and gender influences with in vivo systems. The touch process can be divided into two main phases: The first phase is the first contact between the finger and the object, during this contact, an adhesive force has been created which is the needed force to permit an initial movement of the finger. In the second phase, the finger mechanical properties with their surface topography play an important role in the obtained sensation. In order to understand the age and gender effects on the touch sense, we develop different ideas and systems for each phase. To better characterize the contact, the mechanical properties and the surface topography of human finger, in vivo studies on the pulp of 40 subjects (20 of each gender) of four age groups of 26±3, 35+-3, 45+-2 and 58±6 have been performed. To understand the first touch phase a classical indentation system has been adapted to measure the finger contact properties. The normal force load, the indentation speed, the contact time, the penetration depth and the indenter geometry have been optimized. The penetration depth of a glass indenter is recorded as a function of the applied normal force. Main assessed parameter is the adhesive force F_ad. For the second phase, first, an innovative approach is proposed to characterize the dynamic finger mechanical properties. A contactless indentation test inspired from the techniques used in ophthalmology has been used. The test principle is to blow an air blast to the finger and measure the caused deformation by a linear laser. The advantage of this test is the real observation of the skin free return without any outside influence. Main obtained parameters are the wave propagation speed and the Young's modulus E. Second, negative silicon replicas of subject’s fingerprint have been analyzed by a probe laser defocusing. A laser diode transmits a light beam on the surface to be measured, and the reflected signal is returned to a set of four photodiodes. This technology allows reconstructing three-dimensional images. In order to study the age and gender effects on the roughness properties, a multi-scale characterization of roughness has been realized by applying continuous wavelet transform. After determining the decomposition of the surface, the method consists of quantifying the arithmetic mean of surface topographic at each scale SMA. Significant differences of the main parameters are shown with ageing and gender. The comparison between men and women groups reveals that the adhesive force is higher for women. The results of mechanical properties show a Young’s modulus higher for women and also increasing with age. The roughness analysis shows a significant difference in function of age and gender.

Keywords: ageing, finger, gender, touch

Procedia PDF Downloads 241