Search results for: Automatic keyphrase extraction.

Authors: S. Visetpotjanakit, N. Nakkaew

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Determination of low level ⁹⁰Sr in seawater has been widely developed for the purpose of environmental monitoring and radiological research because ⁹⁰Sr is one of the most hazardous radionuclides released from atmospheric during the testing of nuclear weapons, waste discharge from the generation nuclear energy and nuclear accident occurring at power plants. A liquid extraction technique using bis-2-etylhexyl-phosphoric acid to separate and purify yttrium followed by Cherenkov counting using a liquid scintillation counter to determine ⁹⁰Y in secular equilibrium to ⁹⁰Sr was developed to monitor ⁹⁰Sr in the Asia Pacific Ocean. The analytical performance was validated for the accuracy, precision, and trueness criteria. Sr-90 determination in seawater using various low concentrations in a range of 0.01 – 1 Bq/L of 30 liters spiked seawater samples and 0.5 liters of IAEA-RML-2015-01 proficiency test sample was performed for statistical evaluation. The results had a relative bias in the range from 3.41% to 12.28%, which is below accepted relative bias of ± 25% and passed the criteria confirming that our analytical approach for determination of low levels of ⁹⁰Sr in seawater was acceptable. Moreover, the approach is economical, non-laborious and fast.

Keywords: Proficiency test, radiation monitoring, seawater, strontium determination.

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Authors: Ali Alzaabi, Georges Alquié, Hussain Tassadaq, Ali Seba

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This article presents the developments of efficient algorithms for tablet copies comparison. Image recognition has specialized use in digital systems such as medical imaging, computer vision, defense, communication etc. Comparison between two images that look indistinguishable is a formidable task. Two images taken from different sources might look identical but due to different digitizing properties they are not. Whereas small variation in image information such as cropping, rotation, and slight photometric alteration are unsuitable for based matching techniques. In this paper we introduce different matching algorithms designed to facilitate, for art centers, identifying real painting images from fake ones. Different vision algorithms for local image features are implemented using MATLAB. In this framework a Table Comparison Computer Tool “TCCT" is designed to facilitate our research. The TCCT is a Graphical Unit Interface (GUI) tool used to identify images by its shapes and objects. Parameter of vision system is fully accessible to user through this graphical unit interface. And then for matching, it applies different description technique that can identify exact figures of objects.

Keywords: Harris Extraction and SIFT Matching

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Authors: Yiqin Lin, Wenbo Li

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Dimensionality reduction and feature extraction are of crucial importance for achieving high efficiency in manipulating the high dimensional data. Two-dimensional discriminant locality preserving projection (2D-DLPP) and two-dimensional discriminant supervised LPP (2D-DSLPP) are two effective two-dimensional projection methods for dimensionality reduction and feature extraction of face image matrices. Since 2D-DLPP and 2D-DSLPP preserve the local structure information of the original data and exploit the discriminant information, they usually have good recognition performance. However, 2D-DLPP and 2D-DSLPP only employ single-sided projection, and thus the generated low dimensional data matrices have still many features. In this paper, by combining the discriminant supervised LPP with the bidirectional projection, we propose the bidirectional discriminant supervised LPP (BDSLPP). The left and right projection matrices for BDSLPP can be computed iteratively. Experimental results show that the proposed BDSLPP achieves higher recognition accuracy than 2D-DLPP, 2D-DSLPP, and bidirectional discriminant LPP (BDLPP).

Keywords: Face recognition, dimension reduction, locality preserving projection, discriminant information, bidirectional projection.

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Authors: Youngji Yoo, Seung Hwan Park, Daewoong An, Sung-Shick Kim, Jun-Geol Baek

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The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns.

Keywords: Semiconductor, wafer bin map (WBM), feature extraction, spatial point patterns, contour map.

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Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd Zaizu Ilyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

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This paper presents two techniques, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapped on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non- Gaussian in the feature space and by using combination of several Gaussian functions that has different statistical properties, the best feature representation can be modelled using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculated GMM components. The method is tested using FERET datasets and is able to achieved 92% recognition rates.

Keywords: Local features modelling, face recognition system, Gaussian mixture models.

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Authors: K. Anusarn, P. Chuttrakul, M. Schmidt, T. Kangsadan, A. Pfennig

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Liquid-liquid extraction is a process using two immiscible liquids to extract compounds from one phase without high temperature requirement. Mostly, the technical implementation of this process is carried out in mixer-settlers or extraction columns. In real chemical processes, chemicals may have high viscosity and contain impurities. These impurities may change the settling behavior of the process without measurably changing the physical properties of the phases. In the current study, the settling behavior and the affected parameters in a high-viscosity system were observed. Batchsettling experiments were performed to experimentally quantify the settling behavior and the mixer-settler model of Henschke [1] was used to evaluate the behavior of the toluene + water system. The viscosity of the system was increased by adding polyethylene glycol 4000 to the aqueous phase. NaCl and Na2SO4 were used to study the influence of electrolytes. The results from this study show that increasing the viscosity of water has a higher influence on the settling behavior in comparison to the effects of the electrolytes. It can be seen from the experiments that at high salt concentrations, there was no effect on the settling behavior.

Keywords: Coalescence; electrolytes; liquid-liquid separation; high viscosity; mixer- settler.

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Authors: Prasanth Sabarish Nair, Thomas Binu, Madiajagan Muthaiyan

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This paper discusses a software application to aid in the smooth functioning of a university internship program, including a student, faculty and an administration module. The software can also calculate the most apt combination of students to stations and allocate them respectively.

Keywords: Academic evaluation, administration monitoring, automatic allocation system, internship, student preferences.

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Authors: Mohamed K. Shahin, Ahmed M. Badawi, Mohamed E. M. Rasmy

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Prior research evidenced that unimodal biometric systems have several tradeoffs like noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. In order for the biometric system to be more secure and to provide high performance accuracy, more than one form of biometrics are required. Hence, the need arise for multimodal biometrics using combinations of different biometric modalities. This paper introduces a multimodal biometric system (MMBS) based on fusion of whole dorsal hand geometry and fingerprints that acquires right and left (Rt/Lt) near-infra-red (NIR) dorsal hand geometry (HG) shape and (Rt/Lt) index and ring fingerprints (FP). Database of 100 volunteers were acquired using the designed prototype. The acquired images were found to have good quality for all features and patterns extraction to all modalities. HG features based on the hand shape anatomical landmarks were extracted. Robust and fast algorithms for FP minutia points feature extraction and matching were used. Feature vectors that belong to similar biometric traits were fused using feature fusion methodologies. Scores obtained from different biometric trait matchers were fused using the Min-Max transformation-based score fusion technique. Final normalized scores were merged using the sum of scores method to obtain a single decision about the personal identity based on multiple independent sources. High individuality of the fused traits and user acceptability of the designed system along with its experimental high performance biometric measures showed that this MMBS can be considered for med-high security levels biometric identification purposes.

Keywords: Unimodal, Multi-Modal, Biometric System, NIR Imaging, Dorsal Hand Geometry, Fingerprint, Whole Hands, Feature Extraction, Feature Fusion, Score Fusion

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Authors: Mohammed Rziza, Mohamed El Aroussi, Mohammed El Hassouni, Sanaa Ghouzali, Driss Aboutajdine

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In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA, and independent component Analysis (ICA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet-LDA algorithm. Experimental results on ORL, YALE and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Keywords: Curvelet, Linear Discriminant Analysis (LDA) , Contourlet, Discreet Wavelet Transform, DWT, Block-based analysis, face recognition (FR).

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Authors: Ishtiaq Ahmed, Muhammad Anjum Zia, Hafiz Muhammad Nasir Iqbal

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The purpose of the present work was to study the production and process parameters optimization for the synthesis of cellulase from Trichoderma viride in solid state fermentation (SSF) using an agricultural wheat straw as substrates; as fungal conversion of lignocellulosic biomass for cellulase production is one among the major increasing demand for various biotechnological applications. An optimization of process parameters is a necessary step to get higher yield of product. Several kinetic parameters like pretreatment, extraction solvent, substrate concentration, initial moisture content, pH, incubation temperature and inoculum size were optimized for enhanced production of third most demanded industrially important cellulase. The maximum cellulase enzyme activity 398.10±2.43 μM/mL/min was achieved when proximally analyzed lignocellulosic substrate wheat straw inocubated at 2% HCl as pretreatment tool along with distilled water as extraction solvent, 3% substrate concentration 40% moisture content with optimum pH 5.5 at 45°C incubation temperature and 10% inoculum size.

Keywords: Cellulase, Lignocellulosic residue, Processoptimization, Proximal analysis, SSF, Trichoderma viride.

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Authors: M. Almas Anjum, M. Younus Javed, A. Basit

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In this paper a new approach to face recognition is presented that achieves double dimension reduction, making the system computationally efficient with better recognition results and out perform common DCT technique of face recognition. In pattern recognition techniques, discriminative information of image increases with increase in resolution to a certain extent, consequently face recognition results change with change in face image resolution and provide optimal results when arriving at a certain resolution level. In the proposed model of face recognition, initially image decimation algorithm is applied on face image for dimension reduction to a certain resolution level which provides best recognition results. Due to increased computational speed and feature extraction potential of Discrete Cosine Transform (DCT), it is applied on face image. A subset of coefficients of DCT from low to mid frequencies that represent the face adequately and provides best recognition results is retained. A tradeoff between decimation factor, number of DCT coefficients retained and recognition rate with minimum computation is obtained. Preprocessing of the image is carried out to increase its robustness against variations in poses and illumination level. This new model has been tested on different databases which include ORL , Yale and EME color database.

Keywords: Biometrics, DCT, Face Recognition, Illumination, Computation, Feature extraction.

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Authors: Cristina S. Decano, Vitaliana U. Malamug, Melissa E. Agulto, Helen F. Gavino

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This study aimed to introduce new natural fiber to be used in the production of geotextile net for mitigation of soil erosion. Fiber extraction from the stalks was the main challenge faced during the processing of stalks to ropes. Thus, an investigation on the extraction procedures of corn (Zea mays L.) stalk under biological and chemical retting was undertaken. Results indicated significant differences among percent fiber yield as affected by the retting methods used with values of 15.07%, 12.97%, 11.60%, and 9.01%, for dew, water, chemical (1 day after harvest and15 days after harvest), respectively, with the corresponding average extracting duration of 70, 82, 89, and 94 minutes. Physical characterization of the developed corn stalk geotextile net resulted to average mass per unit area of 806.25 g/m² and 241% water absorbing capacity. The effect of corn stalk geotextile net in mitigating soil erosion was evaluated in a laboratory experiment for 30^o and 60^o inclinations with three treatments: bare soil (A₁), corn stalk geotextile net (A₂) and combined cornstalk geotextile net and vegetation cover (A₃). Results revealed that treatment A₂ and A₃ significantly decreased sediment yield and an increase in terms of soil loss reduction efficiency. The cost of corn stalk geotextile net is Php 62.41 per square meter.

Keywords: Corn stalk, natural geotextile, retting, soil erosion.

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Authors: Anjin Park, Sungju Yun, Jungwhan Kim, Seungk Min, Keechul Jung

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Finger spelling is an art of communicating by signs made with fingers, and has been introduced into sign language to serve as a bridge between the sign language and the verbal language. Previous approaches to finger spelling recognition are classified into two categories: glove-based and vision-based approaches. The glove-based approach is simpler and more accurate recognizing work of hand posture than vision-based, yet the interfaces require the user to wear a cumbersome and carry a load of cables that connected the device to a computer. In contrast, the vision-based approaches provide an attractive alternative to the cumbersome interface, and promise more natural and unobtrusive human-computer interaction. The vision-based approaches generally consist of two steps: hand extraction and recognition, and two steps are processed independently. This paper proposes real-time vision-based Korean finger spelling recognition system by integrating hand extraction into recognition. First, we tentatively detect a hand region using CAMShift algorithm. Then fill factor and aspect ratio estimated by width and height estimated by CAMShift are used to choose candidate from database, which can reduce the number of matching in recognition step. To recognize the finger spelling, we use DTW(dynamic time warping) based on modified chain codes, to be robust to scale and orientation variations. In this procedure, since accurate hand regions, without holes and noises, should be extracted to improve the precision, we use graph cuts algorithm that globally minimize the energy function elegantly expressed by Markov random fields (MRFs). In the experiments, the computational times are less than 130ms, and the times are not related to the number of templates of finger spellings in database, as candidate templates are selected in extraction step.

Keywords: CAMShift, DTW, Graph Cuts, MRF.

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Authors: Nurhani Kasuan, Zakariah Yusuf, Mohd Nasir Taib, Mohd Hezri Fazalul Rahiman, Nazurah Tajuddin, Mohd Azri Abdul Aziz

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This paper presents a hybrid fuzzy-PD plus PID (HFPP) controller and its application to steam distillation process for essential oil extraction system. Steam temperature is one of the most significant parameters that can influence the composition of essential oil yield. Due to parameter variations and changes in operation conditions during distillation, a robust steam temperature controller becomes nontrivial to avoid the degradation of essential oil quality. Initially, the PRBS input is triggered to the system and output of steam temperature is modeled using ARX model structure. The parameter estimation and tuning method is adopted by simulation using HFPP controller scheme. The effectiveness and robustness of proposed controller technique is validated by real time implementation to the system. The performance of HFPP using 25 and 49 fuzzy rules is compared. The experimental result demonstrates the proposed HFPP using 49 fuzzy rules achieves a better, consistent and robust controller compared to PID when considering the test on tracking the set point and the effects due to disturbance.

Keywords: Fuzzy Logic controller, steam temperature, steam distillation, real time control.

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Authors: B. Kabane, G. G. Redhi

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An ammonium based ionic liquid (methyltrioctylammonium chloride) [N_{8 8 8 1}] [Cl] was investigated as an extraction potential solvent for volatile organic solvents (in this regard, solutes), which includes alkenes, alkanes, ketones, alkynes, aromatic hydrocarbons, tetrahydrofuran (THF), alcohols, thiophene, water and acetonitrile based on the experimental activity coefficients at infinite THF measurements were conducted by the use of gas-liquid chromatography at four different temperatures (313.15 to 343.15) K. Experimental data of activity coefficients obtained across the examined temperatures were used in order to calculate the physicochemical properties at infinite dilution such as partial molar excess enthalpy, Gibbs free energy and entropy term. Capacity and selectivity data for selected petrochemical extraction problems (heptane/thiophene, heptane/benzene, cyclohaxane/cyclohexene, hexane/toluene, hexane/hexene) were computed from activity coefficients data and compared to the literature values with other ionic liquids. Evaluation of activity coefficients at infinite dilution expands the knowledge and provides a good understanding related to the interactions between the ionic liquid and the investigated compounds.

Keywords: Separation, activity coefficients, ionic liquid, methyltrioctylammonium chloride, capacity.

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Authors: Sandipan Chakroborty, Anindya Roy, Goutam Saha

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A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank, it captures vocal tract characteristics more effectively in the lower frequency regions. This paper proposes a new set of features using a complementary filter bank structure which improves distinguishability of speaker specific cues present in the higher frequency zone. Unlike high level features that are difficult to extract, the proposed feature set involves little computational burden during the extraction process. When combined with MFCC via a parallel implementation of speaker models, the proposed feature set outperforms baseline MFCC significantly. This proposition is validated by experiments conducted on two different kinds of public databases namely YOHO (microphone speech) and POLYCOST (telephone speech) with Gaussian Mixture Models (GMM) as a Classifier for various model orders.

Keywords: Complementary Information, Filter Bank, GMM, IMFCC, MFCC, Speaker Identification, Speaker Recognition.

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Authors: Stella O. Olubodun, George E. Eriyamremu

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The study assessed the effect of crude oil applied at rates, 0, 2, 5, and 10% on the fractional chemical forms and availability of some metals in soils from Usen, Edo State, with no known crude oil contamination and soil from a crude oil spill site in Ubeji, Delta State, Nigeria. Three methods were used to determine the bioavailability of metals in the soils: maize (Zea mays) plant, EDTA and BCR sequential extraction. The sequential extract acid soluble fraction of the BCR extraction (most labile fraction of the soils, normally associated with bioavailability) were compared with total metal concentration in maize seedlings as a means to compare the chemical and biological measures of bioavailability. Total Fe was higher in comparison to other metals for the crude oil contaminated soils. The metal concentrations were below the limits of 4.7% Fe, 190mg/kg Cu and 720mg/kg Zn intervention values and 36mg/kg Cu and 140mg/kg Zn target values for soils provided by the Department of Petroleum Resources (DPR) guidelines. The concentration of the metals in maize seedlings increased with increasing rates of crude oil contamination. Comparison of the metal concentrations in maize seedlings with EDTA extractable concentrations showed that EDTA extracted more metals than maize plant.

Keywords: Availability, crude oil contamination, EDTA, maize, metals.

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Authors: Kwangjin Hong, Chulhan Lee, Keechul Jung, Kyoungsu Oh

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For the communication between human and computer in an interactive computing environment, the gesture recognition is studied vigorously. Therefore, a lot of studies have proposed efficient methods about the recognition algorithm using 2D camera captured images. However, there is a limitation to these methods, such as the extracted features cannot fully represent the object in real world. Although many studies used 3D features instead of 2D features for more accurate gesture recognition, the problem, such as the processing time to generate 3D objects, is still unsolved in related researches. Therefore we propose a method to extract the 3D features combined with the 3D object reconstruction. This method uses the modified GPU-based visual hull generation algorithm which disables unnecessary processes, such as the texture calculation to generate three kinds of 3D projection maps as the 3D feature: a nearest boundary, a farthest boundary, and a thickness of the object projected on the base-plane. In the section of experimental results, we present results of proposed method on eight human postures: T shape, both hands up, right hand up, left hand up, hands front, stand, sit and bend, and compare the computational time of the proposed method with that of the previous methods.

Keywords: Fast 3D Feature Extraction, Gesture Recognition, Computer Vision.

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Authors: A. M. H. Elyasir, K. S. M. Anbananthen

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The internet is growing larger and becoming the most popular platform for the people to share their opinion in different interests. We choose the education domain specifically comparing some Malaysian universities against each other. This comparison produces benchmark based on different criteria shared by the online users in various online resources including Twitter, Facebook and web pages. The comparison is accomplished using opinion mining framework to extract, process the unstructured text and classify the result to positive, negative or neutral (polarity). Hence, we divide our framework to three main stages; opinion collection (extraction), unstructured text processing and polarity classification. The extraction stage includes web crawling, HTML parsing, Sentence segmentation for punctuation classification, Part of Speech (POS) tagging, the second stage processes the unstructured text with stemming and stop words removal and finally prepare the raw text for classification using Named Entity Recognition (NER). Last phase is to classify the polarity and present overall result for the comparison among the Malaysian universities. The final result is useful for those who are interested to study in Malaysia, in which our final output declares clear winners based on the public opinions all over the web.

Keywords: Entity Recognition, Education Domain, Opinion Mining, Unstructured Text.

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Authors: Fatemeh Cheraghali, Saeedeh Shojaee-Aliabadi, Seyede Marzieh Hosseini, Leila Mirmoghtadaie

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In recent years, the field of natural antimicrobial and antioxidant compounds is one of the main research topics in the food industry. Application of agricultural residues is mainly cheap, and available resources are receiving increased attention. Walnut green husk is one of the agricultural residues that is considered as natural compounds with biological properties because of phenolic compounds. In this study, maltodextrin 10% was used for microencapsulation of walnut green husk extract. At first, the extract was examined to consider extraction yield, total phenolic compounds, and antioxidant activation. The results showed the extraction yield of 81.43%, total phenolic compounds of 3997 [mg GAE/100 g], antioxidant activity [DPPH] of 84.85% for walnut green husk extract. Antioxidant activity is about 75%-81% and by DPPH. At the next stage, microencapsulation was done by spry-drying method. The microencapsulation efficiency was 72%-79%. The results of SEM tests confirmed this microencapsulation process. In addition, microencapsulated and free extract was more effective on gram-positive bacteria’s rather than the gram-negative ones. According to the study, walnut green husk can be used as a cheap antioxidant and antimicrobial compounds due to sufficient value of phenolic compounds.

Keywords: Biopolymer, microencapsulation, Spray-drying, Walnut green husk.

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Authors: Alexandre Boum, Salomon Madinatou

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This paper aims at bringing a scientific contribution to the cardiac arrhythmia biomedical diagnosis systems; more precisely to the study of the amelioration of cardiac arrhythmia classification performance using artificial neural network, adaptive neuro-fuzzy and fuzzy inference systems classifiers. The purpose of this amelioration is to enable cardiologists to make reliable diagnosis through automatic cardiac arrhythmia analyzes and classifications based on high confidence classifiers. In this study, six classes of the most commonly encountered arrhythmias are considered: the Right Bundle Branch Block, the Left Bundle Branch Block, the Ventricular Extrasystole, the Auricular Extrasystole, the Atrial Fibrillation and the Normal Cardiac rate beat. From the electrocardiogram (ECG) extracted parameters, we constructed a matrix (360x360) serving as an input data sample for the classifiers based on neural networks and a matrix (1x6) for the classifier based on fuzzy logic. By varying three parameters (the quality of the neural network learning, the data size and the quality of the input parameters) the automatic classification permitted us to obtain the following performances: in terms of correct classification rate, 83.6% was obtained using the fuzzy logic based classifier, 99.7% using the neural network based classifier and 99.8% for the adaptive neuro-fuzzy based classifier. These results are based on signals containing at least 360 cardiac cycles. Based on the comparative analysis of the aforementioned three arrhythmia classifiers, the classifiers based on neural networks exhibit a better performance.

Keywords: Adaptive neuro-fuzzy, artificial neural network, cardiac arrythmias, fuzzy inference systems.

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Authors: Khaldon Lweesy, Luay Fraiwan, Christoph Maier, Hartmut Dickhaus

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This paper describes a new method for extracting the fetal heart rate (fHR) and the fetal heart rate variability (fHRV) signal non-invasively using abdominal maternal electrocardiogram (mECG) recordings. The extraction is based on the fundamental frequency (Fourier-s) theorem. The fundamental frequency of the mother-s electrocardiogram signal (fo-m) is calculated directly from the abdominal signal. The heart rate of the fetus is usually higher than that of the mother; as a result, the fundamental frequency of the fetal-s electrocardiogram signal (fo-f) is higher than that of the mother-s (fo-f > fo-m). Notch filters to suppress mother-s higher harmonics were designed; then a bandpass filter to target fo-f and reject fo-m is implemented. Although the bandpass filter will pass some other frequencies (harmonics), we have shown in this study that those harmonics are actually carried on fo-f, and thus have no impact on the evaluation of the beat-to-beat changes (RR intervals). The oscillations of the time-domain extracted signal represent the RR intervals. We have also shown in this study that zero-to-zero evaluation of the periods is more accurate than the peak-to-peak evaluation. This method is evaluated both on simulated signals and on different abdominal recordings obtained at different gestational ages.

Keywords: Aabdominal ECG, fetal heart rate variability, frequency harmonics, fundamental frequency.

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Authors: К. R. Aida–Zade, C. Ardil, S. S. Rustamov

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Statement of the automatic speech recognition problem, the assignment of speech recognition and the application fields are shown in the paper. At the same time as Azerbaijan speech, the establishment principles of speech recognition system and the problems arising in the system are investigated. The computing algorithms of speech features, being the main part of speech recognition system, are analyzed. From this point of view, the determination algorithms of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients expressing the basic speech features are developed. Combined use of cepstrals of MFCC and LPC in speech recognition system is suggested to improve the reliability of speech recognition system. To this end, the recognition system is divided into MFCC and LPC-based recognition subsystems. The training and recognition processes are realized in both subsystems separately, and recognition system gets the decision being the same results of each subsystems. This results in decrease of error rate during recognition. The training and recognition processes are realized by artificial neural networks in the automatic speech recognition system. The neural networks are trained by the conjugate gradient method. In the paper the problems observed by the number of speech features at training the neural networks of MFCC and LPC-based speech recognition subsystems are investigated. The variety of results of neural networks trained from different initial points in training process is analyzed. Methodology of combined use of neural networks trained from different initial points in speech recognition system is suggested to improve the reliability of recognition system and increase the recognition quality, and obtained practical results are shown.

Keywords: Speech recognition, cepstral analysis, Voice activation detection algorithm, Mel Frequency Cepstral Coefficients, features of speech, Cepstral Mean Subtraction, neural networks, Linear Predictive Coding.

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Authors: Zharama M. Llarena

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Green chemistry for plant extraction of active principles is the main interest of many researchers concerned with climate change. While classical organic solvents are detrimental to our environment, greener alternatives to ionic liquids are very promising for sustainable organic chemistry. This study focused on the determination of functional groups observed in the main constituents from the ionic liquid extracts of Coleus aromaticus Benth leaves using FT-IR Spectroscopy. Moreover, this research aimed to determine the best ionic liquid that can separate functionalized plant constituents from the leaves Coleus aromaticus Benth using Fourier Transform Infrared Spectroscopy. Coleus aromaticus Benth leaf extract in different ionic liquids, elucidated pharmacologically important functional groups present in major constituents of the plant, namely, rosmarinic acid, caffeic acid and chlorogenic acid. In connection to distinctive appearance of functional groups in the spectrum and highest % transmittance, potassium chloride-glycerol is the best ionic liquid for green extraction.

Keywords: Coleus aromaticus, ionic liquid, rosmarinic acid, caffeic acid, chlorogenic acid.

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Authors: Stefan Menzel

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As a result of the daily workflow in the design development departments of companies, databases containing huge numbers of 3D geometric models are generated. According to the given problem engineers create CAD drawings based on their design ideas and evaluate the performance of the resulting design, e.g. by computational simulations. Usually, new geometries are built either by utilizing and modifying sets of existing components or by adding single newly designed parts to a more complex design. The present paper addresses the two facets of acquiring components from large design databases automatically and providing a reasonable overview of the parts to the engineer. A unified framework based on the topographic non-negative matrix factorization (TNMF) is proposed which solves both aspects simultaneously. First, on a given database meaningful components are extracted into a parts-based representation in an unsupervised manner. Second, the extracted components are organized and visualized on square-lattice 2D maps. It is shown on the example of turbine-like geometries that these maps efficiently provide a wellstructured overview on the database content and, at the same time, define a measure for spatial similarity allowing an easy access and reuse of components in the process of design development.

Keywords: Design decomposition, topographic non-negative matrix factorization, parts-based representation, self-organization, unsupervised feature extraction.

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Authors: Fariba Tadayon, Elmira Hassanlou, Hasan Bagheri, Mostafa Jafarian

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Heavy metals are released into water, plants, soil, and food by natural and human activities. Lead has toxic roles in the human body and may cause serious problems even in low concentrations, since it may have several adverse effects on human. Therefore, determination of lead in different samples is an important procedure in the studies of environmental pollution. In this work, an ultrasonic assisted-ionic liquid based-liquid-liquid microextraction (UA-IL-DLLME) procedure for the determination of lead in zucchini, pomegranate, and lettuce has been established and developed by using flame atomic absorption spectrometer (FAAS). For UA-IL-DLLME procedure, 10 mL of the sample solution containing Pb2+ was adjusted to pH=5 in a glass test tube with a conical bottom; then, 120 μL of 1-Hexyl-3-methylimidazolium hexafluoro phosphate (CMIM)(PF6) was rapidly injected into the sample solution with a microsyringe. After that, the resulting cloudy mixture was treated by ultrasonic for 5 min, then the separation of two phases was obtained by centrifugation for 5 min at 3000 rpm and IL-phase diluted with 1 cc ethanol, and the analytes were determined by FAAS. The effect of different experimental parameters in the extraction step including: ionic liquid volume, sonication time and pH was studied and optimized simultaneously by using Response Surface Methodology (RSM) employing a central composite design (CCD). The optimal conditions were determined to be an ionic liquid volume of 120 μL, sonication time of 5 min, and pH=5. The linear ranges of the calibration curve for the determination by FAAS of lead were 0.1-4 ppm with R2=0.992. Under optimized conditions, the limit of detection (LOD) for lead was 0.062 μg.mL-1, the enrichment factor (EF) was 93, and the relative standard deviation (RSD) for lead was calculated as 2.29%. The levels of lead for pomegranate, zucchini, and lettuce were calculated as 2.88 μg.g-1, 1.54 μg.g-1, 2.18 μg.g-1, respectively. Therefore, this method has been successfully applied for the analysis of the content of lead in different food samples by FAAS.

Keywords: Dispersive liquid-liquid microextraction, Central composite design, Food samples, Flame atomic absorption spectrometry.

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Authors: Ramaswamy Palaniappan, Nai-Jen Huan

Abstract:

Classification of electroencephalogram (EEG) signals extracted during mental tasks is a technique that is actively pursued for Brain Computer Interfaces (BCI) designs. In this paper, we compared the classification performances of univariateautoregressive (AR) and multivariate autoregressive (MAR) models for representing EEG signals that were extracted during different mental tasks. Multilayer Perceptron (MLP) neural network (NN) trained by the backpropagation (BP) algorithm was used to classify these features into the different categories representing the mental tasks. Classification performances were also compared across different mental task combinations and 2 sets of hidden units (HU): 2 to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different mental tasks from 4 subjects were used in the experimental study and combinations of 2 different mental tasks were studied for each subject. Three different feature extraction methods with 6th order were used to extract features from these EEG signals: AR coefficients computed with Burg-s algorithm (ARBG), AR coefficients computed with stepwise least square algorithm (ARLS) and MAR coefficients computed with stepwise least square algorithm. The best results were obtained with 20 to 100 HU using ARBG. It is concluded that i) it is important to choose the suitable mental tasks for different individuals for a successful BCI design, ii) higher HU are more suitable and iii) ARBG is the most suitable feature extraction method.

Keywords: Autoregressive, Brain-Computer Interface, Electroencephalogram, Neural Network.

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Authors: Wei Liu, Xionghui Zhou, Qiang Niu, Yunhao Ni

Abstract:

3D part library is an ideal approach to reuse the existing design and thus facilitates the modeling process, which will enhance the efficiency. In this paper, we implemented the thought on the SolidWorks platform. The system supports the functions of type and parameter selection, 3D template driving and part assembly. Finally, BOM is exported in Excel format. Experiment shows that our method can satisfy the requirement of die and mold designers.

Keywords: Intelligent, SolidWorks, automatic assembly, part library.

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Authors: Rehab F. Abdel-Kader, Rabab M. Ramadan, Rawya Y. Rizk

Abstract:

The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the logpolar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the proposed system outperforms other face recognition methods. The overall recognition rate for the rotated test images being 97%, demonstrating that the extracted feature vector is an effective rotation invariant feature set with minimal set of selected features.

Keywords: Discrete Cosine Transform, Face Recognition, Feature Extraction, Log Polar Transform, Particle SwarmOptimization.

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Authors: Bharathi P. T, P. Subashini

Abstract:

Ice cover County has a significant impact on rivers as it affects with the ice melting capacity which results in flooding, restrict navigation, modify the ecosystem and microclimate. River ices are made up of different ice types with varying ice thickness, so surveillance of river ice plays an important role. River ice types are captured using infrared imaging camera which captures the images even during the night times. In this paper the river ice infrared texture images are analysed using first-order statistical methods and secondorder statistical methods. The second order statistical methods considered are spatial gray level dependence method, gray level run length method and gray level difference method. The performance of the feature extraction methods are evaluated by using Probabilistic Neural Network classifier and it is found that the first-order statistical method and second-order statistical method yields low accuracy. So the features extracted from the first-order statistical method and second-order statistical method are combined and it is observed that the result of these combined features (First order statistical method + gray level run length method) provides higher accuracy when compared with the features from the first-order statistical method and second-order statistical method alone.

Keywords: Gray Level Difference Method, Gray Level Run Length Method, Kurtosis, Probabilistic Neural Network, Skewness, Spatial Gray Level Dependence Method.

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