Search results for: recognition and compression
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2464

Search results for: recognition and compression

2434 Prediction Modeling of Compression Properties of a Knitted Sportswear Fabric Using Response Surface Method

Authors: Jawairia Umar, Tanveer Hussain, Zulfiqar Ali, Muhammad Maqsood

Abstract:

Different knitted structures and knitted parameters play a vital role in the stretch and recovery management of compression sportswear in addition to the materials use to generate this stretch and recovery behavior of the fabric. The present work was planned to predict the different performance indicators of a compression sportswear fabric with some ground parameters i.e. base yarn stitch length (polyester as base yarn and spandex as plating yarn involve to make a compression fabric) and linear density of the spandex which is a key material of any sportswear fabric. The prediction models were generated by response surface method for performance indicators such as stretch & recovery percentage, compression generated by the garment on body, total elongation on application of high power force and load generated on certain percentage extension in fabric. Certain physical properties of the fabric were also modeled using these two parameters.

Keywords: Compression, sportswear, stretch and recovery, statistical model, kikuhime

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2433 Elastic Constants of Fir Wood Using Ultrasound and Compression Tests

Authors: Ergun Guntekin

Abstract:

Elastic constants of Fir wood (Abies cilicica) have been investigated by means of ultrasound and compression tests. Three modulus of elasticity in principal directions (EL, ER, ET), six Poisson’s ratios (ʋLR, ʋLT, ʋRT, ʋTR, ʋRL, ʋTL) and three shear modules (GLR, GRT, GLT) were determined. 20 x 20 x 60 mm samples were conditioned at 65 % relative humidity and 20ºC before testing. Three longitudinal and six shear wave velocities propagating along the principal axes of anisotropy, and additionally, three quasi-shear wave velocities at 45° angle with respect to the principal axes of anisotropy were measured. 2.27 MHz longitudinal and 1 MHz shear sensors were used for obtaining sound velocities. Stress-strain curves of the samples in compression tests were obtained using bi-axial extensometer in order to calculate elastic constants. Test results indicated that most of the elastic constants determined in the study are within the acceptable range. Although elastic constants determined from ultrasound are usually higher than those determined from compression tests, the values of EL and GLR determined from compression tests were higher in the study. The results of this study can be used in the numerical modeling of elements or systems under load using Fir wood.

Keywords: compression tests, elastic constants, fir wood, ultrasound

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2432 Elastic Constants of Heat Treated Wood

Authors: Ergun Guntekin

Abstract:

Effects of heat treatment on elastic constants of Black pine (Pinus nigra) wood were investigated. Specimens were exposed to heat under atmospheric pressure at two different temperatures (180 and 210 °C) and three different time levels (2, 5, 8 hours). Three Young’s modulus in three anatomical directions, six Poisson’s ratios and three Shear modulus values associated with the main directions were evaluated by compression tests. Compression strength of the samples in three principal directions was also determined. All of the properties of the specimens tested were altered by heat treatment. The degree of alteration depends on the temperature as well as duration applied. Results indicate that EL and compression strength in L direction were not significantly influenced, compression strength in R direction significantly decreased, ER, ET and compression strength in T direction were increased for shorter periods, then dropped for 8-hour application of 180 ºC. ER was not significantly affected, compression strength in R direction and EL was significantly decreased, ET and compression strength in T direction were increased for shorter periods, then decreased for 8-hour application of 210 ºC. The shear modulus of the samples was decreased with application of treatment combinations. Most of the Poisson’s ratios were not affected by heat treatment.

Keywords: black pine, elastic constants, heat treatment, wood

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2431 Comparing Emotion Recognition from Voice and Facial Data Using Time Invariant Features

Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

Abstract:

The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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2430 Ab-Initio Study of Native Defects in SnO Under Strain

Authors: A. Albar, D. B. Granato, U. Schwingenschlogl

Abstract:

Tin monoxide (SnO) has promising properties to be applied as a p-type semiconductor in transparent electronics. To this end, it is necessary to understand the behavior of defects in order to control them. We use density functional theory to study native defects of SnO under tensile and compressive strain. We show that Sn vacancies are more stable under tension and less stable under compression, irrespectively of the charge state. In contrast, O vacancies behave differently for different charge. It turns out that the most stable defect under compression is the +1 charged O vacancy in a Sn-rich environment and the charge neutral O interstitial in an O-rich environment. Therefore, compression can be used to transform SnO from an n-type into un-doped semiconductor.

Keywords: native defects, ab-initio, point defect, tension, compression, semiconductor

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2429 Plastic Deformation of Mg-Gd Solid Solutions between 4K and 298K

Authors: Anna Kula, Raja K. Mishra, Marek Niewczas

Abstract:

Deformation behavior of Mg-Gd solid solutions have been studied by a combination of measurements of mechanical response, texture and dislocation substructure. Increase in Gd content strongly influences the work-hardening behavior and flow characteristics in tension and compression. Adiabatic instabilities have been observed in all alloys at 4K under both tension and compression. The frequency and the amplitude of adiabatic stress oscillations increase with Gd content. Profuse mechanical twinning has been observed under compression, resulting in a texture dominated by basal component parallel to the compression axis. Under tension, twining is less active and the texture evolution is affected mostly by slip. Increasing Gd concentration leads to the reduction of the tension and compression asymmetry due to weakening of the texture and stabilizing more homogenous twinning and slip, involving basal and non-basal slip systems.

Keywords: Mg-Gd alloys, mechanical properties, work hardening, twinning

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2428 Geometry of the Bandaging Procedure and Its Application while Wrapping Bandages for Treatment of Leg Ulcers

Authors: Monica Puri Sikka, Subrato Ghosh Arunangshu Mukhopadhyay

Abstract:

Appropriate compression bandaging is important for compression therapeutic medical diseases. The high compression approach employed for treating venous leg ulcers should be used correctly so that sufficient (but not excessive) pressure is applied. Bandages used to treat venous disease by compression should achieve and sustain effective levels and gradients of pressure and minimise the risk of pressure trauma. To maintain graduated compression on the limb the bandage needs to be applied at same tension for each layer from ankle to the knee. In this paper the geometry for various bandaging procedures is used to wrap each layer of bandage by marking the relaxed length of the bandage. The relaxed length is calculated depending on the stretch, average circumference of the limb on which it is to be applied and the bandaging technique to be used. This paper aims at developing a scientific approach while applying the bandage to reduce the inter operator variability in applying same tension on each successive layer of bandage.

Keywords: bandaging, compression, inter operator variability, graduated, relaxed length, stretch

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2427 Effect of Curing Temperature on Unconfined Compression Strength of Bagasse Ash-Calcium Carbide Residue Treated Organic Clay

Authors: John Trihatmoko, Luky Handoko

Abstract:

A series of experimental program was undertaken to study the effect of curing temperature on the unconfined compression strength of bagasse ash (BA) - calcium carbide residue (CCR) stabilized organic clay (OC). A preliminary experiment was performed to get the physical properties of OC, and to get the optimum water content (OMC), the standard compaction test was done. The stabilizing agents used in this research was (40% BA + 60% CCR) . Then to obtain the best binder proportion, unconfined compression test was undertaken for OC + 3, 6, 9, 12 and 15% of binder with 7, 14, 21, 28 and 56 days curing period. The best quantity of the binder was found on 9%. Finally, to study the effect of curing temperature, the unconfined compression test was performed on OC + 9% binder with 7, 14, 21, 28 and 56 days curing time with 20O, 25O, 30O, 40O, and 50O C curing temperature. The result indicates that unconfined compression strength (UCS) of treated OC improve according to the increase of curing temperature at the same curing time. The improvement of UCS is probably due to the degree of cementation and pozzolanic reactions.

Keywords: curing temperature, organic clay, bagasse ash, calcium carbide residue, unconfined compression strength

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2426 Possibilities, Challenges and the State of the Art of Automatic Speech Recognition in Air Traffic Control

Authors: Van Nhan Nguyen, Harald Holone

Abstract:

Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this field. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air traffic control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as specific approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed.

Keywords: automatic speech recognition, asr, air traffic control, atc

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2425 A Contribution to Human Activities Recognition Using Expert System Techniques

Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

Abstract:

This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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2424 Switching to the Latin Alphabet in Kazakhstan: A Brief Overview of Character Recognition Methods

Authors: Ainagul Yermekova, Liudmila Goncharenko, Ali Baghirzade, Sergey Sybachin

Abstract:

In this article, we address the problem of Kazakhstan's transition to the Latin alphabet. The transition process started in 2017 and is scheduled to be completed in 2025. In connection with these events, the problem of recognizing the characters of the new alphabet is raised. Well-known character recognition programs such as ABBYY FineReader, FormReader, MyScript Stylus did not recognize specific Kazakh letters that were used in Cyrillic. The author tries to give an assessment of the well-known method of character recognition that could be in demand as part of the country's transition to the Latin alphabet. Three methods of character recognition: template, structured, and feature-based, are considered through the algorithms of operation. At the end of the article, a general conclusion is made about the possibility of applying a certain method to a particular recognition process: for example, in the process of population census, recognition of typographic text in Latin, or recognition of photos of car numbers, store signs, etc.

Keywords: text detection, template method, recognition algorithm, structured method, feature method

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2423 Effect of 3-Dimensional Knitted Spacer Fabrics Characteristics on Its Thermal and Compression Properties

Authors: Veerakumar Arumugam, Rajesh Mishra, Jiri Militky, Jana Salacova

Abstract:

The thermo-physiological comfort and compression properties of knitted spacer fabrics have been evaluated by varying the different spacer fabric parameters. Air permeability and water vapor transmission of the fabrics were measured using the Textest FX-3300 air permeability tester and PERMETEST. Then thermal behavior of fabrics was obtained by Thermal conductivity analyzer and overall moisture management capacity was evaluated by moisture management tester. Spacer Fabrics compression properties were also tested using Kawabata Evaluation System (KES-FB3). In the KES testing, the compression resilience, work of compression, linearity of compression and other parameters were calculated from the pressure-thickness curves. Analysis of Variance (ANOVA) was performed using new statistical software named QC expert trilobite and Darwin in order to compare the influence of different fabric parameters on thermo-physiological and compression behavior of samples. This study established that the raw materials, type of spacer yarn, density, thickness and tightness of surface layer have significant influence on both thermal conductivity and work of compression in spacer fabrics. The parameter which mainly influence on the water vapor permeability of these fabrics is the properties of raw material i.e. the wetting and wicking properties of fibers. The Pearson correlation between moisture capacity of the fabrics and water vapour permeability was found using statistical software named QC expert trilobite and Darwin. These findings are important requirements for the further designing of clothing for extreme environmental conditions.

Keywords: 3D spacer fabrics, thermal conductivity, moisture management, work of compression (WC), resilience of compression (RC)

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2422 Recognizing an Individual, Their Topic of Conversation and Cultural Background from 3D Body Movement

Authors: Gheida J. Shahrour, Martin J. Russell

Abstract:

The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

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2421 Human Activities Recognition Based on Expert System

Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

Abstract:

Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

Procedia PDF Downloads 101
2420 New Efficient Method for Coding Color Images

Authors: Walaa M.Abd-Elhafiez, Wajeb Gharibi

Abstract:

In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio.

Keywords: image compression, color image, q-coder, quantization, edge-detection

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2419 Lapped Gussets Joints in Compression

Authors: K. R. Tshunza, A. Elvin, A. Gabremmeskel

Abstract:

Final results of an extensive laboratory research program on “lapped gusset joints in compression” are presented. The investigation was carried out at the Heavy structures laboratory at the University of the Witwatersrand in Johannesburg, South Africa. A proposed, relatively easy to use analytical equation was found to be reasonably adequate in determining the global compressive capacity of lapped gussets joints under compressive load. A wide range of lapped mild steel plates of varying slenderness, welded on 219*10 and 127*6 Mild steel circular hollow sections of 1m length were tested in compression and the formula was validated with experimental results. The investigation show that the connection’s capacity is controlled by flexure due to the eccentricity between the plates that are connected side to side.

Keywords: compression, eccentricity, lapped gussets joints, moment resistance

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2418 Effect of Nano-SiO2 Solution on the Strength Characteristics of Kaolinite

Authors: Reza Ziaie Moayed, Hamidreza Rahmani

Abstract:

Today, with developments in science and technology, there is an excessive potential for the use of nanomaterials in various fields of geotechnical project such as soil stabilization. This study investigates the effect of Nano-SiO2 solution on the unconfined compression strength and Young's elastic modulus of Kaolinite. For this purpose, nano-SiO2 was mixed with kaolinite in five different contents: 1, 2, 3, 4 and 5% by weight of the dry soil and a series of the unconfined compression test with curing time of one-day was selected as laboratory test. Analyses of the tests results show that stabilization of kaolinite with Nano-SiO2 solution can improve effectively the unconfined compression strength of modified soil up to 1.43 times compared to  the pure soil.

Keywords: kaolinite, Nano-SiO2, stabilization, unconfined compression test, Young's modulus

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2417 Image Compression Based on Regression SVM and Biorthogonal Wavelets

Authors: Zikiou Nadia, Lahdir Mourad, Ameur Soltane

Abstract:

In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement.

Keywords: image compression, 2D discrete wavelet transform (DWT-2D), support vector regression (SVR), SVM Kernels, run-length, arithmetic coding

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2416 Enhanced Face Recognition with Daisy Descriptors Using 1BT Based Registration

Authors: Sevil Igit, Merve Meric, Sarp Erturk

Abstract:

In this paper, it is proposed to improve Daisy descriptor based face recognition using a novel One-Bit Transform (1BT) based pre-registration approach. The 1BT based pre-registration procedure is fast and has low computational complexity. It is shown that the face recognition accuracy is improved with the proposed approach. The proposed approach can facilitate highly accurate face recognition using DAISY descriptor with simple matching and thereby facilitate a low-complexity approach.

Keywords: face recognition, Daisy descriptor, One-Bit Transform, image registration

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2415 Influence of the Compression Force and Powder Particle Size on Some Physical Properties of Date (Phoenix dactylifera) Tablets

Authors: Djemaa Megdoud, Messaoud Boudaa, Fatima Ouamrane, Salem Benamara

Abstract:

In recent years, the compression of date (Phoenix dactylifera L.) fruit powders (DP) to obtain date tablets (DT) has been suggested as a promising form of valorization of non commercial valuable date fruit (DF) varieties. To further improve and characterize DT, the present study aims to investigate the influence of the DP particle size and compression force on some physical properties of DT. The results show that independently of particle size, the hardness (y) of tablets increases with the increase of the compression force (x) following a logarithmic law (y = a ln (bx) where a and b are the constants of model). Further, a full factorial design (FFD) at two levels, applied to investigate the erosion %, reveals that the effects of time and particle size are the same in absolute value and they are beyond the effect of the compression. Regarding the disintegration time, the obtained results also by means of a FFD show that the effect of the compression force exceeds 4 times that of the DP particle size. As final stage, the color parameters in the CIELab system of DT immediately after their obtaining are differently influenced by the size of the initial powder.

Keywords: powder, tablets, date (Phoenix dactylifera L.), hardness, erosion, disintegration time, color

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2414 Modern Machine Learning Conniptions for Automatic Speech Recognition

Authors: S. Jagadeesh Kumar

Abstract:

This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

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2413 Advances in Artificial intelligence Using Speech Recognition

Authors: Khaled M. Alhawiti

Abstract:

This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

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2412 Biometric Recognition Techniques: A Survey

Authors: Shabir Ahmad Sofi, Shubham Aggarwal, Sanyam Singhal, Roohie Naaz

Abstract:

Biometric recognition refers to an automatic recognition of individuals based on a feature vector(s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. These features are used to provide an authentication for computer based security systems. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on 'who she/he is' rather than 'what she/he has' (card, token, key) or 'what she/he knows' (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal and their advantages and disadvantages, will be presented.

Keywords: biometric, DNA, fingerprint, ear, face, retina scan, gait, iris, voice recognition, unimodal biometric, multimodal biometric

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2411 Application of Compressed Sensing Method for Compression of Quantum Data

Authors: M. Kowalski, M. Życzkowski, M. Karol

Abstract:

Current quantum key distribution systems (QKD) offer low bit rate of up to single MHz. Compared to conventional optical fiber links with multiple GHz bitrates, parameters of recent QKD systems are significantly lower. In the article we present the conception of application of the Compressed Sensing method for compression of quantum information. The compression methodology as well as the signal reconstruction method and initial results of improving the throughput of quantum information link are presented.

Keywords: quantum key distribution systems, fiber optic system, compressed sensing

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2410 Printed Thai Character Recognition Using Particle Swarm Optimization Algorithm

Authors: Phawin Sangsuvan, Chutimet Srinilta

Abstract:

This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function.

Keywords: character recognition, histogram projection, particle swarm optimization, pattern recognition techniques

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2409 Enhanced Thai Character Recognition with Histogram Projection Feature Extraction

Authors: Benjawan Rangsikamol, Chutimet Srinilta

Abstract:

This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters.

Keywords: character recognition, histogram projection, multilayer perceptron, Thai character features extraction

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2408 Experimental Investigation of the Effect of Compression Ratio in a Direct Injection Diesel Engine Running on Different Blends of Rice Bran Oil and Ethanol

Authors: Perminderjit Singh, Randeep Singh

Abstract:

The performance, emission and combustion characteristics of a single cylinder four stroke variable compression ratio multifuel engine when fueled with different blends of rice bran oil methyl ester and ethanol are investigated and compared with the results of standard diesel. Biodiesel produced from rice bran oil by transesterification process has been used in this study. The experiment has been conducted at a fixed engine speed of 1500 rpm, 50% load and at compression ratios of 16.5:1, 17:1, 17.5:1 and 18:1. The impact of compression ratio on fuel consumption, brake thermal efficiency and exhaust gas emissions has been investigated and presented. Optimum compression ratio which gives the best performance has been identified. The results indicate longer ignition delay, the maximum rate of pressure rise, lower heat release rate and higher mass fraction burnt at higher compression ratio for waste cooking oil methyl ester when compared to that of diesel. The brake thermal efficiency at 50% load for rice bran oil methyl ester blends and diesel has been calculated and the blend B40 is found to give maximum thermal efficiency. The blends when used as fuel results in the reduction of carbon monoxide, hydrocarbon and increase in nitrogen oxides emissions.

Keywords: biodiesel, rice bran oil, transesterification, ethanol, compression ratio

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2407 Speaker Recognition Using LIRA Neural Networks

Authors: Nestor A. Garcia Fragoso, Tetyana Baydyk, Ernst Kussul

Abstract:

This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: extreme learning, LIRA neural classifier, speaker identification, voice recognition

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2406 Laser Ultrasonic Diagnostics and Acoustic Emission Technique for Examination of Rock Specimens under Uniaxial Compression

Authors: Elena B. Cherepetskaya, Vladimir A. Makarov, Dmitry V. Morozov, Ivan E. Sas

Abstract:

Laboratory studies of the stress-strain behavior of rocks specimens were conducted by using acoustic emission and laser-ultrasonic diagnostics. The sensitivity of the techniques allowed changes in the internal structure of the specimens under uniaxial compressive load to be examined at micro- and macro scales. It was shown that microcracks appear in geologic materials when the stress level reaches about 50% of breaking strength. Also, the characteristic stress of the main crack formation was registered in the process of single-stage compression of rocks. On the base of laser-ultrasonic echoscopy, 2D visualization of the internal structure of rocky soil specimens was realized, and the microcracks arising during uniaxial compression were registered.

Keywords: acoustic emission, geomaterial, laser ultrasound, uniaxial compression

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2405 A New Framework for ECG Signal Modeling and Compression Based on Compressed Sensing Theory

Authors: Siavash Eftekharifar, Tohid Yousefi Rezaii, Mahdi Shamsi

Abstract:

The purpose of this paper is to exploit compressed sensing (CS) method in order to model and compress the electrocardiogram (ECG) signals at a high compression ratio. In order to obtain a sparse representation of the ECG signals, first a suitable basis matrix with Gaussian kernels, which are shown to nicely fit the ECG signals, is constructed. Then the sparse model is extracted by applying some optimization technique. Finally, the CS theory is utilized to obtain a compressed version of the sparse signal. Reconstruction of the ECG signal from the compressed version is also done to prove the reliability of the algorithm. At this stage, a greedy optimization technique is used to reconstruct the ECG signal and the Mean Square Error (MSE) is calculated to evaluate the precision of the proposed compression method.

Keywords: compressed sensing, ECG compression, Gaussian kernel, sparse representation

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