Search results for: sound recognition

Authors: Marina Velikova, Hennie Daniels, Ad Feelders

Abstract:

In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: mixture models, monotone neural networks, partially monotone models, partially monotone problems.

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Authors: Dimitrios Koukopoulos, Yiannis Stamatiou

Abstract:

In this work, we present for the first time in our perception an efficient digital watermarking scheme for mpeg audio layer 3 files that operates directly in the compressed data domain, while manipulating the time and subband/channel domain. In addition, it does not need the original signal to detect the watermark. Our scheme was implemented taking special care for the efficient usage of the two limited resources of computer systems: time and space. It offers to the industrial user the capability of watermark embedding and detection in time immediately comparable to the real music time of the original audio file that depends on the mpeg compression, while the end user/audience does not face any artifacts or delays hearing the watermarked audio file. Furthermore, it overcomes the disadvantage of algorithms operating in the PCMData domain to be vulnerable to compression/recompression attacks, as it places the watermark in the scale factors domain and not in the digitized sound audio data. The strength of our scheme, that allows it to be used with success in both authentication and copyright protection, relies on the fact that it gives to the users the enhanced capability their ownership of the audio file not to be accomplished simply by detecting the bit pattern that comprises the watermark itself, but by showing that the legal owner knows a hard to compute property of the watermark.

Keywords: Audio watermarking, mpeg audio layer 3, hardinstance generation, NP-completeness.

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Authors: Changhwan Choi, Yongwan Park

Abstract:

In this paper, we propose a method that allows faster and more accurate detection of traffic lights by a vision sensor during driving, DGPS is used to obtain physical location of a traffic light, extract from the image information of the vision sensor only the traffic light area at this location and ascertain if the sign is in operation and determine its form. This method can solve the problem in existing research where low visibility at night or reflection under bright light makes it difficult to recognize the form of traffic light, thus making driving unstable. We compared our success rate of traffic light recognition in day and night road environments. Compared to previous researches, it showed similar performance during the day but 50% improvement at night.

Keywords: Traffic light, Intelligent vehicle, Night, Detection, DGPS (Differential Global Positioning System).

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Authors: Syed Akhter Hossain, M. Lutfar Rahman, Farruk Ahmed

Abstract:

Bangla Vowel characterization determines the spectral properties of Bangla vowels for efficient synthesis as well as recognition of Bangla vowels. In this paper, Bangla vowels in isolated word have been analyzed based on speech production model within the framework of Analysis-by-Synthesis. This has led to the extraction of spectral parameters for the production model in order to produce different Bangla vowel sounds. The real and synthetic spectra are compared and a weighted square error has been computed along with the error in the formant bandwidths for efficient representation of Bangla vowels. The extracted features produced good representation of targeted Bangla vowel. Such a representation also plays essential role in low bit rate speech coding and vocoders.

Keywords: Speech, vowel, formant, synthesis, spectrum, LPC.

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Authors: Dan Ohev Zion, Alon Cohen

Abstract:

In recent years, the presence of short-range millimeter-wave radar in civil application has increased significantly. Autonomous driving, security, 3D imaging and high data rate communication systems are a few examples. The next challenge is the integration inside small form-factor devices, such as smartphones (e.g. gesture recognition). The main challenge is implementation of a truly low-power, low-complexity high-resolution radar. The most popular approach is the Frequency Modulated Continuous Wave (FMCW) radar, with an analog multiplication front-end. In this paper, we present an approach for adaptive estimation and correction of impairments of such front-end, specifically implemented using the Six-Port Device (SPD) as the multiplier element. The proposed algorithm was simulated and implemented on a 60 GHz radar lab prototype.

Keywords: Radar, millimeter-wave, six-port, FMCW Radar, IQ mismatch.

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Authors: G. Mokryani, M. R. Haghifam, J. Esmaeilpoor

Abstract:

Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Competitive Neural Network used for classification. Ferroresonance data and other transients was obtained by simulation using EMTP program. Using Daubechies wavelet transform signals has been decomposed till six levels. The energy of six detailed signals that obtained by wavelet transform are used for training and trailing Competitive Neural Network. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.

Keywords: Competitive Neural Network, Ferroresonance, EMTP program, Wavelet transform.

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Authors: A. H. Tadevosyan, S. K. Mayrapetyan, N. B. Tavakalyan, K. I. Pyuskyulyan, A. H. Hovsepyan, S. N. Sergeeva

Abstract:

Soil contamination with radiocesium has a long-term radiological impact due to its long physical half-life (30.1 years for ¹³⁷Cs and 2 years for ¹³⁴Cs) and its high biological availability. ¹³⁷Cs causes the largest concerns because of its deleterious effect on agriculture and stock farming, and, thus, human life for decades. One of the important aspects of the problem of contaminated soils remediation is understand of protective actions aimed at the reduction of biological migration of radionuclides in soil-plant system. The most effective way to bind radionuclides is the use of selective sorbents. The proposed research mainly aims to achieve control on transfer of ¹³⁷Cs in a system growing media – plant due to counter ions variation in the polymeric sorbents. As research object Japanese basil - Perilla frutescens was chosen. Productivity of plants depending on the presence (control-without presence of polymer) and type of polymer material, as well as content of ¹³⁷Cs in plant material has been determined. The character of different polymers influences on the ¹³⁷Cs migration in growing media – plant system as well as accumulation in the plants has been cleared up.

Keywords: Radioceaseum, Japanese basil, polymer, soil-plant system.

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Authors: Soo- Young Ye, Ki-Gon Nam, Ki-Won Byun

Abstract:

In this paper, we were introduces a skin detection method using a histogram approximation based on the mean shift algorithm. The proposed method applies the mean shift procedure to a histogram of a skin map of the input image, generated by comparison with standard skin colors in the CbCr color space, and divides the background from the skin region by selecting the maximum value according to brightness level. The proposed method detects the skin region using the mean shift procedure to determine a maximum value that becomes the dividing point, rather than using a manually selected threshold value, as in existing techniques. Even when skin color is contaminated by illumination, the procedure can accurately segment the skin region and the background region. The proposed method may be useful in detecting facial regions as a pretreatment for face recognition in various types of illumination.

Keywords: Skin region detection, mean shift, histogram approximation.

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Authors: Ashraf Anwar

Abstract:

In this work, I present a review on Sparse Distributed Memory for Small Cues (SDMSCue), a variant of Sparse Distributed Memory (SDM) that is capable of handling small cues. I then conduct and show some cognitive experiments on SDMSCue to test its cognitive soundness compared to SDM. Small cues refer to input cues that are presented to memory for reading associations; but have many missing parts or fields from them. The original SDM failed to handle such a problem. SDMSCue handles and overcomes this pitfall. The main idea in SDMSCue; is the repeated projection of the semantic space on smaller subspaces; that are selected based on the input cue length and pattern. This process allows for Read/Write operations using an input cue that is missing a large portion. SDMSCue is augmented with the use of genetic algorithms for memory allocation and initialization. I claim that SDM functionality is a subset of SDMSCue functionality.

Keywords: Artificial intelligence, recall, recognition, SDM, SDMSCue.

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Authors: Mohammed Kamil, M. M. Rahman, Rosli A. Bakar

Abstract:

Modeling of hydrogen fueled engine (H₂ICE) injection system is a very important tool that can be used for explaining or predicting the effect of advanced injection strategies on combustion and emissions. In this paper, a common rail injection system (CRIS) is proposed for 4-strokes 4-cylinders hydrogen fueled engine with port injection feeding system (PIH₂ICE). For this system, a numerical one-dimensional gas dynamic model is developed considering single injection event for each injector per a cycle. One-dimensional flow equations in conservation form are used to simulate wave propagation phenomenon throughout the CR (accumulator). Using this model, the effect of common rail on the injection system characteristics is clarified. These characteristics include: rail pressure, sound velocity, rail mass flow rate, injected mass flow rate and pressure drop across injectors. The interaction effects of operational conditions (engine speed and rail pressure) and geometrical features (injector hole diameter) are illustrated; and the required compromised solutions are highlighted. The CRIS is shown to be a promising enhancement for PIH₂ICE.

Keywords: Common rail, hydrogen engine, port injection, wave propagation.

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Authors: K. A. Sumithradevi, Vijayalakshmi. M. N., Annamma Abraham., Dr. Vasanta

Abstract:

The various applications of VLSI circuits in highperformance computing, telecommunications, and consumer electronics has been expanding progressively, and at a very hasty pace. This paper describes a new model for partitioning a circuit using DBSCAN and fuzzy ARTMAP neural network. The first step is concerned with feature extraction, where we had make use DBSCAN algorithm. The second step is the classification and is composed of a fuzzy ARTMAP neural network. The performance of both approaches is compared using benchmark data provided by MCNC standard cell placement benchmark netlists. Analysis of the investigational results proved that the fuzzy ARTMAP with DBSCAN model achieves greater performance then only fuzzy ARTMAP in recognizing sub-circuits with lowest amount of interconnections between them The recognition rate using fuzzy ARTMAP with DBSCAN is 97.7% compared to only fuzzy ARTMAP.

Keywords: VLSI, Circuit partitioning, DBSCAN, fuzzyARTMAP.

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Authors: Venugopal Kummamuru

Abstract:

Corporate Governance (CG) is of utmost importance for running a company ethically. It is essential for the growth and success of the corporation. It is intended to increase the accountability of an organization to the larger context of the business environment. The general principles of CG include and are related to Shareholder recognition, Stakeholder interests, and focus on Corporate Social Responsibility (CSR), Clear Board responsibilities, Ethical behavior, and Business transparency. Network Marketing Organizations (NMOs) focus on marketing through direct-sales using people who are associated with the organization but are not their employees. This paper tries to study the importance of Ethics and CSR in an NMO and suggest a basic guideline for CG in NMO(s). This paper could be used as a basis or starting point for conducting an in-depth research to understand the difference in CG practices between NMO(s) and other organizations and define a standard set of guidelines for CG practice.

Keywords: Corporate governance, corporate responsibility, direct selling, network marketing.

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Authors: Luminita Dumitriu, Cristina Segal, Marian Craciun, Adina Cocu, Lucian P. Georgescu

Abstract:

There are several approaches in trying to solve the Quantitative 1Structure-Activity Relationship (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.

Keywords: association rules, classification, data mining, Quantitative Structure - Activity Relationship.

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Authors: Katsumi Hirata

Abstract:

To develop the useful acoustic environmental recognition system, the method of estimating 3D-position of a stationary random acoustic source using bispectral analysis of 4-point detected signals is proposed. The method uses information about amplitude attenuation and propagation delay extracted from amplitude ratios and angles of auto- and cross-bispectra of the detected signals. It is expected that using bispectral analysis affects less influence of Gaussian noises than using conventional power spectral one. In this paper, the basic principle of the method is mentioned first, and its validity and features are considered from results of the fundamental experiments assumed ideal circumstances.

Keywords: 4-point detection, a stationary random acoustic source, auto- and cross-bispectra, estimation of 3D-position.

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Authors: Dimitrios Koukopoulos, Yiannis Stamatiou

Abstract:

In this work, we present for the first time in our perception an efficient digital watermarking scheme for mpeg audio layer 3 files that operates directly in the compressed data domain, while manipulating the time and subband/channel domain. In addition, it does not need the original signal to detect the watermark. Our scheme was implemented taking special care for the efficient usage of the two limited resources of computer systems: time and space. It offers to the industrial user the capability of watermark embedding and detection in time immediately comparable to the real music time of the original audio file that depends on the mpeg compression, while the end user/audience does not face any artifacts or delays hearing the watermarked audio file. Furthermore, it overcomes the disadvantage of algorithms operating in the PCMData domain to be vulnerable to compression/recompression attacks, as it places the watermark in the scale factors domain and not in the digitized sound audio data. The strength of our scheme, that allows it to be used with success in both authentication and copyright protection, relies on the fact that it gives to the users the enhanced capability their ownership of the audio file not to be accomplished simply by detecting the bit pattern that comprises the watermark itself, but by showing that the legal owner knows a hard to compute property of the watermark.

Keywords: Audio watermarking, mpeg audio layer 3, hard instance generation, NP-completeness.

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Authors: Jaroslav Polec, Petra Heribanová, Tomáš Hirner

Abstract:

In this paper we proposed a method for finding video frames representing one sign in the finger alphabet. The method is based on determining hands location, segmentation and the use of standard video quality evaluation metrics. Metric calculation is performed only in regions of interest. Sliding mechanism for finding local extrema and adaptive threshold based on local averaging is used for key frames selection. The success rate is evaluated by recall, precision and F1 measure. The method effectiveness is compared with metrics applied to all frames. Proposed method is fast, effective and relatively easy to realize by simple input video preprocessing and subsequent use of tools designed for video quality measuring.

Keywords: Key frame, video, quality, metric, MSE, MSAD, SSIM, VQM, sign language, finger alphabet.

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Authors: Erika Pigliapoco, Valerio Freschi, Alessandro Bogliolo

Abstract:

This work presents a new phonetic transcription system based on a tree of hierarchical pronunciation rules expressed as context-specific grapheme-phoneme correspondences. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context levels, eventually representing a minimum set of exhaustive rules that pronounce without errors all the words in the training dictionary and that can be applied to out-of-vocabulary words. The proposed approach improves upon existing rule-tree-based techniques in that it makes use of graphemes, rather than letters, as elementary orthographic units. A new linear algorithm for the segmentation of a word in graphemes is introduced to enable outof- vocabulary grapheme-based phonetic transcription. Exhaustive rule trees provide a canonical representation of the pronunciation rules of a language that can be used not only to pronounce out-of-vocabulary words, but also to analyze and compare the pronunciation rules inferred from different dictionaries. The proposed approach has been implemented in C and tested on Oxford British English and Basic English. Experimental results show that grapheme-based rule trees represent phonetically sound rules and provide better performance than letter-based rule trees.

Keywords: Automatic phonetic transcription, pronunciation rules, hierarchical tree inference.

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Authors: Fernando Alonso, Rafael Fernandez, Sonia Frutos, Javier Soriano

Abstract:

CIM is the standard formalism for modeling management information developed by the Distributed Management Task Force (DMTF) in the context of its WBEM proposal, designed to provide a conceptual view of the managed environment. In this paper, we propose the inclusion of formal knowledge representation techniques, based on Description Logics (DLs) and the Web Ontology Language (OWL), in CIM-based conceptual modeling, and then we examine the benefits of such a decision. The proposal is specified as a CIM metamodel level mapping to a highly expressive subset of DLs capable of capturing all the semantics of the models. The paper shows how the proposed mapping can be used for automatic reasoning about the management information models, as a design aid, by means of new-generation CASE tools, thanks to the use of state-of-the-art automatic reasoning systems that support the proposed logic and use algorithms that are sound and complete with respect to the semantics. Such a CASE tool framework has been developed by the authors and its architecture is also introduced. The proposed formalization is not only useful at design time, but also at run time through the use of rational autonomous agents, in response to a need recently recognized by the DMTF.

Keywords: CIM, Knowledge-based Information Models, Ontology Languages, OWL, Description Logics, Integrated Network Management, Intelligent Agents, Automatic Reasoning Techniques.

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Authors: P. Heribanová, J. Polec, S. Ondrušová, M. Hosťovecký

Abstract:

This paper discusses the cued speech recognition methods in videoconference. Cued speech is a specific gesture language that is used for communication between deaf people. We define the criteria for sentence intelligibility according to answers of testing subjects (deaf people). In our tests we use 30 sample videos coded by H.264 codec with various bit-rates and various speed of cued speech. Additionally, we define the criteria for consonant sign recognizability in single-handed finger alphabet (dactyl) analogically to acoustics. We use another 12 sample videos coded by H.264 codec with various bit-rates in four different video formats. To interpret the results we apply the standard scale for subjective video quality evaluation and the percentual evaluation of intelligibility as in acoustics. From the results we construct the minimum coded bit-rate recommendations for every spatial resolution.

Keywords: cued speech, inteligibility, logatom, video

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Authors: Vasudha Hegde, Narendra Chaulagain, H. M. Ravikumar, Sonu Mishra, Siva Yellampalli

Abstract:

Acoustic sensors are extensively used in recent days not only for sensing and condition monitoring applications but also for small scale energy harvesting applications to power wireless sensor networks (WSN) due to their inherent advantages. The natural frequency of the structure plays a major role in energy harvesting applications since the sensor key element has to operate at resonant frequency. In this paper, circular diaphragm based MEMS acoustic sensor is modelled by Lumped Element Model (LEM) and the natural frequency is compared with the simulated model using Finite Element Method (FEM) tool COMSOL Multiphysics. The sensor has the circular diaphragm of 3000 µm radius and thickness of 30 µm to withstand the high SPL (Sound Pressure Level) and also to withstand the various fabrication steps. A Piezoelectric ZnO layer of thickness of 1 µm sandwiched between two aluminium electrodes of thickness 0.5 µm and is coated on the diaphragm. Further, a channel with radius 3000 µm radius and length 270 µm is connected at the bottom of the diaphragm. The natural frequency of the structure by LEM method is approximately 16.6 kHz which is closely matching with that of simulated structure with suitable approximations.

Keywords: Acoustic sensor, diaphragm based, lumped element modeling, natural frequency, piezoelectric.

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

Abstract:

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

Keywords: Vehicle classification, signal processing, road traffic model, magnetic sensing.

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Authors: Mohd Nuri Al-Amin Endut, Wan Suhaimi Wan Abdullah, Zulqarnain Abu Bakar

Abstract:

The element of justice or al-‘adl in the context of Islamic critical thinking deals with the notion of justice in a thinking process which critically rationalizes the truth in a fair and objective manner with no irrelevant interference that can jeopardize a sound judgment. This Islamic axiological element is vital in technological decision making as it addresses the issues of religious values and ethics that are primarily set to fulfill the purpose of human life on earth. The main objective of this study was to examine and analyze the perception of Muslim engineering students in Malaysian higher education institutions towards the concept of al-‘adl as an essential element of Islamic critical thinking. The study employed mixed methods approach that comprises data collection from the questionnaire survey and the interview responses. A total of 557 Muslim engineering undergraduates from six Malaysian universities participated in the study. The study generally indicated that Muslim engineering undergraduates in the higher institutions have rather good comprehension and consciousness for al-‘adl with a slight awareness on the importance of objective thinking. Nonetheless there were a few items on the concept that have implied a comparatively low perception on the rational justice in Islam as the means to grasp the ultimate truth.

Keywords: Engineering education, Islamic critical thinking, rational justice, perception, tertiary education.

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Authors: Hamid Reza Boveiri

Abstract:

Classification of Persian printed numeral characters has been considered and a proposed system has been introduced. In representation stage, for the first time in Persian optical character recognition, extended moment invariants has been utilized as characters image descriptor. In classification stage, four different classifiers namely minimum mean distance, nearest neighbor rule, multi layer perceptron, and fuzzy min-max neural network has been used, which first and second are traditional nonparametric statistical classifier. Third is a well-known neural network and forth is a kind of fuzzy neural network that is based on utilizing hyperbox fuzzy sets. Set of different experiments has been done and variety of results has been presented. The results showed that extended moment invariants are qualified as features to classify Persian printed numeral characters.

Keywords: Extended moment invariants, optical characterrecognition, Persian numerals classification.

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Authors: Alessio Plebe

Abstract:

One astonishing capability of humans is to recognize thousands of different objects visually, and to learn the semantic association between those objects and words referring to them. This work is an attempt to build a computational model of such capacity,simulating the process by which infants learn how to recognize objects and words through exposure to visual stimuli and vocal sounds.One of the main fact shaping the brain of a newborn is that lights and colors come from entities of the world. Gradually the visual system learn which light sensations belong to same entities, despite large changes in appearance. This experience is common between humans and several other mammals, like non-human primates. But humans only can recognize a huge variety of objects, most manufactured by himself, and make use of sounds to identify and categorize them. The aim of this model is to reproduce these processes in a biologically plausible way, by reconstructing the essential hierarchy of cortical circuits on the visual and auditory neural paths.

Keywords: Auditory cortex, object recognition, self-organizingmaps

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Authors: Kevin T. O'Toole, Mark M. McGrath

Abstract:

The psychological and physical trauma associated with the loss of a human limb can severely impact on the quality of life of an amputee rendering even the most basic of tasks very difficult. A prosthetic device can be of great benefit to the amputee in the performance of everyday human tasks. This paper outlines a proposed mechanical design of a 12 degree-of-freedom SMA actuated artificial hand. It is proposed that the SMA wires be embedded intrinsically within the hand structure which will allow for significant flexibility for use either as a prosthetic hand solution, or as part of a complete lower arm prosthetic solution. A modular approach is taken in the design facilitating ease of manufacture and assembly, and more importantly, also allows the end user to easily replace SMA wires in the event of failure. A biomimetric approach has been taken during the design process meaning that the artificial hand should replicate that of a human hand as far as is possible with due regard to functional requirements. The proposed design has been exposed to appropriate loading through the use of finite element analysis (FEA) to ensure that it is structurally sound. Theoretical analysis of the mechanical framework was also carried out to establish the limits of the angular displacement and velocity of the finger tip as well finger tip force generation. A combination of various polymers and Titanium, which are suitably lightweight, are proposed for the manufacture of the design.

Keywords: Hand prosthesis, mechanical design, shape memory alloys, wire bundle actuation.

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Authors: Elham Rastegari, Amirmasood Rahmani, Saeed Setayeshi

Abstract:

The hospital and the health-care center of a community, as a place for people-s life-care and health-care settings, must provide more and better services for patients or residents. After Establishing Electronic Medical Record (EMR) system -which is a necessity- in the hospital, providing pervasive services is a further step. Our objective in this paper is to use pervasive computing in a case study of healthcare, based on EMR database that coordinates application services over network to form a service environment for medical and health-care. Our method also categorizes the hospital spaces into 3 spaces: Public spaces, Private spaces and Isolated spaces. Although, there are many projects about using pervasive computing in healthcare, but all of them concentrate on the disease recognition, designing smart cloths, or provide services only for patient. The proposed method is implemented in a hospital. The obtained results show that it is suitable for our purpose.

Keywords: Pervasive computing, RFID, Health-care.

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Authors: G.Wiselin Jiji, L.Ganesan

Abstract:

Textures are replications, symmetries and combinations of various basic patterns, usually with some random variation one of the gray-level statistics. This article proposes a new approach to Segment texture images. The proposed approach proceeds in 2 stages. First, in this method, local texture information of a pixel is obtained by fuzzy texture unit and global texture information of an image is obtained by fuzzy texture spectrum. The purpose of this paper is to demonstrate the usefulness of fuzzy texture spectrum for texture Segmentation. The 2nd Stage of the method is devoted to a decision process, applying a global analysis followed by a fine segmentation, which is only focused on ambiguous points. The above Proposed approach was applied to brain image to identify the components of brain in turn, used to locate the brain tumor and its Growth rate.

Keywords: Fuzzy Texture Unit, Fuzzy Texture Spectrum, andPattern Recognition, segmentation.

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Authors: B. S. Fatoyinbo, D. Stretch, O. T. Amoo, D. Allopi

Abstract:

This study extends the use of the Drainage Area Regionalization (DAR) method in generating synthetic data and calibrating PyTOPKAPI stream yield for an ungauged basin at a daily time scale. The generation of runoff in determining a river yield has been subjected to various topographic and spatial meteorological variables, which integers form the Catchment Characteristics Model (CCM). Many of the conventional CCM models adapted in Africa have been challenged with a paucity of adequate, relevance and accurate data to parameterize and validate the potential. The purpose of generating synthetic flow is to test a hydrological model, which will not suffer from the impact of very low flows or very high flows, thus allowing to check whether the model is structurally sound enough or not. The employed physically-based, watershed-scale hydrologic model (PyTOPKAPI) was parameterized with GIS-pre-processing parameters and remote sensing hydro-meteorological variables. The validation with mean annual runoff ratio proposes a decent graphical understanding between observed and the simulated discharge. The Nash-Sutcliffe efficiency and coefficient of determination (R²) values of 0.704 and 0.739 proves strong model efficiency. Given the current climate variability impact, water planner can now assert a tool for flow quantification and sustainable planning purposes.

Keywords: Ungauged Basin, Catchment Characteristics Model, Synthetic data, GIS.

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Authors: Hadi Seyedarabi, Ali Aghagolzadeh, Sohrab Khanmohammadi

Abstract:

Face and facial expressions play essential roles in interpersonal communication. Most of the current works on the facial expression recognition attempt to recognize a small set of the prototypic expressions such as happy, surprise, anger, sad, disgust and fear. However the most of the human emotions are communicated by changes in one or two of discrete features. In this paper, we develop a facial expressions synthesis system, based on the facial characteristic points (FCP's) tracking in the frontal image sequences. Selected FCP's are automatically tracked using a crosscorrelation based optical flow. The proposed synthesis system uses a simple deformable facial features model with a few set of control points that can be tracked in original facial image sequences.

Keywords: Deformable face model, facial animation, facialcharacteristic points, optical flow.

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Authors: Adian Fatchur Rochim, Abdul Muis, Riri Fitri Sari

Abstract:

H-index has been widely used as a performance indicator of researchers around the world especially in Indonesia. The Government uses Scopus and Google scholar as indexing references in providing recognition and appreciation. However, those two indexing services yield to different H-index values. For that purpose, this paper evaluates the difference of the H-index from those services. Researchers indexed by Webometrics, are used as reference’s data in this paper. Currently, Webometrics only uses H-index from Google Scholar. This paper observed and compared corresponding researchers’ data from Scopus to get their H-index score. Subsequently, some researchers with huge differences in score are observed in more detail on their paper’s publisher. This paper shows that the H-index of researchers in Google Scholar is approximately 2.45 times of their Scopus H-Index. Most difference exists due to the existence of uncertified publishers, which is considered in Google Scholar but not in Scopus.

Keywords: Google Scholar, H-index, Scopus.

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