Search results for: Music Feature and Digital Music.

Authors: Wei-Da Toh, Yuan Gao, Minkyu Je

Abstract:

In this paper, an energy efficient digital baseband circuit for piezoelectric (PE) harvester powered batteryless remote control system is presented. Pulse mode PE harvester, which provides short duration of energy, is adopted to replace conventional chemical battery in wireless remote controller. The transmitter digital baseband repeats the control command transmission once the digital circuit is initiated by the power-on-reset. A power efficient data frame format is proposed to maximize the transmission repetition time. By using the proposed frame format and receiver clock and data recovery method, the receiver baseband is able to decode the command even when the received data has 20% error. The proposed transmitter and receiver baseband are implemented using FPGA and simulation results are presented.

Keywords: Clock and Data Recovery (CDR), Correlator, Digital Baseband, Gold Code, Power-On-Reset.

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Authors: Vânia Graça, Paula Quadros-Flores, Altina Ramos

Abstract:

The use of Digital Technologies in teaching and learning processes is currently a reality, namely in initial teacher training. This study aims at knowing the digital reality of students in initial teacher training in order to improve training in the educational use of ICT and to promote digital technology integration strategies in an educational context. It is part of the IFITIC Project "Innovate with ICT in Initial Teacher Training to Promote Methodological Renewal in Pre-school Education and in the 1st and 2nd Basic Education Cycle" which involves the School of Education, Polytechnic of Porto and Institute of Education, University of Minho. The Project aims at rethinking educational practice with ICT in the initial training of future teachers in order to promote methodological innovation in Pre-school Education and in the 1st and 2nd Cycles of Basic Education. A qualitative methodology was used, in which a questionnaire survey was applied to teachers in initial training. For data analysis, the techniques of content analysis with the support of NVivo software were used. The results point to the following aspects: a) future teachers recognize that they have more technical knowledge about ICT than pedagogical knowledge. This result makes sense if we consider the objective of Basic Education, so that the gaps can be filled in the Master's Course by students who wish to follow the teaching; b) the respondents are aware that the integration of digital resources contributes positively to students' learning and to the life of children and young people, which also promotes preparation in life; c) to be a teacher in the digital age there is a need for the development of digital literacy, lifelong learning and the adoption of new ways of teaching how to learn. Thus, this study aims to contribute to a reflection on the teaching profession in the digital age.

Keywords: Digital technologies, initial teacher training, pedagogical use of ICT, skills.

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Authors: Jin Zhi

Abstract:

This paper covered a series of key points in terms of 2D to 3D stereoscopic conversion. A successfully applied stereoscopic conversion approach in current visual effects industry was presented. The purpose of this paper is to cover a detailed workflow and concept, which has been successfully used in 3D stereoscopic conversion for feature films in visual effects industry, and therefore to clarify the process in stereoscopic conversion production and provide a clear idea for those entry-level artists to improve an overall understanding of 3D stereoscopic in digital compositing field as well as to the higher education factor of visual effects and hopefully inspire further collaboration and participants particularly between academia and industry.

Keywords: Clean plates, Mattes, Stereoscopic conversion, 3Dprojection, Z-depth.

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Authors: Dominik Krimpmann

Abstract:

Information technology and information systems are currently at a tipping point. The digital age fundamentally transforms a large number of industries in the ways they work. Lines between business and technology blur. Researchers have acknowledged that this is the time in which the IT/IS organisation needs to re-strategize itself. In this paper, the author provides a structured review of the IS and organisation design literature addressing the question of how the digital age changes the design categories of an IT/IS organisation design. The findings show that most papers just analyse single aspects of either IT/IS relevant information or generic organisation design elements but miss a holistic ‘big-picture’ onto an IT/IS organisation design. This paper creates a holistic IT/IS organisation design framework bringing together the IS research strand, the digital strand and the generic organisation design strand. The research identified four IT/IS organisation design categories (strategy, structure, processes and people) and discusses the importance of two additional categories (sourcing and governance). The authors findings point to a first anchor point from which further research needs to be conducted to develop a holistic IT/IS organisation design framework.

Keywords: IT/IS strategy, IT/IS organisation design, digital age, organisational effectiveness, literature review.

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Authors: Sarah K. Taylor, Miratun M. Saharuddin, Zabri A. Talib

Abstract:

Cybercrime is on the rise, and yet many Law Enforcement Agencies (LEAs) in Malaysia have no Digital Forensics Laboratory (DFL) to assist them in the attrition and analysis of digital evidence. From the estimated number of 30 LEAs in Malaysia, sadly, only eight of them owned a DFL. All of the DFLs are concentrated in the capital of Malaysia and none at the state level. LEAs are still depending on the national DFL (CyberSecurity Malaysia) even for simple and straightforward cases. A survey was conducted among LEAs in Malaysia owning a DFL to understand their history of establishing the DFL, the challenges that they faced and the significance of the DFL to their case investigation. The results showed that the while some LEAs faced no challenge in establishing a DFL, some of them took seven to 10 years to do so. The reason was due to the difficulty in convincing their management because of the high costs involved. The results also revealed that with the establishment of a DFL, LEAs were better able to get faster forensic result and to meet agency’s timeline expectation. It is also found that LEAs were also able to get more meaningful forensic results on cases that require niche expertise, compared to sending off cases to the national DFL. Other than that, cases are getting more complex, and hence, a continuous stream of budget for equipment and training is inevitable. The result derived from the study is hoped to be used by other LEAs in justifying to their management the benefits of establishing an in-house DFL.

Keywords: Digital forensics, digital forensics laboratory, digital evidence, law enforcement agency.

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Authors: Salam Khalifa, Naveed Ahmed

Abstract:

We present a new method to reconstruct a temporally coherent 3D animation from single or multi-view RGB-D video data using unbiased feature point sampling. Given RGB-D video data, in form of a 3D point cloud sequence, our method first extracts feature points using both color and depth information. In the subsequent steps, these feature points are used to match two 3D point clouds in consecutive frames independent of their resolution. Our new motion vectors based dynamic alignement method then fully reconstruct a spatio-temporally coherent 3D animation. We perform extensive quantitative validation using novel error functions to analyze the results. We show that despite the limiting factors of temporal and spatial noise associated to RGB-D data, it is possible to extract temporal coherence to faithfully reconstruct a temporally coherent 3D animation from RGB-D video data.

Keywords: 3D video, 3D animation, RGB-D video, Temporally Coherent 3D Animation.

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Authors: Ahmed Kamil Hasan Al-Ali, Bouchra Senadji, Ganesh Naik

Abstract:

We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR.

Keywords: Noisy forensic speaker verification, ICA algorithm, MFCC, MFCC feature warping.

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

Abstract:

The aim of this work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. With our research and based on a feature selection in different phases, we are trying to design a neural network system with an optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each region of interest (ROI), 6 distinct sets of texture features are extracted such as: first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. When analyzing more phases, we show that the injection of liquid cause changes to the high relevant features in each region. Our results demonstrate that for detecting HCC tumor phase 3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between pathology and healthy classes, according to our method, relates to first order histogram parameters with accuracy of 85% in phase 1, 95% in phase 2, and 95% in phase 3.

Keywords: Feature selection, Multi-phasic liver images, Neural network, Texture analysis.

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Authors: Gelareh Mohammadi, Parisa Shoushtari, Behnam Molaee Ardekani, Mohammad B. Shamsollahi

Abstract:

A direct connection between ElectroEncephaloGram (EEG) and the genetic information of individuals has been investigated by neurophysiologists and psychiatrists since 1960-s; and it opens a new research area in the science. This paper focuses on the person identification based on feature extracted from the EEG which can show a direct connection between EEG and the genetic information of subjects. In this work the full EO EEG signal of healthy individuals are estimated by an autoregressive (AR) model and the AR parameters are extracted as features. Here for feature vector constitution, two methods have been proposed; in the first method the extracted parameters of each channel are used as a feature vector in the classification step which employs a competitive neural network and in the second method a combination of different channel parameters are used as a feature vector. Correct classification scores at the range of 80% to 100% reveal the potential of our approach for person classification/identification and are in agreement to the previous researches showing evidence that the EEG signal carries genetic information. The novelty of this work is in the combination of AR parameters and the network type (competitive network) that we have used. A comparison between the first and the second approach imply preference of the second one.

Keywords: Person Identification, Autoregressive Model, EEG, Neural Network

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Authors: Say Wei Foo, Qi Dong

Abstract:

In this paper, a novel feature-based image watermarking scheme is proposed. Zernike moments which have invariance properties are adopted in the scheme. In the proposed scheme, feature points are first extracted from host image and several circular patches centered on these points are generated. The patches are used as carriers of watermark information because they can be regenerated to locate watermark embedding positions even when watermarked images are severely distorted. Zernike transform is then applied to the patches to calculate local Zernike moments. Dither modulation is adopted to quantize the magnitudes of the Zernike moments followed by false alarm analysis. Experimental results show that quality degradation of watermarked image is visually transparent. The proposed scheme is very robust against image processing operations and geometric attacks.

Keywords: Image watermarking, Zernike moments, Featurepoint, Invariance, Robustness.

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Authors: K. Akilandeswari, G. M. Nasira

Abstract:

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable recognition accuracy. It is a vital step affecting pattern recognition system performance. This study presents a new Binary Particle Swarm Optimization (BPSO) based feature selection algorithm. Multi-layer Perceptron Neural Network (MLPNN) classifier with backpropagation training algorithm and Levenberg-Marquardt training algorithm classify selected features.

Keywords: Brain-Computer Interfaces (BCI), Feature Selection (FS), Walsh–Hadamard Transform (WHT), Binary Particle Swarm Optimization (BPSO), Multi-Layer Perceptron (MLP), Levenberg–Marquardt algorithm.

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Authors: H. Kelkoul, Y. Zaz

Abstract:

Nowadays, the vigorous popularity of video processing techniques has resulted in an explosive growth of multimedia data illegal use. So, watermarking security has received much more attention. The purpose of this paper is to explore some watermarking techniques in order to observe their specificities and select the finest methods to apply in digital cinema domain against movie piracy by creating an invisible watermark that includes the date, time and the place where the hacking was done. We have studied three principal watermarking techniques in the frequency domain: Spread spectrum, Wavelet transform domain and finally the digital cinema watermarking transform domain. In this paper, a detailed technique is presented where embedding is performed using direct sequence spread spectrum technique in DWT transform domain. Experiment results shows that the algorithm provides high robustness and good imperceptibility.

Keywords: Digital cinema, watermarking, wavelet, spread spectrum, JPEG2000.

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Authors: Francesco Coraci, Abdul-Hadi G. Abulrub

Abstract:

Digital technology is transforming the landscape of the industrial sector at a precedential level by connecting people, processes, and machines in real-time. It represents the means for a new pathway to achieve innovative, dynamic competitive advantages, deliver unique customers’ values, and sustain critical relationships. Thus, success in a constantly changing environment is governed by the ability of an organization to revolutionize their business models, deliver innovative solutions, and capture values from big data analytics and insights. Businesses need to re-strategize operations and develop extra capabilities to cope with the necessity for additional flexibility and agility. The traditional “command and control” leadership style is structurally and operationally incompatible with the digital era. In this paper, the authors discuss how transformational leaders can act as a glue in the social, organizational context, which is crucial to enable the workforce and develop a psychological attachment to the digital vision.

Keywords: Internet of things, strategy, change leadership, dynamic competitive advantage, digital transformation.

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

Abstract:

Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: Feature selection, multi-objective evolutionary computation, unsupervised classification, behavior assessment system for children.

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Authors: Hossein Nezamabadi-pour, Saeid Saryazdi

Abstract:

In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.

Keywords: Object-based image retrieval, DCT domain, Image indexing, Image classification.

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Authors: Elif Derya UBEYLI, Inan GULER

Abstract:

A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.

Keywords: Chaotic signal, Electroencephalogram (EEG) signals, Feature extraction/selection, Lyapunov exponents

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Authors: Provides Ng

Abstract:

Platform economy is a peer-to-peer model of distributing resources facilitated by community-based digital platforms. In recent years, digital platforms are rapidly reconfiguring the public realm using hyper-personalisation techniques. This paper aims at investigating how urban planning can leapfrog into the digital age to help relieve the rising tension of the global issue of labour flow; it discusses the means to transfer techniques of hyper-personalisation into urban planning for plasticity using platform technologies. This research first denotes the limitations of the current system of urban residency, where the system maintains itself on the circulation of documents, which are data on paper. Then, this paper tabulates how some of the institutions around the world, both public and private, digitise data, and streamline communications between a network of systems and citizens using platform technologies. Subsequently, this paper proposes ways in which hyper-personalisation can be utilised to form a digital planning platform. Finally, this paper concludes by reviewing how the proposed strategy may help to open up new ways of thinking about how we affiliate ourselves with cities.

Keywords: Platform urbanism, hyper-personalisation, urban residency, digital data.

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Authors: Alexandros Leontitsis, Archana P. Sangole

Abstract:

This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map.

Keywords: Parameter estimation, self-organizing feature maps, spherical topology.

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Authors: Einalou Z., Najafi Z., Maghooli K. Zandi Y, Sheibeigi A

Abstract:

In this study, an investigation over digestive diseases has been done in which the sound acts as a detector medium. Pursue to the preprocessing the extracted signal in cepstrum domain is registered. After classification of digestive diseases, the system selects random samples based on their features and generates the interest nonstationary, long-term signals via inverse transform in cepstral domain which is presented in digital and sonic form as the output. This structure is updatable or on the other word, by receiving a new signal the corresponding disease classification is updated in the feature domain.

Keywords: Cepstrum, databank, digestive disease, acousticsignal.

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Authors: Rafal Maciag

Abstract:

Due to the development of the current civilization, one must create suitable models of its pervasive massive phenomena. Such a phenomenon is the digital transformation, which has a substantial number of disciplined, methodical interpretations forming the diversified reflection. This reflection could be understood pragmatically as the current temporal, a local differential state of knowledge. The model of the discursive space is proposed as a model for the analysis and description of this knowledge. Discursive space is understood as an autonomous multidimensional space where separate discourses traverse specific trajectories of what can be presented in multidimensional parallel coordinate system. Discursive space built on the world of facts preserves the complex character of that world. Digital transformation as a discursive space has a relativistic character that means that at the same time, it is created by the dynamic discourses and these discourses are molded by the shape of this space.

Keywords: Knowledge, digital transformation, discourse, discursive space, complexity.

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Authors: G. Renuka Devi

Abstract:

This paper presents design and analysis of fault tolerate feature of n-phase induction motor drive. The n-phase induction motor (more than 3-phases) has a number of advantages over conventional 3-phase induction motor, it has low torque pulsation with increased torque density, more fault tolerant feature, low current ripple with increased efficiency. When increasing the number of phases, it has reduced current per phase without increasing per phase voltage, resulting in an increase in the total power rating of n-phase motors in the same volume machine. In this paper, the theory of operation of a multi-phase induction motor is discussed. The detailed study of d-q modeling of n-phase induction motors is elaborated. The d-q model of n-phase (5, 6, 7, 9 and 12) induction motors is developed in a MATLAB/Simulink environment. The steady state and dynamic performance of the multi-phase induction motor is studied under varying load conditions. Comparison of 5-phase induction is presented under normal and fault conditions.

Keywords: d-q model, dynamic Response, fault tolerant feature, matlab/simulink, multi-phase induction motor, transient response.

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Authors: Hasmayadi Abdul Majid, Rohana Musa

Abstract:

This paper presents the development of a 38.5 kS/s 10-bit low power SAR ADC which is realized in MIMOS’s 0.35 µm CMOS process. The design uses a resistive DAC, a dynamic comparator with pre-amplifier and SAR digital logic to create 10 effective bits while consuming less than 7.8 mW with a 3.3 V power supply.

Keywords: Successive Approximation Register Analog-to- Digital Converter, SAR ADC, Resistive DAC.

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Authors: Ramaswamy Palaniappan

Abstract:

In this paper, several improvements are proposed to previous work of automated classification of alcoholics and nonalcoholics. In the previous paper, multiplayer-perceptron neural network classifying energy of gamma band Visual Evoked Potential (VEP) signals gave the best classification performance using 800 VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the dataset is extended to include 3560 VEP signals from 102 subjects: 62 alcoholics and 40 non-alcoholics. Three modifications are introduced to improve the classification performance: i) increasing the gamma band spectral range by increasing the pass-band width of the used filter ii) the use of Multiple Signal Classification algorithm to obtain the power of the dominant frequency in gamma band VEP signals as features and iii) the use of the simple but effective knearest neighbour classifier. To validate that these two modifications do give improved performance, a 10-fold cross validation classification (CVC) scheme is used. Repeat experiments of the previously used methodology for the extended dataset are performed here and improvement from 94.49% to 98.71% in maximum averaged CVC accuracy is obtained using the modifications. This latest results show that VEP based classification of alcoholics is worth exploring further for system development.

Keywords: Alcoholic, Multilayer-perceptron, Nearest neighbour, Gamma band, MUSIC, Visual evoked potential.

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Authors: Diamantino Ribeiro, António Pedro Costa, Jorge Remondes

Abstract:

Dubai and the UAE governments have been investing in technology and digital communication for a long time. These governments are pioneers in introducing innovative strategies, policies and projects. They are also recognized worldwide for defining and implementing long term public programs. In terms of eGovernment Dubai and the UAE rank among the world’s most advanced. Both governments have surprised the world a few years ago by creating a Happiness Ministry. This paper focuses on UAE’s government digital strategies and its approach to the next era. The main goal of this exploratory study is to understand the new era of eGovernment and transfer of the happiness and wellness programs. Data were collected from the corpus latente and analysis was anchored in qualitative methodology using content analysis and observation as analysis techniques. The study allowed to highlight that the 2020 government reshuffle has a strong focus on digital reorganisation and digital sustainability, one of the newest trends in sustainability. Regarding happiness and wellbeing portfolio, we were able to observe that there has been a major change within the government organisation: The Ministry of Happiness was extinct and the Ministry of Community Development will manage the so-called ‘Happiness Portfolio’. Additionally, our observation allowed to note the government dual approach to governance: one through digital transformation, thus enhancing the digital sustainability process and, the second one trough government development.

Keywords: Ministry of Happiness, eGovernment, communication, digital sustainability.

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Authors: Benmabrouk. Zaineb, Ben Hamed. Mouna, Lassad. Sbita

Abstract:

This article deals to describe the simulation investigation of the digital phase locked loop implemented in software (SDPLL). SDPLL has been developed for speed drives of an induction motor in scalar strategy. A drive was implemented and simulation results are presented to verify the robustness against motor parameter variation and regulation speed.

Keywords: Induction motor, Software Digital Phase LockedLoop, Speed control, Simulation.

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

Abstract:

Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feedforward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on selforganizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: Classification, SOFM, neural network, RGB images.

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Authors: Yu-Sheng Lee, Qi An

Abstract:

Offset mismatch, gain mismatch, and time-skew error between time-interleaved channels limit the performance of time-interleaved analog-to-digital converters (TIADC). This paper focused on the time-skew error. A new technique for calibrating time-skew error in M-channels TIADC is described, and simulation results are also presented.

Keywords: Calibration, time-skew error, time-interleavedanalog-to-digital converters.

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Authors: Hossein Sahoolizadeh, Mahdi Rahimi, Hamid Dehghani

Abstract:

We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network-s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.

Keywords: Face recognition, Neural Networks, Multi-layer Perceptron, masking.

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Authors: V. Kabeer, N.K.Narayanan

Abstract:

This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.

Keywords: Face recognition, Image analysis, Wavelet feature extraction, Pattern recognition, Classifier algorithms

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Authors: Rike Brecht, Heidi Krömker, Adrian Kühlewind

Abstract:

This paper presents a digital engineering library – the Digital Mechanism and Gear Library, DMG-Lib – providing a multimedia collection of e-books, pictures, videos and animations in the domain of mechanisms and machines. The specific characteristic about DMG-Lib is the enrichment and cross-linking of the different sources. DMG-Lib e-books not only present pages as pixel images but also selected figures augmented with interactive animations. The presentation of animations in e-books increases the clearness of the information. To present the multimedia e-books and make them available in the DMG-Lib internet portal a special e-book reader called StreamBook was developed for optimal presentation of digitized books and to enable reading the e-books as well as working efficiently and individually with the enriched information. The objective is to support different user tasks ranging from information retrieval to development and design of mechanisms.

Keywords: E-books, digital library, multimedia, enrichment and cross-linking

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