Search results for: Feature collection

Authors: Ahmad T. Al-Taani

Abstract:

In this paper, an efficient structural approach for recognizing on-line handwritten digits is proposed. After reading the digit from the user, the slope is estimated and normalized for adjacent nodes. Based on the changing of signs of the slope values, the primitives are identified and extracted. The names of these primitives are represented by strings, and then a finite state machine, which contains the grammars of the digits, is traced to identify the digit. Finally, if there is any ambiguity, it will be resolved. Experiments showed that this technique is flexible and can achieve high recognition accuracy for the shapes of the digits represented in this work.

Keywords: Digits Recognition, Pattern Recognition, FeatureExtraction, Structural Primitives, Document Processing, Handwritten Recognition, Primitives Selection.

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Authors: Cheng-Chi Lee, Min-Shiang Hwang, Shu-Yin Hsiao

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Network exchange is now widely used. However, it still cannot avoid the problems evolving from network exchange. For example. A buyer may not receive the order even if he/she makes the payment. For another example, the seller possibly get nothing even when the merchandise is sent. Some studies about the fair exchange have proposed protocols for the design of efficiency and exploited the signature property to specify that two parties agree on the exchange. The information about purchased item and price are disclosed in this way. This paper proposes a new fair network payment protocol with off-line trusted third party. The proposed protocol can protect the buyers- purchase message from being traced. In addition, the proposed protocol can meet the proposed requirements. The most significant feature is Non-transfer property we achieved.

Keywords: E-commerce, digital signature, fair exchange, security.

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Authors: Lenka Krulikovská, Jaroslav Polec, Tomáš Hirner

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In this paper we present a novel method, which reduces the computational complexity of abrupt cut detection. We have proposed fast algorithm, where the similarity of frames within defined step is evaluated instead of comparing successive frames. Based on the results of simulation on large video collection, the proposed fast algorithm is able to achieve 80% reduction of needed frames comparisons compared to actually used methods without the shot cut detection accuracy degradation.

Keywords: Abrupt cut, fast algorithm, shot cut detection, Pearson correlation coefficient.

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Authors: Zia Amjad, Salem S. Alghamdi

Abstract:

The study was conducted at the student educational farm at the College of Food and Agriculture in the Kingdom of Saudi Arabia. The aim of study was to characterize 154 Vicia faba L. accessions using agro-morphological traits based on The International Union for the Protection of New Varieties of Plants (UPOV) and The International Board for Plant Genetic Resources (IBPGR) descriptors. This research is significant as it contributes to the understanding of the genetic diversity and potential yield of V. faba in Saudi Arabia. In the study, 24 agro-morphological characters including 11 quantitative and 13 qualitative were observed for genetic variation. All the results were analyzed using multivariate analysis i.e., principal component analysis (PCA). First, six principal components (PC) had eigenvalues greater than one; accounted for 72% of available V. faba genetic diversity. However, first three components revealed more than 10% of genetic diversity each i.e., 22.36%, 15.86% and 10.89% respectively. PCA distributed the V. faba accessions into different groups based on their performance for the characters under observation. PC-1, which represented 22.36% of the genetic diversity, was positively associated with stipule spot pigmentation, intensity of streaks, pod degree of curvature and to some extent with 100 seed weight. PC-2 covered 15.86 of the genetic diversity and showed positive association for average seed weight per plant, pod length, number of seeds per plant, 100 seed weight, stipule spot pigmentation, intensity of streaks (same as in PC-1) and to some extent for pod degree of curvature and number of pods per plant. PC-3 revealed 10.89% of genetic diversity and expressed positive association for number of pods per plant and number of leaflets per plant. This study contributes to the understanding of the genetic diversity and potential yield of V. faba in the Kingdom of Saudi Arabia. By establishing a core collection of V. faba, the research provides a valuable resource for future conservation and utilization of this crop worldwide.

Keywords: Agro-morphological characterization, genetic diversity, core collection, PCA, Vicia faba L.

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Authors: Girija Chetty, Monica Singh

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In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..

Keywords: Biometrics, gait recognition, PCA, LDA, Eigenface, Fisherface, Multivariate Gaussian Classifier

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Authors: Iacopo Testi, Diego Pajarito, Nicoletta Roberto, Carmen Greco

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Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.

Keywords: Data science, spatial analysis, composite index, Rome, urban planning, youth economic discomfort index.

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Authors: Nebi Gedik

Abstract:

The aim of the brain-computer interface studies on electroencephalogram (EEG) signals containing motor imagery is to extract the effective features that will provide the highest possible classification accuracy for the detection of the desired motor movement. However, achieving this goal is difficult as the most suitable frequency band and time frame vary from subject to subject. In this study, the classification success of the two-feature data obtained from raw EEG signals and the coefficients of the multi-resolution analysis method applied to the EEG signals were analyzed comparatively. The method was applied to several EEG channels (C3, Cz and C4) signals obtained from the EEG data set belonging to the publicly available BCI competition III.

Keywords: Motor imagery, EEG, wave atom transform, k-NN.

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Authors: Dewi Nasien, Habibollah Haron, Siti SophiayatiYuhaniz

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This paper presents a comparison of metaheuristic algorithms, Genetic Algorithm (GA) and Ant Colony Optimization (ACO), in producing freeman chain code (FCC). The main problem in representing characters using FCC is the length of the FCC depends on the starting points. Isolated characters, especially the upper-case characters, usually have branches that make the traversing process difficult. The study in FCC construction using one continuous route has not been widely explored. This is our motivation to use the population-based metaheuristics. The experimental result shows that the route length using GA is better than ACO, however, ACO is better in computation time than GA.

Keywords: Handwriting Recognition, Feature Extraction, Freeman Chain Code, Genetic Algorithm and Ant ColonyOptimization.

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Authors: Jian-hua Yeh, Shi-yi Kuo

Abstract:

This paper discusses the classification process for medical data. In this paper, we use the data from ACM KDDCup 2008 to demonstrate our classification process based on latent topic discovery. In this data set, the target set and outliers are quite different in their nature: target set is only 0.6% size in total, while the outliers consist of 99.4% of the data set. We use this data set as an example to show how we dealt with this extremely biased data set with latent topic discovery and noise reduction techniques. Our experiment faces two major challenge: (1) extremely distributed outliers, and (2) positive samples are far smaller than negative ones. We try to propose a suitable process flow to deal with these issues and get a best AUC result of 0.98.

Keywords: classification, latent topics, outlier adjustment, feature scaling

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Authors: Magdalena Pejas

Abstract:

This article concerns the presentation of an integrated method for detection of steganographic content embedded by new unknown programs. The method is based on data mining and aggregated hypothesis testing. The article contains the theoretical basics used to deploy the proposed detection system and the description of improvement proposed for the basic system idea. Further main results of experiments and implementation details are collected and described. Finally example results of the tests are presented.

Keywords: Steganography, steganalysis, data embedding, data mining, feature extraction, knowledge base, system learning, hypothesis testing, error estimation, black box program, file structure.

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Authors: S.Mousavian, D.Ashouri, M.abdolahi, M.H.Vakili, Y.Rahnama

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In this study, the dispersed model is used to predict gas phase concentration, liquid drop concentration. The venturi scrubber efficiency is calculated by gas phase concentration. The modified model has been validated with available experimental data of Johnstone, Field and Tasler for a range of throat gas velocities, liquid to gas ratios and particle diameters and is used to study the effect of some design parameters on collection efficiency.

Keywords: Ammonia, Modelling, Purge gas, Removal efficiency, Venturi scrubber

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Authors: Seyed Nima Tayarani Bathaie

Abstract:

In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: Face detection algorithm, Haar features, Security of ATM.

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Authors: H. B. Darbandi, M. R. Ito, J. Little

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This paper describes a probabilistic method for three-dimensional object recognition using a shared pool of surface signatures. This technique uses flatness, orientation, and convexity signatures that encode the surface of a free-form object into three discriminative vectors, and then creates a shared pool of data by clustering the signatures using a distance function. This method applies the Bayes-s rule for recognition process, and it is extensible to a large collection of three-dimensional objects.

Keywords: Object recognition, modeling, classification, computer vision.

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Authors: Ram Kumar Soni, Alok Jain, Rajiv Saxena

Abstract:

The tree structured approach of non-uniform filterbank (NUFB) is normally used in perfect reconstruction (PR). The PR is not always feasible due to certain limitations, i.e, constraints in selecting design parameters, design complexity and some times output is severely affected by aliasing error if necessary and sufficient conditions of PR is not satisfied perfectly. Therefore, there has been generalized interest of researchers to go for near perfect reconstruction (NPR). In this proposed work, an optimized tree structure technique is used for the design of NPR non-uniform filterbank. Window functions of Blackman family are used to design the prototype FIR filter. A single variable linear optimization is used to minimize the amplitude distortion. The main feature of the proposed design is its simplicity with linear phase property.

Keywords: Tree structure, NUFB, QMF, NPR.

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Authors: F. Rarbi, D. Dzahini, W. Uhring

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In this paper, a dynamic and power efficient 8-bit and 100-MSPS Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) is presented. The circuit uses a non-differential capacitive Digital-to-Analog (DAC) architecture segmented by 2. The prototype is produced in a commercial 65-nm 1P7M CMOS technology with 1.2-V supply voltage. The size of the core ADC is 208.6 x 103.6 µm². The post-layout noise simulation results feature a SNR of 46.9 dB at Nyquist frequency, which means an effective number of bit (ENOB) of 7.5-b. The total power consumption of this SAR ADC is only 1.55 mW at 100-MSPS. It achieves then a figure of merit of 85.6 fJ/step.

Keywords: CMOS analog to digital converter, dynamic comparator, image sensor application, successive approximation register.

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Authors: Akram A. Mustafa

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In this paper an approaches for increasing the effectiveness of error detection in computer network channels with Pulse-Amplitude Modulation (PAM) has been proposed. Proposed approaches are based on consideration of special feature of errors, which are appearances in line with PAM. The first approach consists of CRC modification specifically for line with PAM. The second approach is base of weighted checksums using. The way for checksum components coding has been developed. It has been shown that proposed checksum modification ensure superior digital data control transformation reliability for channels with PAM in compare to CRC.

Keywords: Pulse-Amplitude Modulation, checksum, transmission, discrete.

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Authors: P. Soda, G.Iannello

Abstract:

In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.

Keywords: Artificial neural networks, computer aided diagnosis, image classification, indirect immuno-fluorescence, pattern recognition.

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Authors: Meysam Mohamad pour, Fardad Farokhi

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In this paper in consideration of each available techniques deficiencies for speech recognition, an advanced method is presented that-s able to classify speech signals with the high accuracy (98%) at the minimum time. In the presented method, first, the recorded signal is preprocessed that this section includes denoising with Mels Frequency Cepstral Analysis and feature extraction using discrete wavelet transform (DWT) coefficients; Then these features are fed to Multilayer Perceptron (MLP) network for classification. Finally, after training of neural network effective features are selected with UTA algorithm.

Keywords: Multilayer perceptron (MLP) neural network, Discrete Wavelet Transform (DWT) , Mels Scale Frequency Filter , UTA algorithm.

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Authors: Hoon Lee, Soonyoung Park, Kyoungho Choi

Abstract:

Understanding road features such as lanes, the color of lanes, and sidewalks in a live video captured from a moving vehicle is essential to build video-based navigation systems. In this paper, we present a novel idea to understand the road features using support vector machines. Various feature vectors including color components of road markings and the difference between two regions, i.e., chosen AOIs, and so on are fed into SVM, deciding colors of lanes and sidewalks robustly. Experimental results are provided to show the robustness of the proposed idea.

Keywords: video-based navigation system, lane detection, SVMs, autonomous vehicles

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Authors: Awais Adnan, Muhammad Ali, Amir Hanif Dar

Abstract:

Image Searching was always a problem specially when these images are not properly managed or these are distributed over different locations. Currently different techniques are used for image search. On one end, more features of the image are captured and stored to get better results. Storing and management of such features is itself a time consuming job. While on the other extreme if fewer features are stored the accuracy rate is not satisfactory. Same image stored with different visual properties can further reduce the rate of accuracy. In this paper we present a new concept of using polynomials of sorted histogram of the image. This approach need less overhead and can cope with the difference in visual features of image.

Keywords: Sorted Histogram, Polynomial Curves, feature pointsof images, Grayscale, visual properties of image.

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Authors: Adrian Alvarez, Diego Rial

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In this work we present a family of new convergent type methods splitting high order no negative steps feature that allows your application to irreversible problems. Performing affine combinations consist of results obtained with Trotter Lie integrators of different steps. Some examples where applied symplectic compared with methods, in particular a pair of differential equations semilinear. The number of basic integrations required is comparable with integrators symplectic, but this technique allows the ability to do the math in parallel thus reducing the times of which exemplify exhibiting some implementations with simple schemes for its modularity and scalability process.

Keywords: Lie Trotter integrators, Irreversible Problems, Splitting Methods without negative steps, MPI, HPC.

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Authors: K. Ratan Singh, P. K. Das

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An induced graphoidal cover of a graph G is a collection ψ of (not necessarily open) paths in G such that every path in ψ has at least two vertices, every vertex of G is an internal vertex of at most one path in ψ, every edge of G is in exactly one path in ψ and every member of ψ is an induced cycle or an induced path. The minimum cardinality of an induced graphoidal cover of G is called the induced graphoidal covering number of G and is denoted by ηi(G) or ηi. Here we find induced graphoidal cover for some classes of graphs.

Keywords: Graphoidal cover, Induced graphoidal cover, Induced graphoidal covering number.

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Authors: Kamal Ali Albashiri, Khaled Ahmed Kadouh

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A generic and extendible Multi-Agent Data Mining (MADM) framework, MADMF (the Multi-Agent Data Mining Framework) is described. The central feature of the framework is that it avoids the use of agreed meta-language formats by supporting a framework of wrappers. The advantage offered is that the framework is easily extendible, so that further data agents and mining agents can simply be added to the framework. A demonstration MADMF framework is currently available. The paper includes details of the MADMF architecture and the wrapper principle incorporated into it. A full description and evaluation of the framework-s operation is provided by considering two MADM scenarios.

Keywords: Multi-Agent Data Mining (MADM), Frequent Itemsets, Meta ARM, Association Rule Mining, Classifier generator.

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Authors: Seong Woo Kwak, Jung-Min Yang

Abstract:

A strategy of fault diagnosis and tolerance for asynchronous sequential machines is discussed in this paper. With no synchronizing clock, it is difficult to diagnose an occurrence of permanent or stuck-in faults in the operation of asynchronous machines. In this paper, we present a fault detector comprised of a timer and a set of static functions to determine the occurrence of faults. In order to realize immediate fault tolerance, corrective control theory is applied to designing a dynamic feedback controller. Existence conditions for an appropriate controller and its construction algorithm are presented in terms of reachability of the machine and the feature of fault occurrences.

Keywords: Asynchronous sequential machines, corrective control, fault diagnosis and tolerance, fault detector.

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Authors: Qamar Saeed, Vali Uddin, Reza Katebi

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In this paper multivariable predictive PID controller has been implemented on a multi-inputs multi-outputs control problem i.e., quadruple tank system, in comparison with a simple multiloop PI controller. One of the salient feature of this system is an adjustable transmission zero which can be adjust to operate in both minimum and non-minimum phase configuration, through the flow distribution to upper and lower tanks in quadruple tank system. Stability and performance analysis has also been carried out for this highly interactive two input two output system, both in minimum and non-minimum phases. Simulations of control system revealed that better performance are obtained in predictive PID design.

Keywords: Proportional-integral-derivative Control, GeneralizedPredictive Control, Predictive PID Control, Multivariable Systems

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Authors: Jiaji Zhou, Le Liang, Long Chen

Abstract:

Geographic Profiling has successfully assisted investigations for serial crimes. Considering the multi-cluster feature of serial criminal spots, we propose a Multi-point Centrography model as a natural extension of Single-point Centrography for geographic profiling. K-means clustering is first performed on the data samples and then Single-point Centrography is adopted to derive a probability distribution on each cluster. Finally, a weighted combinations of each distribution is formed to make next-crime spot prediction. Experimental study on real cases demonstrates the effectiveness of our proposed model.

Keywords: Geographic profiling, Centrography model, K-means algorithm

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Authors: Y.B.Gandole

Abstract:

Compaction testing methods allow at-speed detecting of errors while possessing low cost of implementation. Owing to this distinctive feature, compaction methods have been widely used for built-in testing, as well as external testing. In the latter case, the bandwidth requirements to the automated test equipment employed are relaxed which reduces the overall cost of testing. Concurrent compaction testing methods use operational signals to detect misbehavior of the device under test and do not require input test stimuli. These methods have been employed for digital systems only. In the present work, we extend the use of compaction methods for concurrent testing of analog-to-digital converters. We estimate tolerance bounds for the result of compaction and evaluate the aliasing rate.

Keywords: Analog-to Digital Converter, Embedded system, Concurrent Testing

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Authors: Maria de Nazaré Moraes Soares, Silvia Maria Dias Pedro Rebouças, José Carlos Lázaro

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The Solidarity Economy (SE) acts in the re-articulation of the economic field to the other spheres of social action. The significant participation of women in SE resulted in the formation of a national network of self-managed enterprises in Brazil: The Solidarity and Feminist Economy Network (SFEN). The objective of the research is to identify factors of gender justice and feminist self-management practices that adhere to the reality of women in SE enterprises. The conceptual apparatus related to feminist studies in this research covers Nancy Fraser approaches on gender justice, and Patricia Yancey Martin approaches on feminist management practices, and authors of postcolonial feminism such as Mohanty and Maria Lugones, who lead the discussion to peripheral contexts, a necessary perspective when observing the women’s movement in SE. The research has a quantitative nature in the phases of data collection and analysis. The data collection was performed through two data sources: the database mapped in Brazil in 2010-2013 by the National Information System in Solidary Economy and 150 questionnaires with women from 16 enterprises in SFEN, in a state of Brazilian northeast. The data were analyzed using the multivariate statistical technique of Factor Analysis. The results show that the factors that define gender justice and feminist self-management practices in SE are interrelated in several levels, proving statistically the intersectional condition of the issue of women. The evidence from the quantitative analysis allowed us to understand the dimensions of gender justice and feminist management practices intersectionality; in this sense, the non-distribution of domestic work interferes in non-representation of women in public spaces, especially in peripheral contexts. The study contributes with important reflections to the studies of this area and can be complemented in the future with a qualitative research that approaches the perspective of women in the context of the SE self-management paradigm.

Keywords: Feminist management practices, gender justice, self-management, solidarity economy.

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Authors: Z. Shaaban

Abstract:

This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.

Keywords: Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinié

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. In this context, the automation of this task is urgent. In this work, we compare classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN and Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches.

Keywords: Image segmentation, stuck particles separation, Sobel operator, thresholding.

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