An Unsupervised and Robust Line and Word Segmentation Method for Handwritten and Degraded Printed Document

Segmentation of text lines and words in an unconstrained handwritten or a machine-printed degraded document is a challenging document analysis problem due to the heterogeneity in the document structure. Often there is un-even skew between the lines and also broken words in a document. In this article, the contribution lies in segmentation of a document page image into lines and words. We have proposed an unsupervised, robust, and simple statistical method to segment a document image that is either handwritten or machine-printed (degraded or otherwise). In our proposed method, the segmentation is treated as a two-class classification problem. The classification is done by considering the distribution of gap size (between lines and between words) in a binary page image. Our method is very simple and easy to implement. Other than the binarization of the input image, no pre-processing is necessary. There is no need of high computational resources. The proposed method is unsupervised in the sense that no annotated document page images are necessary. Thus, the issue of a training database does not arise. In fact, given a document page image, the parameters that are needed for segmentation of text lines and words are learned in an unsupervised manner. We have applied our proposed method on several popular publicly available handwritten and machine-printed datasets (ISIDDI, IAM-Hist, IAM, PBOK) of different Indian and other languages containing different fonts. Several experimental results are presented to show the effectiveness and robustness of our method. We have experimented on ICDAR-2013 handwriting segmentation contest dataset and our method outperforms the winning method. In addition to this, we have suggested a quantitative measure to compute the level of degradation of a document page image.

Robust clustering of functional directional data

A robust approach for clustering functional directional data is proposed. The proposal adapts “impartial trimming” techniques to this particular framework. Impartial trimming uses the dataset itself to tell us which appears to be the most outlying curves. A feasible algorithm is proposed for its practical implementation justified by some theoretical properties. A “warping” approach is also introduced which allows including controlled time warping in that robust clustering procedure to detect typical “templates”. The proposed methodology is illustrated in a real data analysis problem where it is applied to cluster aircraft trajectories.

Importance and Application of Mathematics in Everyday Life

Mathematics is a field of science that studies numbers and how they are used. It includes calculations, computations, and problem solving, among other things. It is a subject that is accurate, precise, methodical, and logical. Mathematics has been defined in a variety of ways throughout history; it is an indispensible component of science and is utilized in virtually every discipline, including natural science, engineering, art, and economics. Mathematics is a vital instrument in our lives and in every scientific field that promotes personal growth and development on a broad scale. To avoid chaos and confusion, mathematics makes life smoother and more organized. Problem solving, creativity, critical thinking, and reasoning capacity are some of the traits and talents fostered by mathematics. Other unique skills include analyzing and communicating effectively. Everyone requires mathematics in their daily lives, whether they are a cook or a farmer, a carpenter or a mechanic, a shopkeeper or a doctor, an engineer or a scientist, a musician or a magician. Therefore, it would be impossible to summarize mathematics applications in each field. Through this research document, it is intended to talk about the importance and applications of mathematics in our daily lives. Keywords: Mathematics, Importance of Mathematics, Application of Mathematics, Analysis, Problem Solving and Critical Thinking Abilities.

Tight Polynomial Bounds for Loop Programs in Polynomial Space

We consider the following problem: given a program, find tight asymptotic bounds on the values of some variables at the end of the computation (or at any given program point) in terms of its input values. We focus on the case of polynomially-bounded variables, and on a weak programming language for which we have recently shown that tight bounds for polynomially-bounded variables are computable. These bounds are sets of multivariate polynomials. While their computability has been settled, the complexity of this program-analysis problem remained open. In this paper, we show the problem to be PSPACE-complete. The main contribution is a new, space-efficient analysis algorithm. This algorithm is obtained in a few steps. First, we develop an algorithm for univariate bounds, a sub-problem which is already PSPACE-hard. Then, a decision procedure for multivariate bounds is achieved by reducing this problem to the univariate case; this reduction is orthogonal to the solution of the univariate problem and uses observations on the geometry of a set of vectors that represent multivariate bounds. Finally, we transform the univariate-bound algorithm to produce multivariate bounds.

Set-Invariance-based Interpretations for the L1 Performance of Nonlinear Systems

This paper is concerend with tackling the L 1 performance analysis problem of continuous and piecewise continuous nonlinear systems with non-unique solutions by using the involved arguments of set-invariance principles. More precisely, this paper derives a sufficient condition for the L 1 performance of continuous nonlinear systems in terms of the invariant set. However, because this sufficient condition intrinsically involves analytical representations of solutions of the differential equations corresponding to the nonlinear systems, this paper also establishes another sufficient condition for the L 1 performance by introducing the so-called extended invariance domain, in which it is not required to directly solving the nonlinear differential equations. These arguments associated with the L 1 performance analysis is further extended to the case of piecewise continuous nonlinear systems, and we obtain parallel results based on the set-invariance principles used for the continuous nonolinear systems. Finally, numerical examples are provided to demonstrate the effectiveness as well as the applicability of the overall results derived in this paper.

ANALYSIS OF TRAFFIC CONGESTION AT TRANSPORT TRANSFER HUBS USING DRONES

This article considers the possibility of using drones to identify and analyze the congestion of traffic-transfer hubs in the urban environment. The article substantiates the need to analyze the congestion of traffic-transfer hubs, due to the unevenness of traffic congestion. Classical models of congestion estimation do not allow taking into account the dynamics of the process. The data obtained by manual calculations contain errors and require lengthy processing. Using the data from cameras allows solving the problem of congestion analysis, but it is not possible to solve this problem with high accuracy during peak hours. The use of data obtained from drones (from an unmanned aerial system) is proposed to solve the congestion analysis problem. To implement this solution, the article presented a list of necessary components that a drone should have, an example of the source code for the implementation of an autonomous flight, an example of the implementation of the traffic-transfer hubs overflight scheme using special software.

A Z-number integrated weighted MULTIMO-ORA method for risk prioritization in FMEA

Current risk prioritization approaches for FMEA models are insufficient to cope with risk analysis problem in which the self-confidence of expert’s judgment and the deviation among risk evaluation information are considered, simultaneously. Therefore, to remedy this limitation, this paper reports an extended risk prioritization approach by integrating the MULTIMOORA approach, Z-numbers and power weighted average (PWA) operator. Firstly, the Z-numbers with triangular fuzzy numbers are applied to reflect the self-confidence and uncertainty of expert’s judgment. Next, the PWA operator for Z-numbers (Z-PWA) with similarity measure is proposed to obtain the group risk evaluation matrix by considering the influence of the deviation among risk evaluation information. Then, an extended version of MULTIMOORA method with developed entropy method is presented to calculate risk priority ranking order of each failure. Finally, the equipment failures in a certain oil and gas plant is utilized to test the extended risk prioritization approach for FMEA model. After that, the sensitivity and comparison studies are led to illustrate the availability and reliability of the proposed risk prioritization approach for FMEA based risk analysis problem.

Training on ant sugar production to increase people's income in Ba'ka Village, Enrekang

Most of the people of Ba'ka Village are conventional palm sugar farmers whose whose selling prices are cheap. The purpose of this service is to provide innovation in palm sugar into ant sugar. Through training in making ant sugar, it is hoped that it can increase people's income. The stages of community service include needs analysis, problem formulation, work program formulation, and work program implementation, and evaluation. This service activity produces ant sugar products that are packaged in a modern way. Ant sugar products can attract consumers because they are more durable, hygienic, and practical.

Fuzzy Entropy-Based Spatial Hotspot Reliability

Cluster techniques are used in hotspot spatial analysis to detect hotspots as areas on the map; an extension of the Fuzzy C-means that the clustering algorithm has been applied to locate hotspots on the map as circular areas; it represents a good trade-off between the accuracy in the detection of the hotspot shape and the computational complexity. However, this method does not measure the reliability of the detected hotspots and therefore does not allow us to evaluate how reliable the identification of a hotspot of a circular area corresponding to the detected cluster is; a measure of the reliability of hotspots is crucial for the decision maker to assess the need for action on the area circumscribed by the hotspots. We propose a method based on the use of De Luca and Termini’s Fuzzy Entropy that uses this extension of the Fuzzy C-means algorithm and measures the reliability of detected hotspots. We test our method in a disease analysis problem in which hotspots corresponding to areas where most oto-laryngo-pharyngeal patients reside, within a geographical area constituted by the province of Naples, Italy, are detected as circular areas. The results show a dependency between the reliability and fluctuation of the values of the degrees of belonging to the hotspots.