Purpose: The main objective of this study is to propose a Keystroke dynamics (KD) based Parkinson’s disease (PD) indicator using both fixed-text and free-text typing habits on conventional keyboards and homogeneous ensemble learning with several fully balanced bootstrapped training sets for more accurate and robust eHealth applications. Furthermore, this study addresses the six key hypotheses related to our main objective of understanding the risks in this screening process that were previously unknown but important for further improvement. Methods: For a predetermined window length, the Colin Bannard and neuroQWERTY MIT-CSXPD datasets were used to extract a series of key hold times and the time gaps between two consecutive presses and releases as a feature set. A wide range of statistical tools were employed to create a machine learning-ready feature arrangement for continuously generated patterns. A homogeneous ensemble learning approach was then developed using bootstrapping and under-sampling while retaining any rare samples. This model was validated using fifteen fixed-text and free-text inputs obtained from early-stage PD patients, De-novo PD patients, and healthy controls. Results: In the leave-one-user-out cross-validation (LOUOCV) evaluation, the maximum observed area under curve (AUC) is 85.99% ± 0.41 for fixed-text input. However, for free-text inputs, AUC is 78.3% ± 0.86, with a sensitivity/specificity of 74.46%/82.13%. The AUC for detecting De-novo patients is 79.83% ± 1.26, which is somewhat lower than the AUC for determining the early stage of the disease, which is 83.81% ± 0.83. Conclusion: The proposed model is more robust, usable (covert way of data acquisition), fast, and has ease of integration into conventional desktops/laptops suitable for real-life eHealth that could help for better diagnosis, early detection in a home environment, future reference, and treatment or therapy management. However, the subject size, severity levels of the disease, typing duration, feature composition, error in typing, and machine learning (ML) method selection influence the performance of this model. Therefore, careful attention is necessary while designing PD indicators using the proposed approach.

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.

Domain knowledge is very important to support the development of analytic models. However, in today's data science projects, domain knowledge is typically documented, but not captured and integrated with the actual analytic model. This raises problems in interoperability and traceability of the relevant domain knowledge that is used to develop an analytic model. To address this challenge, this paper proposes a Knowledge Enriched Analytic Model (KEAM) to enrich analytic models with domain knowledge. To explore the proposed methodology and its benefits, a case study explores the utilization of KEAM to support the development of a Bayesian Network model within the smart manufacturing domain. The case study shows that the efficiency in developing an analytic model is improved by using the proposed KEAM.

Sustainability assessment (SA) has been one of the prime contributors to advanced manufacturing analysis, and it traditionally involves life cycle assessment (LCA) techniques for retrospective and prospective evaluations. One big challenge to reach a reliable sustainability assessment comes from the inadequate understandings of the underlying activities related to each of the product lifecycle stages based on expert knowledge. Data-driven modeling, on the other hand, is an emerging approach that takes advantage of machine-learning methods in building models that would complement or replace the knowledge-based models capturing physical behaviors. Incorporating suitable data analytics models to utilize real-time product and process data could significantly improve LCA techniques. To address the complexity and uncertainty involved in multilevel SA decision-making activities, this paper proposes a modular LCA framework to accommodate a hybrid modeling paradigm that includes knowledge-based and data-driven models. We identify and emphasize on two important challenges: (1) Generalizing knowledge-based and data-driven models into analytics models so that they can be uniformly deployed and interchanged, and (2) Modularizing the LCA decision logics and model structures so that the LCA decision process can be streamlined and easily maintained. The issues related to the decomposition, standardization, deployment and execution of analytics models are discussed in this paper. Three well-adopted standards - STEP (Standard for the Exchange of Product model data), DMN (Decision Model and Notation), and PMML (Predictive Model Markup Language) are employed to capture the product-related data/information, the decision logic decomposition of analytics models, and the structure decomposition of analytics models, respectively. The feasibility and benefits of the proposed modular, hybrid sustainability assessment methodology have been illustrated with an injection molding case study, incorporating an overall modular Scorecard-based LCA architecture with a Bayesian Network predictive model.

In this article, we propose a new framework for segmentation of Bangla handwritten word images into meaningful individual symbols or pseudo-characters. Existing segmentation algorithms are not usually treated as a classification problem. However, in the present study, the segmentation algorithm is looked upon as a two-class supervised classification problem. The method employs an SVM classifier to select the segmentation points on the word image on the basis of various structural features. For training of the SVM classifier, an unannotated training set is prepared first using candidate segmenting points. The training set is then clustered, and each cluster is labeled manually with minimal manual intervention. A semi-automatic bootstrapping technique is also employed to enlarge the training set from new samples. The overall architecture describes a basic step toward building an annotation system for the segmentation problem, which has not so far been investigated. The experimental results show that our segmentation method is quite efficient in segmenting not only word images but also handwritten texts. As a part of this work, a database of Bangla handwritten word images has also been developed. Considering our data collection method and a statistical analysis of our lexicon set, we claim that the relevant characteristics of an ideal lexicon set are present in our handwritten word image database.

Reliable message delivery is a challenging task in Vehicular Ad Hoc Networks (VANETs). Current literature on VANETs generalize all messages and use the same strategy to transmit them. In this paper, we model the cooperative service-based message sharing problem in VANETs as a coalition formation game among nodes. Nodes associate with a coalition based on the type of service-message they process. In the proposed model, service-messages are distinguished from one another by their types. Nodes process different types of service-messages and form a coalition based on the type of messages they process at that time. Some nodes within a coalition can work as a relay, which is modeled as a network formation game to select exactly one relay among a group of potential relay nodes to improve efficiency of the network in terms of improved packet reception rate and reducing transmission delay. The nodes form independent disjoint coalitions and tree structure is formed with the relay nodes within a coalition by using the proposed algorithm, COMES. Simulation results show that COMES, which allows nodes to form independent coalitions among themselves, improves the network performance in terms of incentive received by players by at least 40 percent compared to a non-cooperative function.

Physical products are becoming smarter because of their increased number of embedded sensors and their real-time information-processing capabilities. Data analytics, particularly predictive analytics, is one of the most important of these capabilities because it uses statistical or machine-learning techniques to determine causal relations between input and output parameters. Many researchers have addressed the challenges in creating and evaluating predictive models. Few, however, have discussed how to employ such models effectively throughout a product's life cycle. In this paper, we address this issue by extending Product Lifecycle Management (PLM) systems to include "Smart Component" data models that incorporate predictive models as "parts" or "services" of products in their master records in PLM. These smart-component data models can be modularized, composed, reused, traced, maintained, and replaced on demand. We describe a prototype system to demonstrate the feasibility of the proposed data models using an open-source PLM platform.

The highly networked and real-time data analysis features of smart manufacturing systems (SMS) require different information infrastructure, data analytics technology, and performance assurance methodologies. The main purpose of this paper is to (i) explore the complete product-process performance assurance space to identify the key performance indicators that help evaluate and quantify system performance at different abstraction levels, (ii) discuss models and methodologies for data analytics, and (iii) suggest a digital factory-based simulation technique to evaluate those key indicators for performance prediction. The paper presents a systematic and rigorous approach towards establishing these performance assurance methodologies applicable to complex value chains of smart manufacturing systems by extensively exploring all possible product and process related performance issues. A hypercube information model is proposed for the purpose of formal representation of the highly dimensional and correlated information among different actors in a smart manufacturing system, thus providing a rigorous foundation for the performance assurance space. The relevant taxonomy and an ontology-based framework are then developed for formal representation of the entities, activities and knowledge involved in the performance assurance domain. It provides a detailed insight into the PA space and defines appropriate measures which can be applied to predict and improve system performance assurances.

This paper presents a statistical model based scheme for automatic extraction of text components from digital images. The work is composed of two tasks. First, we perform segmentation of a color image by applying a pair-copula based mixture model. This produces a number of spatially connected components (some of which may be text). From each of these components, we extract certain features that could discriminate text from non-text components. The feature vectors, arising from text components, are assumed to be random samples from a pair-copula based multivariate distribution. This distribution parameters can be estimated using training text samples (i.e., connected components). Here, we use the ICDAR 2011 "Born-Digital Images'' data set since it provides such ground truth text components. We estimate distribution parameters based on the feature vectors obtained from these training text components. The final task remain is to infer whether a test sample is a text component. We apply a non-parametric statistical hypothesis testing to assess whether a test sample is generated from the known multivariate distribution. If so, we may regard the sample to be a text. Our results obtained on ICDAR 2011 "Born-Digital Images'' data set, are satisfactory.

Gulf is a significant region to study due to its economic importance with large number of ports and hydrological significance for its extreme tidal circulation. Remote sensing data of both physical and biological parameters is assimilated using Empirical Orthogonal Function (EOF) and multiscale objective analysis and incorporated into a three dimensional mathematical model to simulate the plankton dynamics and analyze the regional distribution. This three-dimensional circulation model is coupled with five compartment (Nutrient, Chlorophyll a, Phytoplankton, Zooplankton and Detritus) biogeochemical model first time for the Gulf. In the present study, depth integrated phytoplankton for the Gulf of Kutch is estimated. Chlorophyll a (chl_a) data collected from MODIS Aqua chl_a data is found to be higher above 4 mg/m3in almost all the ports except Jamnagar. The model output validated with satellite data show that the model possesses reasonably skill.

In this paper we introduce a stroke based lexicon reduction technique in order to reduce the search space for recognition of handwritten words. The principle of this technique involves mainly two aspects of a word image to constitute a feature vector: one is word-length and the other is shape of the word. The length of the word image is represented by the number of specific vertical strokes present in the word image and, on the other hand, the shape of a word image is realized with the combination of both horizontal and vertical strokes. The experiment has been carried out with a database of 35,700 off-line handwritten Bangla word images. Though our proposed lexicon reduction technique is developed for recognition of Bangla handwritten words, its generalization property can easily be exploited for recognition of handwriting in other scripts also.

This paper presents a recognition system for isolated handwritten Bangla words, with a fixed lexicon, using a left-right hidden Markov model (HMM). A stochastic search method, namely, genetic algorithm (GA) is used to train the HMM. A new shape based direction encoding features has been developed and introduced in our recognition system. Both non-discriminative and discriminative training procedures have been applied iteratively to optimize the parameters of HMM.

Tolerances impose restrictions on the possible deviations of features from their nominal sizes/shapes. These variations of size/shape could be thought of as deviations of a set of generalized coordinates defined at some convenient point on a feature. Any tolerance specification for a feature imposes some kind of restrictions or constraints on its deviation parameters. These constraints, in general, define a bounded region in the deviation space. In this paper, a method has been presented for converting tolerance specifications as per maximum material condition (MMC)/least material condition (LMC)/regardless of feature size (RFS) material conditions for standard mating features (planar, cylindrical, and spherical) into a set of inequalities in a deviation space. Both virtual condition boundaries Virtual condition boundary: A constant boundary generated by the collective effects of a size feature's specified MMC or LMC material condition and the geometric tolerance for that material condition. (VCB) and tolerance zones are utilized for these mappings. The mapping procedures have been illustrated with an example. Note to Practitioners-This paper deals with methods to convert tolerance specification as per ASME Y14.5M into a set of generalized deviation of features and vice-versa. These are intermediate relationships that are required for use in deviation-based tolerance synthesis methods. In this work, different examples have been presented to show how different tolerance specifications (such as positional tolerance at maximum material condition (MMC), least material condition (LMC), etc.) applied to different features could be treated on a generalized basis for tolerancing of manufactured parts.

In this paper, a generic model for the synthesis of tolerances for manufactured parts is presented. The model uses a method of transforming traditional tolerance specifications (as defined in ASME Y14.5M) to a generalized coordinate system (hereinafter referred to as deviation space). Small displacement torsors (SDTs) have been used for representing the deviations. The tolerance synthesis method is formulated as a constrained nonlinear optimization process. Three different types of constraints have been considered for the optimization process: 1) representation of assemblability; 2) mapping of tolerance specification to deviations; and 3) functional requirements. A new deviation-based cost of manufacturing model has been proposed. A working module of the scheme has been implemented and the process has been elaborated with two examples. The possibility of extension of the model and scope for further generalization have been discussed. Note to Practitioners-This paper presents an optimization method for finding tolerance values for different features of an assembly of manufactured parts. Determination of different types of tolerances and their exact values for critical features of any part has been an ad-hoc process; it has been mostly experience-based till recent time. In this paper, efforts have been made to establish a strong mathematically oriented method for synthesis of tolerances. The procedure attempts to minimize cost of manufacturing while the functionality and assemblability are satisfied. It is a generalized method and could be applied for designing rigid manufacture parts. For practical usage, this method should be integrated with three-dimensional computer-aided design packages as a tolerance synthesis module for integrated tolerance design.

The paper analyzes the mating natures of a 2D polygonal assembly and discusses uncertainty in assemblies due to the geometric (size and shape) variations of the assembled parts. To study variant assembly configurations, translational and rotational motion constraints are generated by analyzing geometric contact relations between variant parts. Based on these motion constraints, the uncertainty build-ups in an assembly of toleranced parts are investigated.

This paper presents a scheme for representing form tolerances for polyhedral objects in solid modelers. This includes the development of a complete form tolerance zone definition based on a rigorous mathematical formulation in which derived algebraic constraints define the model variations. The paper also presents a procedure for the representation of the variant boundary surfaces that approximate the real world form variations and simulate the variations by planar surfaces (for tolerance analysis). The proposed scheme has been implemented in an object-oriented programming environment.

A framework has been developed for an expert system that generates process plans for the manufacture of machined parts. The system consults the solid models of the parts (CAD database) for geometrical and technological information, and generates a process plan. Process plans are generated using the blackboard architecture concept through interaction among different experts from the process planning domain. A systematic method has been proposed for creating the different knowledge and databases as required for process planning. The methodology also provides an efficient scheme for exploiting the process knowledge bases. A prototype expert system (BBPP) has been developed using the CLIPS V 6.0 expert system shell and has been integrated with a feature based design system developed over the CAEDS solid modeler.

The paper presents a computational-geometry-based method of determining the roundness error of a measured workpiece. A set of n points (obtained from the measured workpiece) in a plane being given, it is required that the center and the radii of a pair of concentric circles be found such that no point is exterior to the space bounded by the circles, with the condition that the radial separation between the circles is minimum. The paper addresses the mathematical formalization of the problem. The properties of convex-hull and Voronoi diagrams have been exploited to develop a faster algorithm for establishing the circles. The methodology has been implemented, and the results have been presented to validate the computational effectiveness of the approach.

The paper surveys the current state of knowledge of techniques for representing, manipulating and analysing dimensioning and tolerancing data in computer-aided design and manufacturing. The use of solid models and variational geometry, and its implications for the successful integration of CAD and CAM, are discussed. The topics explored so far can be grouped into four categories: (a) the representation of dimensioning and tolerancing (D & T), (b) the synthesis and analysis of D & T, (c) tolerance control, and (d) the implications of D & T in CAM. The paper describes in detail the recent work in each group, and concludes with speculation on a general framework for future research.