Srinivasan Radhakrishnan

Finger-based multitouch interface for performing 3D CAD operations

The area of multitouch interaction research is at its infancy. The commercial sector has seen an exponential growth in this area with ubiquitous products like Apple i-Phone, i-Pad, and Microsoft surface table. In spite of their popularity, developers are still finding it difficult to extend this novel interface to engineering applications such as computer aided design (CAD), due to insufficient understanding of the factors that affect the multitouch interface interaction when applied to CAD operations. The objective of this research is to (1) outline the key elements of the multitouch interface for CAD, (2) identify the factors affecting the performance of a multitouch enabled CAD modeling environment, and (3) lay a foundation for future research and highlight the directions for extending the multitouch interface for CAD and other engineering applications. To demonstrate specific results we have conducted mouse emulation experiments. We compared the performance of two finger touch-based interaction techniques (drag state finger touch and track state finger touch) and a standard mouse device for 3D CAD modeling operations. The results indicated that both the task completion time and error rates are statistically the same for both the finger touch-based techniques. However, the error concentration observed from the experiments revealed that for the edge selection tasks, the track state technique is more suited than the drag state technique. Both the finger touch-based techniques suffered from precise dimension control while executing the tasks. The inclusion of a grid on the design space for modeling purpose reduced user errors. The mouse device outperformed both the finger touch-based techniques and yielded statistically better results in terms of task completion time and error rates.

Correction: Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature

[This corrects the article DOI: 10.1371/journal.pone.0172778.].

Analyzing structural & temporal characteristics of keyword system in academic research articles

Keyword networks, formed from keywords occurring in scholarly articles provide a useful mechanism for understanding academic research trends. In keyword networks, keywords are represented as nodes and a link is formed between a pair of keywords if they appear in the same article. Each link is assigned a weight, representing the number of co-occurrences of the pair in different articles. A statistical and visual analysis of the structural and temporal characteristics of such networks reveals the organizing pattern and the evolution of keywords. In this study we analyse the difference between structured keyword system and unstructured keyword system. We use keywords from two prominent business management journals from USA and India and analyse the corresponding keyword networks. Our results indicate that the network characteristics of structured keyword system are more suitable than unstructured keyword system to analyse research trends and bring forth the emerging areas and popular research methods. The adoption of structured keyword system will aid researchers and funding agencies to optimize their decision on the use of research funding.

Phase Synchronization Approach to Construction and Analysis of Stock Correlation Network

Abstract Stock correlation network, a subset of financial network, is built on the stock price correlation. It is used for observing, analyzing and predicting the stock market dynamics. Existing correlation methods include the minimum spanning tree (MST), planar maximally filtered graph (PMFG), and winner take all (WTA). The MST and PMFG methods lose information due to the connection criterion and thereby fail to include certain highly correlated stocks. The WTA method, when used for a non-linear system such as stock prices, fails to capture the dynamic behavior embedded in the time series of the stocks. In this paper we present a new method, which we call phase synchronization (PS) for constructing and analyzing the stock correlation network. The PS method captures the dynamic behavior of the time series of stocks and mitigates the information loss. To test the proposed PS method we use the weekly closing stock prices of the S&P index (439 stocks) from 2000-2009. The PS method provides valuable insights into the behavior of highly correlated stocks which can be useful for making trading decisions. The network exhibits a scale free degree distribution for both chaotic and non-chaotic periods.

Convergence and divergence in academic and industrial interests on IOT based manufacturing

The manufacturing industry is poised to embrace a major disruption due to the advent of Cyber-Physical Systems (CPS), or popularly known as Internet of Things (IOT). Recent past witnessed a hype phase where diverse set of industries recognized the transformational potential of IOT to amplify efficiencies and reduce costs. Today many organizations have are open to extracting business value from adoption of IOT solutions and some among them have already started implementing or implemented IOT projects at commercial scale. In this work we create a knowledge map of “IOT enabled manufacturing” in academics research and compare it with that in industry. We use keyword co-occurrence network (KCN) to map the connections between keywords appearing in academic work. Statistical properties of KCN enable us to compare academic and industrial direction in IOT enabled manufacturing.

Phase Synchronization based Minimum Spanning Trees for the Analysis and Visualization of Currency Exchange Markets

Abstract Correlation based Minimum Spanning Tree (MST) networks are instrumental in capturing the basic workings of a system and have been accepted for use in the analysis of stock and currency exchange markets. Research in network analysis of financial markets shows that although correlations underlying MST networks capture essential information, they do not faithfully capture dynamic behavior embedded in the time series data of financial systems. We present a new Phase Synchronization (PS) based method for establishing correlations between nonlinear time series data, prior to constructing the MST. In this method, time series data generated by each entity in a system is transformed to a recurrence plot and the recurrence plots are further transformed to trajectories in phase space. For each pair of trajectories, phase synchronization (PS) is quantified based on the degree of phase locking observed. Distances for the MST are then computed as a function of the PS between each entity pair. We demonstrate the method using Thailand Baht exchange rates with 82 countries from 2004 to 2012. We further analyze and compare networks constructed using the PS method (PS-MST) and the existing cross-correlation method (CC-MST), to study the differences in market dynamics captured by these methods.

Supply Chain Resiliency: A Review

This chapter provides a broad overview of the field of supply chain resiliency. First, we define the concept of resiliency from the perspective of a supply chain. Next the terms risk and vulnerability are defined in the context of a resilient supply chain. This connects previous studies in supply chain engineering to the emerging field of resiliency. The second section of the chapter outlines components that contribute to the resiliency of a supply chain. Supply chain flexibility, velocity, visibility, and collaboration are defined and references to additional sources are provided. The third section of the chapter outlines processes that are used for building resilient supply chains. A number of relationships between supply chain risk and profitability are explored, along with their impact on supply chain resiliency. This section further provides a unifying exploration of the various aspects and perspectives on supply chain engineering, including how they can be utilized for developing and measuring the resiliency of a supply chain. The chapter concludes with remarks ongoing research on supply resiliency and identifies knowledge gaps and topics for future research.

Complexity-entropy feature plane for gear fault detection

The Complexity-Entropy Causality Plane (CECP) is a representation space with two dimensions: normalized permutation entropy (Hs) and Jensen-Shannon complexity (Cjs). CECP has wide found applications in non-linear dynamic analysis to classify a given signal according to its randomness and complexity which is a motivation to investigate its application for machine fault diagnostics. In this work we extract features from vibration signals (from gear box) using CECP approach to classify normal operational condition and faulty condition. We observe that the method using CECP is able to identify the changes in underlying dynamics of the input signal, which enables high accuracy classification. The method using CECP generates two-feature vectors with minimal preprocessing of the raw signals. In addition CECP is insensitive to external noise, non-stationarity, and trends; this makes CECP a good candidate for machine fault classification.

Health Care in US: A Combined Simulation Methodology to Assess the Effectiveness of Home-Monitoring Programmes

This article presents a multi-resolution simulation method to assess the effectiveness of the home-bound patient-monitoring programmes in terms of patient satisfaction and system efficiency. In the first stage, an agent-based model is used to assess the patient satisfaction. In the second stage, the first-stage patient satisfaction results are fed to a system dynamics model to analyse the global behaviour of a primary care system supported by home-bound patient-monitoring programmes.