Satish T. S. Bukkapatnam

Distributed architecture for real-time coordination of bus holding in transit networks

A distributed control approach based on multiagent negotiation is presented, wherein stops and buses act as agents that communicate in real-time to achieve dynamic coordination of bus dispatching at various stops. The negotiation between a Bus Agent and a Stop Agent is conducted based on marginal cost calculations. We present optimality conditions for the formulated problem, using a negotiation algorithm, which we derive, to coordinate bus holding at various stops. A comparison between the negotiation algorithm and other simple bus control strategies such as on-schedule and even-headway strategies made through simulations verifies the robustness and efficiency of our negotiation strategy to different transit environments, involving both stationary passenger arrivals as well as a variety of nonstationary passenger arrivals.

Linear affine transformations between 3-lead (Frank XYZ leads) vectorcardiogram and 12-lead electrocardiogram signals.

BACKGROUND Recent advances in computer graphics and wireless technologies have renewed interest in vectorcardiogram (VCG) signals that use fewer leads than the conventional 12-lead electrocardiogram (ECG) signals for medical diagnostic applications. However, most cardiologists are accustomed to the 12-lead ECG even though some of the leads are either nearly aligned with or derived from the others and consequently contain redundant information. The ability to transform from orthogonal 3-lead VCG to 12-lead ECG enables the use of fewer leads for signal analysis, computer visualization, and wireless transmission of signals. This can also improve mobility, albeit limited, to the patients. MATERIALS AND METHODS We present a statistical approach to transform 3-lead Frank VCG to 12-lead ECG signals and vice versa, based on Dowers pioneering work on lead transformation. This approach enables compensation of baseline shifts and other constant biases present in long ECG data streams, so that the resulting statistical transforms can be more consistent and accurate. We compare the performance of the affine transform with that of Dower transform (from 3 to 12 and from 12 to 3) using the data from the PhysioNet PTB database. RESULTS The results show that for both myocardial infarction (MI) and healthy control (HC) subjects, the statistical affine transform presented here maps 3-lead VCG to12-lead ECG more accurately than Dower or other lead transformation matrices of the ECG recordings. DISCUSSION This investigation also shows the limitations associated with single dipole assumption that underlies Dowers geometric transformation. The results also indicate that lead transformation accuracy can be improved using separate customized transforms to, for example, age or pathologic conditions (here, MI vs HC) than a single statistical or geometric transform. Pertinently, we find that the affine transform coefficients can serve as discriminating features for classification/discrimination of MI patients from HC subjects.

Experimental investigation of contour crafting using ceramics materials

This paper presents research about the adaptation of Contour Crafting, a novel prototyping process, for two carefully chosen uncured ceramic materials. The details of the construction and operation of the fabrication machine, as well as procedures and results from our preliminary experimentation are concisely presented.

Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations.

A general method for the development of potential-energy hypersurfaces is presented. The method combines a many-body expansion to represent the potential-energy surface with two-layer neural networks (NN) for each M-body term in the summations. The total number of NNs required is significantly reduced by employing a moiety energy approximation. An algorithm is presented that efficiently adjusts all the coupled NN parameters to the database for the surface. Application of the method to four different systems of increasing complexity shows that the fitting accuracy of the method is good to excellent. For some cases, it exceeds that available by other methods currently in literature. The method is illustrated by fitting large databases of ab initio energies for Si(n) (n=3,4,...,7) clusters obtained from density functional theory calculations and for vinyl bromide (C(2)H(3)Br) and all products for dissociation into six open reaction channels (12 if the reverse reactions are counted as separate open channels) that include C-H and C-Br bond scissions, three-center HBr dissociation, and three-center H(2) dissociation. The vinyl bromide database comprises the ab initio energies of 71 969 configurations computed at MP4(SDQ) level with a 6-31G(d,p) basis set for the carbon and hydrogen atoms and Huzinagas (4333/433/4) basis set augmented with split outer s and p orbitals (43321/4321/4) and a polarization f orbital with an exponent of 0.5 for the bromine atom. It is found that an expansion truncated after the three-body terms is sufficient to fit the Si(5) system with a mean absolute testing set error of 5.693x10(-4) eV. Expansions truncated after the four-body terms for Si(n) (n=3,4,5) and Si(n) (n=3,4,...,7) provide fits whose mean absolute testing set errors are 0.0056 and 0.0212 eV, respectively. For vinyl bromide, a many-body expansion truncated after the four-body terms provides fitting accuracy with mean absolute testing set errors that range between 0.0782 and 0.0808 eV. These errors correspond to mean percent errors that fall in the range 0.98%-1.01%. Our best result using the present method truncated after the four-body summation with 16 NNs yields a testing set error that is 20.3% higher than that obtained using a 15-dimensional (15-140-1) NN to fit the vinyl bromide database. This appears to be the price of the added simplicity of the many-body expansion procedure.

Time series forecasting for nonlinear and non-stationary processes: a review and comparative study

Forecasting the evolution of complex systems is noted as one of the 10 grand challenges of modern science. Time series data from complex systems capture the dynamic behaviors and causalities of the underlying processes and provide a tractable means to predict and monitor system state evolution. However, the nonlinear and non-stationary dynamics of the underlying processes pose a major challenge for accurate forecasting. For most real-world systems, the vector field of state dynamics is a nonlinear function of the state variables; i.e., the relationship connecting intrinsic state variables with their autoregressive terms and exogenous variables is nonlinear. Time series emerging from such complex systems exhibit aperiodic (chaotic) patterns even under steady state. Also, since real-world systems often evolve under transient conditions, the signals obtained therefrom tend to exhibit myriad forms of non-stationarity. Nonetheless, methods reported in the literature focus mostly on forecasting linear and stationary processes. This article presents a review of these advancements in nonlinear and non-stationary time series forecasting models and a comparison of their performances in certain real-world manufacturing and health informatics applications. Conventional approaches do not adequately capture the system evolution (from the standpoint of forecasting accuracy, computational effort, and sensitivity to quantity and quality of a priori information) in these applications.

Spatiotemporal representation of cardiac vectorcardiogram (VCG) signals

BackgroundVectorcardiogram (VCG) signals monitor both spatial and temporal cardiac electrical activities along three orthogonal planes of the body. However, the absence of spatiotemporal resolution in conventional VCG representations is a major impediment for medical interpretation and clinical usage of VCG. This is especially so because time-domain features of 12-lead ECG, instead of both spatial and temporal characteristics of VCG, are widely used for the automatic assessment of cardiac pathological patterns.Materials and methodsWe present a novel representation approach that captures critical spatiotemporal heart dynamics by displaying the real time motion of VCG cardiac vectors in a 3D space. Such a dynamic display can also be realized with only one lead ECG signal (e.g., ambulatory ECG) through an alternative lag-reconstructed ECG representation from nonlinear dynamics principles. Furthermore, the trajectories are color coded with additional dynamical properties of space-time VCG signals, e.g., the curvature, speed, octant and phase angles to enhance the information visibility.ResultsIn this investigation, spatiotemporal VCG signal representation is used to characterize various spatiotemporal pathological patterns for healthy control (HC), myocardial infarction (MI), atrial fibrillation (AF) and bundle branch block (BBB). The proposed color coding scheme revealed that the spatial locations of the peak of T waves are in the Octant 6 for the majority (i.e., 74 out of 80) of healthy recordings in the PhysioNet PTB database. In contrast, the peak of T waves from 31.79% (117/368) of MI subjects are found to remain in Octant 6 and the rest (68.21%) spread over all other octants. The spatiotemporal VCG signal representation is shown to capture the same important heart characteristics as the 12-lead ECG plots and more.ConclusionsSpatiotemporal VCG signal representation is shown to facilitate the characterization of space-time cardiac pathological patterns and enhance the automatic assessment of cardiovascular diseases.

Zero knowledge hidden Markov model inference

Hidden Markov models (HMMs) are widely used in pattern recognition. HMM construction requires an initial model structure that is used as a starting point to estimate the models parameters. To construct a HMM without a priori knowledge of the structure, we use an approach developed by Crutchfield and Shalizi that requires only a sequence of observations and a maximum data window size. Values of the maximum data window size that are too small result in incorrect models being constructed. Values that are too large reduce the number of data samples that can be considered and exponentially increase the algorithms computational complexity. In this paper, we present a method for automatically inferring this parameter directly from training data as part of the model construction process. We present theoretical and experimental results that confirm the utility of the proposed extension.

Analysis of Acoustic Emission Signals in Machining

Acoustic emission (AE) signals are emerging as promising means for monitoring machining processes, but understanding their generation is presently a topic of active research; hence techniques to analyze them are not completely developed. In this paper, we present a novel methodology based on chaos theory, wavelets and neural networks, for analyzing AE signals. Our methodology involves a thorough signal characterization, followed by signal representation using wavelet packets, and state estimation using multilayer neural networks. Our methodology has yielded a compact signal representation, facilitating the extraction of tight set of features for flank wear estimation.

Adaptive Neuro-Fuzzy Inference System Modeling of MRR and WIWNU in CMP Process With Sparse Experimental Data

Availability of only limited or sparse experimental data impedes the ability of current models of chemical mechanical planarization (CMP) to accurately capture and predict the underlying complex chemomechanical interactions. Modeling approaches that can effectively interpret such data are therefore necessary. In this paper, a new approach to predict the material removal rate (MRR) and within wafer nonuniformity (WIWNU) in CMP of silicon wafers using sparse-data sets is presented. The approach involves utilization of an adaptive neuro-fuzzy inference system (ANFIS) based on subtractive clustering (SC) of the input parameter space. Linear statistical models were used to assess the relative significance of process input parameters and their interactions. Substantial improvements in predicting CMP behaviors under sparse-data conditions can be achieved from fine-tuning membership functions of statistically less significant input parameters. The approach was also found to perform better than alternative neural network (NN) and neuro-fuzzy modeling methods for capturing the complex relationships that connect the machine and material parameters in CMP with MRR and WIWNU, as well as for predicting MRR and WIWNU in CMP.

Criticality index analysis based optimal RFID reader placement models for asset tracking

This study is aimed at optimising the RFID network design in the healthcare service sector for tracking medical assets. Two different optimisation models corresponding to two possible scenarios in RFID network design are developed based on the enhancement of location set covering problem (LSCP) and maximal covering location problem (MCLP). They are validated by considering a healthcare facility to optimise the real-time locating system for tracking assets. The methodology is original in that it analyses the trade-off between cost effectiveness and overall RFID system performance and hence provides possible decision guidance to optimise the RFID system. It is vital for healthcare providers to locate crucial assets in the shortest possible time, particularly in emergency situations where human lives are at risk. Hence, increasing the overall RFID system performance will definitely have a valuable effect on real-time information sharing, thereby decreasing related search time for crucial assets.