V Srinivasa Chakravarthy

A crucial insight into handwriting dynamics is embodied in the idea that stable, robust handwriting movements correspond to attractors of an oscillatory dynamical system. We present a phase dynamic model of visuomotor performance involved in copying simple oriented lines. Our studies on human performance in copying oriented lines revealed a systematic error pattern in orientation of drawn lines, i.e., lines at certain orientation are drawn more accurately than at other values. Furthermore, human subjects exhibit "flips" in direction at certain characteristic orientations. It is argued that this flipping behavior has its roots in the fact that copying process is inherently ambiguous-a line of given orientation may be drawn in two different (mutually opposite) directions producing the same end result. The systematic error patterns seen in human copying performance is probably a result of the attempt of our visuomotor system to cope with this ambiguity and still be able to produce accurate copying movements. The proposed nonlinear phase-dynamic model explains the experimentally observed copying error pattern and also the flipping behavior with remarkable accuracy. © 2009 Springer-Verlag.

In this paper, we address some issues in developing an online handwritten character recognition(HCR) system for an Indian language script, Telugu. The number of characters in this script is estimated to be around 5000. A character in this script is written as a sequence of strokes. The set of strokes in Telugu consists of 253 unique strokes. As the similarity among several strokes is high, we propose a modular approach for recognition of strokes. Based on the relative position of a stroke in a character, the stroke set has been divided into three subsets, namely, baseline strokes, bottom strokes and top strokes. Classifiers for the different subsets of strokes are built using support vector machines(SVMs). We study the performance of the classifiers for subsets of strokes and propose methods to improve their performance. A comparative study using hidden Markov models(HMMs) shows that the SVM based approach gives a significantly better performance. © 2007 IEEE.

A system for online recognition of handwritten Tamil characters is presented. A handwritten character is constructed by executing a sequence of strokes. A structure- or shape-based representation of a stroke is used in which a stroke is represented as a string of shape features. Using this string representation, an unknown stroke is identified by comparing it with a database of strokes using a flexible string matching procedure. A full character is recognized by identifying all the component strokes. Character termination, is determined using a finite state automaton. Development of similar systems for other Indian scripts is outlined. © 2004 IEEE.

A neuromotor model of handwritten stroke generation, in which stroke velocities are expressed as a Fourier-style decomposition of oscillatory neural activities, is presented. The neural network architecture consists of an input or stroke-selection layer, an oscillatory layer, and the output layer where stroke velocities are estimated. A separate timing network prepares the network's initial state, which is crucial for accurate stroke generation. Neurobiological significance of this preparation, and a possible mapping of our architecture onto human motor system is suggested. Interaction between timing network and oscillatory layer closely resembles interaction between Basal Ganglia and Supplementary Motor Area in the brain. © Springer-Verlag 2007.

This article proposes an improved XML standard for storing online handwritten data in Indian languages. This standard has evolved over a period of two years, and is currently being used by the Consortium for online handwritten recognition of Indian languages, for annotating about 100,000 handwritten words in each of six Indian languages, namely, Tamil, Kannada, Telugu, Malayalam, Hindi and Bangla. In order that the huge amount of data that is being collected is useable by the future researchers, it is preferable that the data is stored in a format that is unambiguous and easy to read. The uniqueness of this refined standard is that it gives quality labels at different levels to the data, and has provision to annotate all the peculiarities of writing the script of the various Indian languages included in the current consortium project. The current format allows the use of automated and semi-automated annotation tools. Copyright © 2009 ACM.

Although the full physiological significance of vasomotion is still debated, it is generally thought to have a role in optimizing tissue oxygenation parameters. We study the effect of vasomotion rhythm in skeletal muscle on oxygen transport using a computational model. The model is used: (i) to test a novel hypothesis that "vasomotors" form a chemical network in which the rhythm adapts to meet oxygen demand in skeletal muscle and (ii) to study the contribution of desynchronized/chaotic vasomotion in optimizing oxygen delivery to skeletal muscle. We formulate a 2D grid model of skeletal muscle consisting of an interleaved arrangement of vessels and muscle fibers fired by a motor neuronal network. The vasomotors too form a network interacting by chemical means. When positive (negative) synapses dominate, the neuronal network exhibits synchronized (desynchronized) activity. Similarly, when positive (negative) chemical interactions dominate, the vessels exhibit synchronized (desynchronized) activity. Optimal oxygenation is observed when both neuronal network and vasomotor network exhibit desynchronous activity. Muscle oxygenation is thought to result by interactions between the fiber/neuron network and the vessel network; optimal oxygenation depends on precise rhythm-related conditions on the two networks. The model provides interesting insights into the phenomenon of muscle fatigue. © 2009 Elsevier Ltd. All rights reserved.

This chapter describes Complex Hopfield Neural Network (CHNN), a complex-variable version of the Hopfield neural network, which can exist in both fixed point and oscillatory modes. Memories can be stored by a complex version of Hebb's rule. In the fixed-point mode, CHNN is similar to a continuous-time Hopfield network. In the oscillatory mode, when multiple patterns are stored, the network wanders chaotically among patterns. Presence of chaos in this mode is verified by appropriate time series analysis. It is shown that adaptive connections can be used to control chaos and increase memory capacity. Electronic realization of the network in oscillatory dynamics, with fixed and adaptive connections shows an interesting tradeoff between energy expenditure and retrieval performance. It is shown how the intrinsic chaos in CHNN can be used as a mechanism for "annealing" when the network is used for solving quadratic optimization problems. The network's applicability to chaotic synchronization is described. © 2009, IGI Global.

Photoplethysmography (PPG) is typically used to extract cardiac-related information like heart rate and cardiac output, though extra-cardiac information like respiratory rate can also be extracted from PPG. The aim of the current study is to advance this approach further and investigate existence of gastric-related activity in PPG. To this end, we consider pulse rate variability (PRV), which provides information analogous to heart rate variability (HRV). Finger PPG and electrogastrography (EGG) signals were recorded from 8 healthy volunteers in fasting and postprandial state for 30 minutes. Peak-to-peak interval (PPI) analysis shows that the power of high frequency (HF) component in fasting and postprandial state changes significantly. The power ratio (PR), which is the ratio between powers of low frequency band (LF, 0.04-0.15 Hz) to that of high frequency band (HF, 0.15-0.4 Hz) and the EGG power were calculated in fasting and postprandial state. PR was positively correlated with EGG power (r = 0.46; P< 0.05). PR indicates the balancing sympathovagal modulation and vagal nervous activity. The significance of this study is that PR from PRV analysis could be used as a tool for diagnosing gastric system instead of the present invasive/cumbersome methods. ©2009 IEEE.

The phenomenon of cardiac memory refers to persistent response of the heart to an external pacing stimulus, where the response typically outlasts the stimulus. It is a fundamental tenet of neuroscience that synaptic modification is the basis of various forms of learning and memory. Drawing analogy with neural synapses and Gap Junctions (GJs) in the cardiac tissue, we had earlier hypothesized that dynamic, voltage-sensitive changes in GJ conductance may be related to the phenomenon of cardiac memory. In this paper we study a pair of Noble cardiac cell models, - originally proposed as a model of Purkinje cells, and capable of self-excited oscillations - coupled by dynamic GJs. The GJ conductance is allowed to vary as a function of junctional voltage. Simulations show the cell pair and GJ system has two stable states: one at a high value of GJ conductance and another at a lower value. The system can be switched between the two states by an appropriate external input. Such bistable dynamics can support memory operations and most probably underlie the phenomenon of cardiac memory. © 2006 Research Publishing Services.