Cram\'er-Rao Bounds for Polynomial Signal Estimation using Sensors with AR(1) Drift

We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though estimation in the presence of random errors has been extensively studied in the literature, the loss of estimation performance due to systematic errors like drift have rarely been looked into. In this paper, we derive closed-form Fisher Information matrix and subsequently Cram\'er-Rao bounds (upto reasonable approximation) for the estimation accuracy of drift-corrupted signals. We assume a polynomial time-series as the representative signal and an autoregressive process model for the drift. When the Markov parameter for drift \rho<1, we show that the first-order effect of drift is asymptotically equivalent to scaling the measurement noise by an appropriate factor. For \rho=1, i.e., when the drift is non-stationary, we show that the constant part of a signal can only be estimated inconsistently (non-zero asymptotic variance). Practical usage of the results are demonstrated through the analysis of 1) networks with multiple sensors and 2) bandwidth limited networks communicating only quantized observations.Comment: 14 pages, 6 figures, This paper will appear in the Oct/Nov 2012 issue of IEEE Transactions on Signal Processin

Jeeva: Enterprise Grid-enabled Web Portal for Protein Secondary Structure Prediction

This paper presents a Grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise Grid technology. The portal is used by research scientists to discover new prediction structures in a parallel manner. An SVM (Support Vector Machine)-based prediction algorithm is used with 64 sample protein sequences as a case study to demonstrate the potential of enterprise Grids.Comment: 7 page

Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner

KALwEN: a new practical and interoperable key management scheme for body sensor networks

Key management is the pillar of a security architecture. Body sensor networks (BSNs) pose several challenges–some inherited from wireless sensor networks (WSNs), some unique to themselves–that require a new key management scheme to be tailor-made. The challenge is taken on, and the result is KALwEN, a new parameterized key management scheme that combines the best-suited cryptographic techniques in a seamless framework. KALwEN is user-friendly in the sense that it requires no expert knowledge of a user, and instead only requires a user to follow a simple set of instructions when bootstrapping or extending a network. One of KALwEN's key features is that it allows sensor devices from different manufacturers, which expectedly do not have any pre-shared secret, to establish secure communications with each other. KALwEN is decentralized, such that it does not rely on the availability of a local processing unit (LPU). KALwEN supports secure global broadcast, local broadcast, and local (neighbor-to-neighbor) unicast, while preserving past key secrecy and future key secrecy (FKS). The fact that the cryptographic protocols of KALwEN have been formally verified also makes a convincing case. With both formal verification and experimental evaluation, our results should appeal to theorists and practitioners alike

Flow control in networks with multiple paths

We propose two flow control algorithms for networks with multiple paths between each source-destination pair. Both are distributed algorithms over the network to maximize aggregate source utility. Algorithm 1 is a first order Lagrangian method applied to a modified objective function that has the same optimal solution as the original objective function but has a better convergence property. Algorithm 2 is based on the idea that, at optimality, only paths with the minimum price carry positive flows, and naturally decomposes the overall decision into flow control (determines total transmission rate based on minimum path price) and routing (determines how to split the flow among available paths). Both algorithms can be implemented as simply a source-based mechanism in which no link algorithm nor feedback is needed. We present numerical examples to illustrate their behavior

Perturbation in Parasympathetic Nervous System Activity Affects Temporal Structure of Poincaré Plot

Abstract A novel descriptor (Complex Correlation Measure (CCM)) for measuring the variability in the temporal structure of Poincaré plot has been developed to characterize or distinguish between Poincaré plots with similar shapes. This study was designed to assess the changes in temporal structure of the Poincaré plot using CCM during atropine infusion, 70 0 head-up tilt and scopolamine administration. The aim of this study was to asses the changes in temporal structure of the Poincaré plot using CCM during atropine infusion (parasympathetic blockade) and transdermal scopolamine patch administration (enhanced parasympathetic activity) phases. The change in CCM values during these autonomic perturbation phases revealed the physiological relevance of the new descriptor. The concordant reduction and enhancement in CCM values with parasympathetic activity indicates that the temporal variability of Poincaré plot is modulated by the parasympathetic activity which correlates with changes in CCM values. Introduction Heart rate variability (HRV) is one of the powerful noninvasive method for analyzing the function of the autonomic nervous system. It is useful to understand the interplay between the sympathetic and parasympathetic autonomic nervous system Poincaré plot is a visual presentation of time series signal to recognize the hidden patterns. It is also a quantitative technique in the sense that it has various parameters (ex: short-term variability (SD1) and long-term variability (SD2)) to quantify the information from the plot. The Poincaré plot of HRV signal is constructed by plotting consecutive points of RR interval time series (i.e., lag-1 plot). It is a representation of HRV signal on phase space or Cartesian plane In our previous study In this study, we demonstrate the physiological significance of the novel measure CCM by analyzing the effects of perturbations of autonomic function on Poincaré plot descriptors (SD1 and SD2) in young healthy subjects caused by the 70 0 head-up tilt test, atropine infusion an

Deferred decentralized movement pattern mining for geosensor networks

This paper presents an algorithm for decentralized (in-network) data mining of the movement pattern flock amongst mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to &apos;graze&apos; over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors&apos; most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge

Sensitivity of temporal heart rate variability in Poincaré plot to changes in parasympathetic nervous system activity

<p>Abstract</p> <p>Background</p> <p>A novel descriptor (Complex Correlation Measure (CCM)) for measuring the variability in the temporal structure of Poincaré plot has been developed to characterize or distinguish between Poincaré plots with similar shapes.</p> <p>Methods</p> <p>This study was designed to assess the changes in temporal structure of the Poincaré plot using <it>CCM </it>during atropine infusion, 70° head-up tilt and scopolamine administration in healthy human subjects. <it>CCM </it>quantifies the point-to-point variation of the signal rather than gross description of the Poincaré plot. The physiological relevance of <it>CCM </it>was demonstrated by comparing the changes in <it>CCM </it>values with autonomic perturbation during all phases of the experiment. The sensitivities of short term variability (<it>SD</it>1), long term variability (<it>SD</it>2) and variability in temporal structure (<it>CCM</it>) were analyzed by changing the temporal structure by shuffling the sequences of points of the Poincaré plot. Surrogate analysis was used to show <it>CCM </it>as a measure of changes in temporal structure rather than random noise and sensitivity of <it>CCM </it>with changes in parasympathetic activity.</p> <p>Results</p> <p><it>CCM </it>was found to be most sensitive to changes in temporal structure of the Poincaré plot as compared to <it>SD</it>1 and <it>SD</it>2. The values of all descriptors decreased with decrease in parasympathetic activity during atropine infusion and 70° head-up tilt phase. In contrast, values of all descriptors increased with increase in parasympathetic activity during scopolamine administration.</p> <p>Conclusions</p> <p>The concordant reduction and enhancement in <it>CCM </it>values with parasympathetic activity indicates that the temporal variability of Poincaré plot is modulated by the parasympathetic activity which correlates with changes in <it>CCM </it>values. <it>CCM </it>is more sensitive than <it>SD</it>1 and <it>SD</it>2 to changes of parasympathetic activity.</p