Statistical significance of episodes with general partial orders

Frequent episode discovery is one of the methods used for temporal pattern discovery in sequential data. An episode is a partially ordered set of nodes with each node associated with an event type. For more than a decade, algorithms existed for episode discovery only when the associated partial order is total (serial episode) or trivial (parallel episode). Recently, the literature has seen algorithms for discovering episodes with general partial orders. In frequent pattern mining, the threshold beyond which a pattern is inferred to be interesting is typically user-defined and arbitrary. One way of addressing this issue in the pattern mining literature has been based on the framework of statistical hypothesis testing. This paper presents a method of assessing statistical significance of episode patterns with general partial orders. A method is proposed to calculate thresholds, on the non-overlapped frequency, beyond which an episode pattern would be inferred to be statistically significant. The method is first explained for the case of injective episodes with general partial orders. An injective episode is one where event-types are not allowed to repeat. Later it is pointed out how the method can be extended to the class of all episodes. The significance threshold calculations for general partial order episodes proposed here also generalize the existing significance results for serial episodes. Through simulations studies, the usefulness of these statistical thresholds in pruning uninteresting patterns is illustrated. (C) 2014 Elsevier Inc. All rights reserved.

Timer-Based Distributed Node Selection Scheme Exploiting Power Control and Capture

Opportunistic selection in multi-node wireless systems improves system performance by selecting the ``best'' node and by using it for data transmission. In these systems, each node has a real-valued local metric, which is a measure of its ability to improve system performance. Our goal is to identify the best node, which has the largest metric. We propose, analyze, and optimize a new distributed, yet simple, node selection scheme that combines the timer scheme with power control. In it, each node sets a timer and transmit power level as a function of its metric. The power control is designed such that the best node is captured even if. other nodes simultaneously transmit with it. We develop several structural properties about the optimal metric-to-timer-and-power mapping, which maximizes the probability of selecting the best node. These significantly reduce the computational complexity of finding the optimal mapping and yield valuable insights about it. We show that the proposed scheme is scalable and significantly outperforms the conventional timer scheme. We investigate the effect of. and the number of receive power levels. Furthermore, we find that the practical peak power constraint has a negligible impact on the performance of the scheme.

A zero charge-pump mismatch current tracking loop for reference spur reduction in PLLs

The charge-pump (CP) mismatch current is a dominant source of static phase error and reference spur in the nano-meter CMOS PLL implementations due to its worsened channel length modulation effect. This paper presents a charge-pump (CP) mismatch current reduction technique utilizing an adaptive body bias tuning of CP transistors and a zero CP mismatch current tracking PLL architecture for reference spur suppression. A chip prototype of the proposed circuit was implemented in 0.13 mu m CMOS technology. The frequency synthesizer consumes 8.2 mA current from a 13 V supply voltage and achieves a phase noise of -96.01 dBc/Hz @ 1 MHz offset from a 2.4 GHz RF carrier. The charge-pump measurements using the proposed calibration technique exhibited a mismatch current of less than 0.3 mu A (0.55%) over the VCO control voltage range of 0.3-1.0 V. The closed loop measurements show a minimized static phase error of within +/- 70 ps and a similar or equal to 9 dB reduction in reference spur level across the PLL output frequency range 2.4-2.5 GHz. The presented CP calibration technique compensates for the DC current mismatch and the mismatch due to channel length modulation effect and therefore improves the performance of CP-PLLs in nano-meter CMOS implementations. (C) 2015 Elsevier Ltd. All rights reserved.

Inhibition of protein synthesis leading to unfolded protein response is the major event in abrin-mediated apoptosis

Abrin obtained from the plant Abrus precatorius inhibits protein synthesis and also triggers apoptosis in cells. Previous studies from our laboratory suggested a link between these two events. Using an active site mutant of abrin A-chain which exhibits 225-fold lower protein synthesis inhibitory activity than the wild-type abrin A-chain, we demonstrate in this study that inhibition of protein synthesis induced by abrin is the major factor triggering unfolded protein response leading to apoptosis. Since abrin A-chain requires the B-chain for internalization into cells, the wild-type and mutant recombinant abrin A-chains were conjugated to native ricin B-chain to generate hybrid toxins, and the toxic effects of the two conjugates were compared. The rate of inhibition of protein synthesis mediated by the mutant ricin B-rABRA (R167L) conjugate was slower than that of the wild-type ricin B-rABRA conjugate as expected. The mutant conjugate activated p38MAPK and caspase-3 similar to its wild-type counterpart although at later time points. Overall, these results confirm that inhibition of protein synthesis is the major event contributing to abrin-mediated apoptosis.

Event-triggered sampling using signal extrema for instantaneous amplitude and instantaneous frequency estimation

Event-triggered sampling (ETS) is a new approach towards efficient signal analysis. The goal of ETS need not be only signal reconstruction, but also direct estimation of desired information in the signal by skillful design of event. We show a promise of ETS approach towards better analysis of oscillatory non-stationary signals modeled by a time-varying sinusoid, when compared to existing uniform Nyquist-rate sampling based signal processing. We examine samples drawn using ETS, with events as zero-crossing (ZC), level-crossing (LC), and extrema, for additive in-band noise and jitter in detection instant. We find that extrema samples are robust, and also facilitate instantaneous amplitude (IA), and instantaneous frequency (IF) estimation in a time-varying sinusoid. The estimation is proposed solely using extrema samples, and a local polynomial regression based least-squares fitting approach. The proposed approach shows improvement, for noisy signals, over widely used analytic signal, energy separation, and ZC based approaches (which are based on uniform Nyquist-rate sampling based data-acquisition and processing). Further, extrema based ETS in general gives a sub-sampled representation (relative to Nyquistrate) of a time-varying sinusoid. For the same data-set size captured with extrema based ETS, and uniform sampling, the former gives much better IA and IF estimation. (C) 2015 Elsevier B.V. All rights reserved.

Sampling Period Selection of Control Application Connected to FlexRay Bus

Flexray is a high speed communication protocol designed for distributive control in automotive control applications. Control performance not only depends on the control algorithm but also on the scheduling constraints in communication. A balance between the control performance and communication constraints must required for the choice of the sampling rates of the control loops in a node. In this paper, an optimum sampling period of control loops to minimize the cost function, satisfying the scheduling constraints is obtained. An algorithm to obtain the delay in service of each task in a node of the control loop in the hyper period has been also developed. (C) 2015 The Authors. Published by Elsevier B.V.

Event-triggered Sampling and Reconstruction of Sparse Real-valued Trigonometric Polynomials

We propose data acquisition from continuous-time signals belonging to the class of real-valued trigonometric polynomials using an event-triggered sampling paradigm. The sampling schemes proposed are: level crossing (LC), close to extrema LC, and extrema sampling. Analysis of robustness of these schemes to jitter, and bandpass additive gaussian noise is presented. In general these sampling schemes will result in non-uniformly spaced sample instants. We address the issue of signal reconstruction from the acquired data-set by imposing structure of sparsity on the signal model to circumvent the problem of gap and density constraints. The recovery performance is contrasted amongst the various schemes and with random sampling scheme. In the proposed approach, both sampling and reconstruction are non-linear operations, and in contrast to random sampling methodologies proposed in compressive sensing these techniques may be implemented in practice with low-power circuitry.

Small-Signal Stability Analysis of an Open-Loop Induction Motor Drive Including the Effect of Inverter Deadtime

This paper demonstrates light-load instability in a 100-kW open-loop induction motor drive on account of inverter deadtime. An improved small-signal model of an inverter-fed induction motor is proposed. This improved model is derived by linearizing the nonlinear dynamic equations of the motor, which include the inverter deadtime effect. Stability analysis is carried out on the 100-kW415-V three-phase induction motor considering no load. The analysis brings out the region of instability of this motor drive on the voltage versus frequency (V-f) plane. This region of light-load instability is found to expand with increase in inverter deadtime. Subharmonic oscillations of significant amplitude are observed in the steady-state simulated and measured current waveforms, at numerous operating points in the unstable region predicted, confirming the validity of the stability analysis. Furthermore, simulation and experimental results demonstrate that the proposed model is more accurate than an existing small-signal model in predicting the region of instability.

Discovering compressing serial episodes from event sequences

Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevant subset of patterns is a challenging problem of current interest. In this paper, we address this problem in the context of discovering frequent episodes from symbolic time-series data. Motivated by the Minimum Description Length principle, we formulate the problem of selecting relevant subset of patterns as one of searching for a subset of patterns that achieves best data compression. We present algorithms for discovering small sets of relevant non-redundant episodes that achieve good data compression. The algorithms employ a novel encoding scheme and use serial episodes with inter-event constraints as the patterns. We present extensive simulation studies with both synthetic and real data, comparing our method with the existing schemes such as GoKrimp and SQS. We also demonstrate the effectiveness of these algorithms on event sequences from a composable conveyor system; this system represents a new application area where use of frequent patterns for compressing the event sequence is likely to be important for decision support and control.

Modelling and analysis of an open-loop induction motor drive incorporating the effect of inverter dead-time

The objective of this paper is to study the influence of inverter dead-time on steady as well as dynamic operation of an open-loop induction motor drive fed from a voltage source inverter (VSI). Towards this goal, this paper presents a systematic derivation of a dynamic model for an inverter-fed induction motor, incorporating the effect of inverter dead-time, in the synchronously revolving dq reference frame. Simulation results based on this dynamic model bring out the impact of inverter dead-time on both the transient response and steady-state operation of the motor drive. For the purpose of steady-state analysis, the dynamic model of the motor drive is used to derive a steady-state model, which is found to be non-linear. The steady-state model shows that the impact of dead-time can be seen as an additional resistance in the stator circuit, whose value depends on the stator current. Towards precise evaluation of this dead-time equivalent resistance, an analytical expression is proposed for the same in terms of inverter dead-time, switching frequency, modulation index and load impedance. The notion of dead-time equivalent resistance is shown to simplify the solution of the non-linear steady-state model. The analytically evaluated steady-state solutions are validated through numerical simulations and experiments.

Connecting Active-Site Loop Conformations and Catalysis in Triosephosphate Isomerase: Insights from a Rare Variation at Residue96 in the Plasmodial Enzyme

Despite extensive research into triosephosphate isomerases (TIMs), there exists a gap in understanding of the remarkable conjunction between catalytic loop-6 (residues 166-176) movement and the conformational flip of Glu165 (catalytic base) upon substrate binding that primes the active site for efficient catalysis. The overwhelming occurrence of serine at position96 (98% of the 6277 unique TIM sequences), spatially proximal to E165 and the loop-6 residues, raises questions about its role in catalysis. Notably, Plasmodium falciparum TIM has an extremely rare residuephenylalanineat this position whereas, curiously, the mutant F96S was catalytically defective. We have obtained insights into the influence of residue96 on the loop-6 conformational flip and E165 positioning by combining kinetic and structural studies on the PfTIM F96 mutants F96Y, F96A, F96S/S73A, and F96S/L167V with sequence conservation analysis and comparative analysis of the available apo and holo structures of the enzyme from diverse organisms.