A Wavelet based Teager Energy Operator for Spike Detection in Microelectrode Array Recordings

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the `local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike. In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90%and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.

Exploring energy-performance trade-offs for heterogeneous interconnect clustered VLIW processors

Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Although clustering helps by improving clock speed, reducing energy consumption of the logic, and making the design simpler, it introduces extra overheads by way of inter-cluster communication. This communication happens over long global wires which leads to delay in execution and significantly high energy consumption.In this paper, we propose a new instruction scheduling algorithm that exploits scheduling slacks of instructions and communication slacks of data values together to achieve better energy-performance trade-offs for clustered architectures with heterogeneous interconnect. Our instruction scheduling algorithm achieves 35% and 40% reduction in communication energy, whereas the overall energy-delay product improves by 4.5% and 6.5% respectively for 2 cluster and 4 cluster machines with marginal increase (1.6% and 1.1%) in execution time. Our test bed uses the Trimaran compiler infrastructure.

Comparative study of filter-bank mean-energy distance for automated segmentation of speech signals

This paper describes a method of automated segmentation of speech assuming the signal is continuously time varying rather than the traditional short time stationary model. It has been shown that this representation gives comparable if not marginally better results than the other techniques for automated segmentation. A formulation of the 'Bach' (music semitonal) frequency scale filter-bank is proposed. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks considering this model. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. 'Bach' filters are seen to marginally outperform the other filters.

Customer Energy in Relationships

All companies have a portfolio of customer relationships. From a managerial standpoint the value of these customer relationships is a key issue. The aim of the paper is to introduce a conceptual framework for customers’ energy towards a service provider. Customer energy is defined as the cognitive, affective and behavioural effort a customer puts into the purchase of an offering. It is based on two dimensions: life theme involvement and relationship commitment. Data from a survey study of 425 customers of an online gambling site was combined with data about their individual purchases and activity. Analysis showed that involvement and commitment influence both customer behaviour and attitudes. Customer involvement was found to be strongly related to overall spending within a consumption area, whereas relationship commitment is a better predictor of the amount of money spent at a particular company. Dividing the customers into four different involvement / commitment segments revealed differences in churn rates, word-of-mouth, brand attitude, switching propensity and the use of the service for socializing. The framework provides a tool for customer management by revealing differences in fundamental drivers of customer behaviour resulting in completely new customer portfolios. Knowledge of customer energy allows companies to manage their communication and offering development better and provides insight into the risk of losing a customer.

Elucidation of the mechanism of interaction of sheep spleen galectin-1 with splenocytes and its role in cell-matrix adhesion

The binding of a 14 kDa beta-galactoside animal lectin to splenocytes has been studied in detail. The binding data show that there are two classes of binding sites on the cells for the lectin: a high-affinity site with a K-a ranging from 1.1 x 10(6) to 5.1 x 10(5) M-1 and a low affinity binding site with a K-a ranging from 7.7 x 10(4) to 3.4 x 10(4) M-1 The number of receptors per cell for the high- and low-affinity sites is 9 +/- 3 x 10(6) and 2.5 +/- 0.5 x 10(6) respectively. The temperature dependence of the K value yielded the thermodynamic parameters. The energetics of this interaction shows that, although this interaction is essentially enthalpically driven (Delta H - 21 kJ lambda mol(-1)) for the high-affinity sites, there is a very favorable entropy contribution to the free energy of this interaction (-T Delta S - 17.5 Jmol(-1)), suggesting that hydrophobic interaction may also be playing a role in this interaction. Lactose brought about a 20% inhibition of this interaction, whereas the glycoprotein asialofetuin brought about a 75 % inhibition, suggesting that complex carbohydrate structures are involved in the binding of galectin-1 to splenocytes, Galectin-1 also mediated the binding and adhesion of splenocytes to the extracellular matrix glycoprotein laminin, suggesting a role for it in cell-matrix interactions. Copyright (C) 2000 John Wiley & Sons, Ltd.

Optimal Sleep-Wake Policies for an Energy Harvesting Sensor Node

We study a sensor node with an energy harvesting source. In any slot,the sensor node is in one of two modes: Wake or Sleep. The generated energy is stored in a buffer. The sensor node senses a random field and generates a packet when it is awake. These packets are stored in a queue and transmitted in the wake mode using the energy available in the energy buffer. We obtain energy management policies which minimize a linear combination of the mean queue length and the mean data loss rate. Then, we obtain two easily implementable suboptimal policies and compare their performance to that of the optimal policy. Next, we extend the Throughput Optimal policy developed in our previous work to sensors with two modes. Via this policy, we can increase the through put substantially and stabilize the data queue by allowing the node to sleep in some slots and to drop some generated packets. This policy requires minimal statistical knowledge of the system. We also modify this policy to decrease the switching costs.

Joint Power Control, Scheduling and Routing for Multihop Energy Harvesting Sensor Networks

We study wireless multihop energy harvesting sensor networks employed for random field estimation. The sensors sense the random field and generate data that is to be sent to a fusion node for estimation. Each sensor has an energy harvesting source and can operate in two modes: Wake and Sleep. We consider the problem of obtaining jointly optimal power control, routing and scheduling policies that ensure a fair utilization of network resources. This problem has a high computational complexity. Therefore, we develop a computationally efficient suboptimal approach to obtain good solutions to this problem. We study the optimal solution and performance of the suboptimal approach through some numerical examples.

Performance Optimization of Induction Motor-Pump System Using Photovoltaic Energy Source

The optimization of a photovoltaic pumping system based on an induction motor driven pump that is powered by a solar array is presented in this paper. The motor-pump subsystem is analyzed from the point of view of optimizing the power requirement of the induction motor, which has led to an optimum u-f relationship useful in controlling the motor. The complete pumping system is implemented using a dc-dc converter, a three-phase inverter, and an induction motor-pump set. The dc-dc converter is used as a power conditioner and its duty cycle is controlled so as to match the load to the array. A microprocessor-based controller is used to carry out the load-matching.

Empirical torsional potential functions from protein structure data. Phi- and psi-potentials for non-glycyl amino acid residues.

The torsional potential functions Vt(phi) and Vt(psi) around single bonds N--C alpha and C alpha--C, which can be used in conformational studies of oligopeptides, polypeptides and proteins, have been derived, using crystal structure data of 22 globular proteins, fitting the observed distribution in the (phi, psi)-plane with the value of Vtot(phi, psi), using the Boltzmann distribution. The averaged torsional potential functions, obtained from various amino acid residues in L-configuration, are Vt(phi) = 1.0 cos (phi + 60 degrees); Vt(psi) = 0.5 cos (psi + 60 degrees) - 1.0 cos (2 psi + 30 degrees) - 0.5 cos (3 psi + 30 degrees). The dipeptide energy maps Vtot(phi, psi) obtained using these functions, instead of the normally accepted torsional functions, were found to explain various observations, such as the absence of the left-handed alpha helix and the C7 conformation, and the relatively high density of points near the line psi = 0 degrees. These functions derived from observational data on protein structures, will, it is hoped, explain various previously unexplained facts in polypeptide conformation.

A Novel, Balanced, and Energy-Efficient Training Method for Receive Antenna Selection

In receive antenna selection (AS), only signals from a subset of the antennas are processed at any time by the limited number of radio frequency (RF) chains available at the receiver. Hence, the transmitter needs to send pilots multiple times to enable the receiver to estimate the channel state of all the antennas and select the best subset. Conventionally, the sensitivity of coherent reception to channel estimation errors has been tackled by boosting the energy allocated to all pilots to ensure accurate channel estimates for all antennas. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS that transmits one extra pilot symbol that generates accurate channel estimates for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. We derive closed-form expressions for the proposed scheme's symbol error probability for MPSK and MQAM, and optimize the energy allocated to pilot and data symbols. Through an insightful asymptotic analysis, we show that the optimal solution achieves full diversity and is better than the conventional method.

Empirical torsional potential functions from protiesn structure DATA Phi- and Psi-Potentials for Non-glycyl Amino Acid Residues

The torsional potential functions Vt(φ) and Vt(ψ) around single bonds N–Cα and Cα-C, which can be used in conformational studies of oligopeptides, polypeptides and proteins, have been derived, using crystal structure data of 22 globular proteins, fitting the observed distribution in the (φ, ψ)-plane with the value of Vtot(φ, ψ), using the Boltzmann distribution. The averaged torsional potential functions, obtained from various amino acid residues in l-configuration, are Vt(φ) = – 1.0 cos (φ + 60°); Vt(ψ) = – 0.5 cos (ψ + 60°) – 1.0 cos (2ψ + 30°) – 0.5 cos (3ψ + 30°). The dipeptide energy maps Vtot(φ, ψ) obtained using these functions, instead of the normally accepted torsional functions, were found to explain various observations, such as the absence of the left-handed alpha helix and the C7 conformation, and the relatively high density of points near the line ψ = 0°. These functions, derived from observational data on protein structures, will, it is hoped, explain various previously unexplained facts in polypeptide conformation.

Critical phenomena in polymer solutions: Scaling of the free energy

The thermodynamics of monodisperse solutions of polymers in the neighborhood of the phase separation temperature is studied by means of Wilson’s recursion relation approach, starting from an effective ϕ4 Hamiltonian derived from a continuum model of a many‐chain system in poor solvents. Details of the chain statistics are contained in the coefficients of the field variables ϕ, so that the parameter space of the Hamiltonian includes the temperature, coupling constant, molecular weight, and excluded volume interaction. The recursion relations are solved under a series of simplifying assumptions, providing the scaling forms of the relevant parameters, which are then used to determine the scaling form of the free energy. The free energy, in turn, is used to calculate the other singular thermodynamic properties of the solution. These are characteristically power laws in the reduced temperature and molecular weight, with the temperature exponents being the same as those of the 3d Ising model. The molecular weight exponents are unique to polymer solutions, and the calculated values compare well with the available experimental data.

A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) lambda-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The lambda-coverage problem is concerned with finding a set of k key nodes having minimal size that can influence a given percentage lambda of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the lambda-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. Note to Practitioners-In recent times, social networks have received a high level of attention due to their proven ability in improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market using viral marketing techniques, etc. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. This problem is called the target set selection problem and has two variants: 1) the top-k nodes problem, where we are required to identify a set of k influential nodes that maximize the number of nodes being influenced in the network and 2) the lambda-coverage problem which involves finding a set of influential nodes having minimum size that can influence a given percentage lambda of the nodes in the entire network. There are many existing algorithms in the literature for solving these problems. In this paper, we propose a new algorithm which is based on a novel interpretation of information diffusion in a social network as a cooperative game. Using this analogy, we develop an algorithm based on the Shapley value of the underlying cooperative game. The proposed algorithm outperforms the existing algorithms in terms of generality or computational complexity or both. Our results are validated through extensive experimentation on both synthetically generated and real-world data sets.

Mobile Base Stations Placement and Energy Aware Routing in Wireless Sensor Networks

Increasing network lifetime is important in wireless sensor/ad-hoc networks. In this paper, we are concerned with algorithms to increase network lifetime and amount of data delivered during the lifetime by deploying multiple mobile base stations in the sensor network field. Specifically, we allow multiple mobile base stations to be deployed along the periphery of the sensor network field and develop algorithms to dynamically choose the locations of these base stations so as to improve network lifetime. We propose energy efficient low-complexity algorithms to determine the locations of the base stations; they include i) Top-K-max algorithm, ii) maximizing the minimum residual energy (Max-Min-RE) algorithm, and iii) minimizing the residual energy difference (MinDiff-RE) algorithm. We show that the proposed base stations placement algorithms provide increased network lifetimes and amount of data delivered during the network lifetime compared to single base station scenario as well as multiple static base stations scenario, and close to those obtained by solving an integer linear program (ILP) to determine the locations of the mobile base stations. We also investigate the lifetime gain when an energy aware routing protocol is employed along with multiple base stations.

Composition Driven Monolayer to Bilayer Transformation in a Surfactant Intercalated Mg-Al Layered Double Hydroxide

The structure and organization of dodecyl sulfate (DDS) surfactant chains intercalated in an Mg-Al layered double hydroxide (LDH), Mg(1-x)Alx(OH)(2), with differing Al/Mg ratios has been investigated. The Mg-Al LDHs can be prepared over a range of compositions with x varying from 0.167 to 0.37 and therefore provides a simple system to study how the organization of the alkyl chains of the intercalated DDS anions change with packing density; the Al/Mg ratio or x providing a convenient handle to do so. Powder X-ray diffraction measurements showed that at high packing densities (x >= 0.3) the alkyl chains of the intercalated dodecyl sulfate ions are anchored on opposing LDH sheets and arranged as bilayers with an interlayer spacing of similar to 27 angstrom. At lower packing densities (x < 0.2) the surfactant chains form a monolayer with the alkyl chains oriented flat in the galleries with an interlayer spacing of similar to 8 angstrom. For the in between compositions, 0.2 <= x < 0.3, the material is biphasic. MD simulations were performed to understand how the anchoring density of the intercalated surfactant chains in the Mg-Al LDH-DDS affects the organization of the chains and the interlayer spacing. The simulations are able to reproduce the composition driven monolayer to bilayer transformation in the arrangement of the intercalated surfactant chains and in addition provide insights into the factors that decide the arrangement of the surfactant chains in the two situations. In the bilayer arrangement, it is the dispersive van der Waals interactions between chains in opposing layers of the anchored bilayer that is responsible for the cohesive energy of the solid whereas at lower packing densities, where a monolayer arrangement is favored, Coulomb interactions between the positively charged Mg-Al LDH sheets and the negatively charged headgroup of the DDS anion dominate.