Performance analysis of UDP with energy efficient link layer on Markov fading channels

In this paper, we analyze the throughput and energy efficiency performance of user datagram protocol (UDP) using linear, binary exponential, and geometric backoff algorithms at the link layer (LL) on point-to-point wireless fading links. Using a first-order Markov chain representation of the packet success/failure process on fading channels, we derive analytical expressions for throughput and energy efficiency of UDP/LL with and without LL backoff. The analytical results are verified through simulations. We also evaluate the mean delay and delay variation of voice packets and energy efficiency performance over a wireless link that uses UDP for transport of voice packets and the proposed backoff algorithms at the LL. We show that the proposed LL backoff algorithms achieve energy efficiency improvement of the order of 2-3 dB compared to LL with no backoff, without compromising much on the throughput and delay performance at the UDP layer. Such energy savings through protocol means will improve the battery life in wireless mobile terminals.

New Insights into Optimal Discrete Rate Adaptation for Average Power Constrained Single and Multi-Node Systems

The throughput-optimal discrete-rate adaptation policy, when nodes are subject to constraints on the average power and bit error rate, is governed by a power control parameter, for which a closed-form characterization has remained an open problem. The parameter is essential in determining the rate adaptation thresholds and the transmit rate and power at any time, and ensuring adherence to the power constraint. We derive novel insightful bounds and approximations that characterize the power control parameter and the throughput in closed-form. The results are comprehensive as they apply to the general class of Nakagami-m (m >= 1) fading channels, which includes Rayleigh fading, uncoded and coded modulation, and single and multi-node systems with selection. The results are appealing as they are provably tight in the asymptotic large average power regime, and are designed and verified to be accurate even for smaller average powers.

Fuzzy Expert Approach with Curtailed Controllers for Improved Reactive Power Dispatch

This paper addresses the problem of curtailing the number of control actions using fuzzy expert approach for voltage/reactive power dispatch. It presents an approach using fuzzy set theory for reactive power control with the purpose of improving the voltage profile of a power system. To minimize the voltage deviations from pre-desired values of all the load buses, using the sensitivities with respect to reactive power control variables form the basis of the proposed Fuzzy Logic Control (FLC). Control variables considered are switchable VAR compensators, On Load Tap Changing (OLTC) transformers and generator excitations. Voltage deviations and controlling variables are translated into fuzzy set notations to formulate the relation between voltage deviations and controlling ability of controlling devices. The developed fuzzy system is tested on a few simulated practical Indian power systems and modified IEEE-30 bus system. The performance of the fuzzy system is compared with conventional optimization technique and results obtained are encouraging. Results obtained for a modified IEEE-30 bus test system and a 205-node equivalent EHV system a part of Indian southern grid are presented for illustration purposes. The proposed fuzzy-expert technique is found suitable for on-line applications in energy control centre as the solution is obtained fast with significant speedups with few number of controllers.

Free Energy Barriers for Escape of Water Molecules from Protein Hydration Layer

Free energy barriers separating interfacial water molecules from the hydration layer at the surface of a protein to the bulk are obtained by using the umbrella sampling method of free energy calculation. We consider hydration layer of chicken villin head piece (HP-36) which has been studied extensively by molecular dynamics simulations. The free energy calculations reveal a strong sensitivity to the secondary structure. In particular, we find a region near the junction of first and second helix that contains a cluster of water molecules which are slow in motion, characterized by long residence times (of the order of 100 ps or more) and separated by a large free energy barrier from the bulk water. However, these ``slow'' water molecules constitute only about 5-10% of the total number of hydration layer water molecules. Nevertheless, they play an important role in stabilizing the protein conformation. Water molecules near the third helix (which is the important helix for biological function) are enthalpically least stable and exhibit the fastest dynamics. Interestingly, barrier height distributions of interfacial water are quite broad for water surrounding all the three helices (and the three coils), with the smallest barriers found for those near the helix-3. For the quasi-bound water molecules near the first and second helices, we use well-known Kramers' theory to estimate the residence time from the free energy surface, by estimating the friction along the reaction coordinate from the diffusion coefficient by using Einstein relation. The agreement found is satisfactory. We discuss the possible biological function of these slow, quasi-bound (but transient) water molecules on the surface.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Optimal placement of Distributed Generation for a projected load increase using Relative Electrical Distance approach

There is a lot of pressure on all the developed and second world countries to produce low emission power and distributed generation (DG) is found to be one of the most viable ways to achieve this. DG generally makes use of renewable energy sources like wind, micro turbines, photovoltaic, etc., which produce power with minimum green house gas emissions. While installing a DG it is important to define its size and optimal location enabling minimum network expansion and line losses. In this paper, a methodology to locate the optimal site for a DG installation, with the objective to minimize the net transmission losses, is presented. The methodology is based on the concept of relative electrical distance (RED) between the DG and the load points. This approach will help to identify the new DG location(s), without the necessity to conduct repeated power flows. To validate this methodology case studies are carried out on a 20 node, 66kV system, a part of Karnataka Transco and results are presented.

Dual-Stage Power Management Algorithms for Energy Harvesting Sensors

In this paper, we propose power management algorithms for maximizing the utility of energy harvesting sensors (EHS) that operate purely on the basis of energy harvested from the environment. In particular, we consider communication (i.e., transmission and reception) power management issues for EHS under an energy neutrality constraint. We also consider the fixed power loss effects of the circuitry, the battery inefficiency and its storage capacity, in the design of the algorithms. We propose a two-stage structure that exploits the inherent difference in the timescales at which the energy harvesting and channel fading processes evolve, without loss of optimality of the resulting solution. The outer stage schedules the power that can be used by an inner stage algorithm, so as to maximize the long term average utility and at the same time maintain energy neutrality. The inner stage optimizes the communication parameters to achieve maximum utility in the short-term, subject to the power constraint imposed by the outer stage. We optimize the algorithms for different transmission schemes such as the truncated channel inversion and retransmission strategies. The performance of the algorithms is illustrated via simulations using solar irradiance data, and for the case of Rayleigh fading channels. The results demonstrate the significant performance benefits that can be obtained using the proposed power management algorithms compared to the energy efficient (optimum when there is no storage) and the uniform power consumption (optimum when the battery has infinite capacity and is perfectly efficient) approaches.

Energy absorption behaviours of CSM-based GFRC plates with hemispherical features

In the present study, the mechanical behaviour of CSM (chopped strand mat)-based GFRC (glass fibre-reinforced composite) plates with single and multiple hemispheres under compressive loads has been investigated both experimentally and numerically. The basic stress-strain behaviours arc identified with quasi-static tests on two-ply coupon laminates and short cylinders, and these are followed up with compressive tests in a UTM (universal testing machine) on single- and multiple-hemisphere plates. The ability of an explicit LS-DYNA solver in predicting the complex material behaviour of composite hemispheres, including failure, is demonstrated. The relevance and scalability of the present class of structural components as `force-multipliers' and `energy-multipliers' have been justified by virtue of findings that as the number of hemispheres in a panel increased from one to four, peak load and average absorbed energy rose by factors of approximately four and six, respectively. The performance of a composite hemisphere has been compared to similar-sized steel and aluminium hemispheres, and the former is found to be of distinctly higher specific energy than the steel specimen. A simulation-based study has also been carried out on a composite 2 x 2-hemisphere panel under impact loads and its behaviour approaching that of an ideal energy absorber has been predicted. In summary, the present investigation has established the efficacy of composite plates with hemispherical force multipliers as potential energy-absorbing countermeasures and the suitability of CAE (computer-aided engineering) for their design.

Excitation energy transfer from dye molecules to doped graphene

Recently, we have reported theoretical studies on the rate of energy transfer from an electronically excited molecule to graphene. It was found that graphene is a very efficient quencher of the electronically excited states and that the rate infinity z(-4). The process was found to be effective up to 30 nm which is well beyond the traditional FRET limit. In this report, we study the transfer of an amount of energy (h) over bar Omega from a dye molecule to doped graphene. We find a crossover of the distance dependence of the rate from z(-4) to exponential as the Fermi level is increasingly shifted into the conduction band, with the crossover occurring at a shift of the Fermi level by an amount (h) over bar Omega/2.

Liquid-drop model for the surface energy of nanoparticles

Based on the liquid-drop model, we have evaluated the Tolman length and surface energy of nanoparticles for different elements and compared with other theoretical models as well as the available simulated data. The predictions of the model show good agreement with the simulated results. Like the cohesive energy and melting temperature, the size-dependency of surface energy is also shape-dependent. (c) 2012 Elsevier B.V. All rights reserved.

Theoretical analysis of photoinduced H-atom elimination in thiophenol

The photoinduced hydrogen elimination reaction in thiophenol via the conical intersections of the dissociative (1)pi sigma* excited state with the bound (1)pi pi* excited state and the electronic ground state has been investigated with ab initio electronic-structure calculations and time-dependent quantum wave-packet calculations. A screening of the coupling constants of the symmetry-allowed coupling modes at the (1)pi pi*-(1)pi sigma* and (1)pi sigma*-S-0 conical intersection shows that the SH torsional mode is by far the most important coupling mode at both conical intersections. A model including three intersecting potential-energy surfaces (S-0, (1)pi pi*, (1)pi sigma*) and two nuclear degrees of freedom (SH stretch and SH torsion) has been constructed on the basis of ab initio complete-active-space self-consistent field and multireference second-order perturbation theory calculations. The nonadiabatic quantum wave-packet dynamics initiated by optical excitation of the (1)pi pi* and (1)pi sigma* states has been explored for this three-state two-coordinate model. The photodissociation dynamics is characterized in terms of snapshots of time-dependent wave packets, time-dependent electronic population probabilities, and the branching ratio of the (2)sigma/(2)pi electronic states of the thiophenoxyl radical. The dependence of the timescale of the photodissociation process and the branching ratio on the initial excitation of the SH stretching and SH torsional vibrations has been analyzed. It is shown that the node structure, which is imposed on the nuclear wave packets by the initial vibrational preparation as well as by the transitions through the conical intersections, has a profound effect on the photodissociation dynamics. The effect of additional weak coupling modes of CC twist (nu(16a)) and ring-distortion (nu(16b)) character has been investigated with three-dimensional and four-dimensional time-dependent wave-packet calculations, and has been found to be minor. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4709608]

Carrier concentration dependence of donor activation energy in n-type GaN epilayers grown on Si (111) by plasma-assisted MBE

The n-type GaN layers were grown by plasma-assisted MBE and either intentionally doped with Si or unintentionally doped. The optical characteristics of a donor level in Si-doped, GaN were studied in terms of photoluminescence (PL) spectroscopy as a function of electron concentration. Temperature dependent PL measurements allowed us to estimate the activation energy of a Si-related donor from temperature-induced decay of PL intensity. PL peak positions, full width at half maximum of PL and activation energies are found to be proportional to the cube root of carrier density. The involvement of donor levels is supported by the temperature-dependent electron concentration measurements. (C) 2012 Elsevier Ltd. All rights reserved.

Criticality guided energy aware speculation for speculative multithreaded processors

Unending quest for performance improvement coupled with the advancements in integrated circuit technology have led to the development of new architectural paradigm. Speculative multithreaded architecture (SpMT) philosophy relies on aggressive speculative execution for improved performance. However, aggressive speculative execution comes with a mixed flavor of improving performance, when successful, and adversely affecting the energy consumption (and performance) because of useless computation in the event of mis-speculation. Dynamic instruction criticality information can be usefully applied to control and guide such an aggressive speculative execution. In this paper, we present a model of micro-execution for SpMT architecture that we have developed to determine the dynamic instruction criticality. We have also developed two novel techniques utilizing the criticality information namely delaying the non-critical loads and the criticality based thread-prediction for reducing useless computations and energy consumption. Experimental results showing break-up of critical instructions and effectiveness of proposed techniques in reducing energy consumption are presented in the context of multiscalar processor that implements SpMT architecture. Our experiments show 17.7% and 11.6% reduction in dynamic energy for criticality based thread prediction and criticality based delayed load scheme respectively while the improvement in dynamic energy delay product is 13.9% and 5.5%, respectively. (c) 2012 Published by Elsevier B.V.