An efficient and optimized bluetooth scheduling algorithm for piconets

Bluetooth is an emerging standard in short range, low cost and low power wireless networks. MAC is a generic polling based protocol, where a central Bluetooth unit (master) determines channel access to all other nodes (slaves) in the network (piconet). An important problem in Bluetooth is the design of efficient scheduling protocols. This paper proposes a polling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in a Bluetooth Piconet. We present an extensive set of simulation results and performance comparisons with two important existing algorithms. Our results indicate that our proposed scheduling algorithm outperforms the Round Robin scheduling algorithm by more than 40% in all cases tried. Our study also confirms that our proposed policy achieves higher throughput and lower packet delays with reasonable fairness among all the connections.

An efficient pricing based protocol for broadcasting in wireless ad hoc networks

In many applications of wireless ad hoc networks, wireless nodes are owned by rational and intelligent users. In this paper, we call nodes selfish if they are owned by independent users and their only objective is to maximize their individual goals. In such situations, it may not be possible to use the existing protocols for wireless ad hoc networks as these protocols assume that nodes follow the prescribed protocol without deviation. Stimulating cooperation among these nodes is an interesting and challenging problem. Providing incentives and pricing the transactions are well known approaches to stimulate cooperation. In this paper, we present a game theoretic framework for truthful broadcast protocol and strategy proof pricing mechanism called Immediate Predecessor Node Pricing Mechanism (IPNPM). The phrase strategy proof here means that truth revelation of cost is a weakly dominant-strategy (in game theoretic terms) for each node. In order to steer our mechanism-design approach towards practical implementation, we compute the payments to nodes using a distributed algorithm. We also propose a new protocol for broadcast in wireless ad hoc network with selfish nodes based on IPNPM. The features of the proposed broadcast protocol are reliability and a significantly reduced number of packet forwards compared to the number of network nodes, which in turn leads to less system-wide power consumption to broadcast a single packet. Our simulation results show the efficacy of the proposed broadcast protocol.

An efficient reduction algorithm for computation of interconnect delay variability for statistical timing analysis in clock tree planning

In this paper, we propose a novel and efficient algorithm for modelling sub-65 nm clock interconnect-networks in the presence of process variation. We develop a method for delay analysis of interconnects considering the impact of Gaussian metal process variations. The resistance and capacitance of a distributed RC line are expressed as correlated Gaussian random variables which are then used to compute the standard deviation of delay Probability Distribution Function (PDF) at all nodes in the interconnect network. Main objective is to find delay PDF at a cheaper cost. Convergence of this approach is in probability distribution but not in mean of delay. We validate our approach against SPICE based Monte Carlo simulations while the current method entails significantly lower computational cost.

An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes

We develop in this article the first actor-critic reinforcement learning algorithm with function approximation for a problem of control under multiple inequality constraints. We consider the infinite horizon discounted cost framework in which both the objective and the constraint functions are suitable expected policy-dependent discounted sums of certain sample path functions. We apply the Lagrange multiplier method to handle the inequality constraints. Our algorithm makes use of multi-timescale stochastic approximation and incorporates a temporal difference (TD) critic and an actor that makes a gradient search in the space of policy parameters using efficient simultaneous perturbation stochastic approximation (SPSA) gradient estimates. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal policy. (C) 2010 Elsevier B.V. All rights reserved.

NUcache: an efficient multicore cache organization based on next-use distance

The effectiveness of the last-level shared cache is crucial to the performance of a multi-core system. In this paper, we observe and make use of the DelinquentPC - Next-Use characteristic to improve shared cache performance. We propose a new PC-centric cache organization, NUcache, for the shared last level cache of multi-cores. NUcache logically partitions the associative ways of a cache set into MainWays and DeliWays. While all lines have access to the MainWays, only lines brought in by a subset of delinquent PCs, selected by a PC selection mechanism, are allowed to enter the DeliWays. The PC selection mechanism is an intelligent cost-benefit analysis based algorithm that utilizes Next-Use information to select the set of PCs that can maximize the hits experienced in DeliWays. Performance evaluation reveals that NUcache improves the performance over a baseline design by 9.6%, 30% and 33% respectively for dual, quad and eight core workloads comprised of SPEC benchmarks. We also show that NUcache is more effective than other well-known cache-partitioning algorithms.

Efficient Management of Fruit Pests by Pheromone Nanogels

Environment-friendly management of fruit flies involving pheromones is useful in reducing the undesirable pest populations responsible for decreasing the yield and the crop quality. A nanogel has been prepared from a pheromone, methyl eugenol (ME) using a low-molecular mass gelator. This was very stable at open ambient conditions and slowed down the evaporation of pheromone significantly. This enabled its easy handling and transportation without refrigeration, and reduction in the frequency of pheromone recharging in the orchard. Notably the involvement of the nano-gelled pheromone brought about an effective management of Bactrocera dorsalis, a prevalent harmful pest for a number of fruits including guava. Thus a simple, practical and low cost green chemical approach is developed that has a significant potential for crop protection, long lasting residual activity, excellent efficacy and favorable safety profiles. This makes the present invention well-suited for pest management in a variety of crops.

Green synthesis of polysaccharide/gold nanoparticle nanocomposite: An efficient ammonia sensor

A low cost eco-friendly method for the synthesis of gold nanoparticles (AuNPs) using guar gum (GG) as a reducing agent is reported. The nanoparticles obtained are characterized by UV-vis spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD). Based on these results, a potential mechanism for this method of AuNPs synthesis is discussed. GG/AuNPs nanocomposite (GG/AuNPs NC) was exploited for optical sensor for detection of aqueous ammonia based on surface plasmon resonance (SPR). It was found to have good reproducibility, response times of similar to 10 s and excellent sensitivity with a detection limit of 1 ppb (parts-per-billion). This system allows the rapid production of an ultra-low-cost GG/AuNPs NC-based aqueous ammonia sensor.

CUDA-for-clusters: a system for efficient execution of CUDA kernels on multi-core clusters

Rapid advancements in multi-core processor architectures coupled with low-cost, low-latency, high-bandwidth interconnects have made clusters of multi-core machines a common computing resource. Unfortunately, writing good parallel programs that efficiently utilize all the resources in such a cluster is still a major challenge. Various programming languages have been proposed as a solution to this problem, but are yet to be adopted widely to run performance-critical code mainly due to the relatively immature software framework and the effort involved in re-writing existing code in the new language. In this paper, we motivate and describe our initial study in exploring CUDA as a programming language for a cluster of multi-cores. We develop CUDA-For-Clusters (CFC), a framework that transparently orchestrates execution of CUDA kernels on a cluster of multi-core machines. The well-structured nature of a CUDA kernel, the growing popularity, support and stability of the CUDA software stack collectively make CUDA a good candidate to be considered as a programming language for a cluster. CFC uses a mixture of source-to-source compiler transformations, a work distribution runtime and a light-weight software distributed shared memory to manage parallel executions. Initial results on running several standard CUDA benchmark programs achieve impressive speedups of up to 7.5X on a cluster with 8 nodes, thereby opening up an interesting direction of research for further investigation.

Energy Efficient GPS Acquisition with Sparse-GPS

Following rising demands in positioning with GPS, low-cost receivers are becoming widely available; but their energy demands are still too high. For energy efficient GPS sensing in delay-tolerant applications, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose Sparse-GPS(1): a new computing framework for GPS acquisition via sparse approximation. Within the framework, GPS signals can be efficiently compressed by random ensembles. The sparse acquisition information, pertaining to the visible satellites that are embedded within these limited measurements, can subsequently be recovered by our proposed representation dictionary. By extensive empirical evaluations, we demonstrate the acquisition quality and energy gains of Sparse-GPS. We show that it is twice as energy efficient than offloading uncompressed data, and has 5-10 times lower energy costs than standalone GPS; with a median positioning accuracy of 40 m.

Graphene Oxide-MnFe2O4 Magnetic Nanohybrids for Efficient Removal of Lead and Arsenic from Water

We show that the hybrids of single-layer graphene oxide with manganese ferrite magnetic nanoparticles have the best adsorption properties for efficient removal of Pb(II), As(III), and As(V) from contaminated water. The nanohybrids prepared by coprecipitation technique were characterized using atomic force and scanning electron microscopies, Fourier transformed infrared spectroscopy, Raman spectroscopy, X-ray diffraction, and surface area measurements. Magnetic character of the nanohybrids was ascertained by a vibrating sample magnetometer. Batch experiments were carried out to quantify the adsorption kinetics and adsorption capacities of the nanohybrids and compared with the bare nanoparticles of MnFe2O4. The adsorption data from our experiments fit the Langmuir isotherm, yielding the maximum adsorption capacity higher than the reported values so far. Temperature-dependent adsorption studies have been done to estimate the free energy and enthalpy of adsorption. Reusability, ease of magnetic separation, high removal efficiency, high surface area, and fast kinetics make these nanohybrids very attractive candidates for low-cost adsorbents for the effective coremoval of heavy metals from contaminated water.

Pt-Poisoning-Free Efficient CO Oxidation on Pt3Co Supported on MgO(100): An Ab Initio Study

Late-transition-metal-doped Pt clusters are prevalent in CO oxidation catalysis, as they exhibit better catalytic activity than pure Pt, while reducing the effective cost and poisoning However, completely eliminating the critical problem of Pt poisoning still poses a big challenge. Here, we report for the first time that, among the bimetallic clusters ((Pt3M where M = Co, Ni, and Cu)/MgO(100)), the CO adsorption site inverts for Pt3Co/MgO(100) from Pt to Co, due to the complete uptake of Pt d-states by lattice oxygen. While this resolves the problem of Pt poisoning, good reaction kinetics are predicted through low barriers for Langmuir-Hinshelwood and Mars van Krevelen (MvK) mechanisms of CO oxidation for Pt3Co/MgO(100) and Li-doped MgO(100), respectively. Li doping in MgO(100) compensates for the charge imbalance caused by a spontaneous oxygen vacancy formation. Pt-3 Co/Li-doped MgO(100) stands out as an exceptional CO oxidation catalyst, giving an MvK reaction barrier as low as 0.11 eV. We thereby propose a novel design strategy of d-band center inversion for CO oxidation catalysts with no Pt poisoning and excellent reaction kinetics.

Achieving Highly Efficient, Selective, and Stable CO2 Reduction on Nitrogen-Doped Carbon Nanotubes

The challenge in the electrosynthesis of fuels from CO2 is to achieve durable and active performance with cost-effective catalysts. Here, we report that carbon nanotubes (CNTs), doped with nitrogen to form resident electron-rich defects, can act as highly efficient and, more importantly, stable catalysts for the conversion of CO2 to CO. The unprecedented overpotential (-0.18 V) and selectivity (80%) observed on nitrogen-doped CNTs (NCNTs) are attributed to their unique features to facilitate the reaction, including (i) high electrical conductivity, (ii) preferable catalytic sites (pyridinic N defects), and (iii) low free energy for CO2 activation and high barrier for hydrogen evolution. Indeed, DFT calculations show a low free energy barrier for the potential-limiting step to form key intermediate COOH as well as strong binding energy of adsorbed CON and weak binding energy for the adsorbed CO. The highest selective site toward CO production is pyridinic N, and the NCNT-based electrodes exhibit no degradation over 10 h of continuous operation, suggesting the structural stability of the electrode.

An Efficient Load Distribution Strategy for a Distributed Linear Network of Processors with Communication Delays

In this paper, we present an improved load distribution strategy, for arbitrarily divisible processing loads, to minimize the processing time in a distributed linear network of communicating processors by an efficient utilization of their front-ends. Closed-form solutions are derived, with the processing load originating at the boundary and at the interior of the network, under some important conditions on the arrangement of processors and links in the network. Asymptotic analysis is carried out to explore the ultimate performance limits of such networks. Two important theorems are stated regarding the optimal load sequence and the optimal load origination point. Comparative study of this new strategy with an earlier strategy is also presented.

Model Predictive Static Programming: A Computationally Efficient Technique For Suboptimal Control Design

Combining the philosophies of nonlinear model predictive control and approximate dynamic programming, a new suboptimal control design technique is presented in this paper, named as model predictive static programming (MPSP), which is applicable for finite-horizon nonlinear problems with terminal constraints. This technique is computationally efficient, and hence, can possibly be implemented online. The effectiveness of the proposed method is demonstrated by designing an ascent phase guidance scheme for a ballistic missile propelled by solid motors. A comparison study with a conventional gradient method shows that the MPSP solution is quite close to the optimal solution.

Highly efficient C-8 oxidation of substituted xanthines with substitution at the 1-, 3-, and 7- positions using biocatalysts

A bacterial consortium consisting of strains belongings to the genus Klebsiella and Rhodococcus quantitatively converts 1-, 3- and 7-substituted xanthines to their respective 8-oxo compounds.