OpenRaiser/Intern-Atlas huggingface dataset:
An Optimal Load-Frequency Control Method for Inverter-Based Renewable Energy Transmission
Venue: arXiv cs.SY 2013
Year: 2013

The frequency droop response of conventional turbine driven synchronous generators with respect to load increases is normally used in order to have stable operating characteristics for multiple generators operating in parallel over large geographical regions. This presents a challenge for renewable energy sources that interface to the transmission grid through static inverters that do not exhibit an intrinsic frequency droop characteristic. This paper provides a technique for designing optimal load frequency controllers as transmission line inverters fed from renewable energy sources that allows for fast dynamic response due to variable solar and wind conditions while maintain stability to interconnected synchronous generators. A control technique based on LQG optimization theory is presented. Detailed analysis of a three-area system in a region of mixed wind and solar photovoltaic sources is modeled in a manner that confirms the effectiveness of the disclosed load-frequency control method.
DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs
Venue: arXiv cs.OH 2013
Year: 2013

In addition to General Purpose Processors (GPP), Multicore SoCs equipping modern mobile devices contain specialized Digital Signal Processor designed with the aim to provide better performance and low energy consumption properties. However, the experimental measurements we have achieved revealed that system overhead, in case of DSP video decoding, causes drastic performances drop and energy efficiency as compared to the GPP decoding. This paper describes DyPS, a new approach for energy-aware processor switching (GPP or DSP) according to the video quality . We show the pertinence of our solution in the context of adaptive video decoding and describe an implementation on an embedded Linux operating system with the help of the GStreamer framework. A simple case study showed that DyPS achieves 30% energy saving while sustaining the decoding performance
Entitymetrics: Measuring the Impact of Entities
Venue: arXiv cs.DL 2013
Year: 2013

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.
Randomized Consensus with Attractive and Repulsive Links
Venue: arXiv cs.SY 2013
Year: 2013

We study convergence properties of a randomized consensus algorithm over a graph with both attractive and repulsive links. At each time instant, a node is randomly selected to interact with a random neighbor. Depending on if the link between the two nodes belongs to a given subgraph of attractive or repulsive links, the node update follows a standard attractive weighted average or a repulsive weighted average, respectively. The repulsive update has the opposite sign of the standard consensus update. In this way, it counteracts the consensus formation and can be seen as a model of link faults or malicious attacks in a communication network, or the impact of trust and antagonism in a social network. Various probabilistic convergence and divergence conditions are established. A threshold condition for the strength of the repulsive action is given for convergence in expectation: when the repulsive weight crosses this threshold value, the algorithm transits from convergence to divergence. An explicit value of the threshold is derived for classes of attractive and repulsive graphs. The results show that a single repulsive link can sometimes drastically change the behavior of the consensus algorithm. They also explicitly show how the robustness of the consensus algorithm depends on the size and other properties of the graphs.
Empirical Performance Evaluation of Enhanced Throughput Schemes of IEEE802.11 Technology in Wireless Area Networks
Venue: arXiv cs.NI 2013
Year: 2013

The success in the growing wireless standards can be measured by the achievement of quality of service (QoS) specifications by the designers. The IEEE802.11 wireless standards are widely accepted as wireless technology for wireless LAN. Efforts have been made over the years by the task group to provide adequate number of QoS enhancement schemes for the increasing numbers of multimedia applications. This paper examines the empirical performances of ad hoc wireless networks deployed on IEEE802.11 standard variants. A survey to some of the QoS schemes incorporated in IEEE802.11 wireless PHY layers were carried out. Then the effects of this enhancement schemes in relation to data throughput and system capacity and reliability in the newest technology deployed on IEEE802.11ac standards was investigated using real time applications and simulation based approaches.
Indian Buffet Game with Negative Network Externality and Non-Bayesian Social Learning
Venue: arXiv cs.GT 2013
Year: 2013

How users in a dynamic system perform learning and make decision become more and more important in numerous research fields. Although there are some works in the social learning literatures regarding how to construct belief on an uncertain system state, few study has been conducted on incorporating social learning with decision making. Moreover, users may have multiple concurrent decisions on different objects/resources and their decisions usually negatively influence each other's utility, which makes the problem even more challenging. In this paper, we propose an Indian Buffet Game to study how users in a dynamic system learn the uncertain system state and make multiple concurrent decisions by not only considering the current myopic utility, but also taking into account the influence of subsequent users' decisions. We analyze the proposed Indian Buffet Game under two different scenarios: customers request multiple dishes without budget constraint and with budget constraint. For both cases, we design recursive best response algorithms to find the subgame perfect Nash equilibrium for customers and characterize special properties of the Nash equilibrium profile under homogeneous setting. Moreover, we introduce a non-Bayesian social learning algorithm for customers to learn the system state, and theoretically prove its convergence. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms.
Simulation of an Optimum Multilevel Dynamic Round Robin Scheduling Algorithm
Venue: arXiv cs.OS 2013
Year: 2013

CPU scheduling has valiant effect on resource utilization as well as overall quality of the system. Round Robin algorithm performs optimally in time shared systems, but it performs more number of context switches, larger waiting time and larger response time. In order to simulate the behavior of various CPU scheduling algorithms and to improve Round Robin scheduling algorithm using dynamic time slice concept, in this paper we produce the implementation of new CPU scheduling algorithm called An Optimum Multilevel Dynamic Round Robin Scheduling (OMDRRS), which calculates intelligent time slice and warps after every round of execution. The results display the robustness of this software, especially for academic, research and experimental use, as well as proving the desirability and efficiency of the probabilistic algorithm over the other existing techniques and it is observed that this OMDRRS projects good performance as compared to the other existing CPU scheduling algorithms.
Partitioning into Expanders
Venue: arXiv cs.DS 2013
Year: 2013

Let G=(V,E) be an undirected graph, lambda_k be the k-th smallest eigenvalue of the normalized laplacian matrix of G. There is a basic fact in algebraic graph theory that lambda_k > 0 if and only if G has at most k-1 connected components. We prove a robust version of this fact. If lambda_k>0, then for some 1\leq \ell\leq k-1, V can be {\em partitioned} into l sets P_1,\ldots,P_l such that each P_i is a low-conductance set in G and induces a high conductance induced subgraph. In particular, ϕ(P_i)=O(l^3\sqrt{λ_l}) and ϕ(G[P_i]) >= λ_k/k^2). We make our results algorithmic by designing a simple polynomial time spectral algorithm to find such partitioning of G with a quadratic loss in the inside conductance of P_i's. Unlike the recent results on higher order Cheeger's inequality [LOT12,LRTV12], our algorithmic results do not use higher order eigenfunctions of G. If there is a sufficiently large gap between lambda_k and lambda_{k+1}, more precisely, if λ_{k+1} >= \poly(k) lambda_{k}^{1/4} then our algorithm finds a k partitioning of V into sets P_1,...,P_k such that the induced subgraph G[P_i] has a significantly larger conductance than the conductance of P_i in G. Such a partitioning may represent the best k clustering of G. Our algorithm is a simple local search that only uses the Spectral Partitioning algorithm as a subroutine. We expect to see further applications of this simple algorithm in clustering applications.
Transformations between Composite and Visitor implementations in Java
Venue: arXiv cs.SE 2013
Year: 2013

Basic automated refactoring operations can be chained to perform complex structure transformations. This is useful for recovering the initial architecture of a source code which has been degenerated with successive evolutions during its maintenance lifetime. This is also useful for changing the structure of a program so that a maintenance task at hand becomes modular when it would be initially crosscutting. We focus on programs structured according to Composite and Visitor design patterns, which have dual properties with respect to modularity. We consider a refactoring-based round-trip transformation between these two structures and we study how that transformation is impacted by four variations in the implementation of these patterns. We validate that study by computing the smallest preconditions for the resulting transformations. We also automate the transformation and apply it to JHotDraw, where the studied variations occur.
A tabu search algorithm with efficient diversification strategy for high school timetabling problem
Venue: arXiv cs.AI 2013
Year: 2013

The school timetabling problem can be described as scheduling a set of lessons (combination of classes, teachers, subjects and rooms) in a weekly timetable. This paper presents a novel way to generate timetables for high schools. The algorithm has three phases. Pre-scheduling, initial phase and optimization through tabu search. In the first phase, a graph based algorithm used to create groups of lessons to be scheduled simultaneously; then an initial solution is built by a sequential greedy heuristic. Finally, the solution is optimized using tabu search algorithm based on frequency based diversification. The algorithm has been tested on a set of real problems gathered from Iranian high schools. Experiments show that the proposed algorithm can effectively build acceptable timetables.
Delay-Sensitive Communication over Fading Channel: Queueing Behavior and Code Parameter Selection
Venue: arXiv cs.IT 2013
Year: 2013

This article examines the queueing performance of communication systems that transmit encoded data over unreliable channels. A fading formulation suitable for wireless environments is considered where errors are caused by a discrete channel with correlated behavior over time. Random codes and BCH codes are employed as means to study the relationship between code-rate selection and the queueing performance of point-to-point data links. For carefully selected channel models and arrival processes, a tractable Markov structure composed of queue length and channel state is identified. This facilitates the analysis of the stationary behavior of the system, leading to evaluation criteria such as bounds on the probability of the queue exceeding a threshold. Specifically, this article focuses on system models with scalable arrival profiles, which are based on Poisson processes, and finite-state channels with memory. These assumptions permit the rigorous comparison of system performance for codes with arbitrary block lengths and code rates. Based on the resulting characterizations, it is possible to select the best code parameters for delay-sensitive applications over various channels. The methodology introduced herein offers a new perspective on the joint queueing-coding analysis of finitestate channels with memory, and it is supported by numerical simulations.
Measuring the similarity of PML documents with RFID-based sensors
Venue: arXiv cs.DB 2013
Year: 2013

The Electronic Product Code (EPC) Network is an important part of the Internet of Things. The Physical Mark-Up Language (PML) is to represent and de-scribe data related to objects in EPC Network. The PML documents of each component to exchange data in EPC Network system are XML documents based on PML Core schema. For managing theses huge amount of PML documents of tags captured by Radio frequency identification (RFID) readers, it is inevitable to develop the high-performance technol-ogy, such as filtering and integrating these tag data. So in this paper, we propose an approach for meas-uring the similarity of PML documents based on Bayesian Network of several sensors. With respect to the features of PML, while measuring the similarity, we firstly reduce the redundancy data except information of EPC. On the basis of this, the Bayesian Network model derived from the structure of the PML documents being compared is constructed.
Application of Vertex coloring in a particular triangular closed path structure and in Krafts inequality
Venue: arXiv cs.DM 2013
Year: 2013

A good deal of research has been done and published on coloring of the vertices of graphs for several years while studying of the excellent work of those maestros, we get inspire to work on the vertex coloring of graphs in case of a particular triangular closed path structure what we achieve from the front view of a pyramidal structure. From here we achieve a repetitive nature of vertex coloring in case of odd and even number of horizontal lines within this triangular structure. In order to apply this repetitive nature of vertex coloring in case of a binary tree, we get a success in Krafts Inequality. Actually our work mainly deals with a particular triangular closed path vertex coloring and repetition of the vertex coloring nature in case of the Krafts inequality in the field of Information Theory and Coding.
Haze: Privacy-Preserving Real-Time Traffic Statistics
Venue: arXiv cs.CR 2013
Year: 2013

We consider traffic-update mobile applications that let users learn traffic conditions based on reports from other users. These applications are becoming increasingly popular (e.g., Waze reported 30 million users in 2013) since they aggregate real-time road traffic updates from actual users traveling on the roads. However, the providers of these mobile services have access to such sensitive information as timestamped locations and movements of its users. In this paper, we describe Haze, a protocol for traffic-update applications that supports the creation of traffic statistics from user reports while protecting the privacy of the users. Haze relies on a small subset of users to jointly aggregate encrypted speed and alert data and report the result to the service provider. We use jury-voting protocols based on threshold cryptosystem and differential privacy techniques to hide user data from anyone participating in the protocol while allowing only aggregate information to be extracted and sent to the service provider. We show that Haze is effective in practice by developing a prototype implementation and performing experiments on a real-world dataset of car trajectories.
Unified Sum-BER Performance Analysis of AF MIMO Beamforming in Two-Way Relay Networks
Venue: arXiv cs.IT 2013
Year: 2013

Unified performance analysis is carried out for amplify-and-forward (AF) multiple-input-multiple-output (MIMO) beamforming (BF) two-way relay networks in Rayleigh fading with five different relaying protocols including two novel protocols for better performance. As a result, a novel closed-form sum-bit error rate (BER) expression is presented in a unified expression for all protocols. A new closed-form high signal-to-noise-ratio (SNR) performance is also obtained in a single expression, and an analytical high-SNR gap expression between the five protocols is provided. We compare the performance of the five relaying protocols with respect to sum-BER with appropriately normalized rate and power, and show that the proposed protocol with four time slots outperforms other protocols when transmit powers from two sources are sufficiently different, and the one with three time slots dominates other protocols when multiple relay antennas are used, at high-SNR.
Optimized projections for compressed sensing via rank-constrained nearest correlation matrix
Venue: arXiv cs.IT 2013
Year: 2013

Optimizing the acquisition matrix is useful for compressed sensing of signals that are sparse in overcomplete dictionaries, because the acquisition matrix can be adapted to the particular correlations of the dictionary atoms. In this paper a novel formulation of the optimization problem is proposed, in the form of a rank-constrained nearest correlation matrix problem. Furthermore, improvements for three existing optimization algorithms are introduced, which are shown to be particular instances of the proposed formulation. Simulation results show notable improvements and superior robustness in sparse signal recovery.
On the Performance Potential of Speculative Execution based on Branch and Value Prediction
Venue: arXiv cs.AR 2013
Year: 2013

Fluid Stochastic Petri Nets are used to capture the dynamic behavior of an ILP processor, and discrete-event simulation is applied to assess the performance potential of predictions and speculative execution in boosting the performance of ILP processors that fetch, issue, execute and commit a large number of instructions per cycle.
Sufficient Conditions on the Optimality of Myopic Sensing in Opportunistic Channel Access: A Unifying Framework
Venue: arXiv cs.IT 2013
Year: 2013

This paper considers a widely studied stochastic control problem arising from opportunistic spectrum access (OSA) in a multi-channel system, with the goal of providing a unifying analytical framework whereby a number of prior results may be viewed as special cases. Specifically, we consider a single wireless transceiver/user with access to $N$ channels, each modeled as an iid discrete-time two-state Markov chain. In each time step the user is allowed to sense $k\leq N$ channels, and subsequently use up to $m\leq k$ channels out of those sensed to be available. Channel sensing is assumed to be perfect, and for each channel use in each time step the user gets a unit reward. The user's objective is to maximize its total discounted or average reward over a finite or infinite horizon. This problem has previously been studied in various special cases including $k=1$ and $m=k\leq N$, often cast as a restless bandit problem, with optimality results derived for a myopic policy that seeks to maximize the immediate one-step reward when the two-state Markov chain model is positively correlated. In this paper we study the general problem with $1\leq m\leq k\leq N$, and derive sufficient conditions under which the myopic policy is optimal for the finite and infinite horizon reward criteria, respectively. It is shown that these results reduce to those derived in prior studies under the corresponding special cases, and thus may be viewed as a set of unifying optimality conditions. Numerical examples are also presented to highlight how and why an optimal policy may deviate from the otherwise-optimal myopic sensing given additional exploration opportunities, i.e., when $m<k$.
Revisiting Optimal Power Control: its Dual Effect on SNR and Contention
Venue: arXiv cs.SY 2013
Year: 2013

In this paper we study a transmission power tune problem with densely deployed 802.11 Wireless Local Area Networks (WLANs). While previous papers emphasize on tuning transmission power with either PHY or MAC layer separately, optimally setting each Access Point's (AP's) transmission power of a densely deployed 802.11 network considering its dual effects on both layers remains an open problem. In this work, we design a measure by evaluating impacts of transmission power on network performance on both PHY and MAC layers. We show that such an optimization problem is intractable and then we investigate and develop an analytical framework to allow simple yet efficient solutions. Through simulations and numerical results, we observe clear benefits of the dual-effect model compared to solutions optimizing solely on a single layer; therefore, we conclude that tuning transmission power from a dual layer (PHY-MAC) point of view is essential and necessary for dense WLANs. We further form a game theoretical framework and investigate above power-tune problem in a strategic network. We show that with decentralized and strategic users, the Nash Equilibrium (N.E.) of the corresponding game is in-efficient and thereafter we propose a punishment based mechanism to enforce users to adopt the social optimal strategy profile under both perfect and imperfect sensing environments.
Multiplicative Approximations, Optimal Hypervolume Distributions, and the Choice of the Reference Point
Venue: arXiv cs.NE 2013
Year: 2013

Many optimization problems arising in applications have to consider several objective functions at the same time. Evolutionary algorithms seem to be a very natural choice for dealing with multi-objective problems as the population of such an algorithm can be used to represent the trade-offs with respect to the given objective functions. In this paper, we contribute to the theoretical understanding of evolutionary algorithms for multi-objective problems. We consider indicator-based algorithms whose goal is to maximize the hypervolume for a given problem by distributing μ points on the Pareto front. To gain new theoretical insights into the behavior of hypervolume-based algorithms we compare their optimization goal to the goal of achieving an optimal multiplicative approximation ratio. Our studies are carried out for different Pareto front shapes of bi-objective problems. For the class of linear fronts and a class of convex fronts, we prove that maximizing the hypervolume gives the best possible approximation ratio when assuming that the extreme points have to be included in both distributions of the points on the Pareto front. Furthermore, we investigate the choice of the reference point on the approximation behavior of hypervolume-based approaches and examine Pareto fronts of different shapes by numerical calculations.