nils-herrmann/clean_scirepeval huggingface dataset:
Estimation of Distribution Algorithm for green resource allocation in cognitive radio systems

In this paper, we formulate a resource allocation optimization problem for a cooperative relay-assisted cognitive radio system, comprising a single source node, multiple relays and multiple destinations. Our formulation takes into account the effects of the resource allocation on CO2 emission, and we refer to it as a green resource allocation problem. The green resource allocation problem is formulated as a non-linear multi-objective optimization problem. We modify the objective function by applying the weighted sum method, which results in a non-convex mixed integer non-linear programming problem. We propose a hybrid evolutionary scheme that utilizes an enhanced version of Estimation of Distributions Algorithm to solve this optimization problem. Simulation results demonstrate the efficiency of our evolutionary algorithm approach in comparison to other schemes such as GA and EDA.
Integrating effective-bandwidth-based QoS routing and best effort routing

A methodology is presented for integrating effective-bandwidth-based routing for QoS-sensitive traffic and datagram routing of the best-effort traffic. To prevent excessive delays of best-effort traffic in a network domain, we develop (1) a constraint, stated in the form of a residual link bandwidth, and (2) a cost function for application to routing of QoS connections. Link-based and path-based problem formulations and algorithms are presented. For the case that a cost quantization condition holds, we develop an efficient implementation of a link-based routing strategy that first minimizes a QoS cost, then secondarily minimizes a best-effort cost. The performance of this approach is further enhanced by explicitly accounting for the difference between the effective bandwidth and the average bandwidth of traffic. Simulation results illustrate the application of our BE-friendly method to an algorithm for path routing with restoration.
Managing harmonic emissions from small low voltage installations

Managing voltage distortion in power systems usually consists of two steps. The first step, mostly utilized outside of North America, involves limiting harmonic emissions from individual pieces of equipment. The second step, utilized worldwide in different ways, is to manage harmonic emissions on a system level. Traditionally, equipment emissions have been managed at low voltage (LV) whereas system-level electromagnetic compatibility (EMC) issues, including harmonics, have been managed at medium, high, and extra-high voltages (MV, HV, and EHV). With the continued growth of lowvoltage networks, it is becoming necessary to implement some type of system-level coordination strategy at low voltage. Toward this end, IEC TC77/SC77A/WG8 has developed a committee draft document outlining an EMC coordination approach for harmonics, flicker, and voltage unbalance in LV systems and networks. This work is intended to bridge the existing gap in EMC coordination between small equipment harmonic limits and (larger) installation-level limits. To effectively bridge this gap, it is necessary to borrow concepts from both equipment and system-level EMC coordination approaches while not overburdening the parties involved in the process. The possibilities of using current harmonic limits for small installations connected to LV are considered in this paper.
A biogeography-based optimization algorithm for energy efficient virtual machine placement

Recently, high levels of energy consumption in datacenters has become a concern not only due to operational costs, but also due to adverse effects on the environment (i.e., carbon emission, climate change, etc.) Virtualization technology can provide better management of physical servers/machines (PM) and may help reduce power consumption. The purpose of this study is to minimize the total energy consumption through good virtual machine (VM) placement. The VM placement problem has a large search space. Finding an optimal solution of such problems using an exhaustive search is impractical. Heuristic algorithms can provide high-quality solutions with limited computing resources in acceptable time. Evolutionary Algorithms (EAs) can be considered as heuristic tools that can provide high-quality solutions to this type of problems. We propose a Biogeography Based Optimization (BBO) Algorithm for energy-efficient VM placement. We compare the BBO results with the Genetic Algorithm (GA). Overall, simulation results show that BBO outperforms GA.
Swarm Intelligence for Sensor Selection Problems

In this paper, we apply the binary particle swarm optimization to the problem of selecting sensors from a set of sensors for the purpose of minimizing the error in parameter estimation. The motivation of selecting sensors rather than utilizing all sensors includes computational efficiency of parameter estimation and also the efficiency of energy consumption of sensor operations. The computational complexity of finding an optimal subset through exhaustive search can grow exponentially with the number ( and ) of sensors. If and are large, then it is not practical to solve this problem by evaluating all possible subsets of sensors. In addition to applying the general binary particle swarm optimization (BPSO) to the sensor selection problem, we also present a specific improvement to this population-based heuristic algorithm, namely, we use cyclical shifts to construct the members of the initial population, with the intention of reducing the average convergence time (the number of iterations until reaching an acceptable solution). The proposed BPSO for the sensor selection problem is computationally efficient, and its performance is verified through simulation results.
Worst closed-loop controlled bulk distributions of stochastic arrival processes for queue performance

This paper presents basic queueing analysis contributing to teletraffc theory, with commonly accessible mathematical tools. This paper studies queueing systems with bulk arrivals. It is assumed that the number of arrivals and the expected number of arrivals in each bulk are bounded by some constraints B and λ, respectively. Subject to these constraints, convexity argument is used to show that the bulk-size probability distribution that results in the worst mean queue performance is an extremal distribution with support {1, B} and mean equal to λ. Furthermore, from the viewpoint of security against denial-of-service attacks, this distribution remains the worst even if an adversary were allowed to choose the bulk-size distribution at each arrival instant as a function of past queue lengths; that is, the adversary can produce as bad queueing performance with an open-loop strategy as with any closed-loop strategy. These results are proven for an arbitrary arrival process with bulk arrivals and a general service model.
A New Perspective on GDSD Risk Management: Agile Risk Management

Risk management in globally distributed software development (GDSD) projects is becoming a critical area of concern for practitioners. The risks in GDSD projects can be dynamic due to the multiplicity in various aspects of GDSD projects (e.g., multi-locations, multi-cultures, multi-groups, multi-standards, and multi-technologies). This multiplicity nature leads to dynamic interactions among the internal (i.e., people, process, and technology) and external elements of a GDSD project. This study aims to develop a new framework to identify the dynamic risks in GDSD projects and mitigate them using agile risk management practices. We reflect the proposed framework on a case of GDSD project in the literature, which experienced high multiplicity and thus high dynamics in its project management.
Efficient dynamic programming for high-dimensional, optimal motion planning by spectral learning of approximate value function symmetries

We demonstrate how to find high-quality motion plans for high-dimensional holonomic systems efficiently using dynamic programming in a learned subspace of vastly reduced dimension. Our approach (SLASHDP) learns the low-dimensional cost structure of an optimal control problem via an efficient spectral method. This structure results in a symmetric value function that serves as a an efficiently-computable surrogate for the true value function. High-quality feedback motion plans can then be obtained from the symmetric value function. Experimental results show that SLASHDP yields higher-quality plans than can be obtained by post-processing plans generated by a sampling-based motion planner, and with less computational effort for very high-dimensional problems. We demonstrate high-quality dynamic programming plans for an arm planning problem of up to 144 dimensions without using any domain-specific knowledge aside from that learned automatically by SLASHDP. Positive results are also shown for a high-dimensional deformable robot planning problem.
Recursive least squares ladder estimation algorithms

Recursive least squares ladder estimation algorithms have attracted much attention recently because of their excellent convergence behavior and fast parameter tracking capability, compared to gradient based algorithms. We present some recently developed square root normalized exact least squares ladder form algorithms that have fewer storage requirements, and lower computational requirements than the unnormalized ones. A Hilbert space approach to the derivations of magnitude normalized signal and gain recursions is presented. The normalized forms are expected to have even better numerical properties than the unnormalized versions. Other normalized forms, such as joint process estimators (e.g., "adaptive line enhancer") and ARMA (pole-zero) models, will also be presented. Applications of these algorithms to fast (or "zero") startup equalizers, adaptive noise- and echo cancellers, non-Gaussian event detectors, and inverse models for control problems are also mentioned.
Monocular visual pose estimation via online sampling for Mars sample-tube pickup

The 2011 Planetary Science Decadal Survey identified making significant progress towards Mars sample return (MSR) as a top priority for NASA, where a three-mission concept for MSR is currently being investigated. The Mars 2020 mission is intended to collect samples which will be sealed in tubes and left on the surface for potential return to Earth by a future mission. This paper studies the problem, autonomous pick-up of sample tubes using a monocular camera attached to a robot's end-effector. We estimate the pose of the tube and the gripper in a single image where both are visible, and compute incremental arm motions based on the relative transformation between the tube and the gripper. To estimate the pose of the tube in the tool-camera frame, we suggest an online sampling-based approach using image gradients in a coarse-to-fine framework. In order to reduce the search space, we employ a biased sampling strategy based on the target shape and projective geometry, and learn promising sampling regions on the fly. Our experiments demonstrate the effectiveness and efficiency of our strategy toward precise Martian sample retrieval.
Lithography technology development and design rule consideration for 0.4-0.35 /spl mu/m DRAM semiconductor processes

The practical issues of i-line lithography process development and design rule considerations for 0.4-0.35 /spl mu/m DRAM (dynamic random access memory) manufacturing were addressed in this paper. Based on published literature and our development experience in 64 Mb DRAM, a list of lithography requirements for DRAM manufacturing was proposed and discussed. The details on focus budget issue were discussed also. A tremendous DOF (depth of focus) loss on the critical layer processes occurred without the considerations of local and global proximity effects and metrology errors on 0.4 /spl mu/m and below processes. Combining an optical proximity correction (OPC) technique with design rule modifications, a factor of 2-3 improvement in UDOF (usable DOF) was proven. Thinner resist thickness and mask quality control which become inevitable for the coming generations were discussed also.
Alignment Between Internal and External IT Governance and Its Effects on Distinctive Firm Performance: An Extended Resource-Based View

Only a few firms possess all of the information technology (IT) resources required to compete effectively in today's dynamic business environment. Consequently, firms face critical challenges in developing their IT governance by integrating, building, and reconfiguring IT resources available internally and externally to achieve a competitive advantage. However, prior studies have mostly examined IT governance only from an internal (e.g., IT organization design) or external (e.g., IT outsourcing) perspective. Therefore, how the internal and external IT governance of firms simultaneously lead to firm performance remains unclear. To address this research gap, we conceptualize internal and external IT governance from the extended resource-based view, propose three alignments between both types of governance, and hypothesize their effects on distinctive firm performance. We then empirically test these hypotheses using 213 Korean firms that have implemented IT outsourcing. Results show that a hierarchy-based alignment (i.e., between internal and external hierarchy governance) optimizes the operational efficiency of firms, a market-based alignment (i.e., between internal and external market governance) offers the greatest advantage in terms of market growth, and a network-based alignment (i.e., between internal and external network governance) leads to the best innovation performance. Furthermore, our post hoc test also reveals some unexpected alignments (e.g., between internal market and external hierarchy governance) that positively influence firm performance, which call for new areas of discussion with alternative theoretical perspectives. This study guides practitioners in choosing outsourcing alignments by comprehensively analyzing the effects of various alignments on distinctive outcomes.
Development of Through Silicon Via (TSV) interposer for memory module flip chip package

With the trend of microelectronics packaging toward more functionality, high performance and smaller form factor, the product is required to deliver more I/Os and better electrical characteristics under a minimum module system. Therefore, a 3D IC integration System in Package (SiP) with passive Through Silicon Via (TSV) interposer technology is proposed to provide high density and heterogeneous integration needed for such requirement. Due to the Coefficients of Thermal Expansion (CTE) mismatch between materials, thermal-mechanical stress and warpage are induced during Through Silicon Interposer (TSI) fabrication process, which may affect TSV crack or Controlled Collapse Chip Connection (C4) bump crack after TSI bonding to organic substrate process. Therefore, this work is a step by step process simulation with various design parameters by finite element method (FEM) to investigate stress and warpage behaviors of processing effect. For both full array dummy bumps layout and thinner top chips are to reduce micro-bump stress. Also, low CTE organic substrate core, high CTE mold compound and thinner top chips perform lower package warpage. Finally, this paper aims to provide a guideline to designer for 3D IC integration SiP with passive TSV interposer structure by using the finite element models.
Bayesian regularization and nonnegative deconvolution for room impulse response estimation

This paper proposes Bayesian Regularization And Nonnegative Deconvolution (BRAND) for accurately and robustly estimating acoustic room impulse responses for applications such as time-delay estimation and echo cancellation. Similar to conventional deconvolution methods, BRAND estimates the coefficients of convolutive finite-impulse-response (FIR) filters using least-square optimization. However, BRAND exploits the nonnegative, sparse structure of acoustic room impulse responses with nonnegativity constraints and L/sub 1/-norm sparsity regularization on the filter coefficients. The optimization problem is modeled within the context of a probabilistic Bayesian framework, and expectation-maximization (EM) is used to derive efficient update rules for estimating the optimal regularization parameters. BRAND is demonstrated on two representative examples, subsample time-delay estimation in reverberant environments and acoustic echo cancellation. The results presented in this paper show the advantages of BRAND in high temporal resolution and robustness to ambient noise compared with other conventional techniques.
Representing surface curvature discontinuities on curved surfaces

A representation scheme is presented for shapes of three-dimensional curved objects whose surface curvatures are not continuous at all points, i.e., surfaces with C/sup 1/ continuity only. A class of surface curves and points called surface structure descriptors for this representation are defined. These descriptors consist of subsets of lines of curvature and their intersections and are based on local descriptions of smooth surfaces. The surface structure curves are derived from the zero-crossings, asymptotes and gradient directions of the Gaussian and mean curvatures of the surface. A smooth surface sketch in terms of the geometrical structures of enclosing boundaries of bumps and dents, ridge and valley lines, and peaks and pits is developed. A hierarchical description of curved surfaces that leads to a global shape representation of the object is obtained from these descriptors. To handle the discontinuities of surface curvatures, a computational scheme is introduced in which gradient directions of surface curvatures at the discontinuities can be inferred. Simulation results on the computation of these descriptors from curved surfaces are shown. Potential applications of these representation schemes in computer vision tasks are discussed.<<ETX>>
UV nonvolatile sensor by using SONOS capacitor device

The silicon-oxide-nitride-oxide-silicon (SONOS) capacitor devices can be candidates for nonvolatile ultraviolet UV radiation sensors. In the case of SONOS type UV radiation sensors, the UV radiation induces a significant increase of threshold voltage for SONOS by UV-assisted-low-gate-voltage program. The change of threshold voltage for SONOS after UV ray expose has a correlation to the dose of UV ray exposure. In this paper, the performance of capacitor types of SONOS used for UV sensor was studied. The data of SONOS UV radiation sensors wrote by UV-assisted-low-gate-voltage program was nonvolatile at least for 1 month. The SONOS capacitor device with ONO gate dielectric in this study has demonstrated the feasibility for UV nonvolatile sensor application.
Gains achieved by symbol-by-symbol rate adaptation on error-constrained data throughput over fading channels

Methods for symbol-by-symbol channel feedback and adaptation of symbol durations have been recently proposed. In this paper, we quantitatively analyze the gain in error-constrained data throughput due to such an extremely rapid adaptation of symbol durations to fast-time-varying channels. The results show that a symbol-by-symbol adaptation can achieve a throughput gain by orders of magnitude over a frame-by-frame adaptation.
Iterative smoothing approach using Gaussian mixture models for nonlinear estimation

An iterative smoothing algorithm is developed using Gaussian mixture models in order to tackle challenging nonlinear estimation problems. Gaussian mixture models naturally capture nonlinear and non-Gaussian systems, while smoothing algorithms provide ability to update using measurements obtained in the past. A tree structure and Gaussian distribution splitting method are proposed to mitigate nonlinearity effects and complexities. Two methods, Children Collapsing and Parent Splitting, are developed to utilize sigma-points smoother for Gaussian mixture model. An indoor localization problem is used to explore and validate the approach. Performance of these new methods is compared to a baseline sigma-points smoother, in both simulation and experiment, and shows much improvement in overall error compared to the truth.
An analytic approach to efficiently computing call blocking probabilities for multiclass WDM networks

For all-optical WDM networks that provide multiple classes of service, we present a methodology for computing approximate blocking probabilities of dynamic routing and wavelength assignment policies. Each service class is characterized by its resource requirements (number of wavelengths needed for a call) and expected call holding time (or subscription period). Under the wavelength continuity constraint on lightpaths and loss network formulation, we develop fixed-point approximation algorithms that compute approximate blocking probabilities of all classes. We then apply them to the random wavelength assignment policy for the following wavelength routing policies: Fixed Routing (FR), Least Loaded Routing (LLR) and Fixed Alternate Routing (FAR). Simulation results on different network topologies and routing policies considered demonstrate that the simulation results match closely with the blocking probabilities computed by our methods for different multiclass call traffic loading scenarios.
Expected file delivery time of immediate nak arq in ccsds file delivery protocol

AbstructIn this paper, we analyze the Immediate NAK ARQ scheme of the CFDP (CCSDS File Delivery Protocol). We consider the single-hop file transfer operation. For the environment of long propagation delay, we present a performance model of CFDP. On the basis of this model, we analyze the relation between protocol parameters and performance. As performance measures, this paper is mainly concerned for the time taken to transfer a file (expected file delivery time) and throughput efficiency, which result from the protocol specification. Our study focuses on setting timer values to minimize the expected file delivery time under the constraint that the throughput efficiency is never compromised.
Worst-case fraction of CBR teletraffic unpunctual due to statistical multiplexing

This paper quantitatively analyzes the queueing delay of the constant-bit-rate (CBR) traffic in a multiplexer, where the CBR traffic is mixed with the traffic of another session (interfering traffic) that has throughput and burstiness constraints. This paper focuses as a quality-of-service (QoS) measure on the fraction of the CBR traffic that fails to meet a certain delay requirement. Results include the worst-case QoS of CBR traffic as a function of the throughput and burstiness parameters imposed on the interfering traffic. Results of this paper can be applied to traffic management and call processing of asynchronous transfer mode (ATM) networks.
Finite element modeling of the vertical and horizontal electric field signatures in the presence of a sprite

Recently Pasko Barrington-Leigh, and Inan have discussed models explaining the electromagnetic and optical behavior of sprites. Research presented herein extends Baginski's lightning transient model by including Pasko's self-consistent conductivity model for parameters consistent with sprite observations. Equations are solved simultaneously in differential form (Maxwell's equations and electron ionization). Cylindrical symmetry is assumed and fields simulated at radius /spl sim/ 0-50 km, and altitude /spl sim/ 40-80 km. Vertical and horizontal field signatures are presented and anomalous behavior discussed. The research differs from earlier models by including the low altitude conductivity in the solutions and over the entire region simulated.