Low Storage Requirements Research Articles

Parallel algorithms for large scale econometric models

Abstract In this paper we have developed algorithms to solve macroeconometric models with forward-looking variables based on Newton method for nonlinear systems of equations. The most difficult step for Newton methods represents the resolution of a large linear system for each iteration. Thus, we compare the performances resulted by solving this linear system using two iterative methods and the direct method. We’ve also described an implementation of the parallel versions of such algorithms using a software package. Our experiments confirm that the iterative methods have a low computational complexity and storage requirements, but the parallel versions of direct methods show a superior speedup.

On the Accuracy and Limits of Peptide Fragmentation Spectrum Prediction

We estimated the reproducibility of tandem mass spectra for the widely used collision-induced dissociation (CID) of peptide ions. Using the Pearson correlation coefficient as a measure of spectral similarity, we found that the within-experiment reproducibility of fragment ion intensities is very high (about 0.85). However, across different experiments and instrument types/setups, the correlation decreases by more than 15% (to about 0.70). We further investigated the accuracy of current predictors of peptide fragmentation spectra and found that they are more accurate than the ad-hoc models generally used by search engines (e.g., SEQUEST) and, surprisingly, approaching the empirical upper limit set by the average across-experiment spectral reproducibility (especially for charge +1 and charge +2 precursor ions). These results provide evidence that, in terms of accuracy of modeling, predicted peptide fragmentation spectra provide a viable alternative to spectral libraries for peptide identification, with a higher coverage of peptides and lower storage requirements. Furthermore, using five data sets of proteome digests by two different proteases, we find that PeptideART (a data-driven machine learning approach) is generally more accurate than MassAnalyzer (an approach based on a kinetic model for peptide fragmentation) in predicting fragmentation spectra but that both models are significantly more accurate than the ad-hoc models.

Performance Comparison of AODV, DSR and ANODR for Grid Placement Model

A mobile ad hoc network (MANET) is an autonomous collection of mobile devices that communicate over relatively bandwidth constrained wireless links. Since the nodes are mobile, the network topology may change rapidly and unpredictably over time. Two nodes that are out of one another’s transmission range need the support of intermediate nodes which relay messages to set up a communication between each other. This resulted in inventing different routing techniques classified as proactive, reactive and hybrid routing protocols. The reactive routing protocols got much significance because of their low storage requirements and high mobility. AODV and DSR are two such protocols which are being mostly used. The ANODR protocol is a relatively new concept which provides route anonymity and location privacy in addition to ondemand routing capability. In this paper we are exposing the performance of these three protocols in Grid node placement model using QualNet Simulator. General Terms This paper exposes the performance of AODV, DSR and ANODR while the nodes are placed in a Grid placement model.

Visualization by Proxy: A Novel Framework for Deferred Interaction with Volume Data

Interactivity is key to exploration of volume data. Interactivity may be hindered due to many factors, e.g. large data size,high resolution or complexity of a data set, or an expensive rendering algorithm. We present a novel framework for visualizing volume data that enables interactive exploration using proxy images, without accessing the original 3D data. Data exploration using direct volume rendering requires multiple (often redundant) accesses to possibly large amounts of data. The notion of visualization by proxy relies on the ability to defer operations traditionally used for exploring 3D data to a more suitable intermediate representation for interaction--proxy images. Such operations include view changes, transfer function exploration, and relighting. While previous work has addressed specific interaction needs, we provide a complete solution that enables real-time interaction with large data sets and has low hardware and storage requirements.

A comparison of high-order time integrators for thermal convection in rotating spherical shells

A numerical study of several time integration methods for solving the three-dimensional Boussinesq thermal convection equations in rotating spherical shells is presented. Implicit and semi-implicit time integration techniques based on backward differentiation and extrapolation formulae are considered. The use of Krylov techniques allows the implicit treatment of the Coriolis term with low storage requirements. The codes are validated with a known benchmark, and their efficiency is studied. The results show that the use of high-order methods, especially those with time step and order control, increase the efficiency of the time integration, and allows to obtain more accurate solutions.

Load balancing routing for single-layered satellite networks

The varying population density leads to imbalanced utilization rate of satellites. To ensure an intelligent engineering of traffic over satellite networks, a distributed routing scheme for single-layered satellite network, load balancing routing protocol based on mobile agent (LBRP-MA) is proposed. For LBRP-MA, mobile agents explore route by migrating autonomously. Upon arriving at destination, mobile agents migrate back. On each intermediate satellite, mobile agents evaluate path cost considering satellite geographical position as well as inter-satellite link (ISL) cost, and finally take ISL congestion index into account to update routing tables. Through simulations on the Courier-like constellation, the proposed approach is shown to achieve guaranteed end-to-end delay bound and decrease packet loss ratio with better throughput, which is especially suitable for data transferring in case of high traffic load. Moreover, results of the complexity analysis demonstrate that LBRP-MA can have low onboard signaling, storage and computation requirements. Furthermore, issues of LBRP-MA such as ISL congestion index and cost modification factor are discussed.

Distributed intelligent sensor agent system for environment mapping

This paper discusses a multi-agent system consisting of a limited set of mobile intelligent sensor agents that are exploring an environment with the goal of minimizing the environment mapping uncertainty, i.e. the entropy. A novel tree in-motion mapping method combining simplicity and speed of computation with low storage and communications requirements is proposed for the management of a network of robotic agents, each possessing limited sensing, processing and communicating operational entities. Simulation and experimental results demonstrate the efficiency of the proposed system architecture and mapping method.

Highly stable RK time advancing schemes for Computational Aero Acoustics

In this paper a brief survey of finite difference methods and time discretization schemes for the numerical simulation of problems in Computational Aero Acoustics (CAA), with special emphasis in the contributions of the authors in the last years to the subject, is presented. Due to the specific properties of these problems it is shown by means of some illustrative examples that standard schemes have some drawbacks and new numerical schemes have been derived taking into account not only the usual stability and accuracy requirements but also the dissipation and dispersion properties as well as low storage requirements. Some relevant contributions to the subject are presented comparing the relative merits by means of a Fourier analysis and numerical experiments.

A globally convergent derivative-free method for solving large-scale nonlinear monotone equations

In this paper, we propose two derivative-free iterative methods for solving nonlinear monotone equations, which combines two modified HS methods with the projection method in Solodov and Svaiter (1998) [5]. The proposed methods can be applied to solve nonsmooth equations. They are suitable to large-scale equations due to their lower storage requirement. Under mild conditions, we show that the proposed methods are globally convergent. The reported numerical results show that the methods are efficient.

Optimal Explicit Strong-Stability-Preserving General Linear Methods

This paper constructs strong-stability-preserving general linear time-stepping methods that are well suited for hyperbolic PDEs discretized by the method of lines. These methods generalize both Runge–Kutta (RK) and linear multistep schemes. They have high stage orders and hence are less susceptible than RK methods to order reduction from source terms or nonhomogeneous boundary conditions. A global optimization strategy is used to find the most efficient schemes that have low storage requirements. Numerical results illustrate the theoretical findings.

Spherical Harmonic Transforms and Convolutions on the GPU

Abstract We present implementations of the spherical harmonic forward and inverse transforms on the GPU using CUDA. We implement two algorithms for the SH transform: the direct method and the semi-naive. Our direct method has low storage requirements due to our on-the-fly computation of the associated Legendre functions, and it can perform large transform sizes and non-power-of-two sizes. Our semi-naive implementation is faster than state-of-the-art CPU implementations by a factor of between five and six, depending on the transform size. We target our implementations at spherical panoramic image processing where a large number of basis functions are required. We apply our tool to decompose panoramic images into an overcomplete spherical wavelet model for spherical convolution. We present timings, errors, and application examples of our implementations.

A novel indexing architecture for the provision of smart playback functions in collaborative telemedicine applications

This paper presents a novel indexing architecture to support a range of smart playback functions in collaborative telemedicine systems. These functions include replaying the session from a specified point in time, replaying all the session segments controlled by a particular physician, replaying all the session segments relating to a specific medical image, and playing a montage of the entire session. Since the contents of telemedicine sessions vary over time depending on the particular commands invoked by the physician(s) during the session, when executing the smart playback functions, it is necessary to restore the target image contents to the appropriate condition before commencing the playback routine. In this study, this is achieved by using an indexing scheme designated as three- level indexing hierarchy ( TIH) to search for the appropriate cut-in point in the session and to identify the commands which should be applied to restore the current image contents to their original condition at the cut-in point. In the proposed indexing scheme, the performance of the cut-in point determination process and the content restoration procedure is enhanced by maintaining a link between all the changes which take place in the image contents over the duration of the session and the commands which induce these changes. The evaluation results confirm that TIH outperforms existing scene-based retrieval systems in terms of both an improved computational efficiency and a lower storage requirement.

3D Underwater Sensor Network Localization

We transform the 3D underwater sensor network (USN) localization problem into its 2D counterpart by employing sensor depth information and a simple projection technique. We first prove that a nondegenerative projection preserves network localizability. We then prove that given a network and a constant k, all of the geometric k-lateration localization methods are equivalent. Based on these results, we design a purely distributed bilateration localization scheme for 3D USNs termed as underwater sensor positioning (USP). Through extensive simulations, we show that USP has the following nice features: (1) improved localization capabilities over existing 3D methods, (2) low storage and computation requirements, (3) predictable and balanced communication overhead, and (4) robustness to errors from the underwater environment.

An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.

Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC = 0.905 +/- 0.024) in performance as compared to the original IT-CAD system (AUC = 0.865 +/- 0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters.

Very-High-Frequency Resonant Boost Converters

This paper presents a resonant boost topology suitable for very-high-frequency (VHF, 30-300 MHz) DC-DC power conversion. The proposed design features low device voltage stress, high efficiency over a wide load range, and excellent transient performance. Two experimental prototypes have been built and evaluated. One is a 110-MHz, 23-W converter that uses a high-performance RF lateral DMOSFET. The converter achieves higher than 87% efficiency at nominal input and output voltages, and maintains good efficiency down to 5% of full load. The second implementation, aimed toward integration, is a 50-MHz, 17-W converter that uses a transistor from a 50-V integrated power process. In addition, two resonant gate drive schemes suitable for VHF operation are presented, both of which provide rapid startup and low-loss operation. Both converters regulate the output using high-bandwidth, on-off hysteretic control, which enables fast transient response and efficient light-load operation. The low energy storage requirements of the converters allow the use of aircore inductors in both designs, thereby eliminating magnetic core loss and introducing the possibility of easy integration.

Scene-based nonuniformity correction technique for infrared focal-plane arrays

A scene-based nonuniformity correction algorithm is presented to compensate for the gain and bias nonuniformity in infrared focal-plane array sensors, which can be separated into three parts. First, an interframe-prediction method is used to estimate the true scene, since nonuniformity correction is a typical blind-estimation problem and both scene values and detector parameters are unavailable. Second, the estimated scene, along with its corresponding observed data obtained by detectors, is employed to update the gain and the bias by means of a line-fitting technique. Finally, with these nonuniformity parameters, the compensated output of each detector is obtained by computing a very simple formula. The advantages of the proposed algorithm lie in its low computational complexity and storage requirements and ability to capture temporal drifts in the nonuniformity parameters. The performance of every module is demonstrated with simulated and real infrared image sequences. Experimental results indicate that the proposed algorithm exhibits a superior correction effect.

Asymmetric Key Pre-distribution Scheme for sensor networks

A key pre-distribution scheme is a method by which initially an off-line trusted authority distributes pieces of information among a set of users. Later, each member of a group of users can compute a common key for secure communication. In this paper we present an asymmetric key pre-distribution scheme. Instead of assuming that the network is comprised entirely of identical users in conventional key pre-distribution schemes, the network now consists of a mix of users with different missions, i.e., ordinary users and keying material servers. A group of users, using secret keys preloaded in their memory and public keying material retrieved from one keying material server, can compute a session key. The properties of this method are that, the compromise of keying material servers does not reveal any information about users' secret keys and the session keys of privileged subset of users; if computational assumptions are considered, each user has very low storage requirement. These properties make it attractive for sensor networks. We first formally define the asymmetric key pre-distribution scheme in terms of the entropy and give lower bounds on user's storage requirement and the public keying material size. Then, we present its constructions and applications for sensor networks.

Noncoherent MIMO Communication: Grassmannian Constellations and Efficient Detection

This paper considers the design of both a transmitter and a receiver for noncoherent communication over a frequency-flat, richly scattered multiple-input multiple-output (MIMO) channel. The design is guided by the fact that at high signal-to-noise ratios (SNRs), the ergodic capacity of the channel can be achieved by input signals that are isotropically distributed on the (compact) Grassmann manifold. The first part of the paper considers the design of Grassmannian constellations that MIMIC the isotropic distribution. A subspace perturbation analysis is used to determine an appropriate metric for the distance between Grassmannian constellation points, and using this metric, greedy, direct and rotation-based techniques for designing constellations are proposed. These techniques offer different tradeoffs between the minimum distance of the constellation and the design complexity. In addition, the rotation-based technique results in constellations that have lower storage requirements and admit a natural ldquoquasi-set-partitioningrdquo binary labeling.

A Fast (k,L,n)-Threshold Ramp Secret Sharing Scheme

Shamir's (k, n)-threshold secret sharing scheme (threshold scheme) has two problems: a heavy computational cost is required to make shares and recover the secret, and a large storage capacity is needed to retain all the shares. As a solution to the heavy computational cost problem, several fast threshold schemes have been proposed. On the other hand, threshold ramp secret sharing schemes (ramp scheme) have been proposed in order to reduce each bit-size of shares in Shamir's scheme. However, there is no fast ramp scheme which has both low computational cost and low storage requirements. This paper proposes a new (k, L, n)-threshold ramp secret sharing scheme which uses just EXCLUSIVE-OR(XOR) operations to make shares and recover the secret at a low computational cost. Moreover, by proving that the fast (k, n)-threshold scheme in conjunction with a method to reduce the number of random numbers is an ideal secret sharing scheme, we show that our fast ramp scheme is able to reduce each bit-size of shares by allowing some degradation of security similar to the existing ramp schemes based on Shamir's threshold scheme.

BB: AnRPackage for Solving a Large System of Nonlinear Equations and for Optimizing a High-Dimensional Nonlinear Objective Function

We discuss <code>R</code> package <b>BB</b>, in particular, its capabilities for solving a nonlinear system of equations. The function <code>BBsolve</code> in <b>BB</b> can be used for this purpose. We demonstrate the utility of these functions for solving: (a) large systems of nonlinear equations, (b) smooth, nonlinear estimating equations in statistical modeling, and (c) non-smooth estimating equations arising in rank-based regression modeling of censored failure time data. The function <code>BBoptim</code> can be used to solve smooth, box-constrained optimization problems. A main strength of <b>BB</b> is that, due to its low memory and storage requirements, it is ideally suited for solving high-dimensional problems with thousands of variables.