OpenRaiser/Intern-Atlas huggingface dataset:
Effective Bandwidth Utilization in IEEE802.11 for VOIP
Venue: arXiv cs.NI 2010
Year: 2010

Voice over Internet protocol (VoIP) is one of the most important applications for the IEEE 802.11 wireless local area networks (WLANs). For network planners who are deploying VoIP over WLANs, one of the important issues is the VoIP capacity. VoIP bandwidth consumption over a WAN is one of the most important factors to consider when building a VoIP infrastructure. Failure to account for VoIP bandwidth requirements will severely limit the reliability of a VoIP system and place a huge burden on the WAN infrastructure. Less bandwidth utilization is the key reasons for reduced number of channel accesses in VOIP. But in the QoS point of view the free bandwidth of atleast 1-5% will improve the voice quality. This proposal utilizes the maximum bandwidth by leaving 1-5% free bandwidth. A Bandwidth Data rate Moderation (BDM) algorithm has been proposed which correlates the data rate specified in IEEE802.11b with the free bandwidth. At each time BDM will calculate the bandwidth utilization before sending the packet to improve performance and voice quality of VoIP. The bandwidth calculation in BDM can be done by using Erlang and VOIP bandwidth calculator. Finally, ns2 experimental study shows the relationship between bandwidth utilization, free bandwidth and data rate. The paper concludes that marginal VoIP call rate has been increased by BDM algorithm.
ECG Feature Extraction Techniques - A Survey Approach
Venue: arXiv cs.NE 2010
Year: 2010

ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis. The amplitudes and intervals value of P-QRS-T segment determines the functioning of heart of every human. Recently, numerous research and techniques have been developed for analyzing the ECG signal. The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and other Signal Analysis techniques. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for extracting feature from an ECG signal. In addition this paper also provides a comparative study of various methods proposed by researchers in extracting the feature from ECG signal.
Artificial Neural Network based Diagnostic Model For Causes of Success and Failures
Venue: arXiv cs.NE 2010
Year: 2010

In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited to the problems that are well-understood and known solution(s). On the other hand the ANNs have learning by example and processing capabilities similar to that of a human brain. ANN has been followed due to its inherent advantage over conversion algorithmic like approaches and having capabilities, training and human like intuitive decision making capabilities. Therefore, this ANN based approach is likely to help researchers and organizations to reach a better solution to the problem of managing the human resource. The study is particularly important as many studies have been carried in developed countries but there is a shortage of such studies in developing nations like India. Here, a model has been derived using connectionist-ANN approach and improved and verified via back-propagation algorithm. This suggested ANN based model can be used for testing the success and failure human factors in any of the communication Industry. Results have been obtained on the basis of connectionist model, which has been further refined by BPNN to an accuracy of 99.99%. Any company to predict failure due to HR factors can directly deploy this model.
Incidences in Three Dimensions and Distinct Distances in the Plane
Venue: arXiv cs.CG 2010
Year: 2010

We first describe a reduction from the problem of lower-bounding the number of distinct distances determined by a set $S$ of $s$ points in the plane to an incidence problem between points and a certain class of helices (or parabolas) in three dimensions. We offer conjectures involving the new setup, but are still unable to fully resolve them. Instead, we adapt the recent new algebraic analysis technique of Guth and Katz \cite{GK}, as further developed by Elekes et al. \cite{EKS}, to obtain sharp bounds on the number of incidences between these helices or parabolas and points in $\reals^3$. Applying these bounds, we obtain, among several other results, the upper bound $O(s^3)$ on the number of rotations (rigid motions) which map (at least) three points of $S$ to three other points of $S$. In fact, we show that the number of such rotations which map at least $k\ge 3$ points of $S$ to $k$ other points of $S$ is close to $O(s^3/k^{12/7})$. One of our unresolved conjectures is that this number is $O(s^3/k^2)$, for $k\ge 2$. If true, it would imply the lower bound $Ω(s/\log s)$ on the number of distinct distances in the plane.
Revenue Optimal Auction for Single-Minded Buyers
Venue: arXiv cs.GT 2010
Year: 2010

We study the problem of characterizing revenue optimal auctions for single-minded buyers. Each buyer is interested only in a specific bundle of items and has a value for the same. Both his bundle and its value are his private information. The bundles that buyers are interested in and their corresponding values are assumed to be realized from known probability distributions independent across the buyers. We identify revenue optimal auctions with a simple structure, if the conditional distribution of any buyer's valuation is nondecreasing, in the hazard rates ordering of probability distributions, as a function of the bundle the buyer is interested in. The revenue optimal auction is given by the solution of a maximum weight independent set problem. We provide a novel graphical construction of the weights and highlight important properties of the resulting auction.
Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators
Venue: arXiv cs.IT 2010
Year: 2010

Optimal and suboptimal decentralized estimators in wireless sensor networks (WSNs) over orthogonal multiple-access fading channels are studied in this paper. Considering multiple-bit quantization before digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case of the MLE with unknown CSI. It implicitly uses the training symbols to estimate the channel coefficients and exploits the estimated CSI in an optimal way. To reduce the computational complexity, we propose suboptimal estimators. These estimators exploit both signal and data level redundant information to improve the estimation performance. The proposed MLEs reduce to traditional fusion based or diversity based estimators when communications or observations are perfect. By introducing a general message function, the proposed estimators can be applied when various analog or digital transmission schemes are used. The simulations show that the estimators using digital communications with multiple-bit quantization outperform the estimator using analog-and-forwarding transmission in fading channels. When considering the total bandwidth and energy constraints, the MLE using multiple-bit quantization is superior to that using binary quantization at medium and high observation signal-to-noise ratio levels.
Variable elimination for building interpreters
Venue: arXiv cs.SE 2010
Year: 2010

In this paper, we build an interpreter by reusing host language functions instead of recoding mechanisms of function application that are already available in the host language (the language which is used to build the interpreter). In order to transform user-defined functions into host language functions we use combinatory logic : lambda-abstractions are transformed into a composition of combinators. We provide a mechanically checked proof that this step is correct for the call-by-value strategy with imperative features.
3/2 Firefighters are not enough
Venue: arXiv cs.DM 2010
Year: 2010

The firefighter problem is a monotone dynamic process in graphs that can be viewed as modeling the use of a limited supply of vaccinations to stop the spread of an epidemic. In more detail, a fire spreads through a graph, from burning vertices to their unprotected neighbors. In every round, a small amount of unburnt vertices can be protected by firefighters. How many firefighters per turn, on average, are needed to stop the fire from advancing? We prove tight lower and upper bounds on the amount of firefighters needed to control a fire in the Cartesian planar grid and in the strong planar grid, resolving two conjectures of Ng and Raff.
On Design of Distributed Beamforming for Two-Way Relay Networks
Venue: arXiv cs.IT 2010
Year: 2010

We consider a two-way relay network, where two source nodes, S1 and S2, exchange information through a cluster of relay nodes. The relay nodes receive the sum signal from S1 and S2 in the first time slot. In the second time slot, each relay node multiplies its received signal by a complex coefficient and retransmits the signal to the two source nodes, which leads to a distributed two-way beamforming system. By applying the principle of analog network coding, each receiver at S1 and S2 cancels the ``self-interference'' in the received signal from the relay cluster and decodes the message. This paper studies the 2-dimensional achievable rate region for such a two-way relay network with distributed beamforming. With different assumptions of channel reciprocity between the source-relay and relay-source channels, the achievable rate region is characterized under two setups. First, with reciprocal channels, we investigate the achievable rate regions when the relay cluster is subject to a sum-power constraint or individual-power constraints. We show that the optimal beamforming vectors obtained from solving the weighted sum inverse-SNR minimization (WSISMin) problems are sufficient to characterize the corresponding achievable rate region. Furthermore, we derive the closed form solutions for those optimal beamforming vectors and consequently propose the partially distributed algorithms to implement the optimal beamforming, where each relay node only needs the local channel information and one global parameter. Second, with the non-reciprocal channels, the achievable rate regions are also characterized for both the sum-power constraint case and the individual-power constraint case. Although no closed-form solutions are available under this setup, we present efficient algorithms to compute the optimal beamforming vectors, which are attained by solving SDP problems after semi-definite relaxation.
Heuristics in Conflict Resolution
Venue: arXiv cs.AI 2010
Year: 2010

Modern solvers for Boolean Satisfiability (SAT) and Answer Set Programming (ASP) are based on sophisticated Boolean constraint solving techniques. In both areas, conflict-driven learning and related techniques constitute key features whose application is enabled by conflict analysis. Although various conflict analysis schemes have been proposed, implemented, and studied both theoretically and practically in the SAT area, the heuristic aspects involved in conflict analysis have not yet received much attention. Assuming a fixed conflict analysis scheme, we address the open question of how to identify "good'' reasons for conflicts, and we investigate several heuristics for conflict analysis in ASP solving. To our knowledge, a systematic study like ours has not yet been performed in the SAT area, thus, it might be beneficial for both the field of ASP as well as the one of SAT solving.
Impact of Mobility on the Performance of Multicast Routing Protocols in MANET
Venue: arXiv cs.NI 2010
Year: 2010

The advent of ubiquitous computing and the proliferation of portable computing devices have raised the importance of mobile ad-hoc network. A major challenge lies in adapting multicast communication into such environments where mobility and link failures are inevitable. The purpose of this paper is to study impact of mobility models in performance of multicast routing protocols in MANET. In this work, three widely used mobility models such as Random Way Point, Reference Point Group and Manhattan mobility models and three popular multicast routing protocols such as On-Demand Multicast Routing Protocol, Multicast Ad hoc On-demand Distance Vector Routing protocol and Adaptive Demand driven Multicast Routing protocol have been chosen and implemented in NS2. Several experiments have been carried out to study the relative strengths, weakness and applicability of multicast protocols to these mobility models.
Binary Matroids and Quantum Probability Distributions
Venue: arXiv cs.CC 2010
Year: 2010

We characterise the probability distributions that arise from quantum circuits all of whose gates commute, and show when these distributions can be classically simulated efficiently. We consider also marginal distributions and the computation of correlation coefficients, and draw connections between the simulation of stabiliser circuits and the combinatorics of representable matroids, as developed in the 1990s.
An Efficient and Secure Routing Protocol for Mobile Ad-Hoc Networks
Venue: arXiv cs.DC 2010
Year: 2010

Efficiency and simplicity of random algorithms have made them a lucrative alternative for solving complex problems in the domain of communication networks. This paper presents a random algorithm for handling the routing problem in Mobile Ad hoc Networks [MANETS].The performance of most existing routing protocols for MANETS degrades in terms of packet delay and congestion caused as the number of mobile nodes increases beyond a certain level or their speed passes a certain level. As the network becomes more and more dynamic, congestion in network increases due to control packets generated by the routing protocols in the process of route discovery and route maintenance. Most of this congestion is due to flooding mechanism used in protocols like AODV and DSDV for the purpose of route discovery and route maintenance or for route discovery as in the case of DSR protocol. This paper introduces the concept of random routing algorithm that neither maintains a routing table nor floods the entire network as done by various known protocols thereby reducing the load on network in terms of number of control packets in a highly dynamic scenario. This paper calculates the expected run time of the designed random algorithm.
On the Minimization of Handover Decision Instability in Wireless Local Area Networks
Venue: arXiv cs.NI 2010
Year: 2010

This paper addresses handover decision instability which impacts negatively on both user perception and network performances. To this aim, a new technique called The HandOver Decision STAbility Technique (HODSTAT) is proposed for horizontal handover in Wireless Local Area Networks (WLAN) based on IEEE 802.11standard. HODSTAT is based on a hysteresis margin analysis that, combined with a utilitybased function, evaluates the need for the handover and determines if the handover is needed or avoided. Indeed, if a Mobile Terminal (MT) only transiently hands over to a better network, the gain from using this new network may be diminished by the handover overhead and short usage duration. The approach that we adopt throughout this article aims at reducing the minimum handover occurrence that leads to the interruption of network connectivity (this is due to the nature of handover in WLAN which is a break before make which causes additional delay and packet loss). To this end, MT rather performs a handover only if the connectivity of the current network is threatened or if the performance of a neighboring network is really better comparing the current one with a hysteresis margin. This hysteresis should make a tradeoff between handover occurrence and the necessity to change the current network of attachment. Our extensive simulation results show that our proposed algorithm outperforms other decision stability approaches for handover decision algorithm.
Compressive Wideband Spectrum Sensing for Fixed Frequency Spectrum Allocation
Venue: arXiv cs.IT 2010
Year: 2010

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio (CR). As the survey shows that the sensed signal has a sparse representation in frequency domain in the mass, compressed sensing (CS) can be used to transfer the sampling burden to the digital signal processor. An analog to information converter (AIC) can randomly sample the received signal with sub-Nyquist rate to obtained the random measurements. Considering that the static frequency spectrum allocation of primary radios means the bounds between different primary radios is known in advance, here we incorporate information of the spectrum boundaries between different primary user as a priori information to obtain a mixed l2/l1 norm denoising operator (MNDO). In the MNDO, the estimated power spectrum density (PSD) vector is divided into block sections with bounds corresponding different allocated primary radios. Different from previous standard l1-norm constraint on the whole PSD vector, a sum of the l2 norm of each section of the PSD vector is minimized to encourage the local grouping distribution while the sparse distribution in mass, while a relaxed constraint is used to improve the denoising performance. Simulation demonstrates that the proposed method outperforms standard sparse spectrum estimation in accuracy, denoising ability, etc.
Cloud Computing: Exploring the scope
Venue: arXiv cs.DC 2010
Year: 2010

Cloud computing refers to a paradigm shift to overall IT solutions while raising the accessibility, scalability and effectiveness through its enabling technologies. However, migrated cloud platforms and services cost benefits as well as performances are neither clear nor summarized. Globalization and the recessionary economic times have not only raised the bar of a better IT delivery models but also have given access to technology enabled services via internet. Cloud computing has vast potential in terms of lean Retail methodologies that can minimize the operational cost by using the third party based IT capabilities, as a service. It will not only increase the ROI but will also help in lowering the total cost of ownership. In this paper we have tried to compare the cloud computing cost benefits with the actual premise cost which an organization incurs normally. However, in spite of the cost benefits, many IT professional believe that the latest model i.e. "cloud computing" has risks and security concerns. This report demonstrates how to answer the following questions: (1) Idea behind cloud computing. (2) Monetary cost benefits of using cloud with respect to traditional premise computing. (3) What are the various security issues? We have tried to find out the cost benefit by comparing the Microsoft Azure cloud cost with the prevalent premise cost.
Random polynomials and expected complexity of bisection methods for real solving
Venue: arXiv cs.SC 2010
Year: 2010

Our probabilistic analysis sheds light to the following questions: Why do random polynomials seem to have few, and well separated real roots, on the average? Why do exact algorithms for real root isolation may perform comparatively well or even better than numerical ones? We exploit results by Kac, and by Edelman and Kostlan in order to estimate the real root separation of degree $d$ polynomials with i.i.d.\ coefficients that follow two zero-mean normal distributions: for SO(2) polynomials, the $i$-th coefficient has variance ${d \choose i}$, whereas for Weyl polynomials its variance is ${1/i!}$. By applying results from statistical physics, we obtain the expected (bit) complexity of \func{sturm} solver, $\sOB(r d^2 τ)$, where $r$ is the number of real roots and $τ$ the maximum coefficient bitsize. Our bounds are two orders of magnitude tighter than the record worst case ones. We also derive an output-sensitive bound in the worst case. The second part of the paper shows that the expected number of real roots of a degree $d$ polynomial in the Bernstein basis is $\sqrt{2d}\pm\OO(1)$, when the coefficients are i.i.d.\ variables with moderate standard deviation. Our paper concludes with experimental results which corroborate our analysis.
On the Finite Time Convergence of Cyclic Coordinate Descent Methods
Venue: arXiv cs.LG 2010
Year: 2010

Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stability, as well as its competitive performance on $\ell_1$ regularized smooth optimization problems. Surprisingly, very little is known about its finite time convergence behavior on these problems. Most existing results either just prove convergence or provide asymptotic rates. We fill this gap in the literature by proving $O(1/k)$ convergence rates (where $k$ is the iteration counter) for two variants of cyclic coordinate descent under an isotonicity assumption. Our analysis proceeds by comparing the objective values attained by the two variants with each other, as well as with the gradient descent algorithm. We show that the iterates generated by the cyclic coordinate descent methods remain better than those of gradient descent uniformly over time.
Sparse Multipath Channel Estimation Using Compressive Sampling Matching Pursuit Algorithm
Venue: arXiv cs.IT 2010
Year: 2010

Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero. To exploit the sparsity of multi-path channel (MPC), two methods have been proposed. They are, namely, greedy algorithm and convex program. Greedy algorithm is easy to be implemented but not stable; on the other hand, the convex program method is stable but difficult to be implemented as practical channel estimation problems. In this paper, we introduce a novel channel estimation strategy using compressive sampling matching pursuit (CoSaMP) algorithm which was proposed in [1]. This algorithm will combine the greedy algorithm with the convex program method. The effectiveness of the proposed algorithm will be confirmed through comparisons with the existing methods.
Online Learning of Noisy Data with Kernels
Venue: arXiv cs.LG 2010
Year: 2010

We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with no restriction other than having zero mean and bounded variance. Our technique relies on a family of unbiased estimators for non-linear functions, which may be of independent interest. We show that a variant of online gradient descent can learn functions in any dot-product (e.g., polynomial) or Gaussian kernel space with any analytic convex loss function. Our variant uses randomized estimates that need to query a random number of noisy copies of each instance, where with high probability this number is upper bounded by a constant. Allowing such multiple queries cannot be avoided: Indeed, we show that online learning is in general impossible when only one noisy copy of each instance can be accessed.