OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of neighbour and isolated node of intersection area based geocasting protocol (IBGP) in VANET
Venue: arXiv cs.OH 2012
Year: 2012

Geocasting is a special variant of multicasting, where data packet or message is transmitted to a predefined geographical location i.e., known as geocast region. The applications of geocasting in VANET are to disseminate information like, collision warning, advertising, alerts message, etc. In this paper, we have proposed a model for highway scenario where the highway is divided into number of cells. The intersection area between two successive cells is computed to find the number of common nodes. Therefore, probabilistic analysis of the nodes present and void occurrence in the intersection area is carried out. Further, we have defined different forwarding zones to restrict the number of participated nodes for data delivery. Number of nodes present and void occurrence in the different forwarding zones have also been analysed based on various node density in the network to determine the successful delivery of data. Our analytical results show that in a densely populated network, data can be transmitted with low radio transmission range. In a densely populated network smaller forwarding zones will be selected for data delivery.
Overview of streaming-data algorithms
Venue: arXiv cs.DB 2012
Year: 2012

Due to recent advances in data collection techniques, massive amounts of data are being collected at an extremely fast pace. Also, these data are potentially unbounded. Boundless streams of data collected from sensors, equipments, and other data sources are referred to as data streams. Various data mining tasks can be performed on data streams in search of interesting patterns. This paper studies a particular data mining task, clustering, which can be used as the first step in many knowledge discovery processes. By grouping data streams into homogeneous clusters, data miners can learn about data characteristics which can then be developed into classification models for new data or predictive models for unknown events. Recent research addresses the problem of data-stream mining to deal with applications that require processing huge amounts of data such as sensor data analysis and financial applications. For such analysis, single-pass algorithms that consume a small amount of memory are critical.
A Greedy Link Scheduler for Wireless Networks with Fading Channels
Venue: arXiv cs.NI 2012
Year: 2012

We consider the problem of link scheduling for wireless networks with fading channels, where the link rates are varying with time. Due to the high computational complexity of the throughput optimal scheduler, we provide a low complexity greedy link scheduler GFS, with provable performance guarantees. We show that the performance of our greedy scheduler can be analyzed using the Local Pooling Factor (LPF) of a network graph, which has been previously used to characterize the stability of the Greedy Maximal Scheduling (GMS) policy for networks with static channels. We conjecture that the performance of GFS is a lower bound on the performance of GMS for wireless networks with fading channels
Near-optimal quantization and linear network coding for relay networks
Venue: arXiv cs.IT 2012
Year: 2012

We introduce a discrete network corresponding to any Gaussian wireless network that is obtained by simply quantizing the received signals and restricting the transmitted signals to a finite precision. Since signals in the discrete network are obtained from those of a Gaussian network, the Gaussian network can be operated on the quantization-based digital interface defined by the discrete network. We prove that this digital interface is near-optimal for Gaussian relay networks and the capacities of the Gaussian and the discrete networks are within a bounded gap of O(M^2) bits, where M is the number of nodes. We prove that any near-optimal coding strategy for the discrete network can be naturally transformed into a near-optimal coding strategy for the Gaussian network merely by quantization. We exploit this by designing a linear coding strategy for the case of layered discrete relay networks. The linear coding strategy is near-optimal for Gaussian and discrete networks and achieves rates within O(M^2) bits of the capacity, independent of channel gains or SNR. The linear code is robust and the relays need not know the channel gains. The transmit and receive signals at all relays are simply quantized to binary tuples of the same length $n$ . The linear network code requires all the relay nodes to collect the received binary tuples into a long binary vector and apply a linear transformation on the long vector. The resulting binary vector is split into smaller binary tuples for transmission by the relays. The quantization requirements of the linear network code are completely defined by the parameter $n$, which also determines the resolution of the analog-to-digital and digital-to-analog convertors for operating the network within a bounded gap of the network's capacity. The linear network code explicitly connects network coding for wireline networks with codes for Gaussian networks.
Multiuser Detection in Asynchronous Multibeam Communications
Venue: arXiv cs.NI 2012
Year: 2012

This paper deals with multi-user detection techniques in asynchronous multibeam satellite communications. The proposed solutions are based on successive interference cancellation architecture (SIC) and channel decoding algorithms. The aim of these detection methods is to reduce the effect of cochannel interference due to co-frequency access, and consequently, improves the capacity of the mulitbeam communications systems, by improving frequency reuse. Channel estimation allows the determination of interference coefficients, which helps their effects compensation. The developed multiuser detections techniques are iterative. Therefore, detection quality is improved from a stage to another. Moreover, a signals combining method, which is integrated into these detection solutions, enhances their capability. The proposed solutions are evaluated through computer simulations, where an asynchronous multibeam satellite link is considered over an AWGN channel. The obtained simulation results showed the robustness of these multi-user detection techniques.
Video Object Tracking and Analysis for Computer Assisted Surgery
Venue: arXiv cs.CV 2012
Year: 2012

Pedicle screw insertion technique has made revolution in the surgical treatment of spinal fractures and spinal disorders. Although X- ray fluoroscopy based navigation is popular, there is risk of prolonged exposure to X- ray radiation. Systems that have lower radiation risk are generally quite expensive. The position and orientation of the drill is clinically very important in pedicle screw fixation. In this paper, the position and orientation of the marker on the drill is determined using pattern recognition based methods, using geometric features, obtained from the input video sequence taken from CCD camera. A search is then performed on the video frames after preprocessing, to obtain the exact position and orientation of the drill. Animated graphics, showing the instantaneous position and orientation of the drill is then overlaid on the processed video for real time drill control and navigation.
On an optimization technique using Binary Decision Diagram
Venue: arXiv cs.DS 2012
Year: 2012

Two-level logic minimization is a central problem in logic synthesis, and has applications in reliability analysis and automated reasoning. This paper represents a method of minimizing Boolean sum of products function with binary decision diagram and with disjoint sum of product minimization. Due to the symbolic representation of cubes for large problem instances, the method is orders of magnitude faster than previous enumerative techniques. But the quality of the approach largely depends on the variable ordering of the underlying BDD. The application of Binary Decision Diagrams (BDDs) as an efficient approach for the minimization of Disjoint Sums-of-Products (DSOPs). DSOPs are a starting point for several applications. The use of BDDs has the advantage of an implicit representation of terms. Due to this scheme the algorithm is faster than techniques working on explicit representations and the application to large circuits that could not be handled so far becomes possible. Theoretical studies on the influence of the BDDs to the search space are carried out. In experiments the proposed technique is compared to others. The results with respect to the size of the resulting DSOP are as good or better as those of the other techniques.
Towards a Unified Architecture for in-RDBMS Analytics
Venue: arXiv cs.DB 2012
Year: 2012

The increasing use of statistical data analysis in enterprise applications has created an arms race among database vendors to offer ever more sophisticated in-database analytics. One challenge in this race is that each new statistical technique must be implemented from scratch in the RDBMS, which leads to a lengthy and complex development process. We argue that the root cause for this overhead is the lack of a unified architecture for in-database analytics. Our main contribution in this work is to take a step towards such a unified architecture. A key benefit of our unified architecture is that performance optimizations for analytics techniques can be studied generically instead of an ad hoc, per-technique fashion. In particular, our technical contributions are theoretical and empirical studies of two key factors that we found impact performance: the order data is stored, and parallelization of computations on a single-node multicore RDBMS. We demonstrate the feasibility of our architecture by integrating several popular analytics techniques into two commercial and one open-source RDBMS. Our architecture requires changes to only a few dozen lines of code to integrate a new statistical technique. We then compare our approach with the native analytics tools offered by the commercial RDBMSes on various analytics tasks, and validate that our approach achieves competitive or higher performance, while still achieving the same quality.
New approximations for DQPSK transmission bit error rate
Venue: arXiv cs.IT 2012
Year: 2012

In this correspondence our aim is to use some tight lower and upper bounds for the differential quaternary phase shift keying transmission bit error rate in order to deduce accurate approximations for the bit error rate by improving the known results in the literature. The computation of our new approximate expressions are significantly simpler than that of the exact expression.
On the Performance Limits of Pilot-Based Estimation of Bandlimited Frequency-Selective Communication Channels
Venue: arXiv cs.IT 2012
Year: 2012

In this paper the problem of assessing bounds on the accuracy of pilot-based estimation of a bandlimited frequency selective communication channel is tackled. Mean square error is taken as a figure of merit in channel estimation and a tapped-delay line model is adopted to represent a continuous time channel via a finite number of unknown parameters. This allows to derive some properties of optimal waveforms for channel sounding and closed form Cramer-Rao bounds.
Performance Guarantees of the Thresholding Algorithm for the Co-Sparse Analysis Model
Venue: arXiv cs.IT 2012
Year: 2012

The co-sparse analysis model for signals assumes that the signal of interest can be multiplied by an analysis dictionary Ω, leading to a sparse outcome. This model stands as an interesting alternative to the more classical synthesis based sparse representation model. In this work we propose a theoretical study of the performance guarantee of the thresholding algorithm for the pursuit problem in the presence of noise. Our analysis reveals two significant properties of Ω, which govern the pursuit performance: The first is the degree of linear dependencies between sets of rows in Ω, depicted by the co-sparsity level. The second property, termed the Restricted Orthogonal Projection Property (ROPP), is the level of independence between such dependent sets and other rows in Ω. We show how these dictionary properties are meaningful and useful, both in the theoretical bounds derived, and in a series of experiments that are shown to align well with the theoretical prediction.
Statistical Characterization and Mitigation of NLOS Errors in UWB Localization Systems
Venue: arXiv cs.IT 2012
Year: 2012

In this paper some new experimental results about the statistical characterization of the non-line-of-sight (NLOS) bias affecting time-of-arrival (TOA) estimation in ultrawideband (UWB) wireless localization systems are illustrated. Then, these results are exploited to assess the performance of various maximum-likelihood (ML) based algorithms for joint TOA localization and NLOS bias mitigation. Our numerical results evidence that the accuracy of all the considered algorithms is appreciably influenced by the LOS/NLOS conditions of the propagation environment.
Mining Education Data to Predict Student's Retention: A comparative Study
Venue: arXiv cs.LG 2012
Year: 2012

The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student retention data.
Evolving Culture vs Local Minima
Venue: arXiv cs.LG 2012
Year: 2012

We propose a theory that relates difficulty of learning in deep architectures to culture and language. It is articulated around the following hypotheses: (1) learning in an individual human brain is hampered by the presence of effective local minima; (2) this optimization difficulty is particularly important when it comes to learning higher-level abstractions, i.e., concepts that cover a vast and highly-nonlinear span of sensory configurations; (3) such high-level abstractions are best represented in brains by the composition of many levels of representation, i.e., by deep architectures; (4) a human brain can learn such high-level abstractions if guided by the signals produced by other humans, which act as hints or indirect supervision for these high-level abstractions; and (5), language and the recombination and optimization of mental concepts provide an efficient evolutionary recombination operator, and this gives rise to rapid search in the space of communicable ideas that help humans build up better high-level internal representations of their world. These hypotheses put together imply that human culture and the evolution of ideas have been crucial to counter an optimization difficulty: this optimization difficulty would otherwise make it very difficult for human brains to capture high-level knowledge of the world. The theory is grounded in experimental observations of the difficulties of training deep artificial neural networks. Plausible consequences of this theory for the efficiency of cultural evolutions are sketched.
Combining Voting Rules Together
Venue: arXiv cs.AI 2012
Year: 2012

We propose a simple method for combining together voting rules that performs a run-off between the different winners of each voting rule. We prove that this combinator has several good properties. For instance, even if just one of the base voting rules has a desirable property like Condorcet consistency, the combination inherits this property. In addition, we prove that combining voting rules together in this way can make finding a manipulation more computationally difficult. Finally, we study the impact of this combinator on approximation methods that find close to optimal manipulations.
Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem
Venue: arXiv cs.NE 2012
Year: 2012

The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Travelling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.
Codes on Graphs: Observability, Controllability and Local Reducibility
Venue: arXiv cs.IT 2012
Year: 2012

This paper investigates properties of realizations of linear or group codes on general graphs that lead to local reducibility. Trimness and properness are dual properties of constraint codes. A linear or group realization with a constraint code that is not both trim and proper is locally reducible. A linear or group realization on a finite cycle-free graph is minimal if and only if every local constraint code is trim and proper. A realization is called observable if there is a one-to-one correspondence between codewords and configurations, and controllable if it has independent constraints. A linear or group realization is observable if and only if its dual is controllable. A simple counting test for controllability is given. An unobservable or uncontrollable realization is locally reducible. Parity-check realizations are controllable if and only if they have independent parity checks. In an uncontrollable tail-biting trellis realization, the behavior partitions into disconnected subbehaviors, but this property does not hold for non-trellis realizations. On a general graph, the support of an unobservable configuration is a generalized cycle.
Distributed Space Time Coding for Wireless Two-way Relaying
Venue: arXiv cs.IT 2012
Year: 2012

We consider the wireless two-way relay channel, in which two-way data transfer takes place between the end nodes with the help of a relay. For the Denoise-And-Forward (DNF) protocol, it was shown by Koike-Akino et. al. that adaptively changing the network coding map used at the relay greatly reduces the impact of Multiple Access interference at the relay. The harmful effect of the deep channel fade conditions can be effectively mitigated by proper choice of these network coding maps at the relay. Alternatively, in this paper we propose a Distributed Space Time Coding (DSTC) scheme, which effectively removes most of the deep fade channel conditions at the transmitting nodes itself without any CSIT and without any need to adaptively change the network coding map used at the relay. It is shown that the deep fades occur when the channel fade coefficient vector falls in a finite number of vector subspaces of $\mathbb{C}^2$, which are referred to as the singular fade subspaces. DSTC design criterion referred to as the \textit{singularity minimization criterion} under which the number of such vector subspaces are minimized is obtained. Also, a criterion to maximize the coding gain of the DSTC is obtained. Explicit low decoding complexity DSTC designs which satisfy the singularity minimization criterion and maximize the coding gain for QAM and PSK signal sets are provided. Simulation results show that at high Signal to Noise Ratio, the DSTC scheme provides large gains when compared to the conventional Exclusive OR network code and performs slightly better than the adaptive network coding scheme proposed by Koike-Akino et. al.
Dynamic Compression-Transmission for Energy-Harvesting Multihop Networks with Correlated Sources
Venue: arXiv cs.IT 2012
Year: 2012

Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data gathering protocol for multihop wireless sensor networks with energy harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable trade-off between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost. It is shown that the presence of side information can improve the network performance both in terms of overall network cost function and queue sizes.
Physical Layer Network Coding for Two-Way Relaying with QAM and Latin Squares
Venue: arXiv cs.IT 2012
Year: 2012

The design of modulation schemes for the physical layer network-coded two way relaying scenario has been extensively studied recently with the protocol which employs two phases: Multiple access (MA) Phase and Broadcast (BC) Phase. It was observed by Koike-Akino et al. that adaptively changing the network coding map used at the relay according to the channel conditions greatly reduces the impact of multiple access interference which occurs at the relay during the MA Phase and all these network coding maps should satisfy a requirement called the exclusive law. Only the scenario in which the end nodes use M-PSK signal sets is extensively studied in \cite{NVR} using Latin Sqaures. In this paper, we address the case in which the end nodes use M-QAM signal sets (where M is of the form $2^{2λ}$, $λ$ being any positive integer). In a fading scenario, for certain channel conditions $γe^{j θ}$, termed singular fade states, the MA phase performance is greatly reduced. We show that the square QAM signal sets give lesser number of singular fade states compared to PSK signal sets. Because of this, the complexity at the relay is enormously reduced. Moreover, lesser number of overhead bits are required in the BC phase. The fade state $γe^{j θ}=1$ is singular for all constellations of arbitrary size including PSK and QAM. For arbitrary PSK constellation it is well known that the Latin Square obtained by bit-wise XOR mapping removes this singularity. We show that XOR mapping fails to remove this singularity for QAM of size more greater than 4 and show that a doubly block circulant Latin Square removes this singularity. Simulation results are presented to show the superiority of QAM over PSK.