OpenRaiser/Intern-Atlas huggingface dataset:
Queueing Analysis for Preemptive Transmission in Underlay Cognitive Radio Networks
Venue: arXiv cs.NI 2015
Year: 2015

In many cognitive radio applications, there are multiple types of message queues. Existing queueing analysis works in underlay CR networks failed to discuss packets heterogeneity. Therefore high priority packets with impatient waiting time that have preemptive transmission opportunities over low class are investigated. We model the system behavior as a M/M/1+GI queue which is represented by a two dimensional state transition graph. The reneging probability of high priority packets and the average waiting time in two-class priority queues is analyzed. Simulation results demonstrate that the average waiting time of high priority packets decreases with the growing interference power threshold and the average waiting time of the low priority packet is proportional to the arrival rate of the high priority packet. This work may lay the foundation to design efficient MAC protocols and optimize long term system performance by carefully choosing system parameters.
Joint Ranging and Clock Parameter Estimation by Wireless Round Trip Time Measurements
Venue: arXiv cs.NI 2015
Year: 2015

In this paper we develop a new technique for estimating fine clock errors and range between two nodes simultaneously by two-way time-of-arrival measurements us- ing impulse-radio ultra-wideband signals. Estimators for clock parameters and the range are proposed that are robust with respect to outliers. They are analyzed numerically and by means of experimental measurement campaigns. The technique and derived estimators achieve accuracies below 1Hz for frequency estimation, below 1 ns for phase estimation and 20 cm for range estimation, at 4m distance using 100MHz clocks at both nodes. Therefore, we show that the proposed joint approach is practical and can simultaneously provide clock synchronization and positioning in an experimental system.
Smoothed Analysis of the Successive Shortest Path Algorithm
Venue: arXiv cs.DS 2015
Year: 2015

The minimum-cost flow problem is a classic problem in combinatorial optimization with various applications. Several pseudo-polynomial, polynomial, and strongly polynomial algorithms have been developed in the past decades, and it seems that both the problem and the algorithms are well understood. However, some of the algorithms' running times observed in empirical studies contrast the running times obtained by worst-case analysis not only in the order of magnitude but also in the ranking when compared to each other. For example, the Successive Shortest Path (SSP) algorithm, which has an exponential worst-case running time, seems to outperform the strongly polynomial Minimum-Mean Cycle Canceling algorithm. To explain this discrepancy, we study the SSP algorithm in the framework of smoothed analysis and establish a bound of $O(mnϕ)$ for the number of iterations, which implies a smoothed running time of $O(mnϕ(m + n\log n))$, where $n$ and $m$ denote the number of nodes and edges, respectively, and $ϕ$ is a measure for the amount of random noise. This shows that worst-case instances for the SSP algorithm are not robust and unlikely to be encountered in practice. Furthermore, we prove a smoothed lower bound of $Ω(m ϕ\min\{n, ϕ\})$ for the number of iterations of the SSP algorithm, showing that the upper bound cannot be improved for $ϕ= Ω(n)$.
Design of a novel convex hull based feature set for recognition of isolated handwritten Roman numerals
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, convex hull based features are used for recognition of isolated Roman numerals using a Multi Layer Perceptron (MLP) based classifier. Experiments of convex hull based features for handwritten character recognition are few in numbers. Convex hull of a pattern and the centroid of the convex hull both are affine invariant attributes. In this work, 25 features are extracted based on different bays attributes of the convex hull of the digit patterns. Then these patterns are divided into four sub-images with respect to the centroid of the convex hull boundary. From each such sub-image 25 bays features are also calculated. In all 125 convex hull based features are extracted for each numeric digit patterns under the current experiment. The performance of the designed feature set is tested on the standard MNIST data set, consisting of 60000 training and 10000 test images of handwritten Roman using an MLP based classifier a maximum success rate of 97.44% is achieved on the test data.
A GA Based approach for selection of local features for recognition of handwritten Bangla numerals
Venue: arXiv cs.CV 2015
Year: 2015

Soft computing approaches are mainly designed to address the real world ill-defined, imprecisely formulated problems, combining different kind of novel models of computation, such as neural networks, genetic algorithms (GAs. Handwritten digit recognition is a typical example of one such problem. In the current work we have developed a two-pass approach where the first pass classifier performs a coarse classification, based on some global features of the input pattern by restricting the possibility of classification decisions within a group of classes, smaller than the number of classes considered initially. In the second pass, the group specific classifiers concentrate on the features extracted from the selected local regions, and refine the earlier decision by combining the local and the global features for selecting the true class of the input pattern from the group of candidate classes selected in the first pass. To optimize the selection of local regions a GA based approach has been developed here. The maximum recognition performance on Bangla digit samples as achieved on the test set, during the first pass of the two pass approach is 93.35%. After combining the results of the two stage classifiers, an overall success rate of 95.25% is achieved.
Learning and Designing Stochastic Processes from Logical Constraints
Venue: arXiv cs.SY 2015
Year: 2015

Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics must be known exactly. As this is seldom the case, many methods have been devised over the last decade to infer (learn) such parameters from observations of the state of the system. In this paper, we depart from this approach by assuming that our observations are {\it qualitative} properties encoded as satisfaction of linear temporal logic formulae, as opposed to quantitative observations of the state of the system. An important feature of this approach is that it unifies naturally the system identification and the system design problems, where the properties, instead of observations, represent requirements to be satisfied. We develop a principled statistical estimation procedure based on maximising the likelihood of the system's parameters, using recent ideas from statistical machine learning. We demonstrate the efficacy and broad applicability of our method on a range of simple but non-trivial examples, including rumour spreading in social networks and hybrid models of gene regulation.
Features modeling with an $α$-stable distribution: Application to pattern recognition based on continuous belief functions
Venue: arXiv cs.AI 2015
Year: 2015

The aim of this paper is to show the interest in fitting features with an $α$-stable distribution to classify imperfect data. The supervised pattern recognition is thus based on the theory of continuous belief functions, which is a way to consider imprecision and uncertainty of data. The distributions of features are supposed to be unimodal and estimated by a single Gaussian and $α$-stable model. Experimental results are first obtained from synthetic data by combining two features of one dimension and by considering a vector of two features. Mass functions are calculated from plausibility functions by using the generalized Bayes theorem. The same study is applied to the automatic classification of three types of sea floor (rock, silt and sand) with features acquired by a mono-beam echo-sounder. We evaluate the quality of the $α$-stable model and the Gaussian model by analyzing qualitative results, using a Kolmogorov-Smirnov test (K-S test), and quantitative results with classification rates. The performances of the belief classifier are compared with a Bayesian approach.
Estimating the effects of water-induced shallow landslides on soil erosion
Venue: arXiv cs.CE 2015
Year: 2015

Rainfall induced landslides and soil erosion are part of a complex system of multiple interacting processes, and both are capable of significantly affecting sediment budgets. These sediment mass movements also have the potential to significantly impact on a broad network of ecosystems health, functionality and the services they provide. To support the integrated assessment of these processes it is necessary to develop reliable modelling architectures. This paper proposes a semi-quantitative integrated methodology for a robust assessment of soil erosion rates in data poor regions affected by landslide activity. It combines heuristic, empirical and probabilistic approaches. This proposed methodology is based on the geospatial semantic array programming paradigm and has been implemented on a catchment scale methodology using Geographic Information Systems (GIS) spatial analysis tools and GNU Octave. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. In order to improve computational reproducibility, the geospatial data transformations implemented in ESRI ArcGis are made available in the free software GRASS GIS. The proposed modelling architecture is flexible enough for future transdisciplinary scenario analysis to be more easily designed. In particular, the architecture might contribute as a novel component to simplify future integrated analyses of the potential impact of wildfires or vegetation types and distributions, on sediment transport from water induced landslides and erosion.
On structural properties of trees with minimal atom-bond connectivity index II
Venue: arXiv cs.DM 2015
Year: 2015

The {\em atom-bond connectivity (ABC) index} is a degree-based graph topological index that found chemical applications. The problem of complete characterization of trees with minimal $ABC$ index is still an open problem. In~\cite{d-sptmabci-2014}, it was shown that trees with minimal ABC index do not contain so-called {\em $B_k$-branches}, with $k \geq 5$, and that they do not have more than four $B_4$-branches. Our main results here reveal that the number of $B_1$ and $B_2$-branches are also bounded from above by small fixed constants. Namely, we show that trees with minimal ABC index do not contain more than four $B_1$-branches and more than eleven $B_2$-branches.
Online Submodular Maximization with Preemption
Venue: arXiv cs.DS 2015
Year: 2015

Submodular function maximization has been studied extensively in recent years under various constraints and models. The problem plays a major role in various disciplines. We study a natural online variant of this problem in which elements arrive one-by-one and the algorithm has to maintain a solution obeying certain constraints at all times. Upon arrival of an element, the algorithm has to decide whether to accept the element into its solution and may preempt previously chosen elements. The goal is to maximize a submodular function over the set of elements in the solution. We study two special cases of this general problem and derive upper and lower bounds on the competitive ratio. Specifically, we design a $1/e$-competitive algorithm for the unconstrained case in which the algorithm may hold any subset of the elements, and constant competitive ratio algorithms for the case where the algorithm may hold at most $k$ elements in its solution.
Infinite Communication Complexity
Venue: arXiv cs.CC 2015
Year: 2015

Suppose that Alice and Bob are given each an infinite string, and they want to decide whether their two strings are in a given relation. How much communication do they need? How can communication be even defined and measured for infinite strings? In this article, we propose a formalism for a notion of infinite communication complexity, prove that it satisfies some natural properties and coincides, for relevant applications, with the classical notion of amortized communication complexity. More-over, an application is given for tackling some conjecture about tilings and multidimensional sofic shifts.
Robust Decentralized Detection and Social Learning in Tandem Networks
Venue: arXiv cs.IT 2015
Year: 2015

We study a tandem of agents who make decisions about an underlying binary hypothesis, where the distribution of the agent observations under each hypothesis comes from an uncertainty class. We investigate both decentralized detection rules, where agents collaborate to minimize the error probability of the final agent, and social learning rules, where each agent minimizes its own local minimax error probability. We then extend our results to the infinite tandem network, and derive necessary and sufficient conditions on the uncertainty classes for the minimax error probability to converge to zero when agents know their positions in the tandem. On the other hand, when agents do not know their positions in the network, we study the cases where agents collaborate to minimize the asymptotic minimax error probability, and where agents seek to minimize their worst-case minimax error probability (over all possible positions in the tandem). We show that asymptotic learning of the true hypothesis is no longer possible in these cases, and derive characterizations for the minimax error performance.
The Spatial Structure of Transnational Human Activity
Venue: arXiv cs.SI 2015
Year: 2015

Recent studies have shown that the spatial structures of animal displacements and local-scale human motion follow Lévy flights. Whether transnational human activity (THA) also exhibits such a pattern has however not been thoroughly examined as yet. To fill this gap, this article examines the planet-scale spatial structure of THA (a) across eight types of mobility and communication and (b) in its development over time. Combining data from various sources, it is shown that the spatial structure of THA can indeed be approximated by Lévy flights with heavy tails that obey power laws. Scaling exponent and power-law fit differ by type of THA, being highest in refuge-seeking and tourism and lowest in student exchange. Variance in the availability of resources and opportunities for satisfying associated needs appears to explain these differences. Over time, the Lévy-flight pattern remains intact and remarkably stable, contradicting the popular idea that socio-technological trends lead to a "death of distance." Longitudinal change occurs only in some types of THA and predominantly at short distances, indicating regional shifts rather than globalization.
A unified framework for modeling and implementation of hybrid systems with synchronous controllers
Venue: arXiv cs.SY 2015
Year: 2015

This paper presents a novel approach to including non-instantaneous discrete control transitions in the linear hybrid automaton approach to simulation and verification of hybrid control systems. In this paper we study the control of a continuously evolving analog plant using a controller programmed in a synchronous programming language. We provide extensions to the synchronous subset of the SystemJ programming language for modeling, implementation, and verification of such hybrid systems. We provide a sound rewrite semantics that approximate the evolution of the continuous variables in the discrete domain inspired from the classical supervisory control theory. The resultant discrete time model can be verified using classical model-checking tools. Finally, we show that systems designed using our approach have a higher fidelity than the ones designed using the hybrid automaton approach.
On MMSE Properties of Codes for the Gaussian Broadcast and Wiretap Channels
Venue: arXiv cs.IT 2015
Year: 2015

This work concerns the behavior of "good" (capacity achieving) codes in several multi-user settings in the Gaussian regime, in terms of their minimum mean-square error (MMSE) behavior. The settings investigated in this context include the Gaussian wiretap channel, the Gaussian broadcast channel (BC) and the Gaussian BC with confidential messages (BCC). In particular this work addresses the effects of transmitting such codes on unintended receivers, that is, receivers that neither require reliable decoding of the transmitted messages nor are they eavesdroppers that must be kept ignorant, to some extent, of the transmitted message. This work also examines the effect on the capacity region that occurs when we limit the allowed disturbance in terms of MMSE on some unintended receiver. This trade-off between the capacity region and the disturbance constraint is given explicitly for the Gaussian BC and the secrecy capacity region of the Gaussian BCC.
Energy Harvesting Wireless Communications: A Review of Recent Advances
Venue: arXiv cs.IT 2015
Year: 2015

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.
Relaxed Polar Codes
Venue: arXiv cs.IT 2015
Year: 2015

Polar codes are the latest breakthrough in coding theory, as they are the first family of codes with explicit construction that provably achieve the symmetric capacity of discrete memoryless channels. Arıkan's polar encoder and successive cancellation decoder have complexities of $N \log N$, for code length $N$. Although, the complexity bound of $N \log N$ is asymptotically favorable, we report in this work methods to further reduce the encoding and decoding complexities of polar coding. The crux is to relax the polarization of certain bit-channels without performance degradation. We consider schemes for relaxing the polarization of both \emph{very good} and \emph{very bad} bit-channels, in the process of channel polarization. Relaxed polar codes are proved to preserve the capacity achieving property of polar codes. Analytical bounds on the asymptotic and finite-length complexity reduction attainable by relaxed polarization are derived. For binary erasure channels, we show that the computation complexity can be reduced by a factor of 6, while preserving the rate and error performance. We also show that relaxed polar codes can be decoded with significantly reduced latency. For AWGN channels with medium code lengths, we show that relaxed polar codes can have lower error probabilities than conventional polar codes, while having reduced encoding and decoding computation complexities.
Accurate automatic segmentation of retina layers with emphasis on first layer
Venue: arXiv cs.CV 2015
Year: 2015

Quantification of intra-retinal boundaries in optical coherence tomography (OCT) is a crucial task for studying and diagnosing neurological and ocular diseases. Since manual segmentation of layers is usually a time consuming task and relay on user, a lot of attempts done to do it automatically and without interference of user. Although for extracting all layers usually same procedure is applied but finding the first layer is usually more difficult due to vanishing it in some region specially close to Fobia. To have a general software, beside using common methods like applying shortest path algorithm on global gradient of image, some extra steps are used here to confine search area for Dijstra algorithm especially for the second layer. Results demonstrates high accuracy in segmenting all present layers, especially the first one that is important for diagnosing issue.
Constrained Extreme Learning Machines: A Study on Classification Cases
Venue: arXiv cs.LG 2015
Year: 2015

Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers. However, its good generalization ability is built on large numbers of hidden neurons, which is not beneficial to real time response in the test process. In this paper, we proposed new ways, named "constrained extreme learning machines" (CELMs), to randomly select hidden neurons based on sample distribution. Compared to completely random selection of hidden nodes in ELM, the CELMs randomly select hidden nodes from the constrained vector space containing some basic combinations of original sample vectors. The experimental results show that the CELMs have better generalization ability than traditional ELM, SVM and some other related methods. Additionally, the CELMs have a similar fast learning speed as ELM.
Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement
Venue: arXiv cs.SY 2015
Year: 2015

This study presents a method of estimating battery cell core and surface temperature using a thermal model coupled with electrical impedance measurement, rather than using direct surface temperature measurements. This is advantageous over previous methods of estimating temperature from impedance, which only estimate the average internal temperature. The performance of the method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate cell fitted with surface and core thermocouples for validation. An extended Kalman filter, consisting of a reduced order thermal model coupled with current, voltage and impedance measurements, is shown to accurately predict core and surface temperatures for a current excitation profile based on a vehicle drive cycle. A dual extended Kalman filter (DEKF) based on the same thermal model and impedance measurement input is capable of estimating the convection coefficient at the cell surface when the latter is unknown. The performance of the DEKF using impedance as the measurement input is comparable to an equivalent dual Kalman filter using a conventional surface temperature sensor as measurement input.
Parallel Magnetic Resonance Imaging
Venue: arXiv cs.NA 2015
Year: 2015

The main disadvantage of Magnetic Resonance Imaging (MRI) are its long scan times and, in consequence, its sensitivity to motion. Exploiting the complementary information from multiple receive coils, parallel imaging is able to recover images from under-sampled k-space data and to accelerate the measurement. Because parallel magnetic resonance imaging can be used to accelerate basically any imaging sequence it has many important applications. Parallel imaging brought a fundamental shift in image reconstruction: Image reconstruction changed from a simple direct Fourier transform to the solution of an ill-conditioned inverse problem. This work gives an overview of image reconstruction from the perspective of inverse problems. After introducing basic concepts such as regularization, discretization, and iterative reconstruction, advanced topics are discussed including algorithms for auto-calibration, the connection to approximation theory, and the combination with compressed sensing.