OpenRaiser/Intern-Atlas huggingface dataset:
Design of Fuzzy self-tuning PID controller for pitch control system of aircraft autopilot
Venue: arXiv cs.SY 2015
Year: 2015

A variety of control systems have been proposed for aircraft autopilot systems. Traditional approaches such as proportional controller and conventional PID (CPID) controller are widely used. PID controller has a good static performance especially for linear and time-invariant systems, but a weak dynamic performance and discouraging function on nonlinear, time-varying, and uncertain systems. Fuzzy control theory can improve dynamic response in various conditions of system performance. This paper designs fuzzy self-tuning PID (FSPID) controller to improve disadvantages of conventional PID in aircraft autopilots. We apply proposed controller to pitch angle of aircraft then the abilities of proposed controller will be compared to the conventional PID and proportional controller. Inner feedback loop acts as oscillation damper in traditional schemes, but here is removed to compare the capabilities of Fuzzy self-tuning PID, conventional PID, and proportional controller. Based on the simulations, both of Conventional and Fuzzy self-tuning PID controllers can properly damp oscillations in lack of the inner feedback loop, but proportional controller cannot do. Then short-period approximation is assumed to assess the function of FSPID and CPID controllers in confront with abrupt and continuous disturbances, in addition to inappropriate tuning of parameters. Simulation results of short-period approximation show a better anti-disturbance function for Fuzzy self-tuning PID compare to the conventional type. Fuzzy self-tuning PID can tune the PID parameters for achieving the optimal response in view of speed, overshoot, and steady-state error in conditions of inappropriate tuning of PID parameters, based on the results of simulation in short-period approximation, the proposed controller can adaptively improve the system response by on-line setting of PID parameters.
Energy-Efficient Infrastructure Sensor Network for Ad Hoc Cognitive Radio Network
Venue: arXiv cs.DC 2015
Year: 2015

We propose an energy-efficient network architecture that consists of ad hoc (mobile) cognitive radios (CRs) and infrastructure wireless sensor nodes. The sensor nodes within communications range of each CR are grouped into a cluster and the clusters of CRs are regularly updated according to the random mobility of the CRs. We reduce the energy consumption and the end-to-end delay of the sensor network by dividing each cluster into disjoint subsets with overlapped sensing coverage of primary user (PU) activity. Respective subset of a CR provides target detection and false alarm probabilities. Substantial energy efficiency is achieved by activating only one subset of the cluster, while putting the rest of the subsets in the cluster into sleep mode.
On Distributed Vector Estimation for Power and Bandwidth Constrained Wireless Sensor Networks
Venue: arXiv cs.IT 2015
Year: 2015

We consider distributed estimation of a Gaussian vector with a linear observation model in an inhomogeneous wireless sensor network, where a fusion center (FC) reconstructs the unknown vector, using a linear estimator. Sensors employ uniform multi-bit quantizers and binary PSK modulation, and communicate with the FC over orthogonal power- and bandwidth-constrained wireless channels. We study transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize mean-square error (MSE). In particular, we derive two closed-form upper bounds on the MSE, in terms of the optimization parameters and propose coupled and decoupled resource allocation schemes that minimize these bounds. We show that the bounds are good approximations of the simulated MSE and the performance of the proposed schemes approaches the clairvoyant centralized estimation when total transmit power or bandwidth is very large. We study how the power and rate allocation are dependent on sensors observation qualities and channel gains, as well as total transmit power and bandwidth constraints. Our simulations corroborate our analytical results and illustrate the superior performance of the proposed algorithms.
Reliability and Survivability Analysis of Data Center Network Topologies
Venue: arXiv cs.NI 2015
Year: 2015

The architecture of several data centers have been proposed as alternatives to the conventional three-layer one.Most of them employ commodity equipment for cost reduction. Thus, robustness to failures becomes even more important, because commodity equipment is more failure-prone. Each architecture has a different network topology design with a specific level of redundancy. In this work, we aim at analyzing the benefits of different data center topologies taking the reliability and survivability requirements into account. We consider the topologies of three alternative data center architecture: Fat-tree, BCube, and DCell. Also, we compare these topologies with a conventional three-layer data center topology. Our analysis is independent of specific equipment, traffic patterns, or network protocols, for the sake of generality. We derive closed-form formulas for the Mean Time To Failure of each topology. The results allow us to indicate the best topology for each failure scenario. In particular, we conclude that BCube is more robust to link failures than the other topologies, whereas DCell has the most robust topology when considering switch failures. Additionally, we show that all considered alternative topologies outperform a three-layer topology for both types of failures. We also determine to which extent the robustness of BCube and DCell is influenced by the number of network interfaces per server.
Concurrent Constraint Machine Improvisation: Models and Implementation
Venue: arXiv cs.LO 2015
Year: 2015

Machine improvisation creates music either by explicit coding of rules or by applying machine learning methods. We deal with the latter case. An improvisation system capable of real-time must execute two process concurrently: one to apply machine learning methods to musical sequences in order to capture prominent musical features, and one to produce musical sequences stylistically consistent with the learned material. As an example, the Concurrent Constraint Factor Oracle Model for Music Improvisation (ccfomi), based upon Non-deterministic Timed Concurrent Constraint (ntcc) calculus, uses the Factor Oracle to store the learned sequences.
Spectrum Sharing in RF-Powered Cognitive Radio Networks using Game Theory
Venue: arXiv cs.IT 2015
Year: 2015

We investigate the spectrum sharing problem of a radio frequency (RF)-powered cognitive radio network, where a multi-antenna secondary user (SU) harvests energy from RF signals radiated by a primary user (PU) to boost its available energy before information transmission. In this paper, we consider that both the PU and SU are rational and self-interested. Based on whether the SU helps forward the PU's information, we develop two different operation modes for the considered network, termed as non-cooperative and cooperative modes. In the non-cooperative mode, the SU harvests energy from the PU and then use its available energy to transmit its own information without generating any interference to the primary link. In the cooperative mode, the PU employs the SU to relay its information by providing monetary incentives and the SU splits its energy for forwarding the PU's information as well as transmitting its own information. Optimization problems are respectively formulated for both operation modes, which constitute a Stackelberg game with the PU as a leader and the SU as a follower. We analyze the Stackelberg game by deriving solutions to the optimization problems and the Stackelberg Equilibrium (SE) is subsequently obtained. Simulation results show that the performance of the Stackelberg game can approach that of the centralized optimization scheme when the distance between the SU and its receiver is large enough.
TSEB: More Efficient Thompson Sampling for Policy Learning
Venue: arXiv cs.LG 2015
Year: 2015

In model-based solution approaches to the problem of learning in an unknown environment, exploring to learn the model parameters takes a toll on the regret. The optimal performance with respect to regret or PAC bounds is achievable, if the algorithm exploits with respect to reward or explores with respect to the model parameters, respectively. In this paper, we propose TSEB, a Thompson Sampling based algorithm with adaptive exploration bonus that aims to solve the problem with tighter PAC guarantees, while being cautious on the regret as well. The proposed approach maintains distributions over the model parameters which are successively refined with more experience. At any given time, the agent solves a model sampled from this distribution, and the sampled reward distribution is skewed by an exploration bonus in order to generate more informative exploration. The policy by solving is then used for generating more experience that helps in updating the posterior over the model parameters. We provide a detailed analysis of the PAC guarantees, and convergence of the proposed approach. We show that our adaptive exploration bonus encourages the additional exploration required for better PAC bounds on the algorithm. We provide empirical analysis on two different simulated domains.
Efficient and Accurate Path Cost Estimation Using Trajectory Data
Venue: arXiv cs.DB 2015
Year: 2015

Using the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs in a road network, including travel time and fuel consumption. The current paradigm represents a road network as a graph, assigns weights to the graph's edges by fragmenting trajectories into small pieces that fit the underlying edges, and then applies a routing algorithm to the resulting graph. We propose a new paradigm that targets more accurate and more efficient estimation of the costs of paths by associating weights with sub-paths in the road network. The paper provides a solution to a foundational problem in this paradigm, namely that of computing the time-varying cost distribution of a path. The solution consists of several steps. We first learn a set of random variables that capture the joint distributions of sub-paths that are covered by sufficient trajectories. Then, given a departure time and a path, we select an optimal subset of learned random variables such that the random variables' corresponding paths together cover the path. This enables accurate joint distribution estimation of the path, and by transferring the joint distribution into a marginal distribution, the travel cost distribution of the path is obtained. The use of multiple learned random variables contends with data sparseness, the use of multi-dimensional histograms enables compact representation of arbitrary joint distributions that fully capture the travel cost dependencies among the edges in paths. Empirical studies with substantial trajectory data from two different cities offer insight into the design properties of the proposed solution and suggest that the solution is effective in real-world settings.
Sharing the Licensed Spectrum of Full-Duplex Systems using Improper Gaussian Signaling
Venue: arXiv cs.IT 2015
Year: 2015

Sharing the spectrum with in-band full-duplex (FD) primary users (PU) is a challenging and interesting problem in the underlay cognitive radio (CR) systems. The self-interference introduced at the primary network may dramatically impede the secondary user (SU) opportunity to access the spectrum. In this work, we attempt to tackle this problem through the use of the so-called improper Gaussian signaling. Such a signaling technique has demonstrated its superiority in improving the overall performance in interference limited networks. Particularly, we assume a system with a SU pair working in half-duplex mode that uses improper Gaussian signaling while the FD PU pair implements the regular proper Gaussian signaling techniques. First, we derive a closed form expression for the SU outage probability and an upper bound for the PU outage probability. Then, we optimize the SU signal parameters to minimize its outage probability while maintaining the required PU quality-of-service based on the average channel state information. Finally, we provide some numerical results that validate the tightness of the PU outage probability bound and demonstrate the advantage of employing the improper Gaussian signaling to the SU in order to access the spectrum of the FD PU.
Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking
Venue: arXiv cs.CV 2015
Year: 2015

Robust online multi-person tracking requires the correct associations of online detection responses with existing trajectories. We address this problem by developing a novel appearance modeling approach to provide accurate appearance affinities to guide data association. In contrast to most existing algorithms that only consider the spatial structure of human appearances, we exploit the temporal dynamic characteristics within temporal appearance sequences to discriminate different persons. The temporal dynamic makes a sufficient complement to the spatial structure of varying appearances in the feature space, which significantly improves the affinity measurement between trajectories and detections. We propose a feature selection algorithm to describe the appearance variations with mid-level semantic features, and demonstrate its usefulness in terms of temporal dynamic appearance modeling. Moreover, the appearance model is learned incrementally by alternatively evaluating newly-observed appearances and adjusting the model parameters to be suitable for online tracking. Reliable tracking of multiple persons in complex scenes is achieved by incorporating the learned model into an online tracking-by-detection framework. Our experiments on the challenging benchmark MOTChallenge 2015 demonstrate that our method outperforms the state-of-the-art multi-person tracking algorithms.
Concentration of Measure Inequalities and Their Communication and Information-Theoretic Applications
Venue: arXiv cs.IT 2015
Year: 2015

During the last two decades, concentration of measure has been a subject of various exciting developments in convex geometry, functional analysis, statistical physics, high-dimensional statistics, probability theory, information theory, communications and coding theory, computer science, and learning theory. One common theme which emerges in these fields is probabilistic stability: complicated, nonlinear functions of a large number of independent or weakly dependent random variables often tend to concentrate sharply around their expected values. Information theory plays a key role in the derivation of concentration inequalities. Indeed, both the entropy method and the approach based on transportation-cost inequalities are two major information-theoretic paths toward proving concentration. This brief survey is based on a recent monograph of the authors in the Foundations and Trends in Communications and Information Theory (online available at http://arxiv.org/pdf/1212.4663v8.pdf), and a tutorial given by the authors at ISIT 2015. It introduces information theorists to three main techniques for deriving concentration inequalities: the martingale method, the entropy method, and the transportation-cost inequalities. Some applications in information theory, communications, and coding theory are used to illustrate the main ideas.
Congestion Control for P2P Live Streaming
Venue: arXiv cs.NI 2015
Year: 2015

In recent years, research efforts tried to exploit peer-to-peer (P2P) systems in order to provide Live Streaming (LS) and Video-on-Demand (VoD) services. Most of these research efforts focus on the development of distributed P2P block schedulers for content exchange among the participating peers and on the characteristics of the overlay graph (P2P overlay) that interconnects the set of these peers. Currently, researchers try to combine peer-to-peer systems with cloud infrastructures. They developed monitoring and control architectures that use resources from the cloud in order to enhance QoS and achieve an attractive trade-off between stability and low cost operation. However, there is a lack of research effort on the congestion control of these systems and the existing congestion control architectures are not suitable for P2P live streaming traffic (small sequential non persistent traffic towards multiple network locations). This paper proposes a P2P live streaming traffic aware congestion control protocol that: i) is capable to manage sequential traffic heading to multiple network destinations , ii) efficiently exploits the available bandwidth, iii) accurately measures the idle peer resources, iv) avoids network congestion, and v) is friendly to traditional TCP generated traffic. The proposed P2P congestion control has been implemented, tested and evaluated through a series of real experiments powered across the BonFIRE infrastructure.
Tasks Scheduling Technique Using League Championship Algorithm for Makespan Minimization in IaaS Cloud
Venue: arXiv cs.DC 2015
Year: 2015

Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers. In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud. The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space. Three other existing algorithms that is, First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All algorithms under consideration assumed to be non-preemptive. The results obtained shows that, the LCA scheduling technique perform moderately better than the other algorithms in minimizing the makespan time of scheduled tasks in IaaS cloud.
Interactive multiclass segmentation using superpixel classification
Venue: arXiv cs.CV 2015
Year: 2015

This paper adresses the problem of interactive multiclass segmentation. We propose a fast and efficient new interactive segmentation method called Superpixel Classification-based Interactive Segmentation (SCIS). From a few strokes drawn by a human user over an image, this method extracts relevant semantic objects. To get a fast calculation and an accurate segmentation, SCIS uses superpixel over-segmentation and support vector machine classification. In this paper, we demonstrate that SCIS significantly outperfoms competing algorithms by evaluating its performances on the reference benchmarks of McGuinness and Santner.
On insertion-deletion systems over relational words
Venue: arXiv cs.FL 2015
Year: 2015

We introduce a new notion of a relational word as a finite totally ordered set of positions endowed with three binary relations that describe which positions are labeled by equal data, by unequal data and those having an undefined relation between their labels. We define the operations of insertion and deletion on relational words generalizing corresponding operations on strings. We prove that the transitive and reflexive closure of these operations has a decidable membership problem for the case of short insertion-deletion rules (of size two/three and three/two). At the same time, we show that in the general case such systems can produce a coding of any recursively enumerable language leading to undecidabilty of reachability questions.
Using Anatomical Markers for Left Ventricular Segmentation of Long Axis Ultrasound Images
Venue: arXiv cs.CV 2015
Year: 2015

Left ventricular segmentation is essential for measuring left ventricular function indices. Segmentation of one or several images requires an initial guess of the contour. It is hypothesized here that creating an initial guess by first detecting anatomical markers, would lead to correct detection of the endocardium. The first step of the algorithm presented here includes automatic detection of the mitral valve. Next, the apex is detected in the same frame. The valve is then tracked throughout the cardiac cycle. Contours passing from the apex to each valve corner are then found using a dynamic programming algorithm. The resulting contour is used as an input to an active contour algorithm. The algorithm was tested on 21 long axis ultrasound clips and showed good agreement with manually traced contours. Thus, this study demonstrates that detection of anatomic markers leads to a reliable initial guess of the left ventricle border.
Dynamic Sketching for Graph Optimization Problems with Applications to Cut-Preserving Sketches
Venue: arXiv cs.DS 2015
Year: 2015

In this paper, we introduce a new model for sublinear algorithms called \emph{dynamic sketching}. In this model, the underlying data is partitioned into a large \emph{static} part and a small \emph{dynamic} part and the goal is to compute a summary of the static part (i.e, a \emph{sketch}) such that given any \emph{update} for the dynamic part, one can combine it with the sketch to compute a given function. We say that a sketch is \emph{compact} if its size is bounded by a polynomial function of the length of the dynamic data, (essentially) independent of the size of the static part. A graph optimization problem $P$ in this model is defined as follows. The input is a graph $G(V,E)$ and a set $T \subseteq V$ of $k$ terminals; the edges between the terminals are the dynamic part and the other edges in $G$ are the static part. The goal is to summarize the graph $G$ into a compact sketch (of size poly$(k)$) such that given any set $Q$ of edges between the terminals, one can answer the problem $P$ for the graph obtained by inserting all edges in $Q$ to $G$, using only the sketch. We study the fundamental problem of computing a maximum matching and prove tight bounds on the sketch size. In particular, we show that there exists a (compact) dynamic sketch of size $O(k^2)$ for the matching problem and any such sketch has to be of size $Ω(k^2)$. Our sketch for matchings can be further used to derive compact dynamic sketches for other fundamental graph problems involving cuts and connectivities. Interestingly, our sketch for matchings can also be used to give an elementary construction of a \emph{cut-preserving vertex sparsifier} with space $O(kC^2)$ for $k$-terminal graphs; here $C$ is the total capacity of the edges incident on the terminals. Additionally, we give an improved lower bound (in terms of $C$) of $Ω(C/\log{C})$ on size of cut-preserving vertex sparsifiers.
Scaling up for high dimensional and high speed data streams: HSDStream
Venue: arXiv cs.DB 2015
Year: 2015

This paper presents a novel high speed clustering scheme for high dimensional data streams. Data stream clustering has gained importance in different applications, for example, in network monitoring, intrusion detection, and real-time sensing are few of those. High dimensional stream data is inherently more complex when used for clustering because the evolving nature of the stream data and high dimensionality make it non-trivial. In order to tackle this problem, projected subspace within the high dimensions and limited window sized data per unit of time are used for clustering purpose. We propose a High Speed and Dimensions data stream clustering scheme (HSDStream) which employs exponential moving averages to reduce the size of the memory and speed up the processing of projected subspace data stream. The proposed algorithm has been tested against HDDStream for cluster purity, memory usage, and the cluster sensitivity. Experimental results have been obtained for corrected KDD intrusion detection dataset. These results show that HSDStream outperforms the HDDStream in all performance metrics, especially the memory usage and the processing speed.