Search

It has become a strategic consensus of the international community for accelerating the deployment of 5G network. This paper presents an approach for the deployment of 5G base stations under the considerations of both the cost and the signal coverage. We formulate an optimization problem for the site selection and location of 5G macro and micro base stations. An implementation procedure is proposed in the paper for the cooperative operation and deployment scheme of optimizing the location of 5G heterogeneous base stations, which aims to optimally reduce the setup cost and strengthen the signal coverage while deploying 5G base stations. A series of numerical examples are solved in the paper to demonstrate the proposed approach, and a cost-benefit analysis is also conducted to determine the optimal deployment plan for the number of macro and micro base stations. In the conclusion, a balanced executable solution is presented to make the signal strength of all demand points in the studied 5G network reach the strongest under the budget constraint.

This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.

The monitoring of the Internet of things networks depends to a great extent on the availability and correct functioning of all the network nodes that collect data. This network nodes all of which must correctly satisfy their purpose to ensure the efficiency and high quality of monitoring and control of the internet of things networks. This paper focuses on the problem of fault-tolerant maintenance of a networked environment in the domain of the internet of things. Based on continuous-time Markov chains, together with a cooperative control algorithm, a novel feedback model-based predictive hybrid control algorithm is proposed to improve the maintenance and reliability of the internet of things network. Virtual sensors are substituted for the sensors that the algorithm predicts will not function properly in future time intervals; this allows for maintaining reliable monitoring and control of the internet of things network. In this way, the internet of things network improves its robustness since our fault tolerant control algorithm finds the malfunction nodes that are collecting incorrect data and self-correct this issue replacing malfunctioning sensors with new ones. In addition, the proposed model is capable of optimising sensor positioning. As a result, data collection from the environment can be kept stable. The developed continuous-time control model is applied to guarantee reliable monitoring and control of temperature in a smart supermarket. Finally, the efficiency of the presented approach is verified with the results obtained in the conducted case study., This paper has been partially supported by the Salamanca Ciudad de Cultura y Saberes Foundation under the Talent Attraction Programme (CHROMOSOME project).

Many modern applications are modeled using graphs of some kind. Given a graph, reachability, that is, discovering whether there is a path between two given nodes, is a fundamental problem as well as one of the most important steps of many other algorithms. The rapid accumulation of very large graphs (up to tens of millions of vertices and edges) from a diversity of disciplines demand efficient and scalable solutions to the reachability problem. General-purpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize algorithms that present a high degree of regularity. In this paper, we extend the applicability of GPU processing to graph-based manipulation, by re-designing a simple but efficient state-of-the-art graph-labeling method, namely the GRAIL (Graph Reachability Indexing via RAndomized Interval) algorithm, to many-core CUDA-based GPUs. This algorithm firstly generates a label for each vertex of the graph, then it exploits these labels to answer reachability queries. Unfortunately, the original algorithm executes a sequence of depth-first visits which are intrinsically recursive and cannot be efficiently implemented on parallel systems. For that reason, we design an alternative approach in which a sequence of breadth-first visits substitute the original depth-first traversal to generate the labeling, and in which a high number of concurrent visits is exploited during query evaluation. The paper describes our strategy to re-design these steps, the difficulties we encountered to implement them, and the solutions adopted to overcome the main inefficiencies. To prove the validity of our approach, we compare (in terms of time and memory requirements) our GPU-based approach with the original sequential CPU-based tool. Finally, we report some hints on how to conduct further research in the area.

A novel nonlinear filter, which incorporates the concept of exponential sinusoidal models into nonlinear filters based on functional link networks (FLNs) has been developed in this paper. The proposed filter is designed to provide improved convergence characteristics over traditional FLN filters. The conventional trigonometric FLN may be considered as a special case of the proposed adaptive exponential FLN (AEFLN). An adaptive exponential least mean square (AELMS) algorithm has been derived and the same has been successfully applied for identification of a couple of nonlinear plants. The AEFLN-based nonlinear active noise control (ANC) system has also been designed and an adaptive exponential filtered-s least mean square (AEFsLMS) algorithm has been developed to update the weights as well as the exponential factor. Simulation study has revealed the improved noise mitigation offered by the AEFLN-based nonlinear ANC system., by Vinal Patel, Vaibhav Gandhi, Shashank Heda, and Nithin V. George

Efficient, robust and precise algorithms for linear quadratic regulator (LQR) and matrix differential Riccati equation (MDRE) are essential in optimal control. However, there are lack of good algorithms for time-varying LQR problem because of the difficulty of solving the nonlinear time-varying MDRE. In this paper, we proved that the $n$ -th order LQR problem is equivalent to $n$ parallel 1-dim Hamiltonian systems and proposed the explicit symplectic-precise iteration method (SPIM) for solving LQR and MDRE. The explicit symplectic-precise iteration algorithms (ESPIA) designed with SPIM have three typical merits: firstly, there are no accumulative errors in the sense of long-term time which inherits from symplectic difference scheme; secondly the stiffness problem due to the inverse of matrix is avoided by the precise iteration method; and finally the algorithmic structure of ESPIA is simple and no extra assumptions are required. Systematic analysis shows that the time complexity of the symplectic algorithms for the $n$ -th order LQR and MDRE is $\mathcal {O}(k_{max}n^{3})$ where $k_{max}$ is the iteration times specified by the time duration. Numerical examples and simulations are provided to validate the performance of the ESPIA.

In typical e-commerce warehouse operations, upon receiving the orders from customers, the purchased items need to be retrieved from shelves and then packaged accordingly for delivery. To automate and speed up the item retrieval process, a Smart Warehouse usually employs a management system, called the Automated Retrieval System (ARS), to control and schedule the retrieval jobs. The working principle of ARS is crucial to the Smart Warehouse because it will have a great impact on the subsequent downstream processes. In short, all the items in a particular order should be considered as an integral part; if one of these items encounters a much larger retrieval delay than others do, then the entire order may experience an unnecessary latency. In the past, the integrality of order has not received much attention for the parallel retrieval process of multiple stackers. To take this into account, this paper proposes using an Order Tag to label all the items that belong to the same order for retrieval job scheduling. The way of calculating the Order Tags will then determine the scheduling discipline of the ARS. With the objectives of minimizing the average delay and ensuring the fairness, two algorithms are proposed. They are named as Dynamic Order-Based (DOB) and Dynamic Order-Based with Threshold (DOBT) Scheduling Algorithms, respectively. Compared with the First-Come-First-Serve and other approaches, the simulation results show that DOB and DOBT are able to reduce the average order retrieval delay by at least 30%, and generate less backlog pressure to the downstream operations.

In recent years, fade detection algorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes and scenes with captions or flashing light sources. This paper proposes a new fade detection algorithm that uses similarity tendencies of luminance transitions to overcome such obstacles. To prevent detection accuracy degradation by letterboxing and captions, video frames are simplified. Then, fade candidates are detected by transition boundary detection using the angular and curvature characteristics of the luminance vectors. Finally, luminance flipping detection improves the detection accuracy by extracting the luminance retrograde phenomenon that occurs with flashing or light source movements. Through objective evaluation using $F_{1}$ score, the detection accuracy of the proposed algorithm was 0.884, which is an increase of 0.187 (21.2% improvement) compared with the average $F_{1}$ score of existing high-performance methods.

Several advanced cyber attacks adopt the technique of "pivoting" through which attackers create a command propagation tunnel through two or more hosts in order to reach their final target. Identifying such malicious activities is one of the most tough research problems because of several challenges: command propagation is a rare event that cannot be detected through signatures, the huge amount of internal communications facilitates attackers evasion, timely pivoting discovery is computationally demanding. This paper describes the first pivoting detection algorithm that is based on network flows analyses, does not rely on any a-priori assumption on protocols and hosts, and leverages an original problem formalization in terms of temporal graph analytics. We also introduce a prioritization algorithm that ranks the detected paths on the basis of a threat score thus letting security analysts investigate just the most suspicious pivoting tunnels. Feasibility and effectiveness of our proposal are assessed through a broad set of experiments that demonstrate its higher accuracy and performance against related algorithms.

This paper presents a self-contained factorization for the Vandermonde matrices associated with true-time delay based wideband analog multi-beam beamforming using antenna arrays. The proposed factorization contains sparse and orthogonal matrices. Novel self-recursive radix-2 algorithms for Vandermonde matrices associated with true time delay based delay-sum filterbanks are presented to reduce the circuit complexity of multi-beam analog beamforming systems. The proposed algorithms for Vandermonde matrices by a vector attain $\mathcal{O}(N \log N)$ delay-amplifier circuit counts. Error bounds for the Vandermode matrices associated with true-time delay are established and then analyzed for numerical stability. The potential for real-world circuit implementation of the proposed algorithms will be shown through signal flow graphs that are the starting point for high-frequency analog circuit realizations., 20 pages, 1 figure

Content-Centric Networking (CCN) is now a research hotspot aiming at building up a new network architecture compared with the traditional IP-based, host-centric one. In this paper, after leaning that CCN's content naming and content-based properties make it suitable for fast network organizing in disaster recovery, we propose methods on access point placement and routing to fast connect users in a middle-scale post-disaster scenario model. Our work includes the design of a placement algorithm using graphic union coverage and a CCN routing strategy based on Breadth-First Searching, both extracting the social attributes of user node distribution. We use real-world maps for simulation and carry out comparative analysis with existing Ad Hoc methods under the same experimental conditions. The simulation results show that CCN can bring more efficient routing and robust framework to fulfill the urgent demands of post-disaster recovery.

In this paper, a new spectral scaling memoryless Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is developed for solving large scale unconstrained optimization problems, where the scaling parameter is chosen so as to minimize all the eigenvalues of search direction matrices. The search directions in this algorithm are proved to satisfy the approximate Dai-Liao conjugate condition. With this advantage of the search directions, a scaling memoryless BFGS update formula is constructed and an algorithm is developed by incorporating acceleration strategy of line search and restart criterion. Under mild assumptions, global convergence of the algorithm is proved. Numerical tests demonstrate that the developed algorithm is more robust and efficient in solving large scale benchmark test problems than the similar ones in the literature.

Radio Access Network (RAN) disaggregation allows operators to mix-and-match multivendor components and bring RAN services from one end to the other. Despite this goal, issues of resource misuse or performance undershoot may arise because of inflexible RAN function deployment and uncoordinated decision-making across different network segments. To address these issues, this paper considers full flexibility in the synthesis of end-to-end RAN services from a set of disaggregated and uncoordinated components. In particular, five design factors are jointly considered to maximize the overall network spectral efficiency: (1) User association, (2) Remote radio unit clustering, (3) RAN functional split, (4) Fronthaul network routing, and (5) Baseband unit placement. To efficiently deal with the formulated problem, we propose a two-level turbo-based solution and compare its performance with several related works. The simulation results show that our proposed solution can not only achieve a 1.33-times spectral efficiency gain compared with state-of-the-art methods, but also provides 1.27 and 1.74 multiplexing benefits for computing and networking resources, respectively. This work is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement 5Growth (No. 856709), Affordable5G (No. 957317), DAEMON (No. 101017109), and 6GBrain (No. 101017226).

This paper proposes a guidance algorithm of a UAV for multiple targets tracking, considering physical constraints of the platform and its sensor. The on-board sensor used for target tracking is a camera. To relax the need of a gimbal system and provide flexibility in utilisation of the vision sensor, a new rotorcraft type of UAV is developed. The main focus in designing tracking guidance is to develop an image based visual servoing approach, appropriate to the newly developed platform. A complementary to the guidance system a control system for the unconventional UAV is also presented. Both control and guidance algorithms are based on the PID control techniques and this paper shows that the tracking guidance can be significantly simplified with the new type of UAV developed. The performance of the proposed tracking guidance along with the controller designed is validated by numerical simulations and flight tests.

The Multi-Rate Support feature has been introduced by the IEEE 802.11 standard to improve system performance, and has been widely exploited by means of Rate Adaptation (RA) strategies within general purpose Wireless LANs. These strategies revealed ineective for real-time industrial communications, and alternative solutions, better tailored for such a specific field of application, were investigated. The preliminary outcomes of the analyses carried out were promising, even if they clearly indicated that further eorts were necessary. In this direction, this paper firstly proposes Rate Selection for Industrial Networks (RSIN), an innovative RA algorithm specifically conceived for the real-time industrial scenario with the goal of minimizing the transmission error probability, while taking into account the deadline imposed to packet delivery. Then, it describes the practical implementation of RSIN on commercial devices, along with that of other formerly introduced RA techniques. Finally, the paper presents a thorough performance analysis, carried out to investigate the behavior of the addressed RA schemes. Such an assessment was performed via both experimental campaigns and simulations. The obtained results, on the one hand, confirm the eectiveness of the RA techniques purposely designed for real-time industrial communication. On the other hand, they clearly indicate that RSIN outperforms all the other strategies.

This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without terminal constraints. Second, the MPC problem is decomposed into smaller subproblems of fixed dimension independent of the horizon length. Third, using the fast alternating minimization algorithm to solve the subproblems, the horizon length is estimated online, by adding or removing subproblems based on a periodic check on the state of the last subproblem to determine whether it belongs to a given control invariant set. We show that the estimated horizon length is bounded and that the control sequence computed using the proposed algorithm is an optimal solution of the CLQR problem. Compared to state-of-the-art algorithms proposed to solve the CLQR problem, our design solves at each iteration only unconstrained least-squares problems and simple gradient calculations. Furthermore, our technique allows the horizon length to decrease online (a useful feature if the initial guess on the horizon is too conservative). Numerical results on a planar system show the potential of our algorithm., Comment: This technical report is an extended version of the paper titled "Constrained LQR Using Online Decomposition Techniques" submitted to the 2016 Conference on Decision and Control

International audience; Lifted Markov chains are Markov chains on graphs with added local "memory" and can be used to mix towards a target distribution faster than their memoryless counterparts. Upper and lower bounds on the achievable performance have been provided under specific assumptions. In this paper, we analyze which assumptions and constraints are relevant for mixing, and how changing these assumptions affects those bounds. Explicitly, we find that requesting mixing on either the original or the full lifted graph, and allowing for reducible lifted chains or not, have no essential influence on mixing time bounds. On the other hand, allowing for suitable initialization of the lifted dynamics and/or imposing invariance of the target distribution for any initialization do significantly affect the convergence performance. The achievable convergence speed for a lifted chain goes from diameter-time to no acceleration over a standard Markov chain, with conductance bounds limiting the effectiveness of the intermediate cases. In addition, we show that the relevance of imposing ergodic flows depends on the other criteria. The presented analysis allows us to clarify in which scenarios designing lifted dynamics can lead to better mixing, and provide a flexible framework to compare lifted walks with other acceleration methods.

This paper investigates distributed continuous-time fault estimation for multiple devices in the Internet-of-Things (IoT) networks by using a hybrid between cooperative control and state prediction techniques. First, a mode-dependent intermediate temperature matrix is designed, which constructs an intermediate estimator to estimate faulty temperature values obtained by the IoT network. Second, the continuous-time Markov chains transition matrix and output temperatures and the sufficient conditions of stability for auto-correct error of the IoT network temperatures are considered. Moreover, faulty devices are replaced by virtual devices to ensure continuous and robust monitoring of the IoT network, preventing in this way false data collection. Finally, the efficiency of the presented approach is verified with the results obtained in the conducted case study.

R Source-code and experiment data of paper "On the performance of the Bayesian Optimization Algorithm with combined scenarios of Search Algorithms and Scoring Metrics" submitted to Genetic Programming and Evolvable Machines - Springer. TO VIEW ALL THE FILES ACCESS THE PROJECT GITHUB ON https://github.com/ciniro/boa_article_gp-em OR DOWNLOAD THE ZIP FILE ON YOUR COMPUTER. The Bayesian Optimization Algorithm (BOA) is one of the most prominent Estimation of Distribution Algorithms (EDAs). It can detect the correlation between multiple variables and extract knowledge about regular patterns in solutions. Bayesian Networks (BNs) are used in BOA to represent the probability distributions of the best individuals. The BN's construction is challenging since there is a trade-off between acuity and computational cost to generate it. This commitment is determined by combining a Search Algorithm (SA) and a Scoring Metric (SM). Some studies have already analyzed how this relationship affects the learning process of a BN. However, such investigation had not yet been performed to determine the bond linking the selection of SA and SM and the BOA's output quality. Acting in this research gap, a detailed comparative analysis involving two constructive heuristics and four scoring metrics is presented in this work. The classic version of BOA with binary and floating-point representations was applied to discrete and continuous optimization problems. The scenarios were compared through graphical analyses, statistical metrics, and difference detection tests. The results showed that the choice of the SA and the SM affects the quality of BOA results. Additionally, scoring metrics that penalize complex BN models present better performance. This study contributes to a discussion on this metaheuristic's practical use, assisting users with implementation decisions., The article is currently under review, so the data in this repository may be changed in the future. This repository is synchronized with Git at https://github.com/ciniro/boa_article_gp-em.

Over the past several decades, function optimization has been a growing topic in the field of computational intelligence. This is partly down to the myriad of real world problems that function optimization can be applied to, but also the fact there are a number of issues facing optimization algorithms that are still yet to be solved. Such problems include getting stuck in local optima, and balancing exploration and exploitation. This paper introduces a novel approach to solving the function optimization problem that utilizes the equations of Newtonian field theory to find good solutions. Like a number of existing optimization algorithms, this approach models a number of particles and their positions in solution space. However, the algorithm proposed in this paper introduces a number of interesting behaviours that can help solve some of the aforementioned issues. The algorithm is explained using both formal mathematics and pseudocode, and the emergent behaviours of the algorithm are discussed. In addition to this, the approach is compared to other optimization algorithms using a set of different functions. The results of these experiments, as well as potential improvements to the proposed algorithm are discussed.

In this paper, the joint support recovery of several sparse signals whose supports exhibit similarities is examined. Each sparse signal is acquired using the same noisy linear measurement process, which returns fewer observations than the dimension of the sparse signals. The measurement noise is assumed additive, Gaussian, and admits different variances for each sparse signal that is measured. Using the theory of compressed sensing, the performance of simultaneous orthogonal matching pursuit (SOMP) is analyzed for the envisioned signal model. The cornerstone of this paper is a novel analysis method upper bounding the probability that SOMP recovers at least one incorrect entry of the joint support during a prescribed number of iterations. Furthermore, the probability of SOMP failing is investigated whenever the number of sparse signals being recovered simultaneously increases and tends to infinity. In particular, convincing observations and theoretical results suggest that SOMP committing no mistake in the noiseless case does not guarantee the absence of error in the noisy case whenever the number of acquired sparse signals scales to infinity. Finally, simulation results confirm the validity of the theoretical results., SCOPUS: ar.j, info:eu-repo/semantics/published

Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data. One important property that is enjoyed by most such methods is uncorrelation among the extracted features. Recently, regularized versions of MVA methods have appeared in the literature, mainly with the goal to gain interpretability of the solution. In these cases, the solutions can no longer be obtained in a closed manner, and more complex optimization methods that rely on the iteration of two steps are frequently used. This paper recurs to an alternative approach to solve efficiently this iterative problem. The main novelty of this approach lies in preserving several properties of the original methods, most notably the uncorrelation of the extracted features. Under this framework, we propose a novel method that takes advantage of the,2,1 norm to perform variable selection during the feature extraction process. Experimental results over different problems corroborate the advantages of the proposed formulation in comparison to state of the art formulations. This work has been partly supported by MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R and TEC2016-75161-C2-2-R, and Comunidad de Madrid projects PRICAM P2013/ICE-2933 and S2013/ICE-2933.

Vehicle-to-Vehicle (V2V) communication systems intend to increase safety and efficiency of the transportation networks. At high vehicle density, the communication channel may become congested, impairing the reliability of the safety applications. As a counter measure, the European Telecommunications Standard Institute (ETSI), proposes the Decentralized Congestion Control (DCC) framework to control the channel load, by tuning message transmission parameters, such as message rate and transmitting power. In this paper, we analyze a congestion control scheme that follows the DCC framework known as Data Rate-DCC (DR-DCC) for various traffic densities. DR-DCC adjusts the data rate based on channel load measurements thus controlling the air time of packets to avoid congestion. Although tuning the data rate has been proposed before we are not aware of any reported work, where its full potential has been investigated in detail. In this paper we intend to provide more insight on the benefits of this approach by analyzing schemes that aim at optimum data rate for various traffic density cases. The objective is not only to avoid congestion but also to provide optimal support to safety applications. We compare the performance of DR-DCC to another DCC approach based on adjusting transmit power for various traffic density cases. DR-DCC outperforms at various traffic densities providing better support to safety applications.

In our prior work, we identified rules for use in recommendation algorithms on Online Social Network (OSN) in order to increase the relevance of content suggested to a user. The resulting recommendation algorithms filter out and prioritize event types for OSN users (such as photo posts by friends, status posts, shared content, etc.), and are thereby intended to reduce information overload. This paper proposes a representation of these rules in a requirements model of a OSN. This is interesting, because recommendation rules influence user behavior, which in turn influences future requirements. If there is a recommendation algorithm, then its behavior should be represented also in requirements models of the system. The paper makes two contributions. We define requirements that OSNs should satisfy in order to produce relevant recommendations of event types to users. We investigate whether an existing requirements modeling language (namely, i-star) can be used to model these requirements.

A novel fuzzy clustering algorithm is presented in this paper, which removes the constraints generally imposed to the cluster shape when a given model is adopted for membership functions. An on-line, sequential procedure is proposed where the cluster determination is performed by using suited membership functions based on geometrically unconstrained kernels and a point-to-shape distance evaluation. Since the performance of on-line algorithms suffers from the pattern presentation order, we also consider the problem of cluster validity aiming at proving the minimal dependence and the robustness with respect to the initialization of inner parameters in the proposed algorithm. The numerical results reported in the paper prove that the proposed approach is able to improve the performances of well-known algorithms on some reference benchmarks.

A low autocorrelation binary sequence (LABS) problem is a hard combinatorial problem and its solutions are important in many practical applications. Till now, the largest best-known skew-symmetric sequence with merit factor greater than 9 had a length of 189. In this paper, a new heuristic algorithm is presented for the LABS problem. The proposed algorithm stores promising solutions and this mechanism enables the algorithm to perform local searches on these solutions in a systematic way. Our algorithm was tested on skew-symmetric sequences and the obtained results are compared with results of the state-of-the-art algorithms. The proposed algorithm was able to find some new best-known skew-symmetric solutions with merit factor greater than 9 in sequence lengths over 200. The obtained results improve the suggestion from 1985 (Beenker et al. ) and 1987 (Bernasconi) greatly, where the merit factor is approximately equal to 6 for long skew-symmetric sequences with length up to 199. Now, the largest best-known skew-symmetric sequence with merit factor greater than 9 has the length 225. Additionally, now all merit factors are greater than 8.5 on the interval from 159 up to 225 for odd lengths.

International audience; Solving to optimality large instances of combinatorial optimization problems using Brand and Bound (B&B) algorithms requires a huge amount of computing resources. In this paper, we investigate the design and implementation of such algorithms on computational grids. Most of existing grid-based B&B algorithms are based on the Master-Worker paradigm, their scalability is therefore limited. In addition, even if the volatility of resources is a major issue in grids fault tolerance is rarely addressed in these works. We thereby propose FTH-B&B, a fault tolerant hierarchical B&B. FTH-B&B is based on different new mechanisms enabling to efficiently build and maintain balanced the hierarchy, and to store and recover work units (sub-problems). FTH-B&B has been implemented on top of the ProActive grid middleware and programming environment and applied to the Flow-Shop scheduling problem. Very often, the validation of existing grid-based B&B works is performed either through simulation or a very small real grid. In this paper, we experimented FTH-B&B on the Grid’5000 real French nation-wide computational grid using up to 1,900 processor cores distributed over six sites. The reported results show that the overhead induced by the proposed mechanisms is very low and an efficiency close to 100 percent can be achieved on some Taillards benchmarks of the Flow-Shop problem. In addition, the results demonstrate the robustness of the proposed mechanisms even in extreme failure situations.

Resource allocation is a usual problem that must be faced during a distributed system design. Despite the large number of algorithms proposed in literature to solve this problem, most papers lack of detailed descriptions about how to turn these algorithms into real-world reliable protocols. This article presents a fault-tolerant algorithm for distributed resource allocation named SLOTS which is implemented as an executable protocol. It allocates resources among members in a fairly manner using simple heuristics and employing a donation approach. SLOTS supports the dynamic behavior of clusters and provides high availability services. It bases its fault-tolerance properties and membership changes in atomic sets of operations (like transactions) using services provided by an underlying Group Communication System., Facultad de Informática

The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 321132, project TRAMAN21. Summarization: The development and deployment of simple, yet efficient, coordinated and integrated control tools for motorway traffic control remains a challenge. A generic integrated feedback-based motorway traffic flow control concept is proposed in this paper. It is based on the combination and suitable extension of control algorithms and tools proposed or deployed in other studies, such as ramp metering or variable speed limit (VSL)-enabled cascade-feedback mainstream traffic flow control, and allows for consideration of multiple bottlenecks. The new controller enables coordination of ramp metering actions at a series of on-ramps, as well as integration with VSL control actions, toward a common control goal, which is bottleneck throughput maximization. While doing this, the approach considers a pre-specified (desired) balancing of the incurred delays upstream of the employed actuators, via a suitably designed knapsack problem. Despite the multitude of the offered configurations, options, and possibilities, the generic control algorithm remains simple, efficient, and suitable for field implementation. The control algorithm is demonstrated and evaluated using a validated macroscopic traffic flow model for a number of scenarios. Presented on: IEEE Transactions on Intelligent Transportation Systems

Wireless power transfer (WPT) is a convenient, flexible, and safe alternative to its wired counterpart. Due to these benefits, WPT has seen rapid growth in recent years. Typical WPT systems are usually used for static applications, where the load operates within a predefined area. On the other hand, dynamic WPT (D-WPT) is usually used to power loads which dynamically change their positions, and one of the ways to achieve this is by increasing the overall charging area via multiple transmitters (Txs). Multiple Txs require communication and control, which increases the overall complexity of the system. This paper proposes a control algorithm which aims to maintain the overall efficiency of the D-WPT system by turning on and off the Tx when the mobile receiver (Rx) approaches and departs, respectively. Transient analysis of the D-WPT system is used to derive its control variables. In addition, energy and power loss of this proposed control algorithm are also investigated. The feasibility of the proposed control algorithm is demonstrated with 1-Tx and 3-Tx experimental setups. The experimental results validate the theoretical analysis model and the proposed control algorithm, and the standby (when not charging) input power forms 7.6% of the charging input power. Accepted version

Conference of 2013 3rd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and Their Applications, ANIMMA 2013 ; Conference Code:102802; International audience; To monitor radioactivity passing through a pipe or in a given container such as a train or a truck, radiation detection systems are commonly employed. These detectors could be used in a network set along the source track to increase the overall detection efficiency. However detection methods are based on counting statistics analysis. The method usually implemented consists in trigging an alarm when an individual signal rises over a threshold initially estimated in regards to the natural background signal. The detection efficiency is then proportional to the number of detectors in use, due to the fact that each sensor is taken as a standalone sensor. A new approach is presented in this paper taking into account the temporal periodicity of the signals taken by all distributed sensors as a whole. This detection method is not based only on counting statistics but also on the temporal series analysis aspect. Therefore, a specific algorithm is then developed in our lab for this kind of applications and shows a significant improvement, especially in terms of detection efficiency and false alarms reduction. We also plan on extracting information from the source vector. This paper presents the theoretical approach and some preliminary results obtain in our laboratory.

The servo motor’s flexible acceleration/deceleration (acc/dec) control is an emerging research topic in the automation field. A velocity control algorithm based on trigonometric function is proposed in this paper. With required parameters, it transforms trigonometric calculations into elementary mathematical operations and calculates the velocity controlling values iteratively, which could avoid trigonometric calculation and reduce the computation time. Experimental results show that the proposed algorithm is suitable for implementation on field programmable gate arrays and achieves a flexible controlling, enhancing both the equipment’s stability and reliability. It is promising to significantly improve the high-speed computerized numerical control equipment’s controlling accuracy, without a huge hardware resource consumption., NSFC - National Natural Science Foundation of China(2017YB129).

Individuals with sensory impairment (hearing or visual) encounter serious communication barriers within society and the world around them. These barriers hinder the communication process and make access to information an obstacle they must overcome on a daily basis. In this context, one of the most common complaints made by the Television (TV) users with sensory impairment is the lack of synchronism between audio and subtitles in some types of programs. In addition, synchronization remains one of the most significant factors in audience perception of quality in live-originated TV subtitles for the deaf and hard of hearing. This paper introduces the Sub-Sync framework intended for use in automatic synchronization of audio-visual contents and subtitles, taking advantage of current well-known techniques used in symbol sequences alignment. In this particular case, these symbol sequences are the subtitles produced by the broadcaster subtitling system and the word flow generated by an automatic speech recognizing the procedure. The goal of Sub-Sync is to address the lack of synchronism that occurs in the subtitles when produced during the broadcast of live TV programs or other programs that have some improvised parts. Furthermore, it also aims to resolve the problematic interphase of synchronized and unsynchronized parts of mixed type programs. In addition, the framework is able to synchronize the subtitles even when they do not correspond literally to the original audio and/or the audio cannot be completely transcribed by an automatic process. Sub-Sync has been successfully tested in different live broadcasts, including mixed programs, in which the synchronized parts (recorded, scripted) are interspersed with desynchronized (improvised) ones.

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms. European Union's Horizon 2020 No 687299 NeuRAM European Union's Horizon 2020 No 824164 HERMES Ministerio de Economía y Competitividad TEC2015-63884-C2-1-P

To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In other words, machine learning itself can be the weakest link in a security system. In this paper, we rely upon a previously-proposed attack framework to categorize potential attack scenarios against learning-based malware detection tools, by modeling attackers with different skills and capabilities. We then define and implement a set of corresponding evasion attacks to thoroughly assess the security of Drebin, an Android malware detector. The main contribution of this work is the proposal of a simple and scalable secure-learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of attack. We finally argue that our secure-learning approach can also be readily applied to other malware detection tasks., Accepted for publication on IEEE Transactions on Dependable and Secure Computing

International audience; This paper exposes a methodology which allows us to address constrained optimal control of non linear systems by interior penalty methods. A constructive choice for the penalty functions that are introduced to account for the constraints is established in the article. It is shown that it allows us to approach the solution of the non linear optimal control problem using a sequence of unconstrained problems, whose solutions are readily characterized by the simple calculus of variations. An illustrative example is given. The paper extends recent contributions, originally focused on linear dynamics.

Bu bildiride, bilgisayarlı görü ve örüntü tanıma alanlarında çok önemli bir yeri olan plaka tanıma sistemlerinin performansını artırmak amacıyla algoritmik kaynaşım yöntemini önermekteyiz. Plaka tanıma sistemleri genellikle plakanın yerinin saptanması, karakter bölütleme ve karakter tanıma şeklinde uç kısımdan oluşur. Karakter bölütleme ve tanıma, görüntü işleme ve örüntü tanıma alanlarında yoğun bir şekilde çalışılmış ve bu problemlerin çözümünde gürbüz sistemler tasarlanmıştır. Ancak plaka tanıma sistemlerinin en temel problemi plaka yerinin tam piksel hassasiyetle saptanmasıdır. Plaka yerinin saptanması için bir önceki çalışmamızda yatay tarama yöntemini önermiştik. Bu bildiride çift satırlı plakaları da okuyabilen dörtgen saptamaya dayalı farklı bir algoritma geliştirdik. Plaka yerinin saptanmasını gürbüz hale getirebilmek için iki farklı algoritmanın başarımlarını algoritmik kaynaşım çatısı ile birleştiren yeni bir yaklaşım sunmaktayız. Gerçekleştirilen benzetimlerde önerilen algoritmik kaynaşım yöntemi ile plaka tanıma sisteminin performansında önemli artışlar gözlemlenmiştir. In this paper, we propose an algorithmic fusion approach for performance improvement of the license plate recognition (LPR) systems which is a very important research topic in computer vision and pattern recognition areas. Basically the building blocks of LPR system is composed of locating the LP, character segmentation and character recognition stages. Character segmentation and recognition phases are well studied in the fields of image processing and pattern recognition and very robust solutions were proposed in the literature. However, the most important problem of the LPR systems is locating the pixel precise position of the LP inside the image. In our previous work, we proposed a horizontal scan method to locate the LP. In this paper, we propose a square detection based LP locating algorithm which also locates the two lined LPs. In order to design a robust stage for the locating plate block, we present a new approach that combines the dexterity of the two different LPR locating algorithms within algorithmic fusion framework. According to our experiments, we observe that the overall performance of the LPR system is increased by the proposed algorithmic fusion method. Publisher's Version

Tremendous quantities of numeric data have been generated as streams in various cyber ecosystems. Sorting is one of the most fundamental operations to gain knowledge from data. However, due to size restrictions of data storage which includes storage inside and outside CPU with respect to the massive streaming data sources, data can obviously overflow the storage. Consequently, all classic sorting algorithms of the past are incapable of obtaining a correct sorted sequence because data to be sorted cannot be totally stored in the data storage. This paper proposes a new sorting algorithm called streaming data sort for streaming data on a uniprocessor constrained by a limited storage size and the correctness of the sorted order. Data continuously flow into the storage as consecutive chunks with chunk sizes less than the storage size. A theoretical analysis of the space bound and the time complexity is provided. The sorting time complexity is O (n), where n is the number of incoming data. The space complexity is O (M), where M is the storage size. The experimental results show that streaming data sort can handle a million permuted data by using a storage whose size is set as low as 35% of the data size. This proposed concept can be practically applied to various applications in different fields where the data always overflow the working storage and sorting process is needed.

International audience; In this paper, we deal with the problem of scheduling streaming applications on unreliable heterogeneous platforms. We use the realistic one-port model with full computation/communication overlap. We deal with three optimization objectives. The first two, latency and throughput, are performance-related while the third, tolerating a given number of processor failures, is reliability-oriented. The major contribution of this paper is the design of a new scheduling algorithm to minimize latency under both throughput and reliability constraints. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed algorithm.

Mobility has a significant impact on the ad hoc network protocol performance. Mainly, the protocol performance has been evaluated in simulations and using synthetic mobility models, which have two main drawbacks: (a) they assume that wireless devices start and remain in the simulation for a user defined simulation time; and, (b) they are unrealistic. Real mobility models that arederived from real user traces challenge the assumption that wireless devices start and remain in the simulation for the entire user defined simulation time, but they rather show that wireless nodes posses dynamic membership (nodes join and leave the simulation dynamically based on some random variable). In this paper, we evaluate the Maximum Node Degree mobility metric for real mobility models, which has got little attention due to the assumption of static connectivity graph on the number of nodes. The contributions of this paper are two-fold. First, we introduce the algorithm that computes the Maximum Node Degree mobility metric, which in turn provides the upper bound on the number of neighbors for a given node. Second, we show that the upper bound can be used to improve the algorithm complexity by introducing a new algorithm metric, namely Efficiency. Its usefulness is shown through a case study for evaluating the gains in the algorithm complexity of incentive protocols

Games became popular, within the formal verification community, after their application to automatic synthesis of circuits from specifications, and they have been receiving more and more attention since then. This paper focuses on coding the "Sokoban" puzzle, i.e., a very complex single-player strategy game. We show how its solution can be encoded and represented as a Bounded Model Checking problem, and then solved with a SAT solver. After that, to cope with very complex instances of the game, we propose two different ad-hoc divide-and-conquer strategies. Those strategies, somehow similar to state-of-the-art abstraction-and-refinement schemes, are able to decompose deep Bounded Model Checking instances into easier subtasks, trading-off between efficiency and completeness. We analyze a vast set of difficult hard-to-solve benchmark games, trying to push forward the applicability of state-of-the-art SAT solvers in the field. Those results show that games may provide one of the next frontier for the SAT community.

In built-in test pattern generation and test set compression, an LFSR is usually employed as the on-chip generator with an arbitrarily selected characteristic polynomial of degree equal, according to a popular rule, to $S_\mathrm{ max}+20$ , where $S_\mathrm{ max}$ is the maximum number of specified bits in any test cube of the test set. By fixing the polynomial a priori a linear system only needs to be solved to compute the required LFSR initial states (seeds) to generate the target test cubes, but the disadvantage is that the polynomial degree (length of the LFSR and seed bit size) may be too large and the fault coverage cannot be guaranteed. In this paper we address the problem of computing a polynomial of small degree directly from the given test set without having to solve multiple non-linear systems and fixing a priori the polynomial degree. The proposed method uses an adaptation of the Berlekamp-Massey algorithm and the Sidorenko-Bossert theorem to perform the computation. In addition, the method guarantees (by design) that all the test cubes in the given test set are generated, thereby achieving 100% coverage, which cannot be guaranteed under the “trial-and-error” $S_\mathrm{ max}+20$ rule. Experimental results verify the advantages that the proposed methodology offers in terms of reduced polynomial degree and 100% coverage.

International audience; Minimal path techniques can efficiently extract geometrically curve-like structures by finding the path with minimal accumulated cost between two given endpoints. Though having found wide practical applications (e.g., line identification, crack detection, and vascular centerline extraction), minimal path techniques suffer from some notable problems. The first one is that they require setting two endpoints for each line to be extracted (endpoint problem). The second one is that the connection might fail when the geodesic distance between the two points is much shorter than the desirable minimal path (shortcut problem). In addition, when connecting two distant points, the minimal path connection might become inefficient as the accumulated cost increases over the propagation and results in leakage into some non-feature regions near the starting point (accumulation problem). To address these problems, this paper proposes an approach termed minimal path propagation with backtracking. We found that the information in the process of backtracking from reached points can be well utilized to overcome the above problems and improve the extraction performance. The whole algorithm is robust to parameter setting and allows a coarse setting of the starting point. Extensive experiments with both simulated and realistic data are performed to validate the performance of the proposed method

Microfluidic laboratories-on-a-chip (LoCs) are replacing the conventional biochemical analyzers and are able to integrate the necessary functions for biochemical analysis on-chip. There are several types of LoCs, each having its advantages and limitations. In this paper we are interested in flow-based LoCs, in which a continuous flow of liquid is manipulated using integrated microvalves. By combining several microvalves, more complex units, such as micropumps, switches, mixers, and multiplexers, can be built. We consider that the architecture of the LoC is given, and we are interested in synthesizing an implementation, consisting of the binding of operations in the application to the functional units of the architecture, the scheduling of operations and the routing and scheduling of the fluid flows, such that the application completion time is minimized. To solve this problem, we propose a list scheduling-based application mapping (LSAM) framework and evaluate it by using real-life as well as synthetic benchmarks. When biochemical applications contain fluids that may adsorb on the substrate on which they are transported, the solution is to use rinsing operations for contamination avoidance. Hence, we also propose a rinsing heuristic, which has been integrated in the LSAM framework.

The odd-even sort algorithm designed by Batcher [1] is a divide-and-conquer parallel sorting algorithm with O(log2 (n)) delay time [2]. It is described in the literature [3] as very practical due to the potentially easy implementation, which is a notable advantage over AKS sorting networks [4]. However, the most basic literature on that topic contains either theoretical background without any implementations usable with the modern compilers [2] or C-like code snippets that are apparently erroneous [3]. In this paper, we propose an alternative to the code from [3] which improves compatibility with the C++ standard [5], fixes the bug that affected the computational complexity, and yields even further practical improvement to the execution time. All the specified enhancements are achieved without increasing the structural complexity of the method, so the proposed code remains as concise as the original.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. In the recent years, solar energy has attended the world as a clean energy. Many researchers are focused on extracting maximum power from solar panels. So, Maximum Power Point Tracking (MPPT) is used for increasing efficiency of solar PV system. This paper presents Model Predictive Controller (MPC) as a method for Maximum Power Point Tracking (MPPT) and compares it, with Perturb and Observe (P&O) and Incremental Conductance algorithm (IC) techniques. The MATLAB/Simulink and SimPower Systems software packages are used to model a proposed strategy. Simulation results show that Perturb and Observe (P&O) and Incremental Conductance algorithm (IC) techniques have limitation for tracking the exact maximum power point in transient insolation conditions. However, Model Predictive Controller (MPC) method is robust against fast varying of insolation conditions. Also, MPC method is faster than P&O and IC technique according to the simulation results.

This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yes-object probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including $\delta$-GLMB, marginalized $\delta$-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments., Comment: 17pages, 23 figures

Modern network elements are increasingly required to deal with heterogeneous traffic. Recent works consider processing policies for buffers that hold packets with different processing requirements (number of processing cycles needed before a packet can be transmitted out) but uniform value, aiming to maximize the throughput, i.e., the number of transmitted packets. Other developments deal with packets of varying value but uniform processing requirement (each packet requires one processing cycle); the objective here is to maximize the total transmitted value. In this paper, we consider a more general problem, combining packets with both nonuniform processing and nonuniform values in the same queue. We study the properties of various processing orders in this setting. We show that in the general case, natural processing policies have poor performance guarantees, with linear lower bounds on their competitive ratio. Moreover, we show several adversarial lower bounds for every priority queue and even for every online policy. On the positive side, in the special case when only two different values are allowed, 1 and $V$ , we present a policy that achieves competitive ratio $\left ({1+({W+2}/{V})}\right)$ , where $W$ is the maximal number of required processing cycles. We also consider copying costs during admission.

The maximum subarray problem (MSP) is to the find maximum contiguous sum in an array. This paper describes a method of Kadanes algorithm (the state of the art) optimization for specific data (continuous sequences of zeros or negative real numbers). When the data are unfavourable, the modification of the algorithm causes a non significant performance loss (1% > decrease in performance). The modification does not improve time complexity but reduces the number of elementary operations. Various experimental data sets have been used to evaluate possible time efficiency improvement. For the most favourable data sets an increase in efficiency of 25% can be achieved.

Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human-robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products. European Commission/H2020/688857/EU