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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.

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

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.

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

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.

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.

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

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.

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.

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.

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

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

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.

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.

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

A software defined network (SDN) enabled mobile access network is a future network with great potential to support scalable and flexible network applications. To support various network applications, the SDN-enabled mobile access network usually uses forwarding rules in SDN devices. With a limited rule space in existing SDN devices, a rule caching mechanism is an efficient way to improve the network performance. In this paper, we propose SRCMN, which is a new caching structure to improve the network performance with a limited rule space in the SDN-enabled mobile access network. We design a twolayer rule space in each SDN device, which is managed by the SDN controller. We also design a cache prefetching mechanism with the consideration of user mobility. By conducting extensive simulations, we demonstrate that our proposed structure and mechanism significantly outperform original rule space management under various network settings.

Continuous development of electrical power systems has resulted in very complex relay protection system with associated numerical protection algorithms. Reliability, selectivity and speed are the basic design requirements for the development of new algorithms. Ideally the amplitude estimation algorithms should determinate the amplitude as fast as possible, theoretically with just one sampled value. Currently there are no algorithms which can do that with a high reliability. Abovementioned requirements are contradictory which makes it impossible to completely fulfil all of them. Current and voltage based phasor estimation algorithms are the core of all numerical protection relays, thus significantly defining protection function performances. A detailed overview of the most common numerical phase estimation algorithms is shown in this paper, including time responses on various input signal combinations. All algorithms are simulated in the LabVIEW development environment.

International audience; This paper aims to optimize the coverage of a given area from a set of views to allow a complete mosaicing. Among the investigated methods to find the best camera positions, two of them are studied, namely the Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA). After having performed experiments to compare the algorithms, the hybridization of GA and PSO is investigated. To validate the proposed method, it is simulated area of irregular shapes with the cameras mounted on a Unmanned Aerial Vehicles (UAVs). V-REP is used to simulate the UAVs in an indoor environment and satellite images are used for a large outdoor area. The simulation validates the efficiency of the proposed method to find the optimal position of cameras. Then by using the images acquired it is possible to monitor the area and to compute a full mosaic of it.

Proceeding of: IEEE Conference on Computer Communications, INFOCOM 2017, Atlanta, Georgia, USA, 1-4 May 2017 In addition to providing substantial performance enhancements, future 5G networks will also change the mobile network ecosystem. Building on the network slicing concept, 5G allows to "slice" the network infrastructure into separate logical networks that may be operated independently and targeted at specific services. This opens the market to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, we need new algorithms for the allocation of network resources that consider these new players. In this paper, we address this issue by designing an algorithm for the admission and allocation of network slices requests that (i) maximises the infrastructure provider's revenue and (ii) ensures that the service guarantees provided to tenants are satisfied. Our key contributions include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modelled as a Semi-Markov Decision Process) and the optimisation of the infrastructure provider's revenue, and (iii) the design of an adaptive algorithm (based on Q-learning) that achieves close to optimal performance. This research work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA (Grant Agreement No. 671584). The work of A. Banchs was partially supported by the Spanish Ministry of Economy and Competitiveness under the THWART project (Grant TEC2015-70836-ERC).

The current and envisaged increase of cellular traffic poses new challenges to mobile network operators (MNO), who must densify their radio access networks (RAN) while maintaining low capital expenditure and operational expenditure to ensure long-Term sustainability. In this context, this paper analyzes optimal clustering solutions based on device-To-device communications to mitigate partially or completely the need for MNOs to carry out extremely dense RAN deployments. Specifically, a low-complexity algorithm that enables the creation of spectral efficient clusters among users from different cells, denoted as enhanced clustering optimization for resources' efficiency is presented. Due to the imbalance between uplink and downlink traffic, a complementary algorithm, known as clustering algorithm for load balancing, is also proposed to create nonspectral efficient clusters when they result in a capacity increase. Finally, in order to alleviate the energy overconsumption suffered by cluster heads, the clustering energy efficient algorithm (CEEa) is also designed to manage the tradeoff between the capacity enhancement and the early battery drain of some users. Results show that the proposed algorithms increase the network capacity and outperform existing solutions, while, at the same time, CEEa is able to handle the cluster heads energy overconsumption. © 1967-2012 IEEE.

Scheduling and spectrum allocation are tasks affecting the performance of cognitive radio wireless networks, where heterogeneity in channel availability limits the performance and poses a great challenge on protocol design. In this paper, we present a distributed algorithm for scheduling and spectrum allocation with the objective of maximizing the network's throughout subject to a delay constraint. During each time slot, the scheduling and spectrum allocation problems involve selecting a subset of links to be activated, and based on spectrum sensing outcomes, allocate the available resources to these links. This problem is addressed as an aggregate utility maximization problem. Since the throughput of any data flow is limited by the throughput of the weakest link along its end-to-end path, the utility of each flow is chosen as a function of this weakest link's throughput. The throughput and delay performance of the network are characterized using a queueing theoretic analysis, and throughput is maximized via the application of Lagrangian duality theory. The dual decomposition framework decouples the problem into a set of subproblems that can be solved locally, hence, it allows us to develop a scalable distributed algorithm. Numerical results demonstrate the fast convergence rates of the proposed algorithm, as well as significant performance gains compared to conventional design methods. 2002-2012 IEEE. Scopus

In application-specific SoCs, the irregularity of the topology ends up in a complex and customized implementation of the routing algorithm, usually relying on routing tables implemented with memory structures at source end nodes. As system size increases, the routing tables also increase in size with nonnegligible impact on power, area, and latency overheads. In this paper, we present a routing implementation for application-specific SoCs able to implement in an efficient manner (with no routing tables and using a small logic block in every switch) a deadlock-free routing algorithm in these irregular networks. The mechanism relies on a tool that maps the initial irregular topology of the SoC system into a logical regular structure where the mechanism can be applied. We provide details for both the mapping tool and the proposed routing mechanism. Evaluation results show the effectiveness of the mapping tool as well as the low area and timing requirements of the mechanism. With the mapping tool and the routing mechanism, complex irregular SoC topologies can now be supported without the need for routing tables., This work was supported by the Spanish MEC and MICINN, as well as European Commission FEDER funds, under Grant CSD2006-00046. It was also partly supported by the COMCAS project (CA501), a project labeled within the framework of CATRENE, the EUREKA cluster for Application and Technology Research in Europe on NanoElectronics.

Combining the benefits of 3D ICs and Networks-on-Chip (NoCs) schemes provides a significant performance gain in Chip Multiprocessors (CMPs) architectures. As multicast communication is commonly used in cache coherence protocols for CMPs and in various parallel applications, the performance of these systems can be significantly improved if multicast operations are supported at the hardware level. In this paper, we present several partitioning methods for the path-based multicast approach in 3D mesh-based NoCs, each with different levels of efficiency. In addition, we develop novel analytical models for unicast and multicast traffic to explore the efficiency of each approach. In order to distribute the unicast and multicast traffic more efficiently over the network, we propose the Minimal and Adaptive Routing (MAR) algorithm for the presented partitioning methods. The analytical and experimental results show that an advantageous method named Recursive Partitioning (RP) outperforms the other approaches. RP recursively partitions the network until all partitions contain a comparable number of switches and thus the multicast traffic is equally distributed among several subsets and the network latency is considerably decreased. The simulation results reveal that the RP method can achieve performance improvement across all workloads while performance can be further improved by utilizing the MAR algorithm. Nineteen percent average and 42 percent maximum latency reduction are obtained on SPLASH-2 and PARSEC benchmarks running on a 64-core CMP.

Detecting the presence of primary users and ability to find white spaces in the spectrum are the key enablers of the opportunistic communication. This paper analyzes the trade-offs in cyclostationary-based spectrum sensing algorithm implementations in terms of performance, hardware complexity, and power consumption. The evaluation of the algorithm implementations is performed on field-programmable gate arrays. The analysis presented will provide the designer understanding of dependency between algorithm complexity and power consumption, which is inherently limiting factor of implementation feasibility for cognitive mobile devices.

International audience; In this paper we study generalized Reed-Solomon codes (GRS codes) over commutative, noncommutative rings, show that the classical Welch-Berlekamp and Guruswami-Sudan decoding algorithms still hold in this context and we investigate their complexities. Under some hypothesis, the study of noncommutative generalized Reed-Solomon codes over finite rings leads to the fact that GRS code over commutative rings have better parameters than their noncommutative counterparts. Also GRS codes over finite fields have better parameters than their commutative rings counterparts. But we also show that given a unique decoding algorithm for a GRS code over a finite field, there exists a unique decoding algorithm for a GRS code over a truncated power series ring with a better asymptotic complexity. Moreover we generalize a lifting decoding scheme to obtain new unique and list decoding algorithms designed to work when the base ring is for example a Galois ring or a truncated power series ring or the ring of square matrices over the latter ring.; Dans cet article on étudie les codes de Reed-Solomon généralisés (codes GRS) sur des anneaux commutatifs, non commutatifs, on montre que les algorithmes classiques de décodage Welch-Berlekamp et Guruswami-Sudan restent valides dans ce contexte et on donne leurs complexités. Sous certaines hypothèses, l'étude des codes de Reed-Solomon généralisés sur les anneaux finis montre que les codes GRS sur les anneaux commutatifs ont des meilleurs paramètres que leurs équivalents non commutatifs. De même, les codes GRS sur les corps finis ont de meilleurs paramètres que leurs équivalents sur les anneaux commutatifs. Mais on montre aussi qu'étant donné un algorithme de décodage unique pour un code GRS sur un corps fini, il existe un algorithme de décodage unique pour un code GRS sur l'anneaux des séries tronquées avec une meilleure complexité asymptotique. De plus on généralise un schéma de remontée pour obtenir de nouveau algorithmes de décodage unique et en liste pour les codes GRS sur certains anneaux tels que les anneaux de Galois ou les anneaux de séries tronquées ou les anneaux de matrices sur ces derniers.

In this paper a novel GF(2m) multiplier based on Karatsuba-Ofman Algorithm is presented. A binary field multiplication in polynomial basis is typically viewed as a two steps process, a polynomial multiplication followed by a modular reduction step. This research proposes a modification to the original Karatsuba-Ofman Algorithm in order to integrate the modular reduction inside the polynomial multiplication step. Modular reduction is achieved by using parallel linear feedback registers. The new algorithm is described in detail and results from a hardware implementation on FPGA technology are discussed. The hardware architecture is described in VHDL and synthesized for a Virtex-6 device. Although the proposed field multiplier can be implemented for arbitrary finite fields, the targeted finite fields are recommended for Elliptic Curve Cryptography. Comparing other KOA multipliers, our proposed multiplier uses 36% less area resources and improves the maximum delay in 10%.

The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modelling language based on LTL for finite traces. In this paper, we use a combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to improve the performances of the Declare Miner. Using synthetic and real life event logs, we show that the new implemented core of the plug-in allows for a significant performance improvement.

The lack of a comprehensive solution for image information mining has often brought confusion and misunderstanding when Earth Observation data based application scenarios were addressed. Considering the variety of dedicated sensors available nowadays, the particularities of the recorded data raises serious issues when explored. Most of the proposed methodologies for data analysis integrate algorithms able to cope with single cases. In order to overcome this limitation, the present paper introduce a compound, configurable framework containing two processing levels, for feature extraction and image classification, that allows different settings depending on the application being handled. The design was proposed such that it facilitates the integration of several methods and algorithm for each level, including a module to serve for validation when reference data is available. The approach is not complete without the interaction with the user, therefore, a human-machine communication strategy was also developed. The validation was performed through a prototype system meeting all the criteria of the defined framework.

Data mining methods like clustering enable police to get a clearer picture of criminal identification and prediction. Clustering algorithms will help to extracts hidden patterns to identify groups and their similarities. In this paper, a modified k-mean algorithm is proposed. The data point has been allocated to its suitable class or cluster more remarkably. The Modified k-mean algorithm reduces the complex nature of the numerical computation, thereby retaining the effectiveness of applying the k-mean algorithm. Firstly, the data are extracted from the communications and movements record after tracking the park visitors over three days. Then, the original data will be visualised in a graphical format to help make a decision about how many numbers to consider as the K cluster. Secondly, the modified k-means algorithm on the clusters initial centre sensitivity will be performed. This will link similar segments and determine the occurrence of each data point in every segment group rather than partitioning the entire space into various segments and calculating the occurrence of the data point in every segment. Thirdly, result checking and a comparison with the normal k-mean will be performed. The investigation will focus on the movement of people around the park where the crime occurred, and how people move and communicate in the park, how patterns change, and the movement of groups and individuals. The experiments show that the modified K-means algorithm leads to a better way of observing the data to identify groups and their similarities and dissimilarities in the criminal dataset as a specific domain.

This paper describes the design, proof-of-concept simulations and laboratory test of an algorithm for controlling active front-end rectifiers to reduce voltage unbalance. Using inputs of RMS voltage, the rectifier controller allocates load unevenly on its 3 phases to compensate for voltage unbalance originating in the power supply network. Two variants of the algorithm are tested: first, using phase-neutral (P-N) voltage as input, second, using phase-phase (P-P) voltage. The control algorithm is described, and evaluated in simulations and laboratory tests. Two metrics for quantifying voltage unbalance are evaluated: one metric based on the maximum deviation of RMS P-N voltage from the average voltage and one metric based on negative sequence voltage. The tests show that controlling P-N voltage can in most cases eliminate the deviations of P-N voltage from the average voltage, but it does not reduce the negative sequence voltage. The controller that uses the P-P voltage as input eliminates the negative sequence voltage, and reduces P-N voltage deviations from the average to approximately half of their initial value. Current unbalance is reduced when the voltage unbalance is caused by asymmetrical loads, but it is increased in a scenario with unbalanced voltage sources. These results suggest that the optimal algorithm to reduce system unbalance depends on which system parameter is most important: RMS P-N voltage unbalance, negative sequence voltage, or current unbalance.

To achieve workflow retrieving and compliance checking based on behavior, it is necessary to identify the behavioral profiles of workflow models. Since the execution timing and the occurrence probability of tasks are crucial to choose appropriate workflows, it is significant to conduct exact quantitative analysis on the ordering relations by considering the time constraints and occurrence probabilities of tasks in workflow models. In this paper, the quantitative ordering relations are formally defined and an algorithm is proposed to efficiently compute the ordering relations with time and probability constraints based on complete firing sequences. Experiments show that the algorithm works well. Refereed/Peer-reviewed

Today's websites (applications) represent an essential part of nearly every business system, therefore it is unacceptable for them to be unavailable due to the ever-increasing competition on the global market. Consequently, such systems are becoming more and more complex to allow for their high availability. To achieve a higher system availability, a greater scalability is used in creating the so-called Web farms or Web clusters. This system requires much more computer power than the traditional solutions. Since such systems are very expensive and complex in nature, the question is how to obtain the best possible results with the least amount of investment. To achieve that, it is necessary to take a look at the component which contains the information on the request or traffic amount, and that is the HTTP/HTTPS load balancer. The system is based on several algorithms, however, there are no comprehensive analyses that indicate which algorithm to use, depending on the Web cluster and the expected amount of traffic. For this reason, this paper provides a detailed comparison of several frequently used algorithms in several different Web cluster scenarios, i.e. loads. Also, examples are given as to when to use a certain algorithm.

In this paper, resource allocation (RA) problem in heterogeneous Software Defined Network (SDN) with infrastructure sharing platform among multiple network service providers (NSPs) is studied. The considered problem is modeled as a reverse combinatorial auction (R-CA) game, which takes competitiveness and fairness of different NSPs into account. The heterogeneous RA associated with personal QoS requirement problem is optimized by maximizing the social welfare, which is demonstrated to be total system throughput. By exploiting the properties of iterative programming, the resulting non-convex Winner Determination Problem (WDP) is transformed into an equivalent convex optimization problem. The proposed R-CA game is strategy- proof and proved to be with low computational complexity. Simulation results illustrate that with SDN controller sharing environment, the proposed iterative ascending price Vickrey (IA-PV) algorithm converges fast and can obtain nearly optimal system throughput. It is also demonstrated to be robust with density changing, enable higher fairness and ensure individual profit among different NSPs. With the fairness guaranteed, this infrastructure sharing SDN platform can attract more NSPs to participate, in order to achieve more profit and cost reduction.

This paper presents a laboratory model of synchronous generator excitation system based on National Instruments cRIO real-time industrial controller. It was specially designed for development and verification of classical linear and modern nonlinear excitation control algorithms. Real-time Clarke and Park transformations were implemented on the FPGA for measurements of the generator load angle, voltages and currents in the generator dq-frame. Automatic voltage regulator (AVR) and power system stabilizer (PSS2A) were implemented and experimentally verified on an 83kVA synchronous generator. Tests of step changes for generator voltage and mechanical power references, and test of transmission line disconnection were conducted. Experimental results were compared to simulation results of the designed model in Matlab/Simulink.

Power systems are prone to cascading outages leading to large-scale blackouts. Intentional controlled islanding (ICI) is an effective corrective control action that limits the consequences of these catastrophic events by splitting the system into smaller sustainable islands. This paper proposes an ICI algorithm based on an exact Mixed Integer Linear Programming Formulation (MILP) that directly determines an islanding solution with minimal power-flow disruption for any given number of islands, while ensuring that each island contains only coherent generators. In addition, the proposed algorithm enables operators to constrain any transmission line to be excluded from the solution, allows the control of the size of islands and ensures their connectivity. To reduce the search space of the MILP and the overall complexity of the problem, a preprocessing procedure that finds the trees which connect the generators of each coherent group with the minimum number of nodes is also presented. The exact ICI algorithm is tested using dynamic models of the IEEE 39- and IEEE 118-Bus test systems. Multiple case studies are developed to demonstrate the effectiveness of the algorithm to different system conditions.

A batch resolution algorithm (BRA) is a channel access policy used by a group of nodes (the batch) that simultaneously generate a packet for a common receiver. The aim is to minimize the batch resolution interval (BRI), i.e., the time it takes for all nodes in the batch to successfully deliver their packet. Most of existing BRAs require immediate feedback after each packet transmission, and typically assume the feedback time is negligible. This conjecture, however, fails to apply in practical high rate wireless systems, so that the classical performance analysis of BRAs may be overoptimistic. In this paper we propose and analyze a novel BRA named Adaptive Batch Resolution Algorithm with Deferred Feedback (ABRADE), which waives the immediate feedback approach in favor of a deferred feedback method, based on a framed ALOHA access scheme. The frame length is optimized by using a dynamic programming technique in order to minimize the BRI, under the assumption that the batch size is known. Successively, we remove this assumption by coupling ABRADE with a batch size estimate module. The new algorithm, called ABRADE+, is compared against the best performing BRAs based on the immediate feedback paradigm, showing better performance both in case of partial and no prior knowledge of the batch multiplicity.

Tennis player silhouette extraction is a preliminary step fundamental for any behavior analysis processing. Automatic systems for the evaluation of player tactics, in terms of position in the court, postures during the game and types of strokes, are highly desired for coaches and training purposes. These systems require accurate segmentation of players in order to apply posture analysis and high level semantic analysis. Background subtraction algorithms have been largely used in sportive context when fixed cameras are used. In this paper an innovative background subtraction algorithm is presented, which has been adapted to the tennis context and allows high precision in player segmentation both for the completeness of the extracted silhouettes. The algorithm is able to achieve interactive frame rates with up to 30 frames per second, and is suitable for smart cameras embedding. Real experiments demonstrate that the proposed approach is suitable in tennis contexts.

The paper examines the problem of cancellation of direct signal, multipath and clutter echoes in passive bistatic radar (PBR). This problem is exacerbated as the transmitted waveform is not under control of the radar designer and the sidelobes of the ambiguity function can mask targets including those displaced in either (or both) range and Doppler from the disturbance. A novel multistage approach is developed for disturbance cancellation and target detection based on projections of the received signal in a subspace orthogonal to both the disturbance and previously detected targets. The resulting algorithm is shown to be effective against typical simulated scenarios with a limited number of stages, and a version with computational savings is also introduced. Finally its effectiveness is demonstrated with the application to real data acquired with an experimental VHF PBR system.

The significant growth in cloud computing has led to increasing number of cloud providers, each offering their service under different conditions – one might be more secure whilst another might be less expensive or more reliable. At the same time user applications have become more and more complex. Often, they consist of a diverse collection of software components, and need to handle variable workloads, which poses different requirements on the infrastructure. Therefore, many organisations are considering using a combination of different clouds to satisfy these needs. It raises, however, a non-trivial issue of how to select the best combination of clouds to meet the application requirements. This paper presents a novel algorithm to deploy workflow applications on federated clouds. First, we introduce an entropy-based method to quantify the most reliable workflow deployments. Second, we apply an extension of the Bell-LaPadula Multi-Level security model to address application security requirements. Finally, we optimise deployment in terms of its entropy and also its monetary cost, taking into account the cost of computing power, data storage and inter-cloud communication. We implemented our new approach and compared it against two existing scheduling algorithms: Extended Dynamic Constraint Algorithm (EDCA) and Extended Biobjective dynamic level scheduling (EBDLS). We show that our algorithm can find deployments that are of equivalent reliability but are less expensive and meet security requirements. We have validated our solution through a set of realistic scientific workflows, using well-known cloud simulation tools (WorkflowSim and DynamicCloudSim) and a realistic cloud based data analysis system (e-Science Central).

Cloud Radio Access Network (C-RAN) is a promising candidate for future mobile networks to sustain the exponentially increasing demand for data rate. The centralized architecture enables C-RAN to exploit multi-cell cooperation and interference management effectively. In C-RAN, one baseband unit (BBU) communicates with users through distributed Remote Radio Heads (RRHs) which are connected to the BBU via high capacity, low latency fronthaul links and perform ``soft" relaying. However, the architecture of C-RAN imposes a shortage of fronthaul bandwidth because raw In-phase/Quadrature-phase (I/Q) samples are exchanged between the RRHs and the BBU. In this paper, we leverage on advanced signal processing to improve the compression efficiency in fronthaul uplinks. Specifically, we propose a joint decoding algorithm at the BBU that exploits the correlation among the RRHs and jointly performs decompressing and decoding. An upper bound of the Block Error Rate (BLER) of the proposed algorithm is derived using pair-wise error probability analysis. Based on the BLER upper bound, we propose an adaptive compression scheme which minimizes the fronthaul transmission rate while satisfying a target quality of service constrain on the BLER. Our proposed adaptive compressor originates from practical scenarios in which most applications tolerate certain non-zero BLER thresholds.