Rough set theory is an important branch of data mining and granular computing, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, which extracts inconsistent objects from a traditional neighborhood. Firstly, a series of interesting properties are obtained for inconsistent neighborhoods. Specially, some properties generate new solutions to compute the quantities in neighborhood rough set. Then, a fast forward attribute reduction algorithm is proposed by applying the obtained properties. Experiments undertaken on twelve UCI datasets show that the proposed algorithm can get the same attribute reduction results as the existing algorithms in neighborhood rough set domain, and it runs much faster than the existing ones. This validates that employing inconsistent neighborhoods is advantageous in the applications of neighborhood rough set. The study would provide a new insight into neighborhood rough set theory.

In this paper, a study of a sufficient condition on the optimality of a decoded codeword of soft-decision decodings for binary linear codes is shown for a quantized case. A typical uniform 4-level quantizer for soft-decision decodings is employed for the analysis. Simulation results on the (64,42,8) Reed-Muller code indicates that the condition is effective for SN ratios at 3[dB] or higher for any iterative style optimum decodings.

Cloud computing, which enables users to enjoy various Internet services provided by data centers (DCs) at anytime and anywhere, has attracted much attention. In cloud computing, however, service quality degrades with user distance from the DC, which is unfair. In this study, we propose a bandwidth allocation scheme based on collectable information to improve fairness and link utilization in DC networks. We have confirmed the effectiveness of this approach through simulation evaluations.

Clustering is known to be an effective means of reducing energy dissipation and prolonging network lifetime in wireless sensor networks (WSNs). Recently, game theory has been used to search for optimal solutions to clustering problems. The residual energy of each node is vital to balance a WSN, but was not used in the previous game-theory-based studies when calculating the final probability of being a cluster head. Furthermore, the node payoffs have also not been expressed in terms of energy consumption. To address these issues, the final probability of being a cluster head is determined by both the equilibrium probability in a game and a node residual energy-dependent exponential function. In the process of computing the equilibrium probability, new payoff definitions related to energy consumption are adopted. In order to further reduce the energy consumption, an assistant method is proposed, in which the candidate nodes with the most residual energy in the close point pairs completely covered by other neighboring sensors are firstly selected and then transmit same sensing data to the corresponding cluster heads. In this paper, we propose an efficient energy-aware clustering protocol based on game theory for WSNs. Although only game-based method can perform well in this paper, the protocol of the cooperation with both two methods exceeds previous by a big margin in terms of network lifetime in a series of experiments.

The existing methods to estimate satellite attitude by using radar cross section (RCS) sequence suffer from problems such as low precision, computation complexity, etc. To overcome these problems, a novel model of satellite attitude estimation by the local maximum points of the RCS sequence is established and can reduce the computational time by downscaling the dimension of the feature vector. Moreover, a particle swarm optimization method is adopted to improve efficiency of computation. Numerical simulations show that the proposed method is robust and efficient.

We propose a policy UCB-SC for budgeted multi-armed bandits. The policy is a variant of recently proposed KL-UCB-SC. Unlike KL-UCB-SC, which is computationally prohibitive, UCB-SC runs very fast while keeping KL-UCB-SC's asymptotical optimality when reward and cost distributions are Bernoulli with means around 0.5, which are verified both theoretically and empirically.

Irreversible thermal paints or temperature sensitive paints are a kind of special temperature sensor which can indicate the temperature grad by judging the color change and is widely used for off-line temperature measurement during aero engine test. Unfortunately, the hot gases flow within the engine during measuring always make the paint color degraded, which means a serious saturation reduction and contrast loss of the paint colors. This phenomenon makes it more difficult to interpret the thermal paint test results. Present contrast enhancement algorithms can significantly increase the image contrast but can't protect the hue feature of the paint images effectively, which always cause color shift. In this paper, we propose a color restoration method for thermal paint image. This method utilizes the atmospheric scattering model to restore the lost contrast and saturation information, so that the hue can be protected and the temperature can be precisely interpreted based on the image.

Erasure codes have been considered as one of the most promising techniques for data reliability enhancement and storage efficiency in modern distributed storage systems. However, erasure codes often suffer from a time-consuming coding process which makes them nearly impractical. The opportunity to solve this problem probably rely on the parallelization of erasure-code-based application on the modern multi-/many-core processors to fully take advantage of the adequate hardware resources on those platforms. However, the complicated data allocation and limited I/O throughput pose a great challenge on the parallelization. To address this challenge, we propose a general multi-threaded parallel coding approach in this work. The approach consists of a general multi-threaded parallel coding model named as MTPerasure, and two detailed parallel coding algorithms, named as sdaParallel and ddaParallel, respectively, adapting to different I/O circumstances. MTPerasure is a general parallel coding model focusing on the high level data allocation, and it is applicable for all erasure codes and can be implemented without any modifications of the low level coding algorithms. The sdaParallel divides the data into several parts and the data parts are allocated to different threads statically in order to eliminate synchronization latency among multiple threads, which improves the parallel coding performance under the dummy I/O mode. The ddaParallel employs two threads to execute the I/O reading and writing on the basis of small pieces independently, which increases the I/O throughput. Furthermore, the data pieces are assigned to the coding thread dynamically. A special thread scheduling algorithm is also proposed to reduce thread migration latency. To evaluate our proposal, we parallelize the popular open source library jerasure based on our approach. And a detailed performance comparison with the original sequential coding program indicates that the proposed parallel approach outperforms the original sequential program by an extraordinary speedups from 1.4x up to 7x, and achieves better utilization of the computation and I/O resources.

We present a design approach and analysis of a multimode stripline resonator (MSR). Furthermore, a bandpass filter (BPF) using a single MSR is presented. MSR has three fundamental modes, incorporating two transmission resonance modes and one quasi-lumped component (LC) resonance mode. The resonant frequencies and unloaded Q factors of those modes are theoretically derived by transmission modes and LC modes. By our equations, it is also explained that the resonant frequencies can be shown to be easily handled by an increase and decrease in the number of via holes. These frequencies calculated by our equations are in good agreement with those of 3-D simulations and measurements. Finally, design approach of a narrow bandpass filter using our resonator is introduced. Good agreement between measured and computed result is obtained.

As the emergence and the thriving development of social networks, a huge number of short texts are accumulated and need to be processed. Inferring latent topics of collected short texts is an essential task for understanding its hidden structure and predicting new contents. A biterm topic model (BTM) was recently proposed for short texts to overcome the sparseness of document-level word co-occurrences by directly modeling the generation process of word pairs. Stochastic inference algorithms based on collapsed Gibbs sampling (CGS) and collapsed variational inference have been proposed for BTM. However, they either require large computational complexity, or rely on very crude estimation that does not preserve sufficient statistics. In this work, we develop a stochastic divergence minimization (SDM) inference algorithm for BTM to achieve better predictive likelihood in a scalable way. Experiments show that SDM-BTM trained by 30% data outperforms the best existing algorithm trained by full data.

Software-Defined Networking (SDN) can be applied for managing application flows dynamically by a logically centralized SDN controller and SDN switches. Because one SDN switch can support just a few thousand forwarding rule installations per second, it is a barrier to dynamic and scalable application flow management. For this reason, it is essential to reduce the number of application flows if they are to be successfully managed. Nowadays, since much attention has been paid to developing a network service that reduces application delay, the allowable delay of application flows has become an important factor. However, there has been no work on minimizing the number of flows while satisfying end-to-end delay of flows. In this paper, we propose a method that can aggregate flows and minimize the number flows in a network while ensuring all flows satisfy their allowable delay in accordance with QoS or SLA. Since the problem is classified as NP-hard, we propose a heuristic algorithm. We compared the aggregation effect of the proposed method, simple aggregation method and optimal solution by simulation. In addition, we clarify the characteristics of the proposed method by performing simulations with various parameter settings. The results show that the proposed method decreases the number of rules than comparative aggregation method and has very shorter computational time than optimal solution.

Bent functions have been applied to cryptography, spread spectrum, coding theory, and combinatorial design. Permutations play an important role in the design of cryptographic transformations such as block ciphers, hash functions and stream ciphers. By using the Kronecker product this paper presents a general recursive construction method of permutations over finite field. As applications of our method, several infinite classes of permutations are obtained. By means of the permutations obtained and M-M functions we construct several infinite families of bent functions.

In this letter, based on orthogonal Golay sequence sets and orthogonal matrices, general constructions of zero correlation zone (ZCZ) aperiodic complementary sequence (ZACS) sets are proposed. The resultant ZACSs have column sequence peak-to-mean envelop power ratio (PMEPR) of at most 2, and the parameters of the sequence sets are optimal with respect to the theoretical bound. The novel ZACS sets are suitable for approximately synchronized multi-carrier CDMA (MC-CDMA) communication systems.

In this letter, a novel on-orbit estimation and calibration method of GPS antenna geometry offsets for attitude determination of LEO satellites is proposed. Both baseline vectors in the NED coordinate system are achieved epoch-by-epoch firstly. Then multiple epochs' baseline vectors are united to compute all the offsets via an UKF for a certain long time. After on-orbit estimation and calibration, instantaneous and accurate attitude can be achieved. Numerical results show that the proposed method can obtain the offsets of each baseline in all directions with high accuracy estimation and small STDs, and effective attitudes can be achieved after antenna geometry calibration using the estimated offsets. The high accuracy give the proposed scheme a strong practical-oriented ability.

The 5G mobile network environment has been studied and developed, and the concept of a vEPC (Virtualized Evolved Packet Core) has been introduced as a framework for Network Functions Virtualization (NFV). Machine-to-Machine (M2M) communications in 5G networks require much faster response than are possible in 4G networks. However, if both the control plane (C-plane) and the data plane (D-plane) functions of the EPC are migrated into a single vEPC server, M2M devices and other user equipments (UEs) share the same resources. To accommodate delay-sensitive M2M sessions in vEPC networks, not only signaling performance on the C-plane but also packet processing performance on the D-plane must be optimized. In this paper, we propose a method for optimizing resource assignment of C-plane and D-plane Virtualized Network Functions (VNFs) in a vEPC server, called the vEPC-ORA method. We distinguish the communications of M2M devices and smartphones and model the vEPC server by using queueing theory. Numerical analysis of optimal resource assignment shows that our proposed method minimizes the blocking rates of M2M sessions and smartphone sessions. We also confirmed that the mean packet processing time is kept within the allowable delay for each communication type, as long as the vEPC server has enough VM resources. Moreover, we study a resource granularity effect on the optimal resource assignment. Numerical analysis under a fixed number of hardware resources of MME and S/P-GW is done for various resource granularities of the vEPC server. The evaluation results of numerical analyses showed that the vEPC-ORA method derives the optimal resource assignment in practical calculation times.

To reduce the backbone video traffic generated by video surveillance, we propose an intelligent video surveillance system that offers multi-modal sensor-based event detection and event-driven video rate adaptation. Our proposed system can detect pedestrian existence and movements in the monitoring area by using multi-modal sensors (camera, laser scanner and infrared distance sensor) and control surveillance video quality according to the detected events. We evaluate event detection accuracy and video traffic volume in the experiment scenarios where up to six pedestrians pass through and/or stop at the monitoring area. Evaluation results conclude that our system can significantly reduce video traffic while ensuring high-quality surveillance.

This paper proposes a technique for broadcasting control of Smart Air Conditioners (SACs) by implementing the Narrowband Powerline Communications (NB-PLC) technology. The Master Control Room (MCR) generates commands for SACs operation that are sent over power lines by using the communication protocol known as Smart Energy Profile 1.0 (SEP 1.0). The proposed system can also offers features like Demand Response (DR) to facilitate the Smart Grid (SG). Field measurements elucidate the performance of NB-PLC systems. Measurements are carried out by injecting and receiving the NB-PLC signal over low voltage (LV) power lines, medium voltage (MV) power lines and across transformers. A hybrid communication system, NB-PLC integrated with Optical Fiber Network (OFN), is developed for the automation of Distribution System (DS) of Lahore Electric Supply Company (LESCO). The developed system was tested for the DR program in order to support the Smart Air Conditioning system within Lahore. It is suggested that by adjusting the commercial SACs parameters, the task of energy conservation can be achieved by optimizing the peak load curves for greater efficiency. Moreover, data of various types of appliances can also be communicated to the MCR for the purpose of demand side management.

This paper presents an approximated log-likelihood ratio calculation scheme with bit shifts and summations. Our previous work yielded a metric calculation scheme that replaces multiplications with bit shifts and summations in the selection of candidate signal points for joint maximum likelihood detection (MLD). Log-likelihood ratio calculation for turbo decoding generally uses multiplications and by replacing them with bit shifts and summations it is possible to reduce the numbers of logic operations under specific transmission parameters. In this paper, an approximated log-likelihood ratio calculation scheme that substitutes bit shifts and summations for multiplications is proposed. In the proposed scheme, additions are used only for higher-order bits. Numerical results obtained through computer simulation show that this scheme can eliminate multiplications in turbo decoding at the cost of just 0.2dB performance degradation at a BER of 10-4.

This paper introduces robust optimization models for minimization of the network congestion ratio that can handle the fluctuation in traffic demands between nodes. The simplest and widely used model to minimize the congestion ratio, called the pipe model, is based on precisely specified traffic demands. However, in practice, network operators are often unable to estimate exact traffic demands as they can fluctuate due to unpredictable factors. To overcome this weakness, we apply robust optimization to the problem of minimizing the network congestion ratio. First, we review existing models as robust counterparts of certain uncertainty sets. Then we consider robust optimization assuming ellipsoidal uncertainty sets, and derive a tractable optimization problem in the form of second-order cone programming (SOCP). Furthermore, we take uncertainty sets to be the intersection of ellipsoid and polyhedral sets, and considering the mirror subproblems inherent in the models, obtain tractable optimization problems, again in SOCP form. Compared to the previous model that assumes an error interval on each coordinate, our models have the advantage of being able to cope with the total amount of errors by setting a parameter that determines the volume of the ellipsoid. We perform numerical experiments to compare our SOCP models with the existing models which are formulated as linear programming problems. The results demonstrate the relevance of our models in terms of congestion ratio and computation time.

The centralized controller of SDN enables a global topology view of the underlying network. It is possible for the SDN controller to achieve globally optimized resource composition and utilization, including optimized end-to-end paths. Currently, resource composition in SDN arena is usually conducted in an imperative manner where composition logics are explicitly specified in high level programming languages. It requires strong programming and OpenFlow backgrounds. This paper proposes declarative path composition, namely Compass, which offers a human-friendly user interface similar to natural language. Borrowing methodologies from Semantic Web, Compass models and stores SDN resources using OWL and RDF, respectively, to foster the virtualized and unified management of the network resources regardless of the concrete controller platform. Besides, path composition is conducted in a declarative manner where the user merely specifies the composition goal in the SPARQL query language instead of explicitly specifying concrete composition details in programming languages. Composed paths are also reused based on similarity matching, to reduce the chance of time-consuming path composition. The experiment results reflect the applicability of Compass in path composition and reuse.