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.

In this paper, a new and an accurate symbol error probability's analytical model of Rectangular Quadrature Amplitude Modulation in α-µ fading channel is presented for single-user single-input multi-output environment, which can be easily extended to generalized fading channels. The maximal-ratio combining technique is utilized at the receiving end and unified moment generating functions are used to derivate the results. The fading mediums considered are independent and non-identical. The mathematical model presented is applicable for slow and frequency non-selective fading channels only. The final expression is presented in terms of Meijer G-function; it contains single integrals with finite limits to evaluate the mathematical expressions with numerical techniques. The beauty of the model will help evaluate symbol error probability of rectangular quadrature amplitude modulation with spatial diversity over various fading mediums not addressed in this article. To check for the validity of derived analytical expressions, comparison is made between theoretical and simulation results at the end.

Efficient optic disc (OD) segmentation plays a significant role in retinal image analysis and retinal disease screening. In this paper, we present a full-automatic segmentation approach called double boundary extraction for the OD segmentation. The proposed approach consists of the following two stages: first, we utilize an unsupervised learning technology and statistical method based on OD boundary information to obtain the initial contour adaptively. Second, the final optic disc boundary is extracted using the proposed LSO model. The performance of the proposed method is tested on the public DIARETDB1 database and the experimental results demonstrate the effectiveness and advantage of the proposed method.

Device-to-device (D2D) networks are expected to play a number of roles, such as increasing frequency spectrum efficiency and improving throughput at hot-spots. In this paper, our interest is on the potential of D2D on reducing delivery latency. To enable fast D2D network forming, quick device discovery is essential. For quickening device discovery, we propose a method of defining and using common channel and group channels so as to avoid the channel scan uncertainty faced by the conventional method. Rules for using the common channel and group channels are designed. We evaluate and compare the discovery performance of the proposed method with conventional method by using the superframe structure defined in IEEE 802.15.8 and a general discovery procedure. IEEE 802.15.8 is a standard under development for fully distributed D2D communications. A Netlogo simulator is used to perform step by step MAC simulations. The simulation results verify the effectiveness of the proposed method.

Human action recognition in videos draws huge research interests in computer vision. The Bag-of-Word model is quite commonly used to obtain the video level representations, however, BoW model roughly assigns each feature vector to its nearest visual word and the collection of unordered words ignores the interest points' spatial information, inevitably causing nontrivial quantization errors and impairing improvements on classification rates. To address these drawbacks, we propose an approach for action recognition by encoding spatio-temporal log Euclidean covariance matrix (ST-LECM) features within the low-rank and sparse representation framework. Motivated by low rank matrix recovery, local descriptors in a spatial temporal neighborhood have similar representation and should be approximately low rank. The learned coefficients can not only capture the global data structures, but also preserve consistent. Experimental results showed that the proposed approach yields excellent recognition performance on synthetic video datasets and are robust to action variability, view variations and partial occlusion.

We present a GPU (graphics processing unit) accelerated stochastic algorithm implementation for simulating biochemical reaction networks using the latest NVidia architecture. To effectively utilize the massive parallelism offered by the NVidia Pascal hardware, we apply a set of performance tuning methods and guidelines such as exploiting the architecture's memory hierarchy in our algorithm implementation. Based on our experimentation results as well as comparative analysis using CPU-based implementations, we report our initial experiences on the performance of modern GPUs in the context of scientific computing.

In this paper, we study a variant of the MINIMUM DOMINATING SET problem. Given an unweighted undirected graph G=(V,E) of n=|V| vertices, the goal of the MINIMUM SINGLE DOMINATING CYCLE problem (MinSDC) is to find a single shortest cycle which dominates all vertices, i.e., a cycle C such that for the set V(C) of vertices in C and the set N(V(C)) of neighbor vertices of C, V(G)=V(C)∪N(V(C)) and |V(C)| is minimum over all dominating cycles in G [6], [17], [24]. In this paper we consider the (in)approximability of MinSDC if input graphs are restricted to some special classes of graphs. We first show that MinSDC is still NP-hard to approximate even when restricted to planar, bipartite, chordal, or r-regular (r≥3). Then, we show the (lnn+1)-approximability and the (1-ε)lnn-inapproximability of MinSDC on split graphs under P≠NP. Furthermore, we explicitly design a linear-time algorithm to solve MinSDC for graphs with bounded treewidth and estimate the hidden constant factor of its running time-bound.

This letter deals with an audio declipping problem and proposes a multiple matrix rank minimization approach. We assume that short-time audio signals satisfy the autoregressive (AR) model and formulate the declipping problem as a multiple matrix rank minimization problem. To solve this problem, an iterative algorithm is provided based on the iterative partial matrix shrinkage (IPMS) algorithm. Numerical examples show its efficiency.

A cograph (complement reducible graph) is a graph which can be generated by disjoint union and complement operations on graphs, starting with a single vertex graph. Cographs arise in many areas of computer science and are studied extensively. With the goal of developing an effective data mining method for graph structured data, in this paper we introduce a graph pattern expression, called a cograph pattern, which is a special type of cograph having structured variables. Firstly, we show that a problem whether or not a given cograph pattern g matches a given cograph G is NP-complete. From this result, we consider the polynomial time learnability of cograph pattern languages defined by cograph patterns having variables labeled with mutually different labels, called linear cograph patterns. Secondly, we present a polynomial time matching algorithm for linear cograph patterns. Next, we give a polynomial time algorithm for obtaining a minimally generalized linear cograph pattern which explains given positive data. Finally, we show that the class of linear cograph pattern languages is polynomial time inductively inferable from positive data.

The present study considers an action-based person identification problem, in which an input action sequence consists of 3D skeletal data from multiple frames. Unlike previous approaches, the type of action is not pre-defined in this work, which requires the subject classifier to possess cross-action generalization capabilities. To achieve that, we present a novel pose-based Hough forest framework, in which each per-frame pose feature casts a probabilistic vote to the Hough space. Pose distribution is estimated from training data and then used to compute the reliability of the vote to deal with the unseen poses in the test action sequence. Experimental results with various real datasets demonstrate that the proposed method provides effective person identification results especially for the challenging cross-action person identification setting.

Network virtualization and slicing technologies create opportunity for infrastructure-less virtual network operators (VNOs) to enter the market anytime and provide diverse services. Multiple VNOs compete to provide the same kinds of services to end users (EUs). VNOs lease virtual resources from the infrastructure provider (InP) and sell services to the EUs by using the leased resources. The difference between the selling and leasing is the gross profit for the VNOs. A VNO that leases resources without precise knowledge of future demand, may not consume all the leased resources through service offers to EUs. Consequently, the VNO experiences loss and resources remain unused. In order to improve resource utilization and ensure that new entrant VNOs do not face losses, proper estimation of initial resource demand is important. In this paper, we propose a Bayesian game with Cournot oligopoly model to properly estimate the optimal initial resource demands for multiple entrant competing VNOs (players) with the objective of maximizing the expected profit for each VNO. The VNOs offer the same kinds of services to EUs with different qualities (player's type), which are public information. The exact service quality with which a VNO competes in the market is private information. Therefore, a VNO assumes the type of its opponent VNOs with certain probability. We derive the Bayesian Nash equilibrium (BNE) of the presented game and evaluate numerically the effect of service qualities and prices on the expected profit and market share of the VNOs.

IoT (Internet of Things) services are emerging and the bandwidth requirements for rich media communication services are increasing exponentially. We propose a virtual edge architecture comprising computation resource management layers and path bandwidth management layers for easy addition and reallocation of new service node functions. These functions are performed by the Virtualized Network Function (VNF), which accommodates terminals covering a corresponding access node to realize fast VNF migration. To increase network size for IoT traffic, VNF migration is limited to the VNF that contains the active terminals, which leads to a 20% reduction in the computation of VNF migration. Fast dynamic bandwidth allocation for dynamic bandwidth paths is realized by proposed Hierarchical Time Slot Allocation of Optical Layer 2 Switch Network, which attain the minimum calculation time of less than 1/100.

In this paper, we define a wireless sensor network with multiple types of sensors as a wireless heterogeneous sensor network (WHSN), and propose an efficient query dissemination scheme (EDT) in the WHSN. The EDT based on total dominant pruning can forward queries to only the nodes with data requested by the user, thereby reducing unnecessary packet transmission. We show that the EDT is suitable for the WHSN environment through a variety of simulations.

The separating redundancy is an important concept in the analysis of the error-and-erasure decoding of a linear block code using a parity-check matrix of the code. In this letter, we derive new constructive upper bounds on the second separating redundancies of low-density parity-check (LDPC) codes constructed from projective and Euclidean planes over the field Fq with q even.

The integration of VANETs with Internet is required if vehicles are to access IP-based applications. A vehicle must have an IP address, and the IP mobility service should be supported during the movement of the vehicle. VANET standards such as WAVE or C-ITS use IPv6 address auto configuration to allocate an IP address to a vehicle. In C-ITS, NEMO-BS is used to support IP mobility. The vehicle moves rapidly, so reallocation of IP address as well as binding update occurs frequently. The vehicle' communication, however, may be disrupted for a considerable amount of time, and the packet loss occurs during these events. Also, the finding of the home address of the peer vehicle is not a trivial matter. We propose a network based identifier locator separation scheme for VANETs. The scheme uses a vehicle identity based address generation scheme. It eliminates the frequent address reallocation and simplifies the finding of the peer vehicle IP address. In the scheme, a network entity tracks the vehicles in its coverage and the vehicles share the IP address of the network entity for their locators. The network entity manages the mapping between the vehicle's identifier and its IP address. The scheme excludes the vehicles from the mobility procedure, so a vehicle needs only the standard IPv6 protocol stack, and mobility signaling does not occur on the wireless link. The scheme also supports seamlessness, so packet loss is mitigated. The results of a simulation show that the vehicles experience seamless packet delivery.

This paper proposes a sequentially iterative equalizer based on Kalman filtering and smoothing (SIEKFS) for multiple-input multiple-output (MIMO) systems under frequency selective fading channels. In the proposed SIEKFS, an iteration consists of sequentially executed subiterations, and each subiteration performs equalization and detection procedures of the symbols transmitted from a specific transmit antenna. During this subiteration, all available observations for the transmission block are utilized in the equalization procedures. Furthermore, the entire soft estimate of the desired symbols to be detected does not participate in the equalization procedures of the desired symbols, i.e., the proposed SIEKFS performs input-by-input equalization procedures for a priori information nulling. Therefore, compared with the original iterative equalizer based on Kalman filtering and smoothing, which performs symbol-by-symbol equalization procedures, the proposed SIEKFS can also perform iterative equalization based on the Kalman framework and turbo principle, with a significant reduction in computation complexity. Simulation results verify that the proposed SIEKFS achieves suboptimum error performance as the size of the antenna configuration and the number of iterations increase.

Given the worldwide proliferation of cyberattacks, it is imperative that cybersecurity education and training are addressed in a timely manner. These activities typically require trainees to do hands-on practice in order to consolidate and improve their skills, for which purpose training environments called cyber ranges are used. In this paper we present an open-source system named CyRIS (Cyber Range Instantiation System) that supports this endeavor by fully automating the training environment setup, thus making it possible for any organization to conduct more numerous and variate training activities. CyRIS uses a text-based representation in YAML format to describe the characteristics of the training environment, including both environment setup and security content generation. Based on this description, CyRIS automatically creates the corresponding cyber range instances on a computer and network infrastructure, for simultaneous use by multiple trainees. We have evaluated CyRIS in various realistic scenarios, and our results demonstrate its suitability for cybersecurity education and training, both in terms of features and performance, including for large-scale training sessions with hundreds of participants.

Two wideband circularly polarized (CP) equilateral triangular slot (ETS) antennas are proposed for communication system and the Global Navigation Satellite System (GNSS) Radio Occultation (RO) sensor of the GAIA-I microsatellite. These wide slot antennas use the ring slot antenna CP generation method due to their shape. The compact antennas employ truncated corners, grounded equilateral triangular perturbation patch and branched feed line to create CP radiation. A 3-dB axial ratio bandwidth (ARBW) enhancement is achieved by inserting a pair of slits into the ETS. A parametric study on the influence of those shape modifications in reflection coefficient and axial ratio is presented. An ETS antenna for communication system of the GAIA-I is fabricated and measured, which is shown to agree well with its simulated performance by providing CP fractional bandwidth of 52%. An ETS antenna designed for the GNSS RO sensor of GAIA-I delivers 3-dB ARBW of 41.6%. The ETS antenna offers uni-directional radiation by mounting a 3D printed truncated cone reflector underneath which also enhances antenna gain.

We have proposed a fish-farm monitoring system. In our system, the transmission range of acoustic waves from sensors attached to the undersides of the fish is not omnidirectional because of obstruction from the bodies of the fish. In addition, energy-efficient control is highly important in our system to avoid the need to replace the batteries. In this letter, we propose a data-gathering method for fish-farm monitoring without the use of control packets so that energy-efficient control is possible. Instead, our method uses the transmission-range volume as calculated from the location of the sensor node to determine the timing of packet transmission. Through simulation evaluations, we show that the data-gathering performance of our proposed method is better than that of comparative methods.

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.