A new method is developed to generate fuzzy rules from numerical data. This method consists of two algorithms: Algorithm 1 is used to identify structures of the given data set, that is, the optimal number of rules of system; Algorithm 2 is used to identify parameter of the used model. The former is belonged to unsupervised learning, and the latter is belonged to supervised learning. To identify parameters of fuzzy model, we developed a neural network which is referred to as Unsymmetrical Gaussian Function Network (UGFN). Unlike traditional fuzzy modelling methods, in the present method, a) the optimal number of rules (clusters) is determinde by input-output data pairs rather than by only output data as in sugeno's method, b) parameter identification of ghe present model is based on a like-RBF network rather than backpropagation algorithm. Our method is simple and effective because it integrates fuzzy logic with neural networks from basic network principles to neural architecture, thereby establishing an unifying framework for different fuzzy modelling methods such as one with cluster analysis or neural networks and so on.

Due to the discontinuity of packet based traffic, the user terminals in next generation mobile telecommunications systems will be equipped with sleep mode operation functions for power saving purpose. The sleep mode parameters should be appropriately configured so that power consumption can be sufficiently decreased while packet queue length and packet delay are restricted within a demanded level. This paper proposes an adaptive sleep mode parameter configuration scheme which is able to jointly optimize the inactivity timer and sleep period in response to the variation of user traffic arrival pattern. The optimization target of this scheme is to minimize mobile terminal power consumption while ensuring that the mean downlink packet queue length do not exceed a certain threshold. Results of computer simulations prove that, the presented approach perfectly manages packet queue length restriction, packet delay control and power saving in a wide range of user packet inter-arrival rates both in single- and dual-service scenarios.

In this letter we propose a new Whiteman generalized cyclotomic sequence of order 4. Meanwhile, we determine its linear complexity and minimal polynomial. The results show that this sequence possesses both high linear complexity and optimal balance on 1 s and 0 s, which may be attractive for cryptographic applications.

Bugs in industrial financial systems have not been extensively studied. To address this gap, we focused on the empirical study of bugs in three systems, PMS, β-Analyzer, and OrderPro. Results showed the 3 most common types of bugs in industrial financial systems to be internal interface (19.00%), algorithm/method (17.67%), and logic (15.00%).

In many wireless sensor applications, skyline monitoring queries that continuously retrieve the skyline objects as well as the complete set of nodes that reported them play an important role. This paper presents SKYMON, a novel energy-efficient monitoring approach. The basic idea is to prune nodes that cannot yield a skyline result at the sink, as indicated by their (error bounded) prediction values, to suppress unnecessary sensor updates. Every node is associated with a prediction model, which is maintained at both the node and the sink. Sensors check sensed data against model-predicted values and transmit prediction errors to the sink. A data representation scheme is then developed to calculate an approximate view of each node's reading based on prediction errors and prediction values, which facilitates safe node pruning at the sink. We also develop a piecewise linear prediction model to maximize the benefit of making the predictions. Our proposed approach returns the exact results, while deceasing the number of queried nodes and transferred data. Extensive simulation results show that SKYMON substantially outperforms the existing TAG-based approach and MINMAX approach in terms of energy consumption.

This letter provides an efficient massive multiple-input multiple-output (MIMO) detector based on quasi-newton methods to speed up the convergence performance under realistic scenarios, such as high user load and spatially correlated channels. The proposed method leverages the information of the Hessian matrix by merging Barzilai-Borwein method and Limited Memory-BFGS method. In addition, an efficient initial solution based on constellation mapping is proposed. The simulation results demonstrate that the proposed method diminishes performance loss to 0.7dB at the bit-error-rate of 10-2 at 128×32 antenna configuration with low complexity, which surpasses the state-of-the-art (SOTA) algorithms.

Cooperative relaying, while effective in mitigating fading effects, might reduce the overall network throughput since its overhead such as additional time slot and frequency band can be significant. In order to overcome this problem, this paper proposes a superposition coding based cooperative relay scheme to provide reliable transmission with little or no overhead. This scheme exploits the superimposed messages for users in the network to achieve the simultaneous transmission of two or more independent data streams. This scheme reduces the number of transmission phases to the same as that of conventional cooperative relay schemes. The symbol error performance of the proposed scheme is analyzed and simulated.

Based on the mutuality between arrival moments of uplink and downlink messages, this paper proposes a scheme that assigns different time-out thresholds for mobile terminal sleep mode operation according to the direction of the message just processed. Simulation results prove that, this approach can increase the power saving factor of a mobile terminal without degrading QoS.

This paper focuses on recovering from processor transient faults in pipelined multiprocessor systems. A pipelined machine may employ out of order execution and branch prediction techniques to increase performance, thus a precise computation state would not be available. We propose an efficient scheme to maintain the precise computation state in a pipelined machine. The goal of this paper is to implement checkpointing and rollback recovery utilizing the technique of precise interrupt in a pipelined system. Detailed analysis is included to demonstrate the effectiveness of this method.

In this paper, we propose a novel fault-tolerant multicast algorithm for n-dimensional wormhole routed hypercubes. The multicast algorithm will remain functional if the number of faulty nodes in an n-dimensional hypercube is less than n. Multicast is the delivery of the same message from one source node to an arbitrary number of destination nodes. Recently, wormhole routing has become one of the most popular switching techniques in new generation multicomputers. Previous researches have focused on fault-tolerant one-to-one routing algorithms for n-dimensional meshes. However, little research has been done on fault-tolerant one-to-many (multicast) routing algorithms due to the difficulty in achieving deadlock-free routing on faulty networks. We will develop such an algorithm for faulty hypercubes. Our approach is not based on adding physical or virtual channels to the network topology. Instead, we integrate several techniques such as partitioning of nodes, partitioning of channels, node label assignments, and dual-path multicast to achieve fault tolerance. Both theoretical analysis and simulation are performed to demonstrate the effectiveness of the proposed algorithm.

This paper investigates two issues of cellular engineering for cellular systems enhanced with two-hop fixed relay nodes (FRNs): spectrum partitioning and relay positioning, under the assumption of frequency reuse distance being equal to one. A channel-dependent spectrum partitioning scheme is proposed. According to this scheme, the ensemble mean of signal-to-interference-ratio on respective sets of links are taken into account to determine the bandwidths assigned to links connecting base station (BS) and FRNs, those connecting FRNs and mobile terminals (MTs) and those connecting BS and MTs. The proper FRN positioning is formulated as a constraint optimization problem, which tries to maximize the mean user data rate while at the same time ensures in probability 95% users being better served than in conventional cellular systems without relaying. It is demonstrated with computer simulations that FRN positioning has a strong impact on system performance. In addition, when FRNs can communicate with BS over line-of-sight channels the FRN enhanced cellular system with our proposed spectrum partitioning can remarkably outperform that with a known channel-borrowing based scheme and the conventional cellular systems without relaying. Simulation results also show that with proper FRN positioning the proposed spectrum partitioning scheme is robust against the unreliability of links connecting BS and FRNs.

A Hyper-Universal Switch Box (HUSB) [1]-[3] can yield a feasible (detailed) routing solution for any given routing requirement of multi-pin nets or multi-point connections of surrounding terminals. This flexible routing structure obviously possesses multiple potential applications for re-configurable systems such as FPGAs and communication switching networks [4],[5]. Based on the same decomposition theory developed in the design scheme of such powerful switching structure, a simple routing algorithm can also be developed. The router is exact in terms of its assured capability in finding a routing solution, and it is efficient due to the divide and conquer nature and simple mapping scheme for pre-analyzed routing patterns saved in data base.

A large-range switchable RF signal generator is demonstrated using a triple-wavelength fiber laser with uneven-frequency-spacing. Due to the birefringence characteristics of the triple-wavelength fiber laser, switchable dual-wavelength operation can be obtained by adjusting a polarization controller. Therefore, we can achieve a stable RF signals at microwave or millimeter-wave band.

In this letter, unlike the previous work in [2], the optimal power allocation in a non-orthogonal, amplify-and-forward (AF) relay-assisted transmission is investigated in the uplink. Here, the inter-user-interference among the signals from MTs and relays exists due to non-zero interference suppression factor (ISF), i.e., finite spreading factor. In this letter, we show that the optimal solution to achieve a 'max-min fairness' among mobile terminals can be alternatively obtained by solving its inverse problem. The impact of various ISFs as well as the Jain's fairness is investigated in comparison with the equal power allocation.

The fast evolving credential attacks have been a great security challenge to current password-based information systems. Recently, biometrics factors like facial, iris, or fingerprint that are difficult to forge rise as key elements for designing passwordless authentication. However, capturing and analyzing such factors usually require special devices, hindering their feasibility and practicality. To this end, we present WiASK, a device-free WiFi sensing enabled Authentication System exploring Keystroke dynamics. More specifically, WiASK captures keystrokes of a user typing a pre-defined easy-to-remember string leveraging the existing WiFi infrastructure. But instead of focusing on the string itself which are vulnerable to password attacks, WiASK interprets the way it is typed, i.e., keystroke dynamics, into user identity, based on the biologically validated correlation between them. We prototype WiASK on the low-cost off-the-shelf WiFi devices and verify its performance in three real environments. Empirical results show that WiASK achieves on average 93.7% authentication accuracy, 2.5% false accept rate, and 5.1% false reject rate.

Under random linear coding (RLC) scheme, we present a simple expression of the probability distribution p(D=K) for decoding delay D incurred by the lossy channel, where K is a positive integer. In contrast with the previous contribution, our focus is firstly on deriving the cumulative distribution function of the discrete random variable D over a perfect channel. One benefit of such dispose is that, from the overall viewpoint, computing the cumulative distribution function of delay D can be related with calculating the cardinalities of sets of some special matrices, so that the former can be obtained from the latter. Moreover, our expression of the probability distribution is an explicit form, and is valid for any number of packets M, freewill field size q and arbitrary channel loss rate ε.

Incorporating sensor nodes with data aggregation capability to transmit less data flow in wireless sensor networks could reduce the total energy consumption. This calls for the efficient and effective data-centric routing algorithm to facilitate this advantage. In the first part of this paper, we model the data-centric routing problem by rigorous mixed integer and linear mathematical formulation, where the objective function is to minimize the total transmission cost subject to multicast tree constraints. With the advancement of sensor network technology, sensor nodes with configurable transmission radius capability could further reduce energy consumption. The second part of this paper considers the transmission radius assignment of each sensor node and the data-centric routing assignment jointly. The objective function is to minimize the total power consumption together with consideration of construction of a data aggregation tree and sensor node transmission radius assignment. The solution approach is based on Lagrangean relaxation in conjunction with the novel optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms calculate better solution than other existing heuristics with improvement ratio up to 169% and 59% with respect to fixed transmission radius and configurable transmission radius for network with 300 random generated nodes.

In recent years, techniques such as multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) have been developed and combined in MIMO-OFDM systems to provide higher data rates. In addition, the system can be optimized by setting modulation and coding adaptively according to the channel conditions. The overall system performance depends on how accurately the system obtains the channel state information (CSI) and feeds it back to the transmitter. In this paper, we propose a signal-to-noise-ratio (SNR) estimation algorithm in which the preamble is known by both sides of the transceiver. Through simulations of several channel environments, we prove that our proposed algorithm is more accurate than traditional algorithms.

In this letter, we present an improved method for the independence test procedure in the convolutional multicast algorithm proposed by Erez and Feder. We employ the linear independence test vectors to check the independence of the partial encoding vectors in the main program of Erez's convolutional multicast algorithm. It turns out that compared with the previous approach of computing the determinants of the correlative matrices, carrying out the independence test vectors can reduce the computational complexity.

Sparse arrays can usually achieve larger array apertures than uniform linear arrays (ULA) with the same number of physical antennas. However, the conventional direction-of-arrival (DOA) estimation algorithms for sparse arrays usually require the spatial smoothing operation to recover the matrix rank which inevitably involves heavy computational complexity and leads to a reduction in the degrees-of-freedom (DOFs). In this paper, a low-complex DOA estimation algorithm by exploiting the discrete Fourier transform (DFT) is proposed. Firstly, the spatial spectrum of the virtual array constructed from the sparse array is established by exploiting the DFT operation. The initial DOA estimation can obtain directly by searching the peaks in the DFT spectrum. However, since the number of array antennas is finite, there exists spectrum power leakage which will cause the performance degradation. To further improve the angle resolution, an iterative process is developed to suppress the spectrum power leakage. Thus, the proposed algorithm does not require the spatial smoothing operation and the computational complexity is reduced effectively. In addition, due to the extention of DOF with the application of the sparse arrays, the proposed algorithm can resolve the underdetermined DOA estimation problems. The superiority of the proposed algorithm is demonstrated by simulation results.