Recently the word-based stream ciphers have been the subject of a considerable amount of research. The theory of such stream ciphers requires the study of the complexity of a multisequence. Let S1, S2, . . . , Sm be m N-ary sequences of period T, i.e., a multisequence. The relationship between the joint N-adic complexity and the number of the nonzero columns of the generalized Fourier transform for the N-ary multisequence is determined which generalizes the well-known result about the joint linear complexity and the generalized Fourier transform for a multisequence to the case of the joint N-adic complexity.

We present a new lower bound on the number of gates in reversible logic circuits that represent a given reversible logic function, in which the circuits are assumed to consist of general Toffoli gates and have no redundant input/output lines. We make a theoretical comparison of lower bounds, and prove that the proposed bound is better than the previous one. Moreover, experimental results for lower bounds on randomly-generated reversible logic functions and reversible benchmarks are given. The results also demonstrate that the proposed lower bound is better than the former one.

A novel Two-dimensional Fuzzy Discriminant Locality Preserving Projections (2D-FDLPP) algorithm is proposed for learning effective subspace of two-dimensional images. The 2D-FDLPP algorithm is derived from the Two-dimensional Locality Preserving Projections (2D-LPP) by exploiting both fuzzy and discriminant properties. 2D-FDLPP algorithm preserves the relationship degree of each sample belonging to given classes with fuzzy k-nearest neighbor classifier. Also, it introduces between-class scatter constrain and label information into 2D-LPP algorithm. 2D-FDLPP algorithm finds the subspace which can best discriminate different pattern classes and weakens the environment factors according to soft assignment method. Therefore, 2D-FDLPP algorithm has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. Experiments are conducted on the MNIST database for handwritten image classification, the JAFFE database and Cohn-Kanade database for facial expression recognition and the ORL database for face recognition. Experimental results reported the effectiveness of our proposed algorithm.

For two-dimensional IIR digital filters described by the Fornasini-Marchesini second model, the problem of jointly optimizing high-order error feedback and realization to minimize the effects of roundoff noise at the filter output subject to l2-scaling constraints is investigated. The problem at hand is converted into an unconstrained optimization problem by using linear-algebraic techniques. The unconstrained optimization problem is then solved iteratively by applying an efficient quasi-Newton algorithm with closed-form formulas for key gradient evaluation. Finally, a numerical example is presented to illustrate the validity and effectiveness of the proposed technique.

Wide area virtualization of wireless transceivers by centrally managed software radio systems is a way to efficiently share the resources for supporting a variety of wireless protocols. In order to enable wide-area virtualization of wireless transceivers, the authors have developed a mechanism to deliver the radio space information which is quantized broadband radio wave information including the radio signals to the transceivers. Delivery mechanism consists of a distribution server which distributes radio space corresponding to the request of the client such as the center frequency and the bandwidth and a client which uses the radio space information. Accumulation of the distribution servers which deliver radio space information simultaneously to a large number of clients will contribute to build an infrastructure for any clients ubiquitously distributed over the globe. In this paper, scale-out architecture of a distribution server is proposed to deliver unlimitedly broadband radio space information to unlimited number of clients. Experimental implementation indicates the architecture to be a scale-out solution, while the number of clients is restricted by the computer resources of the distribution server. The band pass filter processing for individual client in the distribution server consumes the dominant part of the processing power, and the number of CPU cores is the upper limit of clients supportable for the distribution server in the current operating system implementation. The logical increase of the number of CPU cores by hardware multithreading does not contribute to relax this limit. We also discuss the guidance architecture or building server derived from these conclusions.

An accurate time-of-arrival (TOA) estimation method for isolated pulses positioning system is proposed in this paper. The method is based on a multi-level crossing timing (MCT) digitizer and least square (LS) criterion, namely LS-MCT method, in which TOA of the received signal is directly described as a parameterized combination of a set of MCT samples of the leading and trailing edges of the signal. The LS-MCT method performs a receiver training process, in which a GPS synchronized training pulse generator (TPG) is used to obtain training data and determine the parameters of the TOA combination. The LS method is then used to optimize the combination parameters with a minimization criterion. The proposed method is compared to the conventional TOA estimation methods such as leading edge level crossing discriminator (LCD), adaptive thresholding (ATH), and signal peak detection (PD) methods. Simulation results show that the proposed algorithm leads to lower sensitivity to signal-to-noise ratio (SNR) and attains better TOA estimation accuracy than available TOA methods.

The Piece in Hand method is a security enhancement technique for Multivariate Public Key Cryptosystems (MPKCs). Since 2004, many types of this method have been proposed. In this paper, we consider the 2-layer nonlinear Piece in Hand method as proposed by Tsuji et al. in 2009. The key point of this method is to introduce an invertible quadratic polynomial map on the plaintext variables to add perturbation to the original MPKC. An additional quadratic map allows the owner of the secret key to remove this perturbation from the system. By our analysis, we find that the security of the enhanced scheme depends mainly on the structure of the quadratic polynomials of this auxiliary map. The two examples proposed by Tsuji et al. for this map can not resist the Linearization Equations attack. Given a valid ciphertext, we can easily get a public key which is equivalent to that of the underlying MPKC. If there exists an algorithm that can recover the plaintext corresponding to a valid ciphertext of the underlying MPKC, we can construct an algorithm that can recover the plaintext corresponding to a valid ciphertext of the enhanced MPKC.

Most of the previous work on power optimization regarded the capacity of battery power as an ideal constant value. In fact, experiments showed that 30% of the total battery capacity was wasted by improper discharge pattern [1]. In this letter, a battery-aware task scheduling protocol which harnesses one of the typical characteristics of batteries, i.e., battery recovery, is proposed to extend usage time for smart phones. The key idea is to adjust the working schedule of the components in smart phones for more energy recovering. Experiments show that when the proposed protocol is applied in an online music application, as much as 9% lifespan extension for batteries can be obtained.

This paper studies an adaptive high gain observer design for nonlinear systems which have lower triangular nonlinearity with Lipschitz coefficient, depending on the control input. Because the gain of the proposed observer is tuned automatically by a simple update law, our design approach doesn't need any information about the Lipschitz constant. Also, it is shown that under some assumptions, the dynamic gain of the proposed observer is bounded and its estimation error converges to zero asymptotically. Finally, a numerical example is given to verify the effectiveness of our design approach.

In the optimization of decision diagrams, variable reordering approaches are often used to minimize the number of nodes. However, such approaches are less effective for analysis of multi-state systems given by monotone structure functions. Thus, in this paper, we propose algorithms to minimize the number of edges in an edge-valued multi-valued decision diagram (EVMDD) for fast analysis of multi-state systems. The proposed algorithms minimize the number of edges by grouping multi-valued variables into larger-valued variables. By grouping multi-valued variables, we can reduce the number of nodes as well. To show the effectiveness of the proposed algorithms, we compare the proposed algorithms with conventional optimization algorithms based on a variable reordering approach. Experimental results show that the proposed algorithms reduce the number of edges by up to 15% and the number of nodes by up to 47%, compared to the conventional ones. This results in a speed-up of the analysis of multi-state systems by about three times.

The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.

When deploying a new mobile technology such as LTE, it is crucial to identify the factors that affect the radio network in terms of capacity and quality of service. In this context, network coverage is arguably the single most influential factor. This work presents a metaheuristic-optimization approach to automatically adapt the signal losses due to clutter, based on a set of field measurements. The optimization procedure is performed regionally, enabling the calculation of accurate radio-propagation predictions. The evaluation of the proposed approach is carried out on three different regions in Slovenia, where Telekom Slovenije, d.d., provides LTE coverage. The results show radio-propagation predictions of improved quality and the benefits of the presented approach over manual methods, both in terms of problem size and solution accuracy.

We propose a speaker adaptation method based on the probabilistic principal component analysis (PPCA) of acoustic models. We define a training matrix which is represented in a two-way array and decompose the training models by PPCA to construct bases. In the two-way array representation, each training model is represented as a matrix and the columns of each training matrix are treated as training vectors. We formulate the adaptation equation in the maximum a posteriori (MAP) framework using the bases and the prior.

Open-access femtocell networks assure the cellular user of getting a better and stronger signal. However, due to the small range of femto base stations (FBSs), any motion of the user may trigger handover. In a dense environment, the possibility of such handover is very frequent. To avoid frequent communication disruptions due to phenomena such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell selection method. Existing selection methods commonly uses a measured channel/cell quality metric such as the channel capacity (between the user and the target cell). However, the throughput experienced by the user is time-varying because of the channel condition, i.e., owing to the propagation effects or receiver location. In this context, the conventional approach does not reflect the future performance. To ensure the efficiency of cell selection, user's decision needs to depend not only on the current state of the network, but also on the future possible states (horizon). To this end, we implement a learning algorithm that can predict, based on the past experience, the best performing cell in the future. We present in this paper a reinforcement learning (RL) framework as a generic solution for the cell selection problem in a non-stationary femtocell network that selects, without prior knowledge about the environment, a target cell by exploring past cells' behavior and predicting their potential future states based on Q-learning algorithm. Then, we extend this proposal by referring to a fuzzy inference system (FIS) to tune Q-learning parameters during the learning process to adapt to environment changes. Our solution aims at minimizing the frequency of handovers without affecting the user experience in terms of channel capacity. Simulation results demonstrate that· our solution comes very close to the performance of the opportunistic method in terms of capacity, while fewer handovers are required on average.· the use of fuzzy rules achieves better performance in terms of received reward (capacity) and number of handovers than fixing the values of Q-learning parameters.

A 60-GHz power amplifier (PA) with a reliability consideration for a hot-carrier-induced~(HCI) degradation is presented. The supply voltage of the last stage of the PA ($V_{{ m PA}}$) is dynamically controlled by an on-chip digitally-assisted low drop-out voltage regulator (LDO) to alleviate HCI effects. A physical model for estimation of HCI degradation of NMOSFETs is discussed and investigated for dynamic operation. The PA is fabricated in a standard 65-nm CMOS process with a core area of 0.21,mm$^{2}$, which provides a saturation power of 10.1,dBm to 13.2,dBm with a peak power-added efficiency~(PAE) of 8.1% to 15.0% for the supply voltage $V_{{ m PA}}$ which varies from 0.7,V to 1.0,V at 60,GHz, respectively.

We consider a unified approach to the tracking analysis of adaptive filters with error and matrix data nonlinearities. Using energy-conservation arguments, we not only derive earlier results in a unified manner, but we also obtain new performance results for more general adaptive algorithms without requiring the restriction of the regression data to a particular distribution. Numerical simulations support the theoretical results.

Fault localization is a necessary process of locating faults in buggy programs. This paper proposes a novel approach using dynamic slicing and association analysis to improve the effectiveness of fault localization. Our approach utilizes dynamic slicing to generate a reduced candidate set to narrow the range of faults, and introduces association analysis to mine the relationship between the statements in the execution traces and the test results. In addition, we develop a prototype tool DSFL to implement our approach. Furthermore, we perform a set of empirical studies with 12 Java programs to evaluate the effectiveness of the proposed approach. The experimental results show that our approach is more effective than the compared approaches.

Maximizing network lifetime and optimizing aggregate system utility are important but usually conflict goals in wireless multi-hop networks. For the trade-off, we present a matrix game-theoretic cross-layer optimization formulation to jointly maximize the diverse objectives in such networks with network coding. To this end, we introduce a cross-layer formulation of general network utility maximization (NUM) that accommodates routing, scheduling, and stream control from different layers in the coded networks. Specifically, for the scheduling problem and then the objective function involved, we develop a matrix game with the strategy sets of the players corresponding to hyperlink and transmission mode, and design multiple payoffs specific to lifetime and system utility, respectively. In particular, with the inherit merit that matrix game can be solved with mathematical programming, our cross-layer programming formulation actually benefits from both game-based and NUM-based approaches at the same time by cooperating the programming model for the matrix game with that for the other layers in a consistent framework. Finally, our numerical experiments quantitatively exemplify the possible performance trad-offs with respect to the two variants developed on the multiple objectives in question while qualitatively exhibiting the differences between the framework and the other related works.

This letter considers the weighted sum-rate maximization (WSRMax) problem in downlink multicell multiuser orthogonal frequency-division multiplexing system. The WSRMax problem under per base station transmit power constraint is known to be NP-hard, and the optimal solution is computationally very expensive. We propose two less-complex suboptimal convex approximated solutions which are based on sequential parametric convex approximation approach. We derive provably faster convergent iterative convex approximation techniques that locally optimize the weighted sum-rate function. Both the iterative solutions are found to converge to the local optimal solution within a few iterations compared to other well-known techniques. The numerical results demonstrate the effectiveness and superiority of the proposed approaches.

Time synchronization is important for frequency hopping, power management, scheduling, and basic operations of multi-hop ad-hoc networks. The main problem of existing time synchronization methods is that they depend on a particular node that has the fastest time information among neighbor nodes. The Cucker-Smale flocking model describes that global emergent behavior can be obtained by locally averaging the velocity of each bird. Inspired by this flocking model, we propose a time synchronization method not depending on a particular node. In the proposed method, each node revises its time information via the local-averaging procedure in a distributed manner. A self-correcting procedure is added to the proposed method in order to preserve the effect of time adjustment executed by the local-averaging procedure. The simulation results show that the proposed time synchronization method reduces the time difference among nodes, and enhances the performance of time synchronization in the context of IEEE 802.11-based ad-hoc networks.