A new Hybrid-Carry-Selection (HCS) approach for deriving an efficient modulo 2n-1 addition is presented in this study. Its resulting adder architecture is simple and applicable for all n values. Based on 180-nm CMOS technology, the HCS-based modulo 2n-1 adder demonstrates its superiority in Area-Time (AT) performance over existing solutions.

Auto-calibration for structure and motion recovery can be used for match move where the goal is to insert synthetic 3D objects into real scenes and create views as if they were part of the real scene. However, most auto-calibration methods for multi-views utilize bundle adjustment with non-linear optimization, which requires a very good starting approximation. We propose a novel key-frame selection measurement and LMedS (Least Median of Square)-based approach to estimate scene structure and motion from image sequences captured with a hand-held camera. First, we select key-frames considering the ratio of number of correspondences and feature points, the homography error and the distribution of corresponding points in the image. Then, by using LMedS, we reject erroneous frames among the key-frames in absolute quadric estimation. Simulation results demonstrated that the proposed method can select suitable key-frames efficiently and achieve more precise camera pose estimation without non-linear optimization.

We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

The combination of deliberate clipping and an adaptive symbol selection scheme (ASSS) can be used to reduce the peak to average power ratio (PAPR) for Orthogonal Frequency Division Multiplexing (OFDM) signals. The probability density function (pdf) of a sample's amplitude of an adaptively selected OFDM signal without over-sampling has been considered to be approximately equal to the Rayleigh pdf. In this letter, we derive the exact pdf showing the relationship between the probability distribution of the sample's amplitude and the number of candidate OFDM symbols for ASSS. The use of the newly derived pdf can measure the effect of deliberate clipping on the adaptively selected OFDM signal more accurately.

In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.

We propose an adaptive SLM scheme based on peak observation for PAPR reduction of OFDM signals. The proposed scheme is composed of three steps: peak scaling, sequence selection, and SLM procedures. In the first step, the peak signal samples in the IFFT outputs of the original input sequence are scaled down. In the second step, the sub-carrier positions where the power difference between the original input sequence and the FFT output of the scaled signal is large, are identified. Then, the phase sequences having the maximum number of phase-reversed sequence words only for these positions are selected. Finally, the generic SLM procedure is performed by using only the selected phase sequences for the original input sequence. Simulation results show that the proposed scheme significantly reduce the complexity in terms of IFFT and PAPR calculation than the conventional SLM, while maintaining the PAPR reduction performance.

A parametric linear filter for a linear observation model usually requires a parameter selection process so that the filter achieves a better filtering performance. Generally, criteria for the parameter selection need not only the filtered solution but also the filter itself with each candidate of the parameter. Obtaining the filter usually costs a large amount of calculations. Thus, an efficient algorithm for the parameter selection is required. In this paper, we propose a fast parameter selection algorithm for linear parametric filters that utilizes a joint diagonalization of two non-negative definite Hermitian matrices.

For coherent detection, decoding Orthogonal Space-Time Block Codes (OSTBC) requires full channel state information at the receiver, which basically is obtained by channel estimation. However, in practical systems, channel estimation errors are inevitable and may degrade the system performance more as the number of antennas increases. This letter shows that, using fewer receive antennas can enhance the performance of OSTBC systems in presence of channel estimation errors. Furthermore, a novel adaptive receive antenna selection scheme, which adaptively adjusts the number of receive antennas, is proposed. Performance evaluation and numerical examples show that the proposed scheme improves the performance obviously.

The clonal selection algorithm (CS), inspired by the basic features of adaptive immune response to antigenic stimulus, can exploit and explore the solution space parallelly and effectively. However, antibody initialization and premature convergence are two problems of CS. To overcome these two problems, we propose a chaotic distance-based clonal selection algorithm (CDCS). In this novel algorithm, we introduce a chaotic initialization mechanism and a distance-based somatic hypermutation to improve the performance of CS. The proposed algorithm is also verified for numerous benchmark traveling salesman problems. Experimental results show that the improved algorithm proposed in this paper provides better performance when compared to other metaheuristics.

In order to obtain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the generalization error is minimized. However, since the generalization error is inaccessible in practice, the model parameters are usually determined so that an estimator of the generalization error is minimized. The regularized subspace information criterion (RSIC) is such a generalization error estimator for model selection. RSIC includes an additional regularization parameter and it should be determined appropriately for better model selection. A meta-criterion for determining the regularization parameter has also been proposed and shown to be useful in practice. In this paper, we show that there are several drawbacks in the existing meta-criterion and give an alternative meta-criterion that can solve the problems. Through simulations, we show that the use of the new meta-criterion further improves the model selection performance.

In this paper, we present a new method to enhance classification performance of a multiple classifier system by combining a boosting technique called AdaBoost.M2 and Kernel Discriminant Analysis (KDA). To reduce the dependency between classifier outputs and to speed up the learning, each classifier is trained in a different feature space, which is obtained by applying KDA to a small set of hard-to-classify training samples. The training of the system is conducted based on AdaBoost.M2, and the classifiers are implemented by Radial Basis Function networks. To perform KDA at every boosting round in a realistic time scale, a new kernel selection method based on the class separability measure is proposed. Furthermore, a new criterion of the training convergence is also proposed to acquire good classification performance with fewer boosting rounds. To evaluate the proposed method, several experiments are carried out using standard evaluation datasets. The experimental results demonstrate that the proposed method can select an optimal kernel parameter more efficiently than the conventional cross-validation method, and that the training of boosting classifiers is terminated with a fairly small number of rounds to attain good classification accuracy. For multi-class classification problems, the proposed method outperforms both Boosting Linear Discriminant Analysis (BLDA) and Radial-Basis Function Network (RBFN) with regard to the classification accuracy. On the other hand, the performance evaluation for 2-class problems shows that the advantage of the proposed BKDA against BLDA and RBFN depends on the datasets.

In order to obtain better learning results in supervised learning, it is important to choose model parameters appropriately. Model selection is usually carried out by preparing a finite set of model candidates, estimating a generalization error for each candidate, and choosing the best one from the candidates. If the number of candidates is increased in this procedure, the optimization quality may be improved. However, this in turn increases the computational cost. In this paper, we focus on a generalization error estimator called the regularized subspace information criterion and derive an analytic form of the optimal model parameter over a set of infinitely many model candidates. This allows us to maximize the optimization quality while the computational cost is kept moderate.

The Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. In the immune response, according to Burnet's clonal selection principle, the antigen imposes a selective pressure on the antibody population by allowing only those cells which specifically recognize the antigen to be selected for proliferation and differentiation. However ongoing investigations indicate that receptor editing, which refers to the process whereby antigen receptor engagement leads to a secondary somatic gene rearrangement event and alteration of the receptor specificity, is occasionally found in affinity maturation process. In this paper, we extend the traditional CSA approach by incorporating the receptor editing method, named RECSA, and applying it to the Traveling Salesman Problem. Thus, both somatic hypermutation (HM) of clonal selection theory and receptor editing (RE) are utilized to improve antibody affinity. Simulation results and comparisons with other general algorithms show that the RECSA algorithm can effectively enhance the searching efficiency and greatly improve the searching quality within reasonable number of generations.

The support vector machines (SVMs) are one of the most effective classification techniques in several knowledge discovery and data mining applications. However, a SVM requires the user to set the form of its kernel function and parameters in the function, both of which directly affect to the performance of the classifier. This paper proposes a novel method, named a kernel-tree, the function of which is composed of multiple kernels in the form of a tree structure. The optimal kernel tree structure and its parameters is determined by genetic programming (GP). To perform a fine setting of kernel parameters, the gradient descent method is used. To evaluate the proposed method, benchmark datasets from UCI and dataset of text classification are applied. The result indicates that the method can find a better optimal solution than the grid search and the gradient search.

Multiple access points (APs) are much more likely to be available for stations (STAs) due to the popularity of wireless LANs. The serious issue of how an appropriate AP is selected from those that are available in a wireless LAN therefore arises. We discuss the development of a decentralized architecture for selecting APs, and examine its fundamental characteristics. The proposed architecture should be introduced without adversely affecting the performance of the existing common architecture that is currently being deployed. Therefore, the deployability of our architecture is examined in this respect. Furthermore, the dynamic behavior of the proposed architecture is studied in addition to static characteristics to evaluate its robustness against various dynamic changes in situation due to AP breakdowns and bursty arrivals of STAs. Simulations revealed that the proposed architecture can attain excellent performance in all the cases treated here.

Various trajectories of design, arising from the new methodology of analog network design, are analyzed. Several major criteria suggested for optimal selection of initial approximation to the design process permit the minimization of computer time. The initial approximation point is selected with regard to the previously revealed effect of acceleration of the design process. The concept of separatrix is defined making it possible to determine the optimal position of the initial approximation. The numerical results obtained for passive and active networks prove the possibility of optimal choice of the initial point in design process.

Channel state information (CSI) at transmitter plays an important role for multiuser MIMO broadcast channels, but full CSI at transmitter is not available for many practical systems. Previous work has proposed orthonormal random beamforming (ORBF) [16] for MIMO broadcast channels with partial channel state information (CSI) feedback, and shown that ORBF achieves the optimal sum-rate capacity for a large number of users. However, for cellular systems with moderate number of users, i.e., no more than 64, ORBF only achieves slight performance gain. Therefore, we analyze the performance of ORBF with moderate number of users and total transmit power constraint and show that ORBF scheme is more efficient under low SNR. Then we propose an adaptive ORBF scheme that selects the number of random beams for simultaneous transmission according to the average signal-to-noise ratio (SNR). Moreover, a multi-beam selection (MBS) scheme that jointly selects the number and the subset of the multiple beams is proposed to further improve the system performance for low SNR cases. The simulation results show that the proposed schemes achieve significant performance improvement when the number of users is moderate.

Simple but efficient antenna selection schemes are proposed for the downlink of Orthogonal Frequency Division Multiplexing (OFDM) transmission with multiple transmit antennas over frequency selective fading channels, where transmit antennas are selected at the mobile terminal and the base station is informed of the selected antennas through feedback channel. To obtain the optimal antenna selection, channel frequency responses are required and performances have to be evaluated at all the subcarriers. To reduce the computational complexity at mobile terminal, time-domain channels are utilized for antenna selection in place of channel frequency responses. Our scheme does not guarantee the optimal antenna selection but is shown by numerical simulations to yield reasonable selections. Moreover, by using a specially designed pilot OFDM preamble, an antenna selection without channel estimation is developed. Efficiencies of our suboptimal antenna selections with less computational complexities are verified by numerical simulations.

We propose a new genetic fuzzy discretization method with feature selection for the pattern classification problems. Traditional discretization methods categorize a continuous attribute into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However, the number of intervals, the boundaries of intervals, and the degrees of overlapping are intractable to get optimized and a discretization process increases the total amount of data being transformed. We use a genetic algorithm with feature selection not only to optimize these parameters but also to reduce the amount of transformed data by filtering the unconcerned attributes. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization with feature selection.

In this letter, we discuss the problem of receive antenna selection in the downlink of multiuser multiple-input multiple-output (MIMO) systems with Tomlinson-Harashima precoding (THP), where the number of receivers is assumed equal to that of transmit antennas. Based on the criterion of maximum system sum-capacity, a per-layer receive antenna selection scheme is proposed. This scheme, which selects one receive antenna for each receiver, can well exploit the nonlinear and successive characteristics of THP. Two models are established for the proposed per-layer scheme and the conventional per-user scheme. Both the theoretical analysis and simulation results indicate that the proposed scheme can greatly improve the equivalent channel power gains and the system sum-capacity.