Feature selection (FS) plays an important role in pattern recognition and machine learning. FS is applied to dimensionality reduction and its purpose is to select a subset of the original features of a data set which is rich in the most useful information. Most existing FS methods based on rough set theory focus on dependency function, which is based on lower approximation as for evaluating the goodness of a feature subset. However, by determining only information from a positive region but neglecting a boundary region, most relevant information could be invisible. This paper, the maximal lower approximation (Max-Certainty) – minimal boundary region (Min-Uncertainty) criterion, focuses on feature selection methods based on rough set and mutual information which use different values among the lower approximation information and the information contained in the boundary region. The use of this idea can result in higher predictive accuracy than those obtained using the measure based on the positive region (certainty region) alone. This demonstrates that much valuable information can be extracted by using this idea. Experimental results are illustrated for discrete, continuous, and microarray data and compared with other FS methods in terms of subset size and classification accuracy.

Allowing WLANs to exploit opportunistic spectrum access (OSA) is a promising approach to alleviate spectrum congestion problems in overcrowded unlicensed ISM bands, especially in highly dense WLAN deployments. In this context, novel channel assignment mechanisms jointly considering available channels in both unlicensed ISM and OSA-enabled licensed bands are needed. Unlike classical schemes proposed for legacy WLANs, channel assignment mechanisms for OSA-enabled WLAN should face two distinguishing issues: channel prioritization and spectrum heterogeneity. The first refers to the fact that additional prioritization criteria other than interference conditions should be considered when choosing between ISM or licensed band channels. The second refers to the fact that channel availability might not be the same for all WLAN Access Points because of primary users' activity in the OSA-enabled bands. This paper firstly formulates the channel assignment problem for OSA-enabled WLANs as a Binary Linear Programming (BLP) problem. The resulting BLP problem is optimally solved by means of branch and bound algorithms and used as a benchmark to develop more computationally efficient heuristics. Upon such a basis, a novel channel assignment algorithm based on weighted graph coloring heuristics and able to exploit both channel prioritization and spectrum heterogeneity is proposed. The algorithm is evaluated under different conditions of AP density and primary band availability.

A new codebook mapping algorithm for artificial bandwidth extension (ABE) is introduced in this paper. We design a wideband line spectrum pair (LSP) codebook which is coupled with the same index as the LSP codebook of a narrowband speech codec. The received narrowband LSP codebook indices are used to directly induce wideband LSP codewords. Thus, the proposed scheme eliminates codebook search processing to estimate the wideband spectrum envelope. We apply the proposed scheme to bandwidth extension in adaptive multi-rate (AMR) compressed domain. Its performance is assessed via the perceptual evaluation of speech quality (PESQ), informal listening tests, and weighted million operations per second (WMOPS) calculations.

In this paper, we propose a synthesis method for asynchronous circuits with bundled-data implementation. The proposed method iteratively applies behavioral synthesis and floorplanning to obtain a near optimum circuit in the term of latency under given design constraints. To improve latency, behavioral synthesis and floorplanning are carried out so that the delay of the control circuit is minimized and the addition of delay elements to satisfy timing constraints is minimized. We evaluate the effectiveness of the proposed method in terms of latency, area, and the number of timing violations while synthesizing several benchmarks. Experimental results show that the proposed method synthesizes faster circuits compared to the circuit synthesized without the proposed method. Also, the proposed method is effective to reduce the number of timing violations.

A Drude-critical points (D-CP) model for considering metal dispersion is newly incorporated into the frequency-dependent FDTD method using the simple trapezoidal recursive convolution (TRC) technique. Numerical accuracy is investigated through the analysis of pulse propagation in a metal (aluminum) cladding waveguide. The TRC technique with a single convolution integral is found to provide higher accuracy, when compared with the recursive convolution counterpart. The methodology is also extended to the unconditionally stable FDTD based on the locally one-dimensional scheme for efficient frequency-dependent calculations.

In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling technique. The problem of obtaining sparse representation is formulated by cardinality-constrained 2 optimization of the control performance, which is reducible to 1-2 optimization. The low rate random sampling employed in the proposed method based on the compressive sampling, in addition to the fact that the 1-2 optimization can be effectively solved by a fast iteration method, enables us to generate the sparse control signal with reduced computational complexity, which is preferable in remote control systems where computation delays seriously degrade the performance. We give a theoretical result for control performance analysis based on the notion of restricted isometry property (RIP). An example is shown to illustrate the effectiveness of the proposed approach via numerical experiments.

Keith Salvin presented a key exchange protocol using matrices in the general linear group, GL(r,Zn), where n is the product of two distinct large primes. The system is fully specified in the US patent number 7346162 issued in 2008. In the patent claims, it is argued that the best way to break this system is to factor n. Furthermore, for efficiency reasons, it is suggested to use r=2. In this letter, we show that this cryptosystem can be easily broken by solving a set of consistent homogeneous r2 linear equations in 2r unknowns over Zn.

This paper proposes a direction-of-arrival (DOA) estimation method of multiple speech sources from a stereophonic mixture in an underdetermined case where the number of sources exceeds the number of sensors. The method relies on the sparseness of speech signals in time-frequency (T-F) domain representation which means multiple independent speakers have a small overlap. At first, a selection of T-F cells bearing reliable spatial information is proposed by an introduced reliability index which is defined by the estimated interaural phase difference at each T-F cell. Then, a statistical error propagation model between the phase difference at T-F cell and its consequent DOA is introduced. By employing this model and the sparseness in T-F domain the DOA estimation problem is altered to obtaining local peaks of probability density function of DOA. Finally the kernel density estimator approach based on the proposed statistical model is applied. The performance of the proposed method is assessed by conducted experiments. Our method outperforms others both in accuracy for real observed data and in robustness for simulation with additional diffused noise.

The frequency regions and spectral features that can be used to measure the perceived similarity and continuity of voice quality are reported here. A perceptual evaluation test was conducted to assess the naturalness of spoken sentences in which either a vowel or a long vowel of the original speaker was replaced by that of another. Correlation analysis between the evaluation score and the spectral feature distance was conducted to select the spectral features that were expected to be effective in measuring the voice quality and to identify the appropriate speech segment of another speaker. The mel-frequency cepstrum coefficient (MFCC) and the spectral center of gravity (COG) in the low-, middle-, and high-frequency regions were selected. A perceptual paired comparison test was carried out to confirm the effectiveness of the spectral features. The results showed that the MFCC was effective for spectra across a wide range of frequency regions, the COG was effective in the low- and high-frequency regions, and the effective spectral features differed among the original speakers.

No nontrivial optimal sets of frequency-hopping sequences (FHSs) of period 2(2n-1) for a positive integer n ≥ 2 have been found so far, when their frequency set sizes are less than their periods. In this paper, systematic doubling methods to construct new FHS sets are presented under the constraint that the set of frequencies has size less than or equal to 2n. First, optimal FHS sets with respect to the Peng-Fan bound are constructed when frequency set size is either 2n-1 or 2n. And then, near-optimal FHS sets with frequency set size 2n-1 are designed by applying the Chinese Remainder Theorem to Sidel'nikov sequences, whose FHSs are optimal with respect to the Lempel-Greenberger bound. Finally, a general construction is given for near-optimal FHS sets whose frequency set size is less than 2n-1. Our constructions give new parameters not covered in the literature, which are summarized in Table1.

Requirements changes frequently occur at any time of a software development process, and their management is a crucial issue to develop software of high quality. Meanwhile, goal-oriented analysis techniques are being put into practice to elicit requirements. In this situation, the change management of goal graphs and its support are necessary. This paper presents a technique related to the change management of goal graphs, realizing impact analysis on a goal graph when its modifications occur. Our impact analysis detects conflicts that arise when a new goal is added, and investigates the achievability of the other goals when an existing goal is deleted. We have implemented a supporting tool for automating the analysis. Two case studies suggested the efficiency of the proposed approach.

of late, many researchers have been interested in sparse representation of signals and its applications such as Compressive Sensing in Cognitive Radio (CR) networks as a way of overcoming the issue of limited bandwidth. Compressive sensing based wideband spectrum sensing is a novel approach in cognitive radio systems. Also in these systems, using spatial-frequency opportunistic reuse is emerged interestingly by constructing and deploying spatial-frequency Power Spectral Density (PSD) maps. Since the CR sensors are distributed in the region of support, the sensed PSD by each sensor should be transmitted to a master node (base-station) in order to construct the PSD maps in space and frequency domains. When the number of sensors is large, this data transmission which is required for construction of PSD map can be challenging. In this paper, in order to transmit the CR sensors' data to the master node, the compressive sensing based scheme is used. Therefore, the measurements are sampled in a lower sampling rate than of the Nyquist rate. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 30% of full data transmission. Also, simulation results show the robustness of the proposed method against the channel variations in comparison with classical methods. Different solution schemes such as Basis Pursuit, Lasso, Lars and Orthogonal Matching Pursuit are used and the quality performance of them is evaluated by several simulation results over a Rician channel with respect to several different compression and Signal to Noise Ratios. It is also illustrated that the performance of Basis Pursuit and Lasso methods outperform the other compression methods particularly in higher compression rates.

This paper introduces a unified method of spectrum sensing for all existing analog television (TV) signals including NTSC, PAL and SECAM. We propose a correlation based method (CBM) with a single reference signal for sensing any analog TV signals. In addition we also propose an improved energy detection method. The CBM approach has been implemented in a hardware prototype specially designed for participating in Singapore TV white space (WS) test trial conducted by Infocomm Development Authority (IDA) of the Singapore government. Analytical and simulation results of the CBM method will be presented in the paper, as well as hardware testing results for sensing various analog TV signals. Both AWGN and fading channels will be considered. It is shown that the theoretical results closely match with those from simulations. Sensing performance of the hardware prototype will also be presented in fading environment by using a fading simulator. We present performance of the proposed techniques in terms of probability of false alarm, probability of detection, sensing time etc. We also present a comparative study of the various techniques.

It is shown that it is feasible to apply 0.5-V 6-T SRAM cells in a 25-nm high-speed 1-Gb e-SRAM. In particular, for coping with rapidly reduced voltage margin as VDD is reduced, a boosted word-voltage scheme is first proposed. Second, Vt variations are reduced with repair techniques and nanoscale FD-MOSFETs to further widen the voltage margin. Third, a worst case design is developed, for the first time, to evaluate the cell. This design features a dynamic margin analysis and takes subthreshold current, temperature, and Vt variations and their combination in the cell into account. Fourth, the proposed scheme is evaluated by applying the worst-case design and a 25-nm planar FD-SOI MOSFET. It is consequently found that the scheme provides a wide margin and high speed even at 0.5 V. A 0.5-V high-speed 25-nm 1-Gb SRAM is thus feasible. Finally, to further improve the scheme, it is shown that it is necessary to use FinFETs and suppress and compensate process, voltage, and temperature variations in a chip and wafer.

A small-sized leakage-controlled gated sense amplifier (SA) and relevant circuits are proposed for 0.5-V multi-gigabit DRAM arrays. The proposed SA consists of a high-VT PMOS amplifier and a low-VT NMOS amplifier which is composed of high-VT NMOSs and a low-VT cross-coupled NMOS, and achieves 46% area reduction compared to a conventional SA with a low-VT CMOS preamplifier. Separation of the proposed SA and a data-line pair achieves a sensing time of 6 ns and a writing time of 0.6 ns. Momentarily overdriving the PMOS amplifier achieves a restoring time of 13 ns. The gate level control of the high-VT NMOSs and the gate level compensation circuit for PVT variations reduce the leakage current of the proposed SA to 2% of that without the control, and its effectiveness was confirmed using a 50-nm test chip.

In this paper, we propose a joint diversity algorithm for error-rate minimization in multi-user spatial multiplexing (SM) systems with block diagonalization (BD)-precoding. The proposed algorithm adapts or selects the user set, transmit antenna subset, and the number of streams by an exhaustive search over the available resources. The proposed algorithm makes use of the multi-user diversity (MUD) and the spatial diversity gains as well as the array gain through selecting the best set. Exhaustive search, however, imposes a heavy burden in terms of computational complexity which exponentially increases with the size of the total number of users, streams, and transmit antennas. For complexity reduction, we propose two suboptimal algorithms which reduce the search space by first selecting the best user or by both selecting the best user and fixing the number of streams. Simulation results show that the proposed algorithms improve error probability over the conventional algorithm due to their diversity improvement and the signal-to-noise ratio (SNR) gains over the conventional algorithm. We also show that the suboptimal algorithms significantly reduce the computational complexity over exhaustive search with low-SNR loss.

We propose a new cognitive radio network architecture using the IP multimedia subsystem (IMS) functionality. We implement the cognitive radio network entities standardized in IEEE 1900.4 on the IMS that exchanges RAN and terminal context information between the networks and the terminals to make optimum and immediate reconfiguration decisions. In our proposed architecture, RAN context information is obtained from cellular networks which are directly connected to the IMS. The presence management functions of the IMS are applied to exchange those information in a “push” manner, which enables immediate notification of changes in wireless environment. We evaluate the performance of the proposed context information exchange method, by comparing with the cases that adequate and immediate RAN context information is not available. The evaluation results show that the proposed framework gives 10–30% superior performance than the conventional cognitive radio networks.

This letter proposes a scheme to update metrics without loops while minimizing routing instability time in an Open Shortest Path First (OSPF) network. The original OSPF network enters the transient state when metrics are being updated to improve the routing performance, and in this state packets may fall into loops. This may cause packet loss and inefficient network resource utilization. To avoid transient loops, a conventional scheme gives each router a priority that reflects the optimum time for metric update. However, when the updated metrics include both larger and smaller values than the preceding ones, two sequential updating processes, one for larger values and one for smaller values, are required. It takes time to converge on the final metric values in the conventional scheme, given that the interval time between the two processes is not insignificant. The second process starts only when the first process is confirmed to be completed. The interval time including the confirmation time and the time needed to reconfigure the metrics in all routers, lengthens the transient state duration; from several seconds to several tens of seconds. This causes routing instability. The proposed scheme transforms the set of updated metrics into an equivalent set of metrics that are either all larger or all smaller (if changed at all) than the ones before the update. The set of equivalent metrics yield exactly the same results in terms of routing as the conventional scheme, i.e. the result desired by the network operator. The non-mixture update requires only one updating process and so eliminates the interval time. Numerical results indicate that the probability that the proposed scheme can achieve non-mixture update is more than 67% in the networks examined.

Based on our previous work, this work presents a complete method for time-domain processing of frequency-domain data with evenly-spaced frequency indices, together with its application. The proposed method can be used to calculate the cross spectral and power spectral densities for the frequency indices of interest. A promising application for the time-domain processing of frequency-domain data, particularly for calculating the summation of frequency-domain cross- and auto-correlations in orthogonal frequency-division multiplexing (OFDM) systems, is studied. The advantages of the time-domain processing of frequency-domain data are 1) the ability to rapidly acquire the properties that are readily available in the frequency domain and 2) the reduced complexity. The proposed fast algorithm directly employs time-domain samples, and hence, does not need the fast Fourier transform (FFT) operation. The proposed algorithm has a lower complexity (required complex multiplications ∼ O(N)) than conventional techniques.

An important and widely considered signal identification technique for cognitive radios is cyclostationarity-based feature detection because this method does not require time and frequency synchronization and prior information except for information concerning cyclic autocorrelation features of target signals. This paper presents the development and experimental evaluation of cyclostationarity-based signal identification equipment. A spatial channel emulator is used in conjunction with the equipment that provides an environment to evaluate realistic spectrum sharing scenarios. The results reveal the effectiveness of the cyclostationarity-based signal identification methodology in realistic spectrum sharing scenarios, especially in terms of the capability to identify weak signals.