This paper deals with a strategic issue in the stable marriage model with complete preference lists (i.e., a preference list of an agent is a permutation of all the members of the opposite sex). Given complete preference lists of n men over n women, and a marriage µ, we consider the problem for finding preference lists of n women over n men such that the men-proposing deferred acceptance algorithm (Gale-Shapley algorithm) adopted to the lists produces µ. We show a simple necessary and sufficient condition for the existence of a set of preference lists of women over men. Our condition directly gives an O(n2) time algorithm for finding a set of preference lists, if it exists.

In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.

We have proposed a noise reduction method based on a noise reconstruction system (NRS). The NRS uses a linear prediction error filter (LPEF) and a noise reconstruction filter (NRF) which estimates background noise by system identification. In case a fixed step size for updating tap coefficients of the NRF is used, it is difficult to reduce background noise while maintaining the high quality of enhanced speech. In order to solve the problem, a variable step size is proposed. It makes use of cross-correlation between an input signal and an enhanced speech signal. In a speech section, a variable step size becomes small so as not to estimate speech, on the other hand, large to track the background noise in a non-speech section.

Clonal Selection Algorithm (CSA), based on the clonal selection theory proposed by Burnet, has gained much attention and wide applications during the last decade. However, the proliferation process in the case of immune cells is asexual. That is, there is no information exchange during different immune cells. As a result the traditional CSA is often not satisfactory and is easy to be trapped in local optima so as to be premature convergence. To solve such a problem, inspired by the quantum interference mechanics, an improved quantum crossover operator is introduced and embedded in the traditional CSA. Simulation results based on the traveling salesman problems (TSP) have demonstrated the effectiveness of the quantum crossover-based Clonal Selection Algorithm.

To address the low performance for channel scanning in the DVB-T system, we propose an enhanced front-end algorithm in this paper. The proposed algorithm consists of Auto Scan and Normal Scan, which is a part of the tuning algorithm for front-end (tuner) drivers in the DVB-T receiver. The key idea is that the frequency offset is saved when performing Auto Scan in order to reduce the channel change time for Normal Scan. In addition, the results of a performance evaluation demonstrate that our enhanced front-end algorithm improves the performance of channel scanning significantly, as compared to the generic front-end algorithm.

An inverse scattering problem of estimating the reflection coefficient and the surface impedance from two sets of absolute values of the near field with periodic change is investigated. The problem is formulated in terms of a nonlinear simultaneous equations which is derived from the relation between the two sets of absolute values and the field defined by a finite summation of the modal functions by applying the Fourier analysis. The reflection coefficient is estimated by solving the equations by Newton's method through the successive algorithm with the increment of the number of truncation in the summation one after another. Numerical examples are given and the accuracy of the estimation is discussed.

The Green's function of free space for the fast inhomogeneous plane wave algorithm is represented by an integration in the complex plane. The error in the computational process is determined by the number of sampling points, the truncation of the integration path, and the extrapolation. Therefore, the error control method is different from that for the fast multipole method. We will discuss the worst-case interactions of the fast inhomogeneous plane wave algorithm for the box implementation and define the upper and lower bounds of the computational error.

This letter proposes a novel method of large-scale IP traffic matrix (TM) estimation, called algebraic reconstruction technique inference (ARTI), which is based on the partial flow measurement and Fratar model. In contrast to previous methods, ARTI can accurately capture the spatio-temporal correlations of TM. Moreover, ARTI is computationally simple since it uses the algebraic reconstruction technique. We use the real data from the Abilene network to validate ARTI. Simulation results show that ARTI can accurately estimate large-scale IP TM and track its dynamics.

This paper presents a high-level synthesis approach to minimize the total power consumption in behavioral synthesis under time and area constraints. The proposed method has two stages, functional unit (FU) energy optimization and interconnect energy optimization. In the first stage, active and inactive energies of the FUs are optimized using a multiple supply and threshold voltage scheme. Genetic algorithm (GA) based simultaneous assignment of supply and threshold voltages and module selection is proposed. The proposed GA based searching method can be used in large size problems to find a near-optimal solution in a reasonable time. In the second stage, interconnects are simplified by increasing their sharing. This is done by exploiting similar data transfer patterns among FUs. The proposed method is evaluated for several benchmarks under 90 nm CMOS technology. The experimental results show that more than 40% of energy savings can be achieved by our proposed method.

Intra coding in H.264/AVC has significantly enhanced video compression efficiency. However, computation complexity increases by the rate-distortion (RD) based mode decision. This paper proposes a novel fast mode decision algorithm in H.264/AVC intra prediction and its VLSI architecture. A novel edge-detection pattern is proposed and both edge-detection technique and spatial mode prediction technique are combined together to reduce the number of intra 44 candidate modes from 9 to an average of 2.50. VLSI architecture of intra mode decision module is designed with TSMC 0.18 µm CMOS technology. The maximum frequency of 285 MHz is achieved and 13.1k NAND gates are required. High frequency, efficient processing cycle reduction and small area make this design to be an excellent accelerator for HDTV 1080p@30 fps real time encoder.

A novel fast lock-in digitally controlled phase-locked loop (DCPLL) is proposed in this letter. This DCPLL adopts a novel frequency search algorithm to reduce the lock-in time. Furthermore, to reduce the power consumption, the frequency divider is reused as a frequency detector during the frequency acquisition, and reused as a time-to-digital converter module during the phase acquisition. To verify the proposed algorithm and architecture, a DCPLL design is implemented by SMIC 0.18 µm 1P6M CMOS technology. The Spice simulation results show that the DCPLL can achieve frequency acquisition in 3 reference cycles and complete phase acquisition in 11 reference cycles when locking to 200 MHz. The corresponding power consumption of DCPLL is 3.71 mW.

In this paper, we propose a partially-parallel irregular LDPC decoder based on IEEE 802.11n standard targeting high throughput and small area applications. The design is based on a novel sum-delta message passing algorithm characterized as follows: (i) Decoding throughput is greatly improved by utilizing the difference value between the updated and the original value to remove redundant computations. (ii) Registers and memory are optimized to store only the frequently used messages to decrease the hardware cost. (iii) Techniques such as binary sorting, parallel column operation, high performance pipelining are used to further speed up the message passing procedure. The synthesis result in TSMC 0.18 CMOS technology demonstrates that for (648,324) irregular LDPC code, our decoder achieves 7.5X improvement in throughput, which reaches 402 Mbps at the frequency of 200 MHz, with 11% area reduction. The synthesis result also demonstrates the competitiveness to the fully-parallel regular LDPC decoders in terms of the tradeoff between throughput, area and power.

Task preemption is a critical mechanism for building an effective multi-tasking environment on dynamically reconfigurable processors. When a task is preempted, its necessary state information must be correctly preserved in order for the task to be resumed later. Not only do coarse-grained Dynamically Reconfigurable Processing Array (DRPAs) devices have different architectures using a variety of development tools, but the great amount of state data of hardware tasks executing on such devices are usually distributed on many different storage elements. To address these difficulties, this paper aims at studying a general method for capturing the state data of hardware tasks targeting coarse-grained DRPAs. Based on resource usage, algorithms for identifying preemption points and inserting preemption states subject to user-specified preemption latency are proposed. Moreover, a modification to automatically incorporate proposed steps into the system design flow is also discussed. The performance degradation caused by additional preemption states is minimized by allowing preemption only at predefined points where demanded resources are small. The evaluation result using a model based on NEC Electronics' DRP-1 shows that the proposed method can produce preemption points satisfying a given preemption latency with reasonable hardware overhead (from 6% to 15%).

This paper proposes an efficient design algorithm for power/ground (P/G) network synthesis with dynamic signal consideration, which is mainly caused by Ldi/dt noise and Cdv/dt decoupling capacitance (DECAP) current in the distribution network. To deal with the nonlinear global optimization under synthesis constraints directly, the genetic algorithm (GA) is introduced. The proposed GA-based synthesis method can avoid the linear transformation loss and the restraint condition complexity in current SLP, SQP, ICG, and random-walk methods. In the proposed Hybrid Grid Synthesis algorithm, the dynamic signal is simulated in the gene disturbance process, and Trapezoidal Modified Euler (TME) method is introduced to realize the precise dynamic time step process. We also use a hybrid-SLP method to reduce the genetic execute time and increase the network synthesis efficiency. Experimental results on given power distribution network show the reduction on layout area and execution time compared with current P/G network synthesis methods.

The mixture modeling framework is widely used in many applications. In this paper, we propose a component reduction technique, that collapses a Gaussian mixture model into a Gaussian mixture with fewer components. The EM (Expectation-Maximization) algorithm is usually used to fit a mixture model to data. Our algorithm is derived by extending mixture model learning using the EM-algorithm. In this extension, a difficulty arises from the fact that some crucial quantities cannot be evaluated analytically. We overcome this difficulty by introducing an effective approximation. The effectiveness of our algorithm is demonstrated by applying it to a simple synthetic component reduction task and a phoneme clustering problem.

In this letter, we propose combinatorial and search construction methods of 2-D multi-weight optical orthogonal codes (OOCs) with autocorrelation 0 and crosscorrelation 1, called multi-weight single or no pulse per row (MSNPR) codes. An upper bound on the size of MSNPR codes is derived and the performance of MSNPR codes is compared to those of other OOCs in terms of the bit error rate (BER) and evaluated using blocking probability. It is also demonstrated that the MSNPR codes can be flexibly constructed for different applications, providing the scalability to optical CDMA networks.

Recently, a data-selective method has been proposed to achieve low misalignment in affine projection algorithm (APA) by keeping the condition number of an input data matrix small. We present an improved method, and a complexity reduction algorithm for the APA with the data-selective method. Experimental results show that the proposed algorithm has lower misalignment and a lower condition number for an input data matrix than both the conventional APA and the APA with the previous data-selective method.

Wavelet-based features with simplicity and high efficacy have been used in many pattern recognition (PR) applications. These features are usually generated from the wavelet coefficients of coarse levels (i.e., high octaves) in the discrete periodized wavelet transform (DPWT). In this paper, a new 1-D non-recursive DPWT (NRDPWT) is presented for real-time high octave decomposition. The new 1-D NRDPWT referred to as the 1-D RRO-NRDPWT can overcome the word-length-growth (WLG) effect based on two strategies, resisting error propagation and applying a reversible round-off linear transformation (RROLT) theorem. Finite precision performance analysis is also taken to study the word length suppression efficiency and the feature efficacy in breast lesion classification on ultrasonic images. For the realization of high octave decomposition, a segment accumulation algorithm (SAA) is also presented. The SAA is a new folding technique that can reduce multipliers and adders dramatically without the cost of increasing latency.

An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.

A uniform circular array (UCA) is a well-known array configuration which can accomplish estimation of 360 field of view with identical accuracy. However, a UCA cannot estimate coherent signals because we cannot apply the SSP owing to the structure of UCA. Although a variety of studies on UCA in coherent multipath environments have been done, it is impossible to estimate the DOA of coherent signals with different incident polar angles. Then, we have proposed Root-MUSIC algorithm with a cylindrical array. However, the estimation performance is degraded when incident signals arrive with close polar angles. To solve this problem, in the letter, we propose to use SAGE algorithm with a cylindrical array. Here, we adopt a CLA Root-MUSIC for the initial estimation and decompose two-dimensional search to double one-dimensional search to reduce the calculation load. The results show that the proposal achieves high resolution with low complexity.