Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed, which requires a single microphone only and doesn't need any priori-information on both noise spectrum and pitch. It works in the presence of noise with high amplitude and unknown direction of arrival. More specifically, an adaptive noise suppression algorithm applicable to real-life speech recognition is proposed without assuming the Gaussian white noise, which performs effectively even though the noise statistics and the fluctuation form of speech signal are unknown. The effectiveness of the proposed method is confirmed by applying it to real speech signals contaminated by noises.

A low-complexity Reed-Solomon (RS) decoder design based on the modified Euclidean (ME) algorithm proposed by Truong is presented in this paper. Low complexity is achieved by reformulating Truong's ME algorithm using the proposed polynomial manipulation scheme so that a more compact polynomial representation can be derived. Together with the developed folding scheme and simplified boundary cell, the resulting design effectively reduces the hardware complexity while meeting the throughput requirements of optical communication systems. Experimental results demonstrate that the developed RS(255, 239) decoder, implemented in the TSMC 0.18 µm process, can operate at up to 425 MHz and achieve a throughput rate of 3.4 Gbps with a total gate count of 11,759. Compared to related works, the proposed decoder has the lowest area requirement and the smallest area-time complexity.

The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

To reduce the huge search space when customizing accelerators for the application specific instruction-set processor (ASIP), this paper proposes an automated customization method based on the data flow graph exploration. This method integrates the instruction identification and selection using an iterative improvement strategy, which uses a seed-growth algorithm to select the valid patterns that can bring higher performance enhancement. The search space is reduced by considering the performance factors during the identification stage. The experimental results indicate that the proposed method is feasible enough compared to the previous exhaustive algorithms.

Wireless sensor networks (WSN) is composed of so many small sensor nodes which have limited resources. So the technique that raises energy efficiency is the key to prolong the network life time. In the paper, we propose an agent based framework which takes the biological characteristics of gene. The gene represents an operation policy to control agent behavior. Agents are aggregated to reduce duplicate transmissions in active period. And it selects next hop based on the information of neighbor agents. Among neighbors, the node which has enough energy is given higher priority. The base station processes genetic evolution to refine the behavior policy of agent. Each agent is taken latest gene and spread recursively to find the optimal gene. Our proposed framework yields sensor nodes that have the properties of self-healing, self-configuration, and self-optimization. Simulation results show that our proposed framework increases the lifetime of each node.

We introduce a “generalized small inverse problem (GSIP)” and present an algorithm for solving this problem. GSIP is formulated as finding small solutions of f(x0, x1, ..., xn)=x0 h(x1, ..., xn)+C=0 (mod ; M) for an n-variate polynomial h, non-zero integers C and M. Our algorithm is based on lattice-based Coppersmith technique. We provide a strategy for construction of a lattice basis for solving f=0, which is systematically transformed from a lattice basis for solving h=0. Then, we derive an upper bound such that the target problem can be solved in polynomial time in log M in an explicit form. Since GSIPs include some RSA-related problems, our algorithm is applicable to them. For example, the small key attacks by Boneh and Durfee are re-found automatically.

In ubiquitous sensor networks, extra energy savings can be achieved by selecting the filtering solution to counter the attack. This adaptive selection process employs a fuzzy rule-based system for selecting the best solution, as there is uncertainty in the reasoning processes as well as imprecision in the data. In order to maximize the performance of the fuzzy system the membership functions should be optimized. However, the efforts required to perform this optimization manually can be impractical for commonly used applications. This paper presents a GA-based membership function optimizer for fuzzy adaptive filtering (GAOFF) in ubiquitous sensor networks, in which the efficiency of the membership functions is measured based on simulation results and optimized by GA. The proposed optimization consists of three units; the first performs a simulation using a set of membership functions, the second evaluates the performance of the membership functions based on the simulation results, and the third constructs a population representing the membership functions by GA. The proposed method can optimize the membership functions automatically while utilizing minimal human expertise.

In an undirected graph, the feedback vertex set (FVS for short) problem is to find a set of vertices of minimum cardinality whose removal makes the graph acyclic. The FVS has applications to several areas such that combinatorial circuit design, synchronous systems, computer systems, VLSI circuits and so on. The FVS problem is known to be NP-hard on general graphs but interesting polynomial solutions have been found for some special classes of graphs. In this paper, we present an O(n2.68 + γn) time algorithm for solving the FVS problem on trapezoid graphs, where γ is the total number of factors included in all maximal cliques.

When a link or node fails in a network, the affected flows are automatically rerouted. This increases the hop counts of the flows, which can drastically degrade network performance. Keeping the hop lengths as stable as possible, i.e., minimizing the difference in hop length between the original flow and the rerouted flow is important for network reliability. Therefore, network service providers need a method for designing networks that stabilizes the flow hop length and maintains connectivity during a link or node failure with limited investment cost. First, we formulate the network design problem used for determining the set of links to be added that satisfies the required constraints on flow hop length stability, connectivity, and node degree. Next, we prove that this problem is NP-complete and present two approximation algorithms for the optimization problem so as to minimize the number of links added. Evaluation of the performance of these algorithms by using 39 backbone networks of commercial ISPs and networks generated by two well-known models showed that the proposed algorithms provide effective solutions in sufficiently short computation time.

This paper develops planar circuit filters consisting of arbitrarily-shaped conductor patches and slots on a conductor-backed dielectric substrate, which are designed by an optimization technique based on the genetic algorithm. The developed filter has multiple resonators and their mutual couplings in the limited space by using both sides of the substrate, so that its compactness is realized. We first demonstrate the effectiveness of the present filter structure from some design samples numerically and experimentally. Then as a practical application, we design compact UWB filters, and their filter characteristics are verified from the measurements.

In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.

This paper presents a simplifying method of the two previous fault attacks to pairing and the Miller algorithms based on a practical fault assumption. Our experimental result shows that the assumption is feasible and easy to implement.

In this paper, we deal with the algebraic immunity of the symmetric Boolean functions. The algebraic immunity is a property which measures the resistance against the algebraic attacks on symmetric ciphers. It is well known that the algebraic immunity of the symmetric Boolean functions is completely determined by a narrow class of annihilators with low degree which is denoted by G(n,). We study and determine the weight support of part of these functions. Basing on this, we obtain some relations between the algebraic immunity of a symmetric Boolean function and its simplified value vector. For applications, we put forward an upper bound on the number of the symmetric Boolean functions with algebraic immunity at least d and prove that the algebraic immunity of the symmetric palindromic functions is not high.

The Krivine-style evaluation mechanism is well-known in the implementation of higher-order functions, allowing to avoid some useless closure building. There have been a few type systems that can verify the safety of the mechanism. The incorporation of the proposed ideas into an existing compiler, however, would require significant changes in the type system of the compiler due to the use of some dedicated form of types and typing rules in the proposals. This limitation motivates us to propose an alternative light-weight Krivine typing mechanism that does not need to extend any existing type system significantly. This paper shows how GADTs (Generalized algebraic data types) can be used for typing a ZINC machine following the Krivine-style evaluation mechanism. This idea is new as far as we know. Some existing typed compilers like GHC (Glasgow Haskell compiler) already support GADTs; they can benefit from the Krivine-style evaluation mechanism in the operational semantics with no particular extension in their type systems for the safety. We show the GHC type checker allows to prove mechanically that ZINC instructions are well-typed, which highlights the effectiveness of GADTs.

A new compact form of the sliding window recursive least squares (SWRLS) algorithm, the I-SWRLS algorithm, is derived using an indefinite matrix. The resultant algorithm has a form similar to that of the traditional recursive least squares (RLS) algorithm, and is more computationally efficient than the conventional SWRLS algorithm including two Riccati equations. Furthermore, a computationally reduced version of the I-SWRLS algorithm is developed utilizing a shift property of the correlation matrix of input data. The resulting fast algorithm reduces the computational complexity from O(N2) to O(N) per iteration when the filter length (tap number) is N, but retains the same tracking performance as the original algorithm. This fast algorithm is much easier to implement than the existing SWC FTF algorithms.

Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.

Although a large number of query processing algorithms in spatial network database (SNDB) have been studied, there exists little research on route-based queries. Since moving objects move only in spatial networks, route-based queries, like in-route nearest neighbor (IRNN), are essential for Location-based Service (LBS) and Telematics applications. However, the existing IRNN query processing algorithm has a problem in that it does not consider time and space constraints. Therefore, we, in this paper, propose IRNN query processing algorithms which take both time and space constraints into consideration. Finally, we show the effectiveness of our IRNN query processing algorithms considering time and space constraints by comparing them with the existing IRNN algorithm.

This paper presents a new and robust framework for real-coded genetic algorithm, called real-code conditional genetic algorithm (rc-CGA). The most important characteristic of the proposed rc-CGA is the implicit self-adaptive feature of the crossover and mutation mechanism. Besides, a new crossover operator with laplace distribution following a few promising descent directions (FPDD-LX) is proposed for the rc-CGA. The proposed genetic algorithm (rc-CGA+FPDD-LX) is tested using 31 benchmark functions and compared with four existing algorithms. The simulation results show excellent performance of the proposed rc-CGA+FPDD-LX for continuous function optimization.

In the case of Barreto-Naehrig pairing-friendly curves of embedding degree 12 of order r, recent efficient Ate pairings such as R-ate, optimal, and Xate pairings achieve Miller loop lengths of(1/4) ⌊log2 r⌋. On the other hand, the twisted Ate pairing requires (3/4) ⌊log2 r⌋ loop iterations, and thus is usually slower than the recent efficient Ate pairings. This paper proposes an improved twisted Ate pairing using Frobenius maps and a small scalar multiplication. The proposed idea splits the Miller's algorithm calculation into several independent parts, for which multi-pairing techniques apply efficiently. The maximum number of loop iterations in Miller's algorithm for the proposed twisted Ate pairing is equal to the (1/4) ⌊log2 r ⌋ attained by the most efficient Ate pairings.

Recent works on MIMO receiver design were mainly focused on sphere decoding, which provides a trade-off between the performance and complexity by suitably choosing the “radius” or the number of candidates in the search space. Meanwhile, another approach, called poly-diagonalization and trellis detection, has been proposed to compromise the complexity and performance. In this paper, we compare various MIMO receiver algorithms in terms of both performance and complexity. The performance is evaluated in a frequency selective fading channel environment on the basis of orthogonal frequency division multiplexing with channel coding, for which the generation of soft decision values is crucial. The simulations show that the poly-diagonalization approach matches the performance of sphere decoding at similar computational complexity.