A new artificial fish swarm algorithm (AFSA) for solving the multiple knapsack problem (MKP) is introduced in this paper. In the proposed AFSA, artificial fish (AF) individuals are only allowed to search the region near constraint boundaries of the problem to be solved. For this purpose, several behaviors to be performed by AF individuals, including escaping behavior, randomly moving behavior, preying behavior and following behavior, were specially designed. Exhaustive experiments were implemented in order to investigate the proposed AFSA's performance. The results demonstrated the proposed AFSA has the ability of finding high-quality solutions with very fast speed, as compared with some other versions of AFSA based on different constraint-handling methods. This study is also meaningful for solving other constrained problems.

Multiple access (MA) technology is of most importance for beyond long term evolution (LTE) system. Non-orthogonal multiple access (NOMA) utilizing power domain and advanced receiver has been considered as a candidate MA technology recently. In this paper, power assignment method, which plays a key role in performance of NOMA, is investigated. The power assignment on the basis of maximizing geometric mean user throughput requires exhaustive search and thus has an unacceptable computational complexity for practical systems. To solve this problem, a novel power assignment method is proposed by exploiting tree search and characteristic of serial interference cancellation (SIC) receiver. The proposed method achieves the same performance as the exhaustive search while greatly reduces the computational complexity. On the basis of the proposed power assignment method, the performance of NOMA is investigated by link-level and system-level simulations in order to provide insight into suitability of using NOMA for future MA. Simulation results verify effectiveness of the proposed power assignment method and show NOMA is a very promising MA technology for beyond LTE system.

We consider two methods for constructing high rate punctured convolutional codes. First, we present the best high rate R=(n-1)/n punctured convolutional codes, for n=5,6,…,16, which are obtained by exhaustive searches. To obtain the best code, we use a regular convolutional code whose weight spectrum is equivalent to that of each punctured convolutional code. We search these equivalent codes for the best one. Next, we present a method that searches for good punctured convolutional codes by partial searches. This method searches the codes that are derived from rate 1/2 original codes obtained in the first method. By this method, we obtain some good punctured convolutional codes relatively faster than the case in which we search for the best codes.

This paper describes a near-optimal allocation method for web-based multi-player online role-playing games (MORPGs), which must be able to cope with a large number of users and high frequency of user requests. Our previous work introduced a dynamic data reallocation method. It uses multiple web servers and divides the entire game world into small blocks. Each ownership of block is allocated to a web server. Additionally, the ownership is reallocated to the other web server according to the user's requests. Furthermore, this block allocation was formulated as a combinational optimization problem. And a simulation based experiment with an exact algorithm showed that our system could achieve 31% better than an ad-hoc approach. However, the exact algorithm takes too much time to solve a problem when the problem size is large. This paper proposes a meta-heuristic approach based on a tabu search to solve a problem quickly. A simulation result shows that our tabu search algorithm can generate solutions, whose average correctness is only 1% different from that of the exact algorithm. In addition, the average calculation time for 50 users on a system with five web servers is about 25.67 msec while the exact algorithm takes about 162 msec. An evaluation for a web-based MORPG system with our tabu search shows that it could achieve 420 users capacity while 320 for our previous system.

Nature-inspired devices and architectures are attracting considerable attention for various purposes, including the development of novel computing techniques based on spatiotemporal dynamics, exploiting stochastic processes for computing, and reducing energy dissipation. This paper demonstrates that networks of optical energy transfers between quantum nanostructures mediated by optical near-field interactions occurring at scales far below the wavelength of light could be utilized for solving a constraint satisfaction problem (CSP), the satisfiability problem (SAT), and a decision making problem. The optical energy transfer from smaller quantum dots to larger ones, which is a quantum stochastic process, depends on the existence of resonant energy levels between the quantum dots or a state-filling effect occurring at the larger quantum dots. Such a spatiotemporal mechanism yields different evolutions of energy transfer patterns in multi-quantum-dot systems. We numerically demonstrate that networks of optical energy transfers can be used for solution searching and decision making. We consider that such an approach paves the way to a novel physical informatics in which both coherent and dissipative processes are exploited, with low energy consumption.

This letter presents a technique to reduce the complexity of the soft-output multiple-input multiple-output symbol detection based on Dijkstra's algorithm. By observing that the greedy behavior of Dijkstra's algorithm can entail unnecessary tree-visits for the symbol detection, this letter proposes a technique to evict non-promising candidates early from the search space. The early eviction technique utilizes layer information to determine if a candidate is promising, which is simple but effective. When the SNR is 30dB for 6×6 64-QAM systems, the average number of tree-visits in the proposed method is reduced by 72.1% in comparison to that in the conventional Dijkstra's algorithm-based symbol detection without the early eviction.

Ternary content addressable memory (TCAM) is popular LSI for use in high-throughput forwarding engines on routers. However, the unique structure applied in TCAM consume huge amounts of power, therefore it restricts the ability to handle large lookup table capacity in IP routers. In this paper, we propose a commodity-memory based hardware architecture for the forwarding information base (FIB) application that solves the substantial problems of power and density. The proposed architecture is examined by a fabricated test chip with 40 nm embedded DRAM (eDRAM) technology, and the effect of power reduction verified is greatly lower than conventional TCAM based and the energy metric achieve 0.01 fJ/bit/search. The power consumption is almost 0.5 W at 250 Msps and 8M entries.

Corpus-based concatenative speech synthesis has been widely investigated and deployed in recent years since it provides a highly natural synthesized speech quality. The amount of computation required in the run time, however, can often be quite large. In this paper, we propose early stopping schemes for Viterbi beam search in the unit selection, with which we can stop early in the local Viterbi minimization for each unit as well as in the exploration of candidate units for a given target. It takes advantage of the fact that the space of the acoustic parameters of the database units is fixed and certain lower bounds of the concatenation costs can be precomputed. The proposed method for early stopping is admissible in that it does not change the result of the Viterbi beam search. Experiments using probability-based concatenation costs as well as distance-based costs show that the proposed methods of admissible stopping effectively reduce the amount of computation required in the Viterbi beam search while keeping its result unchanged. Furthermore, the reduction effect of computation turned out to be much larger if the available lower bound for concatenation costs is tighter.

Many deterministic small-world network models have been proposed so far, and they have been proven useful in describing some real-life networks which have fixed interconnections. Search efficiency is an important property to characterize small-world networks. This paper tries to clarify how the search procedure behaves when random walks are performed on small-world networks, including the classic WS small-world network and three deterministic small-world network models: the deterministic small-world network created by edge iterations, the tree-structured deterministic small-world network, and the small-world network derived from the deterministic uniform recursive tree. Detailed experiments are carried out to test the search efficiency of various small-world networks with regard to three different types of random walks. From the results, we conclude that the stochastic model outperforms the deterministic ones in terms of average search steps.

We propose a novel threshold-free salient object detection approach which integrates both saliency density and edge response. The salient object with a well-defined boundary can be automatically detected by our approach. Saliency density and edge response maximization is used as the quality function to direct the salient object discovery. The global optimal window containing a salient object is efficiently located through the proposed saliency density and edge response based branch-and-bound search. To extract the salient object with a well-defined boundary, the GrabCut method is applied, initialized by the located window. Experimental results show that our approach outperforms the methods only using saliency or edge response and achieves a comparable performance with the best state-of-the-art method, while being without any threshold or multiple iterations of GrabCut.

With the successful adoption of link analysis techniques such as PageRank and web spam filtering, current web search engines well support “navigational search”. However, due to the use of a simple conjunctive Boolean filter in addition to the inappropriateness of user queries, such an engine does not necessarily well support “informational search”. Informational search would be better handled by a web search engine using an informational retrieval model combined with enhancement techniques such as query expansion and relevance feedback. Moreover, the realization of such an engine requires a method to prosess the model efficiently. In this paper we propose a novel extension of an existing top-k query processing technique to improve search efficiency. We add to it the technique utilizing a simple data structure called a “term-document binary matrix,” resulting in more efficient evaluation of top-k queries even when the queries have been expanded. We show on the basis of experimental evaluation using the TREC GOV2 data set and expanded versions of the evaluation queries attached to this data set that the proposed method can speed up evaluation considerably compared with existing techniques especially when the number of query terms gets larger.

Initial cell search in wideband code-division multiple-access (W-CDMA) systems is a challenging process. On the one hand, channel impairments such as multipath fading, Doppler shift, and noise create frequency and time offsets in the received signal. On the other hand, the residual synchronization error of the crystal oscillator at the mobile station also causes time and frequency offsets. Such offsets can affect the ability of a mobile station to perform cell search. Previous work concentrated on cell synchronization algorithms that considered multipath channels and frequency offsets, but ignored clock and timing offsets due to device tolerances. This work discusses a robust initial cell search algorithm, and quantifies its performance in the presence of frequency and time offsets due to two co-existing problems: channel impairments and clock drift at the receiver. Another desired performance enhancement is the reduction of power consumption of the receiver, which is mainly due to the computational complexity of the algorithms. This power reduction can be achieved by reducing the computational complexity by a divide and conquer strategy during the synchronization process.

We present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a running-shoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA.

This paper presents an approach based on character recognition to searching for keywords in on-line handwritten Japanese text. It employs an on-line character classifier and an off-line classifier or a combined classifier, which produce recognition candidates, and it searches for keywords in the lattice of candidates. It integrates scores to individually recognize characters and their geometric context. We use quadratic discriminant function(QDF) or support vector machines(SVM) models to evaluate the geometric features of individual characters and the relationships between characters. This paper also presents an approach based on feature matching that employs on-line or off-line features. We evaluate three recognition-based methods, two feature-matching-based methods, as well as ideal cases of the latter and concluded that the approach based on character recognition outperformed that based on feature matching.

We propose a new hidden vector encryption (HVE) scheme that we call a ciphertext-policy delegatable hidden vector encryption (CP-dHVE) scheme. Several HVE schemes have been proposed and their properties have been analyzed extensively. Nonetheless, the definition of the HVE has been left unchanged. We therefore reconsider it, and point out that the conventional HVE should be categorized as the key-policy HVE, because the vectors corresponding to the secret keys can contain wildcards (which specify an access policy) whereas those corresponding to the ciphertexts cannot contain them. We then formalize its dual concept, the ciphertext-policy HVE, and propose a concrete scheme. Then, as an application of our scheme, we propose a public-key encryption with conjunctive keyword search scheme that can be used in the hierarchical user systems. Our scheme is novel in that the ciphertext size grows logarithmically to the number of uses in the system, while that of a conventional scheme grows linearly.

In this paper, a block-constrained trellis coded vector quantization (BC-TCVQ) algorithm is combined with an algebraic codebook to produce an algebraic trellis vector code (ATVC) to be used in ACELP coding. ATVC expands the set of allowed algebraic codebook pulse position, and the trellis branches are labeled with these subsets. The Viterbi algorithm is used to select the excitation codevector. A fast codebook search method using an efficient non-exhaustive search technique is also proposed to reduce the complexity of the ATVC search procedure while maintaining the quality of the reconstructed speech. The ATVC block code is used as the fixed codebook of AMR-NB (12.2 kbps), which reduces the computational complexity compared to the conventional algebraic codebook.

In this paper, we propose a hardware solution for overcoming the problem of high computational demands in a nearest neighbor (NN) based multi-prototype learning system. The multiple prototypes are obtained by a high-speed K-means clustering algorithm utilizing a concept of software-hardware cooperation that takes advantage of the flexibility of the software and the efficiency of the hardware. The one nearest neighbor (1-NN) classifier is used to recognize an object by searching for the nearest Euclidean distance among the prototypes. The major deficiency in conventional implementations for both K-means and 1-NN is the high computational demand of the nearest neighbor searching. This deficiency is resolved by an FPGA-implemented coprocessor that is a VLSI circuit for searching the nearest Euclidean distance. The coprocessor requires 12.9% logic elements and 58% block memory bits of an Altera Stratix III E110 FPGA device. The hardware communicates with the software by a PCI Express (4) local-bus-compatible interface. We benchmark our learning system against the popular case of handwritten digit recognition in which abundant previous works for comparison are available. In the case of the MNIST database, we could attain the most efficient accuracy rate of 97.91% with 930 prototypes, the learning speed of 1.310-4 s/sample and the classification speed of 3.9410-8 s/character.

The mining of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences. A method, called GTRACE, has been proposed to mine frequent patterns from graph sequences under the assumption that changes in graphs are gradual. Although GTRACE mines the frequent patterns efficiently, it still needs substantial computation time to mine the patterns from graph sequences containing large graphs and long sequences. In this paper, we propose a new version of GTRACE that permits efficient mining of frequent patterns based on the principle of a reverse search. The underlying concept of the reverse search is a general scheme for designing efficient algorithms for hard enumeration problems. Our performance study shows that the proposed method is efficient and scalable for mining both long and large graph sequence patterns and is several orders of magnitude faster than the original GTRACE.

To provide scalability against group size, Global Location Search based Hierarchical Geographic Multicast Protocols (GLS-HGMPs) have recently been proposed for wireless sensor networks. To reduce the communication overhead imposed by the global location search and prevent the multicast data detour imposed by the hierarchical geographic multicasting in GLS-HGMPs, this letter proposes Local Location Search based Progressive Geographic Multicast Protocol (LLS-PGMP). Simulation results show that LLS-PGMP is superior to GLS-HGMPs.

A method of identifying JPEG 2000 images with different coding parameters, such as code-block sizes, quantization-step sizes, and resolution levels, is presented. It does not produce false-negative matches regardless of different coding parameters (compression rate, code-block size, and discrete wavelet transform (DWT) resolutions levels) or quantization step sizes. This feature is not provided by conventional methods. Moreover, the proposed approach is fast because it uses the number of zero-bit-planes that can be extracted from the JPEG 2000 codestream by only parsing the header information without embedded block coding with optimized truncation (EBCOT) decoding. The experimental results revealed the effectiveness of image identification based on the new method.