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.

In this paper, cell searching and direction-of-arrival (DoA) estimation methods are proposed for mobile relay stations with a uniform linear arrays in OFDM-based cellular systems. The proposed methods can improve the performance of cell searching and DoA estimation, even when there exist symbol timing offsets among the signals received from adjacent base stations and Doppler frequency shifts caused by the movement of the mobile relay station. The performances and computational complexities of the proposed cell searching and DoA estimation methods are evaluated by computer simulation under a mobile WiMAX environment.

Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW +s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by Class-Bench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.

In multimedia communication, due to the limited computational capability of the personal information machine, a coder with low computational complexity is needed to integrate services from several media sources. This paper presents two efficient candidate schemes to simplify the most computationally demanding operation, the excitation codebook search procedure. For fast adaptive codebook search, we propose an algorithm that uses residual signals to predict the candidate gain-vectors of the adaptive codebook. For the fixed codebook, we propose a fast search algorithm using an energy function to predict the candidate pulses, and we redesign the codebook structure to twin multi-track positions architecture. Overall simulation results indicate that the average perceptual evaluation of speech quality (PESQ) score is degraded slightly, by 0.049, and our proposed methods can reduce total computational complexity by about 67% relative to the original G.723.1 encoder computation load, and with perceptually negligible degradation. Objective and subjective evaluations verify that the more efficient candidate schemes we propose can provide speech quality comparable to that using the original coder approach.

This paper discusses the task of human action detection. It requires not only classifying what type the action of interest is, but also finding actions' spatial-temporal locations in a video. The novelty of this paper lies on two significant aspects. One is to introduce a new graph based representation for the search space in a video. The other is to propose a novel sub-volume search method by Minimum Cycle detection. The proposed method has a low computation complexity while maintaining a high action detection accuracy. It is evaluated on two challenging datasets which are captured in cluttered backgrounds. The proposed approach outperforms other state-of-the-art methods in most situations in terms of both Precision-Recall values and running speeds.

For personalized search, a user must provide her personal information. However, this sometimes includes the user's sensitive information about individuals such as health condition and private lifestyle. It is not sufficient just to protect the communication channel between user and service provider. Unfortunately, the collected personal data can potentially be misused for the service providers' commercial advantage (e.g. for advertising methods to target potential consumers). Our aim here is to protect user privacy by filtering out the sensitive information exposed from a user's query input at the system level. We propose a framework by introducing the concept of query generalizer. Query generalizer is a middleware that takes a query for personalized search, modifies the query to hide user's sensitive personal information adaptively depending on the user's privacy policy, and then forwards the modified query to the service provider. Our experimental results show that the best-performing query generalization method is capable of achieving a low traffic overhead within a reasonable range of user privacy. The increased traffic overhead varied from 1.0 to 3.3 times compared to the original query.

Carrier aggregation (CA) is one of the most important techniques for LTE-Advanced because of its capability to support a wide transmission bandwidth of up to 100 MHz and heterogeneous networks effectively while achieving backward compatibility with the Release 8 LTE. In order to improve the performance of control information transmission in heterogeneous networks, cross-carrier scheduling is supported, i.e., control information on one component carrier (CC) can assign radio resources on another CC. To convey the control information efficiently, a search space is defined and used in Release 8 LTE. In cross-carrier scheduling, the optimum design for the search space for different CCs is a paramount issue. This paper presents two novel methods for search space design. In the first method using one hash function, a user equipment (UE)-specific offset is introduced among search spaces associated with different CCs. Due to the UE-specific offsets, search spaces of different UEs are staggered and the probability that the search space of one UE is totally overlapped by that of another UE can be greatly reduced. In the second method using multiple hash functions, a novel randomization scheme is proposed to generate independent hash functions for search spaces of different CCs. Because of the perfect randomization effect of the proposed method, search space overlapping of different UEs is reduced. Simulation results show that both the proposed methods effectively reduce the blocking probability of the control information compared to existing methods.

Although IP address lookup schemes using ternary content addressable memory (TCAM) can perform high speed packet forwarding, TCAM is much more expensive than ordinary memory in implementation cost. As a low-cost solution, binary search algorithms such as a binary trie or a binary search tree have been widely studied. This paper proposes an efficient IP address lookup scheme using balanced binary search with minimal entries and optimal prefix vectors. In the previous scheme with prefix vectors, there were numerous pairs of nearly identical entries with duplicated prefix vectors. In our scheme, these overlapping entries are combined, thereby minimizing entries and eliminating the unnecessary prefix vectors. As a result, the small balanced binary search tree can be constructed and used for a software-based address lookup in small-sized routers. The performance evaluation results show that the proposed scheme offers faster lookup speeds along with reduced memory requirements.

The dynamic encoding algorithm for searches (DEAS) is a recently developed algorithm that comprises a series of global optimization methods based on variable-length binary strings that represent real variables. It has been successfully applied to various optimization problems, exhibiting outstanding search efficiency and accuracy. Because DEAS manages binary strings or matrices, the decoding rules applied to the binary strings and the algorithm's structure determine the aspects of local search. The decoding rules used thus far in DEAS have some drawbacks in terms of efficiency and mathematical analysis. This paper proposes a new decoding rule and applies it to univariate DEAS (uDEAS), validating its performance against several benchmark functions. The overall optimization results of the modified uDEAS indicate that it outperforms other metaheuristic methods and obviously improves upon older versions of DEAS series.

An efficient pruning method is proposed for the infinity-norm sphere decoding based on Schnorr-Euchner enumeration in multiple-input multiple-output spatial multiplexing systems. The proposed method is based on the characteristics of the infinity norm, and utilizes the information of the layer at which the infinity-norm value is selected in order to decide unnecessary sub-trees that can be pruned without affecting error-rate performance. Compared to conventional pruning, the proposed pruning decreases the average number of tree-visits by up to 37.16% in 44 16-QAM systems and 33.75% in 66 64-QAM systems.

Kennedy has proposed the bare bones particle swarm (BBPS) by the elimination of the velocity formula and its replacement by the Gaussian sampling strategy without parameter tuning. However, a delicate balance between exploitation and exploration is the key to the success of an optimizer. This paper firstly analyzes the sampling distribution in BBPS, based on which we propose an adaptive BBPS inspired by the cloud model (ACM-BBPS). The cloud model adaptively produces a different standard deviation of the Gaussian sampling for each particle according to the evolutionary state in the swarm, which provides an adaptive balance between exploitation and exploration on different objective functions. Meanwhile, the diversity of the swarms is further enhanced by the randomness of the cloud model itself. Experimental results show that the proposed ACM-BBPS achieves faster convergence speed and more accurate solutions than five other contenders on twenty-five unimodal, basic multimodal, extended multimodal and hybrid composition benchmark functions. The diversity enhancement by the randomness in the cloud model itself is also illustrated.

This paper presents a content-addressable memory (CAM) using a phase-change device. A hierarchical match-line structure and a one-hot-spot block code are indispensable to suppress the resistance ratio of the phase-change device and the area overhead of match detectors. As a result, an 8-nsec 72-bit-parallel-search CAM is implemented using a phase-change-device/MOS-hybrid circuitry, where high and low resistances are higher than 2.3 MΩ and lower than 97 kΩ, respectively, while maintaining one-day retention.

This paper proposes a low-complexity signal detection algorithm for spatially correlated multiple-input multiple-output (MIMO) channels. The proposed algorithm sets a minimum mean-square error (MMSE) detection result to the starting point, and searches for signal candidates in multi-dimensions of the noise enhancement from which the MMSE detection suffers. The multi-dimensional search is needed because the number of dominant directions of the noise enhancement is likely to be more than one over the correlated MIMO channels. To reduce the computational complexity of the multi-dimensional search, the proposed algorithm limits the number of signal candidates to O(NT) where NT is the number of transmit antennas and O( ) is big O notation. Specifically, the signal candidates, which are unquantized, are obtained as the solution of a minimization problem under a constraint that a stream of the candidates should be equal to a constellation point. Finally, the detected signal is selected from hard decisions of both the MMSE detection result and unquantized signal candidates on the basis of the log likelihood function. For reducing the complexity of this process, the proposed algorithm decreases the number of calculations of the log likelihood functions for the quantized signal candidates. Computer simulations under a correlated MIMO channel condition demonstrate that the proposed scheme provides an excellent trade-off between BER performance and complexity, and that it is superior to conventional one-dimensional search algorithms in BER performance while requiring less complexity than the conventional algorithms.

Nearest neighbor search (NNS) among large-scale and high-dimensional vectors plays an important role in recent large-scale multimedia search applications. This paper proposes an optimized multiple codebook construction method for an approximate NNS scheme based on product quantization, where sets of residual sub-vectors are clustered according to their distribution and the codebooks for product quantization are constructed from these clusters. Our approach enables us to adaptively select the number of codebooks to be used by trading between the search accuracy and the amount of memory available.

Generally, two problems of bag-of-features in image retrieval are still considered unsolved: one is that spatial information about descriptors is not employed well, which affects the accuracy of retrieval; the other is that the trade-off between vocabulary size and good precision, which decides the storage and retrieval performance. In this paper, we propose a novel approach called Hilbert scan based bag-of-features (HS-BoF) for image retrieval. Firstly, Hilbert scan based tree representation (HSBT) is studied, which is built based on the local descriptors while spatial relationships are added into the nodes by a novel grouping rule, resulting of a tree structure for each image. Further, we give two ways of codebook production based on HSBT: multi-layer codebook and multi-size codebook. Owing to the properties of Hilbert scanning and the merits of our grouping method, sub-regions of the tree are not only flexible to the distribution of local patches but also have hierarchical relations. Extensive experiments on caltech-256, 13-scene and 1 million ImageNet images show that HS-BoF obtains higher accuracy with less memory usage.

This paper presents efficient frame-synchronous beam pruning for HMM-based automatic speech recognition. In the conventional beam pruning, a few hypotheses that have greater potential to reach various words on a lexical tree are likely to be pruned out by a number of hypotheses that have limited potential, since all hypotheses are treated equally without considering this potential. To make the beam pruning less restrictive for hypotheses with greater potential and vice versa, the proposed method adds to the likelihood of each hypothesis a tentative reward as a monotonically increasing function of the number of reachable words from the HMM state where the hypothesis stays in a lexical tree. The reward is designed not to collapse the ASR probabilistic framework. The proposed method reduced 84% of the processing time for a grammar-based 10k-word short sentence recognition task. For a language-model-based dictation task, it also resulted in an additional 23% reduction in processing time from the beam pruning with the language model look-ahead technique.

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.

We propose a new majority voting scheme for identifying downlink primary scrambling code, where two voting processes with different coherent correlation intervals (CCIs) are simultaneously performed. A false alarm probability and a threshold adjustment for the proposed scheme are investigated, and it is shown by computer simulations that the proposed scheme can perform well over a wide range of frequency offsets.

We propose new adaptive tree search algorithms for multiple-input multiple-output (MIMO) systems based on path metric comparison. With the fixed number of survivor paths, the correct path metric may be temporarily larger than the maximum path metric of the survivor paths under an ill-conditioned channel. There have been also adaptive path metric algorithms that control the number of survivor paths according to SNR. However, these algorithms cannot instantaneously adapt to the channel condition. The proposed algorithms accomplish dynamic adaptation based on the ratio of two minimum path metrics as the minimum is significantly smaller than the second minimum under good channel conditions and vice versa. The proposed algorithms are much less complex than the conventional noise variance-based adaptive tree search algorithms while keeping lower or similar error performance. We first employ the proposed adaptive tree search idea to K-best detection and then extend it QRD-M MIMO detection.

This paper presents an adaptive magnification transformation based particle swarm optimizer (AMT-PSO) that provides an adaptive search strategy for each particle along the search process. Magnification transformation is a simple but very powerful mechanism, which is inspired by using a convex lens to see things much clearer. The essence of this transformation is to set a magnifier around an area we are interested in, so that we could inspect the area of interest more carefully and precisely. An evolutionary factor, which utilizes the information of population distribution in particle swarm, is used as an index to adaptively tune the magnification scale factor for each particle in each dimension. Furthermore, a perturbation-based elitist learning strategy is utilized to help the swarm's best particle to escape the local optimum and explore the potential better space. The AMT-PSO is evaluated on 15 unimodal and multimodal benchmark functions. The effects of the adaptive magnification transformation mechanism and the elitist learning strategy in AMT-PSO are studied. Results show that the adaptive magnification transformation mechanism provides the main contribution to the proposed AMT-PSO in terms of convergence speed and solution accuracy on four categories of benchmark test functions.