The dynamic channel selection mechanism used in existing multi-channel MAC protocols selects an idle data channel based on channel usage information from one-hop neighbor nodes. However, this method can cause multi-channel hidden node problem in multi-hop wireless networks. This letter proposes a new approach to channel selection. Nodes snoop data channels during idle times and then select an idle data channel within the carrier sensing range using both the snooping results and the channel usage information. Our simulation results verify that the proposed channel selection approach can effectively avoid the multi-channel hidden node problem and improve the networkwide performance.

Based on the clonal selection principle proposed by Burnet, in the immune response process there is no crossover of genetic material between members of the repertoire, i.e., there is no knowledge communication during different elite pools in the previous clonal selection models. As a result, the search performance of these models is ineffective. To solve this problem, inspired by the concept of the idiotypic network theory, an expanded lateral interactive clonal selection algorithm (LICS) is put forward. In LICS, an antibody is matured not only through the somatic hypermutation and the receptor editing from the B cell, but also through the stimuli from other antibodies. The stimuli is realized by memorizing some common gene segment on the idiotypes, based on which a lateral interactive receptor editing operator is also introduced. Then, LICS is applied to several benchmark instances of the traveling salesman problem. Simulation results show the efficiency and robustness of LICS when compared to other traditional algorithms.

Cooperation can increase the system performance by obtaining the spatial diversity. While most of the present works concentrate on the analysis of the cooperation based on the inter-user channel response and developing a scheme for higher cooperative diversity, in this paper, we focus on practical resource allocation in OFDMA systems. Since the user who uses the same center frequency can not receive the signal when transmitting, this constraint should be considered to apply the cooperation to OFDMA systems. In this paper, we propose the pair-based OFDMA frame structure that overcomes this constraint. Also in this frame structure to achieve the minimum outage probability of system, we select the best partner among the candidate neighbors and allocate the suitable subchannels to bandwidth requested users through a cooperative subchannel allocation (CSA) algorithm. In order to evaluate the proposed resource allocation scheme, we carry out simulations based on IEEE 802.16e. The simulation results show that our proposed algorithm offers smaller outage probability than one based on non-cooperative communications and we get the minimum outage probability when a threshold for selection of candidate neighbors is 10 dB. We analyze that these results can be achieved by helping users located around the edge of the cell.

In this letter, we develop a two-step receive antenna selection method to maximize channel capacity. Different from previous work, we first derive a lower bound on capacity based on Hadamard inequality and arithmetic-geometric mean inequality, which is then used to iteratively drop the worst-performing antennas according to their measure. The recursive nature of this method helps to largely reduce the computational complexity.

In this letter, we propose a coding mode selection method for the AMR-WB+ audio coder based on a decision tree. In order to reduce computation while maintaining good performance, decision tree classifier is adopted with the closed loop mode selection results as the target classification labels. The size of the decision tree is controlled by pruning, so the proposed method does not increase the memory requirement significantly. Through an evaluation test on a database covering both speech and music materials, the proposed method is found to achieve a much better mode selection accuracy compared with the open loop mode selection module in the AMR-WB+.

This paper proposes an antenna selection method for terminal antennas employing orthogonal polarizations and patterns, which is suitable for outdoor MultiUser Multi-Input Multi-Output (MU-MIMO) systems. In addition, this paper introduces and verifies two other antenna selection methods for comparison. For the sake of simplicity, three orthogonal dipoles are considered, and this antenna configuration using the proposed selection method is compared to an antenna configuration with three vertical or horizontal dipoles. In the proposed antenna selection method, we always choose the vertical dipole, and choose one of two horizontal dipoles, which are orthogonal to each other, based on the Signal-to-Noise Ratio (SNR). We measured the MU-MIMO transmission properties and found that the proposed selection method employing the antenna with orthogonal polarizations and patterns can offer fairly high channel capacity in a multiuser scenario.

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

Multi-user MIMO (Multiple Input Multiple Output) systems, in which multiple Mobile Stations (MSs) equipped with multiple antennas simultaneously communicate with a Base Station (BS) equipped with multiple antennas, at the same frequency, are attracting attention because of their potential for improved transmission performance in wireless communications. In the uplink of Space Division Multiplexing based multi-user MIMO (multi-user MIMO/SDM) systems that do not require full Channel State Information (CSI) at the transmitters, selecting active MS antennas, which corresponds to scheduling transmit antennas, is an effective technique. The Full search Selection Algorithm based on exhaustive search (FSA) has been studied as an optimal active MS antenna selection algorithm for multi-user MIMO systems. Unfortunately, FSA suffers from extreme computational complexity given large numbers of MSs. To solve this problem, this paper introduces the Gram-Schmidt orthogonalization based Selection Algorithm (GSSA) to uplink multi-user MIMO/SDM systems. GSSA is a suboptimal active MS antenna selection algorithm that offers lower computational complexity than the optimal algorithm. This paper evaluates the transmission performance improvement of GSSA in uplink multi-user MIMO/SDM systems under realistic propagation conditions such as spatially correlated BS antennas and clarifies the effectiveness of GSSA.

In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.

This paper presents a coexistence model of IEEE 802.15.4 with IEEE 802.11b interference in fading channels and proposes two adaptive channel allocation schemes. The first avoids the IEEE 802.15.4 interference only and the second avoids both of the IEEE 802.15.4 and IEEE 802.11b interferences. Numerical results show that the proposed algorithms are effective for avoiding interferences and for maximizing network capacity since they select a channel which gives the maximum signal to noise ratio to the system.

In this letter, a simple but effective antenna selection algorithm for orthogonal space-time block codes with a linear complex precoder (OSTBC-LCP) is proposed and compared with two conventional algorithms in temporally and spatially correlated fading channels. The proposed algorithm, which minimizes pairwise error probability (MinPEP) with an error codebook (EC) constructed from the error vector quantization, is shown to provide nearly the same performance of MinPEP based on all possible error vectors, while keeping the complexity close to that of antenna selection algorithm based on maximum power criterion (Maxpower).

In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.

Vector precoding is a nonlinear broadcast precoding scheme in the downlink of multi-user MIMO systems which outperforms linear precoding and THP (Tomlinson-Harashima Precoding). This letter discusses the problem of joint receive antenna selection in the multi-user MIMO downlink with vector precoding. Based on random matrix analysis, we derive a simple heuristic selection criterion using singular value decomposition (SVD) and carry out an exhaustive search to determine for each user which receive antenna should be used. Simulation results reveal that receive antenna selection using our proposed criterion obtains the same diversity order as the optimal selection criterion.

This paper proposes a novel Behavioral Synthesis method that tries to reduce the number of clock cycles under clock cycle time and total functional unit area constraints using special functional units efficiently. Special functional units are designed to have shorter delay and/or smaller area than the cascaded basic functional units for specific operation patterns. For example, a Multiply-Accumulator is one of them. However, special functional units may have less flexibility for resource sharing because intermediate operation results may not be able to be obtained. Hence, almost all conventional methods can not handle special functional units efficiently for the reduction of clock cycles in practical time, especially under a tight area constraint. The proposed method makes it possible to solve module selection, scheduling, and functional unit allocation problems using special functional units in practical time with some heuristics. Experimental results show that the proposed method has achieved maximally 33% reduction of the cycles for a small application and 14% reduction for a realistic application in practical time.

In IEEE 802.11 networks, the access point (AP) selection based on the strongest signal strength often results in the extremely unfair bandwidth allocation among mobile users (MUs). In this paper, we propose a distributed AP selection algorithm to achieve a fair bandwidth allocation for MUs. The proposed algorithm gradually balances the AP loads based on max-min fairness for the available multiple bit rate choices in a distributed manner. We analyze the stability and overhead of the proposed algorithm, and show the improvement of the fairness via computer simulation.

Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.

We investigate the MIMO broadcast channels with imperfect channel knowledge due to estimation error and much more users than transmit antennas to exploit multiuser diversity. The channel estimation error causes the interference among users, resulting in the sum-rate loss. A tight upper bound of this sum-rate loss based on zeroforcing beamforming is derived theoretically. This bound only depends on the channel estimation quality and transmit antenna number, but not on the user number. Based on this upper bound, we show this system maintains full multiuser diversity, and always benefits from the increasing transmit power.

This paper proposes a technique for building an effective speech corpus with lower cost by utilizing a statistical multidimensional scaling method. The statistical multidimensional scaling method visualizes multiple HMM acoustic models into two-dimensional space. At first, a small number of voice samples per speaker is collected; speaker adapted acoustic models trained with collected utterances, are mapped into two-dimensional space by utilizing the statistical multidimensional scaling method. Next, speakers located in the periphery of the distribution, in a plotted map are selected; a speech corpus is built by collecting enough voice samples for the selected speakers. In an experiment for building an isolated-word speech corpus, the performance of an acoustic model trained with 200 selected speakers was equivalent to that of an acoustic model trained with 533 non-selected speakers. It means that a cost reduction of more than 62% was achieved. In an experiment for building a continuous word speech corpus, the performance of an acoustic model trained with 500 selected speakers was equivalent to that of an acoustic model trained with 1179 non-selected speakers. It means that a cost reduction of more than 57% was achieved.

To achieve high recognition performance for a wide variety of noise and for a wide range of signal-to-noise ratio, this paper presents methods for integration of four noise reduction algorithms: spectral subtraction with smoothing of time direction, temporal domain SVD-based speech enhancement, GMM-based speech estimation and KLT-based comb-filtering. In this paper, we proposed two types of combination methods of noise suppression algorithms: selection of front-end processor and combination of results from multiple recognition processes. Recognition results on the CENSREC-1 task showed the effectiveness of our proposed methods.

The combination of Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) technologies gives wireless communications systems the advantages of lower bit error rate (BER) and higher data rate in frequency-selective fading environments. However, the main drawbacks of MIMO systems are their high complexity and high cost. Therefore, antenna selection in MIMO systems has been shown to be an effective way to overcome the drawbacks. In this paper, we propose two receive antenna selection methods for a MIMO-OFDM system with radio frequency (RF) switches and polarization antenna elements at the receiver side, taking into consideration low computational complexity. The first method selects a set of polarization antenna elements which gives lower correlation between received signals and larger received signal power, thus achieves a lower BER with low computational complexity. The second method first selects a set of polarization antenna elements based on the criterion of the first method and another set of polarization antenna elements based on the criterion of minimizing the correlation between the received signals; it then calculates the signal-to-interference-plus-noise power ratio (SINR) of the two sets and selects a set with larger SINR. As a result, the second method achieves a better BER than the first one but it also requires higher computational complexity than the first one. We use the measured channel data to evaluate the performance of the two methods and show that they work effectively for the realistic channel.