This letter presents a new automatic musical genre classification method based on an informative song-level representation, in which the mutual information between the feature and the genre label is maximized. By efficiently combining distance-based indexing with informative features, the proposed method represents a song as one vector instead of complex statistical models. Experiments on an audio genre DB show that the proposed method can achieve the classification accuracy comparable or superior to the state-of-the-art results.

An algorithm for the discrimination between human upstairs and downstairs using a tri-axial accelerometer is presented in this paper, which consists of vertical acceleration calibration, extraction of two kinds of features (Interquartile Range and Wavelet Energy), effective feature subset selection with the wrapper approach, and SVM classification. The proposed algorithm can recognize upstairs and downstairs with 95.64% average accuracy for different sensor locations, i.e. located on the subject's waist belt, in the trousers pocket, and in the shirt pocket. Even for the mixed data from all sensor locations, the average recognition accuracy can reach 94.84%. Experimental results have successfully validated the effectiveness of the proposed method.

In this letter, an AGV based relay selection mechanism is developed to ensure relays reporting true information in wireless relay networks. The source selects relays based on the channel state information (CSI) of relay-destination links. Selfish relays may report fake CSI in order to obtain a better chance of being selected, whereas the source is not able to tell the reported in real or in false. In the proposed scheme, a relay node receives some payoffs from the destination with respect to the achievable data rate and also some compensations from the others in terms of the reported CSI of all relays. This mechanism not only enforces truth-telling upon relay nodes with maximum payoff but also ensures fairness among them. The equilibrium of payoff is attained when relay nodes report their true CSI. Simulation results demonstrate the theoretical solutions.

The Medium Access Control (MAC) protocol that uses non-overlapping multiple channels, called the multi-channel MAC protocol, was proposed in order to increase the capacity of ad hoc networks. Since the number of packet interfaces on each node is less than the number of channels in ad hoc networks in general, the node needs to select a suitable channel for data transmission. This means that the multi-channel MAC protocol must be provided with a good channel selection algorithm. In this paper, we design a channel selection algorithm called Conditionally Randomized Channel Selection (CRCS) based on Extended Receiver Directed Transmission (xRDT) protocol that only uses one packet interface. Briefly, CRCS uses the acitve channel for data transmission until the amount of data packets reaches a threshold, at which point it selects one of the available channels other than the active channel. Although CRCS is a very simple channel selection algorithm, by using network simulator we find that CRCS is effective to increase the capacity of ad hoc networks and to keep the load balance of all channels compared to the other channel selection algorithms.

This paper presents an improved Gini-Index algorithm to correct feature-selection bias in text classification. Gini-Index has been used as a split measure for choosing the most appropriate splitting attribute in decision tree. Recently, an improved Gini-Index algorithm for feature selection, designed for text categorization and based on Gini-Index theory, was introduced, and it has proved to be better than the other methods. However, we found that the Gini-Index still shows a feature selection bias in text classification, specifically for unbalanced datasets having a huge number of features. The feature selection bias of the Gini-Index in feature selection is shown in three ways: 1) the Gini values of low-frequency features are low (on purity measure) overall, irrespective of the distribution of features among classes, 2) for high-frequency features, the Gini values are always relatively high and 3) for specific features belonging to large classes, the Gini values are relatively lower than those belonging to small classes. Therefore, to correct that bias and improve feature selection in text classification using Gini-Index, we propose an improved Gini-Index (I-GI) algorithm with three reformulated Gini-Index expressions. In the present study, we used global dimensionality reduction (DR) and local DR to measure the goodness of features in feature selections. In experimental results for the I-GI algorithm, we obtained unbiased feature values and eliminated many irrelevant general features while retaining many specific features. Furthermore, we could improve the overall classification performances when we used the local DR method. The total averages of the classification performance were increased by 19.4 %, 15.9 %, 3.3 %, 2.8 % and 2.9 % (kNN) in Micro-F1, 14 %, 9.8 %, 9.2 %, 3.5 % and 4.3 % (SVM) in Micro-F1, 20 %, 16.9 %, 2.8 %, 3.6 % and 3.1 % (kNN) in Macro-F1, 16.3 %, 14 %, 7.1 %, 4.4 %, 6.3 % (SVM) in Macro-F1, compared with tf*idf, χ2, Information Gain, Odds Ratio and the existing Gini-Index methods according to each classifier.

One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.

In femto/macro cellular networks, the stability and fairness problems caused by the unplanned and random characteristic of femtocells must be solved. By applying queueing theory in IP based femto/macro cellular networks, we found the stability condition, and described two kinds of cell section policies of users. As a main contribution, we provided the adaptive channel distribution algorithm which minimizes the average packet sojourn time at transmitting systems and keeps the whole systems stable and fair among cells. Through experiments in various environments, we analyzed the influence of channel reuse factor, cell selection policies, and the number of femtocells on system performance.

This letter introduces a closed form expression for the channel capacity increase achieved by adding a new pair of transmit and receive antennas. By analyzing this expression, an iterative transmit/receive antenna selection algorithm of low computational complexity is proposed. The new algorithm has higher computational complexity than some existing algorithms, but as the results show, the performance improvement of the proposed algorithm approaching more to the optimal algorithm.

This paper proposes a new method to reduce the cost of nearest neighbor searches in metric spaces. Many similarity search indexes recursively divide a region into subregions by using pivots, and construct a tree-structured index. Most of recently developed indexes focus on pruning objects and do not pay much attention to the tree balancing. As a result, indexes having imbalanced tree-structure may be constructed and the search cost is degraded. We propose a similarity search index called the Partitioning Capacity (PC) Tree. It selects the optimal pivot in terms of the PC that quantifies the balance of the regions partitioned by a pivot as well as the estimated effectiveness of the search pruning by the pivot. As a result, PCTree reduces the search cost for various data distributions. We experimentally compared PCTree with four indexes using synthetic data and five real datasets. The experimental results shows that the PCTree successfully reduces the search cost.

Ad Hoc Routing (AHR) was proposed to replace optimal routing in cluster-based multihop networks since it offers lower implementation complexity. However, this complexity reduction comes at the cost of an increase in the required transmission power. In addition, when the conventional distributed relay selection is applied to implement AHR, another increase in the required transmission power occurs due to the receiver selection error. In this paper, Ad Hoc Cooperative Routing (AHCR) that integrates the cooperative transmission with AHR is presented to reduce the difference between the required transmission power of AHR and that of optimal routing. Besides, Distributed Ad Hoc Cooperative Routing (DAHCR) scheme 1 that combines the cooperative transmission with AHR is proposed to reduce the difference between the required transmission power of DAHR and that of AHR. We then address the problem of DAHCR scheme 1 and propose DAHCR scheme 2. Simulation results show that the required transmission power of AHCR and DAHCR scheme 1 is less than that of AHR and DAHR, respectively. In addition, DAHCR scheme 2 further reduces the required transmission power of DAHCR scheme 1. On the other hand, DAHCR scheme 1 increases the complexity by 43% compared to DAHR. Besides, DAHCR scheme 2 increases the complexity by 1.97% compared to DAHCR scheme 1.

This letter presents structurally simpler symbol error rate (SER) expressions for Transmit Antenna Selection/Maximal-Ratio Combining (TAS/MRC) scheme in independent Nakagami-m fading channels in a comparison with those in the literature. First, the SER is derived as a single infinite series of simple functions for arbitrary m. For integer m, the SER can be attained as a closed-form expression with a double finite series. Moreover, simple asymptotic SER expressions suggest that the TAS/MRC scheme can achieve a full diversity order at high SNR. Numerical and simulation results verify the conciseness of the derived expressions.

Routing is still a challenging issue for wireless sensor networks (WSNs), in particular for WSNs with a non-uniform deployment of nodes. This paper introduces a Node Aggregation Degree-aware Random Routing (NADRR) algorithm for non-uniform WSNs with the help of two new concepts, namely the Local Vertical Aggregation Degree (LVAD) and Local Horizontal Aggregation Degree (LHAD). Our basic idea is to first apply the LVAD and LHAD to determine one size-proper forwarding region (rather than a fixed-size one as in uniform node deployment case) for each node participating in routing, then select the next hop node from the size-proper forwarding region in a probabilistic way, considering both the residual energy and distribution of nodes. In this way, a good adaptability to the non-uniform deployment of nodes can be guaranteed by the new routing algorithm. Extensive simulation results show that in comparison with other classical geographic position based routing algorithms, such as GPSR, TPGF and CR, the proposed NADRR algorithm can result in lower node energy consumption, better balance of node energy consumption, higher routing success rate and longer network lifetime.

The selection cooperation is a basic and attractive scheme of cooperative diversity in the multiple relays scenario. Most previous schemes of selection cooperation consist only one relay-stage in which one relay is selected to retransmit, and the signal from the selected relay is not utilized by other relays. In this paper, we introduce a two relay-stage selection cooperation scheme. The performance can be improved by letting all other relays to utilize the signal from the first selected relay to make another selection and retransmission in the second relay-stage. We derive the closed-form expression of the outage probability of the proposed scheme in the high SNR regime. Both theoretical and numerical results suggest that the proposed scheme can reduce the outage probability compared with the traditional scheme with only one relay-stage. Furthermore, we demonstrate that more than two relay-stage can not further reduce the outage probability. We also study the dependence of the proposed scheme on stage lengths and topology, and analyze the increased overhead.

We deal with a path selection problem for heterogeneous wireless networks integrated with Frequency Agile Access Points. Our goal is to find the minimum achievable amount of radio resources required to set up a transmission path. We propose to formulate the path selection approach as a minimum cost flow problem.

In this letter, we reveal redundant control traffic in the optimized link state routing protocol (OLSR) for MANET. Topology control (TC) messages, which occupy a part of control traffic in OLSR, are used to exchange topology information with other nodes. TC messages are generated and forwarded by only nodes that have been selected as multipoint relays (MPRs) by at least one neighbor node. These nodes selected as MPRs are called TC message senders in this letter. One of solutions to reduce the number of TC messages is to reduce the number of TC message senders. We describe a non-distributed algorithm to minimize the number of TC message senders. Through simulation of static-node scenarios, we show 18% to 37% of TC message senders in RFC-based OLSR are redundant. By eliminating redundant TC message senders, the number of TC packets, each of which contains one or more TC messages, is also reduced from 19% to 46%. We also show that high density scenarios have more redundancy than low density scenarios. This observation can help to consider a cooperative MPR selection in OLSR.

The performance of the kernel-based learning algorithms, such as SVM, depends heavily on the proper choice of the kernel parameter. It is desirable for the kernel machines to work on the optimal kernel parameter that adapts well to the input data and the learning tasks. In this paper, we present a novel method for selecting Gaussian kernel parameter by maximizing a class separability criterion, which measures the data distribution in the kernel-induced feature space, and is invariant under any non-singular linear transformation. The experimental results show that both the class separability of the data in the kernel-induced feature space and the classification performance of the SVM classifier are improved by using the optimal kernel parameter.

Fractional sampling (FS) and Doppler diversity equalization in OFDM receivers can achieve two types of diversity (path diversity and frequency diversity) simultaneously on time-varying multipath channels. However FS with a higher sampling rate requires the large amount of complexity in demodulation. In this paper, a novel sampling point selection (SPS) scheme with MMSE equalization in FS-OFDM receivers is proposed. On fast time-varying multipath channels, the proposed scheme selects the appropriate samples from the fractionally sampled signals. Through the computer simulation, it is demonstrated that with the proposed scheme, both path diversity gain and Doppler diversity gain can increase as compared to a conventional non-SPS scheme.

A new best-relay selection scheme is proposed in this letter in order to maintain a reliable cooperative communications for ubiquitous sensor networks in indoor environments. The suggested technique relies on eigenvalue decomposition to select the best relay. The simulation results confirm that the performance of the proposed approach is better than that of the previous scheme in indoor environments.

Switch and stay combining (SSC) is an attractive diversity technique due to its low complexity and compatibility to resource-constrained wireless networks. This letter proposes a distributed SSC for partial relay selection networks in order to achieve spatial diversity as well as to improve spectral efficiency. Simulation results show that the performance loss (in terms of bit error probability) of the proposed networks relative to partial relay selection networks with selection combining is not substantial.

This paper presents a combined feature extraction method to improve the performance of bag-of-features image classification. We apply 10 relevant operations to global/local statistics of visual words. Because the pairwise combination of visual words is large, we apply feature selection methods including fisher discriminant criterion and L1-SVM. The effectiveness of the proposed method is confirmed through the experiment.