In this paper, we propose a hybrid M-ary Quadrature Amplitude Modulation (M-QAM) transmission scheme that jointly uses discrete-rate adaptation and selection combining for singular value decomposition (SVD)-based multiple-input multiple-output (MIMO) systems, and derive exact closed-form expressions of the performance of the proposed scheme in terms of the average spectral efficiency and the outage probability.

In this paper, we study the performance of dual hop relaying in which the best relay selected by partial relay selection will help the source-destination link to overcome the channel impairment. Specifically, closed-form expressions for outage probability, symbol error probability and achievable diversity gain are derived using the statistical characteristic of the signal-to-noise ratio. Numerical investigation shows that the system achieves diversity of two regardless of relay number and also confirms the correctness of the analytical results. Furthermore, the performance loss due to partial relay selection is investigated.

This letter proposes a nonlinear DCT discriminant feature extraction approach for face recognition. The proposed approach first selects appropriate DCT frequency bands according to their levels of nonlinear discrimination. Then, this approach extracts nonlinear discriminant features from the selected DCT bands by presenting a new kernel discriminant method, i.e. the improved kernel discriminative common vector (KDCV) method. Experiments on the public FERET database show that this new approach is more effective than several related methods.

In this letter, the effect of the time difference of arrival (TDoA) on synchronous types or asynchronous types of OFDMA-based multi-hop relay (MR) systems is investigated. Signal-to-interference plus noise ratios (SINRs) for downlinks and uplinks are defined to measure the degree of interference due to TDoA in asymmetric MR systems. A path selection technique based on the SINR is then proposed to determine the optimal paths for the uplink and downlink independently. It is shown that asymmetric routing can improve the performance of OFDMA-based MR systems when the proposed technique is applied to the path selection of the uplink taking into account the effect of the TDoA.

The multiobjective route selection problem (m-RSP) is a key research topic in the car navigation system (CNS) for ITS (Intelligent Transportation System). In this paper, we propose an interactive multistage weight-based Dijkstra genetic algorithm (mwD-GA) to solve it. The purpose of the proposed approach is to create enough Pareto-optimal routes with good distribution for the car driver depending on his/her preference. At the same time, the routes can be recalculated according to the driver's preferences by the multistage framework proposed. In the solution approach proposed, the accurate route searching ability of the Dijkstra algorithm and the exploration ability of the Genetic algorithm (GA) are effectively combined together for solving the m-RSP problems. Solutions provided by the proposed approach are compared with the current research to show the effectiveness and practicability of the solution approach proposed.

In a Direct-Sequence/Spread-Spectrum (DS/SS) system, a RAKE receiver is used to improve a bit error rate (BER) performance. The RAKE receiver can collect more signal energy through independent paths and achieve path diversity. The RAKE receiver obtains further diversity gain through fractional sampling. However, the power consumption of the RAKE receiver increases in proportion to a sampling rate and does not always maximize the signal-to-noise ratio (SNR). Therefore, sampling rate selection schemes have been proposed to reduce the average sampling rate without degrading the BER. These schemes select the tap positions and the sampling rate depending on channel conditions and the power consumption can be reduced. In this paper, sampling rate selection schemes for the DS/SS system are investigated through an experiment since there have been no numerical results through an experiment. Numerical results show that the power consumption can be reduced even through the experiment without the degradation of the BER.

In the near future, wireless local area networks (WLANs) will overlap to provide continuous coverage over a wide area. In such ubiquitous WLANs, a mobile node (MN) moving freely between multiple access points (APs) requires not only permanent access to the Internet but also continuous communication quality during handover. In order to satisfy these requirements, an MN needs to (1) select an AP with better performance and (2) execute a handover seamlessly. To satisfy requirement (2), we proposed a seamless handover method in a previous study. Moreover, in order to achieve (1), the Received Signal Strength Indicator (RSSI) is usually employed to measure wireless link quality in a WLAN system. However, in a real environment, especially if APs are densely situated, it is difficult to always select an AP with better performance based on only the RSSI. This is because the RSSI alone cannot detect the degradation of communication quality due to radio interference. Moreover, it is important that AP selection is completed only on an MN, because we can assume that, in ubiquitous WLANs, various organizations or operators will manage APs. Hence, we cannot modify the APs for AP selection. To overcome these difficulties, in the present paper, we propose and implement a proactive AP selection method considering wireless link condition based on the number of frame retransmissions in addition to the RSSI. In the evaluation, we show that the proposed AP selection method can appropriately select an AP with good wireless link quality, i.e., high RSSI and low radio interference.

Wireless LANs, which consist of access points and wireless stations, have widely spread in recent years. Routing in wireless LANs suffers the problem that each wireless station selects an access point and a wired path to its destination station. It is desired to design an adaptive routing protocol for wireless LANs since throughputs of communications are dynamically affected by selections of other wireless stations and external environmental changes. In this paper, we propose a routing protocol for wireless LANs based on attractor selection. Attractor selection is a biologically inspired approach, and it has high adaptability to dynamic environmental changes. By applying attractor selection, each wireless station can adaptively select its access point and wired path with high throughput against environmental changes. In addition, we design the protocol with a new technique: combination of multiple attractor selections. The technique is useful because it enables us to divide a problem into several simpler problems. To the best of our knowledge, our protocol is the first one designed around a combination of multiple attractor selections. We show the effectiveness and adaptability of our protocol by simulations.

Multiuser -- Multiple Input Multiple Output (MU-MIMO) techniques were proposed to increase spectrum efficiency; a key assumption was that the Mobile Terminals (MTs) were simple with only a few antennas. This paper focuses on the Block Diagonalization algorithm (BD) based on the equal power allocation strategy as a practical MU-MIMO technique. When there are many MTs inside the service area of the access point (AP), the AP must determine, at each time slot, the subset of the MTs to be spatially multiplexed. Since the transmission performance depends on the subsets of MTs, the user selection method needs to use the Channel State Information (CSI) obtained in the physical layer to maximize the Achievable Transmission Rate (ATR). In this paper, we clarify the relationship between ATR with SU-MIMO and that with MU-MIMO in a high eigenvalue channel. Based on the derived relationship, we propose a new measure for user selection. The new measure, the eigenvalue decay factor, represents the degradation of the eigenvalues in null space compared to those in SU-MIMO; it is obtained from the signal space vectors of the MTs. A user selection method based on the proposed measure identifies the combination of MTs that yields the highest ATR; our approach also reduces the computational load of user selection. We evaluate the effectiveness of user selection with the new measure using numerical formulations and computer simulations.

We propose a thresholding method based on interval-valued fuzzy sets which are used to define the grade of a gray level belonging to one of the two classes, an object and the background of an image. The effectiveness of the proposed method is demonstrated by comparing our classification results on eight test images to results from the conventional methods.

Three experiments were conducted in this study to investigate the human ability to control pen pressure and pen tilt input, by coupling this control with cursor position, angle and scale. Comparisons between pen pressure input and pen tilt input have been made in the three experiments. Experimental results show that decreasing pressure input resulted in very poor performance and was not a good input technique for any of the three experiments. In "Experiment 1-Coupling to Cursor Position", the tilt input technique performed relatively better than the increasing pressure input technique in terms of time, even though the tilt technique had a slightly higher error rate. In "Experiment 2-Coupling to Cursor Angle", the tilt input performed a little better than the increasing pressure input in terms of time, but the gap between them is not so apparent as Experiment 1. In "Experiment 3-Coupling to Cursor Scale", tilt input performed a little better than increasing pressure input in terms of adjustment time. Based on the results of our experiments, we have inferred several design implications and guidelines.

In OLSR (Optimized Link State Routing Protocol), the multipoint relay mechanism has been introduced to minimize routing overhead for flooding control traffic. In order to achieve as low a routing overhead as possible, the selection of MPRs (multipoint relays) is designed to limit the overall number of such relays in the network. OLSR provides the shortest paths in terms of hops; however, it does not take into account the QoS (quality of service) requirements. Therefore, QOLSR (Quality OLSR), which adds a QoS extension to the OLSR, has been proposed. Although QOLSR provides the best QoS path, its selection process does not consider the number of MPRs, which causes an increase in the routing overhead. In this paper, we focus on the selection process of MPRs in link state QoS routing protocol. We propose three heuristics for high-efficiency selections: MIMS, MQES, and MCIS. The basic approach of these selections is to determine a smaller set of MPRs that provide better QoS paths between any two nodes. The main objective in doing so is to maximize the QoS effect while limiting the routing overhead. In addition, we evaluate the performance of the routing protocols with the proposed selections by simulation. The results indicate that MIMS and MCIS achieve high-efficiency selection; as compared to QOLSR, MIMS reduces the maintenance cost by 30%, while the throughput of the resultant path is decreased by 13%, and MCIS reduces the cost by 21% without any decrease in the throughput.

This paper proposes a scheme to select an appropriate gateway based on multiple metrics such as remaining energy, mobility or speed, and number of hops in Mobile Ad Hoc Network (MANET) and the infrastructure network integration. The Multiple Criteria Decision Making (MCDM) method called Simple Additive Weighting (SAW) is used to rank and to select the gateway node. SAW method calculates the weights of gateway node candidates by considering these three metrics. The node with the highest weight will be selected as the gateway. Simulation results show that our scheme can reduce the average energy consumption of MANET nodes, and improve throughput performance, gateway lifetime, Packet Delivery Ratio (PDR) of the MANET and the infrastructure network.

This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

In this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.

In this paper, a new approach to channel order selection of single-input multiple-output (SIMO), finite impulse response (FIR) channels is proposed for blind channel estimation. The approach utilizes cross spectral density (CSD) of the channel outputs, and minimizes the distance between two CSD's, one calculated non-parametrically from the observed output data, and the other calculated from the blindly estimated channel parameters. The CSD criterion is numerically tested on randomly generated SIMO-FIR channels, and shown to be very effective compared to existing channel order selection methods especially under low SNR settings. Blind estimates of the channels with the selected channel order also show superiority of the CSD criterion.

In multiuser MIMO-BC (Multiple-Input Multiple-Output Broadcasting) systems, user selection is important to achieve multiuser diversity. The optimal user selection algorithm is to try all the combinations of users to find the user group that can achieve the multiuser diversity. Unfortunately, the high calculation cost of the optimal algorithm prevents its implementation. Thus, instead of the optimal algorithm, some suboptimal user selection algorithms were proposed based on semiorthogonality of user channel vectors. The purpose of this paper is to achieve multiuser diversity with a small amount of calculation. For this purpose, we propose a user selection algorithm that can improve the orthogonality of a selected user group. We also apply a channel prediction technique to a MIMO-BC system to get more accurate channel information at the transmitter. Simulation results show that the channel prediction can improve the accuracy of channel information for user selections, and the proposed user selection algorithm achieves higher sum rate capacity than the SUS (Semiorthogonal User Selection) algorithm. Also we discuss the setting of the algorithm threshold. As the result of a discussion on the calculation complexity, which uses the number of complex multiplications as the parameter, the proposed algorithm is shown to have a calculation complexity almost equal to that of the SUS algorithm, and they are much lower than that of the optimal user selection algorithm.

A classification method is presented for differentiating honeycombed High Resolution Computed Tomographic (HRCT) images from normal HRCT images. For successful classification of honeycombed HRCT images, a complete set of methods and algorithms is described from segmentation to extraction to feature selection to classification. Wavelet energy is selected as a feature for classification using K-means clustering. Test data of 20 patients are used to validate the method.

In LTE, AC barring check is performed before RRC connection. In some cells with a low access probability, the UEs keep retrying access which results in higher connection failure and longer access delay. We therefore propose balancing the UEs by adjusting the cell reselection criteria based on the access probability, so that the UEs shall be more encouraged to reselect a cell with a higher access probability.

In this paper, we consider an amplify-and-forward (AF) relay network where a source node transmits information to a destination node through the cooperation of multiple relay nodes. It is shown in prior works that the outage behavior and average throughput of the selection AF (S-AF) scheme where only the best relay node is chosen to assist can outperform the conventional all-participate AF (AP-AF) scheme. Assuming multiple antennas at the destination node and single antennas at other nodes in this paper, we propose a relay selection scheme according to the criterion of maximizing receive signal to noise ratio (SNR), where a group of relays is chosen to assist in the transmission simultaneously in a manner similar to cyclic delay diversity (CDD). Compared with S-AF, the proposed scheme achieves better outage behavior and average throughput. It can be seen from simulation results that the performance improvement of symbol error rate (SER) is significant compared with S-AF.