In the conventional multi-input multi-output (MIMO) communication systems, most of the antenna selection methods considered are suitable only for spatially separated uni-polarized system under Rayleigh fading channel in non-line of sight (NLOS) condition. There have a few antenna selection schemes for the cross-polarized system in LOS condition and Ricean fading channel, and no antenna selection scheme for the MIMO channel with both LOS and NLOS. In the practical MIMO channel case, influence of LOS and NLOS conditions in the channel can vary from time to time according to the channel parameters and user movement in the system. Based on these influences and channel condition, uni-polarized system may outperform a cross-polarized. Thus, we should consider this kind of practical MIMO channel environment when developing the antenna selection scheme. Moreover, no research work has been done on reducing the complexity of antenna selection for this kind of practical MIMO channel environment. In this paper, reduced complexity in antenna selection is proposed to give the higher throughput in the practical MIMO channel environment. In the proposed scheme, suitable polarized antennas are selected based on the calculation of singular value decomposition (SVD) of channel matrix and then adaptive bit loading is applied. Simulation results show that throughput of the system can be improved under the constraint of target BER and total transmit power of the MIMO system.

A good abdominal probabilistic atlas can provide important information to guide segmentation and registration applications in the abdomen. Here we build and test probabilistic atlases using 24 abdominal CT scans with available expert manual segmentations. Atlases are built by picking a target and mapping other training scans onto that target and then summing the results into one probabilistic atlas. We improve our previous abdominal atlas by 1) choosing a least biased target as determined by a statistical tool, i.e. multidimensional scaling operating on bending energy, 2) using a better set of control points to model the deformation, and 3) using higher information content CT scans with visible internal liver structures. One atlas is built in the least biased target space and two atlases are built in other target spaces for performance comparisons. The value of an atlas is assessed based on the resulting segmentations; whichever atlas yields the best segmentation performance is considered the better atlas. We consider two segmentation methods of abdominal volumes after registration with the probabilistic atlas: 1) simple segmentation by atlas thresholding and 2) application of a Bayesian maximum a posteriori method. Using jackknifing we measure the atlas-augmented segmentation performance with respect to manual expert segmentation and show that the atlas built in the least biased target space yields better segmentation performance than atlases built in other target spaces.

Generalized selection combining (GSC) was recently proposed as a low-complexity diversity combining technique for diversity-rich environments. This letter proposes a multi-hop Decode-and-Forward Relaying (MDFR) scheme in conjunction with GSC and describes its performance in terms of average bit error probability. We have shown that the proposed protocol offers a remarkable diversity advantage over direct transmission as well as the conventional decode-and-forward relaying (CDFR) scheme. Simulation results are also given to verify the analytical results.

We propose an incremental relaying protocol in conjunction with partial relay selection with the aim of making efficient use of the degrees of freedom of the channels as well as improving the performance of dual hop relaying with partial relay selection (DRPRS). Specifically, whenever the direct link from the source to the destination is not favorable to decoding, the destination will request the help of the opportunistic relay providing highest SNR of the links from the source. Theoretical analyses, as well as simulation results, verify that our scheme outperforms the DRPRS scheme in terms of bit error probability.

This paper presents a novel cross-layer approach to explore selection diversity for distributed clustering based wireless sensor networks (WSNs) by selecting a proper cluster-head. We develop and analyze an instantaneous channel state information (CSI) based cluster-head selection algorithm for a distributed, dynamic and randomized clustering based WSN. The proposed cluster-head selection scheme is also random and capable to distribute the energy uses among the nodes in the network. We present an analytical approach to evaluate the energy efficiency and system lifetime of our proposal. Analysis shows that the proposed scheme outperforms the performance of additive white Gaussian noise (AWGN) channel under Rayleigh fading environment. This proposal also outperforms the existing cooperative diversity protocols in terms of system lifetime and implementation complexity.

This paper focuses on the relationship between the number of interest points and the accuracy rate in scene classification. Here, we accept the common belief that more interest points can generate higher accuracy. But, few effort have been done in this field. In order to validate this viewpoint, in our paper, extensive experiments based on bag of words method are implemented. In particular, three different SIFT descriptors and five feature selection methods are adopted to change the number of interest points. As innovation point, we propose a novel dense SIFT descriptor named Octave Dense SIFT, which can generate more interest points and higher accuracy, and a new feature selection method called number mutual information (NMI), which has better robustness than other feature selection methods. Experimental results show that the number of interest points can aggressively affect classification accuracy.

In wireless networks, sleep mode based power saving mechanisms can reduce the energy consumption at the expense of additional packet delay. This letter analyzes its packet queueing delay and wireless terminals' energy efficiency. Based on the analysis, optimal sleep window size can be derived to optimize terminal energy efficiency with delay constraint.

Motivated by the recent research in crosslayer design of cooperative wireless network, we propose a distributed cooperative routing algorithm for a multihop multi-relay wireless network to achieve selection diversity. We propose two algorithms, rate optimal path selection and outage optimal path selection, to satisfy the different requirements of the systems. Both algorithms work on distributed processing without requiring any centralized controller. Simulations are conducted to evaluate the performance of the proposal. The results of the simulations show that the proposed routing algorithms significantly improve the end-to-end data rate and outage performance compared with noncooperative routing protocols.

Wireless mesh networks have been extensively studied as expandable, flexible, and inexpensive access networks to the Internet. This paper focuses on one composed of multiple access points (APs) connected through multihop wireless communications mainly by the wireless distribution system (WDS). For scalability, the proper partition of APs into multiple WDS clusters is essential, because the number of APs in one cluster is limited due to the increasing radio interference and control packets. In this paper, we formulate this WDS clustering problem and prove the NP-completeness of its decision version through reduction from a known NP-complete problem. Then, we propose its heuristic algorithm, using a greedy method and a variable depth search method, to satisfy the complex constraints while optimizing the cost function. We verify the effectiveness of our algorithm through extensive simulations, where the results confirm its superiority to the existing algorithm in terms of throughput.

In this letter, we propose a novel antenna selection algorithm for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems with Zero-Forcing (ZF) processing applied both at the source node and at the destination node. We obtain the optimum antenna selection criterion by deriving an iterative closed-form expression for capacity maximization.

This paper considers the use of an antenna selection mechanism to reduce the cost of multiple analog transmit/receive chains in multiple-input multiple-output (MIMO) systems. With the optimal antenna selection scheme, radio-frequency chains can optimally connect with the best subset of transmitter and/or receiver antennas. However, the optimal antenna selection algorithm requires an exhaustive search of all possible combinations to find the optimum subset at the transmitter and/or receiver, thus resulting in high complexity. In order to reduce the computational load while still maximizing channel capacity, we introduce the simulated annealing (SA) method, an effective algorithm that solves various combinatorial optimization problems, to search the optimal subset. The simulation results show that the performance of the proposed SA method provides almost the same channel capacity as that of the optimal exhaustive search algorithm while maintaining low complexity.

Antenna selection is a practical way to decrease system complexity and the hardware cost of radio frequency (RF) chains in multiple input multiple output (MIMO) system. In this study, we give a simple characterization of the optimal diversity and multiplexing tradeoff (DMT) curve of the MIMO system with antenna subset selection at both the transmitter and the receiver for Rayleigh fading channel.

In this paper, we investigate the antenna and node selection issues for amplify-and-forward (AF) and decode-and-forward (DF) multi-antenna relay networks in correlated channels. Based on the channel statistics, optimal selection criteria for antenna and relay node are derived jointly, aiming to maximize the ergodic capacity. Instantaneous channel knowledge-based selection schemes, motivated by traditional antenna selection algorithms, are investigated as well. It is shown that the proposed node selection schemes derived from antenna selection on relay nodes are feasible and effective in relay systems. Statistical selection shows considerable capacity gain compared to full complexity scheme and random selection strategy in AF mode, while instantaneous selection performs better in DF relaying. Furthermore, the proposed schemes are shown to be robust to channel estimation errors due to their correlation-oriented nature.

This paper suggests utterance verification system using state-level log-likelihood ratio with frame and state selection. We use hidden Markov models for speech recognition and utterance verification as acoustic models and anti-phone models. The hidden Markov models have three states and each state represents different characteristics of a phone. Thus we propose an algorithm to compute state-level log-likelihood ratio and give weights on states for obtaining more reliable confidence measure of recognized phones. Additionally, we propose a frame selection algorithm to compute confidence measure on frames including proper speech in the input speech. In general, phone segmentation information obtained from speaker-independent speech recognition system is not accurate because triphone-based acoustic models are difficult to effectively train for covering diverse pronunciation and coarticulation effect. So, it is more difficult to find the right matched states when obtaining state segmentation information. A state selection algorithm is suggested for finding valid states. The proposed method using state-level log-likelihood ratio with frame and state selection shows that the relative reduction in equal error rate is 18.1% compared to the baseline system using simple phone-level log-likelihood ratios.

Selection relaying is a promising technique for practical implementation of cooperative systems with multiple relay nodes. However, to select the best relay, global channel knowledge is required at the selecting entity, which may result in considerable signaling overhead. In this paper, we consider the relay selection problem in dual-hop amplify-and-forward (AF) communication systems with partial channel state information (CSI). Relay selection strategies aiming at minimizing either the outage probability or the bit error rate (BER) with quantized CSI available are presented. We also propose a target rate based quantizer to efficiently partition the SNR range for outage minimized relay selection, and a target BER based quantizer for BER minimized relay selection. Simulation results show that near optimal performance is achievable with a few bits feedback to the selecting entity.

The "geometric AIC" and the "geometric MDL" have been proposed as model selection criteria for geometric fitting problems. These correspond to Akaike's "AIC" and Rissanen's "BIC" well known in the statistical estimation framework. Another well known criterion is Schwarz' "BIC", but its counterpart for geometric fitting has not been known. This paper introduces the corresponding criterion, which we call the "geometric BIC", and shows that it is of the same form as the geometric MDL. Our result gives a justification to the geometric MDL from the Bayesian principle.

Critical path selection is very important in delay testing. Critical paths found by conventional static timing analysis (STA) tools are inadequate to represent the real timing of the circuit, since neither the testability of paths nor the statistical variation of cell delays caused by process variation is considered. This paper proposed a novel path selection method considering process variation. The circuit is firstly simplified by eliminating non-critical edges under statistical timing model, and then divided into sub-circuits, while each sub-circuit has only one prime input (PI) and one prime output (PO). Critical paths are selected only in critical sub-circuits. The concept of partially critical edges (PCEs) and completely critical edges (CCEs) are introduced to speed up the path selection procedure. Two path selection strategies are also presented to search for a testable critical path set to cover all the critical edges. The experimental results showed that the proposed circuit division approach is efficient in path number reduction, and PCEs and CCEs play an important role as a guideline during path selection.

Spectrum sensing is an important function for dynamic spectrum access (DSA) type cognitive radio systems to detect opportunities for sharing the spectrum with a primary system. The key requirements for spectrum sensing are stability in controlling the probability of false alarm as well as detection performance of the primary signals. However, false alarms can be triggered by noise uncertainty at the secondary devices or unknown interference signals from other secondary systems in realistic radio environments. This paper proposes a robust spectrum sensing method against such uncertainties; it is a kind of cyclostationary feature detection (CFD) approaches. Our proposed method, referred to as maximum cyclic autocorrelation selection (MCAS), compares the peak and non-peak values of the cyclic autocorrelation function (CAF) to detect primary signals, where the non-peak value is the CAF value calculated at cyclic frequencies between the peaks. In MCAS, the desired probability of false alarm can be obtained by setting the number of the non-peak values. In addition, the multiple peak values are combined in MCAS to obtain noise reduction effect and coherent combining gain. Through computer simulations, we show that MCAS can control the probability of false alarm under the condition of noise uncertainty and interference. Furthermore, our method achieves better performance with much less computational complexity in comparison to conventional CFD methods.

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.

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.