It is known that MIMO channel matrix condition number influences detectors performance. Several authors have proposed combined decoders, mainly suboptimal, to cope with this fact. These combined algorithms require an estimation of the MIMO channel matrix condition number and a selection of a suitable threshold condition number. This letter presents practical algorithms to carry out the referred tasks and shows their performance in practice.

Cooperative transmission is an efficient approach to improve the performance of wireless communications over fading channels without the need for physical co-located antenna arrays. In this paper, we propose a novel cooperative protocol with selective decode-and-forward relays and generalized selection combining (GSC) technique at destination. The advantage of this scheme is that it not only allows us to optimize the structure of destination but also to fully exploit the diversity offered by the channels with an appropriate number of chosen strongest paths. For an arbitrary number of relays, an exact and closed-form expression of the Symbol Error Rate (SER) is derived for M-ary PSK in independent but not identically distributed Rayleigh fading channels. Various simulations are performed and their results exactly match the results of analyses.

Recently, there has been growing interest in the design of wireless cooperative protocol to achieve higher diversity-multiplexing tradeoff among single antenna devices. We propose an automatic request for cooperation (ARC) scheme for wireless networks which can achieve higher order diversity by selecting the best relay. In this scheme, a source transmits a data packet towards a destination and a group of relays. The destination tries to decode the information from the source and if the detection is correct the process will stop. Otherwise, the destination transmits an ARC towards the relays. We utilize this ARC signal for selecting the best relay from the set of relays that have successfully decoded the source packet. The selected relay generates and transmits redundant information for the source packet. The destination combines the two packets received from the source and the best relay to improve the reliability of the packet. We analyze the packet error rate, spectral efficiency and diversity-multiplexing tradeoff of our proposal and compare them with some existing protocols. Analysis shows that our proposal can achieve higher diversity multiplexing tradeoff than conventional cooperative protocols.

In MIMO systems, the deployment of a multiple antenna technique can enhance the system performance. However, since the cost of RF transmitters is much higher than that of antennas, there is growing interest in techniques that use a larger number of antennas than the number of RF transmitters. These methods rely on selecting the optimal transmitter antennas and connecting them to the respective. In this case, feedback information (FBI) is required to select the optimal transmitter antenna elements. Since FBI is control overhead, the rate of the feedback is limited. This motivates the study of limited feedback techniques where only partial or quantized information from the receiver is conveyed back to the transmitter. However, in MIMO/OFDM systems, it is difficult to develop an effective FBI quantization method for choosing the space-time, space-frequency, or space-time-frequency processing due to the numerous subchannels. Moreover, MIMO/OFDM systems require antenna separation of 5 10 wavelengths to keep the correlation coefficient below 0.7 to achieve a diversity gain. In this case, the base station requires a large space to set up multiple antennas. To reduce these problems, in this paper, we propose the link correlation based transmit sector antenna selection for Alamouti coded OFDM without FBI.

We address the issue of MIMO channel estimation with the aid of a priori temporal correlation statistics of the channel as well as the spatial correlation. The temporal correlations are incorporated to the estimation scheme by assuming the Gauss-Markov channel model. Under the MMSE criteria, the Kalman filter performs an iterative optimal estimation. To take advantage of the enhanced estimation capability, we focus on the problem of channel estimation from a partial channel measurement in the MIMO antenna selection system. We discuss the optimal training sequence design, and also the optimal antenna subset selection for channel measurement based on the statistics. In a highly correlated channel, the estimation works even when the measurements from some antenna elements are omitted at each fading block.

Several quasi-orthogonal STBCs (QOSTBCs) designs using four transmit antennas have been proposed. However, these STBC codes do not maintain full diversity due to the limitations of their complex orthogonal design. In this paper, we propose a code selection method with single-phase feedback which eliminates most of the interference by selecting the QOSTBC with less interference from among two predefined QOSTBCs. The BER performance improvement is investigated in relation with the single-phase feedback scheme at the expense of one bit.

We propose a new filter method for feature selection for SELDI-TOF mass spectrum datasets. In the method, a new relevance index was defined to represent the goodness of a feature by considering the distribution of samples based on the counts. The relevance index can be used to obtain the feature sets for classification. Our method can be applied to mass spectrum datasets with extremely high dimensions and process the clinical datasets with practical sizes in acceptable calculation time since it is based on simple counting of samples. The new method was applied to the three public mass spectrum datasets and showed better or comparable results than conventional filter methods.

In this letter, a novel power allocation scheme is proposed to improve the outage performance of an amplify-and-forward (AF) cooperative communication network with multiple potential relays under the assumption that only mean channel gains are available at the transmitters. In this scheme, power allocation is studied jointly with a relay selection algorithm which has low computational complexity. Simulation results demonstrate the performance improvement of the proposed scheme in terms of outage behavior.

Clonal Selection Algorithm (CSA), based on the clonal selection theory proposed by Burnet, has gained much attention and wide applications during the last decade. However, the proliferation process in the case of immune cells is asexual. That is, there is no information exchange during different immune cells. As a result the traditional CSA is often not satisfactory and is easy to be trapped in local optima so as to be premature convergence. To solve such a problem, inspired by the quantum interference mechanics, an improved quantum crossover operator is introduced and embedded in the traditional CSA. Simulation results based on the traveling salesman problems (TSP) have demonstrated the effectiveness of the quantum crossover-based Clonal Selection Algorithm.

This letter analyzes the outage probability of limited feedback beamforming systems with receive antenna selection. Tight analytical closed-form expressions of outage performance are derived for both cases, with and without spatial fading correlation, which allow for evaluation of the performance as a function of the codebook size, the level of fading correlation, and the number of transmit and receive antennas. Simulation results are also provided to verify the analysis.

In order to timely response to a user query at run-time, next generation Semantic Web applications demand a robust mechanism to dynamically select one or more existing ontologies available on the Web and combine them automatically if needed. Although existing ontology retrieval systems return a lengthy list of resultant ontologies, they cannot identify which ones can completely meet the query requirements nor determine a minimum set of resultant ontologies that can jointly satisfy the requirements if no single ontology is available to satisfy them. Therefore, this paper presents an ontology retrieval system, namely combiSQORE, which can return single or combinative ontologies that completely satisfy a submitted query when the available ontology database is adequate to answer such query. In addition, the proposed system ranks the returned results based on their semantic similarities to the given query and their modification (integration) costs. The experimental results show that combiSQORE system yields practical combinative ontologies and useful rankings.

Various types of data from environment are used to provide user-adaptive services. Among them, data of a user's past moving paths are useful to predict a moving user's next location and provide related services. This paper proposes a method to predict a moving user's location through analyzing his/her past moving paths. This method analyzes the user's moving path by using three elements of distance, time and direction of moving paths and Dynamic Time Warping (DTW), and selects the representative path, which is the one most similar to the current moving path in the past paths. The selected path can be used to provide service like space and time estimation.

The Letter deals with constant false alarm rate (CFAR) detection of random Gaussian target signals embedded in Gaussian clutter with unknown covariance. The proposed detector is analyzed on the assumption that clutter covariance is not known and a random target signal has low-rank property. The low-dimensional subspace-based approach leads to a robust false alarm rate (RFAR) detector. The detection performance loss and the false alarm stability loss to unknown clutter covariance have been evaluated for example scenario.

A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640512 pixel input images can be processed in real-time with three agents at a rate of 9 fps in 48 MHz operation.

Recent rapid development of high-speed wireless access technologies has created mixed WLAN (Wireless LAN) environments where QoS capable APs coexist with legacy APs. To provide QoS guarantee in this mixed WLAN environment, this paper proposes a new AP selection algorithm. The proposed algorithm assigns an STA (Station) to an AP in the overall WLAN service area. Simulation results show improvement in the VoIP performance in terms of an eMOS (estimated Mean Opinion Score) value and the FTP throughput compared to conventional algorithms.

Selecting transparently a proper network connection for voice communication will be a fundamental requirement in future multi-mode heterogeneous wireless network. This paper presented a smart session selection (S3) scheme to meet this requirement. Instead of selecting a best access network as in conventional Always Best Connected (ABC) paradigm, S3 enables users to select a best network connection, which consists of source and destination access network pair, to satisfy quality constraint and users' preference. To support S3, we develop a user profile to specify network connection priority. Meanwhile IP multimedia subsystem (IMS) is extended to make smart decision for users. Finally, Analytic Hierarchy Process (AHP) is used to recommend a network connection with assistance of user profile and IMS signaling. An example is illustrated to show that AHP can successfully select a good network connection that fulfills the requirement of users.

This paper proposes a DCT-based steganographic method named StegErmelc in the JPEG domain. Three strategies are proposed, namely (i) edge-like block selection, (ii) recursive matrix encoding, and (iii) largest coefficient serving, to form a novel steganographic method for achieving scalable carrier capacity, low detectability by universal blind steganalyzer, and high image quality, simultaneously. For a given message length, StegErmelc flexibly scales its carrier capacity to accommodate the message while trading off with stego detectability. At full capacity, StegErmelc has comparable carrier capacity relative to the existing methods. When embedding the same amount of information, StegErmelc remarkably reduces the stego detection rate to about 0.3-0.5 lower than that of the existing methods considered, and consequently StegErmelc can withstand blind steganalyzer when embedding up to 0.10 bpc. Under the same condition, StegErmelc produces stego image with quality higher than that of the existing methods considered. Graphical comparison with three additional evaluation metrics is also presented to show the relative performance of StegErmelc with respect to the existing methods considered.

We propose an antenna grouping method that improves the error rate performance of space-time codes in a wide range of mobility environments. The idea is to group symbols to antennas based on limited feedback from the mobile station to utilize all antennas. Our approach requires only two bits of feedback information to achieve better link performance and full rate for a certain four transmit antenna system. Numerical results confirm the bit/frame error gains over the Alamouti-based space-time block code and antenna subset selection strategies.

Combining relaying and multi-input multi-output (MIMO) transmission is a generic way to overcome the channel-fading impairments. Best antenna selection is a simple but efficient MIMO method that achieves the full diversity and also serves as a lower bound reference of MIMO performance. For a dual-hop MIMO system with an ideal amplify-and-forward (AF) relaying gain and best antenna selection, we provide a probability density function (PDF) of received signal-to-noise ratio (SNR) and an analytic BER equation when using M-ary PSK in Rayleigh fading channels. The analytic result is shown to exactly match with simulated one. Furthermore, the effect of link unbalance between the first hop and the second hop, due to differences in the number of antennas deployed in both hops as well as in the average power of channel coefficients, on the BER performance is numerically investigated and the results show that the links with better balance give better 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.