The specification of a Multi-Agent System (MAS) involves the identification of a large number of entities and their relationships. This is a non-trivial task that requires managing different views of the system. Many problems concerning this issue originate in the presence of contradictory goals and tasks, inconsistencies, and unexpected behaviours. Such troublesome configurations should be detected and prevented during the development process in order to study alternative ways to cope with them. In this paper, we present methods and tools that support the management of contradictions during the analysis and design of MAS. Contradiction management in MAS has to consider both individual (i.e. agent) and social (i.e. organization) aspects, and their dynamics. Such issues have already been considered in social sciences, and more concretely in the Activity Theory, a social framework for the study of interactions in activity systems. Our approach applies knowledge from Activity Theory in MAS, especially its base of contradiction patterns. That requires a formalization of this social theory in order to be applicable in a software engineering context and its adaptation to agent-oriented methodologies. Then, it will be possible to check the occurrence of contradiction patterns in a MAS specification and provide solutions to those situations. This technique has been validated by implementing an assistant for the INGENIAS Development Kit and has been tested with several case studies. This paper shows part of one of these experiments for a web application.

This paper proposes a new computational optimization method modified from the dynamic encoding algorithm for searches (DEAS). Despite the successful optimization performance of DEAS for both benchmark functions and parameter identification, the problem of exponential computation time becomes serious as problem dimension increases. The proposed optimization method named univariate DEAS (uDEAS) is especially implemented to reduce the computation time using a univariate local search scheme. To verify the algorithmic feasibility for global optimization, several test functions are optimized as benchmark. Despite the simpler structure and shorter code length, function optimization performance show that uDEAS is capable of fast and reliable global search for even high dimensional problems.

An explicit expression for the impulse response coefficients of the predictive FIR digital filters is derived. The formula specifies a four-parameter family of smoothing FIR digital filters containing the Savitsky-Goaly filters, the Heinonen-Neuvo polynomial predictors, and the smoothing differentiators of arbitrary integer orders. The Hahn polynomials, which are orthogonal with respect to a discrete variable, are the main tool employed in the derivation of the formula. A recursive formula for the computation of the transfer function of the filters, which is the z-transform of a terminated sequence of polynomial ordinates, is also introduced. The formula can be used to design structures with low computational complexity for filters of any order.

For reducing bit errors on wireless channels, we have proposed the multiroute coding scheme on multiple routes for wireless multihop networks. In this paper, we introduce ARQ to our multiroute coding scheme. In our multiroute coding scheme, a destination node combines and decodes subpackets which are encoded and divided by a source node. Each intermediate node relays a subpacket, that is, only a part of a packet. Therefore, intermediate nodes cannot detect packet errors, and only a destination node can do so after combining and decoding subpackets. We propose an ARQ scheme between a source node and a destination node. We analyze the proposed ARQ scheme and evaluate the system performance.

Digital hearing aid users often complain of difficulty in understanding speech in the presence of background noise. To improve speech perception in a noisy environment, various speech enhancement algorithms have been applied in digital hearing aids. In this study, a speech enhancement algorithm using modified spectral subtraction and companding is proposed for digital hearing aids. We adjusted the biases of the estimated noise spectrum, based on a subtraction factor, to decrease the residual noise. Companding was applied to the channel of the formant frequency based on the speech presence indicator to enhance the formant. Noise suppression was achieved while retaining weak speech components and avoiding the residual noise phenomena. Objective and subjective evaluation under various environmental conditions confirmed the improvement due to the proposed algorithm. We tested segmental SNR and Log Likelihood Ratio (LLR), which have higher correlation with subjective measures. Segmental SNR has the highest and LLR the lowest correlation of the methods tested. In addition, we confirmed by spectrogram that the proposed method significantly reduced the residual noise and enhanced the formants. A mean opinion score that represented the global perception score was tested; this produced the highest quality speech using the proposed method. The results show that the proposed speech enhancement algorithm is beneficial for hearing aid users in noisy environments.

This paper discusses a new structure of M-channel IIR perfect reconstruction filter banks. A novel building block defined as a cascade connection of some IIR building blocks and FIR building blocks is presented. An IIR building block is written by state space representation, where we easily obtain a stable filter bank by setting eigenvalues of the state transition matrix into the unit circle. Due to cascade connection of building blocks, we are able to design a system with a larger number of free parameters while keeping the stability. We introduce the condition which obtains the new building block without increasing of the filter order in spite of cascade connection. Additionally, by showing the simulation results, we show that this implementation has a better stopband attenuation than conventional methods.

Recently, space-time block codes (STBCs) obtained from coordinate interleaved orthogonal designs (CIODs) have attracted considerable attention, due to the advantages of full-diversity transmission and single-symbol decodability. In this letter, we design a novel STBC from CIOD for two transmit antennas. The proposed code guarantees full-diversity and full-rate along with low peak-to-minimum power ratio (PMPR). Furthermore, in contrast to the existing Alamouti code, the performance of the proposed code is not degraded even in severely time-selective fading channels.

A biometrics system for identifying individuals using the pattern of veins in a finger was previously proposed. The system has the advantage of being resistant to forgery because the pattern is inside a finger. Infrared light is used to capture an image of a finger that shows the vein patterns, which have various widths and brightnesses that change temporally as a result of fluctuations in the amount of blood in the vein, depending on temperature, physical conditions, etc. To robustly extract the precise details of the depicted veins, we developed a method of calculating local maximum curvatures in cross-sectional profiles of a vein image. This method can extract the centerlines of the veins consistently without being affected by the fluctuations in vein width and brightness, so its pattern matching is highly accurate. Experimental results show that our method extracted patterns robustly when vein width and brightness fluctuated, and that the equal error rate for personal identification was 0.0009%, which is much better than that of conventional methods.

Channel state information (CSI) at transmitter plays an important role for multiuser MIMO broadcast channels, but full CSI at transmitter is not available for many practical systems. Previous work has proposed orthonormal random beamforming (ORBF) [16] for MIMO broadcast channels with partial channel state information (CSI) feedback, and shown that ORBF achieves the optimal sum-rate capacity for a large number of users. However, for cellular systems with moderate number of users, i.e., no more than 64, ORBF only achieves slight performance gain. Therefore, we analyze the performance of ORBF with moderate number of users and total transmit power constraint and show that ORBF scheme is more efficient under low SNR. Then we propose an adaptive ORBF scheme that selects the number of random beams for simultaneous transmission according to the average signal-to-noise ratio (SNR). Moreover, a multi-beam selection (MBS) scheme that jointly selects the number and the subset of the multiple beams is proposed to further improve the system performance for low SNR cases. The simulation results show that the proposed schemes achieve significant performance improvement when the number of users is moderate.

A new framework for reconstruction of missing textures in digital images is introduced in this paper. The framework is based on a projection onto convex sets (POCS) algorithm including a novel constraint. In the proposed method, a nonlinear eigenspace of each cluster obtained by classification of known textures within the target image is applied to the constraint. The main advantage of this approach is that the eigenspace can approximate the textures classified into the same cluster in the least-squares sense. Furthermore, by monitoring the errors converged by the POCS algorithm, a selection of the optimal cluster to reconstruct the target texture including missing intensities can be achieved. This POCS-based approach provides a solution to the problem in traditional methods of not being able to perform the selection of the optimal cluster due to the missing intensities within the target texture. Consequently, all of the missing textures are successfully reconstructed by the selected cluster's eigenspaces which correctly approximate the same kinds of textures. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

In this letter, we discuss the problem of receive antenna selection in the downlink of multiuser multiple-input multiple-output (MIMO) systems with Tomlinson-Harashima precoding (THP), where the number of receivers is assumed equal to that of transmit antennas. Based on the criterion of maximum system sum-capacity, a per-layer receive antenna selection scheme is proposed. This scheme, which selects one receive antenna for each receiver, can well exploit the nonlinear and successive characteristics of THP. Two models are established for the proposed per-layer scheme and the conventional per-user scheme. Both the theoretical analysis and simulation results indicate that the proposed scheme can greatly improve the equivalent channel power gains and the system sum-capacity.

This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.

Adaptive hybrid genetic algorithm concatenated with improved parallel interference cancellation, i.e. adaptive hybrid genetic algorithm parallel interference cancellation (AHGAPIC) was proposed. A study is conducted on the application of AHGAPIC to soft decoding high rate multi-user detection with diversity reception for dual-rate wideband DS-CDMA spread spectrum communications, aiming to mitigate the effect of multiple access interference. The relevant research has revealed that the local search capability of hybrid genetic algorithm (HGA) is still not good enough. Therefore, first, two evolutionary operations, i.e. inversion and insertion are merged into HGA to constitute a novel algorithm. With its moderate local search capability, this new algorithm can search for the global optimum region according to the information entropy, and then it is made adaptively vary its probabilities of crossover and mutation depending on the fitness values of the solutions to form the adaptive hybrid genetic algorithm (AHGA). Second, AHGA is utilized to effectively identify the better and better binary string to maximize the log-likelihood function of dual-rate multi-user detection. As AHGA converges to the optimum region, the control factor of the improved parallel interference cancellation (IPIC) detector is set to be the ratio of the average fitness value to the maximum fitness value of the population of AHGA. Finally, equipped with both the control factor and the binary string with the maximum fitness value as the initial data, the IPIC detector can rapidly find out the approximately optimum soft decoding vector. Then, it can obtain the approximately global optimum estimate point on the basis of the soft decoding rule, corresponding to the transmitted data bits. A lower bound of computational complexity has been achieved through simulations and qualitative analyses. The property of the proposed algorithm to converge rapidly leads to lower computational complexity. Emulation results have shown that the AHGAPIC soft decoding high rate multi-user detector is superior to other suboptimum detectors considered in this paper in terms of two points. They are the mitigation of multiple access interference and the resistance to near-far effects. Its performance is close to the sequential group optimum multi-user detector but with a shorter time delay.

In wireless ad hoc networks, network services are provided through the cooperation of all nodes. Albeit that good teamwork could smoothly run a mobile network, selfish node behaviors would probably cause it to break down. Some examples of these selfish node behaviors would include, "listening only" for saving energy or "receiving the valuable" without forwarding the packets to others. To cope with this problem, we propose PDM, a price-demand function based pricing model, to restrains the selfish behaviors of mobile nodes. PDM is based on the packet sending requirements of the source nodes and the forwarding cost of relay nods. Using this pricing methodology, the packet forwarding activities will be profitable for the relay node and further stimulate cooperation in the network. In particular, the new model enjoys the merit of giving relay nodes no reason to dishonestly report their forwarding costs, because an honest cost claim has proven to be an optimal strategy for relay nodes. Furthermore, our new model uses a price-demand function to reflect the relationship between the service demand of the source nodes and the service supply of the relay nodes. As a consequence, our approach reduces the source nodes' payments to send packets, and at the same time guarantees that the packets sent by the source nodes are delivered to the destination.

In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.

We propose a robust iterative multiuser receiver for decoding convolutional coded code-division multiple access (CDMA) signals in both Gaussian and non-Gaussian channel noise. The receiver is derived from a modified maximum a-posteriori (MAP) algorithm called the max-log-MAP algorithm for robustness against erroneous channel variance estimation. Furthermore, the effect of destructive outliers arising from impulsive noise is mitigated in the proposed receiver by incorporating the robust Huber penalty function into the multiuser detector. The proposed receiver is shown to perform satisfactorily over Gaussian and non-Gaussian impulsive channels. In every iteration, cumulative improvement in the quality of the a-posteriori probabilities is also demonstrated.

A new hybrid model to calculate echoes and ultrasonic fields scattered by flaws is presented. In this model, transmitted fields by a transducer and fields scattered by flaws are calculated using the Rayleigh integral and the FDTD method, respectively. Received echo signals by the transducer are obtained by the inner product of the transmitted fields and the scattered ones at each grid for the FDTD method, and integrating those in a calculation area. Since the calculation area for the FDTD method can be limited around flaws, the calculation time is short and computer memory usage can be reduced. Experiments are done for two types of test object to confirm the validity of the hybrid model. As a result, experimental results are in good agreement with calculated ones.

In this work particle swarm optimization (PSO) aided with radial basis functions (RBF) has been suggested to carry out multiuser detection (MUD) for synchronous direct sequence code division multiple access (DS-CDMA) systems. The performance of the proposed algorithm is compared to that of other standard suboptimal detectors and genetic algorithm (GA) assisted MUD. It is shown to offer better performance than the others especially if there are many users.

As semiconductor processing technology advances, complex, high density circuits can be integrated in a chip. However, increasing energy consumption is becoming one of the most important limiting factors. Power estimation at the early stage of design is essential since design changes at later stages may significantly lengthen the design period and increase the costs. For efficient power estimation, we analyze the "key" control signals of a digital circuit and develop power models for several operational modes. The trade-off between accuracy and complexity can be made by choosing the number and the complexity of the power models. When compared with those of logic simulation based estimation, experimental results show that 13 to 15 times faster power estimation with an estimation error of about 5% is possible. We have also developed new logic-level power modeling techniques in which logic gates are levelized and several levels are selected to build power model tables. This table based method shows significant improvement in estimation accuracy and a slight improvement in efficiency when compared to a well-known previous method. The average estimation error has been reduced from 13.3% to 3.8%.

We study the problem of optimizing admission control policies in mobile multimedia cellular networks when predictive information regarding movement is available and we evaluate the gains that can be achieved by making such predictive information available to the admission controller. We consider a general class of prediction agents which forecast the number of future handovers and we evaluate the impact on performance of aspects like: whether the prediction refers to incoming and/or outgoing handovers, inaccurate predictions, the anticipation of the prediction and the way that predictions referred to different service classes are aggregated. For the optimization process we propose a novel Reinforcement Learning approach based on the concept of afterstates. The proposed approach, when compared with conventional Reinforcement Learning, yields better solutions and with higher precision. Besides it tackles more efficiently the curse of dimensionality inherent to multimedia scenarios. Numerical results show that the performance gains measured are higher when more specific information is provided about the handover time instants, i.e. when the anticipation time is deterministic instead of stochastic. It is also shown that the utilization of the network is maintained at very high values, even when the highest improvements are observed. We also compare an optimal policy obtained deploying our approach with a previously proposed heuristic prediction scheme, showing that plenty of room for technological innovation exists.