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Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, and spatial databases etc. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work.

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In this paper, we address the issue of joint beamforming (BF) and power control (PC) for a device-to-device (D2D) communication underlaying cellular network (CN), where the wireless channels of D2D link and the base station (BS) to user equipment (UE) link experience Rician and correlated Rayleigh fading, respectively. Based on the property of the integral network, we first formulate a constrained optimization problem to minimize the total transmit power of the devices in the network, while meeting the quality-of-service (QoS) requirement of both the D2D and cellular users and suppressing the mutual interference to a certain level. Then, by adopting the available statistical channel state information (CSI) and proposing an approximation method to relax the constraints, a support vector machine (SVM) based algorithm is presented to solve the optimization problem for the transmit powers and BF weight vectors of each user. Furthermore, we derive the analytical expressions for the cumulative density function (CDF) and the generalized moments of the output signal-to-interference-plus-noise ratios (SINRs), thereby developing some novel theoretical formulas for the ergodic capacity (EC) and the average symbol error rate (ASER) of each user in the network. Finally, computer simulation results are provided to demonstrate the validity and efficiency of the proposed scheme as well as its performance analysis.Index Terms-Device-to-device communication, beamforming, power control, support vector machine. 0733-8716 (c)

In this paper, we present an effective and efficient computer aided diagnosis (CAD) system based on principle component analysis (PCA) and extreme learning machine (ELM) to assist the task of thyroid disease diagnosis. The CAD system is comprised of three stages. Focusing on dimension reduction, the first stage applies PCA to construct the most discriminative new feature set. After then, the system switches to the second stage whose target is model construction. ELM classifier is explored to train an optimal predictive model whose parameters are optimized. As we known, the number of hidden neurons has an important role in the performance of ELM, so we propose an experimental method to hunt for the optimal value. Finally, the obtained optimal ELM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative new feature set and the optimal parameters. The effectiveness of the resultant CAD system (PCA-ELM) has been rigorously estimated on a thyroid disease dataset which is taken from UCI machine learning repository. We compare it with other related methods in terms of their classification accuracy. Experimental results demonstrate that PCA-ELM outperforms other ones reported so far by 10-fold cross-validation method, with the mean accuracy of 97.73% and with the maximum accuracy of 98.1%. Besides, PCA-ELM performs much faster than support vector machines (SVM) based CAD system. Consequently, the proposed method PCA-ELM can be considered as a new powerful tools for diagnosing thyroid disease with excellent performance and less time.