Integration of beamforming and uncertainty-of-observation techniques for robust ASR in multi-source environments

This paper presents a new approach for increasing the robustness of multi-channel automatic speech recognition in noisy and reverberant multi-source environments. The proposed method uses uncertainty propagation techniques to dynamically compensate the speech features and the acoustic models for the observation uncertainty determined at the beamforming stage. We present and analyze two methods that allow integrating classical multi-channel signal processing approaches like delay and sum beamformers or Zelinski-type Wiener filters, with uncertainty-of-observation techniques like uncertainty decoding or modified imputation. An analysis of the results on the PASCAL-CHiME task shows that this approach consistently outperforms conventional beamformers with a minimal increase in computational complexity. The use of dynamic compensation based on observation uncertainty also outperforms conventional static adaptation with no need of adaptation data.     

Cooperation Between UAS and Wireless Sensor Networks for Efficient Data Collection in Large Environments

This paper describes a method for collection of data from Wireless Sensor Network (WSN) deployed in large environments using Unmanned Aerial Systems (UAS). Unlike existing approaches, in which the WSN and the UAS act as independent units, the main novelty of the proposed method is that UAS and WSN cooperate to increase the performance of the mission. The proposed method presents two main cooperative behaviors: (1) the results of the WSN operation are used to update the UAS flight plan and; (2) the UAS trajectory is considered in the operation of the WSN in order to improve the data collection performance. The proposed method outperforms non-cooperative UAS-based collection approaches and traditional ground multi-hop collection schemes. The method has been experimented in the airfield of Bellavista in Seville (Spain) in March 2011.     

Clustering interval data through kernel-induced feature space

Recently, kernel-based clustering in feature space has shown to perform better than conventional clustering methods in unsupervised classification. In this paper, a partitioning clustering method in kernel-induce feature space for symbolic interval-valued data is introduced. The distance between an item and its prototype in feature space is expanded using a two-component mixture kernel to handle intervals. Moreover, tools for the partition and cluster interpretation of interval-valued data in feature space are also presented. To show the effectiveness of the proposed method, experiments with real and synthetic interval data sets were performed and a study comparing the proposed method with different clustering algorithms of the literature is also presented. The clustering quality furnished by the methods is measured by an external cluster validity index (corrected Rand index). These experiments showed the usefulness of the kernel K-means method for interval-valued data and the merit of the partition and cluster interpretation tools.     

Response Surface Estimates of the Cross-Sectionally Augmented IPS Tests for Panel Unit Roots

This paper estimates response surface coefficients for a large range of quantiles of the cross-sectionally augmented IPS (CIPS) test of Pesaran (2007), for different specifications of the deterministic components. An Excel programme is available to calculate the P value associated with a CIPS test statistic.     

Handwritten Data Clustering Using Agents Competition in Networks

In this paper, we study a new type of competitive learning scheme realized on large-scale networks. The model consists of several agents walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder agents. In the end of the process, each agent dominates a community (a strongly connected subnetwork). Here, the model is described by a stochastic dynamical system. In this paper, a mathematical analysis for uncovering the system’s properties is presented. In addition, the model is applied to solve handwritten digits and letters clustering problems. An interesting feature is that the model is able to group the same digits or letters even with considerable distortions into the same cluster. Computer simulations reveal that the proposed technique presents high precision of cluster detections, as well as low computational complexity.     

Tracking in scale quad-rotors through adaptive augmentation

This paper illustrates the application of an adaptive flight control architecture to a scale quad-rotor. For autonomous vertical takeoff and landing flight, it is common to separate the control problem into an inner fast loop that controls attitude and an outer slow loop that controls the trajectory tracking. In this paper, we augment a conventional proportional and derivative controller conceived mainly for hovering, with an adaptive element using a real-time tuning single hidden layer neural network in a inner–outer loop combined architecture to account for model inversion error cancelation, issued in the feedback linearization process. The results shown in simulations reveal the superior performance of the augmented controller in tracking maneuvers.     

A keyword-driven approach for generating OWL DL conformance test data

The conformance of semantic technologies has to be systematically evaluated to measure and verify the real adherence of these technologies to the Semantic Web standards. Current evaluations of semantic technology conformance are not exhaustive enough and do not directly cover user requirements and use scenarios, which raises the need for a simple, extensible and parameterizable method to generate test data for such evaluations. To address this need, this paper presents a keyword-driven approach for generating ontology language conformance test data that can be used to evaluate semantic technologies, details the definition of a test suite for evaluating OWL DL conformance using this approach, and describes the use and extension of this test suite during the evaluation of some tools.     

Student progress assessment with the help of an intelligent pupil analysis system

Students and lecturers would like to know how well students have learned the study materials being taught. A formal test or exam would cause needless stress for students. To resolve this problem, the authors of this article have developed an Intelligent Pupil Analysis (IPA) System. A sufficient amount of studies worldwide prove an interrelation between pupil size and a person's cognitive load. The obtained research results are comparable with the results from other similar studies. The original contribution of this article, compared to the research results published earlier, is as follows: the IPA System developed by the authors is superior to the traditional pupil analysis research due to the integration of pupil analysis with subsystems of decision support, recommender and intelligent tutoring systems and innovative Models of the Model-base, which permit a more detailed analysis of the knowledge attained by a student. This article ends with a case study to demonstrate the practical operation of the IPA System.     

Statistical inference in a redesigned Radial Basis Function neural network

A Hybrid Learning Process method was fitted into a RBF. The resulting redesigned RBF intends to show how to test if the statistical assumptions are fulfilled and to apply statistical inference to the redesigned RBFNN bearing in mind that it allows to determine the relationship between a response (to a process) and one or more independent variables, testing how much each factor contributes to the total variation of the response is also feasible. The results show that statistical methods such as inference, Residual Analysis, and statistical metrics are all good alternatives and excellent methods for validation of the effectiveness of the Neural Network models. The foremost conclusion is that the resulting redesigned Radial Basis Function improved the accuracy of the model after using a Hybrid Learning Process; moreover, the new model also validates the statistical assumptions for using statistical inference and statistical analysis, satisfying the assumptions required for ANOVA to determine the statistical significance and the relationship between variables.     

Congestion-aware ant colony based routing algorithms for efficient application execution on Network-on-Chip platform

Networks-on-Chip (NoC) is an interesting option in design of communication infrastructures for embedded systems. It provides a scalable structure and balanced communication between the cores. Parallel applications that take advantage of the NoC architectures, are usually are communication-intensive. Thus, a big deal of data packets is transmitted simultaneously through the network. In order to avoid congestion delays that deteriorate the execution time of the implemented applications, an efficient routing strategy must be thought of carefully. In this paper, the ant colony optimization paradigm is explored to find and optimize routes in a mesh-based NoC. The proposed routing algorithms are simple yet efficient. The routing optimization is driven by the minimization of total latency during packets transmission between the tasks that compose the application. The presented performance evaluation is threefold: first, the impact of well-known synthetic traffic patterns is assessed; second, randomly generated applications are mapped into the NoC infrastructure and some synthetic communication traffics, that follow known patterns, are used to simulate real situations; third, sixteen real-world applications of the E3S and one specific application for digital image processing are mapped and their execution time evaluated. In both cases, the obtained results are compared to those obtained with known general purpose algorithms for deadlock free routing. The comparison avers the effectiveness and superiority of the ant colony inspired routing.