A Parallelization Approach for Hard Real-Time Systems and Its Application on Two Industrial Programs

Applications in industry often have grown and improved over many years. Since their performance demands increase, they also need to benefit from the availability of multi-core processors. However, a reimplementation from scratch and even a restructuring of these industrial applications is very expensive, often due to high certification efforts. Therefore, a strategy for a systematic parallelization of legacy code is needed. We present a parallelization approach for hard real-time systems, which ensures a high reusage of legacy code and preserves timing analysability. To show its applicability, we apply it on the core algorithm of an avionics application as well as on the control program of a large construction machine. We create models of the legacy programs showing the potential of parallelism, optimize them and change the source codes accordingly. The parallelized applications are placed on a predictable multi-core processor with up to 18 cores. For evaluation, we compare the worst case execution times and their speedups. Furthermore, we analyse limitations coming up at the parallelization process.     

Robust probabilistic planning with ilao

In probabilistic planning problems which are usually modeled as Markov Decision Processes (MDPs), it is often difficult, or impossible, to obtain an accurate estimate of the state transition probabilities. This limitation can be overcome by modeling these problems as Markov Decision Processes with imprecise probabilities (MDP-IPs). Robust LAO* and Robust LRTDP are efficient algorithms for solving a special class of MDP-IPs where the probabilities lie in a given interval, known as Bounded-Parameter Stochastic-Shortest Path MDP (BSSP-MDP). However, they do not make clear what assumptions must be made to find a robust solution (the best policy under the worst model). In this paper, we propose a new efficient algorithm for BSSP-MDPs, called Robust ILAO* which has a better performance than Robust LAO* and Robust LRTDP, considered the-state-of-the art of robust probabilistic planning. We also define the assumptions required to ensure a robust solution and prove that Robust ILAO* algorithm converges to optimal values if the initial value of all states is admissible.     

Spatial data warehouses and spatial OLAP come towards the cloud: design and performance

Cloud computing systems handle large volumes of data by using almost unlimited computational resources, while spatial data warehouses (SDWs) are multidimensional databases that store huge volumes of both spatial data and conventional data. Cloud computing environments have been considered adequate to host voluminous databases, process analytical workloads and deliver database as a service, while spatial online analytical processing (spatial OLAP) queries issued over SDWs are intrinsically analytical. However, hosting a SDW in the cloud and processing spatial OLAP queries over such database impose novel obstacles. In this article, we introduce novel concepts as cloud SDW and spatial OLAP as a service, and afterwards detail the design of novel schemas for cloud SDW and spatial OLAP query processing over cloud SDW. Furthermore, we evaluate the performance to process spatial OLAP queries in cloud SDWs using our own query processor aided by a cloud spatial index. Moreover, we describe the cloud spatial bitmap index to improve the performance to process spatial OLAP queries in cloud SDWs, and assess it through an experimental evaluation. Results derived from our experiments revealed that such index was capable to reduce the query response time from 58.20 up to 98.89 %.     

Hand gesture recognition from depth and infrared Kinect data for CAVE applications interaction

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a ‘Gesture’ or ‘Non-Gesture’. The ILSP counterparts of the frames labeled as “Gesture” are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.     

Two-space variability compensation technique for speaker verification in short length and reverberant environments

The performance of state-of-the-art speaker verification in uncontrolled environment is affected by different variabilities. Short duration variability is very common in these scenarios and causes the speaker verification performance to decrease quickly while the duration of verification utterances decreases. Linear discriminant analysis (LDA) is the most common session variability compensation algorithm, nevertheless it presents some shortcomings when trained with insufficient data. In this paper we introduce two methods for session variability compensation to deal with short-length utterances on i-vector space. The first method proposes to incorporate the short duration variability information in the within-class variance estimation process. The second proposes to compensate the session and short duration variabilities in two different spaces with LDA algorithms (2S-LDA). First, we analyzed the behavior of the within and between class scatters in the first proposed method. Then, both proposed methods are evaluated on telephone session from NIST SRE-08 for different duration of the evaluation utterances: full (average 2.5 min), 20, 15, 10 and 5 s. The 2S-LDA method obtains good results on different short-length utterances conditions in the evaluations, with a EER relative average improvement of 1.58%, compared to the best baseline (WCCN[LDA]). Finally, we applied the 2S-LDA method in speaker verification under reverberant environment, using different reverberant conditions from Reverb challenge 2013, obtaining an improvement of 8.96 and 23% under matched and mismatched reverberant conditions, respectively.     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

Path Flow and Trip Matrix Estimation Using Link Flow Density

A macroscopic model is presented that simultaneously estimates route flows and trip matrices for congested road networks using data on link densities instead of link flows. The advantage of this approach is that it avoids errors that may occur in the individual links’ flow-cost relationships when congestion is heavy. Under the proposed methodology, both the flows and the matrices are estimated by the model using an image of the network such as an aerial photograph in which the number of vehicles on each link can be identified. The model itself is formulated as a maximum entropy optimization problem subject to linear constraints given by vehicle densities on the links, and is validated using analytic examples and traffic microsimulations. The results demonstrate the superiority of the link-density approach over the traditional flow-based method.     

Evolutionary computing in recommender systems: a review of recent research

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.     

A versatile technology platform for microfluidic handling systems,part II: channel design and technology

Microfluidic devices often require channels of a specific size and shape. These devices are then made in a fabrication process that is often specialized to produce only those (and very similar) channels. As a result, devices requiring channels of different size and shape cannot easily be integrated on the same chip. This paper presents a method to fabricate microfluidic channels in a wide range of shape and size on the same chip by using a slit pattern through which the channels are etched. The fabrication process to fabricate these channels is discussed in detail, and an empirical model is presented to find the optimal slit pattern for a required size and shape. This part of the paper focusses on the channel design and fabrication. Details on the whole fabrication process and optional functionalization of the channels are presented in part I of this paper.