An 802.11e HCCA scheduler with an end-to-end quality aware territory method

In this paper we present a solution for the IEEE 802.11e HCCA (Hybrid coordination function Controlled Channel Access) mechanism which aims both at supporting strict real-time traffic requirements and, simultaneously, at handling TCP applications efficiently. Our proposal combines a packet scheduler and a dynamic resource allocation algorithm. The scheduling discipline is based on the Monolithic Shaper-Scheduler, which is a modification of a General Processor Sharing (GPS) related scheduler. It supports minimum-bandwidth and delay guarantees and, at the same time, it achieves the optimal latency of all the GPS-related schedulers. In addition, our innovative resource allocation procedure, called the territory method, aims at prioritizing real time services and at improving the performance of TCP applications. For this purpose, it splits the wireless channel capacity (in terms of transmission opportunities) into different territories for the different types of traffic, taking into account the end-to-end network dynamics. In order to give support to the desired applications, we consider the following traffic classes: conversational, streaming, interactive and best-effort. The so called territories shrink or expand depending on the current quality experienced by the corresponding traffic class. We evaluated the performance of our solution through extensive simulations in a heterogeneous wired-cum-wireless scenario under different traffic conditions. Additionally, we compare our proposal to other HCCA scheduling algorithms, the HCCA reference scheduler and Fair Hybrid Coordination Function (FHCF). The results show that the combination of the MSS and the territory method obtains higher system capacity for VoIP traffic (up to 32 users) in the simulated scenario, compared to FHCF and the HCCA reference scheduler (13 users). In addition, the MSS with the territory method also improves the throughput of TCP sources (one FTP application achieves between 6.1 Mbps without VoIP traffic and 2.1 Mbps with 20 VoIP users) compared to the reference scheduler (at most 388 kbps) and FHCF (with a maximum FTP throughput of 4.8 Mbps).     

Compressed Multiresolution Hierarchies for High‐Quality Precomputed Shadows

The quality of shadow mapping is traditionally limited by texture resolution. We present a novel lossless compression scheme for high‐resolution shadow maps based on precomputed multiresolution hierarchies. Traditional multiresolution trees can compactly represent homogeneous regions of shadow maps at coarser levels, but require many nodes for fine details. By conservatively adapting the depth map, we can significantly reduce the tree complexity. Our proposed method offers high compression rates, avoids quantization errors, exploits coherency along all data dimensions, and is well‐suited for GPU architectures. Our approach can be applied for coherent shadow maps as well, enabling several applications, including high‐quality soft shadows and dynamic lights moving on fixed‐trajectories.     

FLAME: a formal framework for the automated analysis of software product lines validated by automated specification testing

In a literature review on the last 20 years of automated analysis of feature models, the formalization of analysis operations was identified as the most relevant challenge in the field. This formalization could provide very valuable assets for tool developers such as a precise definition of the analysis operations and, what is more, a reference implementation, i.e., a trustworthy, not necessarily efficient implementation to compare different tools outputs. In this article, we present the FLAME framework as the result of facing this challenge. FLAME is a formal framework that can be used to formally specify not only feature models, but other variability modeling languages (VML s) as well. This reusability is achieved by its two-layered architecture. The abstract foundation layer is the bottom layer in which all VML-independent analysis operations and concepts are specified. On top of the foundation layer, a family of characteristic model layers—one for each VML to be formally specified—can be developed by redefining some abstract types and relations. The verification and validation of FLAME has followed a process in which formal verification has been performed traditionally by manual theorem proving, but validation has been performed by integrating our experience on metamorphic testing of variability analysis tools, something that has shown to be much more effective than manually designed test cases. To follow this automated, test-based validation approach, the specification of FLAME, written in Z, was translated into Prolog and 20,000 random tests were automatically generated and executed. Tests results helped to discover some inconsistencies not only in the formal specification, but also in the previous informal definitions of the analysis operations and in current analysis tools. After this process, the Prolog implementation of FLAME is being used as a reference implementation for some tool developers, some analysis operations have been formally specified for the first time with more generic semantics, and more VML s are being formally specified using FLAME.     

Guided Image Filtering for Interactive High‐quality Global Illumination

Interactive computation of global illumination is a major challenge in current computer graphics research. Global illumination heavily affects the visual quality of generated images. It is therefore a key attribute for the perception of photo‐realistic images. Path tracing is able to simulate the physical behaviour of light using Monte Carlo techniques. However, the computational burden of this technique prohibits interactive rendering times on standard commodity hardware in high‐quality. Trying to solve the Monte Carlo integration with fewer samples results in characteristic noisy images. Global illumination filtering methods take advantage of the fact that the integral for neighbouring pixels may be very similar. Averaging samples of similar characteristics in screen‐space may approximate the correct integral, but may result in visible outliers. In this paper, we present a novel path tracing pipeline based on an edge‐aware filtering method for the indirect illumination which produces visually more pleasing results without noticeable outliers. The key idea is not to filter the noisy path traced images but to use it as a guidance to filter a second image composed from characteristic scene attributes that do not contain noise by default. We show that our approach better approximates the Monte Carlo integral compared to previous methods. Since the computation is carried out completely in screen‐space it is therefore applicable to fully dynamic scenes, arbitrary lighting and allows for high‐quality path tracing at interactive frame rates on commodity hardware.     

A new DSATUR-based algorithm for exact vertex coloring

This paper describes a new exact algorithm PASS for the vertex coloring problem based on the well known DSATUR algorithm. At each step DSATUR maximizes saturation degree to select a new candidate vertex to color, breaking ties by maximum degree w.r.t. uncolored vertices. Later Sewell introduced a new tiebreaking strategy, which evaluated available colors for each vertex explicitly. PASS differs from Sewell in that it restricts its application to a particular set of vertices. Overall performance is improved when the new strategy is applied selectively instead of at every step. The paper also reports systematic experiments over 1500 random graphs and a subset of the DIMACS color benchmark.     

A metaheuristic for a teaching assistant assignment-routing problem

The Flemish Ministry of Education promotes the integrated education of disabled children by providing educational opportunities in common schools. In the current system, disabled children receive ambulant help from a teaching assistant (TA) employed at an institute for extra-ordinary education. The compensation that the TAs receive for driving to visit the pupils is a major cost factor for the institute that provides the assistance. Therefore, the institute's management desires a schedule that minimizes the accumulated distance traveled by all TAs combined. We call this optimization problem the teaching assistants assignment-routing problem (TAARP). It involves three decisions that have to be taken simultaneously: (1) pupils have to be assigned to TAs; (2) pupils assigned to a given TA have to be spread over the TA's different working days; and (3) the order in which to visit the pupils on each day has to be determined. We propose a solution strategy based on an auction algorithm and a variable neighborhood search heuristic which has an excellent performance when applied to both simulated and real instances. The total distance traveled in the solution obtained for the institute's data set improves the current solution by about 22% which represents a saving of approximately 9% on the annual budget of the institute for integrated education.     

Intrinsically universal n-dimensional quantum cellular automata

Several non-axiomatic approaches have been taken to define Quantum Cellular Automata (QCA); Partitioned QCA (PQCA) are the most canonical. Here we show any QCA can be put into PQCA form. Our construction reconciles the non-axiomatic definitions of QCA, showing that they can all simulate one another, thus they are all equivalent to the axiomatic definition. A simple n-dimensional QCA capable of simulating all others to arbitrary precision is described, where the initial configuration and the evolution of any QCA can be encoded within the initial configuration of the intrinsically universal QCA. Several steps then correspond to one step of the simulated QCA, achieved via a non-trivial reduction of the problem to universality in quantum circuits. Results are formalised by defining generalised n-dimensional intrinsic simulation, preserving topology in that each cell of the simulated QCA is encoded as a group of adjacent cells in the universal QCA. Implications are discussed.     

udpSkeduler: A Web architecture based decision support system for course and classroom scheduling

A key process for post-secondary educational institutions is the definition of course timetables and classroom assignments. Manual scheduling methods require enormous amounts of time and resources to deliver results of questionable quality, and multiple course and classroom conflicts usually occur. This article presents a scheduling system implemented in a Web environment. This system generates optimal schedules via an integer-programming model. Among its functionalities, this system enables direct interaction with instructors in order to gather data on their time availability for teaching courses. The results demonstrate that significant improvements over the typical fully manual process were obtained.     

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones.Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically.The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.     

Cell formation in group technology using constraint programming and Boolean satisfiability

Cell formation consists in organizing a plant as a set of cells, each of them containing machines that process similar types or families of parts. The idea is to minimize the part flow among cells in order to reduce costs and increase productivity. The literature presents different approaches devoted to solve this problem, which are mainly based on mathematical programming and on evolutionary computing. Mathematical programming can guarantee a global optimal solution, however at a higher computational cost than an evolutionary algorithm, which can assure a good enough optimum in a fixed amount of time. In this paper, we model and solve this problem by using state-of-the-art constraint programming (CP) techniques and Boolean satisfiability (SAT) technology. We present different experimental results that demonstrate the efficiency of the proposed optimization models. Indeed, CP and SAT implementations are able to reach the global optima in all tested instances and in competitive runtime.