GPGPU implementation of growing neural gas: Application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6×$6\times$ compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.     

FAIRNESS IN RECURRENT AUCTIONS WITH COMPETING MARKETS AND SUPPLY FLUCTUATIONS

Auctions have been used to deal with resource allocation in multiagent environments, especially in service‐oriented electronic markets. In this type of market, resources are perishable and auctions are repeated over time with the same or a very similar set of agents. In this scenario it is advisable to use recurrent auctions: a sequence of auctions of any kind where the result of one auction may influence the following one. Some problems do appear in these situations, as for instance, the bidder drop problem, the asymmetric balance of negotiation power or resource waste, which could cause the market to collapse. Fair mechanisms can be useful to minimize the effects of these problems. With this aim, we have analyzed four previous fair mechanisms under dynamic scenarios and we have proposed a new one that takes into account changes in the supply as well as the presence of alternative marketplaces. We experimentally show how the new mechanism presents a higher average performance under all simulated conditions, resulting in a higher profit for the auctioneer than with the previous ones, and in most cases avoiding the waste of resources.     

QoS and energy management with Petri nets: A self-adaptive framework

Energy use is becoming a key design consideration in computing infrastructures and services. In this paper we focus on service-based applications and we propose an adaptation framework that can be used to reduce power consumption according to the observed workload. The adaptation guarantees a trade-off between energy consumption and system performance. The approach is based on the principle of proportional energy consumption obtained by scaling down energy for unused resources, considering both the number of servers switched on and their operating frequencies. Stochastic Petri nets are proposed for the modeling of the framework concerns, their analyses give results about the trade-offs. The application of the approach to a simple case study shows its usefulness and practical applicability. Finally, different types of workloads are analyzed with validation purposes.     

Intrinsic Images by Clustering

Decomposing an input image into its intrinsic shading and reflectance components is a long‐standing ill‐posed problem. We present a novel algorithm that requires no user strokes and works on a single image. Based on simple assumptions about its reflectance and luminance, we first find clusters of similar reflectance in the image, and build a linear system describing the connections and relations between them. Our assumptions are less restrictive than widely‐adopted Retinex‐based approaches, and can be further relaxed in conflicting situations. The resulting system is robust even in the presence of areas where our assumptions do not hold. We show a wide variety of results, including natural images, objects from the MIT dataset and texture images, along with several applications, proving the versatility of our method.     

Stronger Lempel-Ziv Based Compressed Text Indexing

Given a text T[1..u] over an alphabet of size σ, the full-text search problem consists in finding the occ occurrences of a given pattern P[1..m] in T. In indexed text searching we build an index on T to improve the search time, yet increasing the space requirement. The current trend in indexed text searching is that of compressed full-text self-indices, which replace the text with a more space-efficient representation of it, at the same time providing indexed access to the text. Thus, we can provide efficient access within compressed space. The Lempel-Ziv index (LZ-index) of Navarro is a compressed full-text self-index able to represent T using 4uH _k (T)+o(ulog?σ) bits of space, where H _k (T) denotes the k-th order empirical entropy of T, for any k=o(log? _σ u). This space is about four times the compressed text size. The index can locate all the occ occurrences of a pattern P in T in O(m ³log?σ+(m+occ)log?u) worst-case time. Although this index has proven very competitive in practice, the O(m ³log?σ) term can be excessive for long patterns. Also, the factor 4 in its space complexity makes it larger than other state-of-the-art alternatives. In this paper we present stronger Lempel-Ziv based indices (LZ-indices), improving the overall performance of the original LZ-index. We achieve indices requiring (2+ε)uH _k (T)+o(ulog?σ) bits of space, for any constant ε>0, which makes them the smallest existing LZ-indices. We simultaneously improve the search time to O(m ²+(m+occ)log?u), which makes our indices very competitive with state-of-the-art alternatives. Our indices support displaying any text substring of length ? in optimal O(?/log? _σ u) time. In addition, we show how the space can be squeezed to (1+ε)uH _k (T)+o(ulog?σ) to obtain a structure with O(m ²) average search time for m≥2log? _σ u. Alternatively, the search time of LZ-indices can be improved to O((m+occ)log?u) with (3+ε)uH _k (T)+o(ulog?σ) bits of space, which is much less than the space needed by other Lempel-Ziv-based indices achieving the same search time. Overall our indices stand out as a very attractive alternative for space-efficient indexed text searching.     

Composite indicator for railway infrastructure management

Railway Engineering Science - The assessment and analysis of railway infrastructure capacity is an essential task in railway infrastructure management carried out to meet the required quality and...     

基于物联网的苹果树病虫防治专家系统的设计与应用

提出一种基于物联网技术的专家系统结构,并以苹果树病虫防治专家系统为例,介绍专家系统的设计方法,以及物联网技术在专家系统中的应用和管理.     

移动设备上移动代理应用的安全性研究

移动代理的安全性问题一直制约着移动代理的广泛应用。以往的研究主要集中在解决移动代理安全的一般问题，但随着移动设备的大量普及，移动代理技术应用于这些受限设备，其适用场合和安全问题应区别对待。本文首先讨论了在这些移动设备上的移动代理典型应用，然后阐述了相关的安全问题及解决办法。最后给出了一个基于J2ME的实现范例，并提供了一些实验结果。     

无取向硅钢热轧板形控制与轧制特性的关系

无取向硅钢板形质量要求日趋严苛。热轧是控制无取向硅钢成品横截面厚差偏大、尤其是边部减薄显著的关键工序。通过测试并分析无取向硅钢板形控制与专有轧制特性的关系,提出可通过采用合理的ASR非对称自补偿工作辊和常规工作辊的辊形配置及相应的窜辊策略可有效控制带钢边部减薄和凸度,使板形质量达到用户要求。