Scanning tunneling spectroscopy studies on YBa2Cu3O7−δ

Scanning Tunneling Spectroscopy (STS) measurements have been carried out on the high T_c superconductor (HTS) YBa₂Cu₃O_7– (YBCO). Using very high quality single crystals as well as hard tips, we obtained differential conductance spectra which showed various conductance features in agreement with other spectroscopic techniques. The reproducibility of our spectra allowed us to map for the first time the vortex lattice on a HTS using a STS technique¹. The vortices are arranged in a short range order oblique lattice, while the cores of the flux lines show an anisotropic shape. These observations can be related to the in-plane anisotropy of YBCO.     

An improved method for voice pathology detection by means of a HMM-based feature space transformation

This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.     

A modeling approach on the TelosB WSN platform power consumption

Substantial characteristics of wireless sensor networks, such as autonomy and miniature size, are achieved at the expense of restricted energy resources. Optimal resource management is thus among the most important challenges in WSNs development and its success requires accurate and practical models based on detailed insight concerning the factors contributing to the overall power consumption of a WSN mote. To achieve such awareness, that will enable models development, appropriate measuring test-beds and methodologies are needed, facilitating reliable and accurate power consumption measurements of critical functionalities.To cover the need for energy models that precisely define the power consumption behavior of WSN hardware platforms, this paper contributes with a measuring methodology including three steps: the design and implementation of a measuring system for a wide range of power consumption thresholds, the identification, isolation and measurement of elementary functionalities of a WSN platform with respect to their contribution to the overall mote power consumption, and the extraction of valuable conclusions based on the respective measurements resulting in the composition of a practical, yet accurate power consumption model.     

Designing adaptive learning itineraries using features modelling and swarm intelligence

In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user??s success on the course. Therefore, the path can change in order to adapt itself to learners?? preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.     

Embedding a fault-free hamiltonian cycle in a class of faulty generalized honeycomb tori

Generalized honeycomb torus (GHT) is recognized as an attractive alternative to existing torus interconnection networks in parallel computing systems. Assume that m and d are integers with m ? 2 and d ? 8. This paper addresses the fault-tolerant hamiltonicity of GHT(m, 2d, d) with fault set F = {(w, y), (x, y)}, where w < x, w + y is even and x + y is odd. We show that such a faulty GHT is hamiltonian by presenting a systematic method for constructing a fault-free hamiltonian cycle. This result reveals another appealing feature of GHTs.     

具有计算可靠性的符号模型

MW方法仅考虑使用公钥加密原语,不包含{{mk}k类型消息的双方协议。针对该问题,使用公钥加密和对称加密,建立扩展标记符号模型与扩展计算模型,论证扩展标记符号模型的计算可靠性,实现对MW方法的扩展。     

基于层次管理的按需路由协议研究

动态源路由（DSR）协议能较好适用中小型对等自组网络环境,但在非对等网络环境中面临一定应用局限。针对该问题,分析DSR协议的特点和存在问题,结合非对等环境和层次路由思想,提出新的协议CDSR,对CDSR路由协议进行性能评估。仿真结果表明,该协议具有较好的执行效能,可提升网络性能。     

时间序列分析在轨道交通客流预报中的应用研究

时间序列分析是动态数据分析的重要方法,在多学科领域中得到广泛的研究和运用.研究时间序列分析在城市轨道交通自动售检票系统数据分析中的应用方法,并结合某线路AFC系统的历史沉淀数据,给出两个完整的时间序列分析案例,其分析方法能够为AFC运营管理提供有效决策的手段.