Comparing knowledge management in health-care and technical support organizations.

Although knowledge management (KM) tools are well established in technical support organizations, health-care organizations have only recently become aware of their benefits. This research investigates whether health care should adopt the same tools taking into account the different KM requirements in the two industries. This study analyzes narratives from key personnel in a technical support organization and a health-care organization to understand and compare their KM process components and facilitating information technology. The empirical data reveal that health care needs a personalization approach to KM focusing on new problem identification using interactive knowledge webs, while technical support relies on a codification approach for problem resolution using interpretive knowledge and a chain structure.     

自适应系统中基于场景的信噪比预测算法

首先定义了过时信道状态信息（ OCSI,Outdated Channel State Information）对系统频谱效率的影响因子,分析了OCSI对频谱效率的影响;接着提出了一种基于信噪比预测的无线链路自适应系统模型,应用文中提出的基于信道变化场景的信噪比预测算法（ SSPA,Scenario-based SNR Prediction Algorithm）对发送数据时的信噪比进行预测,以克服OCSI的影响。仿真表明,与现有技术比较,基于SSPA的自适应系统具有近似的误比特率性能,频谱效率得到了有效的提升,特别是在高信噪比区域,频谱效率的改善十分显著。     

Regularized Spectral Matching for Blind Source Separation. Application to fMRI Imaging

The main contribution of this paper is to present a Bayesian approach for solving the noisy instantaneous blind source separation problem based on second-order statistics of the time-varying spectrum. The success of the blind estimation relies on the nonstationarity of the second-order statistics and their intersource diversity. Choosing the time-frequency domain as the signal representation space and transforming the data by a short-time Fourier transform (STFT), our method presents a simple EM algorithm that can efficiently deal with the time-varying spectrum diversity of the sources. The estimation variance of the STFT is reduced by averaging across time-frequency subdomains. The algorithm is demonstrated on a standard functional resonance imaging (fMRI) experiment involving visual stimuli in a block design. Explicitly taking into account the noise in the model, the proposed algorithm has the advantage of extracting only relevant task-related components and considers the remaining components (artifacts) to be noise.     

Adaptive time-varying cancellation of wideband interferences inspread-spectrum communications based on time-frequencydistributions

The paper proposes an adaptive method for suppressing wideband interferences in spread-spectrum (SS) communications. The proposed method is based on the time-frequency representation of the received signal from which the parameters of an adaptive time-varying interference excision filter are estimated. The approach is based on the generalized Wigner-Hough transform as an effective way to estimate the instantaneous frequency of parametric signals embedded in noise. The performance of the proposed approach is evaluated in the presence of linear and sinusoidal FM interferences plus white Gaussian noise in terms of the SNR improvement factor and bit error rate (BER)     

Developing and Evaluating New ICT Innovation System: Case Study of Korea's Smart Media Industry

The smart media (SM) industry has demonstrated that it has the characteristics to increase user innovative activities, enhance open innovativeness, and increase the segmentation of innovation value. This study introduces and evaluates an innovation system that reflects the characteristics of the SM industry. We categorize the SM industry into hardware, network, platform, and content industries and perform an AHP analysis (based on a survey of 96 experts) to evaluate the relative importance of the factors/factor groups affecting the creation of innovation. The results show that “collaboration activity” is a more important factor than other innovation factor groups (financial support, R&D, policy environment, human resources) in the SM industry. The results also show that the important factors/factor groups differ by industry.     

Greedy Geographic Routing using Dynamic Potential Field for Wireless Ad Hoc Networks

Greedy geographic routing has been considered as an attractive approach due to its simplicity and scalability. However, greedy routing is insufficient in terms of routing convergence because of the local maximum problem. In this letter, we propose a novel greedy routing scheme based on a potential field method that dynamically eliminates the local maximum condition     

Improving Radio Resource Utilization and User Level Fairness in OFDMA Femtocell Networks

Poor indoor coverage and high cost of cellular network operators are among the main motivations for the employment of femtocell networks. Since femto access points (FAPs) and macrocells share same spectrum resources, radio resource allocation is an important challenge in OFDMA femtocell networks. Mitigating interference and improving fairness among FAPs are the main objectives in previous resource allocation methods. However, the main drawback is that user level fairness has not been adequately addressed in the previous methods, and moreover, most of them suffer from inefficient utilization of radio resources. In this paper, modeling the problem as a graph multi-coloring, a centralized algorithm is proposed to obtain both user level fairness and spectrum efficiency. This method employs a priority-based greedy coloring algorithm in order to increase the reuse factor and consequently the spectrum efficiency. Moreover, in situations where the number of available OFDM resources is not sufficient, the proposed method employs a novel fairness index to fairly share those remaining resources among users of FAPs. The performance comparison between the proposed and previous methods shows that the proposed method improves the balance between user-level fairness and resource utilization. In addition, the presented analyses show that the time complexity of the proposed method is less than that of conventional methods.     

利用组稀疏特性的宽带压缩频谱感知

基于认知无线电的动态频谱接入需对宽带信道进行频谱感知,而越来越高的采样速率日益成为宽带频谱感知的瓶颈。压缩感知作为一种新的信号获取技术为亚奈奎斯特采样速率下的宽带频谱感知提供了一种可行方案。在相关应用场景中,如果能够挖掘相关先验信息并在重构算法中整合这些信息,将大幅提高压缩感知的性能。本文基于压缩感知技术,利用信道的划分信息及宽带信号的组稀疏特性,提出了一种组稀疏贪婪算法GOMP。该方法在成熟的贪婪算法基础上,利用子信道内多频点的组测量信息,根据组测量的概率分布特性来识别宽带信道的活动子信道。这种组测量识别方式使算法能以较少的观测数据实现对宽带信道的快速准确感知,极大地降低了宽带频谱感知所需的采样速率。实验结果表明:该算法比传统的OMP算法及BP算法不仅具有更好的重构效果及频谱检测性能,而且具有更好的压缩性能及实时性能。      

Linear prediction error method for blind identification of periodically time-varying channels

Blind channel estimation for single-input multiple-output (SIMO) periodically time-varying channels is considered using only the second-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). The linear prediction error method for blind identification of time-invariant channels is extended to time-varying channels represented by a CE-BEM. Sufficient conditions for identifiability are investigated. The cyclostationary nature of the received signal is exploited to consistently estimate the time-varying correlation function of the data from a single observation record. The proposed method requires the knowledge of the active basis functions but not the channel length (an upper bound suffices). Several existing methods require precise knowledge of the channel length. Equalization of the time-varying channel, given the estimated channel, is investigated. Computer simulation examples are presented to illustrate the approach and to compare it with two existing approaches.     

Short-time Fourier transform receiver for nonstationaryinterference excision in direct sequence spread spectrum communications

A new adaptive excision approach for nonstationary interference excision in direct sequence spread spectrum (DS/SS) communications is introduced. The proposed excision approach is based on the attractive localization properties of the impulse responses of the multiple pole filters. These impulse responses have Gaussian-like shapes and decrease in bandwidth with higher pole multiplicities. When used as data windows, they field a large class of computationally efficient short-time Fourier transforms (STFTs). Localization measures can be applied to determine the optimum window that maximally concentrates the interference in the time-frequency (t.-f.) domain. Interference mitigation is then achieved by applying a binary excision mask to the corresponding STFT for each data bit. We show that the proposed interference excision method permits both data-dependent windowing and time-varying filtering and leads to improved BER performance of the DS/SS system. The paper also derives the general optimum receiver implementing the STFT-based interference excision system     

Pre-reservation based spectrum allocation for cognitive radio network

Studies on the current usage of the radio spectrum by several agencies have already revealed that a large fraction of the radio spectrum is inadequately utilized. This basic finding has led to numerous research initiatives. Cognitive radio technology is one of the key candidate technologies to solve the problems of spectrum scarcity and low spectrum utilization. However, random behavior of the primary user (PU) appears to be an enormous challenge. In this paper, a Pre-reservation based spectrum allocation method for cognitive radio network is proposed to apply a PU behavior aware joint spectrum band (SB) selection and allocation scheme. In the first step, the SB is observed in terms of PU usage statistics whereas in the second phase, a network operator (NO) using a spectrum allocation scheme is employed to allocate SBs among secondary users (SUs). We also introduce the concept of reservation and exchange functionality under the priority serving strategy in a time-varying framing process. Simulation results show that the proposed scheme outperforms existing schemes in terms of the spectrum utilization and network revenue. In addition, it helps NO to manage the spectrum on a planned basis with a systematical spectrum reservation management where the NO has the status of time slots. Moreover, SUs have an opportunity to reserve or instantly request a SB that maximizes the SUs satisfaction in terms of quality of experience.     

Blind identification of time-varying channels using multistep linear predictors

Blind estimation of a class of single-input multiple-output (SIMO) time-varying channels is considered using only the second-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). The multistep linear predictors-based method for blind identification of time-invariant channels is extended to time-varying channels represented by a CE-BEM. Sufficient conditions for channel identifiability are investigated. The proposed method requires the knowledge of the active basis functions in the CE-BEM. It is not as sensitive to overestimation of the channel length as some of the existing methods. Computer simulation examples are presented to illustrate the approach and to compare it with three existing approaches.     

联合稀疏信号恢复的贪婪增强贝叶斯算法

本文针对联合稀疏信号恢复问题,提出了一种贪婪增强贝叶斯算法.算法首先利用联合稀疏的特点对信号进行建模,然后在贝叶斯框架下,提出一种贪婪推理方式对信号恢复问题进行迭代求解.在迭代过程中,提出算法利用贝叶斯估计的方差信息来增强支撑恢复的结果,极大地提高了算法对信号恢复性能.理论分析表明:提出算法与同步正交匹配追踪算法具有相同的计算复杂度,远低于其他联合稀疏信号恢复算法.提出方法在具有高恢复精度和较低计算复杂度的同时,兼具贝叶斯方法和贪婪算法的优点.数值仿真验证了理论分析的有效性.     

A Cluster-Based MAC Protocol for Cognitive Radio Ad Hoc Networks

The cognitive radio (CR) technology is an efficient approach to share the spectrum. But how to access the idle channels effectively is a challenging problem. To solve this problem, we propose a MAC protocol for cluster-based CR ad hoc networks, with the aim of making cluster structure more robust to the primary users’ channel re-occupancy activities, so that the CR users can use the idle spectrum more efficiently. Under this framework, a cluster formation algorithm based on available channels, geographical position and experienced statistics is proposed to maximize the network throughput and maintain the cluster topology stability. The schemes for new CR user joining a cluster and cluster maintenance are also designed. An experience database is proposed to store the experienced statistics which are obtained from spectrum occupancy history. Such a database can be used to support the acts of nodes, such as neighbor discovery and cluster forming. Simulation results reveal that our proposed framework not only outperforms conventional CR MAC protocols in terms of throughput and packet delay, but also generates a smaller number of clusters and has more stable cluster structure with less reclustering time.     

Does fragmentation avoidance improve the performance of dynamic spectrum allocation in elastic optical networks?

Most spectrum allocation algorithms in elastic optical networks apply a greedy approach: A new connection is allocated as long as there are enough spectrum slots to accommodate it. Recently, a different approach was proposed. Named Deadlock–Avoidance (DA), it only establishes a new connection if the portion of spectrum left after allocating it is zero (full-link utilization) or is big enough to accommodate future requests. Otherwise, the connection request is blocked as a way to avoid fragmentation. The performance of DA has been evaluated in a single-link scenario, where its performance is not affected by the slot continuity constraint. In this paper, we evaluate for the first time the blocking performance and fragmentation level of DA in a fully dynamic network scenario with different bitrates and number of slots for a single link, a 4-node bus and a mesh topology. The performance was evaluated by simulation, and a lower bound was also derived using a continuous Markov chain model. Results are obtained for DA and three greedy algorithms: First Fit, Exact Fit and First–Last Fit. Results show that DA significantly decreases fragmentation, and thus, it exhibits a much lower blocking due to fragmentation than the greedy algorithms. However, this decrease is compensated by a new type of blocking due to the selective acceptance of connections. As a result, the extra computational complexity of DA does not compensate a gain in performance.     

空间相关信道下一种合作感知节点集合选择算法

在空间相关信道条件下随着合作感知节点数目的增加,新增合作感知节点所带来的性能增益将会越来越小并趋近于零,同时系统资源使用效率也将会随着合作感知节点数目的增加而降低,因此在选择合作感知节点数目时需要在感知性能与资源使用效率之间进行折衷。本文提出了一种合作感知节点集合的贪婪选择算法。该算法首先在对感知性能和资源使用效率进行折衷的基础上得到判决门限,并依此判决门限选择满足条件的合作感知节点集合。仿真结果证明了所提出算法的有效性与可靠性。      

Semantic Knowledge Management for Education

ldquoSemantic technologiesrdquo are touted as the next big wave in educational technology and as the solution to many problems in this arena. Interdisciplinary work between the fields of knowledge management (KM) and educational technology (ET) is booming. But the crop of actual systems and semantically enhanced learning objects is still meager, maybe because KM and EL are lacking a consensus on the underlying notions, e.g., of ldquosemantics,rdquo yielding specific problems in their interplay. In this paper, we look at semantic educational technologies and draw conclusions for their approach in KM. In particular, we (re)evaluate the notions of semantics, knowledge, and learning; their role for learning materials in ET; and how they interact with the contexts involved in the learning/teaching process. Based on this, we distill a list of conditions the underlying knowledge representation format must fulfil to support these. As these conditions are still rather abstract, we show how they can be realized in a concrete language design, taking in our open mathematical documents format OMDoc as a point of departure.     

认知无线电网络中基于协作中继的资源分配算法

在认知无线电网络的协作中继机制下,中继节点利用其和源节点以及目的节点的不同公共信道为二者的通信转发数据,可以有效解决次用户的通信需求和可用带宽之间的矛盾,提高频谱利用率和系统吞吐量。基于协作中继的认知无线电网络中,不同通信链路上可能存在公共可用信道,使信道和中继的分配问题变得复杂。本文研究了公共信道存在的情况下系统的资源分配问题,基于网络最大流理论提出了两种算法:并行算法和贪婪算法,并分析了算法复杂度。仿真结果表明,两种算法都能够更有效地分配资源,提高频谱利用率,改善网络吞吐量。并行算法可以得到最优解,但其复杂度随信道公用程度的上升增长迅速,受节点并行处理能力的限制,只适用于信道公用程度较低的情况。贪婪算法不一定能得到最优解,但其复杂度较低,并且信道公用程度高时接近最优解,因此超出节点的并行处理能力后,可以选择贪婪算法。      

Opportunistic call admission control for wireless broadband cognitive networks

Wireless Broadband Cognitive Networks (WBCN) are new trend to better utilization of spectrum and resources. However, in multiservice WBCN networks, call admission control (CAC) is a challenging point to effectively control different traffic loads and prevent the network from being overloaded and thus provide promised quality of service. In this paper, we propose a CAC framework and formulate it as an optimization problem, where the demands of both WBCN service providers and cognitive subscribers are taken into account. To solve the optimization problem, we developed an opportunistic multivariate CAC algorithm based on a joint optimization of utility, weighted fairness, and greedy revenue algorithms. Extensive simulation results show that, the proposed call admission control framework can meet the expectations of both service providers and subscribers in wireless broadband cognitive networks.     

An improved adaptive constrained constant modulus reduced-rank algorithm with sparse updates for beamforming

In this work, we propose an adaptive set-membership (SM) reduced-rank filtering algorithm using the constrained constant modulus criterion for beamforming. We develop a stochastic gradient type algorithm based on the concept of SM techniques for adaptive beamforming. The filter weights are updated only if the bounded constraint cannot be satisfied. We also propose a scheme of time-varying bound and incorporate parameter dependence to characterize the environment for improving the tracking performance. A detailed analysis of the proposed algorithm in terms of computational complexity and stability is carried out. Simulation results verify the analytical results and show that the proposed adaptive SM reduced-rank beamforming algorithms with a dynamic bound achieve superior performance to previously reported methods at a reduced update rate.