Game theoretic distributed uplink power control for CDMA networks with real-time services

In this paper, the issue of efficient power allocation in the uplink of CDMA wireless networks supporting real-time services with various QoS constraints, is addressed. Within the proposed framework, utility functions are adopted to reflect a user’s degree of satisfaction with respect to its actual throughput requirements satisfaction and respective power consumption. The corresponding problem is formulated as a non-cooperative game where users aim selfishly at maximizing their utility-based performance under the imposed limitations. The existence and uniqueness of a Nash equilibrium point of the proposed Uplink Power Control (UPC) game is proven, at which all users have attained a targeted SINR threshold value or transmit with their maximum power, leading essentially to an SINR-balanced system. Moreover, a distributed iterative algorithm for reaching UPC game’s equilibrium is provided. The properties of equilibrium in a pure optimization theoretical framework are studied, and the tradeoffs between users’ overall throughput performance and real-time services’ QoS requirements satisfaction, in channel aware resource allocation processes, are revealed and quantified. Through modeling and simulation the efficacy of the introduced framework and proposed UPC algorithm are demonstrated and evaluated.     

Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis inspired criteria has been proposed, which achieves an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance. However, this approach possesses certain limitations, since it assumes that the underlying data distribution is unimodal, which is often unrealistic. To remedy this limitation, we regard that data inside each class have a multimodal distribution, thus forming clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information. The developed algorithm has been applied for both facial expression and face recognition on three popular databases. Experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.     

Cache-aided full-duplex: delivery time analysis and optimization

Edge caching has received much attention as a promising technique to overcome the stringent latency and data-hungry challenges in the future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit at the same frequency band simultaneously. In this paper, we investigate the delivery time performance of a cache-aided FD system, in which an edge node, operates in FD mode, serves users via wireless channels and is equipped with a cache memory. Firstly, we derive a closed-form expression for the average delivery time by taking into account the uncertainties of both backhaul and access wireless channels. The derived analysis allows the examination of the impact of key parameters, e.g., cache size and transmit power. Secondly, a power optimization problem is formulated to minimize the average delivery time. To deal with the non-convexity of the formulated problem, we propose an iterative optimization algorithm based on the bisection method. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithm. A significant delivery time reduction is achieved by the proposed optimization compared to the FD reference and half-duplex counterpart.

     

Community detection in Social Media

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.     

Minimum Moment Method for Resource Leveling Using Entropy Maximization

The minimum moment method for resource leveling is revisited and restated as an entropy-maximization problem. The minimum moment method assumes that the moment of the daily resource demands about the horizontal axis of a project’s resource histogram is a good measure of the resource utilization and that the optimal resource allocation exists when the total moment is at a minimum, thus when the resource histogram is of rectangular shape. The entropy-maximization method proposed in this paper makes use of the general theory of entropy and two of its principal properties (subadditivity and maximality) to revisit the minimum moment method for resource leveling. The entropy-maximization method presented allows for activity stretching and provides resource allocation solutions that show improvement over previous approaches. A case study is also presented that validates the results.     

An axiomatization of graphs

The magmoid of hypergraphs labelled over a finite doubly ranked alphabet, is characterized as the quotient of the free magmoid generated by this alphabet divided by a finite set of equations. Thus a relevant open problem, posed by Engelfriet and Vereijken, is being solved.Received: 27 April 2004, Published online: 14 October 2004     

Picture deformation

Algebraic picture generation based on a pixel deformation theory is presented. The main tool used is the deformation monoid which simulates the algebraic structure of pictures viewed as rectangular arrays with operations the horizontal and vertical concatenation. Picture languages generated by grammatical systems are considered and a Chomsky-like normal form as well as an iteration lemma are established. Infinite pictures are obtained as the ω-completion of the set of finite pictures ordered by picture refinement. Regular fractal pictures (such as the Sierpinski Carpet, the Cantor dust, etc.) are defined as the components of the least solution of systems whose right hand side members are finite pictures. They constitute the least class of pictures containing the finite pictures and closed under substitution and the self similarity operation. Solving non deterministic picture program schemes we get the so called ∞-refinement languages which consist of finite and infinite pictures. For such languages the emptiness and finiteness problems are decidable.     

Proactive Risk-Based Integrity Assessment of Water Distribution Networks

Sustainable management of urban water distribution networks should include not only new methods for monitoring, repairing or replacing aging infrastructure, but also (and more importantly) expanded methods for modelling deteriorating infrastructure, for pro-actively assessing the risk of failure and for devising replace or repair strategies. The study presented herein describes a framework for proactive risk-based integrity monitoring of urban water distribution networks and the results obtained from a case-study based on a 5-year data sample. A combination of artificial neural network and statistical modelling techniques stemming from parametric and nonparametric survival analysis (Kaplan–Meier survival curves with Epanechnikov’s kernel) are utilized in the investigation of identified risk factors and for estimation of the forecasted time to failure metric. The data is stratified for different pipe groups for a more targeted analysis.     

An Autonomic QoS-centric Architecture for Integrated Heterogeneous Wireless Networks

In this paper the problem of seamless mobility and proficient joint radio resource management over an all-IP internetworked wireless heterogeneous environment is addressed. Nodes’ autonomicity is envisioned as the enabler to devise a Quality of Service (QoS) aware architecture for supporting a variety of services, founded on a common utility based framework that provides enhanced flexibility in reflecting different access networks’ type of resources and diverse QoS prerequisites, under a unified QoS-aware resource allocation optimization problem. This allows a more in-depth intrinsic wireless network convergence, beyond All-IP, driven by QoS-oriented resource management. This vision is demonstrated and instantiated for integrated WLAN and cellular (both CDMA and OFDMA) networks, providing a viable path towards the evolution and realization of the future wireless networking paradigm. Initial numerical results demonstrate the effectiveness of the proposed architecture and reveal the benefits of such a service oriented paradigm against other existing access oriented autonomic designs.