Robust adaptive control for greenhouse climate using neural networks

This paper presents a general framework for robust adaptive neural network (NN)‐based feedback linearization controller design for greenhouse climate system. The controller is based on the well‐known feedback linearization, combined with radial basis functions NNs, which allows the feedback linearization technique to be used in an adaptive way. In addition, a robust sliding mode control is incorporated to deal with the bounded disturbances and the approximation errors of NNs. As a result, an inherently nonlinear robust adaptive control law is obtained, which not only provides fast and accurate tracking of varying set‐points, but also guarantees asymptotic tracking even if there are inherent approximation errors. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive discrete neural observer design for nonlinear systems with unknown time‐delay

This paper focuses on the adaptive observer design for nonlinear discrete‐time MIMO systems with unknown time‐delay and nonlinear dynamics. The delayed states involved in the system are arguments of a nonlinear function and only the estimated delay is utilized. By constructing an appropriate Lyapunov–Krasovskii function, the delay estimation error is considered in the observer parameter design. The proposed method is then extended to the system with a nonlinear output measurement equation and the delayed dynamics. With the help of a high‐order neural network (HONN), the requirement for a precise system model, the linear‐in‐the‐parameters (LIP) assumption of the delayed states, the Lipschitz or norm‐boundedness assumption of unknown nonlinearities are removed. A novel converse Lyapunov technical lemma is also developed and used to prove the uniform ultimate boundedness of the proposed observer. The effectiveness of the proposed results is verified by simulations. Copyright © 2010 John Wiley & Sons, Ltd.     

Study of polymer‐network structure on polymer‐stabilized blue phase

Abstract— To understand the mechanism of the disclination defect of the liquid‐crystal (LC) phase, this study was conducted to directly analyze the polymer‐network (PN) structure of polymer‐stabilized blue phase (PSBP), which is minutely formed on all LC layers. The PN was examined after first removing the glass decap and then the LC. Important to note is that the removal of the glass decap did not affect or damage the PN structure. The PN was determined to be a stable structure without any change to the thickness of the layer. When removing the LC, both hexane and acetone solutions were used. Moreover, there was no structural deformation to the PN when using the hexane solution. The results of the study show that the actual size of the polymer chain is in fact 50–60 nm, five times larger than previous theories which estimated the size to be only 10 nm. In addition, this study confirmed that the pores between the PN are 100–200 nm. The PN structure was shown to be susceptible to change based on different heating temperatures. In summation, now that defect lines of a LC display (LCD) could be directly measured, further progress and development in the theoretical interpretations of the Kerr effect on PSBP can be realized.     

A survey on fraud and service misuse in voice over IP (VoIP) networks

The migration from circuit-switched networks to packet-switched networks necessitates the investigation of related issues such as service delivery, QoS, security, and service fraud and misuse. The latter can be seen as a combination of accounting and security aspects. In traditional telecommunication networks, fraud accounts for annual losses at an average of 3%–5% of the operators’ revenue and still increasing at a rate of more than 10% yearly. It is also expected that in VoIP networks, the situation will be worse due to the lack of strong built-in security mechanisms, and the use of open standards. This paper discusses the fraud problem in VoIP networks and evaluates the related available solutions.     

Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.     

A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors

Ultra-wideband (UWB) has been widely recommended for significant commercial and military applications. However, the well-derived coherent structures for UWB signal detection are either computationally complex or hardware impractical in the presence of the intensive multipath propagations. In this article, based on the nonparametric Parzen window estimator and the probabilistic neural networks, we suggest a low-complexity and noncoherent UWB detector in the context of distributed wireless sensor networks (WSNs). A novel characteristic spectrum is firstly developed through a sequence of blind signal transforms. Then, from a pattern recognition perspective, four features are extracted from it to fully exploit the inherent property of UWB multipath signals. The established feature space is further mapped into a two-dimensional plane by feature combination in order to simplify algorithm complexity. Consequently, UWB signal detection is formulated to recognize the received patterns in this formed 2-D feature plane. With the excellent capability of fast convergence and parallel implementation, the Parzen Probabilistic Neural Network (PPNN) is introduced to estimate a posteriori probability of the developed patterns. Based on the underlying Bayesian rule of PPNN, the asymptotical optimal decision bound is finally determined in the feature plane. Numerical simulations also validate the advantages of our proposed algorithm.     

Global exponential synchronization of stochastic switching networks with time‐varying delay

This paper is concerned with the global exponential synchronization of stochastic delayed switching networks via hybrid control. The network under investigation is quite general to reflect the reality, where the network topology consists of r modes and switches from one mode to another according to a Markovian chain with known transition probability. Parameter uncertainties, time varying delay and stochastic disturbances are all taken into account in this study. Based on the Lyapunov functional method and stochastic analysis techniques, some new criteria for the global robust synchronization via hybrid control are established. Finally, one example with numerical simulation is given for illustration. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Traceable content protection based on chaos and neural networks

In this paper, a media content encryption/decryption algorithm is designed based on a chaos system and neural networks, which generates random sequences with chaos, and encrypts or decrypts media contents with neural networks in a parallel way. In this scheme, different decryption keys can be used to recover the media content into different copies. That is, the decryption operation gets the content containing certain random sequence that can be used as the identification. With respect to this property, the scheme is used for secure content distribution. Taking the audio content for example, it is encrypted by a key at the sender side and decrypted by different keys at the receiver side. The differences between decryption keys lead to different decrypted audio copies. If one customer distributes his copy to other unauthorized customers, the chaotic sequence contained in the copy can tell the illegal customer. The performances, including security, imperceptibility and robustness, are analyzed, and some experimental results are given to show the scheme's practicability.     

Opportunistic resource utilization networks—A new paradigm for specialized ad hoc networks

We present opportunistic resource utilization networks or oppnets, a novel paradigm of specialized ad hoc networks. We believe that applications can benefit from using specialized ad hoc networks that provide a natural basis for them, the basis more efficient and effective than what general-purpose ad hoc networks can offer. Oppnets constitute the subcategory of ad hoc networks where diverse systems, not employed originally as nodes of an oppnet, join it dynamically in order to perform certain tasks they have been called to participate in. Oppnets have a significant potential to improve a variety of applications, and to create new application niches. We categorize opportunistic networks currently known in the literature as class 1opportunistic networks that use opportunistically only communication resources, and class 2opportunistic networks or oppnets that use opportunistically all kinds of resources, including not only communication but also computation, sensing, actuation, storage, etc. After describing the oppnets and the basics of their operation, we discuss the Oppnet Virtual Machine (OVM)—a proposed standard implementation framework for oppnet applications. It is followed by a discussion of an example application scenario using the OVM primitives. Next, we discuss the design and operations of a small-scale oppnet, named MicroOppnet, originally developed as a proof of concept. MicroOppnet is now being extended to serve as a testbed for experimentation and pilot implementations of oppnet architectures and their components. We conclude with a summary and a list of some open issues for oppnets.     

A CDMA/TDD approach for wireless mesh networks

Wide band mesh or star oriented networks have recently become a subject of greater interest. Providing wideband multimedia access for a variety of applications has led to the inception of mesh networks. Classic access techniques such as FDMA and TDMA have been the norm for such networks. CDMA maximum transmitter power is much less than TDMA and FDMA counter parts, which is an important asset for mobile operation. In this paper we introduce a code division multiple access/time division duplex technique CDMA/TDD for such networks. The CDMA approach is an almost play and plug technology for wireless access, making it amenable for implementation by the mesh network service station, SS. Further it inherently allows mesh network service stations to use a combination of turbo coding and dynamic parallel orthogonal transmission to improve network efficiency. We outline briefly the new transmitter and receiver structures then evaluate the efficiency, delay and delay jitter. By analysis we show the advantages over classic counter parts with respect to the total network efficiency achievable especially for larger number of hops.