The outline of this chapter is as follows. Section 2 reviews various types of existing finite impulse response (FIR) and infinite impulse response (IIR) two-channel filter banks. The basic operations of these filter banks are considered and the requirements are stated for alias-free, perfect-reconstruction (PR), and nearly perfect-reconstruction (NPR) filter banks. Also some efficient synthesis techniques are referred to. Furthermore, examples are included to compare various two-channel filter banks with each other. Section 3 concentrates on the design of multi-channel (M-channel) uniform filter banks. The main emphasis is laid on designing these banks using tree-structured filter banks with the aid of two-channel filter banks and on generating the overall bank with the aid of a single prototype filter and a proper cosine-modulation or MDFT technique. In Section 4, it is shown how octave filter banks can be generated using a single two-channel filter bank as the basic building block. Also, the relations between the frequency-selective octave filter banks and discrete-time wavelet banks are briefly discussed. Finally, concluding remarks are given in Section 5.

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Multirate Systems and Filter Banks

Trends in extreme learning machines

Cognitive users need to detect primary users continuously and rapidly.Cooperative spectrum sensing in cognitive radio(CR) networks is analyzed.Cooperative spectrum sensing in the ideal network with two cognitive users can reduce the mean detection time.Detection probability to primary users can be improved by multi-cooperative users in multi-user networks.Finally,the realization of multi-user networks is considered.

Cooperative Spectrum Sensing in Cognitive Radio Networks

Underwater sensor networks consist of sensors and vehicles deployed to perform collaborative monitoring tasks over a given region. Underwater sensor networks will find applications in oceano-graphic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation, tactical surveillance, and mine reconnaissance. Underwater acoustic networking is the enabling technology for these applications. In this paper, an architecture for three-dimensional underwater sensor networks is considered, and a model characterizing the acoustic channel utilization efficiency is introduced, which allows investigating some fundamental characteristics of the underwater environment. In particular, the model allows setting the optimal packet size for underwater communications given monitored volume, density of the sensor network, and application requirements. Moreover, the problem of data gathering is investigated at the network layer by considering the cross-layer interactions between the routing functions and the characteristics of the underwater acoustic channel. Two distributed routing algorithms are introduced for delay-insensitive and delay-sensitive applications. The proposed solutions allow each node to select its next hop, with the objective of minimizing the energy consumption taking the varying condition of the underwater channel and the different application requirements into account. The proposed routing solutions are shown to achieve the performance targets by means of simulation.

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Routing algorithms for delay-insensitive and delay-sensitive applications in underwater sensor networks

Wireless Mesh Networks (WMNs) are envisaged to extend Internet access and other networking services in personal, local, campus, and metropolitan areas. Mesh routers (MR) form the connectivity backbone while performing the dual tasks of packet forwarding as well as providing network access to the mesh clients. However, the performance of such networks is limited by traffic congestion, as only limited bandwidth is available for supporting the large number of nodes in close proximity. This problem can be alleviated by the cognitive radio paradigm that aims at devising spectrum sensing and management techniques, thereby allowing radios to intelligently locate and use frequencies other than those in the 2.4 GHz ISM band. These promising technologies are integrated in our proposed Cognitive Mesh NETwork (COMNET) algorithmic framework, thus realizing an intelligent frequency-shifting self-managed mesh network. The contribution of this paper is threefold: (1) A new approach for spectrum sensing is devised without any change to the working of existing de facto mesh protocols. (2) An analytical model is proposed that allows MRs to estimate the power in a given channel and location due to neighboring wireless LAN traffic, thus creating a virtual map in space and frequency domains. (3) These models are used to formulate the task of channel assignment within the mesh network as an optimization problem, which is solved in a decentralized manner. Our analytical models are validated through simulation study, and results reveal the benefits of load sharing by adopting unused frequencies for WMN traffic.

Cognitive Wireless Mesh Networks with Dynamic Spectrum Access

Cognitive radio (CR)-based Internet of Things (IoT) system is an effective step toward a world of smart technology. Many frameworks have been proposed to build CR-based IoT systems. The CR-based IoT frameworks are the key points on which this survey focuses. Efficient spectrum sensing and sharing are the main functional components of the CR-based IoT. Reviews of recent SS and sharing approaches are presented in this survey. This survey classifies the SS and sharing approaches and discusses the merits and limitations of those approaches. Moreover, this survey discusses the design factors of the CR-based IoT and the criteria by which the proper SS and access approaches are selected. Furthermore, the survey explores the integration of newly emerging technologies with the CR-based IoT systems. Finally, the survey highlights some emerging challenges and concludes with suggesting future research directions and open issues.

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Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey

Spectrum sensing is one of the essential tasks to have a cognitive radio system, which will allow an unlicensed user, called secondary user, to utilize the spectrum while the licensed user, called primary user, is not occupying it. The spectrum sensing approaches can be classified as blind and knowledge aided approaches. This tutorial summarizes blind spectrum sensing (BSS) approaches that require no prior knowledge of the licensed user’s signal characteristics, specifically for an interweave cognitive radio network model. The tutorial provides a thorough background, major implementations, and limitations of the BSS approaches, which are energy detector approach, maximum to minimum eigenvalue approach, maximum eigenvalue approach, covariance absolute value approach, and covariance Frobenius norm approach. Moreover, the tutorial compares these approaches based on performance metrics and complexity requirements. Furthermore, for a higher interference protection, the combination of two different spectrum sensing approaches, namely two-stage detection technique is presented and discussed. Besides, the tutorial discusses the challenges and possible future research directions. The fundamental objective of this tutorial is to provide insightful views and design aspects of BSS approach to researchers. For this purpose, the tutorial includes pseudo codes and simulation examples to illustrate more about the practical aspects of the above-mentioned approaches.

Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course

This paper presents a Markov chain analysis for studying the performance of the IEEE 802.11 Distributed Coordination Function. The model supports an arbitrary back-off strategy. We found that the most important parameter affecting the performance of binary exponential back-off is the initial back-off window size. Our experimental results show that the probability of collision can be reduced when the initial back-off window size equals the number of stations. Thus, the throughput of the system increases and, at the same time, the delay to transmit the frame is reduced.

Performance Analysis of the IEEE 802.11 DCF

Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion

Human face recognition has recently become one of the hottest topics in the area of pattern recognition due to its applications in identity validation and recognition. Moment Invariants are pattern sensitive features and are used in pattern recognition applications. In this paper different moment invariants have been used to extract features from human face images for recognition application.  Moment invariants of Hu (HMI), Bamieh (BMI), Zernike (ZMI), Pseudo Zernike (PZMI), Teague-Zernike (TZMI), Normalized Zernike (NZMI) ,Normalized Pseudo Zernike (NPZMI) and also regular Moment Invariant (RMI) have been applied to the AT&T face database and the results have been compared. Our results show that pseudo Zernike moments yields the best recognition accuracy of 95%.

Esam Abdel-Raheem

Papers

Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey

Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course

Performance Analysis of the IEEE 802.11 DCF

Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion

Human Face Recognition Using Different Moment Invariants: A Comparative Study