The overall performance of multi-hop cognitive radio networks (MHCRNs) can be improved significantly by employing the diversity of orthogonal licensed channels in underlay fashion. However, the mutual interference between secondary links and primary links and the congestion due to the contention among traffic flows traversing the shared link become obstacles to this realizing technique. How to control congestion efficiently in coordination with power and spectrum allocation optimally in order to obtain a high end-to-end throughput is motivating cross-layer designs for MHCRNs. In this paper, by taking into account the problem of joint rate adaption, power control, and spectrum allocation (JRPS), we propose a new cross-layer optimization framework for MHCRNs using orthogonal frequency division multiple access (OFDMA). Specifically, the JRPS formulation is shown to be a mix-integer non-linear programming (MINLP) problem, which is NP-Hard in general. To solve the problem, we first develop a partially distributed algorithm, which is shown to converge to the global optimum within a reasonable time interval. We next propose a suboptimal solution which addresses the shortcomings of the first. Using numerical results, we finally demonstrate the efficiency of the proposed algorithms.

This study proposes an improvement to the Triangular Inequality Elimination (TIE) algorithm for vector quantization (VQ). The proposed approach uses recursive and intersection (RI) rules to compensate and enhance the TIE algorithm. The recursive rule changes reference codewords dynamically and produces the smallest candidate group. The intersection rule removes redundant codewords from these candidate groups. The RI-TIE approach avoids over-reliance on the continuity of the input signal. This study tests the contribution of the RI rules using the VQ-based, G.729 standard LSP encoder and some classic images. Results show that the RI rules perform excellently in the TIE algorithm.

Dynamic virtual network allocation is a promising traffic control model for cloud resident data center which offers virtual data centers for customers from the provider's substrate cloud. Unfortunately, dynamic virtual network allocation designed in the past was aimed to the Internet so it needs distributed control methods to scale with such a large network. The price for the scalability of the completely distributed control method at both virtual layer and substrate layer is the slow convergence of algorithm and the less stability of traffic. In this paper, we argue that the distributed controls in both virtual and substrate networks are not necessary for the cloud resident data center environment, because cloud resident data center uses centralized controller as the way to give network control features to customers. In fact, we can use the centralized algorithm in each virtual data center which is not very large network and the distributed algorithm is only needed in substrate network. Based on the specific properties of this model, we have used optimization theory to re-design the substrate algorithm for periodically re-adjusting virtual link capacity. Results from theoretical analysis, simulations, and experiments show that our algorithm has faster convergence time, simpler calculation and can make better use of the feedback information from virtual networks than the previous algorithm.

Precise localization of the Wi-Fi Access Point (AP) is becoming increasingly important with the rise of diverse location-based and smart phone-based services. In this article, we propose a new method for precise Wi-Fi AP localization using GPS information of a smart phone. The idea is that the possible area of Wi-Fi AP location, termed AP_Area, is first determined by measuring GPS information and the received signal strength of smart phones. As the number of measurements from users' smart phones increases, the AP_Area is successively narrowed down to the true AP location. Simulation shows the proposed algorithm can detect the Wi-Fi AP's localization within 5 m (probability exceeds 90%).

Cloud computing is an emerging computing paradigm that may have a significant impact on various aspects of the development of information infrastructure. In a Cloud environment, different types of network resources need to be virtualized as a series of service components by network virtualization, and these service components should be further composed into Cloud services provided to end users. Therefore Quality of Service (QoS) aware service composition plays a crucial role in Cloud service provisioning. This paper addresses the problem on how to compose a sequence of service components for QoS guaranteed service provisioning in a virtualization-based Cloud computing environment. The contributions of this paper include a system model for Cloud service provisioning and two approximation algorithms for QoS-aware service composition. Specifically, a system model is first developed to characterize service provisioning behavior in virtualization-based Cloud computing, then a novel approximation algorithm and a variant of a well-known QoS routing procedure are presented to resolve QoS-aware service composition. Theoretical analysis shows that these two algorithms have the same level of time complexity. Comparison study conducted based on simulation experiments indicates that the proposed novel algorithm achieves better performance in time efficiency and scalability without compromising quality of solution. The modeling technique and algorithms developed in this paper are general and effective; thus are applicable to practical Cloud computing systems.

This paper proposes a dynamic bandwidth allocation algorithm that improves the network performance and bandwidth sharing efficiency in the upstream channels of a hybrid passive optical network (PON) that combines a fiber-to-the-home (FTTH) access network and wireless sensor networks (WSNs). The algorithm is called the adaptive limited dynamic bandwidth allocation (ALDBA) algorithm. Unlike existing algorithms, the ALDBA algorithm is not limited to controlling just FTTH access networks, it also supports WSNs. For the proposed algorithm, we investigate the difference in the lengths of generated data packets between the FTTH terminals and sensor nodes of WSN to effectively evaluate the end-to-end average packet delay, bandwidth utilization, time jitter, and upstream efficiency. Two variants of the proposed algorithm and a limited service (LS) scheme, which is an existing well-known algorithm, are compared under non-uniform traffic conditions without taking into consideration priority scheduling. We demonstrate the proposed scheme through simulation by generating a realistic network traffic model, called self-similar network traffic. We conducted a detailed simulation using several performance parameters to validate the effectiveness of the proposed scheme. The results of the simulation showed that both ALDBA variants outperformed the existing LS scheme in terms of average packet delay, bandwidth utilization, jitter, and upstream efficiency for both low and high traffic loads.

A dual-band dual-polarization array antenna with improved bandwidth for an advanced multi-function radio function concept (AMRFC) radar application is proposed. To improve the S-band impedance bandwidth, the proposed antenna uses modified coupling feed patch. The measured bandwidth of the prototype array is 19.8% and 25.7% for the S- and X-band, respectively. The isolation between the two orthogonal polarizations is higher than 15 dB and cross-polarization level is less than -17 dB for both S- and X-bands.

A simple yet effective time domain correlation channel estimation method is proposed for multiple-input multiple-output (MIMO) systems over dispersive channels. It is known that the inherent co-channel interference (CCI) and inter-symbol interference (ISI) coexist when the signals propagate through MIMO frequency selective channels, which renders the MIMO channel estimation intractable. By elaborately devising the quasi-orthogonal training sequences between multiple antennas which have constant autocorrelation property with different cyclic shifts in the time domain, the interferences induced by ISI and CCI can be simultaneously maintained at a constant and identical value under quasi-static channels. As a consequence, it is advisable to implement the joint ISI and CCI cancelation by solving the constructed linear equation on the basis of the correlation output with optional correlation window. Finally, a general and simplified closed-form expression of the estimated channel impulse response can be acquired without matrix inversion. Additionally, the layered space-time (LST) minimum mean square error (MMSE) (LST-MMSE) frequency domain equalization is briefly described. We also provide some meaningful discussions on the beginning index of the variable correlation window and on the cyclic shift number of m-sequence of other antennas relative to the first antenna. Simulation results demonstrate that the proposed channel estimation approach apparently outperforms the existing schemes with a remarkable reduction in computational complexity.

The Generalized Feistel Structure (GFS) generally uses the sub-block-wise cyclic shift in the permutation layer, the layer between the two F function layers. For Type 2 GFS, at FSE 2010, Suzaki and Minematsu showed that a better diffusion property can be obtained if one uses some other sub-block-wise permutation. In this paper, we consider Type 1, Type 3, Source-Heavy (SH), and Target-Heavy (TH) GFSs, and study if their diffusion properties can be improved by changing the sub-block-wise cyclic shift. For Type 1 GFS and Type 3 GFS, we show that better permutations in terms of diffusion exist. For SH and TH GFSs, we show that the diffusion property does not change even if we change the sub-block-wise cyclic shift. We also experimentally derive optimum permutations in terms of diffusion, and evaluate the security of the resulting schemes against saturation, impossible differential, differential, and linear attacks.

In OFDM systems, residual inter-block interference can be suppressed by a time-domain equalizer that blindly shortens the effective length of a channel impulse response. To further improve the performance of blind equalizers, we propose a channel shortening method that attempts to maximize the minimum FFT output power over data subcarriers. Simulation results indicate that the max-min strategy has performance improvement over a conventional channel shortening method.

As one innovative research that heavily depends on the network virtualization for its realization and deployment on an Internet-scale, we propose an approach to utilize user resources in information-centric network (ICN). We try to fully benefit from the in-network cache that is one attractive feature of ICN by expanding the in-network cache indirectly based on the user resources. To achieve this, in this paper, we focus on how to encourage users to contribute their resources in ICN. Through simulations, we examine a feasibility of our approach and an effect of user participation on the content distribution performance in ICN. We also briefly discuss how the network virtualization technique can be utilized for our research in terms of its evaluation and deployment.

This article presents a differential fault analysis (DFA) technique using round addition for a generalized Feistel network (GFN) including CLEFIA and RC6. Here the term “round addition” means that the round operation executes twice using the same round key. The proposed DFA needs bypassing of an operation to count the number of rounds such as increment or decrement. To verify the feasibility of our proposal, we implement several operations, including increment and decrement, on a microcontroller and experimentally confirm the operation bypassing. The proposed round addition technique works effectively for the generalized Feistel network with a partial whitening operation after the last round. In the case of a 128-bit CLEFIA, we show a procedure to reconstruct the round keys or a secret key using one correct ciphertext and two faulty ciphertexts. Our DFA also works for DES and RC6.

The cooperative orthogonal frequency-division multiplexing (OFDM) relaying system is widely regarded as a key design for future broadband mobile cellular systems. This paper focuses on channel estimation in such a system that uses amplify-and-forward (AF) as the relaying strategy. In the cooperative AF relaying, the destination requires the individual (disintegrated) channel state information (CSI) of the source-relay (S-R) and relay-destination (R-D) links for optimum combination of the signals received from source and relay. Traditionally, the disintegrated CSIs are obtained with two channel estimators: one at the relay and the other at the destination. That is, the CSI of the S-R link is estimated at relay and passed to destination, and the CSI of the R-D link is estimated at destination with the help of pilot symbols transmitted by relay. In this paper, a new disintegrated channel estimator is proposed; based on an expectation-maximization (EM) algorithm, the disintegrated CSIs can be estimated solely by the estimator at destination. Therefore, the new method requires neither signaling overhead for passing the CSI of the S-R link to destination nor pilot symbols for the estimation of the R-D link. Computer simulations show that the proposed estimator works well under the signal-to-noise ratios of interest.

This paper identifies a ripple effect problem (REP) that spreads interference to neighbors and proposes a novel channel localization mechanism to decrease the REP in a Wi-Fi system. The proposed mechanism has less blocking probability when compared to a random channel allocation mechanism and also has increased channel reusability. The proposed mechanism in simulation yields less channels BEm as the number of users and Tused increase.

We introduce a new kind of P2P traffic localization technique, called Netpherd, benefiting from the network virtualization technique for its successful deployment. Netpherd exploits one feature of P2P applications, a peer selection adaptation (i.e., preferring peers who are likely to provide better performance) for the traffic localization. Netpherd tries to enable local peers (i.e., peers in target network domain) to communicate with each other by affecting the peer selection adaptation. To affect the peer selection adaptation, Netpherd adds artificial delay to inter-domain traffic going to local peers. Our experiment conducted over Internet testbed verifies that Netpherd achieves the traffic localization and also improves the content download performance with the network delay insertion. In addition, we show that how the network virtualization technique can be utilized for efficient and graceful implementation of Netpherd.

In this letter, we propose an extension to the classical logarithmic total variation (LTV) model for face recognition under variant illumination conditions. LTV treats all facial areas with the same regularization parameters, which inevitably results in the loss of useful facial details and is harmful for recognition tasks. To address this problem, we propose to assign the regularization parameters which balance the large-scale (illumination) and small-scale (reflectance) components in a spatially adaptive scheme. Face recognition experiments on both Extended Yale B and the large-scale FERET databases demonstrate the effectiveness of the proposed method.

Existing salient object detection methods either simply use a threshold to detect desired salient objects from saliency map or search the most promising rectangular window covering salient objects on the saliency map. There are two problems in the existing methods: 1) The performance of threshold-dependent methods depends on a threshold selection and it is difficult to select an appropriate threshold value. 2) The rectangular window not only covers the salient object but also contains background pixels, which leads to imprecise salient object detection. For solving these problems, a novel saliency threshold-free method for detecting the salient object with a well-defined boundary is proposed in this paper. We propose a novel window search algorithm to locate a rectangular window on our saliency map, which contains as many as possible pixels belonging the salient object and as few as possible background pixels. Once the window is determined, GrabCut is applied to extract salient object with a well-defined boundary. Compared with existing methods, our approach doesn't need any threshold to binarize the saliency map and additional operations. Experimental results show that our approach outperforms 4 state-of-the-art salient object detection methods, yielding higher precision and better F-Measure.

In this paper, we study decentralized supervisory control of timed discrete event systems, where we adopt the OR rule for fusing local enablement decisions and the AND rule for fusing local enforcement decisions. Under these rules, necessary and sufficient conditions for the existence of a decentralized supervisor that achieves a given specification language are easily obtained from the result of literature. If a given specification language does not satisfy these existence conditions, we must compute its sublanguage satisfying them. The main contribution of this paper is proposing a method for computing such a sublanguage.

In this letter we propose a new Whiteman generalized cyclotomic sequence of order 4. Meanwhile, we determine its linear complexity and minimal polynomial. The results show that this sequence possesses both high linear complexity and optimal balance on 1 s and 0 s, which may be attractive for cryptographic applications.

Network slicing for wide-area coordinated packet processing has attracted attentions for improving efficiency of handling network traffic. We have recently proposed a tag-based network slicing mechanism called OpenTag, and introduced the prototype implementation of the OpenTag redirector on an evaluation board. In this paper, we investigate the integration of the OpenTag redirector into a commercial network device. Our contributions are three-fold: (1) designing the architecture aiming OpenTag-capable intermediaries embedded on commercial network devices, (2) implementing a prototype of the embedded OpenTag redirector using the Advanced Mezzanine Card (AMC) which has an OCTEON network processor, (3) showing our implementation can tolerate high bandwidth environment.