Spectrum sharing cognitive radio (CR) with maximal ratio combining (MRC) diversity under asymmetric fading is studied. Specifically, the channel on the secondary transmitter (STx) to the secondary receiver (SRx) link is Nakagami-m distributed while the channel on the STx to the primary receiver (PRx) link is Rayleigh distributed, and the channel state information (CSI) on the STx-PRx link is assumed to be outdated due to feedback delay. The outage capacity of the secondary user (SU) is derived under the average interference and peak transmit power constraints. The results supported by simulations are presented and show the effects of various system parameters on the outage capacity. Particularly, it is shown that the outdated CSI has no impact on the outage capacities in the cases of low peak transmit power constraint and zero-outage probability. It is also shown that MRC diversity can significantly improve the outage capacity especially for the zero-outage capacity and the outage capacity under low outage probability.

This paper presents an approach for cross-pose face recognition by virtual view generation using an appearance clustering based local view transition model. Previously, the traditional global pattern based view transition model (VTM) method was extended to its local version called LVTM, which learns the linear transformation of pixel values between frontal and non-frontal image pairs from training images using partial image in a small region for each location, instead of transforming the entire image pattern. In this paper, we show that the accuracy of the appearance transition model and the recognition rate can be further improved by better exploiting the inherent linear relationship between frontal-nonfrontal face image patch pairs. This is achieved based on the observation that variations in appearance caused by pose are closely related to the corresponding 3D structure and intuitively frontal-nonfrontal patch pairs from more similar local 3D face structures should have a stronger linear relationship. Thus for each specific location, instead of learning a common transformation as in the LVTM, the corresponding local patches are first clustered based on an appearance similarity distance metric and then the transition models are learned separately for each cluster. In the testing stage, each local patch for the input non-frontal probe image is transformed using the learned local view transition model corresponding to the most visually similar cluster. The experimental results on a real-world face dataset demonstrated the superiority of the proposed method in terms of recognition rate.

We present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a running-shoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA.

Existing large-scale systems suffer from various hardware/software failures, motivating the research of fault-tolerance techniques. Checkpoint-restart techniques are widely applied fault-tolerance approaches, especially in scientific computing systems. However, the overhead of checkpoint largely influences the overall system performance. Recently, the emerging byte-addressable, persistent memory technologies, such as phase change memory (PCM), make it possible to implement checkpointing in arbitrary data granularity. However, the impact of data granularity on the checkpointing cost has not been fully addressed. In this paper, we investigate how data granularity influences the performance of a checkpoint system. Further, we design and implement a high-performance checkpoint system named AG-ckpt. AG-ckpt is a hybrid-granularity incremental checkpointing scheme through: (1) low-cost modified-memory detection and (2) fine-grained memory duplication. Moreover, we also formulize the performance-granularity relationship of checkpointing systems through a mathematical model, and further obtain the optimum solutions. We conduct the experiments through several typical benchmarks to verify the performance gain of our design. Compared to conventional incremental checkpoint, our results show that AG-ckpt can reduce checkpoint data amount up to 50% and provide a speedup of 1.2x-1.3x on checkpoint efficiency.

We address the global asymptotic stability of FAST TCP, especially considering cross traffics, time-varying network feedback delay, and queuing delay dynamics at link. Exploiting the inherent dynamic property of FAST TCP, we construct two sequences that represent the lower and upper bound variations of the congestion window in time. By showing that the sequences converge to the equilibrium point of the congestion window, we establish that FAST TCP in itself is globally asymptotically stable without any specific conditions on the tuning parameter α or the update gain γ.

This paper proposes a novel face recognition approach using a centralized gradient pattern image and image covariance-based facial feature extraction algorithms, i.e. a two-dimensional principal component analysis and an alternative two-dimensional principal component analysis. The centralized gradient pattern image is obtained by AND operation of a modified center-symmetric local binary pattern image and a modified local directional pattern image, and it is then utilized as input image for the facial feature extraction based on image covariance. To verify the proposed face recognition method, the performance evaluation was carried out using various recognition algorithms on the Yale B, the extended Yale B and the CMU-PIE illumination databases. From the experimental results, the proposed method showed the best recognition accuracy compared to different approaches, and we confirmed that the proposed approach is robust to illumination variation.

By using electric-field-induced optical second-harmonic generation (EFISHG) measurement, we investigated interfacial carrier behavior in organic solar cells (OSCs) with a blocking layer of bathocuproine (BCP). Results evidently showed that the Maxwell-Wagner type excess charges accumulate on both pentacene/C60 and C60/BCP interfaces. Also, the measurement under short-circuit and open-circuit conditions clearly showed the different photo-induced carrier behaviors in the OSCs devices. Our work proved that the dielectric nature of OSCs dominates the operation of our OSCs and the EFISHG technique is very effective to characterize the dielectric performance of the OSCs.

A self-assembled monolayer having a benzophenone unit as a photoreactive terminal group (BP-SAM) was prepared on an indium-tin oxide (ITO) electrode, on which a hole-transport layer of a phenoxazine-dioctylfluorene copolymer (H5) was spin-coated and irradiated with UV light. After washing the physisorbed H5 molecules, contact angle measurement and ellipsometry showed that the H5 molecules can be tethered to the ITO surface via the BP-SAM. Organic light-emitting diodes (OLEDs) were prepared in the structure of ITO/H5 hole transport layer/tris(8-hydroxyquinolato) aluminum/bathocuproin/LiF/Al electrode with and without the BP-SAM layer on the surface of ITO. The device with the BP-SAM showed higher current density and higher luminance due to the improvement of contact at the ITO/H5 interface by forming covalent bonds via the BP-SAM.

We report enhanced photocurrent properties of dye/Au-loaded titanium dioxide (TiO2) films on Au gratings. Au-loaded TiO2 nanopowders were first synthesized by a modified sol-gel method and then prepared by the impregnation method. We also fabricated dye-sensitized solar cells, which were composed of Au grating/Au-TiO2/TMPyP-SCC LbL (20 bilayers)/electrolyte/ITO substrates. Short-circuit photo-current measurements showed that Au-loaded TiO2 with grating-coupled surface plasmon excitation can enhance the short-circuit photocurrentof the fabricated cells.

Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised subspace learning in statistical pattern recognition. In this paper, a novel method of LDA based on a new L1-norm optimization technique and its variances are proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers, since it used the L2-norm to measure the between-class and within-class distances. In addition, the conventional LDA often suffers from the so-called small sample size (3S) problem since the number of samples is always smaller than the dimension of the feature space in many applications, such as face recognition. Based on L1-norm, the proposed methods have several advantages, first they are robust to outliers because they utilize the L1-norm, which is less sensitive to outliers. Second, they have no 3S problem. Third, they are invariant to rotations as well. The proposed methods are capable of reducing the influence of outliers substantially, resulting in a robust classification. Performance assessment in face application shows that the proposed approaches are more effectiveness to address outliers issue than traditional ones.

The important issue of an adaptive scheduling scheme is to maximize throughput while providing fair services to all users, especially under strict quality of service requirements. To achieve this goal, we consider the problem of multiuser scheduling under a given fairness constraint. A novel Adaptive Fairness and Throughput Control (AFTC) approach is proposed to maximize the network throughput while attaining a given min-max fairness index. Simulation results reveal that comparing to straightforward methods, the proposed AFTC approach can achieve the desired fairness while maximizing the throughput with short convergence time, and is stable in dynamic scenarios. The trade-off between fairness and throughput can be accurately controlled by adjusting the scheduler's parameters.

The imaging of humans using radar is promising for surveillance systems. Although conventional radar systems detect the presence or position of intruders, it is difficult to acquire shape and motion details because the resolution is insufficient. This paper presents a high-resolution human imaging algorithm for an ultra-wideband (UWB) Doppler radar. The proposed algorithm estimates three-dimensional human images using interferometry and, using velocity information, rejects false images created by the interference of body parts. Experiments verify that our proposed algorithm achieves adequate pedestrian imaging. In addition, accurate shape and motion parameters are extracted from the estimated images.

The traditional selection cooperation scheme selects the relay with best instantaneous receive signal-to-noise ratio to forward the message and achieves good outage performance, which may however cause poor fairness among relays. In this letter, we propose two practical selection cooperation schemes in Decode-and-Forward (DF) fashion to improve the fairness of relay selection. Numerical results suggest that both of the proposed schemes can achieve fairness close to the strict fairness scheme without outage performance deterioration. It is also validated that these schemes have lower complexities than traditional ones and therefore are practical for real networks.

We use end-to-end measurements to address the problem of fault diagnosis in computer networks. Since link-level characteristics cannot be uniquely determined from available end-to-end measurements, most existing diagnosis approaches make statistical assumptions of the network to obtain a unique solution. However, the performance of these approaches is not assured due to the uncertainty of the assumptions. Thus the diagnostic accuracy cannot be guaranteed. In this paper, we propose a different paradigm for fault diagnosis which can find all identifiable links and the minimal identifiable link sequences, and infer their loss rates with the least error. Compared with a former representative diagnosis method through experiments, the experimental results show that our method has smaller diagnosis granularity and much less running time for most network topologies. We also conducted experiments using 105 Planetlab hosts. The results validate the performance of our method as well.

This paper proposes a scheme for fairness between uplink and downlink in error-prone 802.11 DCF WLANs by differentiating the contention window of AP. While existing schemes consider only collision, the proposed scheme takes into account packet error due to poor channel condition, too. Instead of complex analytical models based on Markov chain processes, a simpler model based on mean value analysis is proposed. It works on 802.11 DCF and so avoids being dependent on TXOP which lacks applicability. A performance evaluation shows that the proposed method can achieve fairness even in error-prone environments without decrease of total throughput when compared with existing schemes.

In our previous study, we proposed the waveform interpolation (WI) approach to model the excitation signals for hidden Markov model (HMM)-based speech synthesis. This letter presents several techniques to improve excitation modeling within the WI framework. We propose both the time domain and frequency domain zero padding techniques to reduce the spectral distortion inherent in the synthesized excitation signal. Furthermore, we apply non-negative matrix factorization (NMF) to obtain a low-dimensional representation of the excitation signals. From a number of experiments, including a subjective listening test, the proposed method has been found to enhance the performance of the conventional excitation modeling techniques.

This paper describes mobile backhaul optical access network designs for future cellular systems, in particular, for those systems that exploit coordinated multipoints (CoMP) transmission/reception techniques. Wavelength-division-multiplexing passive optical networks (WDM-PON) are primarily considered and two proposals to enhance mobile backhaul capability of WDM-PONs for CoMP are presented. One is physical X2 links that support dedicated low latency and high capacity data exchange between base stations (BSs). The other is multicasting in WDM-PONs. It effectively reduces data/control transmission time from central node to multiple BSs joining CoMP. Evaluation results verify that the proposed X2 links and the multicasting enable more BSs to join CoMP by enhancing the mobile backhaul capability, which results in improved service quality for users.

This paper proposes an IP fast rerouting method which can be implemented in OpenFlow framework. While the current IP is robust, its reactive and global rerouting processes require the long recovery time against failure. On the other hand, IP fast rerouting provides a milliseconds-order recovery time by proactive and local restoration mechanism. Implementation of IP fast rerouting is not common in real systems, however; it requires the coordination of additional forwarding functions to a commercial hardware. We propose an IP fast rerouting mechanism using OpenFlow that separates control function from hardware implementation. Our mechanism does not require any extension of current forwarding hardware. On the contrary, increase of backup routes becomes main overhead of our proposal. We also embed the compression mechanism to our IP fast rerouting mechanism. We show the effectiveness of our IP fast rerouting in terms of the fast restoration and the backup routes compression effect through computer simulations.

The multivariate public key cryptosystem (MPKC), which is based on the problem of solving a set of multivariate systems of quadratic equations over a finite field, is expected to be secure against quantum attacks. Although there are several existing schemes in MPKC that survived known attacks and are much faster than RSA and ECC, there have been few discussions on security against physical attacks, aside from the work of Okeya et al. (2005) on side-channel attacks against Sflash. In this study, we describe general fault attacks on MPKCs including Big Field type (e.g. Matsumoto-Imai, HFE and Sflash) and Stepwise Triangular System (STS) type (e.g. UOV, Rainbow and TTM/TTS). For both types, recovering (parts of) the secret keys S,T with our fault attacks becomes more efficient than doing without them. Especially, on the Big Field type, only single fault is sufficient to recover the secret keys.

One key requirement for achieving network virtualization is resource isolation among slices (virtual networks), that is, to avoid interferences between slices of resources. This paper proposes two methods, per-slice shaping and per-link policing for network-resource isolation (NRI) in terms of bandwidth and delay. These methods use traffic shaping and traffic policing, which are widely-used traffic control methods for guaranteeing QoS. Per-slice shaping utilizes weighted fair queuing (WFQ) usually applied to a fine-grained flow such as a flow from a specific server application to a user. Since the WFQ for fine-grained flows requires many queues, it may not scale to a large number of slices with a large number of virtual nodes. Considering that the purpose of NRI is not thoroughly guaranteeing QoS but avoiding interferences between slices, we believe per-slice (not per virtual link) shaping satisfies our objective. In contrast, per-link policing uses traffic policing per virtual link. It requires less resource and achieves less-strict but more-scalable isolation between hundreds of slices (500 to 700 slices in estimation). Our results show that both methods perform NRI well but the performance of the former is better in terms of delay. Accordingly, per-slice shaping (with/without policing) is effective for delay-sensitive services while per-link policing may be sufficiently used for the other types of services.