In this paper, we analyze the impact of channel estimation errors for both decode-and-forward (DF) and amplify-and-forward (AF) cooperative communication systems over Nakagami-m fading channels. Firstly, we derive the exact one-integral and the approximate expressions of the symbol error rate (SER) for DF and AF relay systems with different modulations. We also present expressions showing the limitations of SER under channel estimation errors. Secondly, in order to quantify the impact of channel estimation errors, the average signal-to-noise-ratio (SNR) gap ratio is investigated for the two types of cooperative communication systems. Numerical results confirm that our theoretical analysis for SER is very efficient and accurate. Comparison of the average SNR gap ratio shows that DF model is less susceptible to channel estimation errors than AF model.

Ad Hoc Routing (AHR) was proposed to replace optimal routing in cluster-based multihop networks since it offers lower implementation complexity. However, this complexity reduction comes at the cost of an increase in the required transmission power. In addition, when the conventional distributed relay selection is applied to implement AHR, another increase in the required transmission power occurs due to the receiver selection error. In this paper, Ad Hoc Cooperative Routing (AHCR) that integrates the cooperative transmission with AHR is presented to reduce the difference between the required transmission power of AHR and that of optimal routing. Besides, Distributed Ad Hoc Cooperative Routing (DAHCR) scheme 1 that combines the cooperative transmission with AHR is proposed to reduce the difference between the required transmission power of DAHR and that of AHR. We then address the problem of DAHCR scheme 1 and propose DAHCR scheme 2. Simulation results show that the required transmission power of AHCR and DAHCR scheme 1 is less than that of AHR and DAHR, respectively. In addition, DAHCR scheme 2 further reduces the required transmission power of DAHCR scheme 1. On the other hand, DAHCR scheme 1 increases the complexity by 43% compared to DAHR. Besides, DAHCR scheme 2 increases the complexity by 1.97% compared to DAHCR scheme 1.

The active shape model (ASM) has been widely adopted by automated bone segmentation approaches for radiographic images. In radiographic images of the distal radius, multiple edges are often observed in the near vicinity of the bone, typically caused by the presence of thin soft tissue. The presence of multiple edges decreases the segmentation accuracy when segmenting the distal radius using ASM. In this paper, we propose an enhanced distal radius segmentation method that makes use of a modified version of ASM, reducing the number of segmentation errors. To mitigate segmentation errors, the proposed method emphasizes the presence of the bone edge and downplays the presence of a soft tissue edge by making use of Dual energy X-ray absorptiometry (DXA). To verify the effectiveness of the proposed segmentation method, experiments were performed with 30 distal radius patient images. For the images used, compared to ASM-based segmentation, the proposed method improves the segmentation accuracy with 47.4% (from 0.974 mm to 0.512 mm).

We review our recent work on ultra-long-haul wavelength division multiplexed (WDM) transmission with high spectral efficiency (SE) employing tight pre-filtering and multi-symbol detection. We start the discussion with a theoretical evaluation of the SE limit of pre-filtered modulation in optical fiber communication systems. We show that pre-filtering induced symbol correlation generates a modulation with memory and thus, a higher SE limit than that of the original memory-less modulation. We also investigate the merits of utilizing the pre-filtering induced symbol correlation with multi-symbol detection to achieve high SE transmission. We demonstrate transoceanic WDM transmission of a pre-filtered polarization division multiplexed return-to-zero quaternary phased shift keying (PDM-RZ-QPSK) modulation format with multi-symbol detection, achieving 419% SE which is higher than the SE limit of the original memory-less PDM-RZ-QPSK format.

A differential cross-correlation cell ID identification algorithm is proposed for IEEE 802.16e OFDMA cellular system. The cell ID represents the number of the preamble selected by the base station in downlink mode. First, we construct the downlink (DL) preamble structure and signal model with carrier frequency offset (CFO) and channel effects. Next, in order to achieve the initial synchronization, a differential receiver with cross correlation for all preamble patterns is proposed to search for cell ID. Simulation results confirm that the proposed structure is suitable for ITU fading channels and outperforms the conventional cell search system.

In this letter, we analyze the effect of the size of observed data on the performance of time delay estimation (TDE) in the chirp spread spectrum (CSS) system. By adjusting the size of observed data, we reduce the effect of DC offsets, which would otherwise degrade the performance of TDE based on CSS, and we optimize the performance of TDE in CSS system. Finally, we derive the optimal size of observed data of TDE in CSS system.

A group key exchange (GKE) protocol allows a group of parties communicating over a public network to establish a common secret key. As group-oriented applications gain popularity over the Internet, a number of GKE protocols have been suggested to provide those applications with a secure multicast channel. In this work, we investigate the security of Wu and Zhu's password-authenticated GKE protocol presented recently in FC'08. Wu and Zhu's protocol is efficient, supports dynamic groups, and can be constructed generically from any password-authenticated 2-party key exchange protocol. However, despite its attractive features, the Wu-Zhu protocol should not be adopted in its present form. Due to a flaw in its design, the Wu-Zhu protocol fails to achieve authenticated key exchange. We here report this security problem with the Wu-Zhu protocol and show how to solve it.

The Laplacian support vector machine (LSVM) is a semi-supervised framework that uses manifold regularization for learning from labeled and unlabeled data. However, the optimal kernel parameters of LSVM are difficult to obtain. In this paper, we propose a multi-kernel LSVM (MK-LSVM) method using multi-kernel learning formulations in combination with the LSVM. Our learning formulations assume that a set of base kernels are grouped, and employ l2 norm regularization for automatically seeking the optimal linear combination of base kernels. Experimental testing reveals that our method achieves better performance than the LSVM alone using synthetic data, the UCI Machine Learning Repository, and the Caltech database of Generic Object Classification.

This paper describes ultra-high capacity wavelength-division multiplexed (WDM) transmission technologies for 100-Tbit/s-class optical transport networks (OTNs). First, we review recent advances in ultra-high capacity transmission technologies focusing on spectrally-efficient multi-level modulation techniques and ultra-wideband optical amplification techniques. Next, we describe an ultra-high capacity WDM transmission experiment, in which high speed polarization-division multiplexed (PDM) 16-ary quadrature amplitude modulation (16-QAM), generated by an optical synthesis technique, in combination with coherent detection based on digital signal processing with pilotless algorithms, realize the high spectral efficiency (SE) of 6.4 b/s/Hz. Furthermore, ultra-wideband hybrid optical amplification utilizing distributed Raman amplification (DRA) and C- and extended L-band erbium-doped fiber amplifiers (EDFAs) is shown to realize 10.8-THz total signal bandwidth. By using these techniques, 69.1-Tbit/s transmission is demonstrated over 240-km of pure silica-core fibers (PSCFs). Furthermore, we describe PDM 64-QAM transmission over 160 km of PSCFs with the SE of 9.0 b/s/Hz.

This paper presents a novel automated microwave filter tuning method based on successive optimization of phase and amplitude characteristics. We develop an optimization procedure to determine how much the adjusting screws of a filter should be rotated. The proposed filter tuning method consists of two stages; coarse and fine tuning stages. In the first stage, called coarse tuning, the phase response error of the target filter is minimized so that the filter roughly approximates almost ideal bandpass characteristics. Then in the second stage, called fine tuning, two different amplitude response errors are minimized in turn and then the resulting filter well approximate the ideal characteristics. Performance of the proposed tuning procedure is evaluated through some experiments of actual filter tuning.

Sequences with good correlation properties are of substantial interest in many applications. By interleaving a perfect array with shift sequences, a new method of constructing binary array set with zero correlation zone (ZCZ) is presented. The interleaving operation can be performed not only row-by-row but also column-by-column on the perfect array. The resultant ZCZ binary array set is optimal or almost optimal with respect to the theoretical bound. The new method provides a flexible choice for the rectangular ZCZ and the set size.

Recently enhancing the visual experience of the user has been a new trend for TV displays. This trend comes from the fact that changes of ambient illuminations while viewing a Liquid Crystal Display (LCD) significantly affect human impressions. However, psychological effects caused by the combination of displayed video image and ambient illuminations have not been investigated. In the present research, we clarify the relationship between ambient illuminations and psychological effects while viewing video image displayed on the LCD by using a questionnaire based semantic differential (SD) method and a factor analysis method. Six kinds of video images were displayed under different colors and layouts of illumination conditions and rated by 15 observers. According to the analysis, it became clear that the illumination control around the LCD with displayed video image, the feeling of 'activity' and 'evaluating' were rated higher than the feeling of fluorescent ceiling condition. In particular, simultaneous illumination control around the display and the ceiling enhanced the feeling of 'activity,' and 'evaluating' with keeping 'comfort.' Moreover, the feeling of 'activity' under the illumination control around the LCD and the ceiling condition while viewing music video image was rated clearly higher than that with natural scene video image.

In reconstruction-based super resolution, a high-resolution image is estimated using multiple low-resolution images with sub-pixel misalignments. Therefore, when only one low-resolution image is available, it is generally difficult to obtain a favorable image. This letter proposes a method for overcoming this difficulty for single- image super resolution. In our method, after interpolating pixel values at sub-pixel locations on a patch-by-patch basis by support vector regression, in which learning samples are collected within the given image based on local similarities, we solve the regularized reconstruction problem with a sufficient number of constraints. Evaluation experiments were performed for artificial and natural images, and the obtained high-resolution images indicate the high-frequency components favorably along with improved PSNRs.

In this letter, the compensation ability of nonlinear distortions for loudspeaker systems is demonstrated using dynamic distortion measurement. Two linearization methods using a Volterra filter and a Mirror filter are compared. The conventional evaluation utilizes swept multi-sinusoidal waves. However, it is unsatisfactory because wideband signals such as those of music and voices are usually applied to loudspeaker systems. Hence, the authours use dynamic distortion measurement employing a white noise. Experimental results show that the two linearization methods can effectively reduce nonlinear distortions for wideband signals.

This paper presents a systemic analysis for phase noise performances of the series quadrature oscillator (QOSC) by using the time-variant impulse sensitivity function (ISF) model. The effective ISF for each noise source in the oscillator is derived mathematically. According to these effective ISFs, the explicit closed-form expression for phase noise due to the total thermal noise in the series QOSC is derived, and the phase noise contribution from the flicker noise in the regenerative and coupling transistors is also figured out. The phase noise contributions from the thermal noise and the flicker noise are verified by SpectreRF simulations.

In many real-world face recognition applications, there might be only one training image per person available. Moreover, the test images may vary in facial expressions and illuminations, or may be partially occluded. However, most classical face recognition techniques assume that multiple images per person are available for training, and they are difficult to deal with extreme expressions, illuminations and occlusions. This paper proposes a novel block-based bag of words (BBoW) method to solve those problems. In our approach, a face image is partitioned into multiple blocks, dense SIFT features are then calculated and vector quantized into different visual words on each block respectively. Finally, histograms of codeword distribution on each local block are concatenated to represent the face image. Our method is able to capture local features on each block while maintaining holistic spatial information of different facial components. Without any illumination compensation or image alignment processing, the proposed method achieves excellent face recognition results on AR and XM2VTS databases. Experimental results show that only using one neutral expression frame per person for training, our method can obtain the best performance ever on face images of AR database with extreme expressions, variant illuminations, and partial occlusions. We also test our method on the standard and darkened sets of XM2VTS database, and achieve the average rates of 100% and 96.10% on the standard and darkened sets of XM2VTS database, respectively.

An interactive support system for image quality enhancement to adjust display equipments according to the user's own subjectivity is developed. Interactive support system for image quality enhancement enable the parameters based on user's preference to be derived by only selecting user's preference images without adjusting image quality parameters directly. In the interactive support system for image quality enhancement, the more the number of parameters is, the more effective this system is. In this paper, lightness, color and sharpness are used as the image quality parameters and the images are enhanced by increasing the number of parameters. Shape of tone curve is controlled by two image quality adjustment parameters for lightness enhancement. Images are enhanced using two image quality adjustment parameters for color enhancement. The two parameters are controlled in L* a* b* color space. Degree and coarseness of image sharpness enhancement are adjusted by controlling a radius of mask of smoothing filter and weight of adding. To confirm the effectiveness of the proposed method, the image quality and derivation time of the proposed method are compared with a manual adjustment method.

The search for lightweight authentication protocols suitable for low-cost RFID tags constitutes an active and challenging research area. In this context, a family of protocols based on the LPN problem has been proposed: the so-called HB-family. Despite the rich literature regarding the cryptanalysis of these protocols, there are no published results about the impact of fault analysis over them. The purpose of this paper is to fill this gap by presenting fault analytic methods against a prominent member of the HB-family: HB+ protocol. We demonstrate that the fault analysis model can lead to a flexible and effective attack against HB-like protocols, posing a serious threat over them.

After the anonymity authentication scheme of Zhu and Ma, several studies have focused on improving its security in wireless mobile environments. Unfortunately, they could not provide untraceability, one of the critical features in anonymity. In this paper, we point out the security concerns of previous studies and propose a novel authentication scheme with improved untraceability.

In this paper, the performance of the induced dimension reduction (IDR) method implemented along with the method of moments (MoM) is described. The MoM is based on a combined field integral equation for solving large-scale electromagnetic scattering problems involving conducting objects. The IDR method is one of Krylov subspace methods. This method was initially developed by Peter Sonneveld in 1979; it was subsequently generalized to the IDR(s) method. The method has recently attracted considerable attention in the field of computational physics. However, the performance of the IDR(s) has hardly been studied or practiced for electromagnetic wave problems. In this study, the performance of the IDR(s) is investigated and clarified by comparing the convergence property and memory requirement of the IDR(s) with those of other representative Krylov solvers such as biconjugate gradient (BiCG) methods and generalized minimal residual algorithm (GMRES). Numerical experiments reveal that the characteristics of the IDR(s) against the parameter s strongly depend on the geometry of the problem; in a problem with a complex geometry, s should be set to an adequately small value in order to avoid the "spurious convergence" which is a problem that the IDR(s) inherently holds. As for the convergence behavior, we observe that the IDR(s) has a better convergence ability than GPBiCG and GMRES(m) in a variety of problems with different complexities. Furthermore, we also confirm the IDR(s)'s inherent advantage in terms of the memory requirements over GMRES(m).