In Long-Term Evolution (LTE)-Advanced, heterogeneous networks where femtocells and picocells are overlaid onto macrocells are being extensively discussed in addition to traditional well-planned macrocell deployment to improve further the system throughput. In heterogeneous networks, cell range expansion (CRE), which is a technique for expanding the cell radius of picocells by biasing the handover criteria, e.g., the downlink received signal power, is applied so that the UEs will more frequently select the picocells. This paper investigates a fractional transmission power control (TPC) method suitable for the heterogeneous networks that employ CRE in the LTE-Advanced uplink and evaluates the cell-edge user throughput and cell throughput performance. Simulation results (2-8 picocells and 25 (30) UEs are located within one macrocell with a uniform (cluster) distribution, the difference in transmission power between the macro and picocells is 16 dB, and the Typical Urban and Pedestrian-A channel models are employed) show that almost the same cell-edge user throughput is obtained by setting an appropriate difference in the target received signal power between the macro and picocells according to the CRE offset value.

Novel joint motion-compensated interpolation using eight-neighbor block motion vectors (8J-MCI) is presented. The proposed method uses bi-directional motion estimation (BME) to obtain the motion vector field of the interpolated frame and adopts motion vectors of the interpolated block and its 8-neighbor blocks to jointly predict the target block. Since the smoothness of the motion vector filed makes the motion vectors of 8-neighbor blocks quite close to the true motion vector of the interpolated block, the proposed algorithm has the better fault-tolerancy than traditional ones. Experiments show that the proposed algorithm outperforms the motion-aligned auto-regressive algorithm (MAAR, one of the state-of-the-art frame rate up-conversion (FRUC) schemes) in terms of the average PSNR for the test image sequence and offers better subjective visual quality.

In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a 'Gaussian Mixture' model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a new thresholding technique to classify image pixels. We assign a distance feature vectors to each pixel and segment the liver by a K-means clustering scheme. This initial boundary of the liver is conditioned by the Fourier transform. Then, a Geodesic Active Contour algorithm uses the boundaries to find the final surface. The novelty in our method is the proper selection and combination of sub-algorithms so as to find the border of an object in a low-contrast image. The number of parameters in the proposed method is low and the parameters have a low range of variations. We applied our method to 30 datasets including normal and abnormal cases of low-contrast/high-contrast images and it was extensively evaluated both quantitatively and qualitatively. Minimum of Dice similarity measures of the results is 0.89. Assessment of the results proves the potential of the proposed method for segmentation in low-contrast images.

In this letter, we consider the ultimate boundedness of the singularly perturbed system with measurement noise. The composite controller is commonly used to regulate the singularly perturbed system. However, in the presence of measurement noise, the composite controller does not guarantee the ultimate boundedness of the singularly perturbed system. Thus, we propose the modified composite controller to show the ultimate boundedness of the singularly perturbed system with measurement noise.

A precoding design for double space-time block coding (STBC) system is investigated in this paper, i.e., the joint processing of STBC and dirty paper coding (DPC) techniques. These techniques are used for avoiding dual spatial streams interference and improving the transmitter diversity. The DPC system is interference free on multi-user or multi-antenna. The STBC transceiver can provide the transmit diversity. Due to the benefits about offered by the STBC and DPC techniques, we propose a new scheme called STBC-DPC system. The transceiver design involves the following procedures. First, the ordering QR decomposition of channel matrix and the maximum likelihood (ML) one-dimensional searching algorithm are proposed to acquire reliable performance. Next, the channel on/off assignment using the water filling algorithm, i.e., maximum capacity criterion, is proposed to overcome the deep fading channel problem. Finally, the STBC-DPC system with the modulus operation to limit the transmit signal level, i.e., the Tomlinson-Harashima precoding (THP) scheme, is proposed to achieve low peak-to-average power ratio (PAPR) performance. Simulation results confirm that the proposed STBC-DPC/THP with water filling ML algorithm can provide the low PAPR and excellent bit error rate (BER) performances.

We propose a compact and high-range resolution 76 GHz millimeter-wave radar system for autonomous unmanned helicopters. The purpose of the radar system is to detect and avoid obstacles that may affect the flight safety. To achieve these objectives, a high range resolution and a long detection range are required for the radar systems with small volume and weight. The radar broadband RF front-end module which employs a simple direct conversion method is proposed. The radar module enables the 6 GHz RF signal transmission as well as the output power of about 8 dBm using commercially available low-cost monolithic microwave integrated circuits. The radar system comprises the broadband RF front-end module, a Ku-band local frequency-modulated continuous wave signal synthesizer, and a very light weight carbon fiber reinforced plastic parabolic reflector antenna. The 5 cm of range resolution is experimentally obtained using the 6 GHz RF signal bandwidth. The results of the power line measurement confirm an about 23 dB signal to noise ratio, which is measured from the reflection of the high-voltage power lines about 150 m ahead. In addition, the results of the radar system on-board test using an unmanned helicopter are evaluated. The real-time radar scope, which is transferred through the wireless connection, confirms the detection of the power lines and the other surrounding objects.

Fluoropolymer thin films were prepared by the ion-assisted vapor deposition polymerization (IAD) of 2-(perfluorohexyl)ethylacrylate (Rf-6). The adhesion strength of the film to substrates was estimated by sonicating the films in water and by immersing the films into dichloro-pentafluoro propane (HCFC225). The Rf-6 polymer films by IAD showed stronger adhesion to glass compared to a spin-coated Teflon AF film. The adhesion strength was improved with increasing ion energy Eion of IAD. The IAD films showed superior adhesion to PET surface compared to the glass substrate. The Rf-6 polymer film was effective as a single-layer antireflective coating. The refractive index of the film was 1.368 (λ = 546 nm), which increased slightly with increasing Eion. IAD can be a promising method to prepare fluoropolymer thin films due to the solvent-less process and the flexibility in controlling the film characteristics by the ion energy.

An automotive operating system is a typical safety-critical software and therefore requires extensive analysis w.r.t its effect on system safety. Our earlier work [1] reported a systematic model checking approach for checking the safety properties of the OSEK/VDX-based operating system Trampoline. This article reports further performance improvement using embeddedC constructs for efficient verification of the Trampoline model developed in the earlier work. Experiments show that the use of embeddedC constructs greatly reduces verification costs.

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.

This paper considers online vertex exploration problems in a simple polygon where starting from a point in the inside of a simple polygon, a searcher is required to explore a simple polygon to visit all its vertices and finally return to the initial position as quickly as possible. The information of the polygon is given online. As the exploration proceeds, the searcher gains more information of the polygon. We give a 1.219-competitive algorithm for this problem. We also study the case of a rectilinear simple polygon, and give a 1.167-competitive algorithm.

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.

This paper presents a novel decision feedback equalizer (DFE) with block delay detection for the joint transceiver design that uses channel state information (CSI). The block delay detection in the proposed DFE offers a degree of freedom for optimizing the precoder of the transmitter, provided the transmission power is constrained. In the proposed DFE, the feedforward matrix is devised to enable a block-based equalizer that can be cooperated with an intrablock decision feedback equalizer for suppressing the intersymbol interference (ISI) for the transmitted block with a certain block delay. In this design, the interblock interference (IBI) for the delay block is eliminated in advance by applying the recently developed oblique projection framework to the implementation of the feedforward matrix. With knowledge of full CSI, the block delay and the associated block-based precoder are jointly designed such that the average bit-error-rate (BER) is minimized, subject to the transmission power constraint. Separate algorithms are derived for directly determining the BER-minimized block delays for intrablock minimum mean-squared error (MMSE) and zero-forcing (ZF) equalization criteria. Theoretical derivations indicate that the proposed MMSE design simultaneously maximize the Gaussian mutual information of a transceiver, even under the cases of existing IBI. Simulation results validate the proposed DFE for devising an optimum transceiver with CSI, and show the superior BER performance of the optimized transceiver using proposed DFE. Relying on analytic results and simulation cases also builds a sub-optimum MMSE design of the proposed DFE using the BER-minimized block delay for ZF criterion, which exhibits almost identical BER performance as the proposed MMSE design in most of the signal-to-noise ratio (SNR) range.

Compensation for the nonlinear systems represented by polynomials involves polynomial inverse. In this paper, a new algorithm is proposed that gives the baseband polynomial inverse with a limited order. The algorithm employs orthogonal basis that is predetermined from the distribution of input signal and finds the coefficients of the inverse polynomial to minimize the mean square error. Compared with the well established p-th order inverse method, the proposed method can suppress the distortions better including higher order distortions. It is also extended to obtain memory polynomial inverse through a feedback-configured structure. Both numerical simulations and experimental results demonstrate that the proposed algorithm can provide good performance for compensating the nonlinear systems represented by baseband polynomials.

In this paper, we investigate a relationship between the length-decreasing self-reducibility and the many-one-like reducibilities for partial multivalued functions. We show that if any parsimonious (many-one or metric many-one) complete function for NPMV (or NPMVg) is length-decreasing self-reducible, then any function in NPMV (or NPMVg) has a polynomial-time computable refinement. This result implies that there exists an NPMV (or NPMVg)-complete function which is not length-decreasing self-reducible unless P = NP.

This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control.

This paper proposes an agent-based modeling and simulation methodology for analyzing the tactical and operational effectiveness of warfare environment. To do this, we adopt the advanced agent modeling principle, HEAP (Hierarchical Encapsulation and Abstraction Principle), as well as the hierarchical modeling and simulation framework, SES/MB (System Entity Structure/Model Base). Proposed methodology is differentiated from other conventional agent-based defense M&S approaches in that; (i) it supports an intelligent hierarchical multi-agent architecture, (ii) it provides an efficient mechanism for analyzing the strategic and operational effectiveness of warfare environment between multiple platforms. The proposed methodology is successfully applied to the two by two warships warfare simulation for analyzing the tactical effectiveness.

PCA-L1 (principal component analysis based on L1-norm maximization) is an approximate solution of L1-PCA (PCA based on the L1-norm), and has robustness against outliers compared with traditional PCA. However, the more dimensions the feature space has, the more calculation time PCA-L1 consumes. This paper focuses on an initialization procedure of PCA-L1 algorithm, and proposes a fast method of PCA-L1 using Gram-Schmidt orthogonalization. Experimental results on face recognition show that the proposed method works faster than conventional PCA-L1 without decrease of recognition accuracy.

Let Gs=(Vs, Es) be a simple connected graph. A vertex v ∈ Vs is an articulation vertex if deletion of v and its incident edges from Gs disconnects the graph into at least two connected components. Finding all articulation vertices of a given graph is called the articulation vertex problem. A vertex u ∈ Vs is called a hinge vertex if there exist any two vertices x and y in Gs whose distance increase when u is removed. Finding all hinge vertices of a given graph is called the hinge vertex problem. These problems can be applied to improve the stability and robustness of communication network systems. In this paper, we propose linear time algorithms for the articulation vertex problem and the hinge vertex problem of circular permutation graphs.

Flat Panel Displays (FPDs) are manufactured using many pieces of different processing equipment arranged sequentially in a line. Although the constant inter-arrival time (i.e., the tact time) of glass substrates in the line should be kept as short as possible, the collision probability between glass substrates increases as tact time decreases. Since the glass substrate is expensive and fragile, collisions should be avoided. In this paper, we derive a closed form formula of the approximate collision probability for a model, in which the processing time on each piece of equipment is assumed to follow Erlang distribution. We also compare some numerical results of the closed form and computer simulation results of the collision probability.

This paper is focused on discussing a low-voltage system for lightning, and in particular the testing equipment of surge arresters. Only by demonstrating the performance and applicability of arresters can we seek the most feasible and economic low-voltage solutions. After performing repeated experiments with the same testing samples, using different testing equipment, we compare the different test results in order to select the most suitable and applicable testing equipment. In addition, the basis of a surge current parameter design theory is confirmed and verified through the test results using a simple and compact Impulse Current Generator to test a wide range of samples. By performing the actual analyzes and experiments, we can understand deeply how R, L, and C affect surge current, current wave, and current wave time. The ideal testing equipment standards have been set as follows: (1) Test Voltage up to 20 kV; (2) Expand current range from 1.5 kA to 46.5 kA, with resolution 1.5 kA; and (3) Simple operational procedures.