A hinge vertex is a vertex in an undirected graph such that there exist two vertices whose removal makes the distance between them longer than before. Identifying hinge vertices in a graph can help detect critical nodes in communication network systems, which is useful for making them more stable. For finding them, an O(n3) time algorithm was developed for a simple graph, and, linear time algorithms were developed for interval and permutation graphs, respectively. Recently, the maximum detour hinge vertex problem is defined by Honma et al. For a hinge vertex u in a graph, the detour degree of u is the largest value of distance between any pair of x and y (x and y are adjacent to u) by removing u. A hinge vertex with the largest detour degree in G is defined as the maximum detour hinge vertex of G. This problem is motivated by practical applications, such as network stabilization with a limited cost, i.e., by enhancing the reliability of the maximum detour hinge vertex, the stability of the network is much improved. We previously developed an O(n2) time algorithm for solving this problem on an interval graph. In this study, we propose an algorithm that identifies the maximum detour hinge vertex on a permutation graph in O(n2) time, where n is the number of vertices in the graph.

Face recognition across pose is generally tackled by either 2D based or 3D based approaches. The 2D-based often require a training set from which the cross-pose multi-view relationship can be learned and applied for recognition. The 3D based are mostly composed of 3D surface reconstruction of each gallery face, synthesis of 2D images of novel views using the reconstructed model, and match of the synthesized images to the probes. The depth information provides crucial information for arbitrary poses but more methods are yet to be developed. Extended from a latest face reconstruction method using a single 3D reference model and a frontal registered face, this study focuses on using the reconstructed 3D face for recognition. The recognition performance varies with poses, the closer to the front, the better. Several ways to improve the performance are attempted, including different numbers of fiducial points for alignment, multiple reference models considered in the reconstruction phase, and both frontal and profile poses available in the gallery. These attempts make this approach competitive to the state-of-the-art methods.

We present a matching method for 3D CAD assembly models consisting of multiple components. Here we need to distinguish the layouts and the materials of the components in addition to their shapes. A set of the feature quantities of an assembly model is extracted using projections from various angles. We show the effectiveness of our method experimentally for 3D CAD assembly models.

This paper presents the effect of transmit diversity on the initial and neighboring cell search time performance and the most appropriate transmit diversity scheme based on system-level simulations employing synchronization signals for the Long Term Evolution (LTE) downlink. The synchronization signals including the primary synchronization signal (PSS) and secondary synchronization signal (SSS) are the first physical channel that a set of user equipment (UE) acquires at the initial radio-link connection. The transmit diversity candidates assumed in the paper are Precoding Vector Switching (PVS), Cyclic Delay Diversity (CDD), Time Switched Transmit Diversity (TSTD), and Frequency Switched Transmit Diversity (FSTD), which are all suitable for simple blind detection at a UE. System-level simulation results show that transmit diversity is effective in improving the detection probabilities of the received PSS timing and PSS sequence in the first step and those of the SSS sequence and radio frame timing in the second step of the cell search process. We also show that PVS achieves fast cell search time performance of less than approximately 20ms at the location probability of 90% regardless of the inter-cell site distance up to 10km. Hence, we conclude that PVS is the best transmit diversity scheme for the synchronization signals from the viewpoint of decreasing the initial and neighboring cell search times.

State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.

For k ≥ 3, a convex geometric graph is called k-locally outerplanar if no path of length k intersects itself. In [D. Boutin, Convex Geometric Graphs with No Short Self-intersecting Path, Congressus Numerantium 160 (2003) 205-214], Boutin stated the results of the degeneracy for 3-locally outerplanar graphs. Later, in [D. Boutin, Structure and Properties of Locally Outerplanar Graphs, Journal of Combinatorial Mathematics and Combinatorial Computing 60 (2007) 169-180], a structural property on k-locally outerplanar graphs was proposed. These results are based on the existence of “minimal corner pairs”. In this paper, we show that a “minimal corner pair” may not exist and give a counterexample to disprove the structural property. Furthermore, we generalize the result on the degeneracy with respect to k-locally outerplanar graphs.

A novel method was developed to expand and adjust the bandwidth of long-period fiber gratings (LPFGs) as band-rejection filters. The band-rejection filters were constructed by concatenating two LPFGs with an appropriate space, that causes a $pi$-phase shift. The component LPFGs with the same period and the different numbers of periods are designed to have $-$3-dB transmission at wavelengths on both sides of a resonance wavelength symmetrically, and the transmission loss of the concatenated LPFGs peaks at the -3-dB transmission wavelengths. The rejection bandwidth was widened by changing the interval between the -3-dB transmission wavelengths. The concatenated LPFGs were simulated by using a transfer-matrix method based on a discrete coupling model, and were fabricated by a point-by-point arc discharge technique on the basis of the simulation results. It was demonstrated that the rejection bandwidth at 20-dB attenuation reached 26.6,nm and was 2.7 times broader than that of a single uniform LPFG.

In this paper, we propose an error-correction low-pass filter (EC-LPF) algorithm for estimating the wireless distance between devices. To measure this distance, the received signal strength indication (RSSI) is a popularly used method because the RSSI of a wireless signal, such as Wi-Fi and Bluetooth, can be measured easily without the need for additional hardware. However, estimating the wireless distance using an RSSI is known to be difficult owing to the occurrence of inaccuracies. To examine the inaccuracy characteristics of Bluetooth RSSI, we conduct a preliminary test to discover the relationship between the actual distance and Bluetooth RSSI under several different environments. The test results verify that the main reason for inaccuracy is the existence of measurement errors in the raw Bluetooth RSSI data. In this paper, the EC-LPF algorithm is proposed to reduce measurement errors by alleviating fluctuations in a Bluetooth signal with responsiveness for real-time applications. To evaluate the effectiveness of the EC-LPF algorithm, distance accuracies of different filtering algorithms are compared, namely, a low-pass filer (LPF), a Kalman filter, a particle filter, and the EC-LPF algorithm under two different environments: an electromagnetic compatibility (EMC) chamber and an indoor hall. The EC-LPF algorithm achieves the best performance in both environments in terms of the coefficient of determination, standard deviation, measurement range, and response time. In addition, we also implemented a meeting room application to verify the feasibility of the EC-LPF algorithm. The results prove that the EC-LPF algorithm distinguishes the inside and outside areas of a meeting room without error.

In this letter, an enhanced detection scheme using threshold and lattice-reduction algorithm is proposed. The first step of the proposed detection scheme finds another basis channel matrix H' which has good properties from the channel matrix H by using lattice-reduction algorithm. And QRD-M detection scheme using threshold algorithm is executed in the next step. Simulation results show that the proposed method has better performance than the conventional QRD-M detection scheme at high SNR. Also, it reduces candidate symbols because of the threshold algorithm.

The main contribution of this paper is to characterize the hyperbentness of two infinite classes of Boolean functions via Dillon-like exponents, and give new classes of semibent functions with Dillon-like exponents and Niho exponents. In this paper, the approaches of Mesnager and Wang et al. are generalized to Charpin-Gong like functions with two additional trace terms. By using the partial exponential sums and Dickson polynomials, it also gives the necessary and sufficient conditions of the hyperbent properties for their subclasses of Boolean functions, and gives two corresponding examples on F230. Thanks to the result of Carlet et al., new classes of semibent functions are obtained by using new hyperbent functions and the known Niho bent functions. Finally, this paper extends the Works of Lisonek and Flori and Mesnager, and gives different characterizations of new hyperbent functions and new semibent functions with some restrictions in terms of the number of points on hyperelliptic curves. These results provide more nonlinear functions for designing the filter generators of stream ciphers.

Optimizing embedded software is a problem having scientific and practical signification. Optimizing embedded software can be done in different phases of the software life cycle under different optimal conditions. Most studies of embedded software optimization are done in forward engineering and these studies have not given an overall model for the optimization problem of embedded software in both forward engineering and reverse engineering. Therefore, in this paper, we propose a new approach to embedded software optimization based on reverse engineering. First, we construct an overall model for the embedded software optimization in both forward engineering and reverse engineering and present a process of embedded software optimization in reverse engineering. The main idea of this approach is that decompiling executable code to source code, converting the source code to models and optimizing embedded software under different levels such as source code and model. Then, the optimal source code is recompiled. To develop this approach, we present two optimization techniques such as optimizing power consumption of assembly programs based on instruction schedule and optimizing performance based on alternating equivalent expressions.

The correct detection of the start of burst is very important in wideband networking radio operation as it directly affects the Time Division Multiple Access (TDMA) adaptive time slot algorithm. In this paper, we propose a robust Data Aided (DA) algorithm for burst detection in a hybrid CDMA/Adaptive TDMA based wideband networking waveform of a software defined radio. The proposed algorithm is based on a novel differentially modulated training sequence designed by using precoding sequence. The training sequence structure and precoding sequence are exploited in the calculation of proposed timing metric which is normalized by the signal energy. The precoding sequence is adequately designed for the timing metric to have a sharp peak. The algorithm shows excellent performance for multiuser scenario. It is shown through computer simulations that by increasing the active users from 1 to 8, the performance degradation is only about 1∼2dB. The proposed algorithm is compared with other algorithms and found to outperform them even in the presence of multipath fading effects. The proposed algorithm has been implemented on Field Programmable Gate Array (FPGA) platform for high data rate applications and it is shown that the results from hardware are identical to the simulation results.

The global avalanche characteristics measure the overall avalanche properties of Boolean functions, an n-variable balanced Boolean function of the sum-of-square indicator reaching σƒ=22n+2n+3 is an open problem. In this paper, we prove that there does not exist a balanced Boolean function with σƒ=22n+2n+3 for n≥4, if the hamming weight of one decomposition function belongs to the interval Q*. Some upper bounds on the order of propagation criterion of balanced Boolean functions with n (3≤n≤100) variables are given, if the number of vectors of propagation criterion is equal and less than 7·2n-3-1. Two lower bounds on the sum-of-square indicator for balanced Boolean functions with optimal autocorrelation distribution are obtained. Furthermore, the relationship between the sum-of-squares indicator and nonlinearity of balanced Boolean functions is deduced, the new nonlinearity improves the previously known nonlinearity.

In recent years, the time variation of Internet traffic has increased due to the growth of streaming and cloud services. Backbone networks must accommodate such traffic without congestion. Traffic engineering with traffic prediction is one approach to stably accommodating time-varying traffic. In this approach, routes are calculated from predicted traffic to avoid congestion, but predictions may include errors that cause congestion. We propose prediction-based traffic engineering that is robust against prediction errors. To achieve robust control, our method uses model predictive control, a process control method based on prediction of system dynamics. Routes are calculated so that future congestion is avoided without sudden route changes. We apply calculated routes for the next time slot, and observe traffic. Using the newly observed traffic, we again predict traffic and re-calculate the routes. Repeating these steps mitigates the impact of prediction errors, because traffic predictions are corrected in each time slot. Through simulations using backbone network traffic traces, we demonstrate that our method can avoid the congestion that the other methods cannot.

Weak target detection is a key problem in passive bistatic radar (PBR). Track-before-detect (TBD) is an effective solution which has drawn much attention recently. However, TBD has not been fully developed in PBR. In this paper, the transition function and the selection of parameters in dynamic programming are analyzed in PBR. Then a novel processing scheme of dynamic programming based TBD is proposed to reduce the computation complexity without severely decreasing the detection performance. Discussions including complexity, detection performance, threshold determination, selection of parameters and detection of multitarget, are presented in detail. The new method can provide fast implementation with only a slight performance penalty. In addition, good multitarget detection performance can be achieved by using this method. Simulations are carried out to present the performance of the proposed processing scheme.

We propose a systematic method for improving the response time of forward collision warning (FCW) on vehicles. First, a performance metric, called the warning lag time, is introduced. We use the warning lag time because its measurement is practical in real driving situations. Next, we discuss two ideas to improve this warning lag time, vertical and horizontal methods. The vertical method gives an additional warning, derived from the cause of a car crash, to a normal FCW system. The experiment showed that it can improve the warning lag time by an average of 0.31sec. compared with a traditional FCW system. The horizontal method uses distributed sensing among vehicles, which helps the vehicle see farther. It can also improve the warning lag time by an average of 1.08sec. compared with a single vehicle FCW.

In this paper, we investigate iterative detection and decoding, a.k.a. turbo detection, for multiple-input multiple-output (MIMO) transmission. Specifically, we consider using a low complexity soft-in/soft-out MIMO detector based on belief propagation over a pair-wise graph that accepts a priori information feedback from a channel decoder. Simulation results confirm that considerable performance improvement can be obtained with only a few detection-and-decoding iterations if convolutional channel coding is used. A brief estimate is given of the overall complexity of turbo detectors, to verify the key argument that the performance of a maximum a posteriori (MAP) detector (without turbo iteration) can be achieved, at much lower computation cost, by using the low complexity soft-in/soft-out MIMO detector under consideration.

This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.

In this work, resistive switching random-access memory (RRAM) devices having a structure of metal/Si$_{3}$N$_{4}$/Si with different top electrode metals were fabricated to investigate the changes in switching and conduction mechanisms depending on electrode metals. It is shown that the metal workfunction is not strongly related with either high-resistance state (HRS) and forming voltage. Top electrodes (TEs) of Al, Cu, and Ni show both bipolar and unipolar switching characteristics. The changes of resistances in these devices can be explained by the different defect arrangements in the switching layer (SL). Among the devices with different TE metals, one with Ag electrode does not show unipolar switching unlike the others. The conducting filaments of Ag-electrode device in the low-resistance state (LRS) demonstrated metallic behaviors in the temperature-controlled experiments, which supports that Ag substantially participates in the conduction as a filament source. Moreover, the difference in switching speed is identified depending on TE metals.

Tonal signals are shown as spectral peaks in the frequency domain. When the number of spectral peaks is small and the spectral signal is sparse, Compressive Sensing (CS) can be adopted to locate the peaks with a low-cost sensing system. In the CS scheme, a time domain signal is modelled as $oldsymbol{y}=Phi F^{-1}oldsymbol{s}$, where y and s are signal vectors in the time and frequency domains. In addition, F-1 and $Phi$ are an inverse DFT matrix and a random-sampling matrix, respectively. For a given y and $Phi$, the CS method attempts to estimate s with l0 or l1 optimization. To generate the peak candidates, we adopt the frequency-domain information of $ esmile{oldsymbol{s}}$ = $oldsymbol{F} esmile{oldsymbol{y}}$, where $ esmile{y}$ is the extended version of y and $ esmile{oldsymbol{y}}left(oldsymbol{n} ight)$ is zero when n is not elements of CS time instances. In this paper, we develop Gaussian statistics of $ esmile{oldsymbol{s}}$. That is, the variance and the mean values of $ esmile{oldsymbol{s}}left(oldsymbol{k} ight)$ are examined.