We propose an optimal access-point (AP) selection algorithm for maximizing the aggregated throughput of each AP (system throughput) while preserving newly arrived-user throughput in multi rate WLAN system. In our algorithm, newly arrived users cooperate with a wireless local area network (WLAN) system they are trying to use, i.e., they are willing to move toward an appropriate AP before the newly arrived user connects to AP. To select the AP by using our AP selection algorithm, the newly arriving users request two novel parameter values, “the minimum acceptable throughput” with which newly arrived users can be satisfied and “the minimum movable distance” in which a user can move to an appropriate AP. While preserving these conditions, we maximize system throughput. When users cannot obtain a throughput greater than “the minimum acceptable throughput” with our proposed AP selection algorithm, they are rejected. Because, if users use streaming applications, which have strict bandwidth demands, with a very low bit-rate connection, they will not be satisfied. Thus, the newly arrived users having low bit-rate connection may be allowed to be rejected before the newly arrived user connects. In this paper, we show the optimal AP by using theoretical proof. We discuss the effectiveness of our proposed AP selection algorithm by using numerical analysis. We also clarify and analyze the characteristics of system throughput. Moreover, we show that a newly arrived user can select the movable distance and acceptable throughput by using examples from graphs depicting every position of newly arrived users. By using the graphs, we also show the relationship between the two parameters (the movable distance and the acceptable throughput) and the optimal AP, and the relationship between the two parameters and optimal system throughput when the movable distance and acceptable throughput are variable.

This paper proposes applying our inter-cell interference (ICI) cancellation method to fractional frequency reuse (FFR) and evaluates the resulting spectral efficiency improvement. With our ICI cancellation method based on base station cooperation, the control station generates ICI replica signals by simple linear processing. Moreover, FFR effectively utilizes frequency resources by both allowing users in the cell-center region to access all available sub-channels and increasing the transmission power to users in the cell-edge region. FFR provides the conditions under which the ICI cancellation method works effectively. Computer simulations show that the average spectral efficiency of the proposed method is comparable to that of cooperative MU-MIMO, which can completely remove ICI.

The market growths of smart-phones and thin clients have been significantly increasing communication traffic in mobile networks. To handle the increased traffic, network operators should consider how to leverage distributed wireless resources such as distributed spots of wireless local access networks. In this paper, we consider the system where multiple moving users share distributed wireless access points on their traveling routes between their start and goal points and formulate as an optimization problem. Then, we come up with three algorithms as a solution for the problem. The key idea here is 'longcut route instruction', in which users are instructed to choose a traveling route where less congested access points are available; even if the moving distance increases, the throughput for users in the system would improve. In this paper, we define the gain function. Moreover, we analyze the basic characteristics of the system using as a simple model as possible.

This report focuses on a design method for gradient index (GRIN) lens antennas with controllable aperture field distributions. First, we derive differential equations representing optical paths in a gradient index medium with two optical surfaces by using geometrical optics, and then we formulate a novel design method for GRIN lens antennas based on these equations. The Levenberg-Marquardt algorithm is applied as a nonlinear least squares method to satisfy two conditions-focusing and shaping the aperture field distribution-thus realizing a prescribed radiation pattern. The conditions can be fulfilled by optimizing only the index (or permittivity) distribution, whereas the shapes of the optical surfaces remain as free parameters that can be utilized for other purposes, such as reducing reflection losses that occur on the surfaces, as illustrated in this report. A plano-concave GRIN lens is designed as an example, applying the proposed method, to realize a sidelobe level of -30 dB pseudo Taylor distribution, and a maximum sidelobe level of -29.1 dB was observed, indicating it is sufficiently accurate for practical use. In addition, we discuss the convergence of this method considering the relationship between the number of the initial conditions and the differential order of the design equations, factoring in scale invariance of the design equations.

This paper is about a new efficient method for the implementation of a Block Proportionate Normalized Least Mean Square (BPNLMS++) adaptive filter using the Fermat Number Transform (FNT) and its inverse (IFNT). These transforms present advantages compared to Fast Fourier Transform (FFT) and the inverse (IFFT). An efficient state space method for implementing the FNT over rectangular windows is used in the cases where there is a large overlap between the consecutive input signals. This is called Generalized Sliding Fermat Number Transform (GSFNT) and is useful for reducing the computational complexity of finite ring convolvers and correlators. In this contribution, we propose, as a first objective, an efficient state algorithm with the purpose of reducing the complexity of IFNT. This algorithm, called Inverse Generalized Sliding Fermat Number Transform (IGSFNT), uses the technique of Generalized Sliding associated to matricial calculation in the Galois Field. The second objective is to realize an implementation of the BPNLMS++ adaptive filter using GSFNT and IGSFNT, which can significantly reduce the computation complexity of the filter implantation on digital signal processors.

Internet threats caused by botnets/worms are one of the most important security issues to be addressed. Darknet, also called a dark IP address space, is one of the best solutions for monitoring anomalous packets sent by malicious software. However, since darknet is deployed only on an inactive IP address space, it is an inefficient way for monitoring a working network that has a considerable number of active IP addresses. The present paper addresses this problem. We propose a scalable, light-weight malicious packet monitoring system based on a multi-dimensional IP/port analysis. Our system significantly extends the monitoring scope of darknet. In order to extend the capacity of darknet, our approach leverages the active IP address space without affecting legitimate traffic. Multi-dimensional monitoring enables the monitoring of TCP ports with firewalls enabled on each of the IP addresses. We focus on delays of TCP syn/ack responses in the traffic. We locate syn/ack delayed packets and forward them to sensors or honeypots for further analysis. We also propose a policy-based flow classification and forwarding mechanism and develop a prototype of a monitoring system that implements our proposed architecture. We deploy our system on a campus network and perform several experiments for the evaluation of our system. We verify that our system can cover 89% of the IP addresses while darknet-based monitoring only covers 46%. On our campus network, our system monitors twice as many IP addresses as darknet.

To improve the observability during the post-silicon validation, it is the key to select the limited trace signals effectively for the data acquisition. This paper proposes an automated trace signal selection algorithm, which uses the pruning-based strategy to reduce the exploration space. First, the restoration range is covered for each candidate signals. Second, the constraints are generated based on the conjunctive normal form (CNF) to avoid the conflict. Finally the candidates are selected through pruning-based enumeration. The experimental results indicate that the proposed algorithm can bring higher restoration ratios and is more effective compared to existing methods.

We present a new framework of the data-reusing (DR) adaptive algorithms by incorporating a constraint on noise, referred to as a noise constraint. The motivation behind this work is that the use of the statistical knowledge of the channel noise can contribute toward improving the convergence performance of an adaptive filter in identifying a noisy linear finite impulse response (FIR) channel. By incorporating the noise constraint into the cost function of the DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive algorithms are derived. Experimental results clearly indicate their superior performance over the conventional DR ones.

Kudekar et al. proved an interesting result in low-density parity-check (LDPC) convolutional codes: The belief-propagation (BP) threshold is boosted to the maximum-a-posteriori (MAP) threshold by spatial coupling. Furthermore, the authors showed that the BP threshold for code-division multiple-access (CDMA) systems is improved up to the optimal one via spatial coupling. In this letter, a phenomenological model for elucidating the essence of these phenomenon, called threshold improvement, is proposed. The main result implies that threshold improvement occurs for spatially-coupled general graphical models.

In the framework of decentralized supervisory control of timed discrete event systems (TDESs), each local supervisor decides the set of events to be enabled to occur and the set of events to be forced to occur under its own local observation in order for a given specification to be satisfied. In this paper, we focus on fusion rules for the enforcement decisions and adopt the combined fusion rule using the AND rule and the OR rule. We first derive necessary and sufficient conditions for the existence of a decentralized supervisor under the combined fusion rule for a given partition of the set of forcible events. We next study how to find a suitable partition.

Nowadays, clustering is a popular tool for exploratory data analysis, with one technique being K-means clustering. Determining the appropriate number of clusters is a significant problem in K-means clustering because the results of the k-means technique depend on different numbers of clusters. Automatic determination of the appropriate number of clusters in a K-means clustering application is often needed in advance as an input parameter to the K-means algorithm. We propose a new method for automatic determination of the appropriate number of clusters using an extended co-occurrence matrix technique called a tri-co-occurrence matrix technique for multispectral imagery in the pre-clustering steps. The proposed method was tested using a dataset from a known number of clusters. The experimental results were compared with ground truth images and evaluated in terms of accuracy, with the numerical result of the tri-co-occurrence providing an accuracy of 84.86%. The results from the tests confirmed the effectiveness of the proposed method in finding the appropriate number of clusters and were compared with the original co-occurrence matrix technique and other algorithms.

Visually saliency detection provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. One of the main aims of visual attention in computer vision is to detect and segment the salient regions in an image. In this paper, we employ matrix decomposition to detect salient object in nature images. To efficiently eliminate high contrast noise regions in the background, we integrate global context information into saliency detection. Therefore, the most salient region can be easily selected as the one which is globally most isolated. The proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. Experiments show that our approach achieves much better performance than that from the existing state-of-art methods.

Although there have been enormous investments into English education all around the world, not many differences have been made to change the English instruction style. Considering the shortcomings for the current teaching-learning methodology, we have been investigating advanced computer-assisted language learning (CALL) systems. This paper aims at summarizing a set of POSTECH approaches including theories, technologies, systems, and field studies and providing relevant pointers. On top of the state-of-the-art technologies of spoken dialog system, a variety of adaptations have been applied to overcome some problems caused by numerous errors and variations naturally produced by non-native speakers. Furthermore, a number of methods have been developed for generating educational feedback that help learners develop to be proficient. Integrating these efforts resulted in intelligent educational robots – Mero and Engkey – and virtual 3D language learning games, Pomy. To verify the effects of our approaches on students' communicative abilities, we have conducted a field study at an elementary school in Korea. The results showed that our CALL approaches can be enjoyable and fruitful activities for students. Although the results of this study bring us a step closer to understanding computer-based education, more studies are needed to consolidate the findings.

In this paper, we analyze the key factors underlying the implementation, evaluation, and optimization of image processing and computer vision algorithms on embedded GPU using OpenGL ES 2.0 shader model. First, we present the characteristics of the embedded GPU and its inherent advantage when compared to embedded CPU. Additionally, we propose techniques to achieve increased performance with optimized shader design. To show the effectiveness of the proposed techniques, we employ cartoon-style non-photorealistic rendering (NPR), speeded-up robust feature (SURF) detection, and stereo matching as our example algorithms. Performance is evaluated in terms of the execution time and speed-up achieved in comparison with the implementation on embedded CPU.

In this paper, we derive a simple formula to generate a wide-sense systematic generator matrix(we call it quasi-systematic) B for a Reed-Solomon code. This formula can be utilized to construct an efficient interpolation based erasure-only decoder with time complexity O(n2) and space complexity O(n). Specifically, the decoding algorithm requires 3kr + r2 - 2r field additions, kr + r2 + r field negations, 2kr + r2 - r + k field multiplications and kr + r field inversions. Compared to another interpolation based erasure-only decoding algorithm derived by D.J.J. Versfeld et al., our algorithm is much more efficient for high-rate Reed-Solomon codes.

A transmission control protocol (TCP) using an additive increase multiplicative decrease (AIMD) algorithm for congestion control plays a leading role in advanced Internet services. However, the AIMD method shows only low link utilization in lossy networks with long delay such as satellite networks. This is because the cwnd dynamics of TCP are reduced by long propagation delay, and TCP uses an inadequate congestion control algorithm, which does not distinguish packet loss from wireless errors from that due to congestion of the wireless networks. To overcome these problems, we propose an exponential recovery (ER) TCP that uses the exponential recovery function for rapidly occupying available bandwidth during a congestion avoidance period, and an adaptive congestion window decrease scheme using timestamp base available bandwidth estimation (TABE) to cope with wireless channel errors. We simulate the proposed ER-TCP under various test scenarios using the ns-2 network simulator to verify its performance enhancement. Simulation results show that the proposal is a more suitable TCP than the several TCP variants under long delay and heavy loss probability environments of satellite networks.

We propose an iterative frequency estimation method for accuracy improvement of discrete Fourier transform (DFT) phase-based methods. It iterates frequency estimation and phase calculation based on the DFT phase-based methods, which maximizes the signal-to-noise floor ratio at the frequency estimation position. We apply it to three methods, the phase difference estimation, the derivative estimation, and the arctan estimation, which are known to be among the best DFT phase-based methods. Experimental results show that the proposed method shows meaningful reductions of the frequency estimation error compared to the conventional methods especially at low signal-to-noise ratio.

In this paper, we investigate the performance of Thorup's algorithm by comparing it to Dijkstra's algorithm for large-scale network simulations. One of the challenges toward the realization of large-scale network simulations is the efficient execution to find shortest paths in a graph with N vertices and M edges. The time complexity for solving a single-source shortest path (SSSP) problem with Dijkstra's algorithm with a binary heap (DIJKSTRA-BH) is O((M + N) log N). An sophisticated algorithm called Thorup's algorithm has been proposed. The original version of Thorup's algorithm (THORUP-FR) has the time complexity of O(M + N). A simplified version of Thorup's algorithm (THORUP-KL) has the time complexity of O(M α(N) + N) where α(N) is the functional inverse of the Ackerman function. In this paper, we compare the performances (i.e., execution time and memory consumption) of THORUP-KL and DIJKSTRA-BH since it is known that THORUP-FR is at least ten times slower than Dijkstra's algorithm with a Fibonaccii heap. We find that (1) THORUP-KL is almost always faster than DIJKSTRA-BH for large-scale network simulations, and (2) the performances of THORUP-KL and DIJKSTRA-BH deviate from their time complexities due to the presence of the memory cache in the microprocessor.

Conventional features for Automatic Speech Recognition and Sound Event Recognition such as Mel-Frequency Cepstral Coefficients (MFCCs) have been shown to perform poorly in noisy conditions. We introduce an auditory feature based on the gammatone filterbank, the Selective Gammatone Envelope Feature (SGEF), for Robust Sound Event Recognition where channel selection and the filterbank envelope is used to reduce the effect of noise for specific noise environments. In the experiments with Hidden Markov Model (HMM) recognizers, we shall show that our feature outperforms MFCCs significantly in four different noisy environments at various signal-to-noise ratios.

In this paper, we report the reduction in the base-collector capacitance (CBC) of InP/InGaAs double heterojunction bipolar transistors with buried SiO2 wires (BG-HBT). In a previous trial, we could not confirm a clear difference between the CBC of the conventional HBT and that of the BG-HBT because the subcollector layer was thicker than expected. In this study, the interface between the collector and the subcollector was shifted to the middle of the SiO2 wires by adjusting the growth temperature, and a reduction in CBC with buried SiO2 wires was confirmed. The estimated CBC of the BG-HBT was 7.6 fF, while that of the conventional HBT was 8.6 fF. This 12% reduction was in agreement with the 10% reduction calculated according to the designed size.