In this letter, we propose a novel method for change detection in multitemporal optical satellite images. Unlike the tradition methods, the proposed method is able to detect changed region even from unregistered images. In order to obtain the change detection map from the unregistered images, we first compute the sum of the color difference (SCD) of a pixel to all pixels in an input image. Then we calculate the SCD of this pixel to all pixels in the other input image. Finally, we use the difference of the two SCDs to represent the change detection map. Experiments on the multitemporal images demonstrates the good performance of the proposed method on the unregistered images.

This paper describes a technique for overcoming the model shrinkage problem in automatic speech recognition (ASR), which allows application developers and users to control the model size with less degradation of accuracy. Recently, models for ASR systems tend to be large and this can constitute a bottleneck for developers and users without special knowledge of ASR with respect to introducing the ASR function. Specifically, discriminative language models (DLMs) are usually designed in a high-dimensional parameter space, although DLMs have gained increasing attention as an approach for improving recognition accuracy. Our proposed method can be applied to linear models including DLMs, in which the score of an input sample is given by the inner product of its features and the model parameters, but our proposed method can shrink models in an easy computation by obtaining simple statistics, which are square sums of feature values appearing in a data set. Our experimental results show that our proposed method can shrink a DLM with little degradation in accuracy and perform properly whether or not the data for obtaining the statistics are the same as the data for training the model.

This paper proposes a wide-band noise reduction method using a zero phase (ZP) signal which is defined as the IDFT of a spectral amplitude. When a speech signal has periodicity in a short observation, the corresponding ZP signal becomes also periodic. On the other hand, when a noise spectral amplitude is approximately flat, its ZP signal takes nonzero values only around the origin. Hence, when a periodic speech signal is embedded in a flat spectral noise in an analysis frame, its ZP signal becomes a periodic signal except around the origin. In the proposed noise reduction method, we replace the ZP signal around the origin with the ZP signal in the second or latter period. Then, we get an estimated speech ZP signal. The major advantages of this method are that it can reduce not only stationary wide-band noises but also non-stationary wide-band noises and does not require a prior estimation of the noise spectral amplitude. Simulation results show that the proposed noise reduction method improves the SNR more than 5 dB for a tunnel noise and 13 dB for a clap noise in a low SNR environment.

Decoding of Reed-Solomon (RS) codes requires many arithmetic operations in the Galois field. While the software decoding of RS codes has the advantage of its flexibility to support RS codes of variable parameters, the speed of the software decoding is slower than dedicated hardware RS decoders because arithmetic operations in the Galois field on an ordinary processor require many instruction steps. To achieve fast software decoding of RS codes, it is effective to accelerate Galois operations by both dedicated circuitry and parallel processing. In this paper, an accelerator is proposed which is attached to the base processor to speed up the software decoding of RS codes by parallel execution of Galois operations.

This paper discusses verification of the security against inference attacks on XML databases in the presence of a functional dependency. So far, we have provided the verification method for k-secrecy, which is a metric for the security against inference attacks on databases. Intuitively, k-secrecy means that the number of candidates of sensitive data (i.e., the result of unauthorized query) of a given database instance cannot be narrowed down to k-1 by using available information such as authorized queries and their results. In this paper, we consider a functional dependency on database instances as one of the available information. Functional dependencies help attackers to reduce the number of the candidates for the sensitive information. The verification method we have provided cannot be naively extended to the k-secrecy problem with a functional dependency. The method requires that the candidate set can be captured by a tree automaton, but the candidate set when a functional dependency is considered cannot be always captured by any tree automaton. We show that the ∞-secrecy problem in the presence of a functional dependency is decidable when a given unauthorized query is represented by a deterministic topdown tree transducer, without explicitly computing the candidate set.

This study shows a fast simulation method of turbo codes over slow Rayleigh fading channels. The reduction of the simulation time is achieved by applying importance sampling (IS). The conventional IS method of turbo codes over Rayleigh fading channels focuses only on modification of additive white Gaussian noise (AWGN) sequences. The proposed IS method biases not only AWGNs but also channel gains of the Rayleigh fading channels. The computer runtime of the proposed method is about 1/5 of that of the conventional IS method on the evaluation of a frame error rate of 10-6. When we compare with the Monte Carlo simulation method, the proposed method needs only 1/100 simulation runtime under the condition of the same accuracy of the estimator.

The quasi-ARX neurofuzzy (Q-ARX-NF) model has shown great approximation ability and usefulness in nonlinear system identification and control. It owns an ARX-like linear structure, and the coefficients are expressed by an incorporated neurofuzzy (InNF) network. However, the Q-ARX-NF model suffers from curse-of-dimensionality problem, because the number of fuzzy rules in the InNF network increases exponentially with input space dimension. It may result in high computational complexity and over-fitting. In this paper, the curse-of-dimensionality is solved in two ways. Firstly, a support vector regression (SVR) based approach is used to reduce computational complexity by a dual form of quadratic programming (QP) optimization, where the solution is independent of input dimensions. Secondly, genetic algorithm (GA) based input selection is applied with a novel fitness evaluation function, and a parsimonious model structure is generated with only important inputs for the InNF network. Mathematical and real system simulations are carried out to demonstrate the effectiveness of the proposed method.

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.

We recently determined the values of intrinsic spin-orbit (SO) parameters for In0.52Al0.48As/In0.53Ga0.47As(10 nm)/In0.52Al0.48As (InGaAs/InAlAs) quantum wells (QW), lattice-matched to (001) InP, from the weak localization/antilocalization analysis of the low-temperature magneto-conductivity measurements [1]. We have then studied the subband energy spectra for the InGaAs/InAlAs double QW system from beatings in the Shubnikov de Haas (SdH) oscillations. The basic properties obtained here for the double QW system provides useful information for realizing nonmagnetic spin-filter devices based on the spin-orbit interaction [2].

This paper employs the nature-inspired approach to investigate the ideal architecture of communication networks as large-scale and complex systems. Conventional architectures are hierarchical with respect to the functions of network operations due entirely to implementation concerns and not to any fundamental conceptual benefit. In contrast, the large-scale systems found in nature are hierarchical and demonstrate orderly behavior due to their space/time scale dependencies. In this paper, by examining the fundamental requirements inherent in controlling network operations, we clarify the hierarchical structure of network operations with respect to time scale. We also describe an attempt to build a new network architecture based on the structure. In addition, as an example of the hierarchical structure, we apply the quasi-static approach to describe user-system interaction, and we describe a hierarchy model developed on the renormalization group approach.

Network structures can be found in almost any kind of natural or artificial systems as transport medium for communication between the respective nodes. In this paper we study certain key topological features of brain functional networks obtained from functional magnetic resonance imaging (fMRI) measurements. We compare complex network measures of the extracted topologies with those from Internet service providers (ISPs). Our goal is to identify important features which will be helpful in designing more robust and adaptive future information network architectures.

The subset sum problem, which is often called as the knapsack problem, is known as an NP-hard problem, and there are several cryptosystems based on the problem. Assuming an oracle for shortest vector problem of lattice, the low-density attack algorithm by Lagarias and Odlyzko and its variants solve the subset sum problem efficiently, when the “density” of the given problem is smaller than some threshold. When we define the density in the context of knapsack-type cryptosystems, weights are usually assumed to be chosen uniformly at random from the same interval. In this paper, we focus on general subset sum problems, where this assumption may not hold. We assume that weights are chosen from different intervals, and make analysis of the effect on the success probability of above algorithms both theoretically and experimentally. Possible application of our result in the context of knapsack cryptosystems is the security analysis when we reduce the data size of public keys.

Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.

In this paper, a novel framework for extracting visual feature-based keyword relationships from an image database is proposed. From the characteristic that a set of relevant keywords tends to have common visual features, the keyword relationships in a target image database are extracted by using the following two steps. First, the relationship between each keyword and its corresponding visual features is modeled by using a classifier. This step enables detection of visual features related to each keyword. In the second step, the keyword relationships are extracted from the obtained results. Specifically, in order to measure the relevance between two keywords, the proposed method removes visual features related to one keyword from training images and monitors the performance of the classifier obtained for the other keyword. This measurement is the biggest difference from other conventional methods that focus on only keyword co-occurrences or visual similarities. Results of experiments conducted using an image database showed the effectiveness of the proposed method.

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.

This letter proposes an efficient Location-Aware Social Routing (LASR) scheme for Delay Tolerant Networks (DTNs). LASR makes forwarding decisions based on a new metric which uses location information to reflect the node relations and community structure. Simulation results are presented to support the effectiveness of our scheme.

The growing use of web services is increasing web browser attacks exponentially. Most attacks use a technique called heap spraying because of its high success rate. Heap spraying executes a malicious code without indicating the exact address of the code by copying it into many heap objects. For this reason, the attack has a high potential to succeed if only the vulnerability is exploited. Thus, attackers have recently begun using this technique because it is easy to use JavaScript to allocate the heap memory area. This paper proposes a novel technique that detects heap spraying attacks by executing a heap object in a real environment, irrespective of the version and patch status of the web browser. This runtime execution is used to detect various forms of heap spraying attacks, such as encoding and polymorphism. Heap objects are executed after being filtered on the basis of patterns of heap spraying attacks in order to reduce the overhead of the runtime execution. Patterns of heap spraying attacks are based on analysis of how an web browser accesses benign web sites. The heap objects are executed forcibly by changing the instruction register into the address of them after being loaded into memory. Thus, we can execute the malicious code without having to consider the version and patch status of the browser. An object is considered to contain a malicious code if the execution reaches a call instruction and then the instruction accesses the API of system libraries, such as kernel32.dll and ws_32.dll. To change registers and monitor execution flow, we used a debugger engine. A prototype, named HERAD(HEap spRAying Detector), is implemented and evaluated. In experiments, HERAD detects various forms of exploit code that an emulation cannot detect, and some heap spraying attacks that NOZZLE cannot detect. Although it has an execution overhead, HERAD produces a low number of false alarms. The processing time of several minutes is negligible because our research focuses on detecting heap spraying. This research can be applied to existing systems that collect malicious codes, such as Honeypot.

This paper presents fundamental concepts, overall process and recent research issues of Mining Software Repositories. The data sources such as source control systems, bug tracking systems or archived communications, data types and techniques used for general MSR problems are also presented. Finally, evaluation approaches, opportunities and challenge issues are given.

In order to improve the address discharge characteristics, we propose the modified selective reset waveform utilizing the address-bias voltage (Va-bias) during the ramp-up period. It is revealed that the proper Va-bias makes the weak discharge between the address and scan electrodes which plays a role in sufficiently removing the wall charge, thereby contributing to minimizing the wall-voltage variation during the address-period. As a result of adopting the Va-bias in the conventional selective reset driving waveform, it was found that the address discharge delay time can be shortened by approximately 40 ns and the address period of each subfield can be significantly reduced by about 43 µs.

Effects of conductive defects on unipolar resistive random access memory (RRAM) are investigated in order to reduce the operation current for high density and low power RRAM applications. It is clarified that forming voltage decreases with increasing charged conductive defects which are a source of conductive filament (CF) path and with decreasing cell thickness. Random circuit breaker (RCB) network simulation model which is a dynamic percolation simulation model is used to elucidate these effects. From this simulation results, the optimal cell thickness with sufficient conductive defect shows improved resistive switching characteristics such as low forming voltage, small set voltage distribution and low reset current. From the deep understanding of relationship between conductive defect in various cell thickness and other resistive switching parameters, RRAM with low forming voltage and reset current can be obtained and it will be one of the most promising next generation nonvolatile memories.