How to reduce noise with less speech distortion is a challenging issue for speech enhancement. We propose a novel approach for reducing noise with the cost of less speech distortion. A noise signal can generally be considered to consist of two components, a "white-like" component with a uniform energy distribution and a "color" component with a concentrated energy distribution in some frequency bands. An approach based on noise eigenspace projections is proposed to pack the color component into a subspace, named "noise subspace". This subspace is then removed from the eigenspace to reduce the color component. For the white-like component, a conventional enhancement algorithm is adopted as a complementary processor. We tested our algorithm on a speech enhancement task using speech data from the Texas Instruments and Massachusetts Institute of Technology (TIMIT) dataset and noise data from NOISEX-92. The experimental results show that the proposed algorithm efficiently reduces noise with little speech distortion. Objective and subjective evaluations confirmed that the proposed algorithm outperformed conventional enhancement algorithms.

The use of frequency-domain interleaving on a frame-by-frame basis for orthogonal frequency division multiplexing (OFDM) combined with time division multiplexing (OFDM/TDM) is presented. In conventional OFDM, FDE is not designed to exploit the channel frequency-selectivity and consequently, the frequency diversity gain cannot be obtained. To further improve the bit error rate (BER) performance of conventional OFDM an interleaving technique may be applied, but FDE cannot be fully exploited. In this letter, the OFDM/TDM signal (i.e., several concatenated OFDM signals) frequency components are interleaved at the transmitter and then, minimum mean square error frequency-domain equalization (MMSE-FDE) is applied at the receiver to obtain a larger frequency diversity gain. It is shown that frequency-domain interleaving on a frame-by-frame basis for OFDM/TDM using MMSE-FDE achieves improved BER performance in comparison with conventional OFDM due to enhanced frequency diversity gain.

In this letter, a new Inter lossless coding method based on a residual DPCM (Differential Pulse Code Modulation) is proposed to improve compression ratio in the H.264 standard. Since the spatial correlation in a residual block can be further exploited among the residual signals after motion estimation/compensation, horizontal or vertical DPCM in the residual signals can be applied to further reduce the magnitudes of the residual signals. The proposed method reduces the average bitrates of 3.5% compared with the Inter lossless coding of the H.264 standard.

In this paper, we consider an amplify-and-forward (AF) relay network where a source node transmits information to a destination node through the cooperation of multiple relay nodes. It is shown in prior works that the outage behavior and average throughput of the selection AF (S-AF) scheme where only the best relay node is chosen to assist can outperform the conventional all-participate AF (AP-AF) scheme. Assuming multiple antennas at the destination node and single antennas at other nodes in this paper, we propose a relay selection scheme according to the criterion of maximizing receive signal to noise ratio (SNR), where a group of relays is chosen to assist in the transmission simultaneously in a manner similar to cyclic delay diversity (CDD). Compared with S-AF, the proposed scheme achieves better outage behavior and average throughput. It can be seen from simulation results that the performance improvement of symbol error rate (SER) is significant compared with S-AF.

In this letter, we propose a novel approach using wireless sensor networks (WSNs) to enhance the safety and efficiency of four-way stop-sign-controlled (FWSC) intersections. The proposed algorithm provides right of way (RoW) and crash avoidance information by means of an intelligent WSN system. The system is composed of magnetic sensors, embedded in the center of a lane, with relay nodes and a base station placed on the side of the road. The experimental results show that the vehicle detection accuracy is over 99% and the sensor node battery life expectancy is over 3 years for traffic of 5,800 vehicles per day. For the traffic application we consider, a strong effect is observed as the projected conflict rate was reduced by 72% compared to an FWSC intersection operated with only driver perception.

Traffic volume anomalies refer to apparently abrupt changes in the time series of traffic volume, which can propagate through the network. Detecting and tracing these anomalies is a critical and difficult task for network operators. In this paper, we first propose a traffic decomposition method, which decomposes the traffic into three components: the trend component, the autoregressive (AR) component, and the noise component. A traffic volume anomaly is detected when the AR component is outside the prediction band for multiple links simultaneously. Then, the anomaly is traced using the projection of the detection result matrices for the observed links which are selected by a shortest-path-first algorithm. Finally, we validate our detection and tracing method by using the real traffic data from the third-generation Science Information Network (SINET3) and show the detected and traced results.

Exploiting vulnerabilities of remote systems is one of the fundamental behaviors of malware that determines their potential hazards. Understanding what kind of propagation tactics each malware uses is essential in incident response because such information directly links with countermeasures such as writing a signature for IDS. Although recently malware sandbox analysis has been studied intensively, little work is done on securely observing the vulnerability exploitation by malware. In this paper, we propose a novel sandbox analysis method for securely observing malware's vulnerability exploitation in a totally isolated environment. In our sandbox, we prepare two victim hosts. We first execute the sample malware on one of these hosts and then let it attack the other host which is running multiple vulnerable services. As a simple realization of the proposed method, we have implemented a sandbox using Nepenthes, a low-interaction honeypot, as the second victim. Because Nepenthes can emulate a variety of vulnerable services, we can efficiently observe the propagation of sample malware. In the experiments, among 382 samples whose scan capabilities are confirmed, 381 samples successfully started exploiting vulnerabilities of the second victim. This indicates the certain level of feasibility of the proposed method.

In this paper, we propose a three-phase text error correction model consisting of a word spacing error correction phase, a syllable-based spelling error correction phase, and a word-based spelling error correction phase. In order to reduce the text error correction complexity, the proposed model corrects text errors step by step. With the aim of correcting word spacing errors, spelling errors, and mixed errors in SMS messages, the proposed model tries to separately manage the word spacing error correction phase and the spelling error correction phase. For the purpose of utilizing both the syllable-based approach covering various errors and the word-based approach correcting some specific errors accurately, the proposed model subdivides the spelling error correction phase into the syllable-based phase and the word-based phase. Experimental results show that the proposed model can improve the performance by solving the text error correction problem based on the divide-and-conquer strategy.

Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)--which is a semi-supervised and local extension of Fisher discriminant analysis--was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.

In this paper, a new local matching algorithm, to estimate dense disparity map in stereo vision, consisting of two stages is presented. At the first stage, the reduction of search space is carried out with a high efficiency, i.e. remarkable decrease in the average number of candidates per pixel, with low computational cost and high assurance of retaining the correct answer. This outcome being due to the effective use of multiple radial windows, intensity information, and some usual and new constraints, in a reasonable manner, retains those candidates which satisfy more constraints and especially being more promising to satisfy the implied assumption in using support windows; i.e., the disparity consistency of the window pixels. Such an output from the first stage, while speeding up the final selection of disparity in the second stage due to search space reduction, is also promising a more accurate result due to having more reliable candidates. In the second stage, the weighted window, although not necessarily being the exclusive choice, is employed and examined. The experimental results on the standard stereo benchmarks for the developed algorithm are presented, confirming that the massive computations to obtain more precise matching costs in weighted window is reduced to about 1/11 and the final disparity map is also improved.

A classification method is presented for differentiating honeycombed High Resolution Computed Tomographic (HRCT) images from normal HRCT images. For successful classification of honeycombed HRCT images, a complete set of methods and algorithms is described from segmentation to extraction to feature selection to classification. Wavelet energy is selected as a feature for classification using K-means clustering. Test data of 20 patients are used to validate the method.

A lexicon-driven segmentation-recognition scheme on Bangla handwritten city-name recognition is proposed for Indian postal automation. In the proposed scheme, at first, binarization of the input document is done and then to take care of slanted handwriting of different individuals a slant correction technique is performed. Next, due to the script characteristics of Bangla, a water reservoir concept is applied to pre-segment the slant corrected city-names into possible primitive components (characters or its parts). Pre-segmented components of a city-name are then merged into possible characters to get the best city-name using the lexicon information. In order to merge these primitive components into characters and to find optimum character segmentation, dynamic programming (DP) is applied using total likelihood of the characters of a city-name as an objective function. To compute the likelihood of a character, Modified Quadratic Discriminant Function (MQDF) is used. The features used in the MQDF are mainly based on the directional features of the contour points of the components. We tested our system on 84 different Bangla city-names and 94.08% accuracy was obtained from the proposed system.

This paper presents the optimum control interval for intra-cell fractional transmission power control (TPC) for a shared data channel employing frequency domain channel-dependent scheduling and adaptive modulation and coding (AMC) in the Evolved UTRA uplink using single-carrier (SC)-FDMA radio access. The simulation results show that the best attenuation factor in the fractional TPC is approximately 0.6 for achieving the maximum user throughput when the maximum target received signal power, P0 is -60 dBm. Then, we show that the optimum averaging interval for the desired signal level, which corresponds to a substantial control interval for the fractional TPC, is approximately 100-200 msec regardless of the maximum Doppler frequency up to 222 Hz and the distance at the shadowing correlation of 0.5. Throughout the simulation results, we verify that slow intra-cell fractional TPC associated with fast AMC is effective in achieving the maximum cell throughput and cell-edge user throughput.

Code division multiple access (CDMA) technique is used widely since it can flexibly support multi-rate multi-media services by changing the number of orthogonal spreading codes. In this paper, we present a new adaptive code assignment algorithm, which consists of three steps: reserved-space, improved-crowded-first-space, and multi-code combination to fully use the code space. Compared with the existing algorithms, the proposed algorithm can avoid the code blocking problem and lower its total blocking probability while keeping its computational complexity relatively low. Simulation results show that increasing the free space reduces the average total blocking probability while increasing the blocking probability of high rate users.

Interconnection networks play a crucial role in the performance of massively parallel computers. Hierarchical interconnection networks provide high performance at low cost by exploring the locality that exists in the communication patterns of massively parallel computers. A Tori connected Torus Network (TTN) is a 2D-torus network of multiple basic modules, in which the basic modules are 2D-torus networks that are hierarchically interconnected for higher-level networks. This paper addresses the architectural details of the TTN and explores aspects such as node degree, network diameter, cost, average distance, arc connectivity, bisection width, and wiring complexity. We also present a deadlock-free routing algorithm for the TTN using four virtual channels and evaluate the network's dynamic communication performance using the proposed routing algorithm under uniform and various non-uniform traffic patterns. We evaluate the dynamic communication performance of TTN, TESH, MH3DT, mesh, and torus networks by computer simulation. It is shown that the TTN possesses several attractive features, including constant node degree, small diameter, low cost, small average distance, moderate (neither too low, nor too high) bisection width, and high throughput and very low zero load latency, which provide better dynamic communication performance than that of other conventional and hierarchical networks.

Salient edge detection which is mentioned less frequently than salient point detection is another important cue for subsequent processing in computer vision. How to find the salient edges in natural images is not an easy work. This paper proposes a simple method for salient edge detection which preserves the edges with more salient points on the boundaries and cancels the less salient ones or noise edges in natural images. According to the Gestalt Principles of past experience and entirety, we should not detect the whole edges in natural images. Only salient ones can be an advantageous tool for the following step just like object tracking, image segmentation or contour detection. Salient edges can also enhance the efficiency of computing and save the space of storage. The experiments show the promising results.

In this paper, we propose a novel multiuser detection (MUD) method that is robust against timing offset between wireless terminals (WTs) for the multiuser multiple-input multiple-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) uplink. In the proposed method, MUD is carried out in the frequency-domain using overlapping fast Fourier transform (FFT) windows. After the inverse FFT (IFFT) operation, the samples obtained at both ends of each FFT window are discarded to suppress the effect of inter-block interference (IBI). Thus, it realizes an MUD regardless of the arrival timing differences of the signals from the WTs. The achievable bit error rate (BER) performance of the proposed MUD method is evaluated by computer simulations in a frequency selective fading channel.

In this letter, we propose a simple but accurate calculation method, that is, an approximate closed-form equation of average bit error rate in DPSK/OFDM systems with post-detection diversity reception over both time- and frequency-selective Rayleigh fading channels. The validity of the proposed method is verified by the fact that BER performances given by the derived equation coincide with those by Monte Carlo simulation.

This paper compares the expressive power of five language-based access control models. We show that the expressive powers are incomparable between any pair of history-based access control, regular stack inspection and shallow history automata. Based on these results, we introduce an extension of HBAC, of which expressive power exceeds that of regular stack inspection.

In LTE, AC barring check is performed before RRC connection. In some cells with a low access probability, the UEs keep retrying access which results in higher connection failure and longer access delay. We therefore propose balancing the UEs by adjusting the cell reselection criteria based on the access probability, so that the UEs shall be more encouraged to reselect a cell with a higher access probability.