Poor illumination and viewing conditions have negativeinfluences on the quality of an image, especially the contrast of the dark and bright region. Thus, captured and displayed images usually need contrast enhancement. Histogram-based or gamma correction-based methods are generally utilized for this. However, these methods are global contrast enhancement method, and since the sensitivity of the human eye changes locally according to the position of the object and the illumination in the scene, the global contrast enhancement methods have a limit. The spatial adaptive method is needed to overcome these limitations and it has led to the development of an integrated surround retinex (ISR), and estimation of dominant chromaticity (EDC) methods. However, these methods are based on Gray-World Assumption, and they use a general image formation model, so the color constancy is known to get poor results, shown through graying-out, halo-artifacts (ringing effects), and the dominated color. This paper presents a contrast enhancement method using a modified image formation model in which the image is divided into three components: global illumination, local illumination and reflectance. After applying the power constant value to control the contrast in the resulting image, the output image is obtained from their product to avoid or minimize a color distortion, based on the sRGB color representation. The experimental results show that the proposed method yields better performances than conventional methods.

This paper presents a novel method for improving the readability of automatic speech recognition (ASR) results for classroom lectures. Because speech in a classroom is spontaneous and contains many ill-formed utterances with various disfluencies, the ASR result should be edited to improve the readability before presenting it to users, by applying some operations such as removing disfluencies, determining sentence boundaries, inserting punctuation marks and repairing dropped words. Owing to the presence of many kinds of domain-dependent words and casual styles, even state-of-the-art recognizers can only achieve a 30-50% word error rate for speech in classroom lectures. Therefore, a method for improving the readability of ASR results is needed to make it robust to recognition errors. We can use multiple hypotheses instead of the single-best hypothesis as a method to achieve a robust response to recognition errors. However, if the multiple hypotheses are represented by a lattice (or a confusion network), it is difficult to utilize sentence-level knowledge, such as chunking and dependency parsing, which are imperative for determining the discourse structure and therefore imperative for improving readability. In this paper, we propose a novel algorithm that infers clean, readable transcripts from spontaneous multiple hypotheses represented by a confusion network while integrating sentence-level knowledge. Automatic and manual evaluations showed that using multiple hypotheses and sentence-level knowledge is effective to improve the readability of ASR results, while preserving the understandability.

Fidelity Range Extension (FRExt) (i.e. High Profile) was added to the H.264/AVC recommendation in the second version. One of the features included in FRExt is the Adaptive Block-size Transform (ABT). In order to conform to the FRExt, a Fractional Motion Estimation (FME) architecture is proposed to support the 88/44 adaptive Hadamard Transform (88/44 AHT). The 88/44 AHT circuit contributes to higher throughput and encoding performance. In order to increase the utilization of SATD (Sum of Absolute Transformed Difference) Generator (SG) in unit time, the proposed architecture employs two 8-pel interpolators (IP) to time-share one SG. These two IPs can work in turn to provide the available data continuously to the SG, which increases the data throughput and significantly reduces the cycles that are needed to process one Macroblock. Furthermore, this architecture also exploits the linear feature of Hadamard Transform to generate the quarter-pel SATD. This method could help to shorten the long datapath in the second-step of two-iteration FME algorithm. Finally, experimental results show that this architecture could be used in the applications requiring different performances by adjusting the supported modes and operation frequency. It can support the real-time encoding of the seven-mode 4 K2 K@24 fps or six-mode 4 K2 K@30 fps video sequences.

When a cognitive radio system dynamically utilizes a frequency band, channel control information must be communicated over the network in order for the currently available carrier frequencies to be shared. In order to keep efficient spectrum utilization, this control information should also be dynamically transmitted through channels such as cognitive pilot channels based on the channel conditions. If transmitters dynamically select carrier frequencies, receivers must receive the control signal without knowledge of its carrier frequencies. A novel scheme called differential code parallel transmission (DCPT) enables receivers to receive low-rate information without any knowledge of the carrier frequency. The transmitter simultaneously transmits two signals whose carrier frequencies are separated by a predefined value. The absolute values of the carrier frequencies can be varied. When the receiver receives the DCPT signal, it multiplies the signal by a frequency-shifted version of itself; this yields a DC component that represents the data signal, which is then demodulated. However, the multiplication process results in the noise power being squared, necessitating high received signal power. In this paper, to realize a bandpass filter that passes only DCPT signals of unknown frequency and that suppresses noise and interference at other frequencies, a DCPT-adaptive bandpass filter (ABF) that employs an adaptive equalizer is proposed. In the training phase, the received signal is the filter input and the frequency-shifted signal is the training input. Then, the filter is trained to pass the higher-frequency signal of the two DCPT signals. The performance of DCPT-ABF is evaluated through computer simulations. We find that DCPT-ABF operates successfully even under strong interference.

Ultra wideband (UWB) radar is considered a promising technology to complement existing camera-based surveillance systems because, unlike cameras, it provides excellent range resolution. Many of the UWB radar imaging algorithms are based on large-scale antenna arrays that are not necessarily practical because of their complexity and high cost. To resolve this issue, we previously developed a two-dimensional radar imaging algorithm that estimates unknown target shapes and motion using only three antennas. In this paper, we extend this method to obtain three-dimensional images by estimating three-dimensional motions from the outputs of five antennas. Numerical simulations confirm that the proposed method can estimate accurately the target shape under various conditions.

In this paper, we propose a power amplifier linearization technique combined with iterative noise cancelation. This method alleviates the effect of added noises which prevents the predistorter (PD) from estimating the exact characteristics of the power amplifier (PA). To iteratively cancel the noise added in the feedback signal, the output signal of the power amplifier without noise is reconstructed by applying the inverse characteristics of the PD to the predistorted signals. The noise can be revealed by subtracting the reconstructed signals from the feedback signals. Simulation results based on the mean-square error (MSE) and power spectral density (PSD) criteria are presented to evaluate PD performance. The results show that the iterative noise cancelation significantly enhances the MSE performance, which leads to an improvement of the out-of-band power suppression. The performance of the proposed technique is verified by computer simulation and hardware test results.

In this paper, we propose a way to improve the classification performance of support vector machines (SVMs), especially for speech and music frames within a selectable mode vocoder (SMV) framework. A myriad of techniques have been proposed for SVMs, and most of them are employed during the training phase of SVMs. Instead, the proposed algorithm is applied during the test phase and works with existing schemes. The proposed algorithm modifies a kernel parameter in the decision function of SVMs to alter SVM decisions for better classification accuracy based on the previous outputs of SVMs. Since speech and music frames exhibit strong inter-frame correlation, the outputs of SVMs can guide the kernel parameter modification. Our experimental results show that the proposed algorithm has the potential for adaptively tuning classifications of support vector machines for better performance.

As a result of the rapid development of the Internet in recent years, network security has become an urgent issue. Distributed denial of service (DDoS) attacks are one of the most serious security issues. In particular, 60 percent of the DDoS attacks found on the Internet are TCP attacks, including SYN flood attacks. In this paper, we propose adaptive timer-based countermeasures against SYN flood attacks. Our proposal utilizes the concept of soft-state protocols that are widely used for resource management on the Internet. In order to avoid deadlock, a server releases resources using a time-out mechanism without any explicit requests from its clients. If we change the value of the timer in accordance with the network conditions, we can add more flexibility to the soft-state protocols. The timer is used to manage the resources assigned to half-open connections in a TCP 3-way handshake mechanism, and its value is determined adaptively according to the network conditions. In addition, we report our simulation results to show the effectiveness of our approach.

Many researchers have proposed ultrasound imaging techniques for product inspection; however, most of these techniques are aimed at detecting the existence of flaws in products. The acquisition of an accurate three-dimensional image using ultrasound has the potential to be a useful product inspection tool. In this paper we apply the Envelope algorithm, which was originally proposed for accurate UWB (Ultra Wide-Band) radar imaging systems, to ultrasound imaging. We show that the Envelope algorithm results in image deterioration, because it is difficult for ultrasound measurements to achieve high signal to noise (S/N) ratio values as a result of a high level of noise and interference from the environment. To reduce errors, we propose two adaptive smoothing techniques that effectively stabilize the estimated image produced by the Envelope algorithm. An experimental study verifies that the proposed imaging algorithm has accurate 3-D imaging capability with a mean error of 6.1 µm, where the transmit center frequency is 2.0 MHz and the S/N ratio is 23 dB. These results demonstrate the robustness of the proposed imaging algorithm compared with a conventional Envelope algorithm.

Face detection has been an independent technology playing an important role in more and more fields, which makes it necessary and urgent to have its architecture reconfigurable to meet different demands on detection capabilities. This paper proposed a face detection architecture, which could be adjusted by the user according to the background, the sensor resolution, the detection accuracy and speed in different situations. This user adjustable mode makes the reconfiguration simple and efficient, and is especially suitable for portable mobile terminals whose working condition often changes frequently. In addition, this architecture could work as an accelerator to constitute a larger and more powerful system integrated with other functional modules. Experimental results show that the reconfiguration of the architecture is very reasonable in face detection and synthesized report also indicates its advantage on little consumption of area and power.

In this letter, we propose a distributed switch-and-stay combining network with partial relay selection and show that the system spectral efficiency can be improved via adaptive modulation. Analytical expressions for the achievable spectral efficiency and average bit error rate of the proposed system over Rayleigh fading channels are derived for an arbitrary switching threshold. Numerical results are gathered to substantiate the analytical derivation showing that in terms of spectral efficiency, the system with single relay outperforms that with more than one relay at high signal-to-noise ratios (SNRs) and the optimal switching threshold can significantly improve the system performance at medium SNRs.

The explosive growth of Internet usage has caused problems for the current Internet in terms of traffic congestion within networks and performance degradation of end-to-end flows. Therefore, a reconsideration of the current Internet has begun and is being actively discussed worldwide with the goals of enabling efficient share of limited network resources (i.e., the link bandwidth) and improved performance. To directly address the inefficiency of TCP's congestion mitigation solely on the end-to-end basis, in this paper we propose an adaptive split connection scheme on advanced relay nodes; this scheme dynamically splits end-to-end TCP connections on the basis of congestion status in output links. Through simulation evaluations, we examine the effectiveness and potential of the proposed scheme.

The purpose of this study is to propose an advanced phase-based optical flow method with improved tracking accuracy for motion flow. The proposed method is mainly based on adaptive bilateral filtering (ABF) and Gabor based spatial filtering. ABF aims to preserve the maximum boundary information of the original image, while the spatial filtering aims to accurately compute the local variations. Our method tracks the optical flow in three stages. Firstly, the input images are filtered by using ABF and a spatial filter to remove noises and to preserve the maximum contour information. The component velocities are then computed based on the phase gradient of each pixel. Secondly, irregular pixels are eliminated, if the phase differences are not linear over the image frames. Lastly, the entire velocity is derived by integrating the component velocities of each pixel. In order to evaluate the tracking accuracy of the proposed method, we have examined its performance for synthetic and realistic images for which the ground truth data were known. As a result, it was observed that the proposed technique offers higher accuracy than the existing optical flow methods.

In this letter, we propose an adaptive sensing/transmission scheduling policy in which the secondary user senses the spectrum when its channel condition is poor for transmission. The adaptive sensing/transmission scheduling is modeled as a Markov process and a near-optimal algorithm is proposed to determine the sensing/transmission policy. Simulation results verify our analysis and demonstrate the superiority of the proposed algorithm.

Equivalent Transmission Path (ETP) model-based BER map method was proved to be used in fast estimating Bit Error Rate (BER) of Single-Input Single-Output Orthogonal Frequency Division Multiplexing (SISO-OFDM) system, where the delay spreading exceeds the guard interval. In this paper, we propose a method for evaluating transmission characteristics of Single-Input Multiple-Output Orthogonal Frequency Division Multiplexing (SIMO-OFDM) system with application to maximum ratio combining diversity at the reception. The simulation results show that the proposed approximation method is able to be used in estimating trasmission characteristics with high accuracy not only for SISO-OFDM system but also for SIMO-OFDM system.

CLEFIA is a 128-bit block cipher proposed by Shirai et al. in 2007. On its saturation attack, Tsunoo et al. reported peculiar saturation characteristics in 2010. They formulated some hypotheses on the existence of the characteristics with no proof. In this paper we have theoretically proved their hypotheses. In their attack scenario, we show that the mod-2 distribution is a code word of Extended Hamming code, and then proof is given by using the property of Hadamard transform.

The channel characteristics of IEEE 802.11 WLAN vary with time and this can affect packet transmission performance. For achieving robust and efficient transmission, the transmission rate is controlled by exploiting the multi-rate capability of the IEEE 802.11 physical layer (PHY) to respond to the time-varying channel condition. In this paper, we propose a novel rate adaptation scheme, called RA-MCE, in which the transmitter estimates channel quality in the MAC layer to enhance throughput performance without the need to use the RTS-CTS mechanism nor to modify the IEEE 802.11 standard. RA-MCE adaptively controls the transmission rate according to the estimated channel quality by the MAC layer channel quality estimator (MCE) that uses only local MAC layer measurements. Through extensive simulations, we validate the accuracy of MCE and evaluate the performance of RA-MCE to show that it achieves higher throughput performance than other rate adaptation schemes under various circumstances.

Beamforming with sparse constraint has shown significant performance improvement. In this letter, a least squares constant modulus blind adaptive beamforming with sparse constraint is proposed. Simulation results indicate that the proposed approach exhibits better performance than the well-known least squares constant modulus algorithm (LSCMA).

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.

We consider the position estimation system for targets which exist in near wide area. The system has multiple sensors and estimates the position with multiple receivers. In the past, if receivers were arranged on a straight line, the large position error in the same direction of the line is generated. In order to reduce the error, we propose a novel estimation algorithm using transmitter's directivity information. Our system use directional emission made by an array of antennas in a transmitter. In this paper, the error characteristic which should be solved is introduced firstly. After that, our algorithm is presented. Finally the performance of the error reduction is shown by computer simulations. And we also confirm the reduction by experimental trials. The results indicate good reduction of the error.