Solid-state disks (SSDs) have received much attention as replacements for hard disk drives (HDDs). One of their noticeable advantages is their high-speed read/write operation. To achieve good performance, SSDs have an internal memory hierarchy which includes several volatile memories, such as DRAMs and SRAMs. Furthermore, many SSDs adopt aggressive memory management schemes under the assumption of stable power supply. Unfortunately, the data stored in the volatile memories are lost when the power supplied to SSDs is abruptly shut off. Such power failure is often observed in portable devices. For this reason, it is critical to provide a power failure protection scheme for reliable SSDs. In this work, we propose a power-failure protection scheme for SSDs to increase their reliability. The contribution of our work is three-fold. First, we design a power failure protection circuit which incorporates super-capacitors as well as rechargeable batteries. Second, we provide a method to determine the capacity of backup power sources. Third, we propose a data backup procedure when the power failure occurs. We implemented our method on a real board and applied it to a notebook PC with a contemporary SSD. The board measurement and simulation results prove that our method is robust in cases of sudden power failure.

Many simulated annealing algorithms use the Cauchy neighbors for fast convergence, and the conventional method uses the product of n one-dimensional Cauchy distributions as an approximation. However, this method slows down the search severely as the dimension gets high because of the dimension-wise neighbor generation. In this paper, we analyze the orthogonal neighbor characteristics of the conventional method and propose a method of generating symmetric neighbors from the n-dimensional Cauchy distribution. The simulation results show that the proposed method is very effective for the search in the simulated annealing and can be applied to many other stochastic optimization algorithms.

Side match vector quantization (SMVQ) has been originally developed for image compression and is also useful for steganography. SMVQ requires to create its own state codebook for each block in both encoding and decoding phases. Since the conventional method for the state codebook generation is extremely time-consuming, this letter proposes a fast generation method. The proposed method is tens times faster than the conventional one without loss of perceptual visual quality.

This letter proposes a new face sketch recognition method. Given a query sketch and face photos in a database, the proposed method first synthesizes pseudo sketches by computing the locality sensitive histogram and dense illumination invariant features from the resized face photos, then extracts discriminative features by computing histogram of averaged oriented gradients on the query sketch and pseudo sketches, and finally find a match with the shortest cosine distance in the feature space. It achieves accuracy comparable to the state-of-the-art while showing much more robustness than the existing face sketch recognition methods.

This letter proposes a QoS control method for video streaming service over wireless networks. Based on statistical analysis, the time-varying MAC parameters highly related to channel condition are selected to predict available bitrate. Adaptive bitrate control of scalably-encoded video guarantees continuity in streaming service even if the channel condition changes abruptly.

Image deformations caused by different steganographic methods are typically extremely small and highly similar, which makes their detection and identification to be a difficult task. Although recent steganalytic methods using deep learning have achieved high accuracy, they have been made to detect stego images to which specific steganographic methods have been applied. In this letter, a staganalytic method is proposed that uses hierarchical residual neural networks (ResNet), allowing detection (i.e. classification between stego and cover images) and identification of four spatial steganographic methods (i.e. LSB, PVD, WOW and S-UNIWARD). Experimental results show that using hierarchical ResNets achieves a classification rate of 79.71% in quinary classification, which is approximately 23% higher compared to using a plain convolutional neural network (CNN).

We deal with LTI nonminimum phase (NMP) systems which are difficult to control with conventional methods because of their inherent characteristics of undershoot. In such systems, reducing the undesirable undershoot phenomenon makes the response time of the systems much longer. Moreover, it is impossible to control the magnitude of undershoot in a direct way and to predict the response time. In this paper, we propose a novel two sliding mode control scheme which is capable of stably determining the magnitude of undershoot and thus the response time of NMP systems a priori. To do this, we introduce two sliding lines which are in charge of control in turn. One is used to stabilize the system and achieve asymptotic regulation eventually like the conventional sliding mode methods and the other to stably control the magnitude of undershoot from the beginning of control until the state meets the first sliding line. This control scheme will be proved to have an asymptotic regulation property. The computer simulation shows that the proposed control scheme is very effective and suitable for controlling the NMP systems compared with the conventional ones.

This letter introduces a cost-effective chrominance compensation scheme. The proposed method is applied to both 'INTER 1616' and 'SKIP' modes in only anchor P-pictures. By testing using JVT common test condition, simulation results show that proposed method can obtain average BD-PSNR gains for U and V as 0.14 dB and 0.13 dB, respectively while maintaining almost the same BD-PSNR's for Y. For the range of low bit-rate, it is observed that average BD-PSNR gains for Y, U and V are 0.14 dB, 0.49 dB and 0.53 dB, respectively. Necessary computational complexity is very marginal because the number of anchor P-pictures is very small in comparison with whole coded video sequences. However it can be found that the proposed method can significantly improve the coding efficiencies of color components.

In this letter, we propose a simple framework for accelerating a state-of-the-art histogram-based weighted median filter at no expense. It is based on a process of determining the filter processing direction. The determination is achieved by measuring the local feature variation of input images. Through experiments with natural images, it is verified that, depending on input images, the filtering speed can be substantially increased by changing the filtering direction.

In this letter, we propose an improved single image haze removal algorithm using image segmentation. It can effectively resolve two common problems of conventional algorithms which are based on dark channel prior: halo artifact and wrong estimation of atmospheric light. The process flow of our algorithm is as follows. First, the input hazy image is over-segmented. Then, the segmentation results are used for improving the conventional dark channel computation which uses fixed local patches. Also, the segmentation results are used for accurately estimating the atmospheric light. Finally, from the improved dark channel and atmospheric light, an accurate transmission map is computed allowing us to recover a high quality haze-free image.

This paper introduces an adaptive GOP structure (AGS), which adaptively defines the GOP structure according to the time-varying temporal properties of video sequences, and thus improves the coding efficiency of the MPEG & ITU-T's Joint Scalable Video Coding (JSVC) scheme, the method proposed in this paper, which adaptively modifies the size of GOP based on the image characteristics of video sequence, improves the coding efficiency up to 0.77 dB compared to the JSVC JSVM (Joint Scalable Video Model).

This paper introduces a new frame-based bit-rate control scheme for bandwidth-adaptive video coding. Proposed method can accurately adapt to the rapid varying scene characteristics by reducing the number of occurrences of the extrapolations while updating the rate-distortion model used for determine the appropriate quantization steps.

In this paper, we suggest the interference cancellation (IC) technique suitable for turbo coded code division multiple access (CDMA) systems, that merges IC processes into turbo decoding processes to improve system performance and reduce system complexity. To ensure the reliability of the temporary decision bits for cancellation, we use cyclic redundancy code (CRC) check as a measure. Prior to design turbo coded CDMA system, we first derive the optimized polynomials of low-rate turbo codes appropriate to CDMA systems. According to the simulation results with setting the processing gain (PG) to 120, the turbo coded CDMA system with the proposed IC technique can accommodate 60 users over additive white Gaussian noise (AWGN) channel when signal to noise ratio (SNR) is about 2. 5 dB and required frame error ratio (FER) is 10-2. Compared this result with the performance of single user's system, it requires only additional 1 dB SNR.

An H.264-based selective FGS coding scheme is proposed. It selectively uses the interframe-prediction data inside the enhancement-layer only when those data can significantly reduce the temporal-redundancies. Since this minimizes the drift effects, the overall coding efficiency is improved. Simulations show that average PSNR of the proposed scheme is higher by 1-3 dB and 3-5 dB than those of the H.264-based FGS and the MPEG-4 video FGS profile, respectively.

This letter proposes a method to retrieve the original image X out of multiple sets of SI (Side Information) in distributed video coding (DVC). Using Turbo decoding methods, the most reliable segments from the decoded Yi's were selected for the composition of Y∞, whose conditional entropy H(X| Y∞) became much lower than any individual conditional entropy H(X| Yi). This proposal has improved the peak signal-to-noise ratio (PSNR) by 1.1 to 1.8 dB, compared to the conventional single SI-based approach.

The network duplicating can achieve significant improvements of the Local Area Network (LAN)'s performance, availability, and security. For LAN duplicating, a Dual-Path Ethernet Module (DPEM) is developed. Since a DPEM is simply located at the front end of any network device as a transparent add-on type independent hardware machine, it does not require sophisticated server reconfiguration. We examine the desirable properties and the characteristics on the Dual-LAN structure. Our evaluation results show that the developed scheme is more efficient than the conventional Single-LAN structures in various aspects.

In this letter, we propose a network-adaptive video streaming scheme based on cross-layered hop-by-hop video rate control in wireless multi-hop networks. We argue that existing end-to-end network-adaptive video rate control schemes, which utilize end-to-end statistics, exhibit serious performance degradation in severely interfered wireless network condition. To cope with this problem, in the proposed scheme, intermediate wireless nodes adjust video sending rate depending upon wireless channel condition measured at MAC (Medium Access Control) layer. Extensive experimental results from an IEEE 802.11a-based testbed show that the proposed scheme improves the perceptual video quality compared to an end-to-end scheme.

We propose a new channel adaptive distributed video coding algorithm, which is adaptive to time-varying available bitrate and packet loss ratio. The proposed method controls the quantization parameter according to channel condition of especially error-prone mobile channel. Simulation shows that the proposed algorithm outperforms the conventional rate-control-only algorithm.

Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.

Feedforward neural networks have been successfully developed and applied in many areas because of their universal approximation capability. However, there still remains the problem of determining a suitable network structure for the given task. In this paper, we propose a novel self-organizing neural network which automatically adjusts its structure according to the task. Utilizing both the constructive and the pruning procedures, the proposed algorithm finds a near-optimal network which is compact and shows good generalization performance. One of its important features is reliability, which means the randomness of neural networks is effectively reduced. The resultant networks can have suitable numbers of hidden neurons and hidden layers according to the complexity of the given task. The simulation results for the well-known function regression problems show that our method successfully organizes near-optimal networks.