When the track density of two-dimensional magnetic recording (TDMR) systems is increased, intertrack interference (ITI) inevitably grows, resulting in the extreme degradation of an overall system performance. In this work, we present coding, writing, and reading techniques which allow TDMR systems with multi-readers to overcome severe ITI. A rate-5/6 two-dimensional (2D) modulation code is adopted to protect middle-track data from ITI based on cross-track data dependence. Since the rate-5/6 2D modulation code greatly improves the reliability of the middle-track, there is a bit-error rate gap between middle-track and sidetracks. Therefore, we propose the different track width writing technique to optimize the reliability of all three data tracks. In addition, we also evaluate the TDMR system performance using an user areal density capability (UADC) as a main key parameter. Here, an areal density capability (ADC) can be measured by finding the bit-error rate of the system with sweeping track and linear densities. The UADC is then obtained by removing redundancy from the ADC. Simulation results show that a system with our proposed techniques gains the UADC of about 4.66% over the conventional TDMR systems.

Super resolution (SR) image reconstruction has attracted increasing attention these years and many SR image reconstruction algorithms have been proposed for restoring a high-resolution image from one or multiple low-resolution images. However, how to objectively evaluate the quality of SR reconstructed images remains an open problem. Although a great number of image quality metrics have been proposed, they are quite limited to evaluate the quality of SR reconstructed images. Inspired by this, this paper presents a blind quality index for SR reconstructed images using first- and second-order structural degradation. First, the SR reconstructed image is decomposed into multi-order derivative magnitude maps, which are effective for first- and second-order structural representation. Then, log-energy based features are extracted on these multi-order derivative magnitude maps in the frequency domain. Finally, support vector regression is used to learn the quality model for SR reconstructed images. The results of extensive experiments that were conducted on one public database demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed method is less dependent on the number of training images and has low computational cost.

Low-density parity-check (LDPC) codes can be used to improve the storage reliability of multi-level cell (MLC) flash memories because of their strong error-correcting capability. In order to improve the weighted bit-flipping (WBF) decoding of LDPC codes in MLC flash memories with cell-to-cell interference (CCI), we propose two strategies of normalizing weights and adjusting log-likelihood ratio (LLR) values. Simulation results show that the WBF decoding under the proposed strategies is much advantageous in both error and convergence performances over existing WBF decoding algorithms. Based on complexity analysis, the strategies provide the WBF decoding with a good tradeoff between performance and complexity.

Modulation spectrogram is effective for analyzing underwater signals which consist of tonal and modulated components. This method can analyze the acoustic and modulation frequency at the same time, but has the trade-off issue of time-frequency localization. This letter introduces a reassignment method for overcoming the localization issue in conventional spectrograms, and then presents an alignment scheme for implementing modulation spectrogram. Relevant experiments show improvement in acoustic frequency estimation perspective and an increment in analyzable modulation frequency range.

The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of O(2N), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H∞ framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.

The output feedback consensus problem of lower triangular nonlinear systems under a directed network with a switching topology is studied. It is assumed that every possible network topology contains a directed spanning tree. The proposed design method utilizes a high gain approach to compensate for triangular nonlinearity and to remove the restriction imposed on dwell time. Compared to the previous research, it is shown that the proposed control method can achieve the output feedback consensus of lower triangular nonlinear systems even in the presence of an arbitrarily small average dwell time. A numerical example is given to illustrate the effectiveness of the proposed design method.

Pseudo-random sequences with good statistical property, such as low autocorrelation, high linear complexity and 2-adic complexity, have been widely applied to designing reliable stream ciphers. In this paper, we explicitly determine the 2-adic complexities of two classes of generalized cyclotomic binary sequences with order 4. Our results show that the 2-adic complexities of both of the sequences attain the maximum. Thus, they are large enough to resist the attack of the rational approximation algorithm for feedback with carry shift registers. We also present some examples to illustrate the validity of the results by Magma programs.

In this letter, a construction of sparse measurement matrices is presented. Based on finite fields, a base matrix is obtained. Then a Hadamard matrix or a discrete Fourier transform (DFT) matrix is nested in the base matrix, which eventually formes a new deterministic measurement matrix. The coherence of the proposed matrices is low, which meets the Welch bound asymptotically. Thus these matrices could satisfy the restricted isometry property (RIP). Simulation results demonstrate that the proposed matrices give better performance than Gaussian counterparts.

Sparse arrays can usually achieve larger array apertures than uniform linear arrays (ULA) with the same number of physical antennas. However, the conventional direction-of-arrival (DOA) estimation algorithms for sparse arrays usually require the spatial smoothing operation to recover the matrix rank which inevitably involves heavy computational complexity and leads to a reduction in the degrees-of-freedom (DOFs). In this paper, a low-complex DOA estimation algorithm by exploiting the discrete Fourier transform (DFT) is proposed. Firstly, the spatial spectrum of the virtual array constructed from the sparse array is established by exploiting the DFT operation. The initial DOA estimation can obtain directly by searching the peaks in the DFT spectrum. However, since the number of array antennas is finite, there exists spectrum power leakage which will cause the performance degradation. To further improve the angle resolution, an iterative process is developed to suppress the spectrum power leakage. Thus, the proposed algorithm does not require the spatial smoothing operation and the computational complexity is reduced effectively. In addition, due to the extention of DOF with the application of the sparse arrays, the proposed algorithm can resolve the underdetermined DOA estimation problems. The superiority of the proposed algorithm is demonstrated by simulation results.

The estimation of the matrix rank of harmonic components of a music spectrogram provides some useful information, e.g., the determination of the number of basis vectors of the matrix-factorization-based algorithms, which is required for the automatic music transcription or in post-processing. In this work, we develop an algorithm based on Stein's unbiased risk estimator (SURE) algorithm with the matrix factorization model. The noise variance required for the SURE algorithm is estimated by suppressing the harmonic component via median filtering. An evaluation performed using the MIDI-aligned piano sounds (MAPS) database revealed an average estimation error of -0.26 (standard deviation: 4.4) for the proposed algorithm.

Multi-Mouse Quiz (MMQ) is a quiz application based on the Single Display Groupware (SDG)[1] concept through which several users can answer quizzes by sharing a computer to take the quiz in a classroom or any other learning environment. We conducted a practice, where we used the MMQ to support collaborative learning, which was combined with a museum visit. In the previous research, we found that the 3rd-grade children were able to operate the MMQ without any special assistance from the researchers, and that their use of the MMQ was characterized by high engagement[2]. In this study, we also conducted qualitative evaluation in the form of observation data and a free description of the questionnaire; we found that, compared to previous studies, which used MMQ with 6th-grade children, the 3rd-grade were more willing to use body language to express their emotions, and this tendency made the whole class more active. Furthermore, MMQ quiz learning inspired children with reflection perspectives to participate in the museum activity and activities in the computer room.

We propose a method to realize a subjective super-resolution on a high-speed LED display, which dynamically shows a set of four neighboring pixels on every LED pixel. We have experimentally confirmed the subjective super-resolution effect. This paper proposes a subjective super-resolution hypothesis in human visual system and reports simulation results with pseudo fixation eye movements.

In recent years, flexible liquid crystal displays (LCDs) have attracted much attention due to their thin and lightweight designs, as well as their ease of installation compared with conventional flat-panel LCDs. Most LCDs require backlight units (BLUs) to yield high-quality images. However, the luminance uniformity of flexible BLUs is drastically reduced in the curved state, as the light emitted from a typical BLU is mainly directed in the normal direction. Conventional BLUs do not provide the image quality of flexible BLUs due to uneven luminance distribution from the perspective of the observer. To overcome these issues, here we introduce a novel oblique-angled notched film for improved light distribution of a conventional BLU. The notched structure of the proposed film exhibits V-shaped split behavior during curvature. This elastic deformation is expected to compensate for the spatial luminance of the light emitted from the BLU. We investigated the design requirements for the proposed film based on geometrical calculations. The luminance distribution of a flexible BLU was then simulated using the proposed film, based on geometrical optics theory. The simulation results confirmed that the direction of travel of the light is controllable according to the total internal reflection of the proposed film, thus improving the luminance uniformity of the BLU in a convex-curved state. Based on these results, the proposed approach is expected to improve the luminance uniformity of convex-curved flexible LCDs.

Phase-sensitive amplification (PSA) has unique properties, such as the quantum-limited noise figure of 0 dB and the phase clamping effect. This study investigates PSA characteristics when a chirped pulse is incident. The signal gain, the output waveform, and the noise figure for an optical pulse having been chirped through chromatic dispersion or self-phase modulation before amplification are analyzed. The results indicate that the amplification properties for a chirped pulse are different from those of a non-chirped pulse, such that the signal gain is small, the waveform is distorted, and the noise figure is degraded.

A novel low power sense-amplifier based flip-flop (FF) is presented. By using a simplified SRAM based latch design and pass transistor logic (PTL) circuit scheme, the transistor-count of the FF design is greatly reduced as well as leakage power performance. The performance claims are verified through extensive post-layout simulations. Compared to the conventional sense-amplifier FF design, the proposed circuit achieves 19.6% leakage reduction. Moreover, the delay, and area are reduced by 21.8% and 31%, respectively. The performance edge becomes even better when the flip-flop is integrated in N-bit register file.

Distributed compressive video sensing (DCVS) has received considerable attention due to its potential in source-limited communication, e.g., wireless video sensor networks (WVSNs). Multi-hypothesis (MH) prediction, which treats the target block as a linear combination of hypotheses, is a state-of-the-art technique in DCVS. The common approach is under the supposition that blocks that are dissimilar from the target block are given lower weights than blocks that are more similar. This assumption can yield acceptable reconstruction quality, but it is not suitable for scenarios with more details. In this paper, based on the joint sparsity model (JSM), the authors present a Tikhonov-regularized MH prediction scheme in which the most similar block provides the similar common portion and the others blocks provide respective unique portions, differing from the common supposition. Specifically, a new scheme for generating hypotheses and a Euclidean distance-based metric for the regularized term are proposed. Compared with several state-of-the-art algorithms, the authors show the effectiveness of the proposed scheme when there are a limited number of hypotheses.

A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.

Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.

We propose a rootkit installation method inside a GPU kernel execution process which works through GPU context manipulation. In GPU-based applications such as deep learning computations and cryptographic operations, the proposed method uses the feature by which the execution flow of the GPU kernel obeys the GPU context information in GPU memory. The proposed method consists of two key ideas. The first is GPU code manipulation, which is able to hijack the execution flow of the original GPU kernel to execute an injected payload without affecting the original GPU computation result. The second is a self-page-table update execution during which the GPU kernel updates its page table to access any location in system memory. After the installation, the malicious payload is executed only in the GPU kernel, and any no evidence remains in system memory. Thus, it cannot be detected by conventional rootkit detection methods.

Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.