Convolutional approximate message-passing (CAMP) is an efficient algorithm to solve linear inverse problems. CAMP aims to realize advantages of both approximate message-passing (AMP) and orthogonal/vector AMP. CAMP uses the same low-complexity matched-filter as AMP. To realize the asymptotic Gaussianity of estimation errors for all right-orthogonally invariant matrices, as guaranteed in orthogonal/vector AMP, the Onsager correction in AMP is replaced with a convolution of all preceding messages. CAMP was proved to be asymptotically Bayes-optimal if a state-evolution (SE) recursion converges to a fixed-point (FP) and if the FP is unique. However, no proofs for the convergence were provided. This paper presents a theoretical analysis for the convergence of the SE recursion. Gaussian signaling is assumed to linearize the SE recursion. A condition for the convergence is derived via a necessary and sufficient condition for which the linearized SE recursion has a unique stationary solution. The SE recursion is numerically verified to converge toward the Bayes-optimal solution if and only if the condition is satisfied. CAMP is compared to conjugate gradient (CG) for Gaussian signaling in terms of the convergence properties. CAMP is inferior to CG for matrices with a large condition number while they are comparable to each other for a small condition number. These results imply that CAMP has room for improvement in terms of the convergence properties.

This paper presents examination result of possibility for automatic unclear region detection in the CAD system for colorectal tumor with real time endoscopic video image. We confirmed that it is possible to realize the CAD system with navigation function of clear region which consists of unclear region detection by YOLO2 and classification by AlexNet and SVMs on customizable embedded DSP cores. Moreover, we confirmed the real time CAD system can be constructed by a low power ASIC using customizable embedded DSP cores.

In this letter, we study low-density parity-check (LDPC) codes for noisy channels with insertion and deletion (ID) errors. We first propose a design method of irregular LDPC codes for such channels, which can be used to simultaneously obtain degree distributions for different noise levels. We then show the asymptotic/finite-length decoding performances of designed codes and compare them with the symmetric information rates of cascaded ID-noisy channels. Moreover, we examine the relationship between decoding performance and a code structure of irregular LDPC codes.

In this paper, we propose an image adjustment method for multi-exposure images based on convolutional neural networks (CNNs). We call image regions without information due to saturation and object moving in multi-exposure images lacking areas in this paper. Lacking areas cause the ghosting artifact in fused images from sets of multi-exposure images by conventional fusion methods, which tackle the artifact. To avoid this problem, the proposed method estimates the information of lacking areas via adaptive inpainting. The proposed CNN consists of three networks, warp and refinement, detection, and inpainting networks. The second and third networks detect lacking areas and estimate their pixel values, respectively. In the experiments, it is observed that a simple fusion method with the proposed method outperforms state-of-the-art fusion methods in the peak signal-to-noise ratio. Moreover, the proposed method is applied for various fusion methods as pre-processing, and results show obviously reducing artifacts.

Recently, control theory using machine learning, which is useful for the control of unknown systems, has attracted significant attention. This study focuses on such a topic with optimal control problems for unknown nonlinear systems. Because optimal controllers are designed based on mathematical models of the systems, it is challenging to obtain models with insufficient knowledge of the systems. Kernel functions are promising for developing data-driven models with limited knowledge. However, the complex forms of such kernel-based models make it difficult to design the optimal controllers. The design corresponds to solving Hamilton-Jacobi (HJ) equations because their solutions provide optimal controllers. Therefore, the aim of this study is to derive certain kernel-based models for which the HJ equations are solved in an exact sense, which is an extended version of the authors' former work. The HJ equations are decomposed into tractable algebraic matrix equations and nonlinear functions. Solving the matrix equations enables us to obtain the optimal controllers of the model. A numerical simulation demonstrates that kernel-based models and controllers are successfully developed.

This letter proposes an adaptive beamforming switch algorithm for realistic massive multiple-input multiple-output (MIMO) systems through prototypes. It is analyzed and identified that a rigid single-mode beamforming regime is hard to maintain superior performance all the time due to no adaption to the inevitable channel variation in practice. In order to cope with this practical issue, the proposed systematic beamforming mechanism is investigated to enable dynamic selection between minimum mean-squared error and grid-of-beams beamforming algorithms, which improves system downlink performance, including throughput and block error rate. The significant performance benefits and realistic feasibility have been validated through the field tests in live networks and theoretical analyses. Meanwhile, the adaptive beamforming switch algorithm is applicable to both fourth and fifth generation time-division duplexing cellular communication system using massive-MIMO technology.

Recently, channel-aware steganography has been presented for high security. The corresponding selection-channel-aware (SCA) detecting algorithms have also been proposed for improving the detection performance. In this paper, we propose a novel detecting algorithm of JPEG steganography, where the embedding probability and block evaluation are integrated into the new probability. This probability can embody the change due to data embedding. We choose the same high-pass filters as maximum diversity cascade filter residual (MD-CFR) to obtain different image residuals and a weighted histogram method is used to extract detection features. Experimental results on detecting two typical steganographic methods show that the proposed method can improve the performance compared with the state-of-art methods.

In recent years, several reversible contrast-enhancement methods for color images using digital watermarking have been proposed. These methods can restore an original image from a contrast-enhanced image, in which the information required to recover the original image is embedded with other payloads. In these methods, the hue component after enhancement is similar to that of the original image. However, the saturation of the image after enhancement is significantly lower than that of the original image, and the obtained image exhibits a pale color tone. Herein, we propose a method for enhancing the contrast and saturation of color images and nearly preserving the hue component in a reversible manner. Our method integrates red, green, and blue histograms and preserves the median value of the integrated components. Consequently, the contrast and saturation improved, whereas the subjective image quality improved. In addition, we confirmed that the hue component of the enhanced image is similar to that of the original image. We also confirmed that the original image was perfectly restored from the enhanced image. Our method can contribute to the field of digital photography as a legal evidence. The required storage space for color images and issues pertaining to evidence management can be reduced considering our method enables the creation of color images before and after the enhancement of one image.

In order to expect the single event occurrence on highly integrated CMOS memory circuit, quantitative evaluation of charge sharing between memory cells is needed. In this study, charge sharing area induced by heavy ion incident is quantitatively calculated by using device-simulation-based method. The validity of this method is experimentally confirmed using the charged heavy ion accelerator.

Global software engineering education faces unique challenges to reflect as close as possible real-world distributed team development in various forms. The complex nature of planning, collaborating, and upholding partnerships present administrative difficulties on top of budgetary constrains. These lead to limited opportunities for students to gain international experiences and for researchers to propagate educational and practical insights. This paper presents an empirical view on three different course structures conducted by the same research and educational team over a four-year time span. The courses were managed in Japan and Germany, facing cultural challenges, time-zone differences, language barriers, heterogeneous and homogeneous team structures, amongst others. Three semesters were carried out before and one during the Covid-19 pandemic. Implications for a recent focus on online education for software engineering education and future directions are discussed. As administrational and institutional differences typically do not guarantee the same number of students on all sides, distributed teams can be 1. balanced, where the number of students on one side is less than double the other, 2. unbalanced, where the number of students on one side is significantly larger than double the other, or 3. one-sided, where one side lacks students altogether. An approach for each of these three course structures is presented and discussed. Empirical analyses and reoccurring patterns in global software engineering education are reported. In the most recent three global software engineering classes, students were surveyed at the beginning and the end of the semester. The questionnaires ask students to rank how impactful they perceive factors related to global software development such as cultural aspects, team structure, language, and interaction. Results of the shift in mean perception are compared and discussed for each of the three team structures.

In this letter, we propose a novel transferable sparse regression (TSR) method, for cross-database facial expression recognition (FER). In TSR, we firstly present a novel regression function to regress the data into a latent representation space instead of a strict binary label space. To further alleviate the influence of outliers and overfitting, we impose a row sparsity constraint on the regression term. And a pairwise relation term is introduced to guide the feature transfer learning. Secondly, we design a global graph to transfer knowledge, which can well preserve the cross-database manifold structure. Moreover, we introduce a low-rank constraint on the graph regularization term to uncover additional structural information. Finally, several experiments are conducted on three popular facial expression databases, and the results validate that the proposed TSR method is superior to other non-deep and deep transfer learning methods.

Software developers often use a web search engine to improve work efficiency. However, web search strategies (e.g., frequently changing web search keywords) may be different for each developer. In this study, we attempted to define a better web search strategy. Although many previous studies analyzed web search behavior in programming, they did not provide guidelines for web search strategies. To suggest guidelines for web search strategies, we asked 10 subjects four questions about programming which they had to solve, and analyzed their behavior. In the analysis, we focused on the subjects' task time and the web search metrics defined by us. Based on our experiment, to enhance the effectiveness of the search, we suggest (1) that one should not go through the next search result pages, (2) the number of keywords in queries should be suppressed, and (3) previously used keywords must be avoided when creating a new query.

We analyze the effect of window choice on the zero-padding method and corrected quadratically interpolated fast Fourier transform using a harmonic signal in noise at both high and low signal-to-noise ratios (SNRs) on a theoretical basis. Then, we validate the theoretical analysis using simulations. The theoretical analysis and simulation results using four traditional window functions show that the optimal window is determined depending on the SNR; the estimation errors are the smallest for the rectangular window at low SNR, the Hamming and Hanning windows at mid SNR, and the Blackman window at high SNR. In addition, we analyze the simulation results using the signal-to-noise floor ratio, which appears to be more effective than the conventional SNR in determining the optimal window.

The Canonical Polyadic Decomposition (CPD) is the tensor analog of the Singular Value Decomposition (SVD) for a matrix and has many data science applications including signal processing and machine learning. For the CPD, the Alternating Least Squares (ALS) algorithm has been used extensively. Although the ALS algorithm is simple, it is sensitive to a noise of a data tensor in the applications. In this paper, we propose a novel strategy to realize the noise suppression for the CPD. The proposed strategy is decomposed into two steps: (Step 1) denoising the given tensor and (Step 2) solving the exact CPD of the denoised tensor. Step 1 can be realized by solving a structured low-rank approximation with the Douglas-Rachford splitting algorithm and then Step 2 can be realized by solving the simultaneous diagonalization of a matrix tuple constructed by the denoised tensor with the DODO method. Numerical experiments show that the proposed algorithm works well even in typical cases where the ALS algorithm suffers from the so-called bottleneck/swamp effect.

We present a novel circularly polarized ring microstrip antenna and its design. The shorting pins discretely disposed on the inner edge of the ring microstrip antenna are introduced as a new degree of freedom for improving the resonance frequency control. The number and diameter of the shorting pins control the resonance frequency; the resonance frequency can be almost constant with respect to the inner/outer diameter ratio, which expands the use of the ring microstrip antenna. The dual-band antenna where the proposed antenna includes another ring microstrip antenna is designed and measured, and simulated results agree well with the measured one.

As an emerging technology, device-free localization (DFL) using wireless sensor networks to detect targets not carrying any electronic devices, has spawned extensive applications, such as security safeguards and smart homes or hospitals. Previous studies formulate DFL as a classification problem, but there are still some challenges in terms of accuracy and robustness. In this paper, we exploit a generalized thresholding algorithm with parameter p as a penalty function to solve inverse problems with sparsity constraints for DFL. The function applies less bias to the large coefficients and penalizes small coefficients by reducing the value of p. By taking the distinctive capability of the p thresholding function to measure sparsity, the proposed approach can achieve accurate and robust localization performance in challenging environments. Extensive experiments show that the algorithm outperforms current alternatives.

Active network monitoring based on Boolean network tomography is a promising technique to localize link failures instantly in transport networks. However, the required set of monitoring trails must be recomputed after each link failure has occurred to handle succeeding link failures. Existing heuristic methods cannot compute the required monitoring trails in a sufficiently short time when multiple-link failures must be localized in the whole of large-scale managed networks. This paper proposes an approach for computing the required monitoring trails within an allowable expected period specified beforehand. A random walk-based analysis estimates the number of monitoring trails to be computed in the proposed approach. The estimated number of monitoring trails are computed by a lightweight method that only guarantees partial localization within restricted areas. The lightweight method is repeatedly executed until a successful set of monitoring trails achieving unambiguous localization in the entire managed networks can be obtained. This paper demonstrates that the proposed approach can compute a small number of monitoring trails for localizing all independent dual-link failures in managed networks made up of thousands of links within a given expected short period.

Pruning is an effective technique to reduce computational complexity of Convolutional Neural Networks (CNNs) by removing redundant neurons (or weights). There are two types of pruning methods: holistic pruning and layer-wise pruning. The former selects the least important neuron from the entire model and prunes it. The latter conducts pruning layer by layer. Recently, it has turned out that some layer-wise methods are effective for reducing computational complexity of pruned models while preserving their accuracy. The difficulty of layer-wise pruning is how to adjust pruning ratio (the ratio of neurons to be pruned) in each layer. Because CNNs typically have lots of layers composed of lots of neurons, it is inefficient to tune pruning ratios by human hands. In this paper, we present Pruning Ratio Optimizer (PRO), a method that can be combined with layer-wise pruning methods for optimizing pruning ratios. The idea of PRO is to adjust pruning ratios based on how much pruning in each layer has an impact on the outputs in the final layer. In the experiments, we could verify the effectiveness of PRO.

This paper proposes an analytical method to design septum-type polarizers by assuming a polarizer as a series of four septum elements with a short ridge-waveguide approximation. We determine parameters of respective elements in such a manner that, at the center frequency, the reflection coefficient of the first element is equal to that of the second one, the reflection of the third one equals to that of the forth, and the electrical lengths of the first, second and third elements are 90 deg. We name this method the Short Ridge-waveguide Approximation Method (SRAM). We fabricated an X-band polarizer, which achieves a cross polarization discrimination (XPD) value of 40.7-64.1 dB over 8.0-8.4 GHz, without any numerical optimization.

In this paper, we present a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a CAD system for colorectal endoscopic images with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification on the embedded DSP core. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image.