In this paper, we present a hardware implementation of a colorectal cancer diagnosis support system using a colorectal endoscopic video image on customizable embedded DSP. 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 computer-aided diagnosis (CAD) system for colorectal endoscopic images with Narrow Band Imaging (NBI) magnification with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification. Since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the proposed hardware system on a customizable embedded DSP, which can realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). Before implementing to the customizable embedded DSP, we profile and analyze processing cycles of the CAD system and optimize the bottlenecks. We show the effectiveness of the real-time diagnosis support system on the embedded system for endoscopic video images. The prototyped system demonstrated real-time processing on video frame rate (over 30fps @ 200MHz) and more than 90% accuracy.

This work introduces a measurement model to estimate the naturalness of vibrato. We carried out a subjective evaluation using a mean opinion score (MOS). We then built a measurement model by using two-dimensional Gaussian functions. We found that three Gaussian functions can measure naturalness with an error of 4.0%.

The plain bend in a pair of differential transmission lines causes a path difference, which leads to differential-to-common mode conversion due to the phase difference. This conversion can cause serious common-mode noise issues. We previously proposed a tightly coupled asymmetrically tapered bend to suppress forward differential-to-common mode conversion and derived the constraint conditions for high-density wiring. To provide sufficient suppression of mode conversion, however, the additional correction was required to make the effective path difference vanish. This paper proposes a practical and straightforward design methodology by using a very tightly coupled bend (decreasing the line width and the line separation of the tightly coupled bend). Full-wave simulations below 20GHz demonstrated that sufficient suppression of the forward differential-to-common mode conversion is successfully achieved as designed. Measurements showed that our design methodology is effective.

In this Letter, we propose a blind and robust multiple watermarking scheme using Contourlet transform and singular value decomposition (SVD). The host image is first decomposed by Contourlet transform. Singular values of Contourlet coefficient blocks are adopted to embed watermark information, and a fast calculation method is proposed to avoid the heavy computation of SVD. The watermark is embedded in both low and high frequency Contourlet coefficients to increase the robustness against various attacks. Moreover, the proposed scheme intrinsically exploits the characteristics of human visual system and thus can ensure the invisibility of the watermark. Simulation results show that the proposed scheme outperforms other related methods in terms of both robustness and execution time.

Spectral graph theory provides an algebraic approach to investigate the characteristics of weighted networks using the eigenvalues and eigenvectors of a matrix (e.g., normalized Laplacian matrix) that represents the structure of the network. However, it is difficult to accurately represent the structures of large-scale and complex networks (e.g., social network) as a matrix. This difficulty can be avoided if there is a universality, such that the eigenvalues are independent of the detailed structure in large-scale and complex network. In this paper, we clarify Wigner's Semicircle Law for weighted networks as such a universality. The law indicates that the eigenvalues of the normalized Laplacian matrix of weighted networks can be calculated from a few network statistics (the average degree, average link weight, and square average link weight) when the weighted networks satisfy a sufficient condition of the node degrees and the link weights.

In this letter, we study an interference scenario under a smart interferer which observes color channels and interferes with a visible light communication (VLC) network. We formulate the smart interference problem based on a Stackelberg game and propose an optimal response algorithm to overcome the interference by optimizing transmit power and sub-color channel allocation. The proposed optimization algorithm is composed with Lagrangian dual decomposition and non-linear fractional programming to have stability to get optimum points. Numerical results show that the utility by the proposed algorithm is increased over that of the algorithm based on the Nash game and the baseline schemes.

This paper proposes a dual linear-polarized open-ended waveguide subarray designed for use in phased array antennas. The proposed subarray is a one-dimensional linear array that consists of open-ended waveguide antenna elements and suspended stripline feed networks to realize vertical and horizontal polarizations. The antenna includes a novel suspended stripline-to-waveguide transition that combines double- and quad-ridge waveguides to minimize the size of the transition and enhance the port isolation. Metal posts are installed on the waveguide apertures to eliminate scan-blindness. Prototype subarrays are fabricated and tested in an array of 16 subarrays. The experimental tests and numerical simulations indicate that the prototype subarray offers a low reflection coefficient of less than -11.4dB, low cross-polarization of less than -26dB, and antenna efficiency above 69% in the frequency bandwidth of 14%.

We studied an acoustic anomaly detection system for equipments, where the outlier detection method based on recorded sounds is used. In a real environment, the SNR of the target sound against background noise is low, and there is the problem that it is necessary to catch slight changes in sound buried in noise. In this paper, we propose a system in which a sound source extraction process is provided at the preliminary stage of the outlier detection process. In the proposed system, nonnegative matrix factorization based on generalized Gaussian distribution (GGD-NMF) is used as a sound source extraction process. We evaluated the improvement of the anomaly detection performance in a low-SNR environment. In this experiment, SNR capable of detecting an anomaly was greatly improved by providing GGD-NMF for preprocessing.

Cognitive radio network (CRN) provides an effective way of improving efficiency and flexibility in spectrum usage. Due to the coexistence of secondary user (SU) and primary user (PU), managing interference is a critical issue to be addressed if we are to reap the full benefits. In this paper, we consider the problem of joint interference management and resource allocation in a multi-channel ad hoc CRN. We formulate the problem as an overlapping coalition formation game to maximize the sum rate of SU links while guaranteeing the quality of service (QoS) of PU links. In the game, each SU link can make an autonomous decision and is allowed to participate in one or more cooperative coalitions simultaneously to maximize its payoff. To obtain the solution of the formulated game, a distributed, self-organizing algorithm is proposed for performing coalition formation. We analyze the properties of the algorithm and show that SU links can cooperate to reach a final stable coalition structure. Compared with existing approaches, the proposed scheme achieves appreciable performance improvement in terms of the sum rate of SU links, which is demonstrated by simulation results.

The PCHS (Park-Chang-Hong-Seo) algorithm is a varied Karatsuba algorithm (KA) that utilizes a different splitting strategy with no overlap module. Such an algorithm has been applied to develop efficient hybrid GF(2m) multipliers for irreducible trinomials and pentanomials. However, compared with KA-based hybrid multipliers, these multipliers usually match space complexity but require more gates delay. In this paper, we proposed a new design of hybrid multiplier using PCHS algorithm for irreducible all-one polynomial. The proposed scheme skillfully utilizes redundant representation to combine and simplify the subexpressions computation, which result in a significant speedup of the implementation. As a main contribution, the proposed multiplier has exactly the same space and time complexities compared with the KA-based scheme. It is the first time to show that different splitting strategy for KA also can develop the same efficient multiplier.

This paper proposes a distributed observer on a sensor network, where communication on the network is randomly performed. This work is a natural extension of Kalman consensus filter approach to the cases involving random communication. In both bidirectional and unidirectional communication cases, gain conditions that guarantee improvement of estimation error convergence compared to the case with no communication are obtained. The obtained conditions are more practical than those of previous studies and give appropriate cooperative gains for a given communication probability. The effectiveness of the proposed method is confirmed by computer simulations.

Orthogonal frequency division multiplexing (OFDM) technique has been widely used in communication systems in pursuit of the most efficient utilization of spectrum. However, the increase of the number of orthogonal subcarriers will lead to the rise of the peak-to-average power ratio (PAPR) of the waveform, thus reducing the efficiency of the power amplifiers. In this letter we propose a phase-changed PAPR reduction technique based on windowing function architecture for OFDM systems. This technique is based on the idea of phase change, which makes the spectrum of output signal almost free of regrowth caused by peak clipping. It can reduce more than 28dBc adjacent channel power ratio (ACPR) compared with the traditional peak windowing clipping methods in situation that peak is maximally suppressed. This technique also has low algorithm complexity so it can be easily laid out on hardware. The proposed algorithm has been laid out on a low-cost field-programmable gate array (FPGA) to verify its effectiveness and feasibility. A 64-QAM modulated 20M LTE-A waveform is used for measurement, which has a sampling rate of 245.67M.

Sparse orthogonal matrices are proposed to improve the convergence property of expectation propagation (EP) for sparse signal recovery from compressed linear measurements subject to known dense and ill-conditioned multiplicative noise. As a typical problem, this letter addresses generalized spatial modulation (GSM) in over-loaded and spatially correlated multiple-input multiple-output (MIMO) systems. The proposed sparse orthogonal matrices are used in precoding and constructed efficiently via a generalization of the fast Walsh-Hadamard transform. Numerical simulations show that the proposed sparse orthogonal precoding improves the convergence property of EP in over-loaded GSM MIMO systems with known spatially correlated channel matrices.

Recently, new optimization machines based on non-silicon physical systems, such as quantum annealing machines, have been developed, and their commercialization has been started. These machines solve the problems by searching the state of the Ising spins, which minimizes the Ising Hamiltonian. Such a property of minimization of the Ising Hamiltonian can be applied to various combinatorial optimization problems. In this paper, we introduce the coherent Ising machine (CIM), which can solve the problems in a milli-second order, and has higher performance than the quantum annealing machines especially on the problems with dense mutual connections in the corresponding Ising model. We explain how a target problem can be implemented on the CIM, based on the optimization scheme using the mutually connected neural networks. We apply the CIM to traveling salesman problems as an example benchmark, and show experimental results of the real machine of the CIM. We also apply the CIM to several combinatorial optimization problems in wireless communication systems, such as channel assignment problems. The CIM's ultra-fast optimization may enable a real-time optimization of various communication systems even in a dynamic communication environment.

To reduce peak-to-average power ratio, we propose a method of choosing suitable vectors in a partial transmit sequence technique. Conventional approaches require that a suitable vector be selected from a large number of candidates. By contrast, our method does not include such a selecting procedure, and instead generates random vectors from the Gaussian distribution whose covariance matrix is a solution of a relaxed problem. The suitable vector is chosen from the random vectors. This yields lower peak-to-average power ratio than a conventional method.

The application of network function virtualization (NFV) and software-defined networking (SDN) to passive optical networks (PONs) is attracting attention for the deployment of cost-effective access network systems. This paper presents a novel architecture of a programmable finite state machine (P-FSM) as a hardware accelerator for protocol processing in an optical line terminal (OLT). The P-FSM is programmable hardware that manages various types of FSMs to enhance flexibility in OLTs and achieve wired-rate performance with a negligible increase in total chip area. The P-FSM is implemented using three key technologies: a specific architecture for state management of communications protocols to minimize the logic area, a memory distributed implementation to minimize the program memory, and a new branch operation to minimize the memory area and reduce processing time. Evaluation results show that the P-FSM can handle 10G-EPON/NG-PON2 communications protocols in the same architecture while achieving wired-rate performance. The increase in the total designed area is only 1.5% to 4.9% depending on the number of protocols supported compared to the area of a conventional communications SoC without flexibility. We also clarify that our architecture has the scalability needed to modify the number of FSMs and the maximum number of ONU connections according to the system scale.

Electrical connectors are the basic components of the electric system in automobiles, aircrafts and ships to realize the current and electrical signal transmission. In the aviation electrical system, the electrical connectors are indispensable supporting devices accessories, which play important roles in connecting electrical system, monitoring and controlling equipment, and provide a guarantee for the reliable transmission of electrical signals between the aviation equipment and system. Whether aviation electrical connectors work reliably directly affects the safety and reliability of the entire aircraft aviation system. The random vibration of aircraft caused by turbulence during flight is one of the main factors affecting the contact performance of the electrical connectors. In this paper, the contacts of the circular four-slot three-pin electrical connectors were chosen as the research specimens. The theoretical model of the contact force for contacts of electrical connectors was established. The test method for contact force measurement was determined. According to the test scheme, the detecting device for the contact force and contact resistance of the electrical connectors was designed, and the turbulence test of the electrical connectors was carried out. Through the analysis of the test data, the influence rule of the turbulence degree, flight speed and flight height on the contact force and contact resistance of the aviation electrical connectors was obtained.

The performance of online algorithms for the bin packing problem is usually measured by the asymptotic approximation ratio. However, even if an online algorithm is explicitly described, it is in general difficult to obtain the exact value of the asymptotic approximation ratio. In this paper we show a theorem that gives the exact value of the asymptotic approximation ratio in a closed form when the item sizes and the online algorithm satisfy some conditions. Moreover, we demonstrate that our theorem serves as a powerful tool for the design of online algorithms combined with mathematical optimization.

This paper deals with the problem of enumerating 3-edge-connected spanning subgraphs of an input plane graph. In 2018, Yamanaka et al. proposed two enumeration algorithms for such a problem. Their algorithm generates each 2-edge-connected spanning subgraph of a given plane graph with n vertices in O(n) time, and another one generates each k-edge-connected spanning subgraph of a general graph with m edges in O(mT) time, where T is the running time to check the k-edge connectivity of a graph. This paper focuses on the case of the 3-edge-connectivity in a plane graph. We give an algorithm which generates each 3-edge-connected spanning subgraph of the input plane graph in O(n2) time. This time complexity is the same as the algorithm by Yamanaka et al., but our algorithm is simpler than theirs.

Recently, image compression systems based on convolutional neural networks that use flexible nonlinear analysis and synthesis transformations have been developed to improve the restoration accuracy of decoded images. Although these methods that use objective metric such as peak signal-to-noise ratio and multi-scale structural similarity for optimization attain high objective results, such metric may not reflect human visual characteristics and thus degrade subjective image quality. A method using a framework called a generative adversarial network (GAN) has been reported as one of the methods aiming to improve the subjective image quality. It optimizes the distribution of restored images to be close to that of natural images; thus it suppresses visual artifacts such as blurring, ringing, and blocking. However, since methods of this type are optimized to focus on whether the restored image is subjectively natural or not, components that are not correlated with the original image are mixed into the restored image during the decoding process. Thus, even though the appearance looks natural, subjective similarity may be degraded. In this paper, we investigated why the conventional GAN-based compression techniques degrade subjective similarity, then tackled this problem by rethinking how to handle image generation in the GAN framework between image sources with different probability distributions. The paper describes a method to maximize mutual information between the coding features and the restored images. Experimental results show that the proposed mutual information amount is clearly correlated with subjective similarity and the method makes it possible to develop image compression systems with high subjective similarity.