The design and adjustment of the divergence in audio applications using nonnegative matrix factorization (NMF) is still open problem. In this study, to deal with this problem, we explore a representation of the divergence using neural networks (NNs). Instead of the divergence, our approach extends the multiplicative update algorithm (MUA), which estimates the NMF parameters, using NNs. The design of the extended MUA incorporates NNs, and the new algorithm is referred to as the deep MUA (DeMUA) for NMF. While the DeMUA represents the algorithm for the NMF, interestingly, the divergence is obtained from the incorporated NN. In addition, we propose theoretical guides to design the incorporated NN such that it can be interpreted as a divergence. By appropriately designing the NN, MUAs based on existing divergences with a single hyper-parameter can be represented by the DeMUA. To train the DeMUA, we applied it to audio denoising and supervised signal separation. Our experimental results show that the proposed architecture can learn the MUA and the divergences in sparse denoising and speech separation tasks and that the MUA based on generalized divergences with multiple parameters shows favorable performances on these tasks.

Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.

Visible light communication (VLC) and visible light ranging are applicable techniques for intelligent transportation systems (ITS). They use every unique light-emitting diode (LED) on roads for data transmission and range estimation. The simultaneous VLC and ranging can be applied to improve the performance of both. It is necessary to achieve rapid data rate and high-accuracy ranging when transmitting VLC data and estimating the range simultaneously. We use the signal modulation method of pulse-width modulation (PWM) to increase the data rate. However, when using PWM for VLC data transmission, images of the LED transmitters are captured at different luminance levels and are easily saturated, and LED saturation leads to inaccurate range estimation. In this paper, we establish a novel simultaneous visible light communication and ranging system for ITS using PWM. Here, we analyze the LED saturation problems and apply bicubic interpolation to solve the LED saturation problem and thus, improve the communication and ranging performance. Simultaneous communication and ranging are enabled using a stereo camera. Communication is realized using maximal-ratio combining (MRC) while ranging is achieved using phase-only correlation (POC) and sinc function approximation. Furthermore, we measured the performance of our proposed system using a field trial experiment. The results show that error-free performance can be achieved up to a communication distance of 55 m and the range estimation errors are below 0.5m within 60m.

A super-resolution algorithm is proposed to solve the problem of measuring the thin thickness of oil slick using compressed sensing theory. First, a mathematical model of a single pulse underwater ultrasonic echo is established. Then, the estimation model of the transmit time of flight (TOF) of ultrasonic echo within oil slick is given based on the sparsity of echo signals. At last, the super-resolution TOF value can be obtained by solving the sparse convex optimization problem. Simulations and experiments are conducted to validate the performance of the proposed method.

The estimation problem of structured clutter covariance matrix (CCM) in space-time adaptive processing (STAP) for airborne radar systems is studied in this letter. By employing the prior knowledge and the persymmetric covariance structure, a new estimation algorithm is proposed based on the whitening ability of the covariance matrix. The proposed algorithm is robust to prior knowledge of different accuracy, and can whiten the observed interference data to obtain the optimal solution. In addition, the extended factored approach (EFA) is used in the optimization for dimensionality reduction, which reduces the computational burden. Simulation results show that the proposed algorithm can effectively improve STAP performance even under the condition of some errors in prior knowledge.

Let f be a Boolean function in n variables. The Möbius transform and its converse of f can describe the transformation behaviors between the truth table of f and the coefficients of the monomials in the algebraic normal form representation of f. In this letter, we develop the Möbius transform and its converse into a more generalized form, which also includes the known result given by Reed in 1954. We hope that our new result can be used in the design of decoding schemes for linear codes and the cryptanalysis for symmetric cryptography. We also apply our new result to verify the basic idea of the cube attack in a very simple way, in which the cube attack is a powerful technique on the cryptanalysis for symmetric cryptography.

In this paper, a novel reconfigurable intelligent surface (RIS)-aided full-duplex (FD) cooperative non-orthogonal multiple access (CNOMA) network is investigated over Nakagami-m fading channels, where two RISs are employed to help the communication of paired users. To evaluate the potential benefits of our proposed scheme, we first derive the closed-form expressions of the outage probability. Then, we derive users' diversity orders according to the asymptotic approximation at high signal-to-noise-ratio (SNR). Simulation results validate our analysis and reveal that users' diversity orders are affected by their channel fading parameters, the self-interference of FD, and the number of RIS elements.

Fractional Frequency Reuse (FFR), which was introduced by 3GPP is considered the powerful technique to improve user performance. However, implementation of FFR is a challenge due to strong dependence between base stations (BSs) in terms of resource allocations. This paper studies a modified and flexible FFR scheme that allows all BSs works independently. The analytical and simulation results prove that the modified FFR scheme outperforms the conventional FFR.

Glaucoma is a common high-incidence eye disease. The detection of the optic cup and optic disc in fundus images is one of the important steps in the clinical diagnosis of glaucoma. However, the fundus images are generally intensity inhomogeneity, and complex organizational structure, and are disturbed by blood vessels and lesions. In order to extract the optic disc and optic cup regions more accurately, we propose a segmentation method of the optic disc and optic cup in fundus image based on distance regularized two-layer level with sparse shape prior constraint. The experimental results show that our method can segment the optic disc and optic cup region more accurately and obtain satisfactory results.