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.

In this paper, we investigate the Access Point (AP) selection problem in Cell-Free Massive multiple-input multiple-output (MIMO) system. Firstly, we add a connecting coefficient to the uplink data transmission model. Then, the problem of AP selection is formulated as a discrete combinatorial optimization problem which can be dealt with by the particle swarm algorithm. However, when the number of optimization variables is large, the search efficiency of the traditional particle swarm algorithm will be significantly reduced. Then, we propose an ‘user-centric’ cooperative coevolution scheme which includes the proposed probability-based particle evolution strategy and random-sampling-based particle evaluation mechanism to deal with the search efficiency problem. Simulation results show that proposed algorithm has better performance than other existing algorithms.

Let us consider a regular expression r of length m and a text string T of length n over an alphabet Σ. Then, the RE minimal substring search problem is to find all minimal substrings of T matching r. Yamamoto proposed O(mn) time and O(m) space algorithm using a Thompson automaton. In this paper, we improve Yamamoto's algorithm by introducing parallelism. The proposed algorithm runs in O(mn) time in the worst case and in O(mn/p) time in the best case, where p denotes the number of processors. Besides, we show a parameter related to the parallel time of the proposed algorithm. We evaluate the algorithm experimentally.

Proof of Work (PoW), which is a consensus algorithm for blockchain, entails a large number of meaningless hash calculations and wastage of electric power and computational resources. In 2021, it is estimated that the PoW of Bitcoin consumes as much electricity as Pakistan's annual power consumption (91TWh). This is a serious problem against sustainable development goals. To solve this problem, this study proposes Meaningful-PoW (mPoW), which involves a meaningful calculation, namely the application of a genetic algorithm (GA) to PoW. Specifically, by using the intermediate values that are periodically generated through GA calculations as an input to the Hashcash used in Bitcoin, it is possible to make this scheme a meaningful calculation (GA optimization problem) while maintaining the properties required for PoW. Furthermore, by applying a device-binding technology, mPoW can be ASIC resistant without the requirement of a large memory. Thus, we show that mPoW can reduce the excessive consumption of both power and computational resources.

The damage cost caused by malware has been increasing in the world. Usually, malwares are packed so that it is not detected. It is a hard task even for professional malware analysts to identify the packers especially when the malwares are multi-layer packed. In this letter, we propose a method to identify the packers for multi-layer packed malwares by using k-nearest neighbor algorithm with entropy-analysis for the malwares.

This paper presents a novel optimization-based decoding algorithm for LDPC codes. The proposed decoding algorithm is based on a proximal gradient method for solving an approximate maximum a posteriori (MAP) decoding problem. The key idea of the proposed algorithm is the use of a code-constraint polynomial to penalize a vector far from a codeword as a regularizer in the approximate MAP objective function. A code proximal operator is naturally derived from a code-constraint polynomial. The proposed algorithm, called proximal decoding, can be described by a simple recursive formula consisting of the gradient descent step for a negative log-likelihood function corresponding to the channel conditional probability density function and the code proximal operation regarding the code-constraint polynomial. Proximal decoding is experimentally shown to be applicable to several non-trivial channel models such as LDPC-coded massive MIMO channels, correlated Gaussian noise channels, and nonlinear vector channels. In particular, in MIMO channels, proximal decoding outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding. The simple optimization-based formulation of proximal decoding allows a way for developing novel signal processing algorithms involving LDPC codes.

This paper deals with a state-space approach for adaptive second-order IIR notch digital filters with constrained poles and zeros. A simplified iterative algorithm is derived from the gradient-descent method to minimize the mean-squared output of an adaptive notch digital filter. Then, stability and parameter-estimation bias are analyzed for the simplified iterative algorithm. A numerical example is presented to demonstrate the validity and effectiveness of the proposed adaptive state-space notch digital filter and parameter-estimation bias analysis.

This paper analyzes a blockchain network forming a directed acyclic graph (DAG), called a DAG-type blockchain, from the viewpoint of graph algorithm theory. To use a DAG-type blockchain, NP-hard graph optimization problems on the DAG are required to be solved. Although various problems for undirected and directed graphs can be efficiently solved by using the notions of graph parameters, these currently known parameters are meaningless for DAGs, which implies that it is hopeless to design efficient algorithms based on the parameters for such problems. In this work, we propose a novel graph parameter for directed graphs called a DAG-pathwidth, which represents the closeness to a directed path. This is an extension of the pathwidth, a well-known graph parameter for undirected graphs. We analyze the features of the DAG-pathwidth and prove that computing the DAG-pathwidth of a DAG (directed graph in general) is NP-complete. Finally, we propose an efficient algorithm for a variant of the maximum k-independent set problem for the DAG-type blockchain when the DAG-pathwidth of the input graph is small.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.

Recently, most signal processing algorithms have been developed with floating-point arithmetic, while the fixed-point arithmetic is more popular with most commercial devices and low-power real-time applications which are implemented on embedded/ASIC/FPGA systems. Therefore, the optimal Floating-point to Fixed-point Conversion (FFC) methodology is a promising solution. In this paper, we propose the FFC consisting of signal grouping technique and simulation-based word length optimization. In order to evaluate the performance of the proposed technique, simulations are carried out and hardware co-simulation on Field Programmable Gate Arrays (FPGAs) platform have been applied to complex Digital Signal Processing (DSP) algorithms: Linear Time Invariant (LTI) systems, multi-mode Fast Fourier Transform (FFT) circuit for IEEE 802.11 ax WLAN Devices and the calibration algorithm of gain and clock skew in Time-Interleaved ADC (TI-ADC) using Adaptive Noise Canceller (ANC). The results show that the proposed technique can reduce the hardware cost about 30% while being able to maintain its speed and reliability.

This paper introduces the working principle of continuous wave (CW) illuminator and selects the test space by developing the wave impedance selection algorithm for the CW illuminator. For the vertical polarization and the horizontal polarization of CW illuminator, the law of wave impedance distribution after loading is analyzed and the influence of loading distribution on test space selection is studied. The selection principle of wave impedance based on incident field or total field at the monitoring point is analyzed.

The Euclidean projection operation is the most complex and time-consuming of the alternating direction method of multipliers (ADMM) decoding algorithms, resulting in a large number of resources when deployed on hardware platforms. We propose a simplified line segment projection algorithm (SLSA) and present the hardware design and the quantization scheme of the SLSA. In simulation results, the proposed SLSA module has a better performance than the original algorithm with the same fixed bitwidths due to the centrosymmetric structure of SLSA. Furthermore, the proposed SLSA module with a simpler structure without hypercube projection can reduce time consuming by up to 72.2% and reduce hardware resource usage by more than 87% compared to other Euclidean projection modules in the experiments.

In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to contending neighboring access points (APs) is proposed. The proposed framework enables APs to evaluate observed rewards compositionally for contending APs, allowing both robustness against reward fluctuation due to neighboring APs' varying channels and assessment of even unexplored channels. To realize this framework, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation among APs under contention and forms the basis for expressing reward functions in disentangled form, that is, a linear combination of parameters associated with neighboring APs under contention). This allows the CMAB to be leveraged with a joint linear upper confidence bound (JLinUCB) exploration and to delve into the effectiveness of the proposed framework. Moreover, we address the problem of non-convergence — the channel exploration cycle — by proposing a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round. Numerical evaluations confirm that the proposed method allows APs to assess the channel quality robustly against reward fluctuations by CDFE and achieves better convergence properties by P-JLinUCB.

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop an optimization algorithm for minimizing the empirical risk under the sign constraints. The algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd+d2) computational cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an ε-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.

Inspired by the framework of algorithm unrolling, we propose a scalable network architecture that computes layer patterns for light field displays, enabling control of the trade-off between the display quality and the computational cost on a single pre-trained network.

Multibeam massive multiple-input multiple-output (MIMO) configuration has been proposed that selects high-power beams in an analog part and uses a blind algorithm, such as the constant-modulus algorithm (CMA), in the digital part. The CMA does not require channel state information. However, when least-squares CMA (LS-CMA) is applied to a quadrature amplitude modulation signal whose amplitude changes, the interference cancellation effect decreases as the modulation order increases. In this paper, a variable-step-size-based CMA (VS-CMA), which modifies the step size of the steepest-descent CMA, is proposed as a blind adaptive algorithm to replace LS-CMA. The basic performance of VS-CMA, its success in cancelling interference, and its effectiveness in multibeam massive MIMO transmission are verified via simulation and compared with other blind algorithms such as independent component analysis, particularly when the data smoothing size is small.

In this paper, we propose a non-orthogonal multiple access with adaptive resource allocation. The proposed non-orthogonal multiple access assigns multiple frequency resources for each device to send packets. Even if the number of devices is more than that of the available frequency resources, the proposed non-orthogonal access allows all the devices to transmit their packets simultaneously for high capacity massive machine-type communications (mMTC). Furthermore, this paper proposes adaptive resource allocation algorithms based on factor graphs that adaptively allocate the frequency resources to the devices for improvement of the transmission performances. This paper proposes two allocation algorithms for the proposed non-orthogonal multiple access. This paper shows that the proposed non-orthogonal multiple access achieves superior transmission performance when the number of the devices is 50% greater than the amount of the resource, i.e., the overloading ratio of 1.5, even without the adaptive resource allocation. The adaptive resource allocation enables the proposed non-orthogonal access to attain a gain of about 5dB at the BER of 10-4.

A detailed investigation of DC and RF performance of AlGaN/GaN HEMT on 3C-SiC/low resistive silicon (LR-Si) substrate by introducing a thick GaN layer is reported in this paper. The hetero-epitaxial growth is achieved by metal organic chemical vapor deposition (MOCVD) on a commercially prepared 6-inch LR-Si substrate via a 3C-SiC intermediate layer. The reported HEMT exhibited very low RF loss and thermally stable amplifier characteristics with the introduction of a thick GaN layer. The temperature-dependent small-signal and large-signal characteristics verified the effectiveness of the thick GaN layer on LR-Si, especially in reduction of RF loss even at high temperatures. In summary, a high potential of the reported device is confirmed for microwave applications.

A grid drawing of a plane graph G is a drawing of G on the plane so that all vertices of G are put on plane grid points and all edges are drawn as straight line segments between their endpoints without any edge-intersection. In this paper we give a linear-time algorithm to find a grid drawing of any given 5-connected plane graph G with five or more vertices on the outer face. The size of the drawing satisfies W + H≤n - 2, where n is the number of vertices in G, W is the width and H is the height of the grid drawing.