In a wireless ad hoc network (MANET), nodes can form a centerless, self-organizing, multi-hop dynamic network without any centralized control function, while hidden and exposed terminals seriously affect the network performance. Meanwhile, the wireless network node is evolving from single communication function to jointly providing a self precise positioning function, especially in indoor environments where GPS cannot work well. However, the existing medium access control (MAC) protocols only deal with collision control for data transmission without positioning function. In this paper, we propose a MAC protocol based on 802.11 standard to enable a node with self-positioning function, which is further used to solve hidden and exposed terminal problems. The location of a network node is obtained through exchanging of MAC frames jointly using a time of arrival (TOA) algorithm. Then, nodes use the location information to calculate the interference range, and judge whether they can transmit concurrently. Simulation shows that the positioning function of the proposed MAC protocol works well, and the corresponding MAC protocol can achieve higher throughput, lower average end-to-end delay and lower packet loss rate than that without self-localization function.

Fifth-generation (5G) mobile communication systems employ beamforming technology using massive multiple-input and multiple-output (MIMO) to improve the reception quality and spectrum efficiency within a cell. Meanwhile, coordinated beamforming among multiple base stations is an effective approach to improving the spectrum efficiency at the cell edges, in which massive MIMO is deployed at geographically distant base stations and beamforming control is conducted in a cooperative manner. Codebook-based beamforming is a method for realizing multi-cell coordinated beamforming, in which each base station selects one of multiple beams that are predefined in a codebook. In codebook-based beamforming, it is important to design an efficient codebook that takes into account the beam allocation and the number of beams. In general, the larger the number of beams defined in a codebook, the more finely tuned the beam control can be and a greater improvement in spectrum efficiency can be expected. However, it requires a huge signal processing to optimize the beam combinations with a large number of beams by coordinated beamforming. This paper proposes a novel codebook design that efficiently assigns beam directions and widths in a vertical plane. Computer simulations showed that the proposed codebook performs as well as the conventional method while requiring fewer beam combinations.

This paper presents a deep network based on morphological filters for Gaussian denoising. The morphological filters can be applied with only addition, max, and min functions and require few computational resources. Therefore, the proposed network is suitable for implementation using a small microprocessor. Each layer of the proposed network consists of a top-hat transform, which extracts small peaks and valleys of noise components from the input image. Noise components are iteratively reduced in each layer by subtracting the noise components from the input image. In this paper, the extensions of opening and closing are introduced as linear combinations of the morphological filters for the top-hat transform of this deep network. Multiplications are only required for the linear combination of the morphological filters in the proposed network. Because almost all parameters of the network are structuring elements of the morphological filters, the feature maps and parameters can be represented in short bit-length integer form, which is suitable for implementation with single instructions, multiple data (SIMD) instructions. Denoising examples show that the proposed network obtains denoising results comparable to those of BM3D [1] without linear convolutions and with approximately one tenth the number of parameters of a full-scale deep convolutional neural network [2]. Moreover, the computational time of the proposed method using SIMD instructions of a microprocessor is also presented.

A method to generate color palettes from images is proposed. Here, deep neural networks (DNN) are utilized in order to consider human perception. Two aspects of human perception are considered; one is attention to image, and the other is human preference for colors. This method first extracts N regions with dominant color categories from the image considering human attention. Here, N is the number of colors in a color palette. Then, the representative color is obtained from each region considering the human preference for color. Two deep neural-net systems are adopted here, one is for estimating the image area which attracts human attention, and the other is for estimating human preferable colors from image regions to obtain representative colors. The former is trained with target images obtained by an eye tracker, and the latter is trained with dataset of color selection by human. Objective and subjective evaluation is performed to show high performance of the proposed system compared with conventional methods.

The spectral envelope parameter is a significant speech parameter in the vocoder's quality. Recently, the Vector Quantized Variational AutoEncoder (VQ-VAE) is a state-of-the-art end-to-end quantization method based on the deep learning model. This paper proposed a new technique for improving the embedding space learning of VQ-VAE with the Generative Adversarial Network for quantizing the spectral envelope parameter, called VQ-VAE-EMGAN. In experiments, we designed the quantizer for the spectral envelope parameters of the WORLD vocoder extracted from the 16kHz speech waveform. As the results shown, the proposed technique reduced the Log Spectral Distortion (LSD) around 0.5dB and increased the PESQ by around 0.17 on average for four target bit operations compared to the conventional VQ-VAE.

Cow behavior monitoring is critical for understanding the current state of cow welfare and developing an effective planning strategy for pasture management, such as early detection of disease and estrus. One of the most powerful and cost-effective methods is a neural-network-based monitoring system that analyzes time series data from inertial sensors attached to cows. For this method, a significant challenge is to improve the quality and quantity of teaching data in the development of neural network models, which requires us to collect data that can cover various realistic conditions and assign labels to them. As a result, the cost of data collection is significantly high. This work proposes a data augmentation method to solve two major quality problems in the collection process of teaching data. One is the difficulty and randomicity of teaching data acquisition and the other is the sensor position changes during actual operation. The proposed method can computationally emulate different rotating states of the collar-type sensor device from the measured acceleration data. Furthermore, it generates data for actions that occur less frequently. The verification results showed significantly higher estimation performance with an average accuracy of over 98% for five main behaviors (feeding, walking, drinking, rumination, and resting) based on learning with long short-term memory (LSTM) network. Compared with the estimation performance without data augmentation, which was insufficient with a minimum of 60.48%, the recognition rate was improved by 2.52-37.05pt for various behaviors. In addition, comparison of different rotation intervals was investigated and a 30-degree increment was selected based on the accuracy performances analysis. In conclusion, the proposed data expansion method can improve the accuracy in cow behavior estimation by a neural network model. Moreover, it contributes to a significant reduction of the teaching data collection cost for machine learning and opens many opportunities for new research.

The narrow bandwidth limitation of 300-3400Hz on the public switching telephone network results in speech quality deterioration. In this paper, we propose an artificial bandwidth extension approach that reconstructs the missing lower bandwidth of 50-300Hz using sinusoidal synthesis based on the first formant location. Sinusoidal synthesis generates sinusoidal waves with a harmonic structure. The proposed method detects the fundamental frequency using an autocorrelation method based on YIN algorithm, where a threshold processing avoids the false fundamental frequency detection on unvoiced sounds. The amplitude of the sinusoidal waves is calculated in the time domain from the weighted energy of 300-600Hz. In this case, since the first formant location corresponds to the first peak of the spectral envelope, we reconstruct the harmonic structure to avoid attenuating and overemphasizing by increasing the weight when the first formant location is lower, and vice versa. Consequently, the subjective and objective evaluations show that the proposed method reduces the speech quality difference between the original speech signal and the bandwidth extended speech signal.

Estimating the spatial parameters of the signals by using the effective data of a single snapshot is essential in the field of reconnaissance and confrontation. Major drawback of existing algorithms is that its constructed covariance matrix has a great degree of rank loss. The performance of existing algorithms gets degraded with low signal-to-noise ratio. In this paper, a three-parallel linear array based algorithm is proposed to achieve two-dimensional direction of arrival estimates in a single snapshot scenario. The key points of the proposed algorithm are: 1) construct three pseudo matrices with full rank and no rank loss by using the single snapshot data from the received signal model; 2) by using the rotation relation between pseudo matrices, the matched 2D-DOA is obtained with an efficient parameter matching method. Main objective of this work is on improving the angle estimation accuracy and reducing the loss of degree of freedom in single snapshot 2D-DOA estimation.

To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining increases. We propose a dynamical model of decision-making for miners using an evolutionary game approach and analyze the stability of equilibrium points of the proposed model. The proposed model is described by the 1st-order differential equation. So, it is simple but its theoretical analysis gives an insight into the characteristics of the decision-making. Through the analysis of the equilibrium points, we show the transcritical bifurcations and hysteresis phenomena of the equilibrium points. We also design a controller that determines the mining reward based on the number of participating miners to stabilize the state where all miners participate in the mining. Numerical simulation shows that there is a trade-off in the choice of the design parameters.

Linear codes with a few-weight have important applications in combinatorial design, strongly regular graphs and cryptography. In this paper, we first construct a class of Boolean functions with at most five-valued Walsh spectra, and determine their spectrum distribution. Then, we derive two classes of linear codes with at most six-weight from the new functions. Meanwhile, the length, dimension and weight distributions of the codes are obtained. Results show that both of the new codes are minimal and among them, one is wide minimal code and the other is a narrow minimal code and thus can be used to design secret sharing scheme with good access structures. Finally, some Magma programs are used to verify the correctness of our results.

In this paper, an automatic retransmission request (ARQ) scheme for IEEE 802.11ac is presented, which can solve the problem of severe packet loss and greatly improve the performance in error-prone environments. The proposed solution only requires to be deployed on the sender and is compatible with the 802.11 protocol. The algorithm utilizes the basic strategy of sliding retransmission and then adds the method of copying frames. The media access control (MAC) protocol data unit (MPDU) lost in the transmission and the newly added data frame brought by the sliding window change are replicated. The scheme retransmits the duplicated aggregated packet and further improves the throughput by increasing the probability of successful transmission of sub-frames. Besides, we also establish a mathematical model to analyze the performance of the proposed scheme. We introduce the concept of average aggregated sub-frames and express the sliding retransmission strategy as the aggregated transmission of average aggregated sub-frames, thereby simplifying the model and effectively analyzing the theoretical throughput of the proposed algorithm. The simulation results of Network simulator 3 (NS-3) simulation results demonstrate that the performance of the proposed algorithm is better than the traditional sliding retransmission ARQ algorithm in error-prone channels with a higher physical layer rate.

This paper addresses single image super-resolution (SR) based on convolutional neural networks (CNNs). It is known that recovery of high-frequency components in output SR images of CNNs learned by the least square errors or least absolute errors is insufficient. To generate realistic high-frequency components, SR methods using generative adversarial networks (GANs), composed of one generator and one discriminator, are developed. However, when the generator tries to induce the discriminator's misjudgment, not only realistic high-frequency components but also some artifacts are generated, and objective indices such as PSNR decrease. To reduce the artifacts in the GAN-based SR methods, we consider the set of all SR images whose square errors between downscaling results and the input image are within a certain range, and propose to apply the metric projection onto this consistent set in the output layers of the generators. The proposed technique guarantees the consistency between output SR images and input images, and the generators with the proposed projection can generate high-frequency components with few artifacts while keeping low-frequency ones as appropriate for the known noise level. Numerical experiments show that the proposed technique reduces artifacts included in the original SR images of a GAN-based SR method while generating realistic high-frequency components with better PSNR values in both noise-free and noisy situations. Since the proposed technique can be integrated into various generators if the downscaling process is known, we can give the consistency to existing methods with the input images without degrading other SR performance.

The electromagnetic environment is increasingly complex and changeable, and radar needs to meet the execution requirements of various tasks. Modern radars should improve their intelligence level and have the ability to learn independently in dynamic countermeasures. It can make the radar countermeasure strategy change from the traditional fixed anti-interference strategy to dynamically and independently implementing an efficient anti-interference strategy. Aiming at the performance optimization of target tracking in the scene where multiple signals coexist, we propose a countermeasure method of cognitive radar based on a deep Q-learning network. In this paper, we analyze the tracking performance of this method and the Markov Decision Process under the triangular frequency sweeping interference, respectively. The simulation results show that reinforcement learning has substantial autonomy and adaptability for solving such problems.

Near-field beamforming has played an important role in many scenarios such as radar imaging and acoustic detection. In this paper, the near-field beamforming is implemented in the time modulated array with the harmonic. The beam pattern with a low sidelobe level in precise position is achieved by controlling the switching sequence in time modulated cross array. Numerical results verify the correctness of the proposed method.

High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.

In this letter, we discuss the ambiguity function of interleaved sequences. Furthermore, using the Guassian sum and choosing binary m-sequences as column sequences, we investigate the property of a binary sequence set given by Zhou, Tang, Gong (IEEE Trans. Inf. Theory, 54(9), 2008), which has low ambiguity property in a large region. Those sequences could be used in radar systems.

The energy efficiency of intelligent reflecting surface (IRS) enabled internet of things (IoT) networks is studied in this letter. The energy efficiency is mathematically expressed, respectively, as the number of reflecting elements and the spectral efficiency of the network and is shown to scale in the logarithm of the reflecting elements number in the high regime of transmit power from source node. Furthermore, it is revealed that the energy efficiency scales linearly over the spectral efficiency in the high regime of transmit power, in contrast to conventional studies on energy and spectral efficiency trade-offs in the non-IRS wireless IoT networks. Numerical simulations are carried out to verify the derived results for the IRS enabled IoT networks.

Reducing the power consumption of capsule endoscopy is essential for its further development. We introduce K-SVD dictionary learning to design a dictionary for sparse coding, and improve reconstruction accuracy of capsule endoscopic images captured using compressed sensing. At a compression ratio of 20%, the proposed method improves image quality by approximately 4.4 dB for the peak signal-to-noise ratio.

In recent years, compared with the traditional face super-resolution (SR) algorithm, the face SR based on deep neural network has shown strong performance. Among these methods, attention mechanism has been widely used in face SR because of its strong feature expression ability. However, the existing attention-based face SR methods can not fully mine the missing pixel information of low-resolution (LR) face images (structural prior). And they only consider a single attention mechanism to take advantage of the structure of the face. The use of multi-attention could help to enhance feature representation. In order to solve this problem, we first propose a new pixel attention mechanism, which can recover the structural details of lost pixels. Then, we design an attention fusion module to better integrate the different characteristics of triple attention. Experimental results on FFHQ data sets show that this method is superior to the existing face SR methods based on deep neural network.