The MPEG Immersive Video (MIV) standard for immersive video coding provides users with an immersive sense of 6 degrees of freedom (6DoF) of view position and orientation by efficiently compressing multiview video acquired from different positions in a limited 3D space. In the MIV reference software called Test Model for Immersive Video (TMIV), the number of pixels to be compressed and transmitted is reduced by removing inter-view redundancy. Therefore, the occupancy information that indicates whether each pixel is valid or invalid must also be transmitted to the decoder for viewport rendering. The occupancy information is embedded in a geometry atlas and transmitted to the decoder side. At this time, to prevent occupancy errors that may occur during the compression of the geometry atlas, a guard band is set in the depth dynamic range. Reducing this guard band can improve the rendering quality by allowing a wider dynamic range for depth representation. Therefore, in this paper, based on the analysis of occupancy error of the current TMIV, two methods of occupancy error correction which allow depth dynamic range extension in the case of computer-generated (CG) sequences are presented. The experimental results show that the proposed method gives an average 2.2% BD-rate bit saving for CG compared to the existing TMIV.

In this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At last, the haze-free image can be restored by inverting the atmospheric scattering model. Additionally, brightness adjustment is also performed for preventing the dehazing result too dark. Experimental results on hazy images reveal that the proposed method can effectively remove the haze and yield dehazing results with vivid color and high scene visibility.

The present paper proposes a dynamic spectrum access policy for multi-hop cognitive radio networks (CRNs), where the transmission in each hop suffers a delay waiting for the communication channel to become available. Recognizing the energy constraints, we assume that each secondary user (SU) in the network is powered by a battery with finite initial energy. We develop an energy-efficient policy for CRNs using the Markov decision process, which searches for spectrum opportunities without a common communication channel and assigns each sensor's decision to every time slot. We first consider a single-sensor scenario. Due to the intermittent activation of the sensor, achieving the optimal sensing schedule requires excessive complexity and is computationally intractable, owing to the fact that the state space of the Markov decision process evolves exponentially with time variance. In order to overcome this difficulty, we propose a state-reduced suboptimal policy by relaxing the constrained state space, i.e., assuming that the electrical energy of a node is infinite, because this state-reduced suboptimal approach can substantially reduce the complexity of decision-making for CRNs. We then analyze the performance of the proposed policy and compare it with the optimal solution. Furthermore, we verify the performance of this spectrum access policy under real conditions in which the electrical energy of a node is finite. The proposed spectrum access policy uses the dynamic information of each channel. We prove that this schedule is a good approximation for the true optimal schedule, which is impractical to obtain. According to our theoretical analysis, the proposed policy has less complexity but comparable performance. It is proved that when the operating time of the CRN is sufficiently long, the data reception rate on the sink node side will converge to the optimal rate with probability 1. Based on the results for the single-sensor scenario, the proposed schedule is extended to a multi-hop CRN. The proposed schedule can achieve synchronization between transmitter and receiver without relying on a common control channel, and also has near-optimal performance. The performance of the proposed spectrum access policy is confirmed through simulation.

Infrared and visible image fusion can combine the thermal radiation information and the textures to provide a high-quality fused image. In this letter, we propose a hybrid variational fusion model to achieve this end. Specifically, an ℓ0 term is adopted to preserve the highlighted targets with salient gradient variation in the infrared image, an ℓ1 term is used to suppress the noise in the fused image and an ℓ2 term is employed to keep the textures of the visible image. Experimental results demonstrate the superiority of the proposed variational model and our results have more sharpen textures with less noise.

A class-E power amplifier (PA) with novel dynamic biasing scheme is proposed to enhance power added efficiency (PAE) over a wide power range. A look-up table (LUT) adjusts input power and drain supply voltage simultaneously to keep switch mode condition of a power transistor and to optimize the PAE. Experimental results show that the class-E PA using the proposed scheme with harmonic suppression filter gives the PAE higher than 80% over 8.5,dB range with less than 40,dBc harmonic suppression.

Multi-modal semantic trajectory prediction has become a new challenge due to the rapid growth of multi-modal semantic trajectories with text message. Traditional RNN trajectory prediction methods have the following problems to process multi-modal semantic trajectory. The distribution of multi-modal trajectory samples shifts gradually with training. It leads to difficult convergency and long training time. Moreover, each modal feature shifts in different directions, which produces multiple distributions of dataset. To solve the above problems, MNERM (Mode Normalization Enhanced Recurrent Model) for multi-modal semantic trajectory is proposed. MNERM embeds multiple modal features together and combines the LSTM network to capture long-term dependency of trajectory. In addition, it designs Mode Normalization mechanism to normalize samples with multiple means and variances, and each distribution normalized falls into the action area of the activation function, so as to improve the prediction efficiency while improving greatly the training speed. Experiments on real dataset show that, compared with SERM, MNERM reduces the sensitivity of learning rate, improves the training speed by 9.120 times, increases HR@1 by 0.03, and reduces the ADE by 120 meters.

This paper proposes a method for estimating the number and locations of multiple targets distributed in the ocean. This is achieved by calculating the cross spectral density matrix (CSDM) generated from individual sound sources and applying them to a minimum variance distortionless response (MVDR), a nonlinear processor. The individual CSDMs are calculated by separating and extracting a Sequence CLEAN-based data vector from the CSDM of the data vector received from multiple targets. Numerical simulations demonstrate that the proposed method improves the MVDR performance in the case of multiple targets.

A microwave photonic mixer utilizing an InGaAs photoconductor for radio over fiber applications is proposed and fabricated. Static and dynamic characteristics of the fabricated microwave photonic mixer were investigated. The microwave photonic mixer showed an optical bandwidth of approximately 300 MHz and a uniform conversion loss characteristic for the electrical input frequency up to 20 GHz.

This letter presents a method for active noise cancelation (ANC) for headphone application. The method improves the performance of ANC by deriving a flexible independent component analysis (ICA) algorithm in a hybrid structure combining feedforward and feedback configurations with correlation-based wind detection. The effectiveness of the method is demonstrated through simulation.

Application of transverse magnetic field (TMF) is one of the most important ways to improve electric life and breaking capacity of DC relays. For better understanding of dependence of arc durations on transverse magnetic field, a series of experiments were conducted under an external transverse magnetic field with 12 pairs of AgSnO2 contacts in a DC 28 V 60 A/30 A/5 A circuit, respectively. By using permanent magnets, the transverse magnetic field was obtained and the magnetic flux density at the gap center was varied from 13 to 94 mT. The results show that breaking arc duration is decreased monotonically with increases in the magnetic flux density, but making arc duration isn't decreased monotonically with increases in the magnetic flux density. In addition, both the magnetic flux density and the breaking arc duration have threshold values Bl and Tbmin, respectively, which means the breaking arc duration is almost stable with the value Tbmin even if the magnetic flux density is higher than Bl.

Effective communication strategies with a properly designed source precoding matrix (PM) and a properly designed relay beamforming matrix (BM) can significantly improve the spectral efficiency of multiple-input multiple-output (MIMO) relaying broadcast channels (RBCs). In the present paper, we first propose a general communication scheme with non-regenerative relay that can overcome the half-duplex relay constraint of the general MIMO-RBC. Based on the proposed scheme, the robust source PM and relay BM are designed for imperfect channel state information at the transmitter (CSIT). In contrast to the conventional non-regenerative relaying communication scheme for the MIMO-RBC, in the proposed scheme, the source can send information continuously to the relay and users during two phases. Furthermore, in conjunction with the advanced precoding strategy, the proposed scheme can achieve a full-degree-of-freedom (DoF) MIMO-RBC with that each entry in the related channel matrix is considered to an i.i.d. complex Gaussian variable. The robust source PM and relay BM designs were investigated based on both throughput and fairness criteria with imperfect CSIT. However, solving the problems associated with throughput and fairness criteria for the robust source PM and relay BM designs is computationally intractable because these criteria are non-linear and non-convex. In order to address these difficulties, we first set up equivalent optimization problems based on a tight lower bound of the achievable rate. We then decompose the equivalent throughput problem into several decoupled subproblems with tractable solutions. Finally, we obtain the suboptimal solution for the throughput problem by an alternating optimization approach. We solve the fairness problem by introducing an adjusted algorithm according to the throughput problem. Finally, we demonstrate that, in both cases of throughput and fairness criteria, the proposed relaying communication scheme with precoding algorithms outperforms existing methods.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

There are several optimization techniques available for improving rendering speed of direct volume rendering. An acceleration method using the hierarchical min-max map requires little preprocessing and data storage while preserving image quality. However, this method introduces computational overhead because of unnecessary comparison and level shift between blocks. In this paper, we propose an efficient space-leaping method using optimal-sized blocks. To determine the size of blocks, our method partitions an image plane into several uniform grids and computes the minimum and the maximum depth values for each grid. We acquire optimal block sets suitable for individual rays from these values. Experimental results show that our method reduces rendering time when compared with the previous min-max octree method.

The emerging three degree of freedom plus (3DoF+) video provides more interactive and deep immersive visual experience. 3DoF+ video introduces motion parallax to 360 video providing omnidirectional view with limited changes of the view position. A large set of views are required to support such 3DoF+ visual experience, hence it is essential to compress a tremendous amount of 3DoF+ video. Recently, MPEG is developing a standard for efficient coding of 3DoF+ video that consists of multiple videos, and its test model named Test Model for Immersive Video (TMIV). In the TMIV, the redundancy between the input source views is removed as much as possible by selecting one or several basic views and predicting the remaining views from the basic views. Each unpredicted region is cropped to a bounding box called patch, and then a large number of patches are packed into atlases together with the selected basic views. As a result, multiple source views are converted into one or more atlas sequences to be compressed. In this letter, we present an improved clustering method using patch merging in the atlas construction in the TMIV. The proposed method achieves significant BD-rate reduction in terms of various end-to-end evaluation metrics in the experiment, and was adopted in TMIV6.0.

An adaptive hybrid acquisition system is presented. The proposed system combines an antenna diversity technique and the cell-averaging constant false alarm rate (CA-CFAR) algorithm to acquire a pseudo-noise (PN) sequence for code-division multiple-access (CDMA) systems. Since conventional acquisition systems have a fixed threshold value, they are unable to adapt to varying mobile communications environments, resulting in a high false-alarm rate or low detection probability. Accordingly, an antenna diversity technique and adaptively varying threshold scheme using the CA-CFAR algorithm are applied to improve the detection performance. Based on deriving the detection probability, false alarm rate, miss detection probability, and mean acquisition time in Rayleigh fading channels, the performance of the proposed system is compared to that of a conventional system with a fixed threshold.

A blind symbol synchronization scheme for MIMO and Multi-User OFDM systems is proposed, which utilizes short-time Fourier Transformation (STFT) to obtain 2D (time and frequency) timing information from the received signals. By analyzing the obtained 2D time-frequency amplitude spectrum, intervals where no inter-symbol interference (ISI) exists are checked out for symbol synchronization, and samples during these intervals are used to carry out carrier frequency offset estimation. Theoretical analysis and simulation results show that the proposed method is more robust and provides more accurate carrier frequency offset estimation than traditional schemes.

This paper presents a salient method to find an optimal bandwidth for low noise phase-locked loop (PLL) applications by analyzing a discrete-time model of charge-pump PLLs based on ring oscillator VCOs. The analysis shows that the timing jitter of the PLL system depends on the jitter in the ring oscillator and an accumulation factor which is inversely proportional to the bandwidth of the PLL. Further analysis shows that the timing jitter of the PLL system, however, proportionally depends on the bandwidth of the PLL when an external jitter source is applied. The analysis of the PLL timing jitter of both cases gives the clue to the optimal bandwidth design for low noise PLL applications, Simulation results using a C-language PLL model are compared with the theoretical predictions and show good agreement.

Image dehazing is of great significance in computer vision and other fields. The performance of dehazing mainly relies on the precise computation of transmission map. However, the computation of the existing transmission map still does not work well in the sky area and is easily influenced by noise. Hence, the dark channel prior (DCP) and luminance model are used to estimate the coarse transmission in this work, which can deal with the problem of transmission estimation in the sky area. Then a novel weighted variational regularization model is proposed to refine the transmission. Specifically, the proposed model can simultaneously refine the transmittance and restore clear images, yielding a haze-free image. More importantly, the proposed model can preserve the important image details and suppress image noise in the dehazing process. In addition, a new Gaussian Adaptive Weighted function is defined to smooth the contextual areas while preserving the depth discontinuity edges. Experiments on real-world and synthetic images illustrate that our method has a rival advantage with the state-of-art algorithms in different hazy environments.

An artificial transmission line with variable capacitors as its shunt elements, also known as a nonlinear transmission line, can be used to generate pulsed waveforms with short durations. In this work, the variable capacitors are implemented using ferroelectric materials. Analysis and experimental results of such a ferroelectric-based artificial transmission line are presented. The differential equation that describes the nonlinear transmission line is derived and solved. The analytical expression for the solitary waves propagating along the line is found. An artificial transmission line is fabricated using thin-film barium--strontium--titanate capacitors and commercially available chip inductors. The fabrication process of the ferroelectric-based artificial transmission line is described. On-wafer characterization of the line is performed. Measurement results show that, with proper dc bias and substantial input power, a sinusoidal input waveform turns into a bell-shaped pulse train at the output, demonstrating the pulse-shaping capability of the ferroelectric-based artificial transmission line.

In order to solve the influence of scale change on target tracking using the drone, a multi-scale target tracking algorithm is proposed which based on the color feature tracking algorithm. The algorithm realized adaptive scale tracking by training position and scale correlation filters. It can first obtain the target center position of next frame by computing the maximum of the response, where the position correlation filter is learned by the least squares classifier and the dimensionality reduction for color features is analyzed by principal component analysis. The scale correlation filter is obtained by color characteristics at 33 rectangular areas which is set by the scale factor around the central location and is reduced dimensions by orthogonal triangle decomposition. Finally, the location and size of the target are updated by the maximum of the response. By testing 13 challenging video sequences taken by the drone, the results show that the algorithm has adaptability to the changes in the target scale and its robustness along with many other performance indicators are both better than the most state-of-the-art methods in illumination Variation, fast motion, motion blur and other complex situations.