Pose estimation is a research hot spot in computer vision tasks and the key to computer perception of human activities. The core concept of human pose estimation involves describing the motion of the human body through major joint points. Large receptive fields and rich spatial information facilitate the keypoint localization task, and how to capture features on a larger scale and reintegrate them into the feature space is a challenge for pose estimation. To address this problem, we propose a multi-scale convergence network (MSCNet) with a large receptive field and rich spatial information. The structure of the MSCNet is based on an hourglass network that captures information at different scales to present a consistent understanding of the whole body. The multi-scale receptive field (MSRF) units provide a large receptive field to obtain rich contextual information, which is then selectively enhanced or suppressed by the Squeeze-Excitation (SE) attention mechanism to flexibly perform the pose estimation task. Experimental results show that MSCNet scores 73.1% AP on the COCO dataset, an 8.8% improvement compared to the mainstream CMUPose method. Compared to the advanced CPN, the MSCNet has 68.2% of the computational complexity and only 55.4% of the number of parameters.

Variable strength orthogonal array, as a special form of variable strength covering array, plays an important role in computer software testing and cryptography. In this paper, we study the construction of variable strength orthogonal arrays with strength two containing strength greater than two by Galois field and construct some variable strength orthogonal arrays with strength l containing strength greater than l by Fan-construction.

In this paper, we propose an interpretation method on amplitude intensities for response waveforms of backward transient scattered field components for both E- and H-polarizations by a 2-D coated metal cylinder. A time-domain (TD) asymptotic solution, which is referred to as a TD Fourier transform method (TD-FTM), is derived by applying the FTM to a backward transient scattered field expressed by an integral form. The TD-FTM is represented by a combination of a direct geometric optical ray (DGO) and a reflected GO (RGO) series. We use the TD-FTM to derive amplitude intensity ratios (AIRs) between adjacent backward transient scattered field components. By comparing the numerical values of the AIRs with those of the influence factors that compose the AIRs, major factor(s) can be identified, thereby allowing detailed interpretation method on the amplitude intensities for the response waveforms of backward transient scattered field components. The accuracy and practicality of the TD-FTM are evaluated by comparing it with three reference solutions. The effectiveness of an interpretation method on the amplitude intensities for response waveforms of backward transient scattered field components is revealed by identifying major factor(s) affecting the amplitude intensities.

An approach to dedicated computers is discussed in this study as a possibility for portable, low-cost, and low-power consumption high-performance computing technologies. Particularly, dataflow architecture dedicated computer of the finite integration technique (FIT) for 2D magnetostatic field simulation is considered for use in industrial applications. The dataflow architecture circuit of the BiCG-Stab matrix solver of the FIT matrix calculation is designed by the very high-speed integrated circuit hardware description language (VHDL). The operation of the dedicated computer's designed circuit is considered by VHDL logic circuit simulation.

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 addresses short-length sparse superposition codes (SSCs) over the additive white Gaussian noise channel. Damped approximate message-passing (AMP) is used to decode short SSCs with zero-mean independent and identically distributed Gaussian dictionaries. To design damping factors in AMP via deep learning, this paper constructs deep-unfolded damped AMP decoding networks. An annealing method for deep learning is proposed for designing nearly optimal damping factors with high probability. In annealing, damping factors are first optimized via deep learning in the low signal-to-noise ratio (SNR) regime. Then, the obtained damping factors are set to the initial values in stochastic gradient descent, which optimizes damping factors for slightly larger SNR. Repeating this annealing process designs damping factors in the high SNR regime. Numerical simulations show that annealing mitigates fluctuation in learned damping factors and outperforms exhaustive search based on an iteration-independent damping factor.

Crosstalk between lines plays an important role in the transmission of signal. Hence it is of great significance to establish the transmission lines model accurately to evaluate factors affecting crosstalk coupling between lines and to improve the anti-interference capability of the system. As twisted-pair line is widely used for its unique twist structure which improves the anti-interference performance of cables, this paper presents a method of constructing transmission lines model of the shielded twisted-pair line (STP) with two twisted pairs based on S-parameters. Firstly, the transmission lines model of STP with one twisted pair is established. The establishment of distributed capacitance matrix of this model depends on the dielectric constant of insulation layer that surrounds a conductor, but the dielectric constant is often unknown. In this respect, a method to obtain the distributed capacitance matrix based on the S-parameters of this model is proposed. Due to twisting, there is a great deal of variability between the distribution parameters along the length of the STP. As the spatial distribution of conductors in the cross-section of twisted-pair line vary along with the cable length, the distribution parameters matrices also change as they move. The cable is divided into several segments, and the transmission lines model of STP is obtained with the cascade of each segment model. For the STP with two twisted pairs, the crosstalk between pairs is analyzed based on the mixed mode S-parameters. Combined with the transmission lines model of STP with one twisted pair, that of STP with two twisted pairs is obtained. The terminal response voltage can be calculated from the transmission lines model and cable terminal conditions. The validity of the transmission lines model is verified by the consistency between the terminal responses calculated by the model and by the simulated. As the theoretical and simulation results are compatible, the modeling method for the STP with two twisted pairs can be used for the STP with more twisted pairs. In practical engineering application, S-parameters and mixed mode S-parameters can be obtained by testing. That means the transmission lines model of STP can be established based on the test results.

This paper first introduces the structure of a shipboard equipment control cabinet and the preliminary design of electromagnetic shielding, then introduces the principle of low-frequency magnetic field shielding, and uses silicon steel sheet to shield the low-frequency magnetic field of shipboard equipment control equipment. Based on ANSYS Maxwell simulation software, the low-frequency magnetic field radiation emission of the equipment's conducted harmonic peak frequency point is simulated. Finally, according to MIL-STD-461G test standard, the low-frequency magnetic field radiation emission test is carried out, which meets the limit requirements of the standard. The low-frequency magnetic field shielding technology has practical value. The low-frequency magnetic field radiation emission simulation based on ANSYS Maxwell proposed in this paper is a useful attempt for the quantitative simulation of radiation emission.

The Nyquist folding receiver (NYFR) is a novel analog-to-information architecture, which can achieve wideband receiving with a small amount of system resource. The NYFR uses a radio frequency (RF) non-uniform sampling to realize wideband receiving, and the practical RF non-uniform sample pulse train usually contains an aperture. Therefore, it is necessary to investigate the aperture impact on the NYFR output. In this letter, based on the NYFR output signal to noise ratio (SNR), the aperture impact on the NYFR is analyzed. Focusing on the aperture impact, the corresponding NYFR output signal power and noise power are given firstly. Then, the relation between the aperture and the output SNR is analyzed. In addition, the output SNR distribution containing the aperture is investigated. Finally, combing with a parameter estimation method, several simulations are conducted to prove the theoretical aperture impact.

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.

While the introduction of softwarelization technologies such as software-defined networking and network function virtualization transfers the main focus of network management from hardware to software, network operators still have to deal with various and numerous network and computing equipment located in network centers. Toward fully automated network management, we believe that a robotic approach will be essential, meaning that physical robots will handle network-facility management works on behalf of humans. This paper focuses on robotic assistance for on-site network maintenance works. Currently, for many network operators, some network maintenance works (e.g., hardware check, hardware installation/replacement, high-impact update of software, etc.) are outsourced to computing and network vendors. Attendance (witness work) at the on-site vendor's works is one of the major tasks of network operators. Network operators confirm the work progress for human error prevention and safety improvement. In order to reduce the burden of this, we propose three essential works of robots, namely delegated attendance at on-site meetings, progress check by periodical patrol, and remote monitoring, which support the various forms of attendance. The paper presents our implementation of enabling these forms of support, and reports the results of experiments conducted in a commercial network center.

This paper proposes an efficient scheduling algorithm for the layered decoding of block low-density parity-check (LDPC) codes. To efficiently configure check node-based scheduling groups, the proposed algorithm utilizes the base matrix of the block LDPC code for a block-by-block scheduling group configuration; i.e., the proposed algorithm generates a scheduling group of check nodes, satisfying the weight condition of the layered decoding, which is performed in block units (including several check nodes). Therefore, unlike the conventional scheduling algorithms performed in node units, the proposed algorithm can efficiently generate scheduling groups for layered decoding at low computational complexity and memory requirements. In addition, to accelerate the decoding convergence speed, check nodes are allocated in each scheduling group such that messages from check nodes up to the current group are delivered as evenly as possible to bit nodes. Simulation results confirm that the proposed algorithm can accelerate decoding convergence compared to other block-based scheduling algorithms for layered decoding of block LDPC codes.

In this paper, the author performed an electromagnetic field simulation of a typical bonding wire structure that connects a chip and a package, and evaluated the signal transmission characteristics (S-parameters). In addition, the inductance per unit length was extracted by comparing with the equivalent circuit of the distributed constant line. It turns out that the distributed constant line model is not sufficient because there are frequencies where chip-package resonance occurs. Below the resonance frequency, the conventional low-frequency approximation model was effective, and it was found that the inductance was about 1nH/mm.

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.

In recent years, High-Altitude Platform Station (HAPS) has become the most interesting topic for next generation mobile communication systems, because platforms such as Unmanned Aerial Vehicles (UAVs), balloons, airships can provide ultra-wide coverage, up to 200km in diameter, from altitudes of around 20 km. It also offers resiliency to damage caused by disasters and so ensures the stability and reliability of mobile communications. In order to further integrate HAPS with existing terrestrial mobile communication networks in providing mobile services to users, radio wave propagation models such as terrain, vegetation loss, human shielding loss, building entry loss, urban/suburban areas must be taken into consideration when designing HAPS-based cell configurations. This paper proposes a human body shielding propagation loss model that considers the basic signal attenuation by the human body at high elevation angles. It also analyzes the effect of changes in actual urban/suburban environments due to the arrival of multipath radio waves for HAPS communications in the frequency range of 0.7 to 3.3GHz. Measurements in actual urban/rural environments in Japan and actual stratospheric base station measurements in Kenya are carried out to confirm the validity of the proposed model. Since the measured results agree well with the results predicted by the proposed model, the model is good enough to provide estimates of human loss in various environments.

A light field (LF), which is represented as a set of dense, multi-view images, has been used in various 3D applications. To make LF acquisition more efficient, researchers have investigated compressive sensing methods by incorporating certain coding functionalities into a camera. In this paper, we focus on a challenging case called snapshot compressive LF imaging, in which an entire LF is reconstructed from only a single acquired image. To embed a large amount of LF information in a single image, we consider two promising methods based on rapid optical control during a single exposure: time-multiplexed coded aperture (TMCA) and coded focal stack (CFS), which were proposed individually in previous works. Both TMCA and CFS can be interpreted in a unified manner as extensions of the coded aperture (CA) and focal stack (FS) methods, respectively. By developing a unified algorithm pipeline for TMCA and CFS, based on deep neural networks, we evaluated their performance with respect to other possible imaging methods. We found that both TMCA and CFS can achieve better reconstruction quality than the other snapshot methods, and they also perform reasonably well compared to methods using multiple acquired images. To our knowledge, we are the first to present an overall discussion of TMCA and CFS and to compare and validate their effectiveness in the context of compressive LF imaging.

Logical matrices are binary matrices often used to represent relations. In the map folding problem, each folded state corresponds to a unique partial order on the set of squares and thus could be described with a logical matrix. The logical matrix representation is powerful than graphs or other common representations considering its association with category theory and homology theory and its generalizability to solve other computational problems. On the application level, such representations allow us to recognize map folding intuitively. For example, we can give a precise mathematical description of a folding process using logical matrices so as to solve problems like how to represent the up-and-down relations between all the layers according to their adjacency in a flat-folded state, how to check self-penetration, and how to deduce a folding process from a given order of squares that is supposed to represent a folded state of the map in a mathematical and natural manner. In this paper, we give solutions to these problems and analyze their computational complexity.

We introduce a new type of exponentiation algorithm in GF(2m) using Euclidean inversion. Our approach is based on the fact that Euclidean inversion cost much less logic gates than ordinary multiplication in GF(2m). By applying signed binary form of the exponent instead of classic binary form, the proposed algorithm can reduce the number of operations further compared with the classic algorithms.

An interpretation method of inversion phenomena is newly proposed for backward transient scattered field components for both E- and H-polarizations when an ultra-wideband (UWB) pulse wave radiated from a line source is incident on a two-dimensional metal cylinder covered with a lossless dielectric medium layer (coated metal cylinder). A time-domain (TD) asymptotic solution, which is referred to as a TD saddle point technique (TD-SPT), is derived by applying the SPT in evaluating a backward transient scattered field which is expressed by an integral form. The TD-SPT is represented by a combination of a direct geometric optical ray (DGO) and a reflected GO (RGO) series, thereby being able to extract and calculate any backward transient scattered field component from a response waveform. The TD-SPT is useful in understanding the response waveform of a backward transient scattered field by a coated metal cylinder because it can give us the peak value and arrival time of any field component, namely DGO and RGO components, and interpret analytically inversion phenomenon of any field component. The accuracy, validity, and practicality of the TD-SPT are clarified by comparing it with two kinds of reference solutions.

This study considered an extension of a sparse regularization method with scaling, especially in thresholding methods that are simple and typical examples of sparse modeling. In this study, in the setting of a non-parametric orthogonal regression problem, we developed and analyzed a thresholding method in which soft thresholding estimators are independently expanded by empirical scaling values. The scaling values have a common hyper-parameter that is an order of expansion of an ideal scaling value to achieve hard thresholding. We simply refer to this estimator as a scaled soft thresholding estimator. The scaled soft thresholding method is a bridge method between soft and hard thresholding methods. This new estimator is indeed consistent with an adaptive LASSO estimator in the orthogonal case; i.e., it is thus an another derivation of an adaptive LASSO estimator. It is a general method that includes soft thresholding and non-negative garrote as special cases. We subsequently derived the degree of freedom of the scaled soft thresholding in calculating the Stein's unbiased risk estimate. We found that it is decomposed into the degree of freedom of soft thresholding and the remainder term connecting to the hard thresholding. As the degree of freedom reflects the degree of over-fitting, this implies that the scaled soft thresholding has an another source of over-fitting in addition to the number of un-removed components. The theoretical result was verified by a simple numerical example. In this process, we also focused on the non-monotonicity in the above remainder term of the degree of freedom and found that, in a sparse and large sample setting, it is mainly caused by useless components that are not related to the target function.