This paper presents a meta-structured circular polarized array antenna with wide scan angle. In order to widen the scanning angle of array antennas, this paper investigates unit antenna beamwidth and the coupling effects between array elements, both of which directly affect the steering performance. As a result, the optimal array distance, the mode configuration, and the antenna structure are elucidated. By using the features of the miniaturized mu-zero resonance (MZR) antenna, it is possible to design the antenna at optimum array distance for wide beamwidth. In addition, by modifying via position and gap configuration of the antenna, it is possible to optimize the mode configuration for optimal isolation. Finally, the 3dB steerable angle of 66° is successfully demonstrated using a 1x8 MZR CP antenna array without any additional decoupling structure. The measured beam patterns at a scan angle of 0°, 22°, 44°, and 66°agree well with the simulated beam patterns.

For general multiple-input multiple-output (MIMO) interference networks, determining the feasibility conditions of interference alignment (IA) to achieve the maximum degree of freedom (DoF), is tantamount to accessing the maximum spatial resource of MIMO systems. In this paper, from the view of antenna configuration, we first explore the IA feasibility in the K-user MIMO interference channel (IC), G-cell MIMO interference broadcast channel (IBC) and interference multiple access channel (IMAC). We first give the concept of the equalized antenna, and all antenna configurations are divided into two categories, equalized antennas and non-equalized ones. The feasibility conditions of IA system with equalized antennas are derived, and the feasible and infeasible regions are provided. Furthermore, we study the correlations among IC, IBC and IMAC. Interestingly, the G-cell MIMO IBC and IMAC are two special ICs, and a systemic work on IA feasibility for these three interference channels is provided.

Usowan is one of Nikoli's pencil puzzles. We study the computational complexity of Usowan puzzles. It is shown that deciding whether a given instance of the Usowan puzzle has a solution is NP-complete.

We investigate linear complementary dual (LCD) rank-metric codes in this paper. We construct a class of LCD generalized Gabidulin codes by a self-dual basis of an extension field over the base field. Moreover, a class of LCD MRD codes, which are obtained by Cartesian products of a generalized Gabidulin code, is constructed.

In this letter, we present a novel single-precision floating-point multiply-accumulator (FNA-MAC) to achieve lower hardware resource, reduced computing latency and improved computing accuracy for continuous dot product operations. By further fusing the normalization and alignment in the traditional FMA algorithm, the proposed architecture eliminates the first N-1 normalization and rounding operations for an N-point dot product, and preserves the precision of interim results in a significant bit size that is twice of that in the traditional methods. The normalization and rounding of the final result is processed at the cost of consuming an additional multiply-add operation. The simulation results show that the improvement in computational accuracy is significant. Meanwhile, when comparing to a recently published FMA design, the proposed FNA-MAC can reduce the slice look-up table/flip-flop resource and computing latency by a fact of 18%, 33.3%, respectively.

Recommender systems (RS) exploit user ratings on items and side information to make personalized recommendations. In order to recommend the right products to users, RS must accurately model the implicit preferences of each user and the properties of each product. In reality, both user preferences and item properties are changing dynamically over time, so treating the historical decisions of a user or the received comments of an item as static is inappropriate. Besides, the review text accompanied with a rating score can help us to understand why a user likes or dislikes an item, so temporal dynamics and text information in reviews are important side information for recommender systems. Moreover, compared with the large number of available items, the number of items a user can buy is very limited, which is called the sparsity problem. In order to solve this problem, utilizing item correlation provides a promising solution. Although famous methods like TimeSVD++, TopicMF and CoFactor partially take temporal dynamics, reviews and correlation into consideration, none of them combine these information together for accurate recommendation. Therefore, in this paper we propose a novel combined model called TmRevCo which is based on matrix factorization. Our model combines the dynamic user factor of TimeSVD++ with the hidden topic of each review text mined by the topic model of TopicMF through a new transformation function. Meanwhile, to support our five-scoring datasets, we use a more appropriate item correlation measure in CoFactor and associate the item factors of CoFactor with that of matrix factorization. Our model comprehensively combines the temporal dynamics, review information and item correlation simultaneously. Experimental results on three real-world datasets show that our proposed model leads to significant improvement compared with the baseline methods.

In this paper, the topology optimization method is first applied to obtain high gain characteristics of dielectric flat lens. The topology optimization method used in this study is based on the gradient method with adjoint variable method. The FDTD method is used as the analysis method of electromagnetic fields. Results are compared with those obtained by using metaheuristic methods GA and PSO. As a result, it is shown that the proposed method can efficiently design a high gain dielectric flat lens in a wide frequency band.

Along with the sophistication of society, the requirements for infrastructure systems are also becoming more sophisticated. Conventionally, infrastructure systems have been accepted if they were safe and stable, but nowadays they are required for serviceability as a matter of course. For this reason, not only the expansion of the scope of the control system but also the integration with the information service system has been frequently carried out. In this paper, we describe safety technology based on autonomous decentralized technology as one of the measures to secure safety in a control system integrating such information service functions. And we propose its future studies.

An improved radiometric calibration algorithm by extending the Mitsunaga and Nayar least-square minimization based algorithm with two major ideas is presented. First, a noise & outlier removal procedure based on the analysis of brightness transfer function is included for improving the algorithm's capability on handling noise and outlier in least-square estimation. Second, an alternative minimization formulation based on weighted least square is proposed to improve the weakness of least square minimization when dealing with biased distribution observations. The performance of the proposed algorithm with regards to two baseline algorithms is demonstrated, i.e. the classical least square based algorithm proposed by Mitsunaga and Nayar and the state-of-the-art rank minimization based algorithm proposed by Lee et al. From the results, the proposed algorithm outperforms both baseline algorithms on both the synthetic dataset and the dataset of real-world images.

A single-polarization single-mode (SPSM) photonic crystal fiber (PCF) based on double-hole unit core is proposed in this paper for application to cross-talk free polarization splitter (PS). Birefringence of the PCF is obtained by adopting double-hole unit cells into the core to destroy its symmetry. With an appropriate cladding hole size, single x- or y-polarized PCF can be achieved by arranging the double-hole unit in the core along the x- or y-axis, respectively. Moreover, our proposed SPSM PCF has the potential to be applied to consist a cross-talk free PS. The simulation result by employing a vectorial finite element beam propagation method (FE-BPM) demonstrates that an arbitrary polarized incident light can be completely separated into two orthogonal single-polarized components through the PS. The structural tolerance and wavelength dependence of the PS have also been discussed in detail.

This paper presents a theoretical design of novel THz bandpass filters composed of M-PhC (metallic-photonic-crystal) point-defect-cavities (PDCs) with a centrally-loaded-rod. After a brief review of the properties of the recently-proposed M-PhC PDCs, two inline-type bandpass filters are synthesized in terms of the coupling matrix theory. The FDTD simulation results of the synthesized filters are in good agreement with the theoretical ones, which confirms the validity of the proposed filters' structures and the design scheme.

In recent years, deep learning based image recognition, speech recognition, text translation and other related applications have brought great convenience to people's lives. With the advent of the era of internet of everything, how to run a computationally intensive deep learning algorithm on a limited resources edge device is a major challenge. For an edge oriented computing vector processor, combined with a specific neural network model, a new data layout method for putting the input feature maps in DDR, rearrangement of the convolutional kernel parameters in the nuclear memory bank is proposed. Aiming at the difficulty of parallelism of two-dimensional matrix convolution, a method of parallelizing the matrix convolution calculation in the third dimension is proposed, by setting the vector register with zero as the initial value of the max pooling to fuse the rectified linear unit (ReLU) activation function and pooling operations to reduce the repeated access to intermediate data. On the basis of single core implementation, a multi-core implementation scheme of Inception structure is proposed. Finally, based on the proposed vectorization method, we realize five kinds of neural network models, namely, AlexNet, VGG16, VGG19, GoogLeNet, ResNet18, and performance statistics and analysis based on CPU, gtx1080TI and FT2000 are presented. Experimental results show that the vector processor has better computing advantages than CPU and GPU, and can calculate large-scale neural network model in real time.

The traditional Markov prediction methods of the taxi destination rely only on the previous 2 to 3 GPS points. They negelect long-term dependencies within a taxi trajectory. We adopt a Recurrent Neural Network (RNN) to explore the long-term dependencies to predict the taxi destination as the multiple hidden layers of RNN can store these dependencies. However, the hidden layers of RNN are very sensitive to small perturbations to reduce the prediction accuracy when the amount of taxi trajectories is increasing. In order to improve the prediction accuracy of taxi destination and reduce the training time, we embed suprisal-driven zoneout (SDZ) to RNN, hence a taxi destination prediction method by regularized RNN with SDZ (TDPRS). SDZ can not only improve the robustness of TDPRS, but also reduce the training time by adopting partial update of parameters instead of a full update. Experiments with a Porto taxi trajectory data show that TDPRS improves the prediction accuracy by 12% compared to RNN prediction method in literature[4]. At the same time, the prediction time is reduced by 7%.

In this paper we extend hyperparameter-free sparse signal reconstruction approaches to permit the high-resolution time delay estimation of spread spectrum signals and demonstrate their feasibility in terms of both performance and computation complexity by applying them to the ISO/IEC 24730-2.1 real-time locating system (RTLS). Numerical examples show that the sparse asymptotic minimum variance (SAMV) approach outperforms other sparse algorithms and multiple signal classification (MUSIC) regardless of the signal correlation, especially in the case where the incoming signals are closely spaced within a Rayleigh resolution limit. The performance difference among the hyperparameter-free approaches decreases significantly as the signals become more widely separated. SAMV is sometimes strongly influenced by the noise correlation, but the degrading effect of the correlated noise can be mitigated through the noise-whitening process. The computation complexity of SAMV can be feasible for practical system use by setting the power update threshold and the grid size properly, and/or via parallel implementations.

A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

A composite system consists of many subsystems, which have interconnections with other subsystems. For such a system, in general, we utilize decentralized control, where each subsystem is controlled by a local controller. On the other hand, event-triggered control is one of useful approaches to reduce the amount of communications between a controller and a plant. In the event-triggered control, an event triggering mechanism (ETM) monitors the information of the plant, and determines the time to transmit the data. In this paper, we propose a design of ETMs for the decentralized event-triggered control of nonlinear composite systems using an M-matrix. We consider the composite system where there is an ETM for each subsystem, and ETMs monitor local states of the corresponding subsystems. Each ETM is designed so that the composite system is stabilized. Moreover, we deal with the case of linear systems. Finally, we perform simulation to show that the proposed triggering rules are useful for decentralized control.

Generalized Minimum Distance (GMD) decoding is a well-known soft-decision decoding for linear codes. Previous research on GMD decoding focused mainly on unquantized AWGN channels with BPSK signaling for binary linear codes. In this paper, a study on the design of a 4-level uniform quantizer for GMD decoding is given. In addition, an extended version of a GMD decoding algorithm for a 4-level quantizer is proposed, and the effectiveness of the proposed decoding is shown by simulation.

Accurate pedestrian navigation remains a challenge in urban environments. GNSS receiver behaves poorly because the reflection and blockage of the GNSS signals by buildings or other obstacles. Integration of GNSS positioning and Pedestrian Dead Reckoning (PDR) could provide a more smooth navigation trajectory. However, the integration system cannot present the satisfied performance if GNSS positioning has large error. This situation often happens in the urban scenario. This paper focuses on improving the accuracy of the pedestrian navigation in urban environment using a proposed altitude map aided GNSS positioning method. Firstly, we use consistency check algorithm, which is similar to receiver autonomous integrity monitoring (RAIM) fault detection, to distinguish healthy and multipath contaminated measurements. Afterwards, the erroneous signals are corrected with the help of an altitude map. We called the proposed method altitude map aided GNSS. After correcting the erroneous satellite signals, the positioning mean error could be reduced from 17 meters to 12 meters. Usually, good performance for integration system needs accurately calculated GNSS accuracy value. However, the conventional GNSS accuracy calculation is not reliable in urban canyon. In this paper, the altitude map is also utilized to calculate the GNSS localization accuracy in order to indicate the reliability of the estimated position solution. The altitude map aided GNSS and accuracy are used in the integration with PDR system in order to provide more accurate and continuous positioning results. With the help of the proposed GNSS accuracy, the integration system could achieve 6.5 meters horizontal positioning accuracy in urban environment.

To enable the vision of precision medicine, evidence-based medicine is the key element. Understanding the natural history of complex diseases like brain aneurysm and particularly investigating the evidences of its rupture risk factors relies on the existence of semantic-enabled data preparation technology to conduct clinical trials, survival analysis and outcome prediction. For personalized medicine in the field of neurological diseases, it is very important that multiple health organizations coordinate and cooperate to conduct evidence based observational studies. Without the means of automating the process of privacy and semantic-enabled data preparation to conduct observational studies at intra-organizational level would require months to manually prepare the data. Therefore, this paper proposes a semantic and privacy enabled, multi-party data preparation architecture and a four-tiered semantic similarity algorithm. Evaluation shows that proposed algorithm achieves a precision of 79%, high recall at 83% and F-measure of 81%.

The locally one-dimensional finite-difference time-domain (FDTD) method in cylindrical coordinates is extended to a frequency-dependent version. The fundamental scheme is utilized to perform matrix-operator-free formulations in the right-hand sides. For the analysis of surface plasmon polaritons propagating along a plasmonic grating, the computation time is significantly reduced to less than 10%, compared with the explicit cylindrical FDTD method.