This letter describes a method that characterizes and improves the performance of a time-interleaved (TI) digital-to-analog converter (DAC) system by using multiport signal-flow graphs at microwave frequencies. A commercial signal generator with two TI DACs was characterized through s-parameter measurements and was compared to the conventional method. Moreover, prefilters were applied to correct the response, resulting in an error-vector magnitude improvement of greater than 8 dB for a 64-quadrature-amplitude-modulated signal of 4.8 Gbps. As a result, the bandwidth limitation and the complex post processing of the conventional method could be minimized.

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.

Software-defined networking (SDN) is recognized as the next-generation networking paradigm. The software-defined architecture for underwater acoustic sensor networks (SDUASNs) has become a hot topic. However, the current researches on SDUASNs is still in its infancy, which mainly focuses on network architecture, data transmission and routing. There exists some shortcomings that the scale of the SDUASNs is difficult to expand, and the security maintenance is seldom dabble. Therefore, a scalable software-definition architecture for underwater acoustic sensor networks (SSDUASNs) is introduced in this paper. It realizes an organic combination of the knowledge level, control level, and data level. The new nodes can easily access the network, which could be conducive to large-scale deployment. Then, the basic security authentication mechanism called BSAM is designed based on our architecture. In order to reflect the advantages of flexible and programmable in SSDUASNs, security authentication mechanism with pre-push (SAM-PP) is proposed in the further. In the current UASNs, nodes authentication protocol is inefficient as high consumption and long delay. In addition, it is difficult to adapt to the dynamic environment. The two mechanisms can effectively solve these problems. Compared to some existing schemes, BSAM and SAM-PP can effectively distinguish between legal nodes and malicious nodes, save the storage space of nodes greatly, and improve the efficiency of network operation. Moreover, SAM-PP has a further advantage in reducing the authentication delay.

In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.

In historical learning, to grasp the causal relationship between historical events and to understand factors that bring about important events are significant for fostering the historical thinking. However, some students are not able to find historical events that have causal relationships. The view of observing the historical events is different among individuals, so it is not appropriate to define the historical events that have causal relationships and impose students to remember them. The students need to understand the definition of the causal relationships and find the historical events that satisfy the definition according to their viewpoints. The objective of this paper is to develop a support system for understanding the meaning of a causal relationship and creating causal relation graphs that represent the causal relationships between historical events. When historical events have a causal relationship, a state change caused by one event becomes the cause of the other event. To consider these state changes is critically important to connect historical events. This paper proposes steps for considering causal relationships between historical events by arranging the state changes of historical people along with them. It also develops the system that supports students to create the causal relation graph according to the state changes. In our system, firstly, the interface for arranging state changes of historical people according to the historical events is given. Then, the interface for drawing the causal relation graph of historical events is provided in which state changes are automatically indicated on the created links in the causal relation graph. By observing the indicated state changes on the links, students are able to check by themselves whether their causal relation graphs correctly represent the causal relationships between historical events.

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%.

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.

In recent years, with the rapid development of the Internet and cloud computing, an enormous amount of information is exchanged on various social networking services. In order to handle and maintain such a mountain of information properly by limited resources in the network, it is very important to comprehend the dynamics for propagation of information or activity on the social network. One of many indices used by social network analysis which investigates the network structure is “node centrality”. A common characteristic of conventional node centralities is that it depends on the topological structure of network and the value of node centrality does not change unless the topology changes. The network dynamics is generated by interaction between users whose strength is asymmetric in general. Network structure reflecting the asymmetric interaction between users is modeled by a directed graph, and it is described by an asymmetric matrix in matrix-based network model. In this paper, we showed an oscillation model for describing dynamics on networks generated from a certain kind of asymmetric interaction between nodes by using a symmetric matrix. Moreover, we propose a new extended index of well-known two node centralities based on the oscillation model. In addition, we show that the proposed index can describe various aspect of node centrality that considers not only the topological structure of the network, but also asymmetry of links, the distribution of source node of activity, and temporal evolution of activity propagation by properly assigning the weight of each link. The proposed model is regarded as the fundamental framework for different node centralities.

This paper argues that a layered approach is more suitable for Information Centric Networking (ICN) than a narrow-waist approach and proposes an ICN mechanism called ZINK. In ZINK, a location-independent content name is resolved to a list of node IDs of content servers in the application layer and a node ID is mapped to a node locator in the network layer, which results in scalable locator-based routing. An ID/Locator split approach in the network layer can efficiently support client/serever mobility. Efficient content transfer is achieved by using sophisticated functions in the transport layer such as multipath transfer for bandwidth aggregation or fault tolerance. Existing well-tuned congestion control in the transport layer achieves fairness not only among ICN flows but also among ICN flows and other flows. A proof-of concept prototype of ZINK is implemented on an IPv6 stack. Evaluation results show that the time for content finding is practical, efficient content transfer is possible by using multipath transfer, and the mobility support mechanism is scalable as shown in a nationwide experiment environment in Japan.

In this paper we report the convergence acceleration effect of the extended node patch preconditioner for the iterative full-wave electromagnetic finite element method with more than ten million degrees of freedom. The preconditioner, which is categorized into the multiplicative Schwarz scheme, effectively works with conventional numerical iterative matrix solving methods on a parallel computer. We examined the convergence properties of the preconditioner combined with the COCG, COCR and GMRES algorithms for the analysis domain encompassed by absorbing boundary conditions (ABC) such as perfectly matched layers (PML). In those analyses the properties of the convergence are investigated numerically by sweeping frequency range and the number of PMLs. Memory-efficient nature of the preconditioner is numerically confirmed through the experiments and upper bounds of the required memory size are theoretically proved. Finally it is demonstrated that this extended node patch preconditioner with GMRES algorithm works well with the problems up to one hundred million degrees of freedom.

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.

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.

We propose a cooperative cognitive radio network (CCRN) with secondary users (SUs) employing two simultaneous transmit and receive (STAR) antennas. In the proposed framework of full-duplex (FD) multiple-input-multiple-output (MIMO) CCRN, the region of achievable rate is expanded via FD communication among SUs enabled by the STAR antennas adopted for the SUs. The link capacity of the proposed framework is analyzed theoretically. It is shown through numerical analysis that the proposed FD MIMO-CCRN framework can provide a considerable performance gain over the conventional frameworks of CCRN and MIMO-CCRN.

This paper presents the characteristics of path loss produced by traffic sign blockage. Multi frequency bands including high frequency bands up to 40 GHz are analyzed on the basis of measurement results in urban microcell environments. It is shown that the measured path loss increases compared to free space path loss even on a straight line-of-sight road, and that the excess attenuation is caused by the blockage effects of traffic signs. It is also shown that the measurement area affected by the blockage becomes small as frequency increases. The blocking object occupies the same area for all frequencies, but it takes up a larger portion of the Fresnel Zone as frequency increases. Therefore, if blockage occurs, the excess loss in high frequency bands becomes larger than in low frequency bands. In addition, the validity of two blockage path loss models is verified on the basis of measurement results. The first is the 3GPP blockage model and the second is the proposed blockage model, which is an expanded version of the basic diffraction model in ITU-R P.526. It is shown that these blockage models can predict the path loss increased by the traffic sign blockage and that their root mean square error can be improved compared to that of the 3GPP two slope model and a free space path loss model. The 3GPP blockage model is found to be more accurate for 26.4 and 37.1GHz, while the proposed model is more accurate for 0.8, 2.2, and 4.7GHz. The results show the blockage path loss due to traffic signs is clarified in a wide frequency range, and it is verified that the 3GPP blockage model and the proposed blockage model can accurately predict the blockage path loss.

We prove generalization error bounds of classes of low-rank matrices with some norm constraints for collaborative filtering tasks. Our bounds are tighter, compared to known bounds using rank or the related quantity only, by taking the additional L1 and L∞ constraints into account. Also, we show that our bounds on the Rademacher complexity of the classes are optimal.

Both placing responsibility of message sending on every IoT object and obfuscating the object's location from other objects are essential to realize a secure and privacy-preserved communication service. Two or more short-lived link identifiers (or pseudonyms) authorized by a trustable authority are often used in related studies, instead of a persistent or long-term use link identifier (i.e. vendor assigned MAC address). However, related studies have limitations in terms of frequently changing pseudonyms to enhance location privacy because the cryptographic algorithms used in them fixedly couple object's identifiers with its security keys. To overcome those limitations, we present a new pseudonym and key management scheme that enables dynamic coupling of pseudonym and key pairs without incurring any adverse impacts. Furthermore, we propose two lightweight pseudonym allocation protocols to effectively reduce the volume of message carrying the allocation parameters. Through qualitative analyses, we verify that the proposed scheme is more scalable than related approaches as it can efficiently allocate enough number of pseudonym/key pairs by reducing the control message overhead by more than 90%.

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.

In an X channel, multiple transmitters transmit independent signals to different receivers. Separate zero-forcing (ZF) precoding is used at transmitters in the two-user X channel with two transmitters and two receivers. A closed-form optimal power allocation is derived under the sum power constraint (SPC) to maximize the squared minimum distance. The ZF strategy with optimal power allocation achieves a significant signal to noise ratio (SNR) improvement. Under the individual power constraint (IPC), a suboptimal power allocation that achieves better performance compared to the existing algorithms is also proposed.

This study discusses an area-averaged incident power density to estimate surface temperature elevation from patch antenna arrays with 4 and 9 elements at the frequencies above 10 GHz. We computationally demonstrate that a smaller averaging area (1 cm2) of power density should be considered at the frequency of 30 GHz or higher compared with that at lower frequencies (4 cm2).