Event-triggered control is a method that the control input is updated only when a certain condition is satisfied (i.e., an event occurs). In this paper, event-triggered control over a sensor network is studied based on the notion of uniformly ultimate boundedness. Since sensors are located in a distributed way, we consider multiple event-triggering conditions. In uniformly ultimate boundedness, it is guaranteed that if the state reaches a certain set containing the origin, the state stays within this set. Using this notion, the occurrence of events in the neighborhood of the origin is inhibited. First, the simultaneous design problem of a controller and event-triggering conditions is formulated. Next, this problem is reduced to an LMI (linear matrix inequality) optimization problem. Finally, the proposed method is demonstrated by a numerical example.

In this paper, we investigate a model-free design of decentralized event-triggered mechanism for networked control systems (NCSs). The approach aims at simultaneously tuning the optimal parameters for the controller and the event-triggered condition, such that a prescribed cost function can be minimized. To achieve this goal, we employ the Bayesian optimization (BO), which is known to be an automatic tuning framework for finding the optimal solution to the black-box optimization problem. Thanks to its efficient search strategy for the global optimum, the BO allows us to design the event-triggered mechanism with relatively a small number of experimental evaluations. This is particularly suited for NCSs where network resources such as the limited life-time of battery powered devices are limited. Some simulation examples illustrate the effectiveness of the approach.

Universal filtered multicarrier (UFMC) systems offer a flexibility of filtering sub-bands with arbitrary bandwidth to suppress out-of-band (OoB) emission, while keeping the orthogonality between subcarriers in one sub-band. Oscillator discrepancies between the transmitter and receiver induce carrier frequency offset (CFO) in practical systems. In this paper, we propose a novel CFO estimation method for UFMC systems that has very low computational complexity and can then be used in practical systems. In order to fully exploit the coherence bandwidth of the channel, the training symbols are designed to have several identical segments in the frequency domain. As a result, the integral part of CFO can be estimated by simply determining the correlation between received signal and the training symbol. Simulation results show that the proposed method can achieve almost the same performance as an existing method and even a better performance in channels that have small decay parameter values. The proposed method can also be used in other multicarrier systems, such as orthogonal frequency division multiplexing (OFDM).

This paper considers a distributed subgradient method for online optimization with event-triggered communication over multi-agent networks. At each step, each agent obtains a time-varying private convex cost function. To cooperatively minimize the global cost function, these agents need to communicate each other. The communication with neighbor agents is conducted by the event-triggered method that can reduce the number of communications. We demonstrate that the proposed online algorithm achieves a sublinear regret bound in a dynamic environment with slow dynamics.

With the development of cloud computing, the Mobile Edge Computing has emerged and attracted widespread attentions. In this paper, we focus on the load balancing in MEC with energy harvesting. We first introduce the load balancing in MEC as a problem of minimizing both the energy consumption and queue redundancy. Thereafter, we adapt such a optimization problem to the Lyapunov algorithm and solve this optimization problem. Finally, extensive simulation results validate that the obtained strategy improves the capabilities of MEC systems.

When applying estimation methods, the issue of outliers is inevitable. The extent of their influence has not been clarified, though several studies have evaluated outlier elimination methods. It is unclear whether we should always be sensitive to outliers, whether outliers should always be removed before estimation, and what amount of precaution is required for collecting project data. Therefore, the goal of this study is to illustrate a guideline that suggests how sensitively we should handle outliers. In the analysis, we experimentally add outliers to three datasets, to analyze their influence. We modified the percentage of outliers, their extent (e.g., we varied the actual effort from 100 to 200 person-hours when the extent was 100%), the variables including outliers (e.g., adding outliers to function points or effort), and the locations of outliers in a dataset. Next, the effort was estimated using these datasets. We used multiple linear regression analysis and analogy based estimation to estimate the development effort. The experimental results indicate that the influence of outliers on the estimation accuracy is non-trivial when the extent or percentage of outliers is considerable (i.e., 100% and 20%, respectively). In contrast, their influence is negligible when the extent and percentage are small (i.e., 50% and 10%, respectively). Moreover, in some cases, the linear regression analysis was less affected by outliers than analogy based estimation.

To enhance the user's privacy in electronic ID, anonymous credential systems have been researched. In the anonymous credential system, a trusted issuing organization first issues a certificate certifying the user's attributes to a user. Then, in addition to the possession of the certificate, the user can anonymously prove only the necessary attributes. Previously, an anonymous credential system was proposed, where CNF (Conjunctive Normal Form) formulas on attributes can be proved. The advantage is that the attribute proof in the authentication has the constant size for the number of attributes that the user owns and the size of the proved formula. Thus, various expressive logical relations on attributes can be efficiently verified. However, the previous system has a limitation: The proved CNF formulas cannot include any negation. Therefore, in this paper, we propose an anonymous credential system with constant-size attribute proofs such that the user can prove CNF formulas with negations. For the proposed system, we extend the previous accumulator for the limited CNF formulas to verify CNF formulas with negations.

We propose a technique to reduce compulsory misses of packet processing cache (PPC), which largely affects both throughput and energy of core routers. Rather than prefetching data, our technique called response prediction cache (RPC) speculatively stores predicted data in PPC without additional access to the low-throughput and power-consuming memory (i.e., TCAM). RPC predicts the data related to a response flow at the arrival of the corresponding request flow, based on the request-response model of internet communications. Our experimental results with 11 real-network traces show that RPC can reduce the PPC miss rate by 13.4% in upstream and 47.6% in downstream on average when we suppose three-layer PPC. Moreover, we extend RPC to adaptive RPC (A-RPC) that selects the use of RPC in each direction within a core router for further improvement in PPC misses. Finally, we show that A-RPC can achieve 1.38x table-lookup throughput with 74% energy consumption per packet, when compared to conventional PPC.

Path planning and motion control are fundamental components to realize safe and reliable autonomous driving. The discrimination of the role of these two components, however, is somewhat obscure because of strong mathematical interaction between these two components. This often results in a redundant computation in the implementation. One of attracting idea to overcome this redundancy is a simultaneous path planning and motion control (SPPMC) based on a model predictive control framework. SPPMC finds the optimal control input considering not only the vehicle dynamics but also the various constraints which reflect the physical limitations, safety constraints and so on to achieve the goal of a given behavior. In driving in the real traffic environment, decision making has also strong interaction with planning and control. This is much more emphasized in the case that several tasks are switched in some context to realize higher-level tasks. This paper presents a basic idea to integrate decision making, path planning and motion control which is able to be executed in realtime. In particular, lane-changing behavior together with the decision of its initiation is selected as the target task. The proposed idea is based on the nonlinear model predictive control and appropriate switching of the cost function and constraints in it. As the result, the decision of the initiation, planning, and control of the lane-changing behavior are achieved by solving a single optimization problem under several constraints such as safety. The validity of the proposed method is tested by using a vehicle simulator.

Designing an optimum quantizer can be treated as the optimization problem of finding the quantization indices that minimize the quantization error. One solution to the optimization problem, DP quantization, is based on dynamic programming. Some applications, such as bit-depth scalable codec and tone mapping, require the construction of multiple quantizers with different quantization levels, for example, from 12bit/channel to 10bit/channel and 8bit/channel. Unfortunately, the above mentioned DP quantization optimizes the quantizer for just one quantization level. That is, it is unable to simultaneously optimize multiple quantizers. Therefore, when DP quantization is used to design multiple quantizers, there are many redundant computations in the optimization process. This paper proposes an extended DP quantization with a complexity reduction algorithm for the optimal design of multiple quantizers. Experiments show that the proposed algorithm reduces complexity by 20.8%, on average, compared to conventional DP quantization.

In this work, we introduce notions of quantum frequency arrangements consisting of quantum frequency squares, cubes, hypercubes and a notion of orthogonality between them. We also propose a notion of quantum mixed orthogonal array (QMOA). By using irredundant mixed orthogonal array proposed by Goyeneche et al. we can obtain k-uniform states of heterogeneous systems from quantum frequency arrangements and QMOAs. Furthermore, some examples are presented to illustrate our method.

Autonomous mobility machines, such as self-driving cars, transportation robots, and automated construction machines, are promising to support or enrich human lives. To further improve such machines, they will be connected to the network via wireless links to be managed, monitored, or remotely operated. The autonomous mobility machines must have self-status based on their positioning system to safely conduct their operations without colliding with other objects. The self-status is not only essential for machine operation but also it is valuable for wireless link quality management. This paper presents self-status-based wireless link quality prediction and evaluates its performance by using a prototype mobility robot combined with a wireless LAN system. The developed robot has functions to measure the throughput and receive signal strength indication and obtain self-status details such as location, direction, and odometry data. Prediction performance is evaluated in offline processing by using the dataset gathered in an indoor experiment. The experiments clarified that, in the 5.6 GHz band, link quality prediction using self-status of the robot forecasted the throughput several seconds into the future, and the prediction accuracies were investigated as dependent on time window size of the target throughput, bandwidth, and frequency gap.

For actual multi-channel differential signaling system, the ideal balance or symmetrical topology cannot be established, and hence, an imbalance component is excited. However a theoretical analysis method of evaluating the voltage and current distribution on the differential-paired lines, which allows to anticipate EM radiation at the design stage and to study possible means for suppressing imbalance components, has not been implemented. To provide the basic considerations for electromagnetic (EM) radiation from practical asymmetrical differential-paired lines structure with equi-length routing used in high-speed board design, this paper newly proposes an analytical method for evaluating the voltage and current at any point on differential-paired lines by expressing the differential paired-lines with an equivalent source circuit and an equivalent load circuit. The proposed method can predict S-parameters, distributions of voltage and current and EM radiation with sufficient accuracy. In addition, the proposed method provides enough flexibility for different geometric parameters and can be used to develop physical insights and design guidelines. This study has successfully established a basic method to effectively predict signal integrity and EM interference issues on a differential-paired lines.

We propose a nonphotorealistic rendering method for generating checkered pattern images from photographic images. The proposed method is executed by iterative calculation using a Prewitt filter with an expanded window size and can automatically generate checkered patterns according to changes in edges and shade of photographic images. To verify the effectiveness of the proposed method, an experiment was conducted using various photographic images. An additional experiment was conducted to visually confirm the checkered pattern images generated by changing the iteration number, window size, and parameter to emphasize the checkered patterns.

Hadoop is a popular data-analytics platform based on Google's MapReduce programming model. Hard-disk drives (HDDs) are generally used in big-data analysis, and the effectiveness of the Hadoop platform can be optimized by enhancing its I/O performance. HDD performance varies depending on whether the data are stored in the inner or outer disk zones. This paper proposes a method that utilizes the knowledge of job characteristics to realize efficient data storage in HDDs, which in turn, helps improve Hadoop performance. Per the proposed method, job files that need to be frequently accessed are stored in outer disk tracks which are capable of facilitating sequential-access speeds that are higher than those provided by inner tracks. Thus, the proposed method stores temporary and permanent files in the outer and inner zones, respectively, thereby facilitating fast access to frequently required data. Results of performance evaluation demonstrate that the proposed method improves Hadoop performance by 15.4% when compared to normal cases when file placement is not used. Additionally, the proposed method outperforms a previously proposed placement approach by 11.1%.

Vehicle logo detection technology is one of the research directions in the application of intelligent transportation systems. It is an important extension of detection technology based on license plates and motorcycle types. A vehicle logo is characterized by uniqueness, conspicuousness, and diversity. Therefore, thorough research is important in theory and application. Although there are some related works for object detection, most of them cannot achieve real-time detection for different scenes. Meanwhile, some real-time detection methods of single-stage have performed poorly in the object detection of small sizes. In order to solve the problem that the training samples are scarce, our work in this paper is improved by constructing the data of a vehicle logo (VLD-45-S), multi-stage pre-training, multi-scale prediction, feature fusion between deeper with shallow layer, dimension clustering of the bounding box, and multi-scale detection training. On the basis of keeping speed, this article improves the detection precision of the vehicle logo. The generalization of the detection model and anti-interference capability in real scenes are optimized by data enrichment. Experimental results show that the accuracy and speed of the detection algorithm are improved for the object of small sizes.

In this paper, we propose a notion for high-dimensional generalizations of mutually orthogonal Latin squares (MOLS) and mutually orthogonal diagonal Latin squares (MODLS), called mutually dimensionally orthogonal d-cubes (MOC) and mutually dimensionally orthogonal diagonal d-cubes (MODC). Systematic constructions for MOC and MODC by using polynomials over finite fields are investigated. In particular, for 3-dimensional cubes, the results for the maximum possible number of MODC are improved by adopting the proposed construction.

Cross-project defect prediction (CPDP) is a feasible solution to build an accurate prediction model without enough historical data. Although existing methods for CPDP that use only labeled data to build the prediction model achieve great results, there are much room left to further improve on prediction performance. In this paper we propose a Semi-Supervised Discriminative Feature Learning (SSDFL) approach for CPDP. SSDFL first transfers knowledge of source and target data into the common space by using a fully-connected neural network to mine potential similarities of source and target data. Next, we reduce the differences of both marginal distributions and conditional distributions between mapped source and target data. We also introduce the discriminative feature learning to make full use of label information, which is that the instances from the same class are close to each other and the instances from different classes are distant from each other. Extensive experiments are conducted on 10 projects from AEEEM and NASA datasets, and the experimental results indicate that our approach obtains better prediction performance than baselines.

Simultaneous multithreading technology (SMT) can effectively improve the overall throughput and fairness through improving the resources usage efficiency of processors. Traditional works have proposed some metrics for evaluation in real systems, each of which strikes a trade-off between fairness and throughput. How to choose an appropriate metric to meet the demand is still controversial. Therefore, we put forward suggestions on how to select the appropriate metrics through analyzing and comparing the characteristics of each metric. In addition, for the new application scenario of cloud computing, the data centers have high demand for the quality of service for killer applications, which bring new challenges to SMT in terms of performance guarantees. Therefore, we propose a new metric P-slowdown to evaluate the quality of performance guarantees. Based on experimental data, we show the feasibility of P-slowdown on performance evaluation. We also demonstrate the benefit of P-slowdown through two use cases, in which we not only improve the performance guarantee level of SMT processors through the cooperation of P-slowdown and resources allocation strategy, but also use P-slowdown to predict the occurrence of abnormal behavior against security attacks.

Cross-project defect prediction (CPDP) is a research hot recently, which utilizes the data form existing source project to construct prediction model and predicts the defect-prone of software instances from target project. However, it is challenging in bridging the distribution difference between different projects. To minimize the data distribution differences between different projects and predict unlabeled target instances, we present a novel approach called selective pseudo-labeling based subspace learning (SPSL). SPSL learns a common subspace by using both labeled source instances and pseudo-labeled target instances. The accuracy of pseudo-labeling is promoted by iterative selective pseudo-labeling strategy. The pseudo-labeled instances from target project are iteratively updated by selecting the instances with high confidence from two pseudo-labeling technologies. Experiments are conducted on AEEEM dataset and the results show that SPSL is effective for CPDP.