In this paper, we propose an online antenna-pulse selection method in space time adaptive processing, while maintaining considerable performance and low computational complexity. The proposed method considers the antenna-pulse selection and covariance matrix estimation at the same time by exploiting the structured clutter covariance matrix. Such prior knowledge can enhance the covariance matrix estimation accuracy and thus can provide a better objective function for antenna-pulse selection. Simulations also validate the effectiveness of the proposed method.

Financial Technology (FinTech) is considered a taxonomy that describes a wide range of ICT (information and communications technology) associated with financial transactions and related operations. Improvement of service quality is the main issue addressed in this taxonomy, and there are a large number of emerging technologies including blockchain-based cryptocurrencies and smart contracts. Due to its innovative nature in accounting, blockchain can also be used in lots of other FinTech contexts where token models play an important role for financial engineering. This paper revisits some of the key concepts accumulated behind this trend, and shows a generalized understanding of the technology using an adapted stochastic process. With a focus on financial instruments using blockchain, research directions toward stable applications are identified with the help of a newly proposed stabilizer: interpretation function of token valuation. The idea of adapted stochastic process is essential for the stabilizer, too.

We improve the cycle time performance of EtherCAT networks with embedded Linux-based master by developing a Linux Ethernet driver optimized for EtherCAT operation. The Ethernet driver is developed to establish a direct interface between the master module and Ethernet controllers of embedded systems by removing the involvement of Linux network stack and the New API (NAPI) of standard Ethernet drivers. Consequently, it is achieved that the time-consuming memory copy operations are reduced and the process of EtherCAT frames is accelerated. In order to demonstrate the effect of the developed Ethernet driver, we set up EtherCAT networks composed of an embedded Linux-based master and commercial off-the-shelf slaves, and the experimental results confirm that the cycle time performance is significantly improved.

The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

We propose a parallel precomputation method for real-time model predictive control. The key idea is to use predicted input values produced by model predictive control to solve an optimal control problem in advance. It is well known that control systems are not suitable for multi- or many-core processors because feedback-loop control systems are inherently based on sequential operations. However, since the proposed method does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems without changing the algorithm in applications. A practical evaluation using three real-world model predictive control system simulation programs demonstrates drastic performance improvement without degrading control quality offered by the proposed method.

In real-time 3D ball tracking of sports analysis in computer vision technology, complex algorithms which assure the accuracy could be time-consuming. Particle filter based algorithm has a large potential to accelerate since the algorithm between particles has the chance to be paralleled in heterogeneous CPU-GPU platform. Still, with the target multi-view 3D ball tracking algorithm, challenges exist: 1) serial flowchart for each step in the algorithm; 2) repeated processing for multiple views' processing; 3) the low degree of parallelism in reweight and resampling steps for sequential processing. On the CPU-GPU platform, this paper proposes the double stream system flow, the view priority based threads allocation, and the binary search oriented reweight. Double stream system flow assigns tasks which there is no data dependency exists into different streams for each frame processing to achieve parallelism in system structure level. View priority based threads allocation manipulates threads in multi-view observation task. Threads number is view number multiplied by particles number, and with view priority assigning, which could help both memory accessing and computing achieving parallelism. Binary search oriented reweight reduces the time complexity by avoiding to generate cumulative distribution function and uses an unordered array to implement a binary search. The experiment is based on videos which record the final game of an official volleyball match (2014 Inter-High School Games of Men's Volleyball held in Tokyo Metropolitan Gymnasium in Aug. 2014) and the test sequences are taken by multiple-view system which is made of 4 cameras locating at the four corners of the court. The success rate achieves 99.23% which is the same as target algorithm while the time consumption has been accelerated from 75.1ms/frame in CPU environment to 3.05ms/frame in the proposed system which is 24.62 times speed up, also, it achieves 2.33 times speedup compared with basic GPU implemented work.

The super-resolution technology is one of the solutions to fill the gap between high-resolution displays and lower-resolution images. There are various algorithms to interpolate the lost information, one of which is using a convolutional neural network (CNN). This paper shows an FPGA implementation and a performance evaluation of a novel CNN-based super-resolution system, which can process moving images in real time. We apply horizontal and vertical flips to input images instead of enlargement. This flip method prevents information loss and enables the network to make the best use of its patch size. In addition, we adopted the residue number system (RNS) in the network to reduce FPGA resource utilization. Efficient multiplication and addition with LUTs increased a network scale that can be implemented on the same FPGA by approximately 54% compared to an implementation with fixed-point operations. The proposed system can perform super-resolution from 960×540 to 1920×1080 at 60fps with a latency of less than 1ms. Despite resource restriction of the FPGA, the system can generate clear super-resolution images with smooth edges. The evaluation results also revealed the superior quality in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) index, compared to systems with other methods.

Recently, Super-resolution convolutional neural network (SRCNN) is widely known as a state of the art method for achieving single-image super resolution. However, performance problems such as jaggy and ringing artifacts exist in SRCNN. Moreover, in order to realize a real-time upconverting system for high-resolution video streams such as 4K/8K 60 fps, problems such as processing delay and implementation cost remain. In the present paper, we propose high-performance super-resolution via patch-based deep neural network (SR-PDNN) rather than a convolutional neural network (CNN). Despite the very simple end-to-end learning system, the SR-PDNN achieves higher performance than the conventional CNN-based approach. In addition, this system is suitable for ultra-low-delay video processing by hardware implementation using an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).

In this paper, we consider coded multi-input multi-output (MIMO) systems with low-density parity-check (LDPC) codes and space-time block code (STBC) in MIMO channels. The LDPC code takes the role of a channel code while the STBC provides spatial-temporal diversity. The performance of such coded MIMO system has been shown to be excellent in the past. In this paper, we present a performance analysis for an upper bound on probability of error for coded MIMO schemes. Compared to previous works, the proposed approach for the upper bound can avoid any explicit weight enumeration of codewords and provide a significant step for the upper bound by using a multinomial theorem. In addition, we propose a log domain convolution that enables us to handle huge numbers, e.g., 10500. Comparison of system simulations and numerical evaluations shows that the proposed upper bound is applicable for various coded MIMO systems.

As real-time embedded systems are required to accommodate various tasks with different levels of criticality, scheduling algorithms for MC (Mixed-Criticality) systems have been widely studied in the real-time systems community. Most studies have focused on MC uniprocessor systems whereas there have been only a few studies to support MC multiprocessor systems. In particular, although the ZL (Zero-Laxity) policy has been known to an effective technique in improving the schedulability performance of base scheduling algorithms on SC (Single-Criticality) multiprocessor systems, the effectiveness of the ZL policy on MC multiprocessor systems has not been revealed to date. In this paper, we focus on realizing the potential of the ZL policy for MC multiprocessor systems, which is the first attempt. To this end, we design the ZL policy for MC multiprocessor systems, and apply the policy to EDF (Earliest Deadline First), yielding EDZL (Earliest Deadline first until Zero-Laxity) tailored for MC multiprocessor systems. Then, we develop a schedulability analysis for EDZL (as well as its base algorithm EDF) to support its timing guarantee. Our simulation results show a significant schedulability improvement of EDZL over EDF, demonstrating the effectiveness of the ZL policy for MC multiprocessor systems.

Wireless information and power transfer technology is a promising means of supplying power for remote terminals in future communication systems. This paper investigates time-splitting (TS) recource allocation schemes for multi-cell massive MIMO systems with downlink (DL) wireless power transfer and uplink (UL) user information transmission under a harvest-then-transmit protocol. In order to jointly optimize the power and time allocation, two power minimization problems are formulated under different constraints on the minimal quality-of-service (QoS) requirement. Then, these original non-convex problems are transformed into their convex approximated ones which can be solved iteratively by successive convex approximation. Simulation results show that by exploiting the diversity effect of large-scale antenna arrays, the complexity-reduced asymptotic recourse allocation scheme almost match the power efficiency of the nonasymptotic scheme.

To achieve high end-to-end availability in nationwide optical transport network across thousands of office buildings, it is important to properly make each function redundant, and execute protection switching, repair failed functions and recover redundancy to prevent multiple simultaneous failures. High redundancy leads to high system cost and high power consumption, and tight conditions for recovery leads to high maintenance cost. Therefore it is important to optimize the balance between redundancy and maintenance condition based on appropriate availability indicators. We previously proposed a resource-pool control mechanism for a nationwide optical transport network that can optimize the balance. This paper proposes an end-to-end availability evaluation scheme for a nationwide optical transport network with our mechanism, by which network operators can design the pool-resource amount of each function and the maintenance conditions for each network area properly to satisfy the end-to-end availability requirement. Although the maintenance conditions are usually discussed based on failure-recovery times, they should be discussed based on cost- or load-based volumes for this design. This paper proposes a maintenance-operation-load evaluation scheme, which derives the required number of maintenance staff members from failure-recovery times. We also discuss the design of the pool-resource amount and maintenance conditions for each network area of a nationwide network based on the proposed evaluation schemes.

Adaptive noise cancellation using adaptive filters is a known method for removing noise that interferes with signal measurements. The adaptive noise canceller performs filtering based on the current situation through a windowing process. The shape of the window function determines the tracking performance of the adaptive noise canceller with respect to the fluctuation of the property of the unknown system that noise (reference signal) passes. However, the shape of the window function in the field of adaptive filtering has not yet been considered in detail. This study mathematically treats the effect of the window function on the adaptive noise canceller and proposes an optimization method for the window function in situations where offline processing can be performed, such as biomedical signal measurements. We also demonstrate the validity of the optimized window function through numerical experiments.

Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

In this letter, we propose a static wear leveling technique, called Recency-based Wear Leveling (RbWL). The basic idea of RbWL is to execute static wear leveling at minimum levels, because the frequent migrations of cold data by static wear leveling cause significant overhead in a NAND flash memory system. RbWL adjusts the execution frequency according to a threshold value that reflects the lifetime difference of the hot/cold blocks and the total lifetime of the NAND flash memory system. The evaluation results show that RbWL improves the lifetime of NAND flash memory systems by 52%, and it also reduces the overhead of wear leveling from 8% to 42% and from 13% to 51%, in terms of the number of erase operations and the number of page migrations of valid pages, respectively, compared with other algorithms.

This letter addresses the problem of space-time adaptive processing (STAP) for airborne nonuniform linear array (NLA) radar using a generalized sidelobe canceller (GSC). Due to the difficulty of determining the spatial nulls for the NLAs, it is a problem to obtain a valid blocking matrix (BM) of the GSC directly. In order to solve this problem and improve the STAP performance, a BM modification method based on the modified Gram-Schmidt orthogonalization algorithm is proposed. The modified GSC processor can achieve the optimal STAP performance and as well a faster convergence rate than the orthogonal subspace projection method. Numerical simulations validate the effectiveness of the proposed methods.

We consider space time block coded-continuous phase modulation (STBC-CPM), which has the advantages of both STBC and CPM at the same time. A weak point of STBC-CPM is that the normalized spectral efficiency (NSE) is limited by the orthogonality of the STBC and CPM parameters. The purpose of this study is to improve the NSE of STBC-CPM. The NSE depends on the transmission rate (TR), the bit error rate (BER) and the occupied bandwidth (OBW). First, to improve the TR, we adapt quasi orthogonal-STBC (QO-STBC) for four transmit antennas and quasi-group orthogonal Toeplitz code (Q-GOTC) for eight transmit antennas, at the expense of the orthogonality. Second, to evaluate the BER, we derive a BER approximation of STBC-CPM with non-orthogonal STBC (NO-STBC). The theoretical analysis and simulation results show that the NSE can be improved by using QO-STBC and Q-GOTC. Third, the OBW depends on CPM parameters, therefore, the tradeoff between the NSE and the CPM parameters is considered. A computer simulation provides a candidate set of CPM parameters which have better NSE. Finally, the adaptation of non-orthogonal STBC to STBC-CPM can be viewed as a generalization of the study by Silvester et al., because orthogonal STBC can be thought of as a special case of non-orthogonal STBC. Also, the adaptation of Q-GOTC to CPM can be viewed as a generalization of our previous letter, because linear modulation scheme can be thought of as a special case of non-linear modulation.

In this study, we address a problem pertaining to the induced matching enumeration. An edge set M is an induced matching of a graph G=(V,E). The enumeration of matchings has been widely studied in literature; however, there few studies on induced matching. A straightforward algorithm takes O(Δ2) time for each solution that is coming from the time to generate a subproblem, where Δ is the maximum degree in an input graph. To generate a subproblem, an algorithm picks up an edge e and generates two graphs, the one is obtained by removing e from G, the other is obtained by removing e, adjacent edge to e, and edges adjacent to adjacent edge of e. Since this operation needs O(Δ2) time, a straightforward algorithm enumerates all induced matchings in O(Δ2) time per solution. We investigated local structures that enable us to generate subproblems within a short time and proved that the time complexity will be O(1) if the input graph is C4-free. A graph is C4-free if and only if none of its subgraphs have a cycle of length four.

A term is a connected acyclic graph (unrooted unordered tree) pattern with structured variables, which are ordered lists of one or more distinct vertices. A variable of a term has a variable label and can be replaced with an arbitrary tree by hyperedge replacement according to the variable label. The dimension of a term is the maximum number of vertices in the variables of it. A term is said to be linear if each variable label in it occurs exactly once. Let T be a tree and t a linear term. In this paper, we study the graph pattern matching problem (GPMP) for T and t, which decides whether or not T is obtained from t by replacing variables in t with some trees. First we show that GPMP for T and t is NP-complete if the dimension of t is greater than or equal to 4. Next we give a polynomial time algorithm for solving GPMP for a tree of bounded degree and a linear term of bounded dimension. Finally we show that GPMP for a tree of arbitrary degree and a linear term of dimension 2 is solvable in polynomial time.

Electronic payment protocols provide secure service for electronic commerce transactions and protect private information from malicious entities in a network. Formal methods have been introduced to verify the security of electronic payment protocols; however, these methods concentrate on the accountability and fairness of the protocols, without considering the impact caused by timeliness. To make up for this deficiency, we present a formal method to analyze the security properties of electronic payment protocols, namely, accountability, fairness and timeliness. We add a concise time expression to an existing logical reasoning method to represent the event time and extend the time characteristics of the logical inference rules. Then, the Netbill protocol is analyzed with our formal method, and we find that the fairness of the protocol is not satisfied due to the timeliness problem. The results illustrate that our formal method can analyze the key properties of electronic payment protocols. Furthermore, it can be used to verify the time properties of other security protocols.