As IT systems, including network systems using SDN/NFV technologies, become large-scaled and complicated, the cost of system management also increases rapidly. Network operators have to maintain their workflow in constructing and consistently updating such complex systems, and thus these management tasks in generating system update plan are desired to be automated. Declarative system update with state space search is a promising approach to enable this automation, however, the current methods is not enough scalable to practical systems. In this paper, we propose a novel heuristic approach to greatly reduce computation time to solve system update procedure for practical systems. Our heuristics accounts for structural bottleneck of the system update and advance search to resolve bottlenecks of current system states. This paper includes the following contributions: (1) formal definition of a novel heuristic function specialized to system update for A* search algorithm, (2) proofs that our heuristic function is consistent, i.e., A* algorithm with our heuristics returns a correct optimal solution and can omit repeatedly expansion of nodes in search spaces, and (3) results of performance evaluation of our heuristics. We evaluate the proposed algorithm in two cases; upgrading running hypervisor and rolling update of running VMs. The results show that computation time to solve system update plan for a system with 100 VMs does not exceed several minutes, whereas the conventional algorithm is only applicable for a very small system.

This paper presents a novel delta-sigma modulator that uses a switched-capacitor (SC) integrator with the structure of a finite impulse response (FIR) filter in a loop filter configuration. The delta-sigma analog-to-digital converter (ΔΣADC) is used in various conversion systems to enable low-power, high-accuracy conversion using oversampling and noise shaping. Increasing the gain coefficient of the integrator in the loop filter configuration of the ΔΣADC suppresses the quantization noise that occurs in the signal band. However, there is a trade-off relationship between the integrator gain coefficient and system stability. The SC integrator, which contains an FIR filter, can suppress quantization noise in the signal band without requiring an additional operational amplifier. Additionally, it can realize a higher signal-to-quantization noise ratio. In addition, the poles that are added by the FIR filter structure can improve the system's stability. It is also possible to improve the flexibility of the pole placement in the system. Therefore, a noise transfer function that does not contain a large gain peak is realized. This results in a stable system operation. This paper presents the essential design aspects of a ΔΣADC with an FIR filter. Two types of simulation results are examined for the proposed first- and second-order, and these results confirm the effectiveness of the proposed architecture.

Two-dimensional (2-D) maximally flat finite impulse response (FIR) digital filters have flat characteristics in both passband and stopband. 2-D maximally flat diamond-shaped half-band FIR digital filter can be designed very efficiently as a special case of 2-D half-band FIR filters. In some cases, this filter would require the reduction of the filter lengths for one of the axes while keeping the other axis unchanged. However, the conventional methods can realize such filters only if difference between each order is 2, 4 and 6. In this paper, we propose a closed-form frequency response of 2-D low-pass maximally flat diamond-shaped half-band FIR digital filters with arbitrary filter orders. The constraints to treat arbitrary filter orders are firstly proposed. Then, a closed-form transfer function is achieved by using Bernstein polynomial.

A diversity strategy is efficient to reduce the fluctuation of communication quality caused by fading. In order to further maintain the communication quality and improve the communication capacity, this paper proposes a two-dimensional diversity approach by serially-concatenating spectral precoding and power normalized-differential space time block coding (PN-DSTBC). Spectral precoding is able to take benefit from a frequency diversity effect without loss in spectral efficiency. In addition, PN-DSTBC is robust against serious phase noise in an extremely high frequency (EHF) band by exploiting a spatial diversity effect. However, there is a problem that a naive concatenation degrades the performance due to the imbalance of equivalent noise variances over transmit frequencies. Thus, we examine an equalized PN-DSTBC decoder as a modified approach to uniform equivalent noise variances over frequencies. The performance evaluation using computer simulations shows that the proposed modified approach yields the performance improvement at any modulation schemes and at any number of transmit frequencies. Furthermore, in the case of 64QAM and two transmit frequencies, the performance gain of the modified approach is 4dB larger than that of PN-DSTBC only at uncoded BER=10-4.

A class of multimodulus algorithms (MMA(p)) optimized by an optimal step-size (OS) for blind equalization are firstly investigated in this letter. The multimodulus (MM) criterion is essentially a split cost function that separately implements the real and imaginary part of the signal, hence the phase can be recovered jointly with equalization. More importantly, the step-size leading to the minimum of the MM criterion along the search direction can be obtained algebraically among the roots of a higher-order polynomial at each iteration, thus a robust optimal step-size multimodulus algorithm (OS-MMA(p)) is developed. Experimental results demonstrate improved performance of the proposed algorithm in mitigating the inter-symbol interference (ISI) compared with the OS constant modulus algorithm (OS-CMA). Besides, the computational complexity can be reduced by the proposed OS-MMA(2) algorithm.

As an emerging and promising technique, device-free localization (DFL) has drawn considerable attention in recent years. By exploiting the inherent spatial sparsity of target localization, the compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements. In practical scenarios, a prior knowledge about target locations is usually available, which can be obtained by coarse localization or tracking techniques. Among existing CS-based DFL approaches, however, few works consider the utilization of prior knowledge. To make use of the prior knowledge that is partly or erroneous, this paper proposes a novel faulty prior knowledge aided multi-target device-free localization (FPK-DFL) method. It first incorporates the faulty prior knowledge into a three-layer hierarchical prior model. Then, it estimates location vector and learns model parameters under a variational Bayesian inference (VBI) framework. Simulation results show that the proposed method can improve the localization accuracy by taking advantage of the faulty prior knowledge.

With the development of spaceborne synthetic aperture radar (SAR), ultra-high spatial resolution has become a hot topic in recent years. The system with high spatial resolution requests large range bandwidths and long azimuth integration time. However, due to the long azimuth integration time, many problems arise, which cannot be ignored in the operational ultra-high resolution spotlight mode. This paper investigates two critical issues that need to be noticed for the full-aperture processing of ultra-high resolution spaceborne SAR spotlight data. The first one is the inaccuracy of the traditional hyperbolic range model (HRM) when the system approaches decimeter range resolution. The second one is the azimuth spectral folding phenomenon. The problems mentioned above result in significant degradation of the focusing effect. Thus, to solve these problems, a full-aperture processing scheme is proposed in this paper which combines the superiorities of two generally utilized processing algorithms: the precision of one-step motion compensation (MOCO) algorithm and the efficiency of modified two-step processing approach (TSA). Firstly, one-step MOCO algorithm, a state-of-the-art MOCO algorithm which has been applied in ultra-high resolution airborne SAR systems, can precisely correct for the error caused by spaceborne curved orbit. Secondly, the modified TSA can avoid the phenomenon of azimuth spectrum folding effectively. The key point of the modified TSA is the deramping approach which is carried out via the convolution operation. The reference function, varying with the instantaneous range frequency, is adopted by the convolution operation for obtaining the unfolding spectrum in azimuth direction. After these operations, the traditional wavenumber domain algorithm is available because the error caused by spaceborne curved orbit and the influence of the spectrum folding in azimuth direction have been totally resolved. Based on this processing scheme, the ultra-high resolution spaceborne SAR spotlight data can be well focused. The performance of the full-aperture processing scheme is demonstrated by point targets simulation.

Radar-absorbent materials (RAM) with various characteristics, such as broadband, oblique-incidence, and polarization characteristics, have been developed according to applications in recent years. This paper presents the optimized design method of two flat layers RAM with both broadband and oblique-incidence characteristics for the required RAM performance. The oblique-incidence characteristics mean that the RAM is possible to absorb radio waves continuously up to the maximum incidence angle. The index of the wave-absorption amount is 20dB, corresponding to an absorption rate of 99%. Because determination of the electrical material constant of each layer is the most important task with respect to the received frequency and the incidence angle, we optimized the values by using Non-dominated sorting genetic algorithm-II (NSGA-II). Two types of flat-layer RAM composed of dielectric and magnetic materials were designed and their characteristics were evaluated. Consequently, it was confirmed that oblique-incidence characteristics were better for the RAM composed of dielectric materials. The dielectric RAM achieved an incidence angle of up to 60° with broadband characteristics and a relative bandwidth of 77.01% at the transverse-magnetic (TM) wave incidence. In addition, the magnetic RAM could lower the minimum frequency of the system more than the dielectric RAM. The minimum frequency of the magnetic RAM was 1.38GHz with a relative bandwidth of 174.18% at TM-wave incidence and an incidence angle of 45°. We confirmed that it is possible to design RAM with broadband characteristics and continuous oblique-incidence characteristics by using the proposed method.

This letter proposes a comprehensive assessment of the mission-level damage caused by cyberattacks on an entire defense mission system. We experimentally prove that our method produces swift and accurate assessment results and that it can be applied to actual defense applications. This study contributes to the enhancement of cyber damage assessment with a faster and more accurate method.

A novel time-series relationship among four consecutive real-valued single-tone sinusoid samples is proposed based on their linear prediction property. In order to achieve unbiased frequency estimates for a real sinusoid in white noise, based on the proposed four-point time-series relationship, a constrained least squares cost function is minimized based on the unit-norm principle. Closed-form expressions for the variance and the asymptotic expression for the variance of the proposed frequency estimator are derived, facilitating a theoretical performance comparison with the existing three-point counterpart, called as the reformed Pisarenko harmonic decomposer (RPHD). The region of performance advantage of the proposed four-point based constrained least squares frequency estimator over the RPHD is also discussed. Computer simulations are conducted to support our theoretical development and to compare the proposed estimator performance with the RPHD as well as the Cramer-Rao lower bound (CRLB).

To optimize the performance of FIR filters that have low computation complexity, this paper proposes a hybrid design consisting of two optimization levels. The first optimization level is based on cyclic-shift synthesis, in which all possible sub filters (or windowed sub filters) with distinct cycle shifts are averaged to generate a synthesized filter. Due to the fact that the ripples of these sub filters' transfer curves can be individually compensated, this synthesized filter attains improved performance (besides two uprushes occur on the edges of a transition band) and thus this synthesis actually plays the role of ‘natural optimization’. Furthermore, this synthesis process can be equivalently summarized into a 3-step closed-form procedure, which converts the multi-variable optimization into a single-variable optimization. Hence, to suppress the uprushes, what the second optimization level (by Differential Evolution (DE) algorithm) needs to do is no more than searching for the optimum transition point which incurs only minimal complexity . Owning to the combination between the cyclic-shift synthesis and DE algorithm, unlike the regular evolutionary computing schemes, our hybrid design is more attractive due to its narrowed search space and higher convergence speed . Numerical results also show that the proposed design is superior to the conventional DE design in both filter performance and design efficiency, and it is comparable to the Remez design.

Data generated from the Internet of Things (IoT) devices in smart spaces are utilized in a variety of fields such as context recognition, service recommendation, and anomaly detection. However, the missing values in the data streams of the IoT devices remain a challenging problem owing to various missing patterns and heterogeneous data types from many different data streams. In this regard, while we were analyzing the dataset collected from a smart space with multiple IoT devices, we found a continuous missing pattern that is quite different from the existing missing-value patterns. The pattern has blocks of consecutive missing values over a few seconds and up to a few hours. Therefore, the pattern is a vital factor to the availability and reliability of IoT applications; yet, it cannot be solved by the existing missing-value imputation methods. Therefore, a novel approach for missing-value imputation of the continuous missing pattern is required. We deliberate that even if the missing values of the continuous missing pattern occur in one data stream, missing-values imputation is possible through learning other data streams correlated with this data stream. To solve the missing values of the continuous missing pattern problem, we analyzed multiple IoT data streams in a smart space and figured out the correlations between them that are the interdependencies among the data streams of the IoT devices in a smart space. To impute missing values of the continuous missing pattern, we propose a deep learning-based missing-value imputation model exploiting correlation information, namely, the deep imputation network (DeepIN), in a smart space. The DeepIN uses that multiple long short-term memories are constructed according to the correlation information of each IoT data stream. We evaluated the DeepIN on a real dataset from our campus IoT testbed, and the experimental results show that our proposed approach improves the imputation performance by 57.36% over the state-of-the-art missing-value imputation algorithm. Thus, our approach can be a promising methodology that enables IoT applications and services with a reasonable missing-value imputation accuracy (80∼85%) on average, even if a long-term block of values is missing in IoT environments.

In parallel computing systems, the interconnection network forms the critical infrastructure which enables robust and scalable communication between hundreds of thousands of nodes. The traditional packet-switched network tends to suffer from long communication time when network congestion occurs. In this context, we explore the use of circuit switching (CS) to replace packet switches with custom hardware that supports circuit-based switching efficiently with low latency. In our target CS network, a certain amount of bandwidth is guaranteed for each communication pair so that the network latency can be predictable when a limited number of node pairs exchange messages. The number of allocated time slots in every switch is a direct factor to affect the end-to-end latency, we thereby improve the slot utilization and develop a network topology generator to minimize the number of time slots optimized to target applications whose communication patterns are predictable. By a quantitative discrete-event simulation, we illustrate that the minimum necessary number of slots can be reduced to a small number in a generated topology by our design methodology while maintaining network cost 50% less than that in standard tori topologies.

The fifth generation mobile communications (5G) systems will need to support the ultra-reliable and low-latency communications (URLLC) to enable future mission-critical applications, e.g., self-driving cars and remote control. With the aim of verifying the feasibility of URLLC related 5G requirements in real environments, field trials of URLLC using a new frame structure are conducted in Yokohama, Japan. In this paper, we present the trial results and investigate the impact of the new frame structure and retransmission method on the URLLC performance. To reduce the user-plane latency and improve the packet success probability, a wider subcarrier spacing, self-contained frame structure, and acknowledgement/negative acknowledgement-less (ACK/NACK-less) retransmission are adopted. We verify the feasibility of URLLC in actual field tests using our prototype test-bed while implementing these techniques. The results show that for the packet size of 32 bytes the URLLC related requirements defined by the 3GPP are satisfied even at low signal-to-noise ratios or at non-line-of-sight transmission.

In this study, we propose a statistical reputation approach for constructing a reliable packet route in ad-hoc sensor networks. The proposed method uses reputation as a measurement for router node selection through which a reliable data route is constructed for packet delivery. To refine the reputation, a transaction density is defined here to showcase the influence of node transaction frequency over the reputation. And to balance the energy consumption and avoid choosing repetitively the same node with high reputation, node remaining energy is also considered as a reputation factor in the selection process. Further, a shortest-path-tree routing protocol is designed so that data packets can reach the base station through the minimum intermediate nodes. Simulation tests illustrate the improvements in the packet delivery ratio and the energy utilization.

In this work, we provide the first practical secure email filtering scheme based on homomorphic encryption. Specifically, we construct a secure naïve Bayesian filter (SNBF) using the Paillier scheme, a partially homomorphic encryption (PHE) scheme. We first show that SNBF can be implemented with only the additive homomorphism, thus eliminating the need to employ expensive fully homomorphic schemes. In addition, the design space for specialized hardware architecture realizing SNBF is explored. We utilize a recursive Karatsuba Montgomery structure to accelerate the homomorphic operations, where multiplication of 2048-bit integers are carried out. Through the experiment, both software and hardware versions of the SNBF are implemented. On software, 104-105x runtime and 103x storage reduction are achieved by SNBF, when compared to existing fully homomorphic approaches. By instantiating the designed hardware for SNBF, a further 33x runtime and 1919x power reduction are achieved. The proposed hardware implementation classifies an average-length email in under 0.5s, which is much more practical than existing solutions.

This paper proposes a new design criterion of adaptively scaled belief (ASB) in Gaussian belief propagation (GaBP) for large multi-user multi-input multi-output (MU-MIMO) detection. In practical MU detection (MUD) scenarios, the most vital issue for improving the convergence property of GaBP iterative detection is how to deal with belief outliers in each iteration. Such outliers are caused by modeling errors due to the fact that the law of large number does not work well when it is difficult to satisfy the large system limit. One of the simplest ways to mitigate the harmful impact of outliers is belief scaling. A typical approach for determining the scaling parameter for the belief is to create a look-up table (LUT) based on the received signal-to-noise ratio (SNR) through computer simulations. However, the instantaneous SNR differs among beliefs because the MIMO channels in the MUD problem are random; hence, the creation of LUT is infeasible. To stabilize the dynamics of the random MIMO channels, we propose a new transmission block based criterion that adapts belief scaling to the instantaneous channel state. Finally, we verify the validity of ASB in terms of the suppression of the bit error rate (BER) floor.

Electrophoretic deposition (EPD) usingpolydimethylsiloxane(PDMS)-based organic-inorganic hybrid materials as binders can be used to prepare alumina-binder composites on metal substrates. Herein, we investigated the deposition mechanism of PDMS-based polymers. The composition and porosity of EPD composites can be controlled by adjusting the EPD condition, and shape of alumina particles.

We report the control of threshold voltage (Vth) for low voltage (5V) operation in OFET by using double gate dielectric layers composed of poly (vinyl cinnamate) and SiO2. We succeeded in realizing a driving voltage of -5V and Vth shift by c.a. 1.0V. And programmed Vth was almost unchanged for 104s, where the relative change of Vth remains more than 99%.

We have developed the flexible dual-gate OFET based pressure sensor using a thin polyethylene naphthalate (PEN, 25 µm) film as a substrate. The performance was equivalent to that fabricated on the glass substrate, and it could also be used on the curved surface. Drain current in the flexible pressure sensor was increased according to the pressure load without application of gate voltage. The magnitude of the change in drain current with respect to pressure application was about 2.5 times larger than that for the device on the glass substrate.