This paper develops the double generating function method for the discrete-time linear quadratic optimal control problem. This method can give generators for optimal solutions only in terms of pre-computed coefficients and boundary conditions, which is useful for the on-line repetitive computation for different boundary conditions. Moreover, since each generator contains inverse terms, the invertibility analysis is also performed to conclude that the terms in the generators constructed by double generating functions with opposite time directions are invertible under some mild conditions, while the terms with the same time directions will become singular when the time goes infinity which may cause instability in numerical computations. Examples demonstrate the effectiveness of the developed method.

The short-channel effect (SCE) in a MOSFET with an atomically thin MoS$_{2}$ channel was studied using a TCAD simulator. We derived the surface potential roll-up, drain-induced barrier lowering (DIBL), threshold voltage, and subthreshold swing (SS) as indexes of the SCE and analyzed their dependency on the channel thickness (number of atomic layers) and channel length. The minimum scalable channel length for a one-atomic-layer-thick MoS$_{2}$ MOSFET was determined from the threshold voltage roll-off to be 7.6,nm. The one-layer-thick device showed a small DIBL of 87,mV/V at a 20 nm gate length. By using high-k gate insulator, an SS lower than 70,mV/dec is achievable in sub-10-nm-scale devices.

How to retrieve the closest content from an in-network cache is one of the most important issues in Information-Centric Networking (ICN). This paper proposes a novel content discovery scheme called Local Tree Hunting (LTH). By adding branch-cast functionality to a local tree for content requests to a Content-Centric Network (CCN) response node, the discovery area for caching nodes expands. Since the location of such a branch-casting node moves closer to the request node when the content is more widely cached, the discovery range, i.e. the branch size of the local tree, becomes smaller. Thus, the discovery area is autonomously adjusted depending on the content dissemination. With this feature, LTH is able to find the “almost true closest” caching node without checking all the caching nodes in the in-network cache. The performance analysis employed in Zipf's law content distribution model and which uses the Least Recently Used eviction rule shows the superiority of LTH with respect to identifying the almost exact closest cache.

Many wireless communication systems use a relay station for cooperative diversity or cell coverage extension. In this letter, an efficient partial single relay selection scheme is proposed for wireless communications. The conventional schemes such as the best harmonic mean and the threshold-based relay selection should know channel state informaion (CSI), or noise variance at all stations in advance. But the proposed scheme does not require any priori information. It uses a characteristic of the repeated signal pattern at candidates of the relay station. Simulation results show that the performance of proposed scheme is very close to the best harmonic mean relay selection scheme as one of the optimal relay selection schemes.

This paper presents a downlink interference mitigation framework for two-tier heterogeneous networks, that consist of spectrum-sharing macrocells and femtocells*. This framework establishes cooperation between the two tiers through two algorithms, namely, the restricted waterfilling (RWF) algorithm and iterative reweighted least squares interference alignment (IRLS-IA) algorithm. The proposed framework models the macrocell-femtocell two-tier cellular system as an overlay cognitive radio system in which the macrocell system plays the role of the primary user (PU) while the femtocell networks play the role of the cognitive secondary users (SUs). Through the RWF algorithm, the macrocell basestation (MBS) cooperates with the femtocell basestations (FBSs) by releasing some of its eigenmodes to the FBSs to do their transmissions even if the traffic is heavy and the MBS's signal to noise power ratio (SNR) is high. Then, the FBSs are expected to achieve a near optimum sum rate through employing the IRLS-IA algorithm to mitigate both the co-tier and cross-tier interference at the femtocell users' (FUs) receivers. Simulation results show that the proposed IRLS-IA approach provides an improved sum rate for the femtocell users compared to the conventional IA techniques, such as the leakage minimization approach and the nuclear norm based rank constraint rank minimization approach. Additionally, the proposed framework involving both IRLS-IA and RWF algorithms provides an improved total system sum rate compared with the legacy approaches for the case of multiple femtocell networks.

Forty years after the first application of Superconducting Quantum Interference Devices (SQUIDs) [1], [2] for geophysical purposes, they have recently become a valued tool for mineral exploration. One of the most common applications is time domain (or transient) electromagnetics (TEM), an active method, where the inductive response from the ground to a changing current (mostly rectangular) in a loop on the surface is measured. After the current in the transmitter coil is switched, eddy currents are excited in the ground, which decay in a manner dependent on the conductivity of the underlying geologic structure. The resulting secondary magnetic field at the surface is measured during the off-time by a receiver coil (induced voltage) or by a magnetometer (e.g. SQUID or fluxgate). The recorded transient signal quality is improved by stacking positive and negative decays. Alternatively, the TEM results can be inverted and give the electric conductivity of the ground over depth. Since SQUIDs measure the magnetic field with high sensitivity and a constant frequency transfer function, they show a superior performance compared to conventional induction coils, especially in the presence of strong conductors. As the primary field, and especially its slew rate, are quite large, SQUID systems need to have a large slew rate and dynamic range. Any flux jump would make the use of standard stacking algorithms impossible. IPHT and Supracon are developing and producing SQUID systems based on low temperature superconductors (LTS, in our case niobium), which are now state-of-the-art. Due to the large demand, we are additionally supplying systems with high temperature superconductors (HTS, in our case YBCO). While the low temperature SQUID systems have a better performance (noise and slew rate), the high temperature SQUID systems are easier to handle in the field. The superior performance of SQUIDs compared to induction coils is the most important factor for the detection of good conductors at large depth or ore bodies underneath conductive overburden.

This paper considers the problem of enumerating all maximal cliques in unit disk graphs, which is a plausible setting for applications of finding similar data groups. Our primary interest is to develop a faster algorithm using the geometric structure about the metric space where the input unit disk graph is embedded. Assuming that the distance between any two vertices is available, we propose a new algorithm based on two well-known algorithms called Bron-Kerbosch and Tomita-Tanaka-Takahashi. The key idea of our algorithm is to find a good pivot quickly using geometric proximity. We validate the practical impact of our algorithm via experimental evaluations.

In retinal blood vessel extraction through background removal, the vessels in a fundus image which appear in a higher illumination variance area are often missing after the background is removed. This is because the intensity values of the vessel and the background are nearly the same. Thus, the estimated background should be robust to changes of the illumination intensity. This paper proposes retinal blood vessel extraction using background estimation. The estimated background is calculated by using a weight surface fitting method with a high degree polynomial. Bright pixels are defined as unwanted data and are set as zero in a weight matrix. To fit a retinal surface with a higher degree polynomial, fundus images are reduced in size by different scaling parameters in order to reduce the processing time and complexity in calculation. The estimated background is then removed from the original image. The candidate vessel pixels are extracted from the image by using the local threshold values. To identify the true vessel region, the candidate vessel pixels are dilated from the candidate. After that, the active contour without edge method is applied. The experimental results show that the efficiency of the proposed method is higher than the conventional low-pass filter and the conventional surface fitting method. Moreover, rescaling an image down using the scaling parameter at 0.25 before background estimation provides as good a result as a non-rescaled image does. The correlation value between the non-rescaled image and the rescaled image is 0.99. The results of the proposed method in the sensitivity, the specificity, the accuracy, the area under the receiver operating characteristic (ROC) curve (AUC) and the processing time per image are 0.7994, 0.9717, 0.9543, 0.9676 and 1.8320 seconds for the DRIVE database respectively.

In this paper, we present new methods for learning the individual patterns of a card user's transaction amount and the region in which he or she uses the card, for a given period, and for determining whether the specified transaction is allowable in accordance with these learned user transaction patterns. Then, we classify legitimate transactions and fraudulent transactions by setting thresholds based on the learned individual patterns.

Multi-antenna relay transport protocols are analysed, the transmitting matrix of relay node can split into a forward and a backward filters, and these two filters are cascade connection. Based on the zero-forcing relaying protocol, a spatial channel mapping matrix is added between these two filters, and a unified framework of spatial channel mapping matrix is proposed. Then, various linear system designs are summarized, the spatial channel mapping matrix is used to reduce destination noise, so that the relaying noise is suppressed in destination node, and the transmitting power of relay is efficiently utilized. Meanwhile, source node preprocessing operation and destination node equalizer are considered. Simulation results show that the spatial channel mapping matrix has an advantage in terms of system outage probability and capacity performance, and the result is consistent with theoretical analysis.

In order to verify the channel sum-rate improvement by multi-user multiple-input multiple-output (MU-MIMO) transmission in distributed antenna systems (DASs), we investigate and compare the characteristics of channel sum-rates in both centralized antenna systems (CASs) and DASs under the effects of path loss, spatially correlated shadowing, correlated multi-path fading, and inter-cell interference. In this paper, we introduce two different types of functions to model the shadowing, auto-correlation and cross-correlation, and a typical exponential decay function to model the multi-path fading correlation. Thus, we obtain the distribution of the channel sum-rate and investigate its characteristics. Computer simulation results indicate that DAS can improve the performance of the channel sum-rate compared to CAS, even in the case under consideration. However, this improvement decreases as interference power increases. Moreover, the decrease in the channel sum-rate due to the increase in the interference power becomes slow under the effect of shadowing correlation. In addition, some other analyses on the shadowing correlation that occurs on both the transmit and receiver sides are provided. These analysis results show that the average channel sum-rate in a DAS without inter-cell interference considerably decreases because of the shadowing correlation. In contrast, there appears to be no change in the CAS. Furthermore, there are two different types of sum-rate changes in a DAS because of the difference in shadowing auto-correlation and cross-correlation.

In order to improve the motion control performance, a new friction determination method, using the LuGre model, is proposed. The model parameters are determined by performing two-step closed-loop experiments using a proportional-integral observer (PIO). The PIO is also used to develop a robust motion controller to deal with additional uncertainties including the effect of the inaccurate estimation of the friction. The experimental results reveal improved performance compared to that of a single-PIO-based controller.

In this letter, we propose a simple framework for accelerating a state-of-the-art histogram-based weighted median filter at no expense. It is based on a process of determining the filter processing direction. The determination is achieved by measuring the local feature variation of input images. Through experiments with natural images, it is verified that, depending on input images, the filtering speed can be substantially increased by changing the filtering direction.

During the investment process for enhancing the level of IT security, organizations typically rely on two kinds of security countermeasures, i.e., proactive security countermeasures (PSCs) and reactive security countermeasures (RSCs). The PSCs are known to prevent security incidents before their occurrence, while the RSCs identify security incidents and recover the damaged hardware and software during or after their occurrence. Some researchers studied the effect of the integration of PSCs and RSCs, and showed that the integration can control unwanted incidents better than a single type of security countermeasure. However, the studies were made mostly in a qualitative manner, not in a quantitative manner. In this paper, we focus on deriving a quantitative model that analyzes the influence of different conditions on the efficiency of the integrated security countermeasures. Using the proposed model, we analyze for the first time how vulnerability and the potential exploits resulting from such vulnerability can affect the efficiency of the integrated security countermeasures; furthermore, we analytically verify that as the efficiency of PSCs increases, the burden of RSCs decreases, and vice versa. Also, we describe how to select possibly optimal configurations of the integrated security countermeasures.

This letter investigates the consensus problem for an undirected network of high-order integrators with an arbitrarily large communication delay. A consensus protocol with the low gain parameter that can eliminate an effect of time delay on the consensus problem is proposed newly. Moreover, it is proved that under some sufficient conditions, it can solve the consensus problem in the presence of an arbitrarily large communication delay. A simulation example is presented to verify the validness of the proposed design.

Content caching networks like Information-Centric Networking (ICN) are beneficial to reduce the network traffic by storing content data on routers near to users. In ICN, it becomes an important issue to manage system resources, such as storage and network bandwidth, which are influenced by cache characteristics of each cache node. Meanwhile, cache aging techniques based on Time-To-Live (TTL) of content facilitate analyzing cache characteristics and can realize appropriate resource management by setting efficient TTLs. However, it is difficult to search for the efficient TTLs in a distributed cache system connected by multiple cache nodes. Therefore, we propose an adaptive control mechanism of the TTL value of content in distributed cache systems by using predictive models which can estimate the impact of the TTL values on network resources and cache performance. Furthermore, we show the effectiveness of the proposed mechanism.

In this paper, we propose a framework for the real-time estimation of a multidimensional QoE of Multi-View Video and Audio (MVV-A) IP transmission. The framework utilizes linear multiple regression analysis with application-level and transport-level QoS parameters which can be measured in real time. In order to cope with a variety of MVV-A usage-situations, we introduce the concept of usage-situation type for grouping usage-situations with similar features to apply a representative regression line. We deal with two contents, two camera arrangements, and two user interfaces for viewpoint change as representative examples of the usage-situations. We assess multidimensional QoE of MVV-A with various types of average load, playout buffering time, and delay in the network. We then conduct the multiple regression analysis for the multidimensional QoE values represented by a psychological scale. From the comparison of measured values and estimated ones, we notice that real-time estimation of QoE is feasible in MVV-A IP transmission.

The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.

Based on the work by Helleseth [1], for an odd prime p and an even integer n=2m, the cross-correlation values between two decimated m-sequences by the decimation factors 2 and 4pn/2-2 are derived. Their cross-correlation function is at most 4-valued, that is, $igg {rac{-1 pm p^{n/2}}{2}, rac{-1 + 3p^{n/2}}{2}, rac{-1 + 5p^{n/2}}{2} igg }$. From this result, for pm ≠ 2 mod 3, a new sequence family with family size 4N and the maximum correlation magnitude upper bounded by $rac{-1 + 5p^{n/2}}{2} simeq rac{5}{sqrt{2}}sqrt{N}$ is constructed, where $N = rac{p^n-1}{2}$ is the period of sequences in the family.

We report the energy-efficient optical input interface using NbN superconducting nanowire-based optical-to-electrical (SN-OE) converters for a single-flux-quantum (SFQ) data processing system. The SN-OE converters with small active areas ranging from 1$, imes,$1 to 10$, imes,$10,$mu$m$^2$ were fabricated to improve the recovery time by reducing the kinetic inductance of the nanowire. The SN-OE with the smallest area of 1$, imes,$1 $mu$m$^2$ showed the recovery time of around 0.3 ns, while its detection efficiency for a single photon was reduced below 0.1% due to insufficient coupling efficiency with a single-mode optical fiber. However, the optical power dependence of the error rate of this device showed that the required optical power to achieve the error rate below $10^{-12}$ at 10 GHz operation is as large as 70 $mu$W, which is still one order of magnitude lower than semiconductor photo diodes. We also demonstrated the operation of the SN-OE converters combined with the SFQ readout circuit and confirmed the operating speed up to 77~MHz.