This letter presents the safety confirmation system based on Near Field Communication (NFC) and Ultra High Frequency (UHF) band Radio Frequency IDentification (RFID) tags. Because these RFID tags can operate without the need for internal batteries, the proposed safety confirmation system is effective during large-scale disasters that cause loss of electricity and communication infrastructures. Sharing safety confirmation data between the NFC and UHF band RFID tags was studied to confirm the feasibility of the data sharing. The prototype of the proposed system was fabricated, confirming the feasibility of the proposed safety confirmation system.

Power line communication (PLC) networks play an important role in home networks and in next generation hybrid networks, which provide higher data rates (Gbps) and easier connectivity. The standard medium access control (MAC) protocol of PLC networks, IEEE 1901, uses a special carrier sense multiple access with collision avoidance (CSMA/CA) mechanism, in which the deferral counter technology is introduced to avoid unnecessary collisions. Although PLC networks have achieved great commercial success, MAC layer analysis for IEEE 1901 PLC networks received limited attention. Until now, a few studies used renewal theory and strong law of large number (SLLN) to analyze the MAC performance of IEEE 1901 protocol. These studies focus on saturated conditions and neglect the impacts of buffer size and traffic rate. Additionally, they are valid only for homogeneous traffic. Motivated by these limitations, we develop a unified and scalable analytical model for IEEE 1901 protocol in unsaturated conditions, which comprehensively considers the impacts of traffic rate, buffer size, and traffic types (homogeneous or heterogeneous traffic). In the modeling process, a multi-layer discrete Markov chain model is constructed to depict the basic working principle of IEEE 1901 protocol. The queueing process of the station buffer is captured by using Queueing theory. Furthermore, we present a detailed analysis for IEEE 1901 protocol under heterogeneous traffic conditions. Finally, we conduct extensive simulations to verify the analytical model and evaluate the MAC performance of IEEE 1901 protocol in PLC networks.

Aircraft landing scheduling (ALS) is one of the most important challenges in air traffic management. The target of ALS is to decide a landing scheduling sequence and calculate a landing time for each aircraft in terminal areas. These landing times are within time windows, and safety separation distances between aircraft must be kept. ALS is a complex problem, especially with a large number of aircraft. In this study, we propose a novel heuristic called CGIC to solve ALS problems. The CGIC consists of four components: a chunking rule based on costs, a landing subsequence generation rule, a chunk improvement heuristic, and a connection rule. In this algorithm, we reduce the complexity of the ALS problem by breaking it down into two or more subproblems with less aircraft. First, a feasible landing sequence is generated and divided into several subsequences as chunks by a chunking rule based on aircraft cost. Second, each chunk is regenerated by a constructive heuristic, and a perturbative heuristic is applied to improve the chunks. Finally, all chunks constitute a feasible landing sequence through a connection rule, and the landing time of each aircraft is calculated on the basis of this sequence. Simulations demonstrate that (a) the chunking rule based on cost outperforms other chunking rules based on time or weight for ALS in static instances, which have a large number of aircraft; (b) the proposed CGIC can solve the ALS problem up to 500 aircraft optimally; (c) in dynamic instances, CGIC can obtain high-quality solutions, and the computation time of CGIC is low enough to enable real-time execution.

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.

This paper presents a conifer and broad-leaf tree classification scheme that processes high resolution polarimetric synthetic aperture data above X-band. To validate the proposal, fully polarimetric measurements are conducted in a precisely controlled environment to examine the difference between the scattering mechanisms of conifer and broad-leaf trees at 15GHz. With 3.75cm range resolution, scattering matrices of two tree types were measured by a vector network analyzer. Polarimetric analyses using the 4-component scattering power decomposition and alpha-bar angle of eigenvalue decomposition yielded clear distinction between the two tree types. This scheme was also applied to an X-band Pi-SAR2 data set. The results confirm that it is possible to distinguish between tree types using fully polarimetric and high-resolution data above X-band.

The blind multichannel identification is useful in many applications. Although many approaches have been proposed to address this challenging problem, the adaptive filtering-based methods are attractive due to their computational efficiency and good convergence property. The multichannel normalized least mean-square (MCNLMS) algorithm is easy to implement, but it converges very slowly for a correlated input. The multichannel affine projection algorithm (MCAPA) is thus proposed to speed up the convergence. However, the convergence of the MCNLMS and MCAPA is still unsatisfactory in practice. In this paper, we propose a time-domain Kalman filtering approach to the blind multichannel identification problem. Specifically, the proposed adaptive Kalman filter is based on the cross relation method and also uses more past input vectors to explore the decorrelation property. Simulation results indicate that the proposed method outperforms the MCNLMS and MCAPA significantly in terms of the initial convergence and tracking capability.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

This article presents a universal and versatile model of multiservice overflow systems based on Hayward's concept. The model can be used to analyze modern telecommunications and computer networks, mobile networks in particular. The advantage of the proposed approach lies in its ability to analyze overflow systems with elastic and adaptive traffic, systems with distributed resources and systems with non-full-availability in primary and secondary resources.

Affine projection sign algorithm (APSA) is an important adaptive filtering method to combat the impulsive noisy environment. However, the performance of APSA is poor, if its regularization parameter is not well chosen. We propose a variable regularization APSA (VR-APSA) approach, which adopts a gradient-based method to recursively reduce the norm of the a priori error vector. The resulting VR-APSA leverages the time correlation of both the input signal matrix and error vector to adjust the value of the regularization parameter. Simulation results confirm that our algorithm exhibits both fast convergence and small misadjustment properties.

Sectors in the airspace are units of the air traffic control. For airspace traffic data consists of the location of each aircraft with timestamp, we propose an efficient method to identify the sector where each aircraft lies.

In recent years, algebraic attacks and fast algebraic attacks have received a lot of attention in the cryptographic community. There are three Boolean functions achieving optimal algebraic immunity based on primitive element of F2n. The support of Boolean functions in [1]-[3] have the same parameter s, which makes us have a large number of Boolean functions with good properties. However, we prove that the Boolean functions are affine equivalence when s takes different values.

In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.

In this work, gold powder made from gold leaf investigated to have the potential as a filler of conductive ink. The resistance of a conductive film decreased from 6.2kΩ to 1 Ω by adding only 2.0wt% of gold powder to conductive polymer (PEDOT:PSS) ink. The change of the resistance depends on the characteristics of gold powder. Gold powder with smaller and uniform sizes and good dispersibility is beneficial to form a continuous percolation network.

Multi-static Primary Surveillance Radar (MSPSR) has recently attracted attention as a new surveillance technology for civil aviation. Using multiple receivers, Primary Surveillance Radar (PSR) detection performance can be improved by synthesizing the reflection characteristics which change due to the aircraft's position. In this paper, we report experimental results from our proposed optical-fiber-connected passive PSR system with transmit signal installed at the Sendai Airport in Japan. The signal-to noise ratio of experimental data is evaluated to verify moving target detection. In addition, we confirm the operation of the proposed system using a two-receiver setup, to resemble a conventional multi-static radar. Finally, after applying time correction, the delay of the reflected signal from a stationary target remains within the expected range.

Given a network G(V,E), a lightweight method to calculate overlaid origin-destination (O-D) traffic flows on all edges is developed. Each O-D trip shall select the shortest path. While simple implementations for single-source/all-destination and all-pair trips need O(L·n) and O(L·n2) in worst-case time complexity, respectively, our technique is executed with O(m+n) and O(m+n2), where n=|V|, m=|E|, and L represents the maximum arc length. This improvement is achieved by reusing outcomes of priority queue-based algorithms. Using a GIS dataset of a road network in Tokyo, Japan, the effectiveness of our technique is confirmed.

Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.

Traffic matrix (TM) estimation has been extensively studied for decades. Although conventional estimation techniques assume that traffic volumes are unchanged between origins and destinations, packets are often lost on a path due to traffic burstiness, silent failures, etc. Counting every path at every link, we could easily get the traffic volumes with their change, but this approach significantly increases the measurement cost since counters are usually implemented using expensive memory structures like a SRAM. This paper proposes a mathematical model to estimate TMs including volume changes. The method is established on a Boolean fault localization technique; the technique requires fewer counters as it simply determines whether each link is lossy. This paper extends the Boolean technique so as to deal with traffic volumes with error bounds that requires only a few counters. In our method, the estimation errors can be controlled through parameter settings, while the minimum-cost counter placement is determined with submodular optimization. Numerical experiments are conducted with real network datasets to evaluate our method.

A reconfigurable broadband linear power amplifier (PA) for long-range WLAN applications fabricated in a 180nm RF CMOS process is presented here. The proposed reconfigurable in/output matching network provides the PA with broadband capability at the two center frequencies of 0.5GHz and 0.85GHz. The output matching network is realized by a switchable transformer which shows maximum peak passive efficiencies of 65.03% and 73.45% at 0.45GHz and 0.725GHz, respectively. With continuous wave sources, a 1-dB bandwidth (BW1-dB) according to saturated output power is 0.4-1.2GHz, where it shows a minimum output power with a power added efficiency (PAE) of 25.62dBm at 19.65%. Using an adaptive power cell configuration at the common gate transistor, the measured PA under LTE 16-QAM 20MHz (40MHz) signals shows an average output power with a PAE exceeding 20.22 (20.15) dBm with 7.42 (7.35)% at an ACLRE-UTRA of -30dBc, within the BW1-dB.

Many single model methods have been applied to real-time short-term traffic flow prediction. However, since traffic flow data is mixed with a variety of ingredients, the performance of single model is limited. Therefore, we proposed Multi-Long-Short Term Memory Models, which improved traffic flow prediction accuracy comparing with state-of-the-art models.

Defects in spacecraft software may result in loss of life and serious economic damage. To avoid such consequences, the software development process incorporates code review activity. A code review conducted by a third-party organization independently of a software development team can effectively identify defects in software. However, such review activity is difficult for third-party reviewers, because they need to understand the entire structure of the code within a limited time and without prior knowledge. In this study, we propose a tool to visualize inter-module dataflow for source code of spacecraft software systems. To evaluate the method, an autonomous rover control program was reviewed using this visualization. While the tool does not decreases the time required for a code review, the reviewers considered the visualization to be effective for reviewing code.