Named Data Networking (NDN) is a promising architecture for the future Internet and it is mainly designed for efficient content delivery and retrieval. However, producer mobility support is one of the challenging problems of NDN. This paper proposes a scheme which aims to optimize the tunneling-based producer mobility solution in NDN. It does not require NDN routers to change their routing tables (Forwarding Information Base) after a producer moves. Instead, the Interest packet can be sent from a consumer to the moved producer using the tunnel. The piggybacked Data packet which is sent back to the consumer will trigger the consumer to send the following Interest packets through the optimized path to the producer. Moreover, a naming scheme is proposed so that the NDN caching function can be fully utilized. An analysis is carried out to evaluate the performance of the proposal. The results indicate that the proposed scheme reduces the network cost compared to related works and supports route optimization for enhanced producer mobility support in NDN.

In order to satisfy the very high traffic demand in crowded hotspot areas and realize adequate security in future fifth-generation networks, this paper studies physical-layer security in the downlink of a two-tier ultra dense heterogeneous network, where a ubiquitous array formed by ultra dense deployed small-cells surrounds a macrocell base station. In this paper, the locations of legitimate users and eavesdroppers are drawn from Poisson point processes. Then, the cumulative distribution functions of the receive signal-to-interference-plus-noise ratio for legitimate users and eavesdroppers are derived. Further, the average secrecy rate and secrecy coverage probability for each tier as well as for the whole network are investigated. Finally, we analyze the influences on secrecy performance caused by eavesdropper density, transmit power allocation ratio, antenna number allocation ratio, and association area radius.

Web browsing services are expanding as smartphones are becoming increasingly popular worldwide. To provide customers with appropriate quality of web-browsing services, quality design and in-service quality management on the basis of quality of experience (QoE) is important. We propose a web-browsing QoE estimation model. The most important QoE factor for web-browsing is the waiting time for a web page to load. Next, the variation in the communication quality based on a mobile network should be considered. We developed a subjective quality assessment test to clarify QoE characteristics in terms of waiting time using 20 different types of web pages and constructed a web-page QoE estimation model. We then conducted a subjective quality assessment test of web-browsing to clarify the relationship between web-page QoE and web-browsing QoE for three web sites. We obtained the following two QoE characteristics. First, the main factor influencing web-browsing QoE is the average web-page QoE. Second, when web-page QoE variation occurs, a decrease in web-page QoE with a huge amplitude causes the web-browsing QoE to decrease. We used these characteristics in constructing our web-browsing QoE estimation model. The verification test results using non-training data indicate the accuracy of the model. We also show that our findings are applicable to web-browsing quality design and solving management issues on the basis of QoE.

Systematic research on electromagnetic compatibility (EMC) in Japan started in 1977 by the establishment of a technical committee on “environmental electromagnetic engineering” named EMCJ, which was founded both in the Institute of Electronics and Communication Engineers or the present IEICE (Institute of Electronics, Information and Communication Engineers) and in the Institute of Electrical Engineers of Japan or the IEEJ. The research activities have been continued as the basic field of interdisciplinary study to harmonize even in the electromagnetic (EM) environment where radio waves provide intolerable EM disturbances to electronic equipment and to that environment itself. The subjects and their outcomes which the EMCJ has dealt with during about 40 years from the EMCJ establishment include the evaluation of EM environment, EMC of electric and electronic equipment, and EMC of biological effects involving bioelectromagnetics and so on. In this paper, the establishment history and structure of the EMCJ are reviewed along with the change in activities, and topics of the technical reports presented at EMCJ meetings from 2006 to 2016 are surveyed. In addition, internationalization and its related campaign are presented in conjunction with the EMCJ research activities, and the status quo of the EMCJ under the IEICE is also discussed along with the prospects.

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.

This paper looks at the history of research in the Technical Committee on Information Networks from the time of its inception to the present and provides an overview of the latest research in this area based on the topics discussed in recent meetings of the committee. It also presents possible future developments in the field of information networks.

Many DC commutator motors are widely used in automobiles. In recent years, as compact and high output DC motors have been developed due to advanced technology, the faster the rotational speed is required and the commutation arc causes a high rate of wear/erosion of brush and commutator. Therefore, it is important how the motor speed influences commutation phenomena such as arc duration, residual current and erosion and wear of commutator and brush in order to achieve high reliability and extensive lifespan. In this paper waveforms of commutation voltage and current are measured at the rotation speed of 1000 to 5000min-1and the relation between rotation speed and arc duration / residual current is obtained. In addition long term tests are carried out at the rotation speed of 1000 to 5000min-1 the change of arc duration and effective commutation period is examined during the test of 20hours. Further, brush wear is evaluated by the difference of brush length between before and after test. Consequently, it can be made clear that as the speed increases, the effective commutation period decreases and the arc duration is almost same at the speed up to 3000min-1 and is around 42µsec.

Asymmetric bilinear maps using Type-3 pairings are known to be advantageous in several points (e.g., the speed and the size of a group element) to symmetric bilinear maps using Type-1 pairings. Kremer and Mazaré introduce a symbolic model to analyze protocols based on bilinear maps, and show that the symbolic model is computationally sound. However, their model only covers symmetric bilinear maps. In this paper, we propose a new symbolic model to capture asymmetric bilinear maps. Our model allows us to analyze security of various protocols based on asymmetric bilinear maps (e.g., Joux's tripartite key exchange, and Scott's client-server ID-based key exchange). Also, we show computational soundness of our symbolic model under the decisional bilinear Diffie-Hellman assumption.

Scalar multiplication over higher degree rational point groups is often regarded as the bottleneck for faster pairing based cryptography. This paper has presented a skew Frobenius mapping technique in the sub-field isomorphic sextic twisted curve of Kachisa-Schaefer-Scott (KSS) pairing friendly curve of embedding degree 18 in the context of Ate based pairing. Utilizing the skew Frobenius map along with multi-scalar multiplication procedure, an efficient scalar multiplication method for KSS curve is proposed in the paper. In addition to the theoretic proposal, this paper has also presented a comparative simulation of the proposed approach with plain binary method, sliding window method and non-adjacent form (NAF) for scalar multiplication. The simulation shows that the proposed method is about 60 times faster than plain implementation of other compared methods.

In ProvSec 2014, Wang and Tanaka proposed a transformation which converts weakly existentially unforgeable (wEUF) signature schemes into strongly existentially unforgeable (sEUF) ones in the bounded leakage model. To obtain the construction, they combined leakage resilient (LR) chameleon hash functions with the Generalised Boneh-Shen-Waters (GBSW) transformation proposed by Steinfeld, Pieprzyk, and Wang. However, their transformation cannot be used in a more realistic model called continual leakage model since secret keys of LR chameleon hash functions cannot be updated. In this paper, we propose a transformation which can convert wEUF signature schemes into sEUF ones in the continual leakage model. To achieve our goal, we give a new definition of continuous leakage resilient (CLR) chameleon hash function and construct it based on the CLR signature scheme proposed by Malkin, Teranishi, Vahlis, and Yung. Although our CLR chameleon hash functions satisfy the property of strong collision-resistance, due to the existence of the updating algorithm, an adversary may find the kind of collisions such that messages are the same but randomizers are different. Hence, we cannot combine our chameleon hash functions with the GBSW transformation directly, or the sEUF security of the transformed signature schemes cannot be achieved. To solve this problem, we improve the original GBSW transformation by making use of the Groth-Sahai proof system and then combine it with CLR chameleon hash functions.

An inter-subset uncorrelated zero-correlation zone (ZCZ) sequence pair set is one consisting of multiple ZCZ sequence pair subsets. What's more, two arbitrary sequence pairs which belong to different subsets should be uncorrelated sequence pairs in this set, i.e., the cross-correlation function (CCF) between arbitrary sequence pairs in different subsets are zeros at everywhere. Meanwhile, each subset is a typical ZCZ sequence pair set. First, a class of uncorrelated ZCZ (U-ZCZ) sequence pair sets is proposed from interleaving perfect sequence pairs. An U-ZCZ sequence pair set is a type of ZCZ sequence pair set, which of most important property is that the CCF between two arbitrary sequence pairs is zero at any shift. Then, a type of inter-subset uncorrelated ZCZ sequence pair set is obtained by interleaving proposed U-ZCZ sequence pair set. In particular, the novel inter-subset uncorrelated ZCZ sequence pair sets are expected to be useful for designing spreading codes for QS-CDMA systems.

We describe SyncSync, a time-of-arrival (ToA)-based localization method for smartphones. In general, ToA measurements show better precision than time-difference-of-arrival (TDoA) measurements, although ToA systems require a synchronization mechanism between anchor and mobile nodes. For this synchronization, we employ modulated LED light with an acoustic wave for time-of-flight distance measurements. These are detected by the smartphone's video camera and microphone. The time resolution in consumer video cameras is typically only a few tenths of a second, but by utilizing a CMOS image sensor's rolling shutter effect we obtain synchronization resolutions of a few microseconds, which is sufficient for precise acoustic ToA measurements. We conducted experiments to confirm operation of the system, and obtained ToA localization errors within 10mm. The characteristics of the error distributions for both TDoA and ToA measurements were explained by dilution of precision.

In this paper, we propose a method for removing block noise which appears in JPEG (Joint Photographic Experts Group) encoded images. We iteratively perform the 3D wiener filtering and correction of the coefficients. In the wiener filtering, we perform the block matching for each patch in order to get the patches which have high similarities to the reference patch. After wiener filtering, the collected patches are returned to the places where they were and aggregated. We compare the performance of the proposed method to some conventional methods, and show that the proposed method has an excellent performance.

This paper presents a low-latency, low-cost architecture for computing square and cube roots in the fixed-point format. The proposed architecture is designed based on a non-iterative root calculation scheme to achieve fast computations. While previous non-iterative root calculators are restricted to a square-root operation due to the limitation of their mathematical property, the root computation is generalized in this paper to apply an approximation method to the non-iterative scheme. On top of that, a recurrent method is proposed to select parameters, which enables us to reduce the table size while keeping the maximum relative error value low. Consequently, the proposed root calculator can support both square and cube roots at the expense of small delay and low area overheads. This extension can be generalized to compute the nth roots, where n is a positive integer.

Aiming at detecting pronunciation errors produced by second language learners and providing corrective feedbacks related with articulation, we address effective articulatory models based on deep neural network (DNN). Articulatory attributes are defined for manner and place of articulation. In order to efficiently train these models of non-native speech without such data, which is difficult to collect in a large scale, several transfer learning based modeling methods are explored. We first investigate three closely-related secondary tasks which aim at effective learning of DNN articulatory models. We also propose to exploit large speech corpora of native and target language to model inter-language phenomena. This kind of transfer learning can provide a better feature representation of non-native speech. Related task transfer and language transfer learning are further combined on the network level. Compared with the conventional DNN which is used as the baseline, all proposed methods improved the performance. In the native attribute recognition task, the network-level combination method reduced the recognition error rate by more than 10% relative for all articulatory attributes. The method was also applied to pronunciation error detection in Mandarin Chinese pronunciation learning by Japanese native speakers, and achieved the relative improvement up to 17.0% for detection accuracy and up to 19.9% for F-score, which is also better than the lattice-based combination.

Volume segmentation is of great significances for feature visualization and feature extraction, essentially volume segmentation can be viewed as generalized cluster. This paper proposes a hybrid approach via symmetric region growing (SRG) and information diffusion estimation (IDE) for volume segmentation, the volume dataset is over-segmented to series of subsets by SRG and then subsets are clustered by K-Means basing on distance-metric derived from IDE, experiments illustrate superiority of the hybrid approach with better segmentation performance.

We propose a method of interframe prediction in depth map coding that uses pixel-wise 3D motion estimated from encoded textures and depth maps. By using the 3D motion, an approximation of the depth map frame to be encoded is generated and used as a reference frame of block-wise motion compensation.

In this letter, we consider a cognitive radio network where multiple secondary users (SUs) share the spectrum bands with multiple primary users (PUs) who are facing security threats from multiple eavesdroppers. By adopting the PU secrecy outage constraint to protect the PUs, we optimize the joint user and power allocation for the SUs to maximize the SU ergodic transmission rate. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that the proposed algorithm outperforms the existing scheme, especially for a large number of PUs and a small number of SUs. It is also shown that the number of eavesdroppers has negligible impact on the performance improvement of the proposed algorithm compared to the existing scheme. In addition, it is shown that increasing the number of eavesdroppers has insignificant impact on the SU performance if the number of eavesdroppers is already large.

Researchers have already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental workload, but this phenomenon has not been explored adequately. Especially, there still lacks an automated method for accurately predicting mental workload using handwriting features. To solve the problem, we first conducted an experiment to collect handwriting data under different mental workload conditions. Then, a predictive model (called SVM-GA) on two-level handwriting features (i.e., sentence- and stroke-level) was created by combining support vector machines and genetic algorithms. The results show that (1) the SVM-GA model can differentiate three mental workload conditions with accuracy of 87.36% and 82.34% for the child and adult data sets, respectively and (2) children demonstrate different changes in handwriting features from adults when experiencing mental workload.

In this paper, we propose a novel gate delay time mismatch tolerant time-mode signal accumulator whose input and output are represented by a time difference of two digital signal transitions. Within the proposed accumulator, the accumulated value is stored as the time difference between the two pulses running around the same ring of a delay line, so that there is no mismatch between the periods of the two pulses, thus the output drift of the accumulator is suppressed in principle without calibrating mismatch of two rings, which is used to store the accumulated value in the conventional one. A prototype of the proposed accumulator was fabricated in 180nm CMOS. The accumulating operation is confirmed by both time and frequency domain experiments. The standard deviation of the error of the accumulating operation is 9.8ps, and compared with the previous work, the peak error over full-scale is reduced by 46% without calibrating the output drift.