This paper presents the set of procedures to blend GNSS and V2V communication to improve the performance of the stand-alone on-board GNSS receiver and to assure mutual positioning with a bounded error. Particle filter algorithm is applied to enhance mutual positioning of vehicles, and it fuses the information provided by the GNSS receiver, wireless measurements in vehicular environments, odometer, and digital road map data including reachability and zone probabilities. Measurement-based statistical model of relative distance as a function of Time-of-Arrival is experimentally obtained. The number of collaborative vehicles to the mutual positioning procedure is investigated in terms of positioning accuracy and network performance through realistic simulation studies, and the proposed mutual positioning procedure is experimentally evaluated by a fleet of five IEEE 802.11p radio modem equipped vehicles. Collaboration in a VANET improves availability of position measurement and its accuracy up to 40% in comparison with respect to the stand-alone GNSS receiver.

Power line noise is one of critical problems in bio-sensing. Various approaches utilizing both analog and digital techniques has been proposed. However, these approaches need active circuits with a wide dynamic range. N-path notch filters which implementable using passive components can be a promising solution to this problem. However, the notch depth of a conventional N-path notch filter is limited by the number of path. A new N-path notch filter with additional S/H circuit is proposed. Simulation results show that the proposed topology improves the notch depth by 43dB.

Interference alignment (IA) is a promising technology for eliminating interferences while it still achieves the optimal capacity scaling. However, in practical systems, the IA feasibility limit and the heavy signaling overhead obstructs employing IA to large-scale networks. In order to jointly consider these issues, we propose the feedback overhead-aware IA clustering algorithm which comprises two parts: adaptive feedback resource assignment and dynamic IA clustering. Numerical results show that the proposed algorithm offers significant performance gains in comparison with conventional approaches.

Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.

A broadband approach to estimate the relative permittivity of dielectric cuboids has been proposed for materials of weak frequency dispersive characteristic. Our method involves a numerical iterative scheme with appropriate initial values carefully selected to solve for the relative permittivity in a wide range of frequencies. Good agreements between our method and references have been observed for nylon and acrylic samples. An applicable range relation between the minimal thickness, the frequency range and the dielectric property of the material has also been discussed.

This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences. Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme. More specifically, temporal cues of object hypotheses are captured by the motion signature proposed in this paper, which is derived from sparse saliency representation imposed on magnitude of optical flow field. We integrate semantic scores derived from deep networks with location priors that allows us to directly estimate appearance potentials of foreground hypotheses. A unified MRF energy functional is proposed to simultaneously incorporate the information from the motion signature and semantic prediction features. The functional enforces both spatial and temporal consistency and impose appearance constancy and spatio-temporal smoothness constraints directly on the object hypotheses. It inherently handles the challenges of segmenting ambiguous objects with large displacements in high speed videos. Our experiments on video object segmentation benchmarks demonstrate the effectiveness of the proposed method for segmenting high speed objects despite the complicated scene dynamics and large displacements.

Service chaining (SC) is a method for realizing a service by transferring flows among several service functions (SFs) that process packets. A route among SFs is called a service path (SP). Service chaining is being developed to reduce costs, increase flexibility, and shorten time-to-market. SC technologies are expected to be applied to carrier networks so that large communication carriers benefit from them. We assume that SPs process the traffic of services that treat all users in the same way such as an Internet access service for home users. An SP processes flows from several users. We do not assume that each SP is assigned to a user. Because a carrier network accommodates many users, each service will be heavily utilized. Therefore, it is assumed that the amount of traffic of a service is larger than the resource of an SF apparatus. Several SPs are required to process the traffic. SPs are supposed to meet two requirements. One is guaranteeing minimum bandwidth. The other is reducing the number of SF apparatuses, i.e., high resource utilization. Resource utilization depends on the combination of the resource quantities of SF apparatuses. Network operators have to determine the bandwidth of each SP within the range from the minimum bandwidth to the resource quantities of SF apparatuses to maximize resource utilization. Methods for determining the bandwidth of each SP have not been proposed for meeting the two requirements. Therefore, we propose a resource allocation method for this purpose. The proposed method determines the bandwidth of each SP on the basis of the combination of the resource quantities of SF apparatuses for guaranteeing the minimum bandwidth and maximizing resource utilization and allocates necessary resources to each SP. We also evaluate the proposed method and confirm that it can guarantee the minimum bandwidth of SPs and achieve high resource utilization regardless of the combination of the resource quantities of SF apparatuses. Although SF apparatuses are generally produced without considering the combinations of resource quantities of SF apparatuses in SPs, the proposed method can provide more options for selecting SF apparatuses.

Within information hiding technology, digital watermarking is one of the most important technologies for copyright protection of digital content. Many digital watermarking schemes have been proposed in academia. However, these schemes are not used, because they are not practical; one reason for this is that the evaluation criteria are loosely defined. To make the evaluation more concrete and improve the practicality of digital watermarking, watermarking schemes must use common evaluation criteria. To realize such criteria, we organized the Information Hiding and its Criteria for Evaluation (IHC) Committee to create useful, globally accepted evaluation criteria for information hiding technology. The IHC Committee improves their evaluation criteria every year, and holds a competition for digital watermarking based on state-of-the-art evaluation criteria. In this paper, we describe the activities of the IHC Committee and its evaluation criteria for digital watermarking of still images, videos, and audio.

We investigate the coding scheme and error probability in information transmission over continuous-time additive Gaussian noise channels with feedback. As is known, the error probability can be substantially reduced by using feedback, namely, under the average power constraint, the error probability may decrease more rapidly than the exponential of any order. Recently Gallager and Nakibolu proposed, for discrete-time additive white Gaussian noise channels, a feedback coding scheme such that the resulting error probability Pe(N) at time N decreases with an exponential order αN which is linearly increasing with N. The multiple-exponential decay of the error probability has been studied mostly for white Gaussian channels, so far. In this paper, we treat continuous-time Gaussian channels, where the Gaussian noise processes are not necessarily white nor stationary. The aim is to prove a stronger result on the multiple-exponential decay of the error probability. More precisely, for any positive constant α, there exists a feedback coding scheme such that the resulting error probability Pe(T) at time T decreases more rapidly than the exponential of order αT as T→∞.

Many studies have applied the three-dimensional discrete wavelet transform (3D DWT) to video coding. It is known that corruptions of the lowest frequency sub-band (LL) coefficients of 3D DWT severely affect the visual quality of video. Recently, we proposed an error resilient 3D DWT video coding method (the conventional method) that employs dispersive grouping and an error concealment (EC). The EC scheme of our conventional method adopts a replacement technique of the lost LL coefficients. In this paper, we propose a new 3D DWT video transmission method in order to enhance error resilience. The proposed method adopts an error correction scheme using invertible codes to protect LL coefficients. We use half-rate Reed-Solomon (RS) codes as invertible codes. Additionally, to improve performance by using the effect of interleave, we adopt a new configuration scheme at the RS encoding stage. The evaluation by computer simulation compares the performance of the proposed method with that of other EC methods, and indicates the advantage of the proposed method.

A Reed-Solomon (RS) decoder is designed based on the pipelined recursive Euclidean algorithm in the key equation solution. While the Euclidean algorithm uses less Galois multipliers than the modified Euclidean (ME) and reformulated inversionless Berlekamp-Massey (RiBM) algorithms, division between two elements in Galois field is required. By implementing the division with a multi-cycle Galois inverter and a serial Galois multiplier, the proposed key equation solver architecture achieves lower complexity than the conventional ME and RiBM based architectures. The proposed RS (255,239) decoder reduces the hardware complexity by 25.9% with 6.5% increase in decoding latency.

This paper proposes a deep quasi-linear kernel for support vector machines (SVMs). The deep quasi-linear kernel can be constructed by using a pre-trained deep neural network. To realize this goal, a multilayer gated bilinear classifier is first designed to mimic the functionality of the pre-trained deep neural network, by generating the gate control signals using the deep neural network. Then, a deep quasi-linear kernel is derived by applying an SVM formulation to the multilayer gated bilinear classifier. In this way, we are able to further implicitly optimize the parameters of the multilayer gated bilinear classifier, which are a set of duplicate but independent parameters of the pre-trained deep neural network, by using an SVM optimization. Experimental results on different data sets show that SVMs with the proposed deep quasi-linear kernel have an ability to take advantage of the pre-trained deep neural networks and outperform SVMs with RBF kernels.

This paper presents a low power millimeter-wave oscillator consisting of a current-reused topology and buffer-feedback. By connecting a buffer-feedback topology between the core LC-tank of the oscillator and the output buffer stage, the simulated oscillation frequency of the proposed oscillator is increased by 17%, compared to that of the conventional current-reused oscillator. In addition, to obtain the same output power, the proposed oscillator reduces the power dissipation by 47%, compared to that of the conventional buffer-feedback oscillator. The prototype of the proposed oscillator is fabricated in a 65nm CMOS technology with a size of 700µm×480µm including pad. Measurement results indicate an oscillation frequency of 71.3GHz, while dissipating 10mA from a 1.6V supply.

Peer-to-peer (P2P) networks have attracted increasing attention in the distribution of large-volume and frequently accessed content. In this paper, we mainly consider the problem of key leakage in secure P2P content distribution. In secure content distribution, content is encrypted so that only legitimate users can access the content. Usually, users (peers) cannot be fully trusted in a P2P network because malicious ones might leak their decryption keys. If the redistribution of decryption keys occurs, copyright holders may incur great losses caused by free riders who access content without purchasing it. To decrease the damage caused by the key leakage, the individualization of encrypted content is necessary. The individualization means that a different (set of) decryption key(s) is required for each user to access content. In this paper, we propose a P2P content distribution scheme resilient to the key leakage that achieves the individualization of encrypted content. We show the feasibility of our scheme by conducting a large-scale P2P experiment in a real network.

This paper proposes a SAR-VCO hybrid 1-1 MASH ADC architecture, where a fully-passive 1st-order noise-shaping SAR ADC is implemented in the first stage to eliminate Op-amp. A VCO-based ADC quantizes the residue of the SAR ADC with one additional order of noise shaping in the second stage. The inter-stage gain error can be suppressed by a foreground calibration technique. The proposed ADC architecture is expected to accomplish 2nd-order noise shaping without Op-amp, which makes both high SNDR and low power possible. A prototype ADC is designed in a 65nm CMOS technology to verify the feasibility of the proposed ADC architecture. The transistor-level simulation results show that 75.7dB SNDR is achieved in 5MHz bandwidth at 60MS/s. The power consumption is 748.9µW under 1.0V supply, which results in a FoM of 14.9fJ/conversion-step.

This paper presents a novel non-native speech synthesis technique that preserves the individuality of a non-native speaker. Cross-lingual speech synthesis based on voice conversion or Hidden Markov Model (HMM)-based speech synthesis is a technique to synthesize foreign language speech using a target speaker's natural speech uttered in his/her mother tongue. Although the technique holds promise to improve a wide variety of applications, it tends to cause degradation of target speaker's individuality in synthetic speech compared to intra-lingual speech synthesis. This paper proposes a new approach to speech synthesis that preserves speaker individuality by using non-native speech spoken by the target speaker. Although the use of non-native speech makes it possible to preserve the speaker individuality in the synthesized target speech, naturalness is significantly degraded as the synthesized speech waveform is directly affected by unnatural prosody and pronunciation often caused by differences in the linguistic systems of the source and target languages. To improve naturalness while preserving speaker individuality, we propose (1) a prosody correction method based on model adaptation, and (2) a phonetic correction method based on spectrum replacement for unvoiced consonants. The experimental results using English speech uttered by native Japanese speakers demonstrate that (1) the proposed methods are capable of significantly improving naturalness while preserving the speaker individuality in synthetic speech, and (2) the proposed methods also improve intelligibility as confirmed by a dictation test.

As a research project supported jointly by the National Institute of Information and Communications Technology (NICT) in Japan and the European Commission under its 7th Framework Program, the GreenICN Project has been in operation from 2013 to 2016. The GreenICN project focused on two typical application scenarios, one a disaster scenario and the other a video delivery scenario. The disaster scenario assumed a situation of limited resources, and the video delivery scenario assumed a situation of large-scale content delivery. In both situations, the project challenged to provide “green”, i.e. energy-efficient, content delivery mechanism. For this goal, we designed an energy consumption model to lay out energy reduction policies. For the achievement of the policies, we improved ICN architecture, for example a name-based publish/subscribe mechanism, an effective cache management policy,energy-efficient security scheme and a new energy API. This paper provides a summary of our achievements and descriptions of some outcome.

In this paper, we investigate the degrees of freedom (DoF) of a MIMO cellular interfering network (CIN) with L (L≥3) cells and K users per cell. Previous works established the DoF upper bound of LK(M+N)/(LK+1) for the MIMO CIN by analyzing the interference alignment (IA) feasibility, where M and N denote the number of antennas at each base station (BS) and each user, respectively. However, there is still a gap between the DoF upper bound and the achievable DoF in existing designs. To address this problem, we propose two linear IA schemes without symbol extensions to jointly design transmit and receive beamforming matrices to align and eliminate interference. In the two schemes, the transmit beamforming vectors are allocated to different cluster structures so that the inter-cell interference (ICI) data streams from different ICI channels are aligned. The first scheme, named fixed cluster structure (FCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of fixed dimension and can achieve the DoF upper bound under some system configurations. The second scheme, named dynamic cluster structure IA (DCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of dynamic dimension and can get a tradeoff between the number of antennas at BSs and users so that ICI alignment can be applied under various system configurations. Through theoretical analysis and numerical simulations, we verify that the DoF upper bound can be achieved by using the FCS-IA scheme. Furthermore, we show that the proposed schemes can provide significant performance gain over the time division multiple access (TDMA) scheme in terms of DoF. From the perspective of DoF, it is shown that the proposed schemes are more effective than the conventional IA schemes for the MIMO CIN.

File system workloads are increasing write-heavy. The growing capacity of RAM in modern nodes allows many reads to be satisfied from memory while writes must be persisted to disk. Today's sophisticated local file systems like Ext4, XFS and Btrfs optimize for reads but suffer from workloads dominated by microdata (including metadata and tiny files). In this paper we present an LSM-tree-based file system, RFS, which aims to take advantages of the write optimization of LSM-tree to provide enhanced microdata performance, while offering matching performance for large files. RFS incrementally partitions the namespace into several metadata columns on a per-directory basis, preserving disk locality for directories and reducing the write amplification of LSM-trees. A write-ordered log-structured layout is used to store small files efficiently, rather than embedding the contents of small files into inodes. We also propose an optimization of global bloom filters for efficient point lookups. Experiments show our library version of RFS can handle microwrite-intensive workloads 2-10 times faster than existing solutions such as Ext4, Btrfs and XFS.

Excessive IR-drop in capture mode during at-speed scan testing may cause timing errors for defect-free circuits, resulting in undue test yield loss. Previous solutions for achieving capture-power-safety adjust the switching activity around logic paths, especially long sensitized paths, in order to reduce the impact of IR-drop. However, those solutions ignore the impact of IR-drop on clock paths, namely test clock stretch; as a result, they cannot accurately achieve capture-power-safety. This paper proposes a novel scheme, called LP-CP-aware ATPG, for generating high-quality capture-power-safe at-speed scan test vectors by taking into consideration the switching activity around both logic and clock paths. This scheme features (1) LP-CP-aware path classification for characterizing long sensitized paths by considering the IR-drop impact on both logic and clock paths; (2) LP-CP-aware X-restoration for obtaining more effective X-bits by backtracing from both logic and clock paths; (3) LP-CP-aware X-filling for using different strategies according to the positions of X-bits in test cubes. Experimental results on large benchmark circuits demonstrate the advantages of LP-CP-aware ATPG, which can more accurately achieve capture-power-safety without significant test vector count inflation and test quality loss.