The tree-based routing approach has been known as an efficient method for node mobility management and data packet transmission between two long-distance parties; however, its parameter adjustment must balance control overhead against the convergence speed of topology information according to node mobility. Meanwhile, location-based routing works more efficiently when the distance between the source and destination is relatively short. Therefore, this paper proposes a roadside unit (RSU) based hybrid routing protocol, called RSU-HRP that combines the strengths of both protocols while offsetting their weaknesses. In RSU-HRP, the tree construction is modified to take into account the link and route quality to construct a robust and reliable tree against high node mobility, and an optimized broadcast algorithm is developed to reduce control overhead induced by the advertisement message periodically sent from a roadside unit. In addition, the two routing methods are selectively used based on the computed distance in hops between a source and a destination. Simulation results show that RSU-HRP far outperforms TrafRoute in terms of packet delivery ratio, end-to-end delay, and control overhead in both Vehicle-to-Infrastructure and Vehicle-to-Vehicle communication models.

In this letter, we propose a novel supervised pre-training technique for deep neural network (DNN)-hidden Markov model systems to achieve robust speech recognition in adverse environments. In the proposed approach, our aim is to initialize the DNN parameters such that they yield abstract features robust to acoustic environment variations. In order to achieve this, we first derive the abstract features from an early fine-tuned DNN model which is trained based on a clean speech database. By using the derived abstract features as the target values, the standard error back-propagation algorithm with the stochastic gradient descent method is performed to estimate the initial parameters of the DNN. The performance of the proposed algorithm was evaluated on Aurora-4 DB, and better results were observed compared to a number of conventional pre-training methods.

We propose a method of enhancing the performance of a cross-talk canceller for a four-speaker system with respect to sweet spot size and ringing effect. For the large sweet spot of a cross-talk canceller, the speaker layout needs to be symmetrical to the listener's position. In addition, a ringing effect of the cross-talk canceller is reduced when many speakers are located close to each other. Based on these properties, the proposed method first selects the two speakers in a four-speaker system that are most symmetrical to the target listener's position and then adds the remaining speakers between these two to the final selection. By operating only these selected speakers, the proposed method enlarges the sweet spot size and reduces the ringing effect. We conducted objective and subjective evaluations and verified that the proposed method improves the performance of the cross-talk canceller compared to the conventional method.

A conventional HMM-based speech synthesis system for Hanoi Vietnamese often suffers from hoarse quality due to incomplete F0 parameterization of glottalized tones. Since estimating F0 from glottalized waveform is rather problematic for usual F0 extractors, we propose a pitch marking algorithm where pitch marks are propagated from regular regions of a speech signal to glottalized ones, from which complete F0 contours for the glottalized tones are derived. The proposed F0 parameterization scheme was confirmed to significantly reduce the hoarseness whilst slightly improving the tone naturalness of synthetic speech by both objective and listening tests. The pitch marking algorithm works as a refinement step based on the results of an F0 extractor. Therefore, the proposed scheme can be combined with any F0 extractor.

The conventional non-negative matrix factorization (NMF)-based speech enhancement is accomplished by updating iteratively with the prior knowledge of the clean speech and noise spectra bases. With the probabilistic estimation of whether the speech is present or not in a certain frame, this letter proposes a speech enhancement algorithm incorporating the speech presence probability (SPP) obtained via noise estimation to the NMF process. To take advantage of both the NMF-based and statistical model-based approaches, the final enhanced speech is achieved by applying a statistical model-based filter to the output of the SPP weighted NMF. Objective evaluations using perceptual evaluation of speech quality (PESQ) on TIMIT with 20 noise types at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed algorithm over the conventional NMF and statistical model-based baselines.

In this paper, we consider the Reed-Solomon codes over Fqm with evaluations in a subfield Fq. By the “virtual extension”, we can embed these codes into homogeneous interleaved Reed-Solomon codes. Based on this property and the collaborative decoding algorithm, a new probabilistic decoding algorithm that can correct errors up to $rac{m}{m+1}(n-k)$ for these codes is proposed. We show that whether the new decoding algorithm fails or not is only dependent on the error. We also give an upper bound on the failure probability of the new decoding algorithm for the case s=2. The new decoding algorithm has some advantages over some known decoding algorithms.

A novel dynamic element matching (DEM) method, called binary-tree random DEM (BTR-DEM), is presented for a Nyquist-rate current-steering digital-to-analog converter (DAC). By increasing or decreasing the number of unit current sources randomly at the same time, the BTR-DEM encoding reduces switch transition glitches. A 5-bit current-steering DAC with the BTR-DEM technique is implemented in a 65-nm CMOS technology. The measured spurious free dynamic range (SFDR) attains 42 dB for a sample rate of 100 MHz and shows little dependence on signal frequency.

Outsourcing IC design and fabrication is one of the effective solutions to reduce design cost but it may cause severe security risks. Particularly, malicious outside vendors may implement Hardware Trojans (HTs) on ICs. When we focus on IC design phase, we cannot assume an HT-free netlist or a Golden netlist and it is too difficult to identify whether a given netlist is HT-free or not. In this paper, we propose a score-based hardware-trojans identifying method at gate-level netlists without using a Golden netlist. Our proposed method does not directly detect HTs themselves in a gate-level netlist but it detects a net included in HTs, which is called Trojan net, instead. Firstly, we observe Trojan nets from several HT-inserted benchmarks and extract several their features. Secondly, we give scores to extracted Trojan net features and sum up them for each net in benchmarks. Then we can find out a score threshold to classify HT-free and HT-inserted netlists. Based on these scores, we can successfully classify HT-free and HT-inserted netlists in all the Trust-HUB gate-level benchmarks and ISCAS85 benchmarks as well as HT-free and HT-inserted AES gate-level netlists. Experimental results demonstrate that our method successfully identify all the HT-inserted gate-level benchmarks to be “HT-inserted” and all the HT-free gate-level benchmarks to be “HT-free” in approximately three hours for each benchmark.

An Al-N system optical absorption layer has been developed, to be used for Al-based metal mesh electrodes on touch screen panels. The triple-layered electrode effectively suppresses the optical reflection in both visible light and the blue color region and exhibits excellent wet etching property that accommodates micro-fabrication. Due to its high noise immunity and contact sensitivity originating from its low electrical resistivity, the proposed metal mesh electrodes are useful for touch-sensitive panels in the next generation ultra-high-resolution displays.

With the growth of cloud-based services, cloud data centers are experiencing large growth. A key component in a cloud data center is the network technology deployed. In particular, Ethernet technology, commonly deployed in cloud data centers, is already envisioned for 10 Tbps Ethernet. In this paper, we study and analyze the makespan, workload execution times, and virtual machine migrations as the network speed increases. In particular, we consider homogeneous and heterogeneous data centers, virtual machine scheduling algorithms, and workload scheduling algorithms. Results obtained from our study indicate that the increase in a network's speed reduces makespan and workloads execution times, while aiding in the increase of the number of virtual machine migrations. We further observed that the number of migrations' behaviors in relation to the speed of the networks also depends on the employed virtual machines scheduling algorithm.

Most error correction interfaces for speech recognition applications on smartphones require the user to first mark an error region and choose the correct word from a candidate list. We propose a simple multimodal interface to make the process more efficient. We develop Long Context Match (LCM) to get candidates that complement the conventional word confusion network (WCN). Assuming that not only the preceding words but also the succeeding words of the error region are validated by users, we use such contexts to search higher-order n-grams corpora for matching word sequences. For this purpose, we also utilize the Web text data. Furthermore, we propose a combination of LCM and WCN (“LCM + WCN”) to provide users with candidate lists that are more relevant than those yielded by WCN alone. We compare our interface with the WCN-based interface on the Corpus of Spontaneous Japanese (CSJ). Our proposed “LCM + WCN” method improved the 1-best accuracy by 23%, improved the Mean Reciprocal Rank (MRR) by 28%, and our interface reduced the user's load by 12%.

In recent years, many variants of key point based image descriptors have been designed for the image matching, and they have achieved remarkable performances. However, to some images, local features appear to be inapplicable. Since theses images usually have many local changes around key points compared with a normal image, we define this special image category as the image with local changes (IL). An IL pair (ILP) refers to an image pair which contains a normal image and its IL. ILP usually loses local visual similarities between two images while still holding global visual similarity. When an IL is given as a query image, the purpose of this work is to match the corresponding ILP in a large scale image set. As a solution, we use a compressed HOG feature descriptor to extract global visual similarity. For the nearest neighbor search problem, we propose random projection indexed KD-tree forests (rKDFs) to match ILP efficiently instead of exhaustive linear search. rKDFs is built with large scale low-dimensional KD-trees. Each KD-tree is built in a random projection indexed subspace and contributes to the final result equally through a voting mechanism. We evaluated our method by a benchmark which contains 35,000 candidate images and 5,000 query images. The results show that our method is efficient for solving local-changes invariant image matching problems.

We have proposed a new Bayesian network model (BNM) framework for single-trial-EEG-based Brain-Computer Interface (BCI). The BNM was constructed in the following. In order to discriminate between left and right hands to be imaged from single-trial EEGs measured during the movement imagery tasks, the BNM has the following three steps: (1) independent component analysis (ICA) for each of the single-trial EEGs; (2) equivalent current dipole source localization (ECDL) for projections of each IC on the scalp surface; (3) BNM construction using the ECDL results. The BNMs were composed of nodes and edges which correspond to the brain sites where ECDs are located, and their connections, respectively. The connections were quantified as node activities by conditional probabilities calculated by probabilistic inference in each trial. The BNM-based BCI is compared with the common spatial pattern (CSP) method. For ten healthy subjects, there was no significant difference between the two methods. Our BNM might reflect each subject's strategy for task execution.

This paper studies a privacy-preserving decision tree learning protocol (PPDT) for vertically partitioned datasets. In vertically partitioned datasets, a single class (target) attribute is shared by both parities or carefully treated by either party in existing studies. The proposed scheme allows both parties to have independent class attributes in a secure way and to combine multiple class attributes in arbitrary boolean function, which gives parties some flexibility in data-mining. Our proposed PPDT protocol reduces the CPU-intensive computation of logarithms by approximating with a piecewise linear function defined by light-weight fundamental operations of addition and constant multiplication so that information gain for attributes can be evaluated in a secure function evaluation scheme. Using the UCI Machine Learning dataset and a synthesized dataset, the proposed protocol is evaluated in terms of its accuracy and the sizes of trees*.

The hop-limited adaptive routing (HLAR) mechanism and its enhancement (EHLAR), both tailored for the packet-switched non-geostationary (NGEO) satellite networks, are proposed and evaluated. The proposed routing mechanisms exploit both the predictable topology and inherent multi-path property of the NGEO satellite networks to adaptively distribute the traffic via all feasible neighboring satellites. Specifically, both mechanisms assume that a satellite can send the packets to their destinations via any feasible neighboring satellites, thus the link via the neighboring satellite to the destination satellite is assigned a probability that is proportional to the effective transmission to the destination satellites of the link. The satellite adjusts the link probability based on the packet sending information observed locally for the HLAR mechanism or exchanged between neighboring satellites for the EHLAR mechanism. Besides, the path of the packets are bounded by the maximum hop number, thus avoiding the unnecessary over-detoured packets in the satellite networks. The simulation results corroborate the improved performance of the proposed mechanisms compared with the existing in the literature.

The advanced front-end (AFE) for automatic speech recognition (ASR) was standardized by the European Telecommunications Standards Institute (ETSI). The AFE provides speech enhancement realized by an iterative Wiener filter (IWF) in which a smoothed FFT spectrum over adjacent frames is used to design the filter. We have previously proposed robust time-varying complex Auto-Regressive (TV-CAR) speech analysis for an analytic signal and evaluated the performance of speech processing such as F0 estimation and speech enhancement. TV-CAR analysis can estimate more accurate spectrum than FFT, especially in low frequencies because of the nature of the analytic signal. In addition, TV-CAR can estimate more accurate speech spectrum against additive noise. In this paper, a time-invariant version of wide-band TV-CAR analysis is introduced to the IWF in the AFE and is evaluated using the CENSREC-2 database and its baseline script.

In this paper, we consider a distributed power control scheme that can maximize overall capacity of an interference-limited wireless system in which the same radio resource is spatially reused among different transmitter-receiver pairs. This power control scheme employs a gradient-descent method in each transmitter, which adapts its own transmit power to co-channel interference dynamically to maximize the total weighted sum rate (WSR) of the system over a given interval. The key contribution in this paper is to propose a common feedback channel, over which a backward physical signal is accumulated for computing the gradient of the transmit power in each transmitter, thereby significantly reducing signaling overhead for the distributed power control. We show that the proposed power control scheme can achieve almost 95% of its theoretical upper WSR bound, while outperforming the non-power-controlled system by roughly 63% on average.

Millimeter-wave (mmw) frequency bands, especially 60GHz unlicensed band, are considered as a promising solution for gigabit short range wireless communication systems. IEEE standard 802.11ad, also known as WiGig, is standardized for the usage of the 60GHz unlicensed band for wireless local area networks (WLANs). By using this mmw WLAN, multi-Gbps rate can be achieved to support bandwidth-intensive multimedia applications. Exhaustive search along with beamforming (BF) is usually used to overcome 60GHz channel propagation loss and accomplish data transmissions in such mmw WLANs. Because of its short range transmission with a high susceptibility to path blocking, multiple number of mmw access points (APs) should be used to fully cover a typical target environment for future high capacity multi-Gbps WLANs. Therefore, coordination among mmw APs is highly needed to overcome packet collisions resulting from un-coordinated exhaustive search BF and to increase total capacity of mmw WLANs. In this paper, we firstly give the current status of mmw WLANs with our developed WiGig AP prototype. Then, we highlight the great need for coordinated transmissions among mmw APs as a key enabler for future high capacity mmw WLANs. Two different types of coordinated mmw WLAN architecture are introduced. One is distributed antenna type architecture to realize centralized coordination, while the other is autonomous coordination with the assistance of legacy Wi-Fi signaling. Moreover, two heterogeneous network (HetNet) architectures are also introduced to efficiently extend the coordinated mmw WLANs to be used for future 5th Generation (5G) cellular networks.

In this letter, we present a meet-in-the-middle attack on the 5-round reduced Kuznyechik cipher which has been recently chosen to be standardized by the Russian federation. Our attack is based on the differential enumeration approach. However, the application of the exact approach is not successful on Kuznyechik due to its optimal round diffusion properties. Accordingly, we adopt an equivalent representation for the last round where we can efficiently filter ciphertext pairs and launch the attack in the chosen ciphertext setting. We also utilize partial sequence matching which further reduces the memory and time complexities. For the 5-round reduced cipher, the 256-bit master key is recovered with an online time complexity of 2140.3, a memory complexity of 2153.3, and a data complexity of 2113.

Given a graph G, a set of spanning trees of G are completely independent if for any vertices x and y, the paths connecting them on these trees have neither vertex nor edge in common, except x and y. In this paper, we prove that for graphs of order n, with n ≥ 6, if the minimum degree is at least n-2, then there are at least ⌊n/3⌋ completely independent spanning trees.