Recently, Quantum Annealing (QA) has attracted attention as an efficient algorithm for combinatorial optimization problems. In QA, the input data size becomes large and its reduction is important for accelerating by the hardware emulation since the usable memory size and its bandwidth are limited. The paper proposes the compression method of input sparse matrices for QA emulator. The proposed method uses the sparseness of the coefficient matrix and the reappearance of the same values. An independent table is introduced and data are compressed by the search and registration method of two consecutive data in the value table. The proposed method is applied to Traveling Salesman Problem (TSP) with 32, 64 and 96 cities and Nurse Scheduling Problem (NSP). The proposed method could reduce the amount of data by 1/40 for 96 city TSP and could manage 96 city TSP on the hardware emulator. When applied to NSP, we confirmed the effectiveness of the proposed method by the compression ratio ranging from 1/4 to 1/11.8. The data reduction is also useful for the simulation/emulation performance when using the compressed data directly and 1.9 times faster speed can be found on 96 city TSP than the CSR-based method.

SDN (Software-Defined Networking) enables software applications to program individual network devices dynamically and therefore control the behavior of the network as a whole. Incomplete programming and/or inconsistency with the network policy of SDN software applications may lead to verification issues. The objective of this paper is to describe the formal modeling that uses the process algebra called pACSR and then suggest a method to verify the firewall application running on top of the SDN controller. The firewall rules are translated into a pACSR process which acts as the specification, and packet's behaviors in SDN are also translated to a pACSR process which is a role as the implementation. Then we prove the correctness by checking whether the parallel composition of two pACSR processes is deadlock-free. Moreover, in the case of network topology changes, our verification can be directly applied to check whether any mismatches or inconsistencies will occur.

Feedback with carry shift registers (FCSRs) implemented using Galois representation have been found to have a weakness called LFSRization. It leads to powerful attacks against the stream ciphers based on them. A new representation called ring representation has been proposed to avoid the attacks. It was considered to circumvent the weaknesses of Galois FCSRs. This correspondence presents a class of ring FCSRs, which meet the implementation criteria, but are still possible to maintain linear behavior for several clock cycles. Their LFSRization probability and how to improve their security are also mentioned.

This paper presents a novel acoustic modeling technique of large vocabulary automatic speech recognition for under-resourced languages by leveraging well-trained acoustic models of other languages (called source languages). The idea is to use source language acoustic model to score the acoustic features of the target language, and then map these scores to the posteriors of the target phones using a classifier. The target phone posteriors are then used for decoding in the usual way of hybrid acoustic modeling. The motivation of such a strategy is that human languages usually share similar phone sets and hence it may be easier to predict the target phone posteriors from the scores generated by source language acoustic models than to train from scratch an under-resourced language acoustic model. The proposed method is evaluated using on the Aurora-4 task with less than 1 hour of training data. Two types of source language acoustic models are considered, i.e. hybrid HMM/MLP and conventional HMM/GMM models. In addition, we also use triphone tied states in the mapping. Our experimental results show that by leveraging well trained Malay and Hungarian acoustic models, we achieved 9.0% word error rate (WER) given 55 minutes of English training data. This is close to the WER of 7.9% obtained by using the full 15 hours of training data and much better than the WER of 14.4% obtained by conventional acoustic modeling techniques with the same 55 minutes of training data.

BEAN is a newly proposed lightweight stream cipher adopting Fibonacci FCSRs. It is designed for very constrained environments and aims at providing a balance between security, efficiency and cost. A weakness in BEAN was first found by Å gren and Hell in 2011, resulting in a key recovery attack slightly better than brute force. In this paper, we present new correlations between state and keystream with large statistical advantage, leading to a much more efficient key recovery attack. The time and data complexities of this attack are 257.53 and 259.94, respectively. Moreover, two new output functions are provided as alternatives, which are more efficent than the function used in BEAN and are immune to all attacks proposed on the cipher. Also, suggestions for improving the FCSRs are given.

This paper describes a low-power VLSI chip for speaker-independent 60-kWord continuous speech recognition based on a context-dependent Hidden Markov Model (HMM). It features a compression-decoding scheme to reduce the external memory bandwidth for Gaussian Mixture Model (GMM) computation and multi-path Viterbi transition units. We optimize the internal SRAM size using the max-approximation GMM calculation and adjusting the number of look-ahead frames. The test chip, fabricated in 40 nm CMOS technology, occupies 1.77 mm2.18 mm containing 2.52 M transistors for logic and 4.29 Mbit on-chip memory. The measured results show that our implementation achieves 34.2% required frequency reduction (83.3 MHz), 48.5% power consumption reduction (74.14 mW) for 60 k-Word real-time continuous speech recognition compared to the previous work while 30% of the area is saved with recognition accuracy of 90.9%. This chip can maximally process 2.4faster than real-time at 200 MHz and 1.1 V with power consumption of 168 mW. By increasing the beam width, better recognition accuracy (91.45%) can be achieved. In that case, the power consumption for real-time processing is increased to 97.4 mW and the max-performance is decreased to 2.08because of the increased computation workload.

In the construction of a no-linear key-stream generator, self-shrinking is an established way of getting the binary pseudo-random periodic sequences in cryptography design. In this paper, using the theoretical analysis, we mainly study the self-shrinking sequence based on the l-sequence, and the theoretical results reflect its good cryptography properties accurately, such that it has the last period T = pe(p-1)/2 when T is an odd number, and the expected value of its autocorrelation belongs to {0,1/T and the variance is O(T/ln4T). Furthermore, we find that the 2-adic complexity of the self-shrinking sequence based on the l-sequence is large enough to resist the Rational Approximation attack.

This paper presents our recent work in regard to building Large Vocabulary Continuous Speech Recognition (LVCSR) systems for the Thai, Indonesian, and Chinese languages. For Thai, since there is no word boundary in the written form, we have proposed a new method for automatically creating word-like units from a text corpus, and applied topic and speaking style adaptation to the language model to recognize spoken-style utterances. For Indonesian, we have applied proper noun-specific adaptation to acoustic modeling, and rule-based English-to-Indonesian phoneme mapping to solve the problem of large variation in proper noun and English word pronunciation in a spoken-query information retrieval system. In spoken Chinese, long organization names are frequently abbreviated, and abbreviated utterances cannot be recognized if the abbreviations are not included in the dictionary. We have proposed a new method for automatically generating Chinese abbreviations, and by expanding the vocabulary using the generated abbreviations, we have significantly improved the performance of spoken query-based search.

This letter is concerned with cellular controlled short-range communication (CCSRC) systems, which can provide a significant performance gain over the traditional cellular systems as shown in the literature. However, to obtain such a gain, CCSRC systems need perfect channel state information (CSI) of all users and the complexity of setting up the optimal cooperative clusters is factorial with respect to the number of potentially cooperative users, which is very unrealistic in practical systems. To solve this problem, we propose a novel cooperative strategy, where CCSRC systems only need the distances between all user pairs and the complexity of setting up the cooperative clusters is relatively low. Simulation results show that the performance of the proposed strategy is close to optimal.

A new approach to speech feature estimation under noise circumstances is proposed in this paper. It is used in noise-robust continuous speech recognition (CSR). As the noise robust techniques in isolated word speech recognition, the running spectrum analysis (RSA), the running spectrum filtering (RSF) and the dynamic range adjustment (DRA) methods have been developed. Among them, only RSA has been applied to a CSR system. This paper proposes an extended DRA for a noise-robust CSR system. In the stage of speech recognition, a continuous speech waveform is automatically assigned to a block defined by a short time length. The extended DRA is applied to these estimated blocks. The average recognition rate of the proposed method has been improved under several different noise conditions. As a result, the recognition rates are improved up to 15% in various noises with 10 dB SNR.

We propose a novel metamaterial antenna that is based on loading a single complementary split ring resonator (CSRR) onto a substrate integrated waveguide (SIW) structure. Negative order and zeroth-order resonance can be observed in the proposed structure. These resonance modes are used to reduce the antenna size. In addition, a high quality (Q) factor of the CSRR-loaded SIW structure can minimize the radiation loss. The -1st, 0th, and 1st resonances are experimentally observed at 6.63, 13.68, and 20.31 GHz with maximum gains of 1.59, 3.97, 6.83 dBi, respectively. The electrical size of the antenna at the -1st resonance is only 42% of the resonance of a square microstrip patch antenna.

The 2-adic complexity of binary periodic sequences plays an important role in cryptology. In this paper, by means of the usual Fourier transform, we give a simpler form of the upper bound for 2-adic complexity than related result before. For pn-periodic sequences, we discuss the relation between sequences and their Fourier coefficients. Furthermore, based on the relation, we get the lower bound for the number of pn-periodic sequences with given 2-adic complexity.

We propose a committee-based method of active learning for large vocabulary continuous speech recognition. Multiple recognizers are trained in this approach, and the recognition results obtained from these are used for selecting utterances. Those utterances whose recognition results differ the most among recognizers are selected and transcribed. Progressive alignment and voting entropy are used to measure the degree of disagreement among recognizers on the recognition result. Our method was evaluated by using 191-hour speech data in the Corpus of Spontaneous Japanese. It proved to be significantly better than random selection. It only required 63 h of data to achieve a word accuracy of 74%, while standard training (i.e., random selection) required 103 h of data. It also proved to be significantly better than conventional uncertainty sampling using word posterior probabilities.

In this paper, a rat-race hybrid coupler based on an open complementary split ring resonator (OCSRR) is presented. By embedding the OCSRR in the microstrip transmission line, slow-wave effect is introduced to achieve size reduction. The proposed rat-race coupler size is 37% smaller than the conventional rat-race coupler. Besides, the proposed coupler provides better third harmonic suppression up to 35 dB. The simulated results are compared with the measured data and good agreement is reported.

In this paper, we propose a new speaker-class modeling and its adaptation method for the LVCSR system and evaluate the method on the Corpus of Spontaneous Japanese (CSJ). In this method, closer speakers are selected from training speakers and the acoustic models are trained by using their utterances for each evaluation speaker. One of the major issues of the speaker-class model is determining the selection range of speakers. In order to solve the problem, several models which have a variety of speaker range are prepared for each evaluation speaker in advance, and the most proper model is selected on a likelihood basis in the recognition step. In addition, we improved the recognition performance using unsupervised speaker adaptation with the speaker-class models. In the recognition experiments, a significant improvement could be obtained by using the proposed speaker adaptation based on speaker-class models compared with the conventional adaptation method.

The algebra of communicating shared resources (ACSR) is a timed process algebra which extends classical process algebras with the notion of a resource. In analyzing ACSR models, the existing techniques such as bisimulation checking and Hennessy-Milner Logic (HML) model checking are very important in theory of ACSR, but they are difficult to use for large complex system models in practice. In this paper, we suggest a framework to verify ACSR models against their requirements described in an expressive timed temporal logic. We demonstrate the usefulness of our approach with a real world case study.

This paper describes a reading miscue detection system based on the conventional Large Vocabulary Continuous Speech Recognition (LVCSR) framework [1]. In order to incorporate the knowledge of reference (what the reader ought to read) and some error patterns into the decoding process, two methods are proposed: Dynamic Multiple Pronunciation Incorporation (DMPI) and Dynamic Interpolation of Language Model (DILM). DMPI dynamically adds some pronunciation variations into the search space to predict reading substitutions and insertions. To resolve the conflict between the coverage of error predications and the perplexity of the search space, only the pronunciation variants related to the reference are added. DILM dynamically interpolates the general language model based on the analysis of the reference and so keeps the active paths of decoding relatively near the reference. It makes the recognition more accurate, which further improves the detection performance. At the final stage of detection, an improved dynamic program (DP) is used to align the confusion network (CN) from speech recognition and the reference to generate the detecting result. The experimental results show that the proposed two methods can decrease the Equal Error Rate (EER) by 14% relatively, from 46.4% to 39.8%.

This paper describes a morpheme-based pronunciation model that is especially useful to develop the pronunciation lexicon for Large Vocabulary Continuous Speech Recognition (LVCSR) in Korean. To address pronunciation variation in Korean, we analyze phonological rules based on phonemic contexts together with morphological category and morpheme boundary information. Since the same phoneme sequences can be pronounced in different ways at across morpheme boundary, incorporating morphological environment is required to manipulate pronunciation variation modeling. We implement a rule-based pronunciation variants generator to produce a pronunciation lexicon with context-dependent multiple variants. At the lexical level, we apply an explicit modeling of pronunciation variation to add pronunciation variants at across morphemes as well as within morpheme into the pronunciation lexicon. At the acoustic level, we train the phone models with re-labeled transcriptions through forced alignment using context-dependent pronunciation lexicon. The proposed pronunciation lexicon offers the potential benefit for both training and decoding of a LVCSR system. Subsequently, we perform the speech recognition experiment on read speech task with 34K-morpheme vocabulary. Experiment confirms that improved performance is achieved by pronunciation variation modeling based on morpho-phonological analysis.

This investigation proposes a modified equivalent circuit of single complementary split-ring resonator (CSRR) in planar transmission media and a dual-mode ring bandpass filter (BPF) that uses periodic CSRRs to suppress the spurious response. The proposed modified equivalent circuit consists of lumped elements that can be easily extracted from the measured S parameters. The proposed dual-mode ring BPF has exhibits a wide stopband characteristic owing to the bandgap resonant characteristic of CSRRs in the harmonic frequency of the dual-mode ring BPF. Good agreement with EM simulation and measurement is demonstrated.

We investigate the impact of electrical band-limitation on the transmission performance of both carrier suppressed return-to-zero (CSRZ) and CSRZ differential phase shift keying (CSRZ-DPSK) format for high spectral efficiency DWDM systems. Results show that electrical band-limitation improves signal spectral compactness, leading to reduced linear crosstalk and improved tolerance against chromatic dispersion in optical fiber link without causing any degradation to fiber nonlinearity tolerance. In addition, it is shown that the electrical band-limitation is more efficient to CSRZ-DPSK signal than CSRZ signal in reducing signal degradation caused by linear crosstalk and fiber chromatic dispersion.