A third-order low-distortion delta-sigma modulator (DSM), whose third-order noise-shaping ability is achieved by just a single opamp, is proposed. Since only one amplifier is required in the whole circuit, the designed DSM is very power efficient. To realize the adder in front of quantizer without employing the huge-power opamp, a capacitive passive adder, which is the digital-to-analog converter (DAC) array of a successive-approximation-type quantizer, is used. In addition, the feedback path timing is extended from a nonoverlapping interval for the conventional low-distortion structure to half of the clock period, so that the strict operation timing issue with regard to quantization and the dynamic element matching (DEM) logic operation can be solved. In the proposed DSM structure, the features of the unity-gain signal transfer function (STF) and finite-impulse-response (FIR) noise transfer function (NTF) are still preserved, and thus advantages such as a relaxed opamp slew rate and reduced output swing are also maintained, as with the conventional low-distortion DSM. Moreover, the memory effect in the proposed DSM is analyzed when employing the opamp sharing for integrators. The proposed third-order DSM with a 4-bit SAR ADC as the quantizer is implemented in a 90-nm CMOS process. The post-layout simulations show a 79.8-dB signal-to-noise and distortion ratio (SNDR) in the 1.875-MHz signal bandwidth (OSR=16). The active area of the circuit is 0.35mm2 and total power consumption is 2.85mW, resulting in a figure of merit (FOM) of 95 fJ/conversion-step.

Information Security Management Systems (ISMSs) play important roles in helping organizations to manage their information securely. However, establishing, managing, and maintaining ISMSs is not an easy task for most organizations because an ISMS has many participants and tasks, and requires many kinds of documents. Therefore, organizations with ISMSs demand tools that can support them to perform all tasks in ISMS lifecycle processes consistently and continuously. To realize such support tools, a database system that manages ISO/IEC 27000 series, which are international standards for ISMSs, and ISMS documents, which are the products of tasks in ISMS lifecycle processes, is indispensable. The database system should manage data of the standards and documents for all available versions and translations, relationship among the standards and documents, authorization to access the standards and documents, and metadata of the standards and documents. No such database system has existed until now. This paper presents an information security management database system (ISMDS) that manages ISO/IEC 27000 series and ISMS documents. ISMDS is a meta-database system that manages several databases of standards and documents. ISMDS is used by participants in ISMS as well as tools supporting the participants to perform tasks in ISMS lifecycle processes. The users or tools can retrieve data from all versions and translations of the standards and documents. The paper also presents some use cases to show the effectiveness of ISMDS.

A burst of social network services increases the need for in-depth analysis of network activities. Privacy breach for network participants is a concern in such analysis efforts. This paper investigates structural and property changes via several privacy preserving methods (anonymization) for social network. The anonymized social network does not follow the power-law for node degree distribution as the original network does. The peak-hop for node connectivity increases at most 1 and the clustering coefficient of neighbor nodes shows 6.5 times increases after anonymization. Thus, we observe inconsistency of privacy preserving methods in social network analysis.

This paper presents a voice conversion technique using speaker-dependent Restricted Boltzmann Machines (RBM) to build high-order eigen spaces of source/target speakers, where it is easier to convert the source speech to the target speech than in the traditional cepstrum space. We build a deep conversion architecture that concatenates the two speaker-dependent RBMs with neural networks, expecting that they automatically discover abstractions to express the original input features. Under this concept, if we train the RBMs using only the speech of an individual speaker that includes various phonemes while keeping the speaker individuality unchanged, it can be considered that there are fewer phonemes and relatively more speaker individuality in the output features of the hidden layer than original acoustic features. Training the RBMs for a source speaker and a target speaker, we can then connect and convert the speaker individuality abstractions using Neural Networks (NN). The converted abstraction of the source speaker is then back-propagated into the acoustic space (e.g., MFCC) using the RBM of the target speaker. We conducted speaker-voice conversion experiments and confirmed the efficacy of our method with respect to subjective and objective criteria, comparing it with the conventional Gaussian Mixture Model-based method and an ordinary NN.

A whitelisting approach is a promising solution to prevent unwanted processes (e.g., malware) getting executed. However, previous solutions suffer from limitations in that: 1) Most methods place the whitelist information in the kernel space, which could be tempered by attackers; 2) Most methods cannot prevent the execution of kernel processes. In this paper, we present VAW, a novel application whitelisting system by using the virtualization technology. Our system is able to block the execution of unauthorized user and kernel processes. Compared with the previous solutions, our approach can achieve stronger security guarantees. The experiments show that VAW can deny the execution of unwanted processes effectively with a little performance overhead.

It is well known that solving randomly chosen Multivariate Quadratic equations over a finite field (MQ-Problem) is NP-hard, and the security of Multivariate Public Key Cryptosystems (MPKCs) is based on the MQ-Problem. However, this problem can be solved efficiently when the number of unknowns n is sufficiently greater than that of equations m (This is called “Underdefined”). Indeed, the algorithm by Kipnis et al. (Eurocrypt'99) can solve the MQ-Problem over a finite field of even characteristic in a polynomial-time of n when n ≥ m(m+1). Therefore, it is important to estimate the hardness of the MQ-Problem to evaluate the security of Multivariate Public Key Cryptosystems. We propose an algorithm in this paper that can solve the MQ-Problem in a polynomial-time of n when n ≥ m(m+3)/2, which has a wider applicable range than that by Kipnis et al. We will also compare our proposed algorithm with other known algorithms. Moreover, we implemented this algorithm with Magma and solved the MQ-Problem of m=28 and n=504, and it takes 78.7 seconds on a common PC.

For frequency hopping spread spectrum communication systems, the average Hamming correlation (AHC) among frequency hopping sequences (FHSs) is an important performance indicator. In this letter, a sufficient and necessary condition for a set of FHSs with optimal AHC is given. Based on interleaved technique, a new construction for optimal AHC FHS sets is also proposed, which generalizes the construction of Chung and Yang. Several optimal AHC FHS sets with more flexible parameters not covered in the literature are obtained by the new construction, which are summarized in Table 1.

We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.

This paper presents a voice conversion (VC) technique for noisy environments based on a sparse representation of speech. Sparse representation-based VC using Non-negative matrix factorization (NMF) is employed for noise-added spectral conversion between different speakers. In our previous exemplar-based VC method, source exemplars and target exemplars are extracted from parallel training data, having the same texts uttered by the source and target speakers. The input source signal is represented using the source exemplars and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. However, this exemplar-based approach needs to hold all training exemplars (frames), and it requires high computation times to obtain the weights of the source exemplars. In this paper, we propose a framework to train the basis matrices of the source and target exemplars so that they have a common weight matrix. By using the basis matrices instead of the exemplars, the VC is performed with lower computation times than with the exemplar-based method. The effectiveness of this method was confirmed by comparing its effectiveness (in speaker conversion experiments using noise-added speech data) with that of an exemplar-based method and a conventional Gaussian mixture model (GMM)-based method.

Grapheme-to-phoneme (g2p) conversion, used to estimate the pronunciations of out-of-vocabulary (OOV) words, is a highly important part of recognition systems, as well as text-to-speech systems. The current state-of-the-art approach in g2p conversion is structured learning based on the Margin Infused Relaxed Algorithm (MIRA), which is an online discriminative training method for multiclass classification. However, it is known that the aggressive weight update method of MIRA is prone to overfitting, even if the current example is an outlier or noisy. Adaptive Regularization of Weight Vectors (AROW) has been proposed to resolve this problem for binary classification. In addition, AROW's update rule is simpler and more efficient than that of MIRA, allowing for more efficient training. Although AROW has these advantages, it has not been applied to g2p conversion yet. In this paper, we first apply AROW on g2p conversion task which is structured learning problem. In an evaluation that employed a dataset generated from the collective knowledge on the Web, our proposed approach achieves a 6.8% error reduction rate compared to MIRA in terms of phoneme error rate. Also the learning time of our proposed approach was shorter than that of MIRA in almost datasets.

One of the most important problems in web services application is the integration of different existing services into a new composite service. Existing work has the following disadvantages: (i) developers are often required to provide a composite service model first and perform formal verifications to check whether the model is correct. This makes the synthesis process of composite services semi-automatic, complex and inefficient; (ii) there is no assurance that composite services synthesized by using the fully-automatic approaches are correct; (iii) some approaches only handle simple composition problems where existing services are atomic. To address these problems, we propose a correct assurance approach for automatically synthesizing composite services based on finite state machine model. The syntax and semantics of the requirement model specifying composition requirements is also proposed. Given a set of abstract BPEL descriptions of existing services, and a composition requirement, our approach automatically generate the BPEL implementation of the composite service. Compared with existing approaches, the composite service generated by utilizing our proposed approach is guaranteed to be correct and does not require any formal verification. The correctness of our approach is proved. Moreover, the case analysis indicates that our approach is feasible and effective.

A multi-source Tri-Training transfer learning algorithm is proposed by integrating transfer learning and semi-supervised learning. First, multiple weak classifiers are respectively trained by using both weighted source and target training samples. Then, based on the idea of co-training, each target testing sample is labeled by using trained weak classifiers and the sample with the same label is selected as the high-confidence sample to be added into the target training sample set. Finally, we can obtain a target domain classifier based on the updated target training samples. The above steps are iterated till the high-confidence samples selected at two successive iterations become the same. At each iteration, source training samples are tested by using the target domain classifier and the samples tested as correct continue with training, while the weights of samples tested as incorrect are lowered. Experimental results on text classification dataset have proven the effectiveness and superiority of the proposed algorithm.

Break arcs are generated in a DC48V and 12A resistive circuit. Silver electrical contacts are separated at constant opening speed. The cathode contact surface is irradiated by a blue LED. The center wavelength of the emission of the LED is 470nm. There is no spectral line of the light emitted from the break arcs. Only the images of contact surface are observed by a high-speed camera and an optical band pass filter. Another high-speed camera observes only the images of the break arc. Time evolutions of the cathode surface morphology being eroded by the break arcs and the motion of the break arcs are observed with these cameras, simultaneously. The images of the cathode surface are investigated by the image analysis technique. The results show that the moments when the expanded regions on the cathode surface are formed during the occurrence of the break arcs. In addition, it is shown that the expanded regions are not contacted directly to the cathode roots of the break arcs.

Substrate noise coupling in RF receiver front-end circuitry for LTE wireless communication was examined by full-chip level simulation and on-chip measurements, with a demonstrator built in a 65nm CMOS technology. A CMOS digital noise emulator injects high-order harmonic noises in a silicon substrate and induces in-band spurious tones in an RF receiver on the same chip through substrate noise interference. A complete simulation flow of full-chip level substrate noise coupling uses a decoupled modeling approach, where substrate noise waveforms drawn with a unified package-chip model of noise source circuits are given to mixed-level simulation of RF chains as noise sensitive circuits. The distribution of substrate noise in a chip and the attenuation with distance are simulated and compared with the measurements. The interference of substrate noise at the 17th harmonics of 124.8MHz — the operating frequency of the CMOS noise emulator creates spurious tones in the communication bandwidth at 2.1GHz.

A three dimensional (3D) chip stack featuring a 4096-bit wide I/O demonstrator incorporates an in-place waveform capturer on an intermediate interposer within the stack. The capturer includes probing channels on paths of signaling as well as in power delivery and collects analog waveforms for diagnosing circuits within 3D integration. The collection of in-place waveforms on vertical channels with through silicon vias (TSVs) are demonstrated among 128 vertical I/O channels distributed in 8 banks in a 9.9mm × 9.9mm die area. The analog waveforms confirm a full 1.2-V swing of signaling at the maximum data transmission bandwidth of 100GByte/sec with sufficiently small deviations of signal skews and slews among the vertical channels. In addition, it is also experimentally confirmed that the signal swing can be reduced to 0.75V for error free data transfer at 100GByte/sec, achieving the energy efficiency of 0.21pJ/bit.

This paper studies the design of cooperative beamforming (CB) and cooperative jamming (CJ) for the physical layer security of an amplify-and-forward (AF) relay network in the presence of multiple multi-antenna eavesdroppers. The secrecy rate maximization (SRM) problem of such a network is to maximize the difference of two concave functions, a problem which is non-convex and has no efficient solution. Based on the inner convex approximation (ICA) and semidefinite relaxation (SDR) techniques, we propose two novel low-complexity schemes to design CB and CJ for SRM in the AF network. In the first strategy, relay nodes adopt the CB only to secure transmission. Based on ICA, this design guarantees convergence to a Karush-Kuhn-Tucker (KKT) solution of the SDR of the original problem. In the second strategy, the optimal joint CB and CJ design is studied and the proposed joint design can guarantee convergence to a KKT solution of the original problem. Moreover, in the second strategy, we prove that SDR always has a rank-1 solution for the SRM problem. Simulation results show the superiority of the proposed schemes.

Honeycomb structures are widely used in aerospace industry because of the lightweight and durable properties they provide. Here we propose to use a honeycomb core as the wave guiding structure in Radial Line Slot Antennas (RLSAs). This paper quantifies the propagation characteristics, especially the loss due to the honeycomb. At 32GHz, by choosing the proper cell size, both good isotropy and reasonably low effective dielectric constants are realized with the honeycomb as a spacer in a radial line waveguide. To estimate the material loss factor, several methods are compared and a factor of about 0.014∼0.018dB/mm is predicted and measured. A fabricated 90cm diameter honeycomb RLSA suffers about a 3.5∼5dB loss, which coincides with the estimates using the predicted loss factor.

We consider the method of evaluating the detection performance of a single pulse monostatic radar for a fluctuating target in compound-Gaussian clutter plus noise background. The system uses a coded pulse compression waveform as its transmitting signal and the linear minimum mean square error (LMMSE) based reiterated filtering, also known as the adaptive pulse compression (APC). We study the theoretical statistical characteristics of the amplitude of the APC estimation for infinite iterations in this scenario. Based on this theory, we derive both the theoretical probability of false alarm and the probability of detection for the ‘ideal constant false alarm rate (CFAR)’ detector that uses amplitude of the APC estimation as the test statistics. Finaly, we verify the validity of the theoretical detection performance calculations with Monte Carlo simulations. The simulations include three different compound-Gaussian clutter models and all theoretical results well fit the simulated ones.

Extremely low voltage operation near or below threshold voltage is a key circuit technology to improve the energy efficiency of information systems and to realize ultra-low power sensor nodes. However, it is difficult to operate conventional analog circuits based on amplifier at low voltage. Furthermore, PVT (Process, Voltage and Temperature) variation and random Vth variation degrade the minimum operation voltage and the energy efficiency in both digital and analog circuits. In this paper, extremely low power analog circuits based on comparator and switched capacitor as well as extremely low power digital circuits are presented. Many kinds of circuit technologies are applied to cope with the variation problem. Finally, image processing SoC that integrates digital and analog circuits is presented, where improvement of total performance by a cooperation of analog circuits and digital circuits is demonstrated.