A novel and compact electro-optic modulator implemented by a combination of a 12 multimode interference (MMI) coupler and an integrated Mach-Zehnder interferometer (MZI) modulator consisting of a microring and a phase modulator (PM) is presented and analyzed theoretically. It is shown that the proposed modulator offers both ultra-linearity and high output RF gain simultaneously, with no requirements for complicated and precise direct current (DC) control.

Characterization of peer-to-peer (P2P) traffic is an essential step to develop workload models towards capacity planning and cyber-threat countermeasure over P2P networks. In this paper, we present a classification scheme for characterizing P2P file-sharing hosts based on transport layer statistical features. The proposed scheme is accessed on a virtualized environment that simulates a P2P-friendly cloud system. The system shows high accuracy in differentiating P2P file-sharing hosts from ordinary hosts. Its tunability regarding monitoring cost, system response time, and prediction accuracy is demonstrated by a series of experiments. Further study on feature selection is pursued to identify the most essential discriminators that contribute most to the classification. Experimental results show that an equally accurate system could be obtained using only 3 out of the 18 defined discriminators, which further reduces the monitoring cost and enhances the adaptability of the system.

In this paper, we propose a novel incremental method for discovering latent variables from multivariate data with high efficiency. It integrates non-Gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. Our proposed method discovers a small number of compact features from a very large number of features and can still achieve good predictive performance in EEG signals. The promising EEG signal classification results from our experiments prove that this approach can successfully extract important features. Our proposed method also has low memory requirements and computational costs.

MPEG spatial audio object coding (SAOC) is a new audio coding standard which efficiently represents various audio objects as a down-mix signal and spatial parameters. MPEG SAOC has a backward compatibility with existing playback systems for the down-mix signal. If a mastering signal is used for providing CD-like sound quality instead of the down-mix signal, an output signal decoded with the mastering signal may be easily degraded due to the difference between the down-mix and the mastering signals. To successfully use the mastering signal in MPEG SAOC, the difference between two signals should be eliminated. As a simple mastering signal processing, we propose a mastering signal processing using the mastering down-mix gain (MDG) which is similar to the arbitrary down-mix gain of MPEG Surround. Also, we propose an enhanced mastering signal processing using the MDG bias in order to reduce quantization errors of the MDG. Experimental results show that the proposed schemes can improve sound quality of the output signal decoded with the mastering signal. Especially, the enhanced method shows better performance than the simple method in the aspects of the quantization errors and the sound quality.

In this paper, we propose a method for designing genetically optimized Linguistic Models (LM) with the aid of fuzzy granulation. The fundamental idea of LM introduced by Pedrycz is followed and their design framework based on Genetic Algorithm (GA) is enhanced. A LM is designed by the use of information granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules represented as a fuzzy set. However, it is difficult to optimize the number of linguistic contexts, the number of clusters generated by each context, and the weighting exponent. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on GA. Experiments on the coagulant dosing process in a water purification plant reveal that the proposed method shows better performance than the previous works and LM itself.

Post-silicon tuning is attracting a lot of attention for coping with increasing process variation. However, its tuning cost via testing is still a crucial problem. In this paper, we propose tuning-friendly body bias clustering with multiple bias voltages. The proposed method provides a small set of compensation levels so that the speed and leakage current vary monotonically according to the level. Thanks to this monotonic leveling and limitation of the number of levels, the test-cost of post-silicon tuning is significantly reduced. During the body bias clustering, the proposed method explicitly estimates and minimizes the average leakage after the post-silicon tuning. Experimental results demonstrate that the proposed method reduces the average leakage by 25.3 to 51.9% compared to non clustering case. In a test case of four clusters, the number of necessary tests is reduced by 83% compared to the conventional exhaustive test approach. We reveal that two bias voltages are sufficient when only a small number of compensation levels are allowed for test-cost reduction. We also give an implication on how to synthesize a circuit to which post-silicon tuning will be applied.

In large-scale collaborative computing, users and resource providers organize various Virtual Organizations (VOs) to share resources and services. A VO organizes other sub-VOs for the purpose of achieving the VO goal, which forms hierarchical VO environments. VO participants agree upon a certain policies, such as resource sharing amount or user accesses. In this letter, we provide an optimal resource sharing mechanism in hierarchical VO environments under resource sharing agreements. The proposed algorithm enhances resource utilization and reduces mean response time of each user.

In wireless sensor networks, unbalanced energy consumption and transmission collisions are two inherent problems and can significantly reduce network lifetime. This letter proposes an unequal clustering and TDMA-like scheduling mechanism (UCTSM) based on a gradient sinking model in wireless sensor networks. It integrates unequal clustering and TDMA-like transmission scheduling to balance the energy consumption among cluster heads and reduce transmission collisions. Simulation results show that UCTSM balances the energy consumption among the cluster heads, saves nodes' energy and so improves the network lifetime.

Geographic routing uses the geographical location information provided by nodes to make routing decisions. However, the nodes can not obtain accurate location information due to the effect of measurement error. A new routing strategy using maximum expected distance and angle (MEDA) algorithm is proposed to improve the performance and promote the successive transmission rate. We firstly introduce the expected distance and angle, and then we employ the principal component analysis to construct the object function for selecting the next hop node. We compare the proposed algorithm with maximum expectation within transmission range (MER) and greedy routing scheme (GRS) algorithms. Simulation results show that the proposed MEDA algorithm outperforms the MER and GRS algorithms with higher successive transmission rate.

A reliable detection of atrial fibrillation (AF) in Electrocardiogram (ECG) monitoring systems is significant for early treatment and health risk reduction. Various ECG mining and analysis studies have addressed a wide variety of clinical and technical issues. However, there is still room for improvement mostly in two areas. First, the morphological descriptors not only between different patients or patient clusters but also within the same patient are potentially changing. As a result, the model constructed using an old training data no longer needs to be adjusted in order to identify new concepts. Second, the number and types of ECG parameters necessary for detecting AF arrhythmia with high quality encounter a massive number of challenges in relation to computational effort and time consumption. We proposed a mixture technique that caters to these limitations. It includes an active learning method in conjunction with an ECG parameter customization technique to achieve a better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed a sensitivity of 95.2%, a specificity of 99.6%, and an overall accuracy of 99.2%.

In this paper, we have addressed the problem of ordering transactions in network-on-chips (NoCs) for post-silicon validation. The main idea is to extract the order of the transactions from the local partial orders in each NoC tile based on a set of “happened-before” rules, assuming transactions do not have a timestamp. The assumption is based on the fact that implementation and usage of a global time as timestamp in such systems may not be practical or efficient. When a new transaction is received in a tile, we send special messages to the neighboring tiles to inform them regarding the new transaction. The process of sending those special messages continues recursively in all the tiles that receive them until another such special message is detected. This way, we relate local orders of different tiles with each other. We show that our method can reconstruct the correct transaction orders when communication delays are deterministic. We have shown the effectiveness of our method by correctly ordering the transaction in NoCs with mesh and torus topologies with different sizes from 5*5 to 9*9. Also, we have implemented the proposed method in hardware to show its feasibility.

This paper proposes a low-complexity concatenated (LCC) soft-in soft-out (SISO) detector for spreading OFDM systems. The LCC SISO detector uses the turbo principle to compute the extrinsic information of the optimal maximum a priori probability (MAP) SISO detector with extremely low complexity. To develop the LCC SISO detector, we first partition the spreading matrix into some concatenated sparse matrices separated by interleavers. Then, we use the turbo principle to concatenate some SISO detectors, which are separated by de-interleavers or interleavers. Each SISO detector computes the soft information for each sparse matrix. By exchanging the soft information between the SISO detectors, we find the extrinsic information of the MAP SISO detector with extremely low complexity. Simulation results show that using the LCC SISO detector produces a near-optimal performance for both uncoded and coded spreading OFDM systems. In addition, by using the LCC SISO detector, the spreading OFDM system significantly improves the BER of the conventional OFDM system.

In this study, we propose a cooperative sensing with distributed pre-detection for gathering sensing information on shared primary system. We have proposed a system that gathers multiple sensing information by using the orthogonal narrowband signal; the system is called the orthogonal frequency-based sensing information gathering (OF-SIG) method. By using this method, sensing information from multiple secondary nodes can be gathered from the surrounding secondary nodes simultaneously by using the orthogonal narrowband signals. The advantage of this method is that the interference from each node is small because a narrowband tone signal is transmitted from each node. Therefore, if appropriate power and transmission control are applied at the surrounding nodes, the sensing information can be gathered in the same spectrum as the primary system. To avoid interference with the primary receiver, we propose a cooperative sensing with distributed pre-detection for gathering sensing information in each node by limiting sensing node power. In the proposed method, the number of sensing information transmitting nodes depends on the pre-detection ability of the individual sensing at each node. Then the secondary node can increase the transmit power by improving the sensing detection ability, and the secondary node can gather the sensing information from the surrounding secondary nodes which are located more far by redesign the transmit power of the secondary nodes. Here, we design the secondary transmit power based on OF-SIG while considering the aggregated interference from multiple sensing nodes and individual sensing ability. Finally we confirm the performance of the cooperative sensing of the proposed method through computer simulation.

This paper precisely characterizes secret sharing schemes based on arbitrary linear codes by using the relative dimension/length profile (RDLP) and the relative generalized Hamming weight (RGHW). We first describe the equivocation Δm of the secret vector =[s1,...,sl] given m shares in terms of the RDLP of linear codes. We also characterize two thresholds t1 and t2 in the secret sharing schemes by the RGHW of linear codes. One shows that any set of at most t1 shares leaks no information about , and the other shows that any set of at least t2 shares uniquely determines . It is clarified that both characterizations for t1 and t2 are better than Chen et al.'s ones derived by the regular minimum Hamming weight. Moreover, this paper characterizes the strong security in secret sharing schemes based on linear codes, by generalizing the definition of strongly-secure threshold ramp schemes. We define a secret sharing scheme achieving the α-strong security as the one such that the mutual information between any r elements of (s1,...,sl) and any α-r+1 shares is always zero. Then, it is clarified that secret sharing schemes based on linear codes can always achieve the α-strong security where the value α is precisely characterized by the RGHW.

A method obtaining a monochrome image which can rebuild colors is proposed. In this method, colors in an input image are quantized under a lightness constraint and a palette, which represents relationship between quantized colors and gray-levels, is generated. Using the palette, an output monochrome image is obtained. Experiments show that the proposed method obtains good monochrome and rebuilt color images.

This letter addresses antenna ordering to improve the performance of the MIMO detectors in [4], where two low complexity MIMO detectors have been proposed based on either fully-connected or ring type pair-wise Markov random field (MRF). The former was shown to be better than the latter, while being more complex. The objective of this letter is to make the performance of the detector based on ring-type MRF (with complexity of O(2M 22m)) close to or better than that of fully-connected MRF (with complexity of O(M (M-1)22m)), by applying appropriate antenna ordering. The simulation results validate the proposed antenna ordering methods.

This paper proposes a third-order low-distortion delta-sigma modulator (DSM). The third-order noise shaping is achieved by a single opamp (excluding the quantizer). In the proposed DSM structure, the timing limitation on the quantizer and dynamic element matching (DEM) logic in a conventional low-distortion structure can be relaxed from a non-overlapping interval to half of the clock period. A cyclic analog-to-digital converter with a loading-free technique is utilized as a quantizer, which shares an opamp with the active adder. The signal transfer function (STF) is preserved as unity, which means that the integrators process only the quantization noise component. As a result, the opamp used for the integrators has lower requirements, as low-distortion DSMs, on slew rate, output swing, and power consumption. The proposed third-order DSM with a 4-bit cyclic-type quantizer is implemented in a 90-nm CMOS process. Under a sampling rate of 80 MHz and oversampling ratio of 16, simulation results show that an 81.97-dB signal-to-noise and distortion ratio and an 80-dB dynamic range are achieved with 4.17-mW total power consumption. The resulting figure of merit (FOM) is 81.5 fJ/conversion-step.

Secret sharing schemes have been proposed to protect content by dividing it into many pieces securely and distributing them over different locations. Secret sharing schemes can also be used for the secure delivery of content. The original content cannot be reconstructed by the attacker if the attacker cannot eavesdrop on all the pieces delivered from multiple content servers. This paper aims to obtain secure delivery routes for the pieces, which minimizes the probability that all the pieces can be stolen on the links composing the delivery routes. Although such a route optimization problem can be formulated using an ILP (Integer Linear Programming) model, optimum route computation based on the ILP model requires large amounts of computational resources. Thus, this paper proposes a lightweight route computation method for obtaining suboptimum delivery routes that achieve a sufficiently small probability of all the pieces being stolen. The proposed method computes the delivery routes successively by using the conventional shortest route algorithm repeatedly. The distance of the links accommodating the routes that have already been calculated is adjusted iteratively and utilized for calculation of the new shortest route. The results of a performance evaluation clarify that sufficiently optimum routes can be computed instantly even in practical large-scale networks by the proposed method, which adjusts the link distance strictly based on the risk level at the considered link.

This paper proposes a video-quality estimation method based on a no-reference model for realtime quality monitoring in video-streaming services. The proposed method analyzes both bitstream information and decoded pixel information to estimate video-quality degradation by transmission errors. Video quality in terms of a mean squared error (MSE) between degraded video frames and error-free video frames is estimated on the basis of the number of impairment macroblocks in which the quality degradation has not been possible to be concealed. Error-concealment effectiveness is evaluated using motion information and luminance discontinuity at the boundaries of impairment regions. Simulation results show a high correlation (correlation coefficients of 0.93) between the actual MSE and the number of macroblocks in which error concealment has not been effective. These results show that the proposed method works well in reatime quality monitoring for video-streaming services.

This paper proposes a bit-split string matcher architecture for a memory-efficient hardware-based parallel pattern matching engine. In the proposed bit-split string matcher, multiple finite-state machine (FSM) tiles share match vectors to reduce the required number of stored match vectors. By decreasing the memory size for storing match vectors, the total memory requirement can be minimized.