Software FMEA is valuable and practically used for embedded software of safety-critical systems. In this paper, a novel method for Software FMEA is presented based on co-analysis of system model and software model. The method is hopeful to detect quantitative and dynamic effects by a targeted software failure. A typical application of the method is provided to illustrate the procedure and the applicable scenarios. In addition, a pattern is refined from the application for further reuse.

In this paper we consider the two-class classification problem with high-dimensional data. It is important to find a class of distributions such that we cannot expect good performance in classification for any classifier. In this paper, when two population variance-covariance matrices are different, we give a reasonable sufficient condition for distributions such that the misclassification rate converges to the worst value as the dimension of data tends to infinity for any classifier. Our results can give guidelines to decide whether or not an experiment is worth performing in many fields such as bioinformatics.

In this letter, the problem of feature quantization in robust hashing is studied from the perspective of approximate nearest neighbor (ANN). We model the features of perceptually identical media as ANNs in the feature set and show that ANN indexing can well meet the robustness and discrimination requirements of feature quantization. A feature quantization algorithm is then developed by exploiting the random-projection based ANN indexing. For performance study, the distortion tolerance and randomness of the quantizer are analytically derived. Experimental results demonstrate that the proposed work is superior to state-of-the-art quantizers, and its random nature can provide robust hashing with security against hash forgery.

This paper evaluates the impact of Through-Silicon Via (TSV) on the performance and power consumption of 3D circuitry. The physical and electrical model of TSV which considers the coupling effects with adjacent TSVs is exploited in our investigation. Simulation results show that the overall performance of 3D IC infused with TSV can be improved noticeably. The frequency of the ring oscillator in 4-tier stacking layout soars up to two times compared with one in 2D planar. Furthermore, TSV process variations are examined by Monte Carlo simulations to figure out the geometrical factor having more impact in manufacturing. An in-depth research on repeater associated with TSV offers a metric to compute the optimization of 3D systems integration in terms of performance and energy dissipation. By such optimization metric with 45 nm MOSFET used in our circuit layout, it is found that the optimal number of tiers in both performance and power consumption approaches 4 since the substantial TSV-TSV coupling effect in the worst case of interference is expected in 3D IC.

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.

A parameterization of perfect sequences over composition algebras over the real number field is presented. According to the proposed parameterization theorem, a perfect sequence can be represented as a sum of trigonometric functions and points on a unit sphere of the algebra. Because of the non-commutativity of the multiplication, there are two definitions of perfect sequences, but the equivalence of the definitions is easily shown using the theorem. A composition sequence of sequences is introduced. Despite the non-associativity, the proposed theorem reveals that the composition sequence from perfect sequences is perfect.

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.

A high-speed low-complexity time-multiplexing Reed-Solomon-based forward error correction architecture based on the pipelined truncated inversionless Berlekamp-Massey algorithm is presented in this paper. The proposed architecture has very high speed and very low hardware complexity compared with conventional Reed-Solomon-based forward error correction architectures. Hardware complexity is improved by employing a truncated inverse Berlekamp-Massey algorithm. A high-speed and high-throughput data rate is facilitated by employing a three-parallel processing pipelining technique and modified syndrome computation block. The time-multiplexing method for pipelined truncated inversionless Berlekamp-Massey architecture is used in the parallel Reed-Solomon decoder to reduce hardware complexity. The proposed architecture has been designed and implemented with 90-nm CMOS technology. Synthesis results show that the proposed 16-channel Reed-Solomon-based forward error correction architecture requires 417,600 gates and can operate at 640 MHz to achieve a throughput of 240 Gb/s. The proposed architecture can be readily applied to Reed-Solomon-based forward error correction devices for next-generation short-reach optical communications.

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.

Two-dimensional (2-D) codes for optical code-division multiple access (O-CDMA) systems can increase the number of subscribers and simultaneous users as compared to one-dimensional time-spreading codes. Multiple-wavelength optical orthogonal code (MWOOC), which is one of the 2-D codes, uses prime sequences as a wavelength-hopping code and an optical orthogonal code (OOC) as a time-spreading code. MWOOCs have some advantages over other 2-D codes especially in high bit-rate O-CDMA systems. The only drawback of MWOOC is that the performance degrades significantly when the number of wavelengths is not prime. Recently a generalized class of modified prime sequence codes (MPSCs), which includes the class of original MPSCs as its subclass, was presented. An important property of generalized MPSCs is that the codes can be constructed over not only prime fields but also extension fields. It has been shown that the correlation property of generalized MPSCs is the same as that of the original MPSCs. This paper investigates MWOOC with generalized prime sequences, which can be obtained in the process of generating the generalized MPSCs, as a wavelength-hopping code. Use of the generalized prime sequences can solve the nonprime problem of MWOOCs. The average error probability of the proposed MWOOCs is formulated theoretically and numerical results are compared with that of the original schemes. It is shown that nonprime numbers, such as 2m, 3m and 5m, can be also taken as the number of wavelengths without degrading the system performance in the proposed systems.

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.

A diversity scheme with Fractional Sampling (FS) in OFDM receivers has been investigated recently. FS path diversity makes use of the imaging components of the desired signal transmitted on the adjacent channel. To increase the diversity gain with FS the bandwidth of the transmit signal has to be enlarged. This leads to the reduction of spectrum efficiency. In this paper non-orthogonal access over multiple channels in the frequency domain with iterative interference cancellation (IIC) and FS is proposed. The proposed scheme transmits the imaging component non-orthogonally on the adjacent channel. In order to accommodate the imaging component, it is underlaid on the other desired signal. Through diversity with FS and IIC, non-orthogonal access on multiple channels is realized. Our proposed scheme can obtain diversity gains for non-orthogonal signals modulated with QPSK.

Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.

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.

This paper proposes a downlink multi-user transmission scheme for the amplify-and-forward(AF)-based multi-relay cellular network, in which Tomlinson-Harashima precoding(TH precoding) and interference alignment(IA) are jointly applied. The whole process of transmission is divided into two phases: TH precoding is first performed at base-station(BS) to support the multiplexing of data streams transmitted to both mobile-stations(MS) and relay-stations(RS), and then IA is performed at both BS and RSs to achieve the interference-free communication. During the whole process, neither data exchange nor strict synchronization is required among BS and RSs thus reducing the cooperative complexity as well as improving the system performance. Theoretical analysis is provided with respect to the channel capacity of different types of users, resulting the upper-bounds of channel capacity. Our analysis and simulation results show that the joint applications of TH precoding and IA outperforms other schemes in the presented multi-relay cellular network.

When the Akebono (EXOS-D) satellite passed through the plasmasphere, a series of lightning whistlers was observed by its analog wideband receiver (WBA). Recently, we developed an intelligent algorithm to detect lightning whistlers from WBA data. In this study, we analyzed two typical events representing the clear dispersion characteristics of lightning whistlers along the trajectory of Akebono. The event on March 20, 1991 was observed at latitudes ranging from 47.83 (47,83N) to -11.09 (11.09S) and altitudes between ∼2232 and ∼7537 km. The other event on July 12, 1989 was observed at latitudes from 34.94 (34.94N) and -41.89 (41.89S) and altitudes ∼1420–∼7911 km. These events show systematic trends; hence, we can easily determine whether the wave packets of lightning whistlers originated from lightning strikes in the northern or the southern hemispheres. Finally, we approximated the path lengths of these lightning whistlers from the source to the observation points along the Akebono trajectory. In the calculations, we assumed the dipole model as a geomagnetic field and two types of simple electron density profiles in which the electron density is inversely proportional to the cube of the geocentric distance. By scrutinizing the dipole model we propose some models of dispersion characteristic that proportional to the electron density. It was demonstrated that the dispersion D theoretically agrees with observed dispersion trend. While our current estimation is simple, it shows that the difference between our estimation and observation data is mainly due to the electron density profile. Furthermore, the dispersion analysis of lightning whistlers is a useful technique for reconstructing the electron density profile in the Earth's plasmasphere.

Limited Random Sequence (LRS) is quite important for Analog-to-Information Converter (AIC) because it determines the random sampling scheme and the resultant performance. LRS is established with the elements of “0” and “1”. The “1” appears randomly in the segment of the sequence, so that the production of the original signal and LRS can be considered as the approximation of the random sampling of the original signal. The random sampling result can perfectly recover the signal with Compressive Sensing (CS) algorithm. In this paper, a high order LRS is proposed for the AIC design in Distributed Compressive Sensing (DCS), which has the following three typical features: 1) The high order LRS has the elements of integer which can indicate the index number of the sensor in DCS. 2) High order LRS can adapt to the sparsity variation of the original signal detected by each sensor. 3) Employing the AIC with high order LRS, the DCS algorithm can recover the signal with very low sampling rate, usually above 2 orders less than the traditional distributed sensors. In the paper, the scheme and the construction algorithm of high order LRS are proposed. The performance is evaluated with the application studies of the distributed sensor network and the camera picture correspondingly.

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.

Fast and accurate estimation of missing relations, e.g., similarity, distance and kernel, among objects is now one of the most important techniques required by major data mining tasks, because the missing information of the relations is needed in many applications such as economics, psychology, and social network communities. Though some approaches have been proposed in the last several years, the practical balance between their required computation amount and obtained accuracy are insufficient for some class of the relation estimation. The objective of this paper is to formalize a problem to quickly and efficiently estimate missing relations among objects from the other known relations among the objects and to propose techniques called “PSD Estimation” and “Row Reduction” for the estimation problem. This technique uses a characteristic of the relations named “Positive Semi-Definiteness (PSD)” and a special assumption for known relations in a matrix. The superior performance of our approach in both efficiency and accuracy is demonstrated through an evaluation based on artificial and real-world data sets.

We observe that the state-of-the-art power-saving mechanisms (PSM) for IEEE 802.16e is neither optimal in terms of delay nor in terms of energy consumption. We propose a new PSM which achieves the optimality in terms of the average buffering delay without increasing energy consumption. In order to do so, we derive a formula which relates the average buffering delay to sleep intervals. Simulation results show that our scheme surpasses the BTE algorithm (used by the current IEEE 802.16e Mobile Stations) by 56.75–76% and the PSID algorithm by 8.52–24.39% in terms of the delay-energy consumption product.