The ITU-T J.83 Annex B is a widely adopted standard in North America for digital video and audio transmission over coaxial cable. This paper proposes a new parallel processing architecture of the parity checksum generator and syndrome generator specified in the standard for packet synchronization and error detection. The proposed parallel processing architecture removes the performance bottleneck occurring in the conventional serial processing architecture, leading to significant decrease in processing time for generating a parity checksum in transmitter and a syndrome in receiver. Implementation results show that the proposed parallel processing architecture reduces the processing time by 92% for parity checksum generation and by 81% for syndrome generation over the conventional serial processing architecture.

The Dst index is the most popular measure of a scale of magnetic storms, and it is widely used as a monitor of the conditions of the Earth's magnetosphere. Since the Dst index contains contributions from multiple magnetospheric phenomena, it is important to distinguish each of the contributions in order to obtain meaningful information about the conditions of the magnetosphere. There have been several efforts which modeled temporal evolution of the Dst index empirically, and these empirical models considers some contributions separately. However, they take only short-term varations into accout, and contributions from phenomena which show long-term variations are neglected. In the present study, we have developed a technique for estimating the component of long-term variations of the Dst index using solar wind data and a nonlinear empirical model. The newly-developed technique adopts an algorithm which is similar to the particle filter. This algorithm allows an on-line processing of a long sequence of Dst data, which would enable a real-time estimation of system variables in a nonlinear system model. The estimates of the long-term variations can be used for accurate estimation of other contributions to the Dst index, which would provide credible information about the conditions of the magnetosphere. The framework proposed in the present study could be applied for the purpose of continuous real-time monitoring of the environment of the magnetosphere.

The situations where several population parameters need to be estimated simultaneously arise frequently in wide areas of applications, including reliability modeling, survival analysis and biological study. In this paper, we propose Bayesian methods of estimation of the ordered parameters of the two exponential populations, which incorporate the prior information about the simple order restriction, but sometimes breaks the order restriction. A simulation study shows that the proposed estimators are more efficient (in terms of mean square errors) than the isotonic regression of the maximum likelihood estimators with equal weights. An illustrative example is finally presented.

Particle filter has attracted increasing attention from researchers of object tracking due to its promising property of handling nonlinear and non-Gaussian systems. In this paper, we mainly explore the problem of precisely estimating observation likelihoods of particles in the joint feature-spatial space. For this purpose, a mixture Gaussian kernel function based similarity is presented to evaluate the discrepancy between the target region and the particle region. Such a similarity can be interpreted as the expectation of the spatial weighted feature distribution over the target region. To adapt outburst of object motion, we also present a method to appropriately adjust state transition model by utilizing the priors of motion speed and object size. In comparison with the standard particle filter tracker, our tracking algorithm shows the better performance on challenging video sequences.

With a Gaussian kernel function, we find that the distance between two classes (DBTC) can be used as a class separability criterion in feature space since the between-class separation and the within-class data distribution are taken into account impliedly. To test the validity of DBTC, we develop a method of tuning the kernel parameters in support vector machine (SVM) algorithm by maximizing the DBTC in feature space. Experimental results on the real-world data show that the proposed method consistently outperforms corresponding hyperparameters tuning methods.

One major drawback of orthogonal frequency-division multiplexing is the high peak-to-average power ratio (PAPR) of the output signal. The selected mapping (SLM) and partial transmit sequences (PTS) methods are two promising techniques for PAPR reduction. However, to generate a set of candidate signals, these techniques need a bank of inverse fast Fourier transforms (IFFT's) and thus require high computational complexity. In this paper, we propose two low-complexity multiplication-free conversion processes to replace the IFFT's in the SLM method, where each conversion process for an N-point IFFT involves only 3N complex additions. Using these proposed conversions, we develop several new SLM schemes and a combined SLM & PTS method, in which at least half of the IFFT blocks are reduced. Computer simulation results show that, compared to the conventional methods, these new schemes have approximately the same PAPR reduction performance under the same number of candidate signals for transmission selection.

This paper proposes a dynamic capture-recapture (DCR) model to estimate not only the total number of software faults but also quantitative software reliability from observed data. Compared to conventional static capture-recapture (SCR) model and usual software reliability models (SRMs) in the past literature, the DCR model can handle dynamic behavior of software fault-detection processes and can evaluate quantitative software reliability based on capture-recapture sampling of software fault data. This is regarded as a unified modeling framework of SCR and SRM with the Bayesian estimation. Simulation experiments under some plausible testing scenarios show that our models are superior to SCR and SRMs in terms of estimation accuracy.

In this letter, we analyze the convergence speed of layered decoding of block-type low-density parity-check codes and verify that the layered decoding gives faster convergence speed than the sequential decoding with randomly selected check node subsets. Also, it is shown that using more subsets than the maximum variable node degree does not improve the convergence speed.

To segment a shape into parts is an important problem in shape representation and analysis. We propose in this paper a novel framework of shape segmentation using deformation models learned from multiple shapes. The deformation model from the target image to every other image is then estimated. Finally, normalized-cut graph partition is applied to the graph constructed based on the similarity of local patches in the target image, and a segmentation of the shape is carried out. Experimental results for images from MPEG7 shape database show the effectiveness of the proposed method.

ε-filter is a nonlinear filter for reducing noise and is applicable not only to speech signals but also to image signals. The filter design is simple and it can effectively reduce noise with an adequate filter parameter. This paper presents a method for estimating the optimal filter parameter of ε-filter based on signal-noise decorrelation and shows that it yields the optimal filter parameter concerning a wide range of noise levels. The proposed method is applicable where the noise to be removed is uncorrelated with signal, and it does not require any other knowledge such as noise variance and training data.

A parallel multiplexing scheduling (PMS) scheme is proposed for distributed antenna systems (DAS), which greatly improves average system throughput due to multi-user diversity and multi-user multiplexing. However, PMS has poor fairness because of the use of the "best channel selection" criteria in the scheduler. Thus we present a parallel proportional fair scheduling (PPFS) scheme, which combines PMS with proportional fair scheduling (PFS) to achieve a tradeoff between average throughput and fairness. In PPFS, the "relative signal to noise ratio (SNR)" is employed as a metric to select the user instead of the "relative throughput" in the original PFS. And only partial channel state information (CSI) is fed back to the base station (BS) in PPFS. Moreover, there are multiple users selected to transmit simultaneously at each slot in PPFS, while only one user occupies all channel resources at each slot in PFS. Consequently, PPFS improves fairness performance of PMS greatly with a relatively small loss of average throughput compared to PFS.

Inductive effect becomes important for on-chip global interconnects, like the power/ground (P/G) grid. Because of the locality property of partial reluctance, the inverse of partial inductance, the window-based partial reluctance extraction has been applied for large-scale interconnect structures. In this paper, an efficient method of partial reluctance extraction is proposed for large-scale regular P/G grid structures. With a block reuse technique, the proposed method makes full use of the structural regularity of the P/G grid. Numerical results demonstrate the proposed method is able to efficiently handle a P/G grid with up to one hundred thousands wire segments. It is several tens times faster than the window-based method, while generating accurate frequency-dependent partial reluctance and resistance.

Power and ground planes on multilayer PCBs can effectively radiate electromagnetic fields excited by the IC simultaneous switching noise. The high frequency electromagnetic radiation is often calculated from the electric field along the edge of the PCB, which can be estimated with a cavity model using magnetic walls. The excitation of the cavity modes is related to the via current passing through the power bus planes at the interconnection between IC package and PCB. Usually the attention is focused on the differential-mode current of the package pins, but in the present paper it is shown that the common-mode current flowing out from package pins plays a very important role in the excitation of cavity modes, and its neglect implies a fatal underestimation of the electromagnetic radiation from the power bus planes in some circumstances. A second important contribute to the radiation is given by the common mode current on the pins, together with the current flowing on the PCB ground plane. With the proposed equivalent circuit, the effectiveness of decoupling inductors depending on their location and on the value of the parasitic capacitance is studied.

A 1-GHz input bandwidth analog-to-digital (A/D) converter for an ultra-wideband impulse radio (UWB-IR) receiver is developed. Both an under-sampling sample-and-hold (S/H) circuit and a dynamic current-reduction comparator are proposed for the A/D converter. An under-sampling S/H circuit, which digitizes an input signal at a higher frequency than the sampling frequency with low power consumption, is required because the UWB-IR system utilizes intermittent ultrashort impulses. The proposed S/H circuit executes sampling by separating a sampling capacitor from an operational amplifier and accumulating the offset voltage of the amplifier in the other capacitor. The proposed dynamic current reduction comparator reduces bias current dynamically corresponding to its input-voltage level. The A/D converter is implemented in a 0.18-µm CMOS process technology, which achieves an effective number of bits of 5.5, 5.4, and 4.9 for input signals with frequencies of 1, 513, and 1057 MHz, respectively, at 32 M samples/s. The converter consumes 0.89 mA and 0.42 mA in the analog and digital component, respectively, at a 1.8-V supply.

Time variation within an OFDM symbol causes inter-carrier interference (ICI). In this letter, frequency-domain partial response coding (PRC) is investigated to reduce ICI in the Alamouti SFBC-OFDM system. Based on the expression of the ICI power in the SFBC-OFDM system with PRC, the near-optimal weights of PRC are derived. Simulation results show that the PRC scheme can reduce ICI effectively.

The electromagnetic fields emitted from an electrostatic discharge (ESD) event occurring between charged metals cause seriously damage high-tech equipment. In order to clarify the generation mechanism of such ESD fields and also to reduce them, we previously proposed a finite-difference time-domain (FDTD) algorithm based on a delta-gap feeding method and a frequency dispersion characteristic formula (Naito's formula) of ferrite material for simulating the ESD fields due to a spark between the charged metals with ferrite core attachment. In the present study, by integrating the above FDTD algorithm and a spark-resistance formula, we simulated both of the ESD itself and the resultant fields for the metal bars with ferrite core attachment, and demonstrated that the core attachment close to the spark gap suppresses the magnetic field level. This finding was also validated via 6-GHz wide-band measurement of the magnetic near-field.

Given a sparse linear system, A x = b, we can solve the equivalent system P A PT y = P b, x = PT y, where P is a permutation matrix. It has been known that, for example, when P is the RCMK (Reverse Cuthill-Mckee) ordering permutation, the convergence rate of the Krylov subspace method combined with the ILU-type preconditioner is often enhanced, especially if the matrix A is highly nonsymmetric. In this paper we offer a reordering heuristic for accelerating the solution of large sparse linear systems by the Krylov subspace methods with the ILUT preconditioner. It is the LRB (Line Red/Black) ordering based on the well-known 2-point Red-Black ordering. We show that for some model-like PDE (partial differential equation)s the LRB ordered FDM (Finite Difference Method)/FEM (Finite Element Method) discretization matrices require much less fill-ins in the ILUT factorizations than those of the Natural ordering and the RCMK ordering and hence, produces a more accurate preconditioner, if a high level of fill-in is used. It implies that the LRB ordering could outperform the other two orderings combined with the ILUT preconditioned Krylov subspace method if the level of fill-in is high enough. We compare the performance of our heuristic with that of the RCMK (Reverse Cuthill-McKee) ordering. Our test matrices are obtained from various standard discretizations of two-dimensional and three-dimensional model-like PDEs on structured grids by the FDM or the FEM. We claim that for the resulting matrices the performance of our reordering strategy for the Krylov subspace method combined with the ILUT preconditioner is superior to that of RCMK ordering, when the proper number of fill-in was used for the ILUT. Also, while the RCMK ordering is known to have little advantage over the Natural ordering in the case of symmetric matrices, the LRB ordering still can improve the convergence rate, even if the matrices are symmetric.

In this paper, a new local matching algorithm, to estimate dense disparity map in stereo vision, consisting of two stages is presented. At the first stage, the reduction of search space is carried out with a high efficiency, i.e. remarkable decrease in the average number of candidates per pixel, with low computational cost and high assurance of retaining the correct answer. This outcome being due to the effective use of multiple radial windows, intensity information, and some usual and new constraints, in a reasonable manner, retains those candidates which satisfy more constraints and especially being more promising to satisfy the implied assumption in using support windows; i.e., the disparity consistency of the window pixels. Such an output from the first stage, while speeding up the final selection of disparity in the second stage due to search space reduction, is also promising a more accurate result due to having more reliable candidates. In the second stage, the weighted window, although not necessarily being the exclusive choice, is employed and examined. The experimental results on the standard stereo benchmarks for the developed algorithm are presented, confirming that the massive computations to obtain more precise matching costs in weighted window is reduced to about 1/11 and the final disparity map is also improved.

Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)--which is a semi-supervised and local extension of Fisher discriminant analysis--was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.

This paper proposes joint maximum a posteriori (MAP) detection and spatial filtering for MIMO-OFDM mobile communications; it offers excellent receiver performance even over interference-limited channels. The proposed joint processor consists of a log likelihood generator and a MAP equalizer. The log likelihood generator suppresses cochannel interference by spatially filtering received signals and provides branch metrics of transmitted signal candidates. Using the branch metrics, the MAP equalizer generates log likelihood ratios of coded bits and performs channel decoding based on the MAP criterion. In the first stage, the log likelihood generator performs spatio-temporal filtering (STF) of the received signals prior to the fast Fourier transform (FFT) and is referred to as preFFT-type STF. Estimation of parameters including tap coefficients of the spatio-temporal filters and equivalent channel impulse responses of desired signals is based on the eigenvalue decomposition of an autocorrelation matrix of both the received and transmitted signals. For further improvement, in the second stage, the generator performs spatial filtering (SF) of the FFT output and is referred to as postFFT-type SF. Estimation of both tap coefficients of the spatial filters and channel impulse responses employs the recursive least squares (RLS) with smoothing. The reason for switching from preFFT-type STF into postFFT-type SF is that preFFT-type STF outperforms postFFT-type SF with a limited number of preamble symbols while postFFT-type SF outperforms preFFT-type STF when data symbols can be reliably detected and used for the parameter estimation. Note that there are two major differences between the proposed and conventional schemes: one is that the proposed scheme performs the two-stage processing of preFFT-type STF and postFFT-type SF, while the other is that the smoothing algorithm is applied to the parameter estimation of the proposed scheme. Computer simulations demonstrate that the proposed scheme can achieve excellent PER performance under interference-limited channel conditions and that it can outperform the conventional joint processing of preFFT-type STF and the MAP equalizer.