Fractals are mathematical or natural objects that are made of parts similar to the whole in certain ways. In this paper a software reliability forecasting method of software failure is proposed based on predictability of fractal time series. The empirical failure data (three data sets of Musa's) are used to demonstrate the performance of the reliability prediction. Compared with other methods, our method is effective.

In this paper, we address the following problems: Given a sequence A of n real numbers, and four parameters I,J,X and Y with I≤ J and X≤ Y, find the longest (or shortest) subsequence of A such that its length is between I and J and its sum is between X and Y. We present an online and an offline algorithm for the problems, both run in O(nlog n) time, which are optimal.

In this paper, we propose a novel stochastic gradient algorithm for efficient adaptive filtering. The basic idea is to sparsify the initial error vector and maximize the benefits from the sparsification under computational constraints. To this end, we formulate the task of algorithm-design as a constrained optimization problem and derive its (non-trivial) closed-form solution. The computational constraints are formed by focusing on the fact that the energy of the sparsified error vector concentrates at the first few components. The numerical examples demonstrate that the proposed algorithm achieves the convergence as fast as the computationally expensive method based on the optimization without the computational constraints.

A (k,δ,ε)-locally decodable code C:Fqn FqN is an error-correcting code that encodes =(x1,x2,...,xn) ∈ Fqn to C() ∈ FqN and has the following property: For any ∈ FqN such that d(,C()) ≤ δ N and each 1 ≤ i ≤ n, the symbol xi of can be recovered with probability at least 1-ε by a randomized decoding algorithm looking at only k coordinates of . The efficiency of a (k,δ,ε)-locally decodable code C:Fqn FqN is measured by the code length N and the number k of queries. For a k-query locally decodable code C:Fqn FqN, the code length N was conjectured to be exponential of n, i.e., N=exp(nΩ(1)), however, this was disproved. Yekhanin [In Proc. of STOC, 2007] showed that there exists a 3-query locally decodable code C:F2n F2N such that N=exp(n1/log log n) assuming that infinitely many Mersenne primes exist. For a 3-query locally decodable code C:Fqn FqN, Efremenko [ECCC Report No.69, 2008] further reduced the code length to N=exp(nO((log log n/ log n)1/2)), and in general showed that for any integer r>1, there exists a 2r-query locally decodable code C:Fqn FqN such that N=exp(nO((log log n/ log n)1-1/r)). In this paper, we will present improved constructions for query-efficient locally decodable codes by introducing a technique of "composition of locally decodable codes," and show that for any integer r>5, there exists a 9 2r-4-query locally decodable code C:Fqn FqN such that N=exp(nO((log log n/ log n)1-1/r)).

This paper reviews the standardization work that has been done in question 2 of ITU-T study group 15, which is the lead group on optical access transport technology. The major topics covered are the progress in the G-PON series and the new point-to-point fiber access recommendation. Finally, a brief view of the future plan of XG-PON is presented.

In this paper, Cellular Neural Networks using genetic algorithm (GA-CNNs) are designed for CMOS image noise reduction. Cellular Neural Networks (CNNs) could be an efficient way to apply to the image processing technique, since CNNs have high-speed parallel signal processing characteristics. Adaptive CNNs structure is designed for the reduction of Photon Shot Noise (PSN) changed according to the average number of photons, and the design of templates for adaptive CNNs is based on the genetic algorithm using real numbers. These templates are optimized to suppress PSN in corrupted images. The simulation results show that the adaptive GA-CNNs more efficiently reduce PSN than do the other noise reduction methods and can be used as a high-quality and low-cost noise reduction filter for PSN. The proposed method is designed for real-time implementation. Therefore, it can be used as a noise reduction filter for many commercial applications. The simulation results also show the feasibility to design the CNNs template for a variety of problems based on the statistical image model.

Structured light vision systems are based on the fact that the pixel location of light in an image has a unique association with the object depth. However, their applications are mainly limited to the properties of the object surface and the lighting conditions. This paper presents a robust vision system for accurate acquisition of 3-D surface data based on optimal structured light. To achieve depth measurement for a dynamic scene, the data acquisition must be performed with only a single image. Our special arrangement makes the image of the light stripe remaining sharp while the background becomes blurred. Moreover, a dynamic programming approach is proposed to track the optimal path while the laser beam is invisible or divergent under extreme condition. The principle and necessary mathematics for implementing the algorithm are presented. The robustness of the system against uncalibrated errors is demonstrated.

This paper proposes a power saving mechanism with variable sleep period to reduce the power consumed by optical network units (ONUs) in passive optical network (PON) systems. In the PON systems based on time division multiplexing (TDM), sleep and periodic wake-up (SPW) control is an effective ONU power saving technique. However, the effectiveness of SPW control is fully realized only if the sleep period changes in accordance with the traffic conditions. This paper proposes an SPW control mechanism with variable sleep period. The proposed mechanism sets the sleep period according to traffic conditions, which greatly improves the power saving effect. In addition, the protocols needed between an optical line terminal (OLT) and ONUs are described on the assumption that the proposed mechanism is applied to 10 Gigabit (10G) class PON systems, i.e. IEEE 802.3av 10G-EPON and FSAN/ITU-T 10G-PON systems. The validity of the proposed mechanism is confirmed by numerical simulations.

The precise noise modeling of complementary metal oxide semiconductor image sensor (CMOS image sensor: CIS) is a significant key in understanding the noise source mechanisms, optimizing sensor design, designing noise reduction circuit, and enhancing image quality. Therefore, this paper presents an accurate random telegraph signal (RTS) noise analysis model and a novel quantitative evaluation method in motion picture for the visual sensory evaluation of CIS. In this paper, two main works will be introduced. One is that the exposure process of a video camera is simulated, in which a Gaussian noise and an RTS noise in pinned-photodiode CMOS pixels are modeled in time domain and spatial domain; the other is that a new video quality evaluation method for RTS noise is proposed. Simulation results obtained reveal that the proposed noise modeling for CIS can approximate its physical process and the proposed video quality evaluation method for RTS noise performs effectively as compared to other evaluation methods. Based on the experimental results, conclusions on how the spatial distribution of an RTS noise affects the quality of motion picture are carried out.

A scalable speech codec consisting of a harmonic codec as the core layer and MDCT-based transform codec as the enhancement layer is often required to provide both very low-rate core communication and fine granular scalability. This structure, however, has a serious drawback for practical use because a time delay caused by transform in each layer is accumulated, resulting in a long overall codec delay. In this letter, a new MDCT structure is proposed to reduce the overall codec delay by eliminating the accumulation of time delay by each transform. In the proposed structure, the time delay is first reduced by forcing two transforms to share a common look-ahead. The error components of MDCT caused by the look-ahead sharing are then analyzed and compensated in the decoder, resulting in perfect reconstruction. The proposed structure reduces the codec delay by the frame size, with an equivalent coding efficiency.

The LC-based Digitally Controlled Oscillator (DCO) is one of the most important components of all digital phase locked loops. The performance of the loops is significantly determined by the DCO's frequency resolution. In order to enhance the frequency resolution, we propose a mismatched capacitor pairs based digitally controlled switched capacitance array, which dramatically reduces the minimum switched varactor capacitance. Furthermore, we implement a DCO based on our proposal in SMIC 0.18 µm and conduct simulation in Spectre. The simulation results show that the frequency resolution is enhanced compared with the existing methods.

This paper deals with the maximum-weight 2-path packing problem (M2PP), which is the problem of computing a set of vertex-disjoint paths of length 2 in a given edge-weighted complete graph so that the total weight of edges in the paths is maximized. Previously, Hassin and Rubinstein gave a randomized cubic-time approximation algorithm for M2PP which achieves an expected ratio of - ε ≈ 0.5223 - ε for any constant ε > 0. We refine their algorithm and derandomize it to obtain a deterministic cubic-time approximation algorithm for the problem which achieves a better ratio (namely, 0.5265 - ε for any constant ε>0).

To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.

This paper presents a novel approach for robot localization using landmark maps. With recent progress in SLAM researches, it has become crucial for a robot to obtain and use large-size maps that are incrementally built by other mapper robots. Our localization approach successfully works with such incremental and large-size maps. In literature, RANSAC map-matching has been a promising approach for large-size maps. We extend the RANSAC map-matching so as to deal with incremental maps. We combine the incremental RANSAC with an incremental LSH database and develop a hybrid of the position-based and the appearance-based approaches. A series of experiments using radish dataset show promising results.

This paper proposes an improved method of reversible data hiding with increased capacity. The conventional method determines whether to embed a data bit in an image block according to the statistics of pixels in that block. Some images have pixel statistics that are inadequate for data hiding, and seldom or never have data embedded in them. The proposed method modulates the statistics invertibility to overcome such disadvantages, and is also able to improve the quality of the image containing the hidden data using block-adaptive modulation. Simulationresults show the effectiveness of the proposed method.

A new low voltage operation of high voltage switching technique, which is capable of reducing leakage current by an order of three compared to conventional circuits, has been developed for sub-1.8 V low voltage mobile NAND flash memory. In addition, by using the proposed high voltage switch, chip size scaling can be realized due to reduced a minimum required space between the N-wells of selected and unselected blocks for isolation. The proposed scheme is essential to achieve low power operation NAND Flash memory, especially for mobile electronics.

The selection of effective features is especially important in achieving highly accurate speech recognition. Although the mel-cepstrum is a popular and effective feature for speech recognition, it is still unclear that the filterbank adopted in the mel-cepstrum always produces the optimal performance regardless of the phonetic environment of any specific speech recognition task. In this paper, we propose a new cepstral domain feature extraction approach utilizing the entropic distance-based filterbank for highly accurate speech recognition. Experimental results showed that the cepstral features employing the proposed filterbank reduce the relative error by 31% for clean as well as noisy speech compared to the mel-cepstral features.

Recent advanced technology makes digital circuits small and the number of digital functional blocks that can be integrated on a single chip is increasing rapidly. On the other hand, reduction in the size of analog circuits has been insufficient. This means that the analog circuit area is relatively large, and reducing analog circuit area can be effective to make a low cost radio receiver. In this paper, a new wireless receiver architecture that occupies small analog area is proposed, and measured results of the core analog blocks are described. To reduce the analog area, a balanced 3-phase analog system is adopted and the functions of analog baseband filters and VGAs are moved to the digital domain. The test chip consists of a 3-phase downconverter and a 3-phase ADC. There is no analog baseband filter on the chip and the analog filter is assumed to be replaced with a digital filter. The downconverter and ADC occupy 0.28 mm2. The measured results show the possibility that the requirements for IMT-2000 are fulfilled even with a small chip area.

In this paper, we propose a novel noncoherent maximum likelihood detection (NMLD) method for differential spatial multiplexing (SM) multiple-input multiple-output (MIMO) systems. Unlike the conventional maximum likelihood detection (MLD) method which needs the knowledge of channel state information (CSI) at the receiver, NMLD method has no need of CSI at either the transmitter or receiver. After repartitioning the observation block of multiple-symbol differential detection (MSDD) and following a decision feedback process, the decision metric of NMLD is derived by reforming that of MSDD. Since the maximum Doppler frequency and noise power are included in the derived decision metric, estimations of both maximum Doppler frequency and noise power are needed at the receiver for NMLD. A fast calculation algorithm (FCA) is applied to reduce the computational complexity of NMLD. The feasibility of the proposed NMLD is demonstrated by computer simulations in both slow and fast fading environments. Simulation results show that the proposed NMLD has good bit error rate (BER) performance, approaching that of the conventional coherent MLD with the extension of reference symbols interval. It is also proved that the BER performance is not sensitive to the estimation errors in maximum Doppler frequency and noise power.

We propose a surface profiling algorithm by white-light interferometry that extends sampling interval to twice of the widest interval among those used in conventional algorithms. The proposed algorithm uses a novel function called an in-phase component of an interferogram to detect the peak of the interferogram, while conventional algorithms used the squared-envelope function or the envelope function. We show that the in-phase component has the same peak as the corresponding interferogram when an optical filter has a symmetric spectral distribution. We further show that the in-phase component can be reconstructed from sampled values of the interferogram using the so-called quadrature sampling technique. Since reconstruction formulas used in the algorithm are very simple, the proposed algorithm requires low computational costs. Simulation results show the effectiveness of the proposed algorithm.