This paper proposes a closed form solution to L2-sensitivity minimization of second-order state-space digital filters. Restricting ourselves to the second-order case of state-space digital filters, we can express the L2-sensitivity by a simple linear combination of exponential functions and formulate the L2-sensitivity minimization problem by a simple polynomial equation. As a result, the L2-sensitivity minimization problem can be converted into a problem to find the solution to a fourth-degree polynomial equation of constant coefficients, which can be algebraically solved in closed form without iterative calculations.

The Bloom Filter (BF), a space-and-time-efficient hash-coding method, is used as one of the fundamental modules in several network processing algorithms and applications such as route lookups, cache hits, packet classification, per-flow state management or network monitoring. BF is a simple space-efficient randomized data structure used to represent a data set in order to support membership queries. However, BF generates false positives, and cannot count the number of distinct elements. A counting Bloom Filter (CBF) can count the number of distinct elements, but CBF needs more space than BF. We propose an alternative data structure of CBF, and we called this structure an Adaptive Bloom Filter (ABF). Although ABF uses the same-sized bit-vector used in BF, the number of hash functions employed by ABF is dynamically changed to record the number of appearances of a each key element. Considering the hash collisions, the multiplicity of a each key element on ABF can be estimated from the number of hash functions used to decode the membership of the each key element. Although ABF can realize the same functionality as CBF, ABF requires the same memory size as BF. We describe the construction of ABF and IABF (Improved ABF), and provide a mathematical analysis and simulation using Zipf's distribution. Finally, we show that ABF can be used for an unpredictable data set such as real network traffic.

Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.

In this paper, we propose memory and performance optimized architecture to accelerate the operation speed of adaptive deblocking filter for H.264/JVT/AVC video coding. The proposed deblocking filter executes loading/storing and filtering operations with only 192 cycles for 1 macroblock. Only 244 internal buffers and 3216 internal SRAM are adopted for the buffering operation of deblocking filter with I/O bandwidth of 32 bit. The proposed architecture can process the filtering operation for 1 macroblock with less filtering cycles and lower memory sizes than some conventional approaches of realizing deblocking filter. The efficient hardware architecture is implemented with novel data arrangement, hybrid filter scheduling and minimum number of buffer. The proposed architecture is suitable for low cost and real-time applications, and the real-time decoding with 1080HD (19201088@30 fps) can be easily achieved when working frequency is 70 MHz.

This paper presents discussion about channel fluctuation on channel estimation in digital terrestrial television broadcasting. This channel estimation uses a two-dimensional (2D) filter. In our previous work, only a structure of a lattice is considered for generation of nonrectangular 2D filter. We investigate generation of nonrectangular 2D filter with adaptive method, because we should refer to not only a lattice but also channel conditions. From the computer simulations, we show that bit error rate of the proposed filter is improved compared to that of the filter depending on only lattices.

In the acoustic echo canceller (AEC), the step-size parameter of the adaptive filter must be varied according to the situation if double talk occurs and/or the echo path changes. We propose an AEC that uses a sub-adaptive filter. The proposed AEC can control the step-size parameter according to the situation. Moreover, it offers superior convergence compared to the conventional AEC even when the double talk and the echo path change occur simultaneously. Simulations demonstrate that the proposed AEC can achieve higher ERLE and faster convergence than the conventional AEC. The computational complexity of the proposed AEC can be reduced by reducing the number of taps of the sub-adaptive filter.

Despite the extensive literature on current conveyor-based universal (namely, low-pass, band-pass, high-pass, notch, and all-pass) biquads with three inputs and one output, no filter circuits have been reported to date which simultaneously achieve the following seven important features: (i) employment of only two current conveyors, (ii) employment of only grounded capacitors, (iii) employment of only grounded resistors, (iv) high-input and low-output impedance, (v) no need to employ inverting type input signals, (vi) no need to impose component choice conditions to realize specific filtering functions, and (vii) low active and passive sensitivity performances. This letter describes a new voltage-mode biquad circuit that satisfies all the above features simultaneously, and without trade-offs.

This paper describes a novel approach for detecting fault-prone modules using a spam filtering technique. Fault-prone module detection in source code is important for the assurance of software quality. Most previous fault-prone detection approaches have been based on using software metrics. Such approaches, however, have difficulties in collecting the metrics and constructing mathematical models based on the metrics. Because of the increase in the need for spam e-mail detection, the spam filtering technique has progressed as a convenient and effective technique for text mining. In our approach, fault-prone modules are detected in such a way that the source code modules are considered text files and are applied to the spam filter directly. To show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can correctly predict 78% of actual fault-prone modules as fault-prone.

Ultrasonic distance measurement using the pulse-echo method is based on the determination of the time of flight of ultrasonic waves. The pulse-compression technique, in which the cross-correlation function of a detected ultrasonic wave and a transmitted ultrasonic wave is obtained, is the conventional method used for improving the resolution of distance measurement. However, the calculation of a cross-correlation operation requires high-cost digital signal processing. This paper presents a new method of sensor signal processing within the pulse-compression technique using a delta-sigma modulated single-bit digital signal. The proposed sensor signal processing method consists of a cross-correlation operation employing single-bit signal processing and a smoothing operation involving a moving average filter. The proposed method reduces the calculation cost of the digital signal processing of the pulse-compression technique.

A new adaptive filter algorithm based on the linear prediction property of sinusoidal signals is proposed for unbiased estimation of the frequency of a real tone in white noise. Similar to the least mean square algorithm, the estimator is computationally simple and it provides unbiased as well as direct frequency measurements. Learning behavior and variance of the estimated frequency are derived and confirmed by computer simulations.

This letter presents low-complexity digital pulse shaping filter structures of a direct sequence code division multiple access (DS-CDMA) ultra wide-band (UWB) modem transmitter with a ternary spreading code. The proposed finite impulse response (FIR) filter structures using a look-up table (LUT) have the effect of saving the amount of memory by about 50% to 80% in comparison to the conventional FIR filter structures, and consequently are suitable for a high-speed parallel data process.

This letter mainly focuses on improving current noise reduction methods to solve the critical speech distortion problems with robust noise reduction in noisy speech signals for speech enhancement over IP networks. For robust noise reduction with packet loss recovery, we propose a novel optimized Wiener filtering technique that uses the estimated SNR (Signal-to-Noise Ratio) with packet loss recovery method which is applied as post-filtering over IP-networks. Simulation results demonstrate that the proposed scheme provides better reduction and recovery rates with considering packet loss and SNR environment than other methods.

Complex speech analysis for an analytic speech signal can accurately estimate the spectrum in low frequencies since the analytic signal provides spectrum only over positive frequencies. The remarkable feature makes it possible to realize more accurate F0 estimation using complex residual signal extracted by complex-valued speech analysis. We have already proposed F0 estimation using complex LPC residual, in which the autocorrelation function weighted by AMDF was adopted as the criterion. The method adopted MMSE-based complex LPC analysis and it has been reported that it can estimate more accurate F0 for IRS filtered speech corrupted by white Gauss noise although it can not work better for the IRS filtered speech corrupted by pink noise. In this paper, robust complex speech analysis based on ELS (Extended Least Square) method is introduced in order to overcome the drawback. The experimental results for additive white Gauss or pink noise demonstrate that the proposed algorithm based on robust ELS-based complex AR analysis can perform better than other methods.

In this comment we point out that the mapping from carry-propagation adders to carry-save adders in the context of shift-and-add multiplication is inconsistent. Based on this it is shown that the implementation in Ref.[1] does not achieve any complexity reduction in practice.

We report on suppressing adjacent-frequency interference (AFI) by using a RF receive bandpass-filter (BPF) with high-selectivity. By considering a high temperature superconducting (HTS) multi-pole BPF as a high selective BPF, the effect was estimated by numerical simulations. The simulations of the RF signals with an OFDM modulation transmitted to the demodulator via the BPF were carried out using the HTS BPF for 5 GHz band. The results confirmed the improvement of the bit error rate (BER) characteristic with the assumed HTS BPF with the high multi-poles under a strong AFI.

We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.

A sweep spectrum analyzer has been improved over the years, but the fundamental method has not been changed before the 'Super Sweep' method appeared. The 'Super Sweep' method has been expected to break the limitation of the conventional sweep spectrum analyzer, a limit of the maximum sweep rate which is in inverse proportion to the square of the frequency resolution. The superior performance of the 'Super Sweep' method, however, has not been experimentally proved yet. This paper gives the experimental evaluation on the 'Super Sweep' spectrum analyzer, of which theoretical concepts have already been presented by the authors of this paper. Before giving the experimental results, we give complete analysis for a sweep spectrum analyzer and express the principle of the super-sweep operation with a complete set of equations. We developed an experimental system whose components operated in an optimum condition as the spectrum analyzer. Then we investigated its properties, a peak level reduction and broadening of the frequency resolution of the measured spectrum, by changing the sweep rate. We also confirmed that the experimental system satisfactorily detected the spectrum at least 30 times faster than the conventional method and the sweep rate was in proportion to the bandwidth of the base band signal to be analyzed. We proved that the 'Super Sweep' method broke the restriction of the sweep rate put on a conventional sweep spectrum analyzer.

In this paper, we propose a recursive filtering algorithm to restore monochromatic images which are corrupted by general dependent additive noise. It is assumed that the equation which describes the image field is not available and a filtering algorithm is obtained using the information provided by the covariance functions of the signal, noise that affects the measurement equation, and the fourth-order moments of the signal. The proposed algorithm is obtained by an innovation approach which provides a simple derivation of the least mean-squared error linear estimators. The estimation of the grey level in each spatial coordinate is made taking into account the information provided by the grey levels located on the row of the pixel to be estimated. The proposed filtering algorithm is applied to restore images which are affected by general signal-dependent additive noise.

This paper addresses the problem of voice activity detection (VAD) in noisy environments. The VAD method proposed in this paper is based on a statistical model approach, and estimates statistical models sequentially without a priori knowledge of noise. Namely, the proposed method constructs a clean speech / silence state transition model beforehand, and sequentially adapts the model to the noisy environment by using a switching Kalman filter when a signal is observed. In this paper, we carried out two evaluations. In the first, we observed that the proposed method significantly outperforms conventional methods as regards voice activity detection accuracy in simulated noise environments. Second, we evaluated the proposed method on a VAD evaluation framework, CENSREC-1-C. The evaluation results revealed that the proposed method significantly outperforms the baseline results of CENSREC-1-C as regards VAD accuracy in real environments. In addition, we confirmed that the proposed method helps to improve the accuracy of concatenated speech recognition in real environments.

A novel dual mode bandpass filter (BPF) with improved spurious response is presented in this letter. To obtain low insertion loss, the coupling structure using the dual mode resonator and the feeding scheme using coplanar-waveguide (CPW) are constructed on the two sides of a dielectric substrate. A defected ground structure (DGS) is designed on the ground plane of the CPW to achieve the goal of spurious suppression of the filter. The filter has been investigated numerically and experimentally. Measured results show a good agreement with the simulated analysis.