This paper proposes an accurate and efficient method to calculate probability distributions of pulse-shaped complex signals. We show that the distribution over the in-phase and quadrature-phase (I/Q) complex plane is obtained by a recursive probability mass function of the accumulator for a pulse-shaping filter. In contrast to existing analytical methods, the proposed method provides complex-plane distributions in addition to instantaneous power distributions. Since digital signal processing generally deals with complex amplitude rather than power, the complex-plane distributions are more useful when considering digital signal processing. In addition, our approach is free from the derivation of signal-dependent functions. This fact results in its easy application to arbitrary constellations and pulse-shaping filters like Monte Carlo simulations. Since the proposed method works without numerical integrals and calculations of transcendental functions, the accuracy degradation caused by floating-point arithmetic is inherently reduced. Even though our method is faster than Monte Carlo simulations, the obtained distributions are more accurate. These features of the proposed method realize a novel framework for evaluating the characteristics of pulse-shaped signals, leading to new modulation, predistortion and peak-to-average power ratio (PAPR) reduction schemes.

Recent trends in designing filters involve development of sparse filters with coefficients that not only have real but also zero values. These sparse filters can achieve a high performance through optimizing the selection of the zero coefficients and computing the real (non-zero) coefficients. Designing an infinite impulse response (IIR) sparse filter is more challenging than designing a finite impulse response (FIR) sparse filter. Therefore, studies on the design of IIR sparse filters have been rare. In this study, we consider IIR filters whose coefficients involve zero value, called sparse IIR filter. First, we formulate the design problem as a linear programing problem without imposing any stability condition. Subsequently, we reformulate the design problem by altering the error function and prepare several possible denominator polynomials with stable poles. Finally, by incorporating these methods into successive thinning algorithms, we develop a new design algorithm for the filters. To demonstrate the effectiveness of the proposed method, its performance is compared with that of other existing methods.

This paper presents a novel delta-sigma modulator that uses a switched-capacitor (SC) integrator with the structure of a finite impulse response (FIR) filter in a loop filter configuration. The delta-sigma analog-to-digital converter (ΔΣADC) is used in various conversion systems to enable low-power, high-accuracy conversion using oversampling and noise shaping. Increasing the gain coefficient of the integrator in the loop filter configuration of the ΔΣADC suppresses the quantization noise that occurs in the signal band. However, there is a trade-off relationship between the integrator gain coefficient and system stability. The SC integrator, which contains an FIR filter, can suppress quantization noise in the signal band without requiring an additional operational amplifier. Additionally, it can realize a higher signal-to-quantization noise ratio. In addition, the poles that are added by the FIR filter structure can improve the system's stability. It is also possible to improve the flexibility of the pole placement in the system. Therefore, a noise transfer function that does not contain a large gain peak is realized. This results in a stable system operation. This paper presents the essential design aspects of a ΔΣADC with an FIR filter. Two types of simulation results are examined for the proposed first- and second-order, and these results confirm the effectiveness of the proposed architecture.

Digitization of analog terrestrial TV broadcasting has recently been accelerated in many countries, and the effective utilization of vacant frequencies has also been investigated for new systems in each country. In Japan, a portion of vacant frequencies in the VHF-high band was allocated to the public broadband mobile communication (PBB) system. To evaluate the current PBB system and develop future broadband communication systems in this band, it is important to analyze the propagation channel more accurately. In this study, we characterize the propagation channel for 200MHz band broadband mobile communication systems, using measured channel impulse responses (CIRs). In the characterization process, the Saleh-Valenzuela (S-V) model is utilized to extract channel model parameters statistically. When evaluating the fluctuation of path power gain, we also propose to model the fluctuation of path power gain using the generalized extreme value distribution instead of the conventional log-normal distribution. The extracted CIR model parameters are validated by cumulative distribution function of root-means-square delay spread and maximum excess delay, comparing simulation result to measurement result. From the extracted CIR model parameters, we clarified the characteristics of 200MHz band broadband mobile communication systems in non-line-of-sight environments based on S-V model with the proposed channel model.

We have proposed and developed new electro-optic modulators for the pre-equalization of signal distortion caused by the optical fiber chromatic dispersion effect. We found that the synthesis of an almost arbitrary impulse response function is obtainable by utilizing an electro-optic modulator composed of a Mach-Zehnder waveguide and travelling-wave electrodes on a ferro-electric material substrate with polarization-reversed structures. In this paper, the operational principle, design and simulation results of the pre-equalization modulator are presented. Some preliminary experimental results are also shown with future prospects.

In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.

The notion of multiple constant multiplication (MCM) is extensively adopted in digital signal processing (DSP) applications such as finite impulse filter (FIR) designs. A set of adders is utilized to replace regular multipliers for the multiplications between input data and constant filter coefficients. Though many algorithms have been proposed to reduce the total number of adders in an MCM block for area minimization, they do not consider the actual bitwidth of each adder, which may not estimate the hardware cost well enough. Therefore, in this article we propose a bitwidth-aware MCM optimization algorithm that focuses on minimizing the total number of adder bits rather than the adder count. It first builds a subexpression graph based on the given coefficients, derives a set of constraints for adder bitwidth minimization, and then optimally solves the problem through integer linear programming (ILP). Experimental results show that the proposed algorithm can effectively reduce the required adder bit count and outperforms the existing state-of-the-art techniques.

Tracking capsule endoscope location is one of the promising applications offered by implant body area networks (BANs). When tracking the capsule endoscope location, i.e., continuously localize it, it is effective to take the weighted sum of its past locations to its present location, in other words, to low-pass filter its past locations. Furthermore, creating an exact mathematical model of location transition will improve tracking performance. Therefore, in this paper, we investigate two tracking methods with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem. One of the two tracking methods is finite impulse response (FIR) filter-based tracking, which tracks the capsule endoscope location by averaging its past locations. The other one is particle filter-based tracking in order to deal with a nonlinear transition model on the capsule endoscope. However, the particle filter requires that the particle weight is calculated according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated from the received wireless signal. Therefore, for the purpose of applying the particle filter to capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrate that the two tracking methods can improve the performance as compared with the conventional maximum likelihood (ML) localization. Furthermore, we confirm that the particle filter-based tracking outperforms the conventional FIR filter-based tracking by taking the realistic capsule endoscope transition model into consideration.

The optimal design of an extrapolated impulse response (EIR) filter (in the mini-max sense) is a non-linear programming problem. In this paper, the optimal design of the EIR filter by the semi-infinite programming (SIP) is investigated and an iterative technique for optimally designing the EIR filter is proposed. The simulation experiment validates the effectiveness of the SIP technique and the proposed iterative technique in the optimal design of the EIR filter.

This letter proposes an iterative learning control with advanced output data (ADILC) scheme using an estimation of the impulse response for non-minimum phase (NMP) systems, whose model is unknown, except for the relative degree and the number of NMP zeros. Although the ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems, at least a partial model should be known in order to apply ADILC. Considering this fact, in this letter, we propose a new ADILC method based on the estimation of the impulse response for NMP systems whose model is unknown. An estimation method for the learning matrix and an ADILC scheme are presented for NMP systems.

A method for the specification of weighting functions for a spaceborne/airborne interferometric synthetic aperture radar (SAR) sensor for Earth observation and environment monitoring is introduced. This method is based on designing an optimum mismatched filter which minimizes the total power in sidelobes located out of a specified range region around the peak value point of the system point-target response, i.e. impulse response function under the constraint imposed on the peak value. It is shown that this method allows achieving appreciable improvement in accuracy performance without degradation in the range resolution.

The information of channel impulse response (CIR) length and noise variance play an important role in blind identification and equalization of wireless multipath channels. In orthogonal frequency division multiplexing (OFDM) systems, multipath fading channels introduce interference between adjacent symbols which can be prevented by inserting a cyclic prefix (CP) before each symbol. In this letter, we find that the interference power in the cyclic prefix (CP) interval and its variation can be used to estimate the CIR length and noise variance jointly and blindly.

For an indoor area where a target node is tracked with anchor nodes, we can calculate the priori probability density functions (pdfs) on the distances between the target and anchor nodes by using its shape, three-dimensional sizes and anchor nodes locations. We call it “the area layout information (ALI)” and apply it for two indoor target tracking methods with received signal strength indication (RSSI) assuming a square location estimation area. First, we introduce the ALI to a target tracking method which tracks a target using the weighted sum of its past-to-present locations by a simple infinite impulse response (IIR) low pass filter. Second, we show that the ALI is applicable to a target tracking method with a particle filter where the motion of the target is nonlinearly modelled. The performances of the two tracking methods are evaluated by not only computer simulations but also experiments. The results demonstrate that the use of ALI can successfully improve the location estimation performance of both target tracking methods, without huge increase of computational complexity.

In this paper, we present a comparative study on directly aligned multi point controlled wavefront synthesis (DMCWS) and wave field synthesis (WFS) for the realization of a high-accuracy sound reproduction system, and the amplitude, phase and attenuation characteristics of the wavefronts generated by DMCWS and WFS are assessed. First, in the case of DMCWS, we derived an optimal control-line coordinate based on a numerical analysis. Next, the results of computer simulations revealed that the wavefront in DMCWS has wide applicability in both the spatial and frequency domains with small amplitude and phase errors, particularly above the spatial aliasing frequency in WFS, and we clarified that the amplitude error in DMCWS has similar behavior to the well-known approximate expression for spatial decay in WFS; this implies the ease of taking into account estimating the amplitude error in DMCWS. Finally, we developed wavefront measurement system and measured a DMCWS wavefront using our wavefront measurement system and algorithm. The results of measurements clarified the frequency characteristics of a loudspeaker. Also, DMCWS has wide applicability in frequency domains in actual environments. From these findings, we concluded the advantageousness of DMCWS compared with WFS.

The proportionate normalized least mean square algorithm (PNLMS) greatly improves the convergence of the sparse impulse response. It exploits the shape of the impulse response to decide the proportionate step gain for each coefficient. This is not always suitable. Actually, the proportionate step gain should be determined according to the difference between the current estimate of the coefficient and its optimal value. Based on this idea, an approach is proposed to determine the proportionate step gain. The proposed approach can improve the convergence of proportionate adaptive algorithms after a fast initial period. It even behaves well for the non-sparse impulse response. Simulations verify the effectiveness of the proposed approach.

Recently, proportionate adaptive algorithms have been proposed to speed up convergence in the identification of sparse impulse response. Although they can improve convergence for sparse impulse responses, the steady-state misalignment is limited by the constant step-size parameter. In this article, based on the principle of least perturbation, we first present a derivation of normalized version of proportionate algorithms. Then by taking the disturbance signal into account, we propose a variable step-size proportionate NLMS algorithm to combine the benefits of both variable step-size algorithms and proportionate algorithms. The proposed approach can achieve fast convergence with a large step size when the identification error is large, and then considerably decrease the steady-state misalignment with a small step size after the adaptive filter reaches a certain degree of convergence. Simulation results verify the effectiveness of the proposed approach.

This letter investigates an ADILC (Iterative Learning Control with Advanced Output Data) scheme for nonminimum phase systems using a partially known impulse response. ADILC has a simple learning structure that can be applied to both minimum phase and nonminimum phase systems. However, in the latter case, the overall control time horizon must be considered in the input update law, which makes the dimension of the matrices in the convergence condition very large. Also, this makes it difficult to find a proper learning gain matrix. In this letter, a new sufficient condition is derived from the convergence condition, which can be used to find the learning gain matrix for nonminimum phase systems if we know the first part of the impulse response up to a sufficient order. Based on this, an iterative learning control scheme is proposed using the estimation of the first part of the impulse response for nonminimum phase systems.

In this letter we purpose adaptive neuro-fuzzy inference system (ANFIS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. To evaluate the performance of this estimator, we compare the ANFIS with least square (LS) algorithm, minimum mean square error (MMSE) algorithm by using bit error rate (BER) and mean square error (MSE) criterias. According to computer simulations the performance of ANFIS has better performance than LS algorithm and close to MMSE algorithm. Besides there is unnecessity to send pilot when used the ANFIS.

This paper discusses a new structure of M-channel IIR perfect reconstruction filter banks. A novel building block defined as a cascade connection of some IIR building blocks and FIR building blocks is presented. An IIR building block is written by state space representation, where we easily obtain a stable filter bank by setting eigenvalues of the state transition matrix into the unit circle. Due to cascade connection of building blocks, we are able to design a system with a larger number of free parameters while keeping the stability. We introduce the condition which obtains the new building block without increasing of the filter order in spite of cascade connection. Additionally, by showing the simulation results, we show that this implementation has a better stopband attenuation than conventional methods.

An explicit expression for the impulse response coefficients of the predictive FIR digital filters is derived. The formula specifies a four-parameter family of smoothing FIR digital filters containing the Savitsky-Goaly filters, the Heinonen-Neuvo polynomial predictors, and the smoothing differentiators of arbitrary integer orders. The Hahn polynomials, which are orthogonal with respect to a discrete variable, are the main tool employed in the derivation of the formula. A recursive formula for the computation of the transfer function of the filters, which is the z-transform of a terminated sequence of polynomial ordinates, is also introduced. The formula can be used to design structures with low computational complexity for filters of any order.