Infinite Impulse Response (IIR)

Fractional-order operators play a fundamental role in the modeling and control of complex dynamical systems; however, their infinite-dimensional nature necessitates rational approximation for practical implementation. This paper presents a unified comparative framework for evaluating widely used approximation methods, including standard and refined Oustaloup filters, continued fraction expansion (CFE), Matsuda, curve-fitting, and modified stability boundary locus (M-SBL) approaches. A systematic evaluation methodology is developed to assess these methods based on frequency-domain accuracy, timedomain performance, and robustness. Furthermore, a Pareto-based multi-objective analysis is introduced to explicitly capture the trade-offs among conflicting performance criteria, enabling the identification of non-dominated solutions without relying on weighted-sum formulations. Extensive simulations are conducted over a wide frequency range to evaluate approximation accuracy and control-oriented performance. The results reveal that different methods exhibit distinct trade-offs between accuracy, robustness, and complexity. In particular, the Oustaloup and M-SBL approaches demonstrate strong overall performance across multiple criteria, while methods such as CFE and curve-fitting show limitations under wideband conditions. The proposed framework provides a systematic and reproducible basis for selecting appropriate approximation techniques in fractional-order control applications, offering valuable insights into their practical implementation and performance trade-offs.

It is a well-known result of estimation theory that biased estimators can outperform unbiased ones in terms of expected quadratic error. In steady-state, many adaptive filtering algorithms offer an unbiased estimation of both the reference signal and the unknown true parameter vector. In this correspondence, we propose a simple yet effective scheme for adaptively biasing the weights of adaptive filters using an output multiplicative factor. We give theoretical results that show that the proposed configuration is able to provide a convenient bias vs variance tradeoff, leading to reductions in the filter mean-square error, especially in situations with a low signal-to-noise ratio (SNR). After reinterpreting the biased estimator as the combination of the original filter and a filter with constant output equal to 0, we propose practical schemes to adaptively adjust the multiplicative factor. Experiments are carried out for the normalized leastmean-squares (NLMS) adaptive filter, improving its mean-square performance in stationary situations and during the convergence phase.

Neste trabalho foi implementado os seguintes filtros: IIR pelo método da Invariância do Impulso, filtro IIR pelo método da Transformada Bilinear e um filtro FIR passa baixa. Nos três projetos serão apresentados todo o desenvolvimento matemático, a função de transferência do filtro de tempo contínuo H(s) e seu diagrama de Bode (diagrama de magnitude e fase); a função de transferência de tempo discreto H(z) e seu diagrama de Bode; representação destes filtros através de equações a diferenças; simulações dentro da banda passante, na frequência de corte e na banda de rejeição; apresentação dos códigos, e sobreposição em cada gráfico, do sinal de entrada e do sinal de saída.

The cost and complexity of large satellite space missions continue to escalate. To reduce costs, more attention is being directed toward small, lightweight satellites where future demand is expected to grow dramatically. Draper Laboratory is currently developing a three-axis stabilized control system using on-board GPS, micromechanical gyroscopes, small reaction wheels, and miniaturized microstrip phased array antennas for active line-of-sight control of a small pointing satellite. A first iteration of the rate gyroscope electronics has been designed and implemented on a single mixed-signal CMOS ASIC. The gyroscope sensor, ASIC, and supporting components are placed in a 2.5-cm X 2.5-cm flat package. Total power dissipation for the gyroscope ASIC is a small fraction of a watt. The micromechanical gyro data is optimally blended with the GPS data, thereby taking advantage of the gyro's ability to measure high-frequency dynamics and the GPS's ability to bound the error growth due to gyro drift. First-order simulation has shown that the performance of the micromechanical gyros, when integrated with GPS, is feasible for a pointing mission of 1 microradian of jitter stability and -2 milliradians (10') absolute error, for a satellite with 1 meter antenna separation.

Matched filtering is broadly used in various radar applications and communication fields whose output indicates some range sidelobes which may mask some stronger targets. This problem can be overcome by using mismatched filter after the matched filter which increases the performance that is in terms of peak sidelobe ratio. The proposed algorithm is suitable to obtain superior performance in range resolution and detection range simultaneously. The characteristics of chaotic codes such as auto-correlation and cross-correlation are same as random codes. So the input is considered as binary and ternary chaotic sequence which is undergone matched filtering process which is then undergo mismatched filter for further reduction in the sidelobe levels. The design of mismatched filter is adapted with cuckoo search algorithm that uses Lévy distribution. It is observed that the performance of the ternary sequence is significantly improved at the output and this method is extended to various len...

This paper demonstrates the application of an evolutionary heuristic search technique called Novel Particle Swarm Optimization (NPSO) for the optimal design of 6th and 8th order low pass and band pass Infinite Impulse Response (IIR) filters. Conventional particle swarm optimization (PSO) has been modified with the attractive features of cognitive information, ( pbest and pworst ) and named as NPSO. NPSO helps to speed up the convergence by means of non-advancement of particle to pworst position. It finds near optimal solution in terms of a set of filter coefficients. The effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, Real coded Genetic Algorithm (RGA) and conventional Particle Swarm Optimization (PSO) with a superior outcome for the designed 6th and 8th order IIR low pass and band pass filters. Simulation results affirm that the proposed NPSO algorithm outperforms its counterparts not only in terms of quality output i.e. sharpness at cut-off, pass band ripple and stop band attenuation but also in convergence speed with assured stability.

The second-order all-pass section is the main building block of the lattice wave digital filters (WDFs). The all-pass sections are conventionally realized using two port adaptors. In this paper, second-order all-pass sections are replaced with three port parallel adaptors. These adaptors, implemented with canonic signed digit coefficients, are proposed to increase maximal sampling frequency of lattice WDFs. The proposed implementation of three port parallel adaptors reduces the latency of the critical loop by reducing the components (adders and multipliers). Further increase in maximal sampling frequency is obtained by integrating these three port parallel adaptors using carry propagation adders (CPA) designed with low power and high performance 1-bit full adders, registers as delay elements and binary multipliers. Here, multipliers are implemented using a network of shifts and adders (or subtractors). An example of a filter implementation where the proposed approaches are applied, ...

We present a method to reduce the effect of full-rate GSM RPE-LTP coding by combining two sets of acoustic models during recognition, one set trained on GSMdistorted speech and the other trained on clean uncoded speech. During recognition, the a posteriori probabilities of an utterance are calculated as a sum of the posteriors of the individual models, weighted according to the distortion class each state in the model represents. We analyze the origin of spectral distortion to the quantized long-term residual introduced by the RPE-LTP codec and discuss how this distortion varies according to phonetic class. For the Research Management corpus, the proposed method reduces the degradation in frame-byframe phonetic recognition accuracy introduced by GSM coding by more than 20 percent.

Abstract- In this paper, the design of Simpson and Alaoui operator based 1/3rd order digital differentiators are presented. To improve the design accuracy of the Simpson operator based 1/3 rd digital differentiator, firstly, the stability criterion is considered, and then the fractional order digital differentiator is designed by using Continued Fraction Expansion (CFE) discretization scheme. Next Alaoui operator is used to design 1/3 rd order digital differentiator. Lastly, the MATLAB simulation results of both the designed fractional order digital differentiators are compared with the ideal fractional order digital differentiator.

This work bridges the gap between activities motivated from statistical signal processing, neuromorphic systems, and microelectronic implementation techniques for blind separation and recovery of mixed signals. The composition adopts both discrete-time and continuous-time formulations with a view towards implementations in the digital as well as the analog domains of microelectronic circuits. This paper focuses on the development and formulation of dynamic architectures with adaptive update laws for multi-source blind signal separation/recovery.

In a previous work we presented a low delay audio coder [1]. This coder responded to a Subband-ADPCM hybrid structure and required 2'5 bits/sample. In this paper we present a new low delay audio coder that demands only 2 bit/sample and responds to the same structure Subband-ADPCM but incorporating adaptive vector quantization [2]. In such sense, each component of the vector (prediction error) is weighted according to its variance estimation. The total coding delay (< 2 ms) is due only to the perfect reconstruction filter bank [3]. Our objective is to find an audio coding scheme that allows to achieve nearly-transparent quality with low delay. In applications where the delay is a critical parameter, i.e. when is important to use a feedback channel, there is not a standard well suited for this case. The profusely used standards ISO-MPEG for audio handle perceptual models that require high frequency precision and introduces high delay [4].

Nowadays, it does not exist an audio coding standard for getting nearly-transparent quality with low delay. The standard ISO-MPEG is profusely used in audio for getting high quality [1]. It uses a perceptual model that requires high frequency precision and introduces high delay. However, in applications where the delay is a critical parameter, e.g. when is important to use a feedback channel, the ISO-MPEG is not adequate and there is not a standard for this case. This paper describes the different steps followed for the obtainment of an audio coder with nearly-transparent quality and low delay. The complexity was another design criterion because the final objective is the real time implementation of the proposed coder on a commercial DSP. A comparative analysis of low delay codification schemes is done (ADPCM, Subband, and Subband-ADPCM coding). For each technique we modify its different parameters (e.g. numbers of bits of the quantizer, number of bands, bit allocations criterion, aliasing filters, etc) in order to get the best configuration. The proposed coder uses a Subband-ADPCM hybrid structure with dynamic bit allocation. In such sense, we propose a very low complexity bit allocation strategy with similar results to the classic strategy [2][3] and no delay. The coder output is scalable from high quality at higher bit-rate to lower quality at lower bit rates, supporting a wide range of service and resource utilization. Nearly-transparent quality is accomplished with 2.5 bits/sample. The less than 2 ms algorithm delay is due only to the perfect reconstruction filter bank [4].

The limited success of most prediction systems has proved that future stock prices are very difficult to predict. The purpose of this paper is to show that future prices do not have to be known to make successful investments and that anticipating movements (increases or decreases) of the price can be sufficient. Probabilistic classification systems based on pattern recognition theory appear to be a good way to reach this objective. Moreover, they include some other advantages, principally in terms of risk management. Results show satisfactory classification hit rates but a rather poor translation into financial gains. This paper tries to identify causes of this problem and proposes some ideas of solution.

This paper presents an optimal design of linear phase digital low pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO). NPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified in the PSO to enhance its search capability that leads to a higher probability of obtaining the global optimal solution. The key feature of the proposed modified inertia weight mechanism is to monitor the weights of particles, which linearly decrease in general applications. In the design process, the filter length, pass band and stop band frequencies, feasible pass band and stop band ripple sizes are specified. FIR filter design is a multi-modal optimization problem. Evolutionary algorithms like real code genetic algorithm (RGA), particle swarm optimization (PSO), and the novel particle swarm optimization (NPSO) have been used in this work for the design of linear phase FIR low pass (LP) filter. A comparison of simulation results reveals the optimization efficacy of the algorithm over the prevailing optimization techniques for the solution of the multimodal, non-differentiable, highly non-linear, and constrained FIR filter design problems.

Digital integer and fractional order integrators and differentiators are very important blocks of digital signal processing. In many situations, integer order integrators and differentiators are not sufficient to model all kind of dynamics. For such systems, fractional order operators give better solution. This paper is based on design of a new family of fractional order integrators and differentiators using various approximation techniques. Here, digital fractional order integrators are designed by direct discretization method using different techniques like continued fraction expansion, Taylor series expansion, and rational Chebyshev approximation on the transfer function of Jain-Gupta-Jain second order integrator. Their response in frequency domain is compared. The frequency response of the proposed integrators with highest efficiency is also compared with the existing ones. It is proved that rational Chebyshev approximation based integrators have highest efficiency among them. The fractional order differentiators are also designed using proposed integrators. It is concluded that proposed family of fractional order operators show remarkable improvement in frequency response compared to all the existing ones over the entire Nyquist frequency range.

This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical submodels. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

Abstract: This study deals with a noise robust distributed speech recognizer for real-world applications by deploying feature parameter compensation technique. To realize this objective, Mel-LP based speech analysis has been used in speech coding on the linear frequency scale by applying a first-order all-pass filter instead of a unit delay. To minimize the mismatch between training and test phases, Cepstral Mean Normalization (CMN) and Blind Equalization (BEQ) have been applied to enhance Mel-LP cepstral coefficient as an ...

The degree to which radiation from a loudspeaker is diffuse may be quantified by a spatial correlation function normalised to the on-axis response. This is true for any loudspeaker type, including the distributed-mode loudspeaker (DML). However, because of the variation in material damping and design-related constraints, correlation commonly varies both with frequency and direction. A modified function, the offset spatial bandwidth of correlation function, is introduced as a means of describing diffuse performance and quantifying its variation over the radiation field.

In this paper, the design of Simpson and Alaoui operator based 1/3rd order digital differentiators are presented. To improve the design accuracy of the Simpson operator based 1/3 rd digital differentiator, firstly, the stability criterion is considered, and then the fractional order digital differentiator is designed by using Continued Fraction Expansion (CFE) discretization scheme. Next Alaoui operator is used to design 1/3 rd order digital differentiator. Lastly, the MATLAB simulation results of both the designed fractional order digital differentiators are compared with the ideal fractional order digital differentiator.

Thenormal trendin Linear Prediction (LP) techniques is fixed frame windowing. In this paper, however, dynamic window concept is introduced whereframe size is kept variable in order to achieve efficient outcome in terms of computational cost, mean square error and prediction gain. The three famous LP techniques namely Normal Equations, Levinson Durbin Algorithm (LDA) and Leroux Gueguen Algorithm (LGA) are briefly discusse dusing variable frame windowing and the above mentioned parameters are analyzed. Simulation results for the above three algorithms suggest that LDA and LGA have shown better performance than Normal Equation method based on reduced prediction error, low computational time and high prediction gain.

Digital integer and fractional order integrators and differentiators are very important blocks of digital signal processing. In many situations, integer order integrators and differentiators are not sufficient to model all kind of dynamics. For such systems, fractional order operators give better solution. This paper is based on design of a new family of fractional order integrators and differentiators using various approximation techniques. Here, digital fractional order integrators are designed by direct discretization method using different techniques like continued fraction expansion, Taylor series expansion, and rational Chebyshev approximation on the transfer function of Jain-Gupta-Jain second order integrator. Their response in frequency domain is compared. The frequency response of the proposed integrators with highest efficiency is also compared with the existing ones. It is proved that rational Chebyshev approximation based integrators have highest efficiency among them. The fractional order differentiators are also designed using proposed integrators. It is concluded that proposed family of fractional order operators show remarkable improvement in frequency response compared to all the existing ones over the entire Nyquist frequency range.

Mitigation of anomalous propagation clutter (AP) is an ongoing problem in radar meteorology. AP clutter routinely contaminates radar data masking weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data but this also eliminates weather data at zero velocity. With the advent of fast digital receivers capable of real time spectral processing, the real time identification and elimination of AP clutter is now possible. A fuzzy logic algorithm is used to distinguish between clutter echo and precipitation echoes. Based on this classification, clutter filters can be applied to only those radar resolution volumes where clutter is present, in real time. In this way weather echoes are preserved while clutter echoes are mitigated. The clutter filters used in this paper are spectral based, i.e., they are applied in the spectral domain. In many cases, after the clutter echoes are filtered, the underlying weather echo signatures ...

Mitigation of anomalous propagation clutter (AP) is an ongoing problem in radar meteorology. AP clutter routinely contaminates radar data masking weather returns causing poor data quality. The problem is typically mitigated by clutter filtering all radar data but this will also eliminate weather data at zero velocity. With the advent of fast digital receivers capable of real time spectral processing, the real time identification and elimination of AP clutter is now possible. A fuzzy logic algorithm is used to distinguish between clutter echo and precipitation echoes. Based on this classification, clutter filters can be applied to only those radar resolution volumes where clutter is present, in real time. In this way weather echoes are preserved while clutter echoes are mitigated. The clutter filters used in this paper are spectral based, i.e., they are applied in the spectral domain. In many cases, after the clutter echoes are filtered, the underlying weather echo signatures are rev...

The steady-state amplitude response measured at listening locations in a room is a widely accepted indicator of sound quality in sound reproduction systems. Room equalization schemes adjust the room curve to match a target believing that this ensures good and consistent sound. This paper examines both small and large venues, home theaters to cinemas, seeking a calibration methodology that could be applied throughout the audio industry. At present most of the industry follows a common philosophy but movie sound is a problematic exception. Some changes to current practice are indicated. PAPERS MEASUREMENT AND CALIBRATION OF SOUND REPRODUCING SYSTEMS monitor LOUDSPEAKERS that are evaluated using RECORDINGS that are made using microphones, equalization, ambience and other EFFECTS evaluated using that are used as test signals to subjectively evaluate audio equipment that are enjoyed using consumer audio systems in homes, cars, etc. monitor LOUDSPEAKERS that are evaluated using RECORDINGS that are made using microphones, equalization, ambience and other EFFECTS evaluated using a eva that are used as test signals to subjectively evaluate audio equipment that are enjoyed using consumer audio systems in homes, cars, etc. CREATING THE ART APPRECIATING THE ART monitor LOUDSPEAKERS that are evaluated using RECORDINGS that are made using microphones, equalization, ambience and other EFFECTS evaluated using that are used as test signals to subjectively evaluate audio equipment that are enjoyed using consumer audio systems in homes, cars, etc.

In this paper, an optimal design of linear phase digital high pass (HP) finite impulse response (FIR) filter has been proposed using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach and Wavelet Mutation (PSOCFIWA-WM). PSOCFIWA-WM incorporates a new definition of swarm updating in PSOCFIWA with the help of wavelet based mutation. Wavelet mutation enhances the PSOCFIWA to explore the solution space more effectively. In the design process, filter length, pass band and stop band edge frequencies, feasible pass band and stop band ripple sizes are specified. Other evolutionary algorithms like real coded genetic algorithm (RGA), particle swarm optimization (PSO), PSO with constriction factor and inertia weight approach (PSOCFIWA), and the proposed PSOCFIWA-WM have been used for the comparative design of linear phase FIR HP filter. A comparison of simulation results reveals the optimization superiority of the proposed technique over the other optimization techniques for the solution of FIR HP filter design.

In this paper a Quadrature Mirror Filter is implemented in VHDL, for wireless communication applications. The Quadrature Mirror Filter (QMF) basically is a parallel combination of a High Pass Filter (HPF) and Low Pass Filter (LPF), which performs the action of frequency subdivision by splitting the signal spectrum into two spectra. The QMF implementation is carried out on FPGA platform. The Xilinx IP Core generator will be used for instantiating the standard Xilinx parts. Xilinx ISE will be used to carry out the synthesis and bit file generation. The obtained Synthesis Report for implemented QMF will be used to analyze the occupied area and power dissipation. The study and implementation will be aimed to realize the equalizer for wireless communication system. Modelsim Xilinx Edition (MXE) will be used for simulation and functional verification. Xilinx ISE will be used for synthesis and bit file generation. The Xilinx Chip scope will be used to test the results on Spartan 3E 500K FP...

Reconfigurable finite impulse response (FIR) filters whose filter coefficients and filter order change dynamically during run-time play an important role in the software defined radio (SDR) systems, multi-channel filters, and digital up/down converters. However, there are not many reports on such reconfigurable designs which can support dynamic variation of filter order and filter coefficients. The purpose of this paper is to provide an architectural solution for the FIR filters to support run-time variation of the filter order and filter coefficients. First, two straightforward designs, namely, (i) single-MAC based design and (ii) fullparallel design are presented. For large variation of the filter order, two designs based on (iii) folded structure and (iv) fast FIR algorithm are presented. Finally, we propose (v) high throughput design which provides significant advantage in terms of hardware and/or time complexities over the other designs. We compare complexities of all the five structures, and provide the synthesis results for verification.

This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Probabilistic Neural Networks (PNN). A LRPNN, in contrast to a PNN, is sensitive to the context in which events occur, and therefore, identification of time or spatial correlations is attainable. Besides the definition of the LRPNN architecture a fast three-step training method is proposed. The first two steps are identical to the training of traditional PNNs, while the third step is based on the Differential Evolution optimization method. Finally, the superiority of LRPNNs over PNNs on the task of text-independent speaker verification is demonstrated.

This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Probabilistic Neural Networks (PNN). A LRPNN, in contrast to a PNN, is sensitive to the context in which events occur, and therefore, identification of time or spatial correlations is attainable. Besides the definition of the LRPNN architecture a fast three-step training method is proposed. The first two steps are identical to the training of traditional PNNs, while the third step is based on the Differential Evolution optimization method. Finally, the superiority of LRPNNs over PNNs on the task of text-independent speaker verification is demonstrated.