Error filtering

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  • Scientists, engineers and the like are a strange lot. Unperturbed by societal norms, they direct their energies to finding better alternatives to existing theories and concocting solutions to unsolved problems. Driven by an insatiable curiosity, they record their observations and crunch the numbers. This tome is about the science of crunching. It’s about digging out something of value from the detritus that others tend to leave behind. The described approaches involve constructing models to process the available data.

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  • Adaptive filtering can be used to characterize unknown systems in time-variant environments. The main objective of this approach is to meet a difficult comprise: maximum convergence speed with maximum accuracy. Each application requires a certain approach which determines the filter structure, the cost function to minimize the estimation error, the adaptive algorithm, and other parameters; and each selection involves certain cost in computational terms, that in any case should consume less time than the time required by the application working in real-time....

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  • We describe refinements to hierarchical translation search procedures intended to reduce both search errors and memory usage through modifications to hypothesis expansion in cube pruning and reductions in the size of the rule sets used in translation. Rules are put into syntactic classes based on the number of non-terminals and the pattern, and various filtering strategies are then applied to assess the impact on translation speed and quality. Results are reported on the 2008 NIST Arabic-toEnglish evaluation task. ...

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  • Some bandpass modulation schemes -M-PAM, M-PSK, M-FSK, M-QAM -How to perform coherent and non-coherent detection -How to calculate the average probability of symbol error for different modulation schemes that we studied?

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  • The invariable motif for analog design is to explore the new circuit topologies, architectures and CAD technologies to overcome the design challenges coming from the new applications and new fabrication technologies. In this book, a new architecture for a SAR ADC is proposed to eliminate the process mismatches and minimize the errors.

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  • In comparison with adaptive finite impulse response (FIR) filters, adaptive infinite impulse response (IIR) filters offer the potential to implement an adaptive filter meeting desired performance levels, as measured by mean-square error, for example, with much less computational complexity.

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  • KALMAN FILTER REVISITED 18.1 INTRODUCTION In Section 2.6 we developed the Kalman filter as the minimization of a quadratic error function. In Chapter 9 we developed the Kalman filter from the minimum variance estimate for the case where there is no driving noise present in the target dynamics model. In this chapter we develop the Kalman filter for more general case [5, pp. 603–618]. The concept of the Kalman filter as a fading-memory filter shall be presented. Also its use for eliminating bias error buildup will be presented.

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  • INTRODUCTION In Section 6.3 we developed a recursive least-squares growing memory-filter for the case where the target trajectory is approximated by a polynomial. In this chapter we develop a recursive least-squares growing-memory filter that is not restricted to having the target trajectory approximated by a polynomial [5. pp. 461–482]. The only requirement is that Y nÀi , the measurement vector at time n À i, be linearly related to X nÀi in the error-free situation. The Y nÀi can be made up to multiple measurements obtained at the time n À i as in (4.

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  • WIENER FILTERS 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 Wiener Filters: Least Square Error Estimation Block-Data Formulation of the Wiener Filter Interpretation of Wiener Filters as Projection in Vector Space Analysis of the Least Mean Square Error Signal Formulation of Wiener Filters in the Frequency Domain Some Applications of Wiener Filters The Choice of Wiener Filter Order Summary W iener theory, formulated by Norbert Wiener, forms the foundation of data-dependent linear least square error filters.

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  • GRAM–SCHMIDT ORTHONORMAL TRANSFORMATION 13.1 CLASSICAL GRAM–SCHMIDT ORTHONORMAL TRANSFORMATION The Gram–Schmidt orthonormalization procedure was introduced in Section 4.3 in order to introduce the orthonormal transformation F applied to the matrix T. The Gram–Schmidt orthonormalization procedure described there is called the classical Gram–Schmidt (CGS) orthogonilization procedure. The CGS procedure was developed in detail for the case s ¼ 3, m 0 ¼ 2, and then these results were extrapolated to the general case of arbitrary s and m 0 .

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  • Another source of error due to filtering effect of the system: -Inter-symbol interference (ISI) -The techniques to reduce ISI -Pulse shaping to have zero ISI at the sampling time -Equalization to combat the filtering effect of the channel

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A New Method for Least-Squares and Minimax Group-Delay Error Design of Allpass Variable Fractional-Delay Digital Filters

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Sensitivity-Based Pole and Input-Output Errors of Linear Filters as Indicators of the Implementation Deterioration in Fixed-Point Context Thibault Hilaire1 and Philippe Chevrel2

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  • Given the observation vector , perform a mapping from to an estimate of the transmitted symbol, , such that the average probability of error in the decision is minimized.Signal vector is deterministic. Elements of noise vector are i.i.d Gaussian random variables with zeromean and variance . The noise vector pdf is

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  • Signal detection in AWGN channels Minimum distance detector. Maximum likelihood. Average probability of symbol error. Union bound on error probability. Upper bound on error probability based on the minimum distance.ISI in the detection process due to the filtering effects of the system Overall equivalent system transfer function

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  • The properties of Convolutional codes. We introduced interleaving as a means to combat bursty errors by making the channel seem uncorrelated. We also studied “Concatenated codes” that simply consist of inner and outer codes. They can provide the required performance at a lower complexity.

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  • Another source of error due to filtering effect of the system: Intersymbol. interference (ISI) The techniques to reduce ISI. Pulse shaping to achieve zero ISI at the sampling time. Equalization to combat the filtering effect of the channel.Some bandpass modulation schemes used in DCS for transmitting information oer channel. MPAM, MPSK, MFSK, MQAM How to detect the transmitted information at the receiver. Coherent detection. Noncoherent detection.

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  • Some bandpass modulation schemes MPAM, MPSK, MFSK, MQAM How to perform coherent and noncoherent detection.How to calculate the average probability of symbol error for different modulation schemes that we studied? How to compare different modulation schemes based on their error performances?

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  • Evaluating the average probability of symbol error for different bandpass modulation schemes Comparing different modulation schemes based on their error performances.Transforming signals to improve communications performance by increasing the robustness against channel impairments (noise, interference, fading, ...)

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  • Another class of linear codes, known as Convolutional codes. We study the structure of the encoder. We study different ways for representing the encoder.Convolutional codes offer an approach to error control coding substantially different from that of block codes. A convolutional encoder: encodes the entire data stream, into a single codeword. does not need to segment the data stream into blocks of fixed size (Convolutional codes are often forced to block structure by periodic truncation). is a machine with memory.

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