Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels

The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Digital signal processing for segmented HPGe detectors preprocessingalgorithms and pulse shape analysis

MINIBALL is an versatile spectrometer consisting of 24 longitudinally six-fold segmented HPGe detectors, build for the efficient detection of rare γ decays in nuclear reactions of radioactive ion beams. MINIBALL was the first spectrometer equipped with digital electronics. Pulse shape analysis algorithms to determine the interaction position of γ -rays were implemented on a Digital Signal Processor and validated in an experiment using a collimated γ -ray source. Emphasis was placed on the properties of the different digital signal processing algorithms, the consequences for the implementation and the applicability for position determination. The next generation of γ -ray spectrometers will consist of highly segmented HPGe detectors equipped with digital electronics, resulting in a more than ten-fold increase in complexity compared to current spectrometers. To enable the construction of a γ -ray tracking spectrometer, new and powerful digital electronics will be developed. Preprocessing algorithms, giving the γ -ray energy and generating event triggers, were implemented on a VME module equipped with fast A/D converters and tested with different detectors and sources. Emphasis was placed on the detailed simulation and understanding of the algorithms as well as the influence of electronics and detector onto the energy resolution

IMPLEMENTATION OF NOISE CANCELLATION WITH HARDWARE DESCRIPTION LANGUAGE

The objective of this project is to implement noise cancellation technique on an FPGA using Hardware Description Language. The performance of several adaptive algorithms is compared to determine the desirable algorithm used for adaptive noise cancellation system. The project will focus on the implementation of adaptive filter with least-meansquares (LMS) algorithm or normalized least-mean-squares (NLMS) algorithm to cancel acoustic noises. This noise consists of extraneous or unwanted waveforms that can interfere with communication. Due to the simplicity and effectiveness of adaptive noise cancellation technique, it is used to remove the noise component from the desired signal. The project is divided into four main parts: research, Matlab simulation, ModelSim simulation and hardware implementation. The project starts with research on several noise cancellation techniques, and then with Matlab code, Simulink and FDA tool, the adaptive noise cancellation system is designed with the implementation of the LMS algorithm, NLMS algorithm and recursive-least-square algorithm to remove the interference noise. By using the Matlab code and Simulink, the noise that interfered with a sinusoidal signal and a record of music can be removed. The original signal in turns can be retrieved from the noise corrupted signal by changing the coefficient of the filter. Since filter is the important component in adaptive filtering process, the filter is designed first before adding adaptive algorithm. A Finite Impulse Response (FIR) filter is designed and the desired result of functional simulation and timing simulation is obtained through ModelSim and Integrated Software Environment (ISE) software and FPGA implementation. Finally the adaptive algorithm is added to the filter, and implemented in the FPGA. The noise is greatly reduced in Matlab simulation, functional simulation and timing simulation. Hence the results of this project show that noise cancellation with adaptive filter is feasible

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides. However, in most articles, the inference formulas for the LSTM network and its parent, RNN, are stated axiomatically, while the training formulas are omitted altogether. In addition, the technique of "unrolling" an RNN is routinely presented without justification throughout the literature. The goal of this paper is to explain the essential RNN and LSTM fundamentals in a single document. Drawing from concepts in signal processing, we formally derive the canonical RNN formulation from differential equations. We then propose and prove a precise statement, which yields the RNN unrolling technique. We also review the difficulties with training the standard RNN and address them by transforming the RNN into the "Vanilla LSTM" network through a series of logical arguments. We provide all equations pertaining to the LSTM system together with detailed descriptions of its constituent entities. Albeit unconventional, our choice of notation and the method for presenting the LSTM system emphasizes ease of understanding. As part of the analysis, we identify new opportunities to enrich the LSTM system and incorporate these extensions into the Vanilla LSTM network, producing the most general LSTM variant to date. The target reader has already been exposed to RNNs and LSTM networks through numerous available resources and is open to an alternative pedagogical approach. A Machine Learning practitioner seeking guidance for implementing our new augmented LSTM model in software for experimentation and research will find the insights and derivations in this tutorial valuable as well.Comment: 43 pages, 10 figures, 78 reference

High-Precision Automotive Radar Target Simulation

Radar target simulators (RTSs) deceive a radar under test (RuT) by creating an artificial environment consisting of virtual radar targets. In this work, new techniques are presented that overcome the rasterization deficiency of current RTS systems and enable the generation of virtual targets at arbitrary high-precision positions. This allows for continuous movement of the targets and thus a more credible simulation environment