Standards for data acquisition and software‐based analysis of in vivo electroencephalography recordings from animals. A TASK1‐WG5 report of the AES/ILAE Translational Task Force of the ILAE

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/1/epi13909.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/2/epi13909_am.pd

Computer-Aided Design of Switched-Capacitor Filters

This thesis describes a series of computer methods for the design of switched-capacitor filters. Current software is greatly restricted in the types of transfer function that can be designed and in the range of filter structures by which they can be implemented. To solve the former problem, several new filter approximation algorithms are derived from Newton's method, yielding the Remez algortithm as a special case (confirming its convergency properties). Amplitude responses with arbitrary passband shaping and stopband notch positions are computed. Points of a specified degree of tangency to attenuation boundaries (touch points) can be placed in the response, whereby a family of transfer functions between Butterworth and elliptic can be derived, offering a continuous trade-off in group delay and passive sensitivity properties. The approximation algorithms have also been applied to arbitrary group delay correction by all-pass functions. Touch points form a direct link to an iterative passive ladder design method, which bypasses the need for Hurwitz factorisation. The combination of iterative and classical synthesis methods is suggested as the best compromise between accuracy and speed. It is shown that passive ladder prototypes of a minimum-node form can be efficiently simulated by SC networks without additional op-amps. A special technique is introduced for canonic realisation of SC ladder networks from transfer functions with finite transmission at high frequency, solving instability and synthesis difficulties. SC ladder structures are further simplified by synthesising the zeros at +/-2fs which are introduced into the transfer function by bilinear transformation. They cause cancellation of feedthrough branches and yield simplified LDI-type SC filter structures, although based solely on the bilinear transform. Matrix methods are used to design the SC filter simulations. They are shown to be a very convenient and flexible vehicle for computer processing of the linear equations involved in analogue filter design. A wide variety of filter structures can be expressed in a unified form. Scaling and analysis can readily be performed on the system matrices with great efficiency. Finally, the techniques are assembled in a filter compiler for SC filters called PANDDA. The application of the above techniques to practical design problems is then examined. Exact correction of sinc(x), LDI termination error, pre-filter and local loop telephone line weightings are illustrated. An optimisation algorithm is described, which uses the arbitrary passband weighting to predistort the transfer function for response distortions. Compensation of finite amplifier gain-bandwidth and switch resistance effects in SC filters is demonstrated. Two commercial filter specifications which pose major difficulties for traditional design methods are chosen as examples to illustrate PANDDA's full capabilities. Significant reductions in order and total area are achieved. Finally, test results of several SC filters designed using PANDDA for a dual-channel speech-processing ASIC are presented. The speed with which high-quality, standard SC filters can be produced is thus proven

Digital quadrature demodulation of Doppler signals

Dissertação de mest., Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2009Ultrasound has for many years been an important tool in the detection and quantification of various health problems. In vascular diseases, for example, the ultrasound can be applied with different techniques such as Transit-Time Flow Measurements (TTFM) [36], Doppler [28][40][41][42] and elastography [37] [38]. Research has been developed focusing the signal processing of Doppler ultrasound signals. In an ongoing project, named Desarrollo de Sistemas Ultras´onicos y Computacionales para Diagn´ostico Cardiovascular (SUCoDiC), Doppler ultrasound signals are processed by an analog signal processing unit, in order to obtain the inphase (I) and quadrature (Q) components of the Doppler ultrasound signals, to allow directional blood flow separation. Problems associated with unbalanced channels’ gain of the employed analog system have been detected, resulting in an inapropriate directional blood flow separation. This thesis reports the research performed to eliminate such problems by substituting the analog system’s demodulator by digital signal processing approaches aiming at the achievement of the same goals, i.e., obtaining the Doppler ultrasound signal’s inphase (I) and quadrature (Q) components, for efficient directional blood flow separation. Five digital quadrature techniques have been studied to achieve such goal. Also, given technical constraints imposed by the nature of the Doppler ultrasound signals to be used, and limitations of the sampling rate of the Analog-to-Digital Converter (ADC) used, two strategies to acquire the Doppler ultrasound signals were studied. Such strategies involved the sampling of a downconversion version of the Doppler ultrasound signals (by application of the heterodyne function) and direct sampling of the Doppler ultrasound signals using uniform bandpass sampling. From the results obtained, three approaches are selected and proposed for real time implementation. Comparison between both signal sampling strategies employed are also presente

Design of multichannel nonrecursive digital filters with applications to seismic reflection data

Imperial Users onl

DSP compensation for distortion in RF filters

There is a growing demand for the high quality TV programs such as High Definition TV (HDTV). The CATV network is often a suitable solution to address this demand using a CATV modem delivering high data rate digital signals in a cost effective manner, thereby, utilizing a complex digital modulation scheme is inevitable. Exploiting complex modulation schemes, entails a more sophisticated modulator and distribution system with much tighter tolerances. However, there are always distortions introduced to the modulated signal in the modulator degrading signal quality. In this research, the effect of distortions introduced by the RF band pass filter in the modulator will be considered which cause degradations on the quality of the output Quadrature Amplitude Modulated (QAM) signal. Since the RF filter's amplitude/group delay distortions are not symmetrical in the frequency domain, once translated into the base band they have a complex effect on the QAM signal. Using Matlab, the degradation effects of these distortions on the QAM signal such as Bit Error Rate (BER) is investigated. In order to compensate for the effects of the RF filter distortions, two different methods are proposed. In the first method, a complex base band compensation filter is placed after the pulse shaping filter (SRRC). The coefficients of this complex filter are determined using an optimization algorithm developed during this research. The second approach, uses a pre-equalizer in the form of a Feed Forward FIR structure placed before the pulse shaping filter (SRRC). The coefficients of this pre-equalizer are determined using the equalization algorithm employed in a test receiver, with its tap weights generating the inverse response of the RF filter. The compensation of RF filter distortions in base band, in turn, improves the QAM signal parameters such as Modulation Error Ratio (MER). Finally, the MER of the modulated QAM signal before and after the base band compensation is compared between the two methods, showing a significant enhancement in the RF modulator performance

Filter Bank Multicarrier Modulation for Spectrally Agile Waveform Design

In recent years the demand for spectrum has been steadily growing. With the limited amount of spectrum available, Spectrum Pooling has gained immense popularity. As a result of various studies, it has been established that most of the licensed spectrum remains underutilized. Spectrum Pooling or spectrum sharing concentrates on making the most of these whitespaces in the licensed spectrum. These unused parts of the spectrum are usually available in chunks. A secondary user looking to utilize these chunks needs a device capable of transmitting over distributed frequencies, while not interfering with the primary user. Such a process is known as Dynamic Spectrum Access (DSA) and a device capable of it is known as Cognitive Radio. In such a scenario, multicarrier communication that transmits data across the channel in several frequency subcarriers at a lower data rate has gained prominence. Its appeal lies in the fact that it combats frequency selective fading. Two methods for implementing multicarrier modulation are non-contiguous orthogonal frequency division multiplexing (NCOFDM)and filter bank multicarrier modulation (FBMC). This thesis aims to implement a novel FBMC transmitter using software defined radio (SDR) with modulated filters based on a lowpass prototype. FBMCs employ two sets of bandpass filters called analysis and synthesis filters, one at the transmitter and the other at the receiver, in order to filter the collection of subcarriers being transmitted simultaneously in parallel frequencies. The novel aspect of this research is that a wireless transmitter based on non-contiguous FBMC is being used to design spectrally agile waveforms for dynamic spectrum access as opposed to the more popular NC-OFDM. Better spectral containment and bandwidth efficiency, combined with lack of cyclic prefix processing, makes it a viable alternative for NC-OFDM. The main aim of this thesis is to prove that FBMC can be practically implemented for wireless communications. The practicality of the method is tested by transmitting the FBMC signals real time by using the Simulink environment and USRP2 hardware modules

Implementation of DSP-based algorithms on USRP for mitigating non-linear distortions in the receiver

In recent years, software-defined radio (SDR) has attracted increasingly more attention in regards to modern communication systems. The concept of SDR defines a radio device that is capable of flexibly reconfiguring its radio interface by software. This opens multiple fields of application and makes SDR an enormously adjustable and versatile radio technology. However, RF impairments induced by cheap and simple RF front-ends turn out to be a significant limitation in practice. Non-linear distortions emerge from non-linear components of the direct down-conversion chain that are driven into their saturation level. This is a result of a finite linearity and limited dynamic range of the RF frontend. The focus of this thesis are non-linear distortions in wideband receivers and a mitigation of them by means of digital signal processing. The idea is to artificially regenerate the non-linear distortions in the digital domain by applying a memoryless, polynomial model. An adaptive filter adjusts these reference distortions in their magnitude and phase and subtracts them from the distorted signal. A hardware implementation of a mitigation algorithm on a typical SDR-platform is presented. No prior implementation of this pure-digital approach is known. An implementation design flow is described following a top-down approach, starting from a fixed-point high-level implementation and ending up with a low-level hardware description language implementation. Both high-level and low-level simulations help to validate and evaluate the implementation. In conclusion, the implementation of the mitigation algorithm is a sophisticated mitigation technique for cleaning a down-converted baseband spectrum of non-linear distortions in real-time. Therefore, the effective linearity of the RF front-end is increased. This may lead to a significant improvement in the bit error rate performance of cleansed modulated signals, as well as to an enhanced sensing reliability in the context of cognitive radio.Zusammenfassung: In den letzten Jahren sorgte Software-Defined Radio (SDR) in Bezug auf moderne Kommunikationssysteme für immer größere Aufmerksamkeit. Das Konzept von SDR bezeichnet ein Funkgerät, das in der Lage ist, seine Funkschnittstelle durch Software flexibel zu rekonfigurieren. Dies ermöglicht eine Vielzahl von Anwendungsmöglichkeiten und macht SDR zu einer enorm anpassungsfähigen und vielseitigen Funktechnologie. Allerdings stellen im HF-Frontend ausgelöste Störungen in der Praxis eine erhebliche Einschränkung dar. In direkt umsetzenden Empfängerstrukturen entstehen durch nichtlineare Komponenten, die in ihren Sättigungsbereich getrieben werden, nichtlineare Verzerrungen. Das ist ein Ergebnis der begrenzten Linearität und des Dynamikbereich des HF-Frontends eingeschränkt sind. Der Fokus der Arbeit liegt auf nichtlinearen Verzerrungen in breitbandigen Empfängern und deren Minderung mit Hilfe von digitaler Signalverarbeitung. Die Idee ist, die nichtlinearen Verzerrungen im digitalen Bereich auf Basis eines gedächtnislosen, Polynom-Modells zu regenerieren. Ein adaptives Filter passt dabei den Betrag der nichtlinearen Referenzverzerrungen an und subtrahiert diese vom verzerrten Signal. In der Arbeit wird eine Hardwareimplementierung eines Störungsminderungsalgorithmus auf einer typischen SDR Plattform vorgestellt. Bisher ist keine Implementierung des rein-digitalen Ansatzes bekannt. Der Implementierungsablauf beschreibt anhand eines Top-Bottom-Ansatzes, wie der Algorithmus zuerst in einer Festpunkt High-Level Realisierung und schließlich in einer Low-Level Implementierung mit einer Hardwarebeschreibungssprache umgesetzt wird. Sowohl High-Level als auch Low-Level Simulationen unterstützen dabei die Validierung und Bewertung der Implementierung. Die Implementierung des Abschwächungsalgorithmus stellt schließlich eine ausgefeilte Methode dar, um ein heruntergeschmischtes Basisbandspektrum in Echtzeit von nichtlinearen Verzerrungen zu befreien. Demzufolge wird die effektive Linearität des HF-Frontends erhöht. Dies kann zu einer erheblichen Verbesserung der Bitfehlerrate von modulierten Signalen führen sowie die Zuverlässigkeit des Sensings in Bezug auf kognitiven Funk steigern.Ilmenau, Techn. Univ., Masterarbeit, 201

An Introduction to Digital Signal Processing

An Introduction to Digital Signal Processing aims at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. The book is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware in which the candidate is introduced to the basic concepts first before embarking to the practical part which comes in the later chapters. Initially Digital Signal Processing evolved as a postgraduate course which slowly filtered into the undergraduate curriculum as a simplified version of the latter. The goal was to study DSP concepts and to provide a foundation for further research where new and more efficient concepts and algorithms can be developed. Though this was very useful it did not arm the student with all the necessary tools that many industries using DSP technology would require to develop applications. This book is an attempt to bridge the gap. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware. The objective is to win the student to use a variety of development tools to develop applications. Contents• Introduction to Digital Signal processing.• The transform domain analysis: the Discrete-Time Fourier Transform• The transform domain analysis: the Discrete Fourier Transform• The transform domain analysis: the z-transform• Review of Analogue Filter• Digital filter design.• Digital Signal Processing Implementation Issues• Digital Signal Processing Hardware and Software• Examples of DSK Filter Implementatio

An Introduction to Digital Signal Processing

An Introduction to Digital Signal Processing aims at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. The book is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware in which the candidate is introduced to the basic concepts first before embarking to the practical part which comes in the later chapters. Initially Digital Signal Processing evolved as a postgraduate course which slowly filtered into the undergraduate curriculum as a simplified version of the latter. The goal was to study DSP concepts and to provide a foundation for further research where new and more efficient concepts and algorithms can be developed. Though this was very useful it did not arm the student with all the necessary tools that many industries using DSP technology would require to develop applications. This book is an attempt to bridge the gap. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware. The objective is to win the student to use a variety of development tools to develop applications. Contents• Introduction to Digital Signal processing.• The transform domain analysis: the Discrete-Time Fourier Transform• The transform domain analysis: the Discrete Fourier Transform• The transform domain analysis: the z-transform• Review of Analogue Filter• Digital filter design.• Digital Signal Processing Implementation Issues• Digital Signal Processing Hardware and Software• Examples of DSK Filter Implementatio

Using Bayesian Inference in Design Applications

This dissertation presents a new approach for solving engineering design problems such as the design of antenna arrays and finite impulse response (FIR) filters. In this approach, a design problem is cast as an inverse problem. The tools and methods previously developed for Bayesian inference are adapted and utilized to solve design problems. Given a desired design output, Bayesian parameter estimation and model comparison are employed to produce designs that meet the prescribed design specifications and requirements. In the Bayesian inference framework, the solution to a design problem is the posterior distribution, which is proportional to the product of the likelihood and priors. The likelihood is obtained via the assignment of a distribution to the error between the desired and achieved design output. The priors are assigned distributions which express constraints on the design parameters. Other design requirements are implemented by modifying the likelihood. The posterior --- which cannot be determined analytically --- is approximated by a Markov chain Monte Carlo method by drawing a reasonable number of samples from it. Each posterior sample represents a design candidate and a designer needs to select a single candidate as the final design based on additional design criteria. The Bayesian inference framework has been applied to design antenna arrays and FIR filters. The antenna array examples presented here use different types of array such as planar array, symmetric, asymmetric and reconfigurable linear arrays to realize various desired radiation patterns which include broadside, end-fire, shaped beam, and three-dimensional patterns. Various practical design requirements such as a minimum spacing between two adjacent elements, limitations in the dynamic range and accuracy of the current amplitudes and phases, the ability to maintain antenna performance over a frequency band, and the ability to sustain the loss of an arbitrary element, have been incorporated. For the filter design application, all presented examples employ a linear phase FIR filter to produce various desired frequency responses. In practice, the filter coefficients are limited in dynamic range and accuracy. This requirement has been incorporated into two examples where the filter coefficients are represented by a sum of signed power-of-two terms