Richard G. Lyons
Bio: Richard G. Lyons is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 107 citations.
01 Jan 2003
TL;DR: Comprehensive in scope, and gentle in approach, this book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.
Abstract: From the Publisher: This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.
22 May 2015
TL;DR: The RTL-SDR can be used to acquire and sample RF (radio frequency) signals transmitted in the frequency range 25MHz to 1.75GHz, and the MATLAB and Simulink environment can be employed to develop receivers using first principles DSP (digital signal processing) algorithms as discussed by the authors.
Abstract: The availability of the RTL-SDR device for less than $20 brings software defined radio (SDR) to the home and work desktops of EE students, professional engineers and the maker community. The RTL-SDR can be used to acquire and sample RF (radio frequency) signals transmitted in the frequency range 25MHz to 1.75GHz, and the MATLAB and Simulink environment can be used to develop receivers using first principles DSP (digital signal processing) algorithms. Signals that the RTL-SDR hardware can receive include: FM radio, UHF band signals, ISM signals, GSM, 3G and LTE mobile radio, GPS and satellite signals, and any that the reader can (legally) transmit of course! In this book we introduce readers to SDR methods by viewing and analysing downconverted RF signals in the time and frequency domains, and then provide extensive DSP enabled SDR design exercises which the reader can learn from. The hands-on SDR design examples begin with simple AM and FM receivers, and move on to the more challenging aspects of PHY layer DSP, where receive filter chains, real-time channelisers, and advanced concepts such as carrier synchronisers, digital PLL designs and QPSK timing and phase synchronisers are implemented. In the book we will also show how the RTL-SDR can be used with SDR transmitters to develop complete communication systems, capable of transmitting payloads such as simple text strings, images and audio across the lab desktop.
03 Feb 2022
TL;DR: The first downsampling layer with learnable strides, DiffStride, which learns the size of a cropping mask in the Fourier domain, that effectively performs resizing in a differentiable way and allows trading off accuracy for efficiency on ImageNet.
Abstract: Convolutional neural networks typically contain several downsampling operators, such as strided convolutions or pooling layers, that progressively reduce the resolution of intermediate representations. This provides some shift-invariance while reducing the computational complexity of the whole architecture. A critical hyperparameter of such layers is their stride: the integer factor of downsampling. As strides are not differentiable, finding the best configuration either requires cross-validation or discrete optimization (e.g. architecture search), which rapidly become prohibitive as the search space grows exponentially with the number of downsampling layers. Hence, exploring this search space by gradient descent would allow finding better configurations at a lower computational cost. This work introduces DiffStride, the first downsampling layer with learnable strides. Our layer learns the size of a cropping mask in the Fourier domain, that effectively performs resizing in a differentiable way. Experiments on audio and image classification show the generality and effectiveness of our solution: we use DiffStride as a drop-in replacement to standard downsampling layers and outperform them. In particular, we show that introducing our layer into a ResNet-18 architecture allows keeping consistent high performance on CIFAR10, CIFAR100 and ImageNet even when training starts from poor random stride configurations. Moreover, formulating strides as learnable variables allows us to introduce a regularization term that controls the computational complexity of the architecture. We show how this regularization allows trading off accuracy for efficiency on ImageNet.
TL;DR: In this paper , the effect of vehicle frequencies is removed by using the vehicle-bridge contact (point) responses and road roughness by the residue of the front and rear contact responses of the two-axle test vehicle.
Abstract: Both vehicle frequencies and road roughness are factors that may render the vehicle scanning method ineffective for bridges. In this paper, both factors will be eliminated via the skillful use of a two‐axle test vehicle. Namely, the effect of vehicle frequencies is removed by using the vehicle–bridge contact (point) responses and road roughness by the residue of the front and rear contact responses of the two‐axle test vehicle. Firstly, the contact responses for the two axles are derived from the vertical and rotational equations of motion for the test vehicle. Next, the contact response is processed by the variational mode decomposition (VMD) to yield the component response and then by the Hilbert transform (HT) to yield the mode shape. The parametric study has demonstrated that (1) more bridge frequencies can be extracted from the contact responses presented due to removal of vehicle frequencies; (2) the VMD‐HT technique for recovering mode shapes is robust with regard to vehicle damping and speed; (3) the proposed procedure in its entity is good not only for single‐span but for multi‐span bridges; and (4) the residue of the axles' responses can effectively reduce road roughness in identifying bridge modal properties.
01 Jan 1988
TL;DR: Digital Filter Design program presents method to compare desired and designed digital filters by minimizing sum-square error of differences in magnitude and phase angles.
Abstract: Digital Filter Design program presents method to compare desired and designed digital filters by minimizing sum-square error of differences in magnitude and phase angles. Written in FORTRAN IV.
27 Apr 2016
TL;DR: In this paper, a nouvelle methode de traitement dusignal is presented, which prend en compte les variations de phase, basee sur the transformee en ondelettes discrete stationnaire (TODS) for debruiter le signal and sur le filtre de Kalman etendu (FKE) for l’estimer the reponse du systeme visuel.
Abstract: L’examen PEV (Potentiel Evoque Visuel) avec stimulation par balayage de frequence spatiale est reconnu pour l’estimation de l’acuite visuelle (AV) chez le jeune enfant en raison de son objectivite et de sa faible duree d’examen. Neanmoins, plusieurs etudes ont souligne la variabilite des reponses et pour certains cas, des attenuations a certaines frequences spatiales. Dans cette these, nous demontrons la relation entre la phase du signal et ces attenuations lorsque la transformee de Fourier a court terme (TFCT) est utilisee. Nous presentons une nouvelle methode de traitement dusignal qui prend en compte les variations de phase, basee sur la transformee en ondelettes discrete stationnaire (TODS) pour le debruiter le signal et sur le filtre de Kalman etendu (FKE) pour l’estimer la reponse du systeme visuel. La nouvelle methode est testee sur deux ensembles d’examens. Le premier provient du CHRU de Lille et le second de notre laboratoire. La TODS ameliore le rapport signal sur bruit de 10.9 dB en moyenne (IC95 [6.3,15.6]) et reduit les artefacts dus aux clignements et aux mouvements. Le FKE permet une estimation de la reponse du systeme visuel plus precise. Grâce a la prise en compte de la phase du signal, la forme de la reponse presente moins de variations. La dispersion entre les balayages est divisee par 1.4 en comparaison a la methode actuelle. La correlation entre l’AV ETDRS et l’estimation de l’acuite visuelle de la nouvelle methode est meilleure (indice de Spearman=0.64, valeur-p=6 10-4, ecart-type=0.34 logMAR) que celle de la methode actuelle (indice de Spearman=0.57, valeur-p=1.1 10-3, ecart type=0.45 logMAR).