Richard G. Lyons

Bio: Richard G. Lyons is an academic researcher. The author has contributed to research in topics: Digital signal processing & Sample rate conversion. The author has an hindex of 1, co-authored 1 publications receiving 208 citations.

Understanding Digital Signal Processing (2nd Edition)

Abstract: Amazon.com's top-selling DSP book for 5 straight years-now fully updated!Real-world DSP solutions for working professionals!Understanding Digital Signal Processing, Second Edition is quite simply the best way for engineers, and other technical professionals, to master and apply DSP techniques. Lyons has updated and expanded his best-selling first edition-building on the exceptionally readable coverage that made it the favorite of professionals worldwide.This book achieves the perfect balance between theory and practice, making DSP accessible to beginners without ever oversimplifying it. Comprehensive in scope and gentle in approach, keeping the math at a tolerable level, this book helps readers thoroughly grasp the basics and quickly move on to more sophisticated techniques.This edition adds extensive new coverage of quadrature signals for digital communications; recent improvements in digital filtering; and much more. It also contains more than twice as many "DSP Tips and Tricks"... including clever techniques even seasoned professionals may have overlooked. Down-to-earth, intuitive, and example-rich, with detailed numerical exercises Stresses practical, day-to-day DSP implementations and problem-solving All-new quadrature processing coverage includes easy-to-understand 3D drawings Extended coverage of IIR filters; plus frequency sampling, interpolated FIR filters New coverage of multirate systems; including both polyphase and cascaded integrator-comb FIR filters Coverage includes: periodic sampling, DFT, FFT, digital filters, discrete Hilbert transforms, sample rate conversion, quantization, signal averaging, and more

Event-Related EEG Time-Frequency Analysis : An Overview of Measures and An Analysis of Early Gamma Band Phase Locking in Schizophrenia

Abstract: An increasing number of schizophrenia studies have been examining electroencephalography (EEG) data using time-frequency analysis, documenting illness-related abnormalities in neuronal oscillations and their synchronization, particularly in the gamma band. In this article, we review common methods of spectral decomposition of EEG, time-frequency analyses, types of measures that separately quantify magnitude and phase information from the EEG, and the influence of parameter choices on the analysis results. We then compare the degree of phase locking (ie, phase-locking factor) of the gamma band (36-50 Hz) response evoked about 50 milliseconds following the presentation of standard tones in 22 healthy controls and 21 medicated patients with schizophrenia. These tones were presented as part of an auditory oddball task performed by subjects while EEG was recorded from their scalps. The results showed prominent gamma band phase locking at frontal electrodes between 20 and 60 milliseconds following tone onset in healthy controls that was significantly reduced in patients with schizophrenia (P = .03). The finding suggests that the early-evoked gamma band response to auditory stimuli is deficiently synchronized in schizophrenia. We discuss the results in terms of pathophysiological mechanisms compromising event-related gamma phase synchrony in schizophrenia and further attempt to reconcile this finding with prior studies that failed to find this effect.

Time Series Forecasting With Deep Learning: A Survey

Abstract: Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time series forecasting -- describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time series data.

Toward an Efficient C-RAN Optical Fronthaul for the Future Networks: A Tutorial on Technologies, Requirements, Challenges, and Solutions

Abstract: The exponential traffic growth, demand for high speed wireless data communications, as well as incessant deployment of innovative wireless technologies, services, and applications, have put considerable pressure on the mobile network operators (MNOs). Consequently, cellular access network performance in terms of capacity, quality of service, and network coverage needs further considerations. In order to address the challenges, MNOs, as well as equipment vendors, have given significant attention to the small-cell schemes based on cloud radio access network (C-RAN). This is due to its beneficial features in terms of performance optimization, cost-effectiveness, easier infrastructure deployment, and network management. Nevertheless, the C-RAN architecture imposes stringent requirements on the fronthaul link for seamless connectivity. Digital radio over fiber-based common public radio interface (CPRI) is the fundamental means of distributing baseband samples in the C-RAN fronthaul. However, optical links which are based on CPRI have bandwidth and flexibility limitations. Therefore, these limitations might constrain or make them impractical for the next generation mobile systems which are envisaged not only to support carrier aggregation and multi-band but also envisioned to integrate technologies like millimeter-wave (mm-wave) and massive multiple-input multiple-output antennas into the base stations. In this paper, we present comprehensive tutorial on technologies, requirements, architectures, challenges, and proffer potential solutions on means of achieving an efficient C-RAN optical fronthaul for the next-generation network such as the fifth generation network and beyond. A number of viable fronthauling technologies such as mm-wave and wireless fidelity are considered and this paper mainly focuses on optical technologies such as optical fiber and free-space optical. We also present feasible means of reducing the system complexity, cost, bandwidth requirement, and latency in the fronthaul. Furthermore, means of achieving the goal of green communication networks through reduction in the power consumption by the system are considered.

Real-time full-field arbitrary optical waveform measurement

Abstract: The development of a real-time optical waveform measurement technique with quantum-limited sensitivity, unlimited record lengths and an instantaneous bandwidth scalable to terahertz frequencies would be beneficial in the investigation of many ultrafast optical phenomena. Currently, full-field (amplitude and phase) optical measurements with a bandwidth greater than 100 GHz require repetitive signals to facilitate equivalent-time sampling methods or are single-shot in nature with limited time records. Here, we demonstrate a bandwidth- and time-record scalable measurement that performs parallel coherent detection on spectral slices of arbitrary optical waveforms in the 1.55 µm telecommunications band. External balanced photodetection and high-speed digitizers record the in-phase and quadrature-phase components of each demodulated spectral slice, and digital signal processing reconstructs the signal waveform. The approach is passive, extendable to other regions of the optical spectrum, and can be implemented as a single silicon photonic integrated circuit. A measurement scheme that is capable of recording the amplitude and phase of arbitrary shaped optical waveforms with a bandwidth of up to 160 GHz is presented. The approach is compatible with integration on a silicon photonic chip and could aid the study of transient ultrafast phenomena.

Radio-Frequency Interference Mitigation for the Soil Moisture Active Passive Microwave Radiometer

Abstract: The Soil Moisture Active Passive (SMAP) radiometer operates in the L-band protected spectrum (1400-1427 MHz) that is known to be vulnerable to radio-frequency interference (RFI). Although transmissions are forbidden at these frequencies by international regulations, ground-based, airborne, and spaceborne radiometric observations show substantial evidence of out-of-band emissions from neighboring transmitters and possibly illegally operating emitters. The spectral environment that SMAP faces includes not only occasional large levels of RFI but also significant amounts of low-level RFI equivalent to a brightness temperature of 0.1-10 K at the radiometer output. This low-level interference would be enough to jeopardize the success of a mission without an aggressive mitigation solution, including special flight hardware and ground software with capabilities of RFI detection and removal. SMAP takes a multidomain approach to RFI mitigation by utilizing an innovative onboard digital detector back end with digital signal processing algorithms to characterize the time, frequency, polarization, and statistical properties of the received signals. Almost 1000 times more measurements than what is conventionally necessary are collected to enable the ground processing algorithm to detect and remove harmful interference. Multiple RFI detectors are run on the ground, and their outputs are combined for maximum likelihood of detection to remove the RFI within a footprint. The capabilities of the hardware and software systems are successfully demonstrated using test data collected with a SMAP radiometer engineering test unit.