Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada

Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada

Mean frequency estimation of surface EMG signals using filterbank methods

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 201

Applied Signal Processing

Being an inter-disciplinary subject, Signal Processing has application in almost all scientific fields. Applied Signal Processing tries to link between the analog and digital signal processing domains. Since the digital signal processing techniques have evolved from its analog counterpart, this book begins by explaining the fundamental concepts in analog signal processing and then progresses towards the digital signal processing. This will help the reader to gain a general overview of the whole subject and establish links between the various fundamental concepts. While the focus of this book is on the fundamentals of signal processing, the understanding of these topics greatly enhances the confident use as well as further development of the design and analysis of digital systems for various engineering and medical applications. Applied Signal Processing also prepares readers to further their knowledge in advanced topics within the field of signal processing

Chaotic global metric analysis of heart rate variability following six power spectral manipulations in malnourished children

Background and Aim: The study objective was to assess chaotic global metrics in malnourished children following power spectral manipulations. Methods: We evaluated the complexity of heart rate (HR) variability (HRV) in malnourished subjects via six power spectra (Welch, multi-taper method (MTM), Burg, covariance, YuleWalker, and periodogram) and then, when adjusted by the MTM parameters, for further refinement. Seventy children were split equally (controls & malnourished) and the HR was monitored for 20 min; 1000 RR-intervals were attained for HRV analysis. Results: The results stipulate that CFP1 (chaotic forward parameter) and CFP3 are the best metrics to distinguish the two groups. The most appropriate power spectra were Welch, MTM, and Yule-Walker. Results indicate that CFP3 calculated using MTM power spectra is the best combination to discriminate between the two groups. Yet, if the RR intervals are set to 400, discrete prolate spheroidal sequences (DPSS) to 3, and Thomson’s nonlinear combination to ‘adaptive’, a greater level of significance can be achieved (Cohen’s ds = -1.57). This significantly outperforms that under default conditions (Glass’s ∆ Delta = -1.06, and Cohen’s ds = -0.95). Conclusion: Malnourished children have a lower response to chaotic global metrics than the control group. CFP3 with the aforementioned settings is the best combination to discriminate between these groups on the basis of RR intervals. It has the greatest significance by Cohen’s ds. Our data suggest impaired autonomic function in malnourished children, which may have consequences for cardiovascular risks

Fatigue Driving Detection Method Based on IPPG Technology

Physiological signal index can accurately reflect the degree of fatigue, but the contact detection method will greatly affect the driver\u27s driving. This paper presents a non-contact method for detecting tired driving. It uses cameras and other devices to collect information about the driver\u27s face. By recording facial changes over a period and processing the captured video, pulse waves are extracted. Then the frequency domain index and nonlinear index of heart rate variability were extracted by pulse wave characteristics. Finally, the experiment proves that the method can clearly judge whether the driver is tired. In this study, the Imaging Photoplethysmography (IPPG) technology was used to realise non-contact driver fatigue detection. Compared with the non-contact detection method through identifying drivers\u27 blinking and yawning, the physiological signal adopted in this paper is more convincing. Compared with other methods that detect physiological signals to judge driver fatigue, the method in this paper has the advantages of being non-contact, fast, convenient and available for the cockpit environment

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain\u27s intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity\u27s intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain