Frequency domain representation

This paper presents an ultra wideband (UWB) channel sounding scheme with a technique for time of arrival (TOA) and angle of arrival (AOA) estimation using transformation between time and frequency domain signals. Since the measured UWB power spectrum can be transformed into a time domain signal, both frequency and time domain signals are available for the channel estimation. We propose the TOA estimation using a peak search over time domain signal, and the AOA estimation using MUSIC and AOA histograms. The AOA histogram is counted for AOA samples of discrete frequencies over the UWB bandwidth. Comparing both AOA histograms before and after TOA subtraction, we can determine pairing the elements of TOA vector and those of AOA vector. The anechoic chamber tests indicated that both TOAs and AOAs were precisely resolved for two-path model, even if either TOAs or AOAs were too close to resolve.

Automatic modulation classification (AMC), which aims to identify the modulation types of unknown signals without any prior knowledge, plays a key role in intelligent wireless communication. In recent years, the outstanding achievements of deep learning in the fields of computer vision and nature language processing have promoted the continuous researches of deep learning in AMC. In view of the fact that the existing deep learning-based AMC ignores the frequency attribute of the modulated signals, this paper proposed an AMC approach based on deep learning which comprehensively considers the contribution of frequency and spatiotemporal characteristics of the modulated signals. Specifically, we utilize the sliding window to divide the raw signals into short-time signal slices. In order to obtain the representation of frequency domain of the signals, each short-time signal slice is decomposed into several modes by variational mode decomposition (VMD), which is an adaptive signal decomposition technique. Finally, all the modes are used as input of the convolutional neural network (CNN) as a tensor to learn high-level features from the frequency and spatiotemporal domain to identify the modulation type. In this paper, we verify the effectiveness of the proposed approach with 10 kinds of modulation types generated by SMW200A Vector Signal Generator. More importantly, the proposed approach achieves 99.2% of the overall classification accuracy at 8dB signal to noise ratio (SNR) and shows better robustness to noise than the existing AMC approaches.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

The effects of electrical vestibular stimulation upon movement and perception suggest two evoked sensations: head roll and inter-aural linear acceleration. The head roll vector causes walking subjects to turn in a direction dependent on head pitch, requiring generation of torque around a vertical axis. Here the effect of vestibular stimulation upon vertical torque (T(z)) was investigated during quiet stance. With the head tilted forward, square-wave stimuli applied to the mastoid processes evoked a polarity-specific T(z) response accompanied by trunk yaw. Stochastic vestibular stimulation (SVS) was used to investigate the effect of head pitch with greater precision; the SVS–T(z) cross-correlation displayed a modulation pattern consistent with the head roll vector and this was also reflected by changes in coherence at 2–3 Hz. However, a separate response at 7–8 Hz was unaffected by head pitch. Head translation (rather than rotation) had no effect upon this high frequency response either, suggesting it is not caused by a sense of body rotation induced by an inter-aural acceleration vector offset from the body. Instead, high coherence between medio-lateral shear force and T(z) at the same frequency range suggests it is caused by mechanical coupling to evoked medio-lateral sway. Consistent with this explanation, the 7–8 Hz response was attenuated by 90 deg head roll or yaw, both of which uncouple the inter-aural axis from the medio-lateral sway axis. These results demonstrate two vertical torque responses to electrical vestibular stimulation in standing subjects. The high frequency response can be attributed to mechanical coupling to evoked medio-lateral sway. The low frequency response is consistent with a reaction to a sensation of head roll, and provides a novel method for investigating proprioceptive-vestibular interactions during stance.

Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.

Numerical analysis methods in time-domain and frequency-domain are commonly considers as two important ways for seismic evaluation of structure responses. FFT plays a unique role in building the equivalent expression between the structural dynamic signals in time domain and frequency domain, such as computing frequency spectrum values at discrete frequency points for time history data. On the basis of radix-4 and radix-2 FFT techniques, an improved district fast Fourier transfer FFT is presented in this paper to improve the transform efficiency, in which radix-4 transform is applied in most iterative steps, besides radix-2 transform used in the last one iterative step. As compared to the traditional radix-2 FFT, the new mixed-radix FFT leads to distinct reducing in computing amount, while the equivalent precision and the same discrete frequency points remain. Also by taking the trigonometric coefficient method as theoretical results, it’s numerically validated through some examples that the new improved FFT transform technique suits the engineering application of the fast numerical transform in the time and frequency domain for structural frequency spectral analysis.

In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing and feature analysis, Python has recently dethroned MATLAB with the rise of data science, machine learning and artificial intelligence. Hence, there is a compelling need for a Python package for physiological feature analysis and extraction to achieve compatibility with downstream models often trained in Python. Thus, we present a novel visualization and feature analysis Python toolbox, PySio, to enable rapid, efficient and user-friendly analysis of physiological signals. First, the user should import the signal-of-interest with the corresponding sampling rate. After importing, the user can either analyze the signal as it is, or can choose a specific region for more detailed analysis. PySio enables the user to (i) visualize and analyze the physiological signals (or user-selected segments of the signals) in time domain, (ii) study the signals (or user-selected segments of the signals) in frequency domain through discrete Fourier transform and spectrogram representations, and (iii) investigate and extract the most common time (energy, entropy, zero crossing rate and peaks) and frequency (spectral entropy, rolloff, centroid, spread, peaks and bandpower) domain features, all with one click. Clinical relevance- As the physiological signals originate directly from the underlying physiological events, proper analysis of the signal patterns can provide valuable information in personalized treatment and wearable technology applications.

Advanced spectral methods for detecting dynamic behaviour

Recognition methods of threshing load conditions based on machine learning algorithms

Power spectral analysis of fetal heart rate variability has been proposed to provide a non-invasive estimate of autonomic balance. However, there are few systematic data before birth. We therefore examined developmental changes in the frequency power spectrum at very low (0-0.04 Hz), low (0.04-0.15 Hz) and high frequencies (0.15-0.4 Hz), as well as the ratio of low- to high-frequency power (LF/HF), in chronically catheterized, healthy fetal sheep at 0.6 (n = 8), 0.7 (n = 7) and 0.8 gestational age (ga; n = 11). In a second study, 0.8 ga fetuses received either atropine (4.8 mg bolus, then 4.8 mg h(-1) for 30 min, n = 6) or 6-hydroxydopamine (20 mg ml(-1) at 2.5 ml h(-1) for 3 h; n = 9). Data were analysed by sleep state, defined by low-voltage-high-frequency (LV) or high-voltage-low-frequency (HV) EEG. Total spectral power increased with gestational age (P < 0.05), while LF/HF decreased from 0.6 to 0.7 ga. At 0.8 ga, heart rate and LF/HF were significantly higher during HV than LV sleep (P < 0.05). Consistent with this, although total spectral power was not significantly greater during HV sleep, there was a significant interaction between sleep state and frequency band (P = 0.02). Both atropine (P = 0.05) and 6-hydroxydopamine (P < 0.05) were associated with an overall reduction in spectral power but no significant effect on the LF/HF ratio. This study does not support substantial, consistent differences between the frequencies of sympathetic and parasympathetic activity in late-gestation fetal sheep.

Influence of water activity on acoustic emission of flat extruded bread

Content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FCM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FCM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.

Frequency and time domain analyses of sensor signals in drilling—I. Correlation with drill wear

To the Editor: In a recent issue of Circulation , Stambler et al1 provide an intriguing but possibly misleading set of results regarding the variability of atrial flutter cycle length (AFCL) and the role of the autonomic nervous system in AFCL modulation. Previous studies2 3 4 5 identified in the AFCL variability the existence of 2 rhythmic oscillations, 1 controlled by the ventricular rate and 1 by respiratory activity, suggesting a modulatory effect of atrial stretch. Using spectral analysis, Stambler et al claimed the existence of an additional low-frequency oscillation (band 1) mediated by sympathetic tone. Their conclusions are not supported by their study design and results. In fact, the newly proposed low-frequency oscillation band was concentrated in the extremely low-frequency region of the spectrum and on average was centered around 0.006±0.002 Hz (Table 2 of the article by Stambler et al). This should correspond to an AFCL oscillation with a period of 166 seconds, clearly longer than the 2 minutes of analyzed recording time. This means that we are dealing with a baseline trend rather than a rhythmic oscillation. In other words, band 1 is simply a DC component of the spectrum reflecting the nonstationary nature of the analyzed time series and thus should be avoided in the interpretation of the power spectra.6 In their study, Stambler et al also examined the role of the autonomic nervous system in the modulation of AFCL by studying the effect of pharmacological interventions on the power-band distribution of AFCL spectra. Stimulation and inhibition of sympathetic tone obtained by intravenous infusion of isoproterenol and long-term therapy with …

Among other things, enhancing spectral contrast for a cochlear implant listener includes detecting a time domain signal. A first transformation is applied to the detected time domain signal to convert the time domain signal to a frequency domain signal. A second transformation is applied to the frequency domain signal to express the frequency domain signal as a sum of two or more components. A sensitivity of the cochlear implant listener to detect modulation of each component is obtained.