Aperiodic Information Research Articles

Exploring the aperiodic nature of parasympathetic activity during sleep in idiopathic gastroparesis.

The parasympathetic nervous system is necessary to regulate both sleep and digestion. Investigating abnormalities during the controlled setting of sleep can shed light on digestion, specifically for patients with idiopathic gastroparesis. In this study, we specifically investigate heartbeat-derived parasympathetic activity during sleep at very low frequencies, relevant to sleep cycle regulation. To do this, we adapt a method that extracts both periodic and aperiodic information from the power spectral density and recognize that the aperiodic activity may contain information relevant to very low frequencies. After testing on both synthetic noise data (pink and white) and overnight data from seven healthy controls and idiopathic gastroparetics, we find that the healthy controls' low-frequency aperiodic activity reflects pink noise structure, while the majority of the patients' aperiodic activity reflects white noise structure. At these low frequencies, these differences suggest differences in autonomic sleep cycle regulation.Clinical Relevance- This methodology can be optimized to track the health of the parasympathetic nervous system and suggest whether individual disease etiology is autonomic-related.

Event-triggered distributed coordinated control of networked autonomous surface vehicles subject to fully unknown kinetics via concurrent-learning-based neural predictor

This paper is concerned with distributed coordinated control of networked autonomous surface vehicles (ASVs) guided by multiple parameterized paths. The model of each ASV is subject to fully unknown kinetics, including environment disturbances, model uncertainties, and unknown input coefficients. A coordinated control method is presented for multiple ASVs to achieve a containment formation based on event-triggered mechanisms. Specifically, an event-triggered kinematic control law is firstly developed based on aperiodic communication. The desired path during the triggering interval is predicted by the last event-triggered velocity. Then, an event-triggered kinetic control law is developed, a third-order linear tracking differentiator is introduced to take the derivative of the kinematic control law, and the control input is updated only if the predefined event is triggered. A concurrent-learning-based neural predictor is constructed by using historical data such that model uncertainties and unknown input coefficients are identified simultaneously. Next, an event-triggered path update law is constructed by using a path-maneuvering design and the aperiodic information received from the network. The proposed method ensures the input-to-state stability of the resulting closed-loop system. Finally, simulation results are provided to demonstrate the effectiveness of the proposed distributed coordinated control for networked ASVs by using event-triggered mechanisms and concurrent-learning-based neural predictors.

Distributed dynamic event-triggered algorithm with minimum inter-event time for multi-agent convex optimisation

In this paper, the distributed convex optimisation problem of the multi-agent system over an undirected network is investigated, in which the local objective function of each agent is only known by itself. To reduce the communication consumption between agents, a state-based dynamic event-triggered algorithm with positive minimum inter-event time (MIET) is provided, where the aperiodic information communication only occurs at some discrete triggering time instants. Moreover, the sampling control technology is combined into the previous event-triggered algorithm for verifying the event-triggered condition at every sampling time, instead of continuous access. Finally, several numerical simulations are presented for illustrating and verifying the proposed algorithms.

Stabilization of Uncertain Discrete-Time Linear System With Limited Communication

This technical note proposes a procedure to control an uncertain discrete-time networked system using an aperiodic stabilizing input information. The system is primarily affected by the time-varying, norm bounded, mismatched parametric uncertainty. Aperiodic exchange of information is done due to bandwidth constraint of the communication network. An event-triggered based robust control strategy is adopted to reduce the effects of system uncertainty in such bandwidth constrained networks. In event-triggered control, the control input is computed and actuated at the system end only when a pre-specified event condition is violated. The robust control input is derived to stabilize the uncertain system by solving an optimal control problem based on a virtual nominal dynamics and a modified cost-functional. It is shown that the robust control law with aperiodic information ensures input-to-state stability (ISS) of the original system in the presence of mismatched uncertainty. Deriving the event-triggering condition for a discrete-time uncertain system and ensuring the stability of such system analytically are the key contributions of this technical note. A numerical example is given to prove the efficacy of the proposed event-based control algorithm over the conventional periodic one.

Hindi phoneme classification using Wiener filtered wavelet packet decomposed periodic and aperiodic acoustic feature

Wavelet packet (WP) acoustic features are found to be very promising in unvoiced phoneme classification task but they are less effective to capture periodic information from voiced speech. This motivated us to develop a wavelet packet based feature extraction technique that signifies both the periodic and aperiodic information. This method is based on parallel distributed processing technique inspired by the human speech perception process. This front end feature processing technique employs Equivalent Rectangular Bandwidth (ERB) filter like wavelet speech feature extraction method called Wavelet ERB Sub-band based Periodicity and Aperiodicity Decomposition (WERB-SPADE). Winer filter is used at front end to minimize the noise for further processing. The speech signal is filtered by 24 band ERB like wavelet filter banks, and then the output of each sub-band is processed through comb filter. Each comb filter is designed individually for each sub-band to decompose the signal into periodic and aperiodic features. Thus it carries the periodic information without losing certain important information like formant transition incorporated in aperiodic features. Hindi phoneme classification experiments with a standard HMM recognizer under both clean-training and multi-training condition is conducted. This technique shows significant improvement in voiced phoneme class without affecting the performance of unvoiced phoneme class.

Feature extraction technique using ERB like wavelet sub-band periodic and aperiodic decomposition for TIMIT phoneme recognition

In the recent years, wavelet transform has been found to be an effective tool for the time---frequency analysis for non-stationary and quasi-stationary signals such as speech signals. In the recent past, wavelet transform has been used as feature extraction in speech recognition applications. Here we propose a wavelet based feature extraction technique that signifies both the periodic and aperiodic information along with sub-band instantaneous frequency of speech signal for robust speech recognition in noisy environment. This technique is based on parallel distributed processing technique inspired by the human speech perception process. This frontend feature processing technique employs equivalent rectangular bandwidth (ERB) filter like wavelet speech feature extraction method called Wavelet ERB Sub-band based Periodicity and Aperiodicity Decomposition (WERB-SPADE), and examines its validity for TIMIT phone recognition task in noisy environments. The speech sound is filtered by 24 band ERB like wavelet filter banks, and then the equal loudness pre-emphasized output of each band is processed through comb filter. Each comb filter is designed individually for each frequency sub-band to decompose the signal into periodic and aperiodic features. Thus it takes the advantage of the robustness shown by periodic features without losing certain important information like formant transition incorporated in aperiodic features. Speech recognition experiments with a standard HMM recognizer under both clean-training and multi-training condition training is conducted. Proposed technique shows more robustness compared to other features especially in noisy condition.

Recent Developments on Hybrid Time-Frequency Numerical Simulation Techniques for RF and Microwave Applications

This paper reviews some of the promising doors that functional analysis techniques have recently opened in the field of electronic circuit simulation. Because of the modulated nature of radio frequency (RF) signals, the corresponding electronic circuits seem to operate in a slow time scale for the aperiodic information and another, much faster, time scale for the periodic carrier. This apparent multirate behavior can be appropriately described using partial differential equations (PDEs) within a bivariate framework, which can be solved in an efficient way using hybrid time-frequency techniques. With these techniques, the aperiodic information dimension is treated in the discrete time domain, while the periodic carrier dimension is processed in the frequency domain, in which the solution is evaluated within a space of harmonically related sinusoidal functions. The objective of this paper is thus to provide a general overview on the most important hybrid time-frequency techniques, as the ones found in commercial tools or the ones recently published in the literature.