Revisiting the Multilayer Network Framework for Electrophysiological Networks.

The chapter deals with Fourier transform (FT) representation for continuous time and discrete time aperiodic signals. The FT of CT signals is called as CTFT and that for DT signals is called as DTFT. The Fourier transform is defined and is evaluated for all standard aperiodic signals such as exponential signal, rectangular pulse, triangular pulse, etc. The IFT of some standard signals such as sinc function is also discussed. The use of Dirac Delta function is explained for evaluation of FT for periodic signals. FT of DT signals are illustrated with some numerical examples. Properties of FT and DTFT are emphasized with their physical significance. The numerical examples for the calculation of FT using FT properties are illustrated. The calculation of response of LTI system to the input signal is simplified using FT. Fourier Transform Representation of Aperiodic CT Signals If the signal x(t) is aperiodic, a similar representation for a signal in frequency domain can be developed in terms of Fourier Transform using exponential signals as basis function. The signal x P ( t ) a periodic signal can be generated by repeating x ( t ) after a period of T 0 . We can now define x ( t ) as The Fourier series representation can be written for the periodic signal x P ( t ). Equation (5.38) becomes The coefficients can be written as As the period tends to infinity, the spacing between the spectral lines, which is equal to 1/ T 0 , will tend to zero and the spectrum will be a continuous spectrum. Now, the summation will turn into the integral. The coefficients will represent a frequency point on the frequency axis represented by X ( f ).

Chapter 4 - Frequency Analysis: The Fourier Series

Chapter 4 - Frequency Analysis: The Fourier Series

Modulation of biological oscillations by stimuli lies at the root of many phenomena, including maintenance of circadian rhythms, propagation of neural signals, and somitogenesis. While it is well established that regular periodic modulation can entrain an oscillator, an aperiodic (noisy) modulation can also robustly entrain oscillations. This latter scenario may describe, for instance, the effect of irregular weather patterns on circadian rhythms, or why irregular neural stimuli can still reliably transmit information. A synthetic gene oscillator approach has already proven to be useful in understanding the entrainment of biological oscillators by periodic signaling, mimicking the entrainment of a number of noisy oscillating systems. We similarly seek to use synthetic biology as a platform to understand how aperiodic signals can strongly correlate the behavior of cells. This study should lead to a deeper understanding of how fluctuations in our environment and even within our body may promote substantial synchrony among our cells. Specifically, we investigate experimentally and theoretically the entrainment of a synthetic gene oscillator in E. coli by a noisy stimulus. This phenomenon was experimentally studied and verified by a combination of microfluidics and microscopy using the real synthetic circuit. Stochastic simulation of an associated model further supports that the synthetic gene oscillator can be strongly entrained by aperiodic signals, especially telegraph noise. Finally, widespread applicability of aperiodic entrainment beyond the synthetic gene oscillator is supported by results derived from both a model for a natural oscillator in D. discoideum and a model for predator-prey oscillations.

Relationship between brain structural network integrity and emotional symptoms in youth with perinatally-acquired HIV

We put forward a new piecewise-linear stochastic resonance (SR) model for detecting the weak signal under the strong noise backgrounde. we show its description equations and haracter parameters. Through numerically simulating its performance under periodic signal and aperiodic signal respectively, we illustrate that this system is very helpful for detecting the weak signal under the strong noise background

In this paper we present the wavelet analysis technique, a powerful tool for the detection and analysis of power system transients. Traditional Fourier analysis techniques are suitable for periodic signals whereas aperiodic signals such as transients on a power system would require a more robust analysis technique such as the wavelet analysis technique. The basic idea of this technique is to decompose the transient signal into a number of levels, which basically contain the high and low-frequency components of the signal. By analyzing the decomposed transient signal at different levels, not only do we get information regarding the frequency content of the transient but also the time at which each of these transients occurred, which helps in distinguishing transients occurring due to different reasons.

Purpose This paper aims to investigate the shock transmission structure among Chinese financial institutions. Design/methodology/approach Based on the market data of 34 listed financial institutions from 2011 to 2023, this study proposes the empirical mode decomposition into the generalized forecast variance decomposition framework to create multilayer networks that include volatility spillover layers at multidimensional time scales for analyzing the shock transmission structure among Chinese financial institutions. Findings Empirical research indicates that layers at different time scales exhibit distinct network structures, and multilayer network analysis provides a more comprehensive understanding of the financial system. The volatility spillover networks exhibit notable time-varying characteristics with significant periodic patterns linked to endogenous and exogenous shocks. The importance of financial institutions is dynamic. Key nodes in the multilayer spillover network mainly come from the banking and securities sectors, with banks being primarily joint-stock commercial banks and urban commercial banks and securities institutions controlled mainly by large central enterprises or local governments. Originality/value This paper explores how to use an adaptive decomposition method to more accurately construct multilayer networks for risk contagion and proposes a new “time-frequency domain” multilayer network analysis framework. By constructing multilayer networks at different time scales, the risk contagion patterns driven by different economic mechanisms are elucidated. Finally, this article provides a more refined and robust evaluation of the systemic importance of financial institutions from a dynamic network perspective with multiple time scales.

The Navy's commitment to the application of data bus technology to shipboard internal communication stems from the necessity ofreducing cable congestion and weight. The Shipboard Data Multiplex System is a general purpose data bus system intended to take the placeof most of the point -to -point cabling and associated hardware presently used for information transfer aboard ships of the Fleet. It uses bothfrequency and time multiplexing of a two -level set of redundant interconnections to provide a highly reliable data bus capable of distributingall forms of periodic and aperiodic signals.We report here a demonstration of the feasibility of substituting fiber optics for the coaxial cable presently used by this system. Primaryconcerns were whether the interface hardware could handle the FDM signals on the bus without excessive distortion and degradation of theirsignal to noise ratios.This paper describes methods used to demonstrate that the intermodulation distortion and signal -to -noise ratio requirements of theSDMS can be met by the necessary interface components.IntroductionThis paper reports the first phase of an effort to demonstrate and evaluate the feasibility of applying fiber optics technology to the ad-vanced development model of the Shipboard Data Multiplex System (SDMS), a multiterminal multiplexing (data bus) system. SDMS is in-tended to serve as a general purpose information transfer system taking the place of most of the point -to -point cabling and associated hard-ware presently used for information transfer aboard naval ships.1 , 2 It has been built by the Autonetics Division of Rockwell Internationalunder contract to the Naval Sea Systems Command. This new system uses frequency and time multiplexing of a two -level set of redundantinterconnections to provide higher reliability and survivability with lower weight than present point -to -point systems. It is potentially capableof distributing throughout a ship all forms of periodic and aperiodic signals, either analog or digital.Two -level multiplexing, wherein user access is provided through remote multiplexers (RMs) which transfer information to area multi-plexers (AMs) and thence to a common data bus, allows many access points to be serviced without the attenuation that would result fromseparate connections to a common data bus. The first stage of multiplexing, consisting primarily of pulse coding in the RMs, is followed bytime multiplex transmission to the AMs at base band. Each AM serves up to eight RMs on a direct basis. Each dual redundant RM is

Voice is the perception of physical complexity of the laryngeal tone modified by a resonating cavity. The voice parameters are the vital signs of an individual to project their personality and gender. The basic acoustical parameters of voice are: Fundamental frequency, Intensity, Jitter, Shimmer and Harmonics to noise ratio (HNR) and among these parameters HNR is the most significant. The voice component represents the periodic and aperiodic signals and predominantly aperiodic signals. There are many research work have been documented on acoustical analysis of voice with narrowed age range at different zonals in India. The motive of the study was mainly focused on the ratio between periodic and aperiodic components of voice and its changes from pre pubertal to post pubertal stages in both the genders. Due to the paucity of literature on changes in HNR in developing children, the present study was conducted to find out how do the changes in HNR occur in both the genders. Observational study design with convenient sampling method was used for the collection of raw data. A total of 198 subjects comprising of group I males (n = 99) and group II females (n = 99). These groups were further subdivided into 9 sub groups in males (n = 99) and females (n = 99) based on the age range. Sustained phonation of /a/ was used as a stimulus for voice sample. The data was analyzed using PRAAT software. The over mean Harmonic to Noise Ratio was found to be higher in males compared to the females. When age wise comparison was done, a significant difference in the Harmonic to Noise Ratio was found in males with no significant changes noticed in females. The transition in the Harmonic to Noise Ratio in males was seen to start at the age of 14-15years.

Simulation of circuits demonstrating stochastic resonance

Noise-enhanced nonlinear detector to improve signal detection in non-Gaussian noise

This paper presents a method to synthesize internal model principle type controllers for rejection of sinusoidal disturbances. The frequencies of the disturbance are not required to be of rational ratios of the sampling interval or each other, thus addressing both periodic and aperiodic signals. The control synthesis utilizes positive feedback around notch filters to create the internal model and employs stable inversion of the plant dynamics to achieve closed loop stability. An example and simulation on a general electromechanical motion control system are presented to demonstrate the effectiveness of the proposed method.

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson’s disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject’s brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2−(i−1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person’s unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33–42 years (n1 = 18, n2 = 14), C: 43–52 years (n1 = 42, n2 = 23), D: 53–62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study.

COVID-19 has been associated with various neurological complications, including cognitive impairments such as memory deficits, attention difficulties, and executive dysfunction. These symptoms raise concerns about potential long-term effects, particularly in individuals with preexisting neurodegenerative conditions. Emerging evidence suggests that systemic inflammation, blood-brain barrier (BBB) dysfunction, and neuroinflammation may contribute to cognitive decline in COVID-19 patients. However, the impact of COVID-19 on functional brain connectivity, particularly in dementia patients, remains unclear.This study aims to investigate the differences in functional connectivity across different frequency bands (delta, theta, alpha, beta, and gamma) in dementia patients with and without a history of COVID-19 (D-COVID and D-nCOVID) compared to a healthy control (HC) group. The study explores whether COVID-19 accelerates neurodegenerative processes by disrupting functional brain networks. Functional connectivity was assessed using electroencephalography (EEG)-based network analysis in three groups: D-COVID, D-nCOVID, and HC. Connectivity metrics were compared across frequency bands, with a focus on local efficiency (LE) and global network alterations. The Kruskal-Wallis test assessed statistical significance, while the Dunn test was used for post-hoc analysis. Findings indicate a significant reduction in functional connectivity across multiple brain regions in dementia patients, with the D-COVID group exhibiting more pronounced declines. The observed decrease in connectivity suggests that COVID-19 may accelerate neurodegenerative processes. Additionally, the HC group demonstrated stronger connectivity and higher LE metrics, highlighting the widespread impact of dementia on brain networks. These findings support the hypothesis that COVID-19 contributes to cognitive decline by exacerbating neurodegenerative mechanisms. The disruption of functional brain connectivity observed in D-COVID patients aligns with previous studies suggesting that SARS-CoV-2 may indirectly promote neuronal degeneration. Further longitudinal studies are needed to determine the long-term cognitive consequences of COVID-19 and potential therapeutic interventions to mitigate these effects.