Assessment of Distortion Product Otoacoustic Emissions Input-Output Function in Individuals with and without Musical Abilities

Background and Aim: Musical training has shown to bring about superior performance in several auditory and non-auditory tasks compared to those without musical exposure. Distortion product otoacoustic emissions (DPOAE) input-output function can be an indicator of the non-linear functioning of the cochlea. The objective of this study was to evaluate and compare the differences in the slope of DPOAE input-output function in individuals with and without musical abilities. Methods: Twenty normal-hearing individuals were considered in the age range of 18–25 years. They were divided based on the scores obtained on the questionnaire of musical abilities, as individuals with and without musical abilities. DPOAE input-output function was done for each of the two groups. The slope of the DPOAE input-output function was compared at different frequencies between the groups. Results: The results of the Mann Whitney test revealed that the slope was significantly steeper at 2000, 3000, 4000 and 6000 Hz in individuals with musical abilities. There was no significant difference in slope at 1000 and 1500 Hz. Conclusion: The increased steepness of the slope indicates a relatively better functioning of the cochlea in individuals with musical abilities. The enhanced perception of music may induce changes in the cochlea resulting in a better appreciation of music.

Passivity-Based Output Feedback Control for Systems with Nonlinear Outputs via High-Gain Observers

In this paper, a high-gain observer with nonlinear output is designed. The scaled estimation error system is constructed with a passive function based on the nonlinear output function and a strictly positive real transfer function of boundary-layer system. The ultimate boundedness and exponential stability of the estimation error for the global and regional two cases are demonstrated, as long as high-gain observer's decay rate is fast enough. For the regional case, due to the restriction on the passive function, the estimation error has a region of attraction which is a subset of the intersection of a positively invariant compact set and the strip coming from the restriction. The extended results under passivity of the output function and strictly positive realness of the transfer function are presented. The performance recovery property of the output feedback using high-gain observer with nonlinear output is validated. Some examples are applied in the simulation to illustrate the proposed results in this paper.

Error factors of AC-DC conversion circuits accuracy

Voltage standard source is an important basic instrument in the electrical measurement and testing field, and it has important guarantee significance for many industries. AC voltage standard source is based on AC-DC conversion technology to achieve output function of high precision AC voltage. In order to get a better effect of AC-DC conversion, the three factors—operational amplifier, resistance and diode—that have a greater impact on the accuracy of the rectifier output were taken the research object, and their impacts on the circuit error were analyzed respectively. The output result was compared with the output of full wave precision rectification circuit under ideal state, and the scheme of reducing error in the precision rectifier circuit was summarized, which provides a reference for improving the accuracy of AC-DC conversion in the design of full wave rectifier circuit.

Adaptive output feedback controller design for high-order stochastic nonlinear systems with uncertain output function and unknown growth rates

This paper concentrates on the adaptive output feedback controller design for a class of high-order stochastic nonlinear systems(SNSs) with uncertain output function. Firstly, a homogeneous output feedback controller for the nominal system is designed through the technique of adding a power integrator. Secondly, a well-designed dynamic gain is introduced into the controller to ensure the original SNSs globally asymptotically stable(GAS) in probability. Besides, the proposed control strategy can be also extended to upper-triangular SNSs. Finally, two numerical examples illustrate the effectiveness of the proposed method.

Bifurcations and Chaos in a Minimal Neural Network: Forced Coupled Neurons

The bifurcations and chaos in a system of two coupled sigmoidal neurons with periodic input are revisited. The system has no self-coupling and no inherent limit cycles in contrast to the previous studies and shows simple bifurcations qualitatively different from the previous results. A symmetric periodic solution generated by the periodic input underdoes a pitchfork bifurcation so that a pair of asymmetric periodic solutions is generated. A chaotic attractor is generated through a cascade of period-doubling bifurcations of the asymmetric periodic solutions. However, a symmetric periodic solution repeats saddle-node bifurcations many times and the bifurcations of periodic solutions become complicated as the output gain of neurons is increasing. Then, the analysis of border collision bifurcations is carried out by using a piecewise constant output function of neurons and a rectangular wave as periodic input. The saddle-node, the pitchfork and the period-doubling bifurcations in the coupled sigmoidal neurons are replaced by various kinds of border collision bifurcations in the coupled piecewise constant neurons. Qualitatively the same structure of the bifurcations of periodic solutions in the coupled sigmoidal neurons is derived analytically. Further, it is shown that another period-doubling route to chaos exists when the output function of neurons is asymmetric.

Ascon v1.2: Lightweight Authenticated Encryption and Hashing

Authenticated encryption satisfies the basic need for authenticity and confidentiality in our information infrastructure. In this paper, we provide the specification of Ascon-128 and Ascon-128a. Both authenticated encryption algorithms provide efficient authenticated encryption on resource-constrained devices and on high-end CPUs. Furthermore, they have been selected as the “primary choice” for lightweight authenticated encryption in the final portfolio of the CAESAR competition. In addition, we specify the hash function Ascon-Hash, and the extendable output function Ascon-Xof. Moreover, we complement the specification by providing a detailed overview of existing cryptanalysis and implementation results.

Identification of Brain Electrical Activity Related to Head Yaw Rotations

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

A Multi-Index Feedback Linearization Control for a Buck-Boost Converter

Due to the nonlinear and nonminimum phase characteristics of the buck-boost converter, the design of its controller has always been a challenging problem. In this paper, a multi-index feedback linearization control strategy is proposed to design the controller of the buck-boost converter. Firstly, by constructing an appropriate output function, the original nonlinear system is mapped into a combination of a linear subsystem and a nonlinear subsystem. Then, according to the structural characteristics of these two subsystems, the linear optimal control theory is adopted for the control design of the linear subsystem to make it have a good output performance, while for the nonlinear subsystem, the coefficient of the output function is adjusted to ensure its stability. Finally, based on the Hartman–Grobman theorem, the internal mechanism and coefficient adjustment basis of the proposed method are revealed; that is, by adjusting the coefficient of the output function and the feedback coefficient of the linear control law, the poles of the system are configured to achieve the purpose of adjusting the static and dynamic performance of the system. The simulation results show the feasibility and superiority of using the multi-index feedback linearization control strategy to design the nonlinear control law of the buck-boost converter.