Journal: Entropy

Article Title: An Analysis of Deterministic Chaos as an Entropy Source for Random Number Generators

doi: 10.3390/e20120957

Figure Lengend Snippet: Relation between absolute value of normalized autocorrelation at one-bit lag and the sampling period τ s for ( a ) bounded chaos, ( b ) unbounded chaos, and ( c ) white Gaussian noise.

Article Snippet: By proper selection of the sampling frequency of the chaotic signal, the absolute value of normalized autocorrelation values at one-bit lag of the bit sequence can be made close to that of the bit sequence generated by regular sampling of Matlab-based white Gaussian noise which has an infinite and flat power spectrum.

Techniques: Sampling

Journal: Entropy

Article Title: An Analysis of Deterministic Chaos as an Entropy Source for Random Number Generators

doi: 10.3390/e20120957

Figure Lengend Snippet: Relation between absolute value of normalized autocorrelation at one-bit lag and the sampling period τ s for ( a ) bounded chaos, ( b ) unbounded chaos, and ( c ) white Gaussian noise.

Article Snippet: By proper selection of the sampling frequency of the chaotic signal, the absolute value of normalized autocorrelation values at one-bit lag of the bit sequence can be made close to that of the bit sequence generated by regular sampling of Matlab-based white Gaussian noise which has an infinite and flat power spectrum.

Techniques: Sampling

Journal: Scientific Reports

Article Title: Phase-based fast 3D high-resolution quantitative T 2 MRI in 7 T human brain imaging

doi: 10.1038/s41598-022-17607-z

Figure Lengend Snippet: Human whole-brain T 2 maps with a 0.85 mm isotropic voxel. ( a ) without denoising, ( b ) with denoising, based on DnCNN model for Gaussian noise removal. Arrows point to the cerebellum region, which especially benefits from denoising. Top row, Sagittal and Coronal planes. Bottom two rows, six slices of the Axial plane, at 10 mm intervals.

Article Snippet: To provide even higher robustness following the reduced SNR of the high-resolution datasets, we also incorporated denoising based on a DnCNN deep-learning network (provided in MATLAB, The Mathworks, Natick MA, for Gaussian noise removal).

Techniques:

Journal: Journal of Neurophysiology

Article Title: Stimulus-timing-dependent modifications of rate-level functions in animals with and without tinnitus

doi: 10.1152/jn.00457.2014

Figure Lengend Snippet: Gap detection assay for behavioral evaluation of tinnitus. A: healthy, sham-exposed animals respond with a robust startle to the presentation of a sound pulse (10 ms, 115 dB, represented by the black, tall bar) embedded in a continuous background sound (60 dB). B: when a silent gap (50 ms) is introduced in the background sound, the animal uses the gap to predict the incoming startle pulse and responds with decreased startle amplitude. C: noise-exposed guinea pigs that developed tinnitus fail to detect the gap due to their tinnitus percept and respond with a strong startle to the pulse presentation. D–E: Gaussian mixture model was employed to partition the distribution of startle observations into normal and tinnitus distributions. D: example of histogram of the normalized startle distribution (gray line) partitioned into a distribution with no evidence for tinnitus (black bars) and a distribution with evidence for tinnitus (light gray bars). E: probability functions that the normalized startle amplitudes belong to tinnitus (light gray curve) or no-tinnitus (black curve) distributions. F–H: example of histogram partitioned distributions of normalized startle observations evaluated at 8 kHz in sham-exposed (F), baseline (G), and noise-exposed animals (H). Percentage of observations classified as tinnitus is shown in F–H.

Article Snippet: Evidence for tinnitus at each frequency band was evaluated based on a Gaussian mixture model (Statistics Toolbox, Matlab release 2012b) applied to the distribution of normalized startle trials from all observations from all animals ( ).

Techniques: Detection Assay

Journal: Journal of Neurophysiology

Article Title: Stimulus-timing-dependent modifications of rate-level functions in animals with and without tinnitus

doi: 10.1152/jn.00457.2014

Figure Lengend Snippet: Schematic of the bimodal protocol and example of rate-level functions (RLFs) with illustration of their modeling. A: schematic of the bimodal stimulation protocol. RLFs are evaluated before and 15 min after bimodal stimulation with a specific bimodal interval (BI) to assess persistent modifications. The BI is defined by the onset difference between the Sp5 electrical stimulation (vertical bar) and the auditory stimulation (sinusoid curve). The illustration is an example of a positive BI for which Sp5 stimulation precedes auditory stimulation. Negative BIs are defined by the opposite order of the stimuli presentation. B and C: representative examples of RLF responses from 2 different units before and after bimodal stimulation recorded in a sham animal (B) and a tinnitus animal (C). The responses before and after bimodal stimulation are indicated by black and gray solid lines. The fit of these RLFs was obtained by employing the two Gaussian-split modeling strategy and is indicated by dotted lines. D: illustration of the two-tail split Gaussian model used to fit the RLFs. The quantifiers in D have direct correspondence to (Eq. 1) as follows: the lower and upper variance are σlow and σhigh, lower and upper DC offset are DClower and DCupper, the best level is μ, and the amplitude is a. E: when a good fit is achieved, additional parameters are determined as indicated: the threshold is defined as the level corresponding to an amplitude equal to 10% of the total amplitude (evaluated as the difference between maximum and minimum amplitude), the upper and lower saturations are defined as the levels corresponding to 90% of the total amplitude, lower and upper dynamic ranges are defined by the difference between corresponding saturation and threshold, and the lower and upper gain are defined as the slope of the monotonic increasing and decreasing component of the RLFs.

Article Snippet: Evidence for tinnitus at each frequency band was evaluated based on a Gaussian mixture model (Statistics Toolbox, Matlab release 2012b) applied to the distribution of normalized startle trials from all observations from all animals ( ).

Techniques: