physical signal
Recently Published Documents


TOTAL DOCUMENTS

43
(FIVE YEARS 11)

H-INDEX

7
(FIVE YEARS 1)

2021 ◽  
Vol 7 (10) ◽  
pp. 209
Author(s):  
Simon Pinzek ◽  
Alex Gustschin ◽  
Tobias Neuwirth ◽  
Alexander Backs ◽  
Michael Schulz ◽  
...  

Grating-based phase-contrast and dark-field imaging systems create intensity modulations that are usually modeled with sinusoidal functions to extract transmission, differential-phase shift, and scatter information. Under certain system-related conditions, the modulations become non-sinusoidal and cause artifacts in conventional processing. To account for that, we introduce a piecewise-defined periodic polynomial function that resembles the physical signal formation process, modeling convolutions of binary periodic functions. Additionally, we extend the model with an iterative expectation-maximization algorithm that can account for imprecise grating positions during phase-stepping. We show that this approach can process a higher variety of simulated and experimentally acquired data, avoiding most artifacts.


2021 ◽  
Vol 30 (2) ◽  
pp. 107-115
Author(s):  
Seiji Tanaka

Synchronous hatching within single egg clutches is moderately common in locusts and other insects and can be mediated by vibrational stimuli generated by adjacent embryos. However, in non-locust grasshoppers, there has been little research on the patterns of egg hatching and the mechanisms controlling the time of hatching. In this study, the hatching patterns of six grasshoppers (Atractomorpha lata, Oxya yezoensis, Acrida cinerea, Chorthippus biguttulus, Gastrimargus marmoratus, and Oedaleus infernalis) were observed under various laboratory treatments. Under continuous illumination and a 25/30°C thermocycle, the eggs of these grasshoppers tended to hatch during the first half of the daily warm period. Eggs removed from egg pods and cultured at 30°C tended to hatch significantly earlier and more synchronously when kept in groups vs. singly. In general, eggs hatched earlier when egg group size was increased. Egg hatching was stimulated by hatched nymphs in some species, but not in others. In all species, two eggs separated by several millimeters on sand hatched less synchronously than those kept in contact with one another, but the hatching synchrony of similarly separated eggs was restored if they were connected by a piece of wire, suggesting that a physical signal transmitted through the wire facilitated synchronized hatching. In contrast, hatching times in the Emma field cricket, Teleogryllus emma, which lays single, isolated eggs, were not influenced by artificial clumping in laboratory experiments. These results are discussed and compared with the characteristics of other insects.


Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1370
Author(s):  
Olga Yablonskaya ◽  
Vladimir Voeikov ◽  
Ekaterina Buravleva ◽  
Aleksei Trofimov ◽  
Kirill Novikov

Water vapor absorbs well in the infrared (IR) region of the spectra. On the other hand, it was recently demonstrated that IR radiation promotes formation of the so-called exclusion zones (EZ) at the interfaces between hydrophilic surfaces and water. EZ-water properties differ significantly from that of bulk water. It was studied for the first time whether treatment of water with humid air irradiated with IR-C band could change its physical-chemical properties, making it EZ-water-like. Humid air irradiated with IR was called coherent humidity (CoHu). Redox potential and surface tension decreased in deionized water and mineral water samples that were treated with CoHu, while dielectric constant increased in such water samples. After such treatment of carbonate or phosphate buffers, their buffer capacity against acidification and leaching significantly increased. No such changes were observed in water samples treated with non-irradiated humid air. Thus, after treatment of tested aqueous systems with humid air exposed to IR radiation, their properties change, making them more like EZ-water. The results suggest that IR irradiation of humid air converts it into a carrier of a certain physical signal that affects water properties.


2021 ◽  
Vol 38 (3) ◽  
pp. 282-292
Author(s):  
André Almeida ◽  
Emery Schubert ◽  
Joe Wolfe

In music, vibrato consists of cyclic variations in pitch, loudness, or spectral envelope (hereafter, “timbre vibrato”—TV) or combinations of these. Here, stimuli with TV were compared with those having loudness vibrato (LV). In Experiment 1, participants chose from tones with different vibrato depth to match a reference vibrato tone. When matching to tones with the same vibrato type, 70% of the variance was explained by linear matching of depth. Less variance (40%) was explained when matching dissimilar vibrato types. Fluctuations in loudness were perceived as approximately the same depth as fluctuations in spectral envelope (i.e., about 1.3 times deeper than fluctuations in spectral centroid). In Experiment 2, participants matched a reference with test stimuli of varying depths and types. When the depths of the test and reference tones were similar, the same type was usually selected, over the range of vibrato depths. For very disparate depths, matches were made by type only about 50% of the time. The study revealed good, fairly linear sensitivity to vibrato depth regardless of vibrato type, but also some poorly understood findings between physical signal and perception of TV, suggesting that more research is needed in TV perception.


2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Alexey V Nemchaninov ◽  
Alena V Nikolaeva ◽  
Sergey Victorovich Ulyanov ◽  
Andrey G Reshetnikov

A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given. As a result, a prototype of manmade prosthesis on a 3D printer as well as a foundation for computational intelligence presented. The application of soft computing technology (the first step of IT) allows to extract knowledge directly from the physical signal of the electroencephalogram, as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state. The possibilities of applying quantum soft computing technologies (the second step of IT) in the processes of robust filtering of electroencephalogram signals for the formation of mental commands and quantum supremacy simulation of robotic prosthetic arm discussed.


Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 936
Author(s):  
Milton A. Garcés

Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.


Author(s):  
Milton Garces

Data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present emerging signal processing challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a highly standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different CWT reconstruction schemas are presented and tested under different SNR conditions. A sparse representation of the Nth order Gabor atoms worked well against a test blast synthetic using the wavelet entropy and a comparable entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for dictionary-based machine learning.


Sign in / Sign up

Export Citation Format

Share Document