Extrinsic Resonance Effects in a Subresonant Mechanical Spectrometer

2006 ◽  
Vol 115 ◽  
pp. 293-298
Author(s):  
Leszek B. Magalas
Keyword(s):  
Loss Tangent ◽  
Precise Measurement ◽  
Low Frequency ◽  
Resonant Mode ◽  
Forced Oscillations ◽  
Signal Quality ◽  
Resonance Effects ◽  
Mechanical Loss Tangent ◽  
Eigen Frequencies

Extrinsic resonance effects observed in a low-frequency subresonant mechanical spectrometer are reported. High resolution of the mechanical spectrometer enables precise measurement of small values of the mechanical loss tangent ( tanϕ = 1- 2 × 10-4) and the apparent high values of the loss tangent detected for extrinsic resonance effects in the vicinity of the eigen frequencies of the spectrometer. Forced oscillations can be used as a ‘signal quality test’ to validate the quality of exponentially damped harmonic oscillations in the resonant mode.

Dynamics of the processes in metal machining

1998 ◽  
Vol 2 ◽  
pp. 115-122
Author(s):  
Donatas Švitra ◽  
Jolanta Janutėnienė
Keyword(s):  
High Frequency ◽  
Machine Tools ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Low Frequency ◽  
Metal Cutting Machine ◽  
Cutting Machine ◽  
Metal Machining

In the practice of processing of metals by cutting it is necessary to overcome the vibration of the cutting tool, the processed detail and units of the machine tool. These vibrations in many cases are an obstacle to increase the productivity and quality of treatment of details on metal-cutting machine tools. Vibration at cutting of metals is a very diverse phenomenon due to both it’s nature and the form of oscillatory motion. The most general classification of vibrations at cutting is a division them into forced vibration and autovibrations. The most difficult to remove and poorly investigated are the autovibrations, i.e. vibrations arising at the absence of external periodic forces. The autovibrations, stipulated by the process of cutting on metalcutting machine are of two types: the low-frequency autovibrations and high-frequency autovibrations. When the low-frequency autovibration there appear, the cutting process ought to be terminated and the cause of the vibrations eliminated. Otherwise, there is a danger of a break of both machine and tool. In the case of high-frequency vibration the machine operates apparently quiently, but the processed surface feature small-sized roughness. The frequency of autovibrations can reach 5000 Hz and more.

Estimating the Quality of Thermionic Cathodes for Microwave Vacuum Tubes Using the Low-Frequency Noise Parameters

Vestnik MEI ◽  
2018 ◽  
Vol 5 (5) ◽  
pp. 120-127
Author(s):  
Mikhail D. Vorobyev ◽  
◽  
Dmitriy N. Yudaev ◽  
Andrey Yu. Zorin ◽  
◽  
...  
Keyword(s):  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Noise Parameters ◽  
Thermionic Cathodes ◽  
Vacuum Tubes

scholarly journals The Optical Signal-to-Crosstalk Ratio for the MBA(N, e, g) Switching Fabric †

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1534
Author(s):  
Remigiusz Rajewski
Keyword(s):  
Optical Switching ◽  
Essential Feature ◽  
Optical Signal ◽  
Point Of View ◽  
Signal Quality ◽  
Switching Networks ◽  
Optical Elements ◽  
Optical Signals ◽  
Switching Fabric

The banyan-type switching networks, well known in switching theory and called the logdN switching fabrics, are composed of symmetrical switching elements of size d×d. In turn, the modified baseline architecture, called the MBA(N,e,g), is only partially built from symmetrical optical switching elements, and it is constructed mostly from asymmetrical optical switching elements. Recently, it was shown that the MBA(N,e,g) structure requires a lower number of passive as well as active optical elements than the banyan-type switching fabric of the same capacity and functionality, which makes it an attractive solution. However, the optical signal-to-crosstalk ratio for the MBA(N,e,g) was not investigated before. Therefore, in this paper, the optical signal-to-crosstalk ratio in the MBA(N,e,g) was determined. Such crosstalk influences the output signal’s quality. Thus, if such crosstalk is lower, the signal quality is better. The switching fabric proposed in the author’s previous work has lower optical signal losses than a typical Beneš and banyan-type switching networks of this same capacity and functionality, which gives better quality of transmitted optical signals at the switching node’s output. The investigated MBA(N,e,g) architecture also contains one stage fewer than banyan-type network of the same capacity, which is an essential feature from the optical switching point of view.

scholarly journals Anisotropy of Mechanical Loss Tangent of Drawn Polyacrylonitrile Film

1968 ◽  
Vol 25 (274) ◽  
pp. 97-101
Author(s):  
Akio Takeuchi ◽  
Saburo Okajima
Keyword(s):  
Loss Tangent ◽  
Mechanical Loss ◽  
Mechanical Loss Tangent

МЕТОД ГЛУБОКОГО ОБУЧЕНИЯ С ПОДКРЕПЛЕНИЕМ ДЛЯ СИСТЕМ ИНТЕРНЕТА ВЕЩЕЙ НА БАЗЕ ТЕХНОЛОГИИ LORA С ОГРАНИЧЕННЫМИ РЕСУРСАМИ И ПОДДЕРЖКОЙ QoS

2021 ◽  
Author(s):  
 M.S. MUTHANNA ◽  
A.S. MUTHANNA ◽  
 A.S. BORODIN
Keyword(s):  
Data Transmission ◽  
Environmental Parameters ◽  
Code Rate ◽  
Frame Structure ◽  
Signal Quality ◽  
High Quality ◽  
Spreading Factor ◽  
Transmission Parameters ◽  
Factor Code

Achieving high Quality of Service (QoS) remains a challenge for LoRa technology. However, high QoS can be achieved via optimizing the transmission policy parameters such as bandwidth and code rate. Existing approaches do not provide an opportunity to optimize the LoRa networks' data transmission parameters. The article proposes transmission policy enforcementfor QoS-aware LoRanetworks.The QoSparameter ranking is implemented for IoT nodes where priority and nonpriority information is identified by the new field of LoRa frame structure(QRank).The optimaltransmissionpolicyenforcement uses fast deep reinforcement learning that utilizes the environmental parameters including QRank, signal quality, and signal-to-interference-plus-noise-ratio. The transmission policy is optimized for spreading factor, code rate, bandwidth, and carrier frequency. Performance evaluation is implemented using an NS3.26 LoRaWAN module. The performance is examined for various metrics such as delay and throughput. Достижение высокого качества обслуживания (QoS) по-прежнему остается достаточно сложной задачей для технологии LoRa. В принципе высокий уровень QoS может быть достигнут за счет оптимизации параметров передачи, например, пропускной способности и скорости передачи информации в сети. Известные на сегодняшний день решения не дают возможности оптимизировать параметры передачи данных для сетей LoRa. В статье предложен эффективный метод передачи данных, обеспечивающий требования по QoS при использовании технологии LoRa. Ранжирование параметров QoS для узлов интернета вещей определяется новым полем структуры фрейма LoRa (QRank) для приоритетной и неприоритетной информации. Для обеспечения эффективной передачи применяется быстрое глубокое обучение с подкреплением, для которого используются как параметры качества обслуживания, так и отношение сигнал/шум. Метод передачи оптимизирован с учетом коэффициента распространения, скорости передачи данных, полосы пропускания и несущей частоты. Оценка производительности при применении предложенного метода проведена с использованием модуля LoRaWAN в пакете имитационного моделирования NS3.26. Производительность оценивается на основе параметров задержки и пропускной способности.

The quality of intrapartum cardiotocography in preterm labour

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zohal Faiz ◽  
Eline M. Van ’t Hof ◽  
Gerard J. Colenbrander ◽  
Ralf Lippes ◽  
Petra C.A.M. Bakker
Keyword(s):  
Preterm Labour ◽  
Statistical Significance ◽  
International Federation ◽  
Signal Quality ◽  
Foetal Heart Rate ◽  
Signal Loss ◽  
Chi Square ◽  
Second Stage ◽  
Second Stage Of Labour

Abstract Objectives The aim of this study is to determine the quality of the foetal heart rate (FHR) recording, defined as signal loss, during preterm labour below 28 weeks gestational age (GA) and contribute to the discussion if cardiotocography (CTG) is of value for the extreme preterm foetus. Methods From January 2010 to December 2019 a retrospective study was conducted with data of 95 FHR recordings of singletons born between 24 and 28 weeks GA at the Amsterdam University Medical Centre, location VUmc. FHR tracings had a duration of at least 30 min and were obtained via external ultrasound mode. Data of all recordings were divided in two groups according to gestation (24–26 weeks and 26–28 weeks). Signal loss was analysed. Statistical significance was calculated by non-parametric tests and chi-square tests. The median signal loss and the proportion of cases exceeding the International Federation of Gynaecology and Obstetrics Guidelines (FIGO) threshold of 20% signal loss were calculated. Results One-third of the recordings exceeded the 20% FIGO-criterion for adequate signal quality during the first stage of labour. In the second stage, this was nearly 75%. Similarly, the median signal loss was 13% during the first and 30% during the second stage of labour (p<0.01). Conclusions The quality of FHR monitoring in the extreme preterm foetus is inadequate in a large proportion of the foetuses, especially during the second stage. FHR monitoring is therefore controversial and should be used with caution.

Research of Wavelet Packet Analysis in the De-Noising of Ultrasonic Echo Signal

2011 ◽  
Vol 403-408 ◽  
pp. 1817-1822
Author(s):  
Xi Feng Zhou ◽  
Xiao Wu ◽  
Qian Gang Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Random Noise ◽  
Flaw Detection ◽  
Low Frequency ◽  
Local Analysis ◽  
Echo Signal ◽  
Wavelet Packet Analysis ◽  
Ultrasonic Flaw

The quality of ultrasonic flaw echo signal is the foundation of achieving qualitative and quantitative analysis in the in ultrasonic flaw detection. In practice, the flaw echo signals are often contaminated or even annihilation by random noise. According to the characteristics of ultrasonic flaw echo signal, wavelet packet has more accurate local analysis ability in low frequency and high frequency part. This paper discusses de-noising in ultrasonic signals based on wavelet packet analysis, and proposes an improved threshold approach for de-noising. The results show that: It remarkably raises the signal-to-noise ratio of ultrasonic flaw echo signal and improves the quality of signal with improved wavelet packet threshold.

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