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In this paper we report the results of extensive research on phase modulation (PM) and amplitude modulation (AM) noise in linear bipolar junction transistor (BJT) amplifiers. BJT amplifiers exhibit 1/f PM and AM noise about a carrier signal that is much larger than the amplifier's thermal noise at those frequencies in the absence of the carrier signal. Our work shows that the 1/f PM noise of a BJT based amplifier is accompanied by 1/f AM noise which can be higher, lower, or nearly equal depending on the circuit implementation. The 1/f AM and PM noise in BJTs is primarily the result of 1/f fluctuations in transistor current, transistor capacitance, circuit supply voltages, circuit impedances, and circuit configuration. We discuss the theory and present experimental data in reference to common emitter amplifiers, but the analysis can be applied to other configurations as well. This study provides the functional dependence of 1/f AM and PM noise on transistor parameters, circuit parameters, and signal frequency, thereby laying the groundwork for a comprehensive theory of 1/f AM and PM noise in BJT amplifiers. We show that in many cases the 1/f PM and AM noise can be reduced below the thermal noise of the amplifier.

A practical implementation of a portable secondary standard for phase modulation (PM) and amplitude modulation (AM) noise at 5, 10, and 100 MHz is described. The accuracy of the standard for both PM and AM noise is +or-0.14 dB, and the temperature coefficient is less than 0.02 dB/K. The noise floor of the standard for PM noise measurements is less than -190 dBC rel to 1 rad/sup 2//Hz at 5, 10, and 100 MHz. The noise floor for AM measurements depends on the configuration. A calibrated level of PM and AM noise of approximately-130+or-0.2 dB rel to 1 rad/sup 2//Hz is used to evaluate the accuracy versus Fourier frequency. This standard can also be used as an alternative to the normal method of the PM/AM detector for PM/AM measurements. >

We show that a high-resolution software package can be adapted to calculate the amplitude modulation (AM) and phase modulation (PM) noise performance of Si Bipolar Junction Transistor (BJT) amplifiers. Our simulations predict that the PM noise induced by bias current noise can be greatly suppressed by operating at special bias conditions. Furthermore, a dc balance amplifier circuit is introduced to stabilize the base-to-collector voltage. The total sensitivity of PM noise to power supply noise using dc balance architecture can be reduced by 40 dB compared to basic amplifier architecture. This is experimentally verified.

A portable amplitude-modulation (AM) and phase-modulation (PM) noise standard has been developed for carrier frequencies of 10.6, 21.2 and 42.4 GHz. The noise level from the carrier 1/2 S/sub /spl phi//(f) (phase noise) or 1/2 S/sub a/(f) (amplitude noise) is -122.6 dBc/Hz at 10.6 GHz, -119.6 dBc/Hz at 21.2 GHz and -116.3 dBc/Hz at 42.4 GHz. For Fourier frequencies of 1 kHz to 10 MHz the added noise is flat with an uncertainty of /spl plusmn/0.25 dB. The uncertainty of the noise level for Fourier frequencies of 1 kHz to 100 MHz is /spl plusmn/0.5 dB. The residual PM noise floor between the signal and reference outputs at 100 MHz is less than -182 dBc/Hz.

When an RF carrier is amplified by a GaAs MESFET, amplitude modulation (AM) and phase modulation (PM) noises are imposed on the carrier. This is generally believed to be caused by transposition to the carrier frequency of the low-frequency flicker noise generated by the FET. The cross correlation between the AM and PM noises and the low-frequency noise observed on the drain of the FET is measured. While the AM noise and the low-frequency noise on the drain of the FET exhibit a high degree of correlation, the PM noise and the low-frequency drain noise are not highly correlated. The latter result may explain the limited success of oscillator phase noise reduction methods which rely on the existence of a large cross correlation between the PM and low-frequency noises. >

The opto-electronic oscillator (OEO) has emerged in recent years as an excellent low-noise source that rivals the best RF oscillators over broad offset frequencies. The main sources of noise in an OEO are the laser and RF modulator, photo detector, loop amplifier, and the long fiber that is needed for high Q. Recent studies have shown that even by using state-of-the-art components and a low-loss long fiber, the phase-modulated (PM) noise of these OEOs fails to meet the theoretical value at offset frequencies close to carrier, from a few hertz to 1 kHz. The main cause for this shortfall is vibration effects on the optical fiber. External, environmental vibration causes mechanical distortions in the fiber that induce time-delay (phase) fluctuations. The spool onto which the fiber is wound is primarily responsible for imparting these vibration-induced delay fluctuations to the fiber and thus diminishing the performance of the OEO. In this paper, we compare the vibration-induced phase fluctuations of a 3 km optical fiber wound on spools made of four materials-metal, ceramic, plastic, and foam-covered plastic. We investigate fiber-on-spool winding and mounting techniques that reduce vibration susceptibility. We present residual PM measurements that compare the vibration sensitivity of an optical fiber wound on these different materials.

An efficient forecasting method for time series based on visibility graph and multi-subgraph similarity

Proper timing of activities is one of the principal challenges faced by most organisms. Organisms need to account for various aspects in decision making like avoiding inordinate risks, synchronizing with resource availability, or finding mates. We provide analytical and simulation models to investigate the influence of life expectancy, resource competition and unpredictable environmental conditions (environmental uncertainty) on the evolutionarily stable distribution of emergence times in organisms depending on seasonally available resources. We focus on the partitioning of total phenotypic variance in emergence times into 1) genetic variance in mean emergence times between lineages and 2) environmental trait variance that determines the intra‐lineage variance in the timing of emergence. Both, life expectancy of organisms and intensity of competition severely influence the evolutionary response to environmental uncertainty. Our main findings can be summarized as follows: 1) in general diversifying bet hedging (environmental trait variance) is the adequate mechanism to reduce the risk arising from environmental uncertainty while conservative bet hedging, i.e. delaying emergence into ‘safe’ phases of the season is restricted to short lived organisms and to situations with vanishing competition. 2) Environmental trait variance increases with increasing environmental uncertainty whereas 3) significant genetic variance evolves only under severe resource competition; it is driven by selection for an ideal free distribution of emergence times. 4) The level of genetic variance evolving declines with increasing life expectancy of organisms. 5) With sufficiently short life expectancy evolutionary branching and coexistence of distinctly different emergence strategies occurs; the number of co‐occurring strategies is determined by the level of environmental uncertainty. Our model provides cues for understanding how different ecological factors contribute and interact to shape the evolution of emergence strategies.

The purpose of this study was to describe objective sleep-wake characteristics and glycemia over 7-14 days in young adults with type 1 diabetes. In addition, person-level associations among objective sleep-wake characteristics (total sleep time, sleep variability, and sleep fragmentation index), daytime sleepiness, and glycemia (glycemic control and glucose variability) were examined. In this cross-sectional study, objective sleep-wake characteristics were measured via actigraphy and glucose variability via continuous glucose monitoring over 6-14 days. At baseline, participants completed the Psychomotor Vigilance Test, the Trail Making Test, and questionnaires on daytime sleepiness, sleep quality, and sleep disturbance including sleep diaries. Forty-six participants (mean age, 22.3 ± 3.2 years) wore a wrist actigraph and underwent continuous glucose monitoring concurrently for 6-14 days. Greater sleep variability was directly associated with greater glucose variability (mean of daily differences; r = .33, P = .036). Higher daytime sleepiness was directly associated with greater glucose variability (mean of daily differences; r = .50, P = .001). The association between sleep variability and glucose variability (mean of daily differences) was no longer significant when accounting for daytime sleepiness and controlling for type 1 diabetes duration (P > .05). A higher sleep fragmentation index was associated with greater glucose variability (B = 1.27, P = .010, pr2 = 0.40) after controlling for type 1 diabetes duration and accounting for higher daytime sleepiness. Sleep-wake variability, sleep fragmentation, daytime sleepiness, and the associations with glycemia are new dimensions to consider in young adults with type 1 diabetes. Sleep habits in this population may explain higher glucose variability, and optimizing sleep may improve overall diabetes management. Griggs S, Hickman RL Jr, Strohl KP, Redeker NS, Crawford SL, Grey M. Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes. J Clin Sleep Med. 2021;17(9):1865-1874.

Enhancing the accuracy of long-term time series forecasting is a crucial task across various fields. Recently, many studies have employed the Discrete Fourier Transform (DFT) to convert time series into the frequency domain for forecasting, as it provides a compact and efficient representation, enabling the capture of deep cyclical patterns and global trends that are often difficult to identify in the time domain. However, the frequency domain information in time series has yet to be fully explored due to three main reasons: they rely on a fixed-resolution DFT, which restricts them to a single frequency resolution and leads to the loss of essential information; they predominantly focus on global dependencies while overlooking local temporal details; they remain highly sensitive to noise in the time series, limiting the model’s ability to capture stable patterns. In this paper, we propose a novel lightweight architecture (FreMixer) that operates entirely in the frequency domain. Firstly, we introduce a multi resolution segmentation mechanism in the frequency domain, enabling features represented at different resolutions to complement each other, effectively overcoming the sparse resolution limitations of DFT in the frequency spectrum. Secondly, we comprehensively extract the frequency information by employing a dual branch architecture that simultaneously captures both global and local features at each frequency resolution, providing a more comprehensive representation of temporal patterns. Moreover, we propose a noise insensitive loss function ArcTanLoss that reduces overfitting to outliers. Extensive experiments conducted on seven different datasets have validated the effectiveness of our proposed model and loss function.

For the time series with high acquisition frequency and high noise, it is difficult to establish the prediction model directly. If we simply take their average values, we will lose a lot of trend information. Therefore, we studied how to accurately obtain the trend information of the time series and establish its accurate prediction model, and proposed a prediction model based on K-means clustering. The first step of the model is to obtain the trend information of the original time series based on the K-means clustering idea, and the second step is to use the gated recurrent unit based on the attention mechanism to establish a prediction model for the trend information. Experiments on three dataset show that the proposed K-means clustering method can effectively reduce noise interference and accurately obtain trend information. Comparative experiments on different prediction models show that our proposed prediction model has the best prediction accuracy.

Phase vs. magnitude information in functional magnetic resonance imaging time series: toward understanding the noise.

This paper utilizes a convolutional neural network and long short‐term memory with attention mechanism (CLA) prediction model for time series forecasting. An innovative master‐slave electric‐hydraulic hybrid vehicle (MSEHV) is introduced. It simplifies the drivetrain structure by utilizing planetary gears for energy transfer and coupling to transform electrical, mechanical, and hydraulic energy. This paper conducts simulation verification using existing control strategies. It combines the velocity time series information with the hydraulic accumulator pressure time series information to create a unified time series. Some of the more popular machine learning and neural network algorithms are considered, and the mean square error, mean absolute error, and coefficient of determination evaluation metrics are calculated for validation. The research presents a developed battery state of charge regression prediction model based on genetic algorithm‐backpropagation by combining the predicted results of composite time series with the current discharge depth of the battery. The validation results indicate that the CLA time series prediction model proposed in this paper for MSEHV has higher prediction accuracy compared to other prediction models.

An effective health assessment guarantees the high accuracy of remaining useful life (RUL) prediction of machinery components. The key to health assessment is the health indicator of machinery components, which are generally constructed by feature fusion from the time and frequency domains in sensorial data. However, existing health indicator construction methods are constrained due to the low-sampling rate and the susceptibility to environmental disturbances of sensorial data. To draw these issues, this paper mainly proposes a health indicator construction method combining condensed image coding and a metrics-constrained deep learning model for improving the accuracy of RUL prediction. Firstly, to extract more information in time series under a low-sampling rate, a recurrence plot-grey (RP-G) image coding is extended from RP images to extract and fuse both the global and local dynamics at multiple scales in time series. Then, a nested residual-convolution autoencoder (NR-CAE) model is proposed to extract degradation-related information from RP-G images containing information disturbed by the environment. Finally, to build a more solid connection between the image-based features and the RUL prediction, a metric-constrained gated recurrent unit (MC-GRU) is proposed by considering characteristics related to RUL prediction and temporal relationships in RP-G image-based features. A commercial case of wind turbine gearboxes in Liaoning, China is investigated and demonstrates that the proposed method owns the ability to construct reliable health indicator of machinery components for accurate RUL prediction, using low-sampling sensorial data under environmental disturbances.

Identifying objects with radar is in great demand to avoid road accidents. Recent research tried to identify moving objects on a road by inputting radar information to a machine learning classifier. In the conventional method, the features used in the machine learning are extracted from the observed radar information with a short time interval. Since the movement of the objects is different depending on the objects, time series information is effective for classification, which has not been exploited before. In this paper, we propose an on-road object identification considering time series of radar information. We measured objects with 79.5 GHz millimeter-wave radar and extract features from a series of time windows by calculating the mean and variance of object information, i.e., velocity, distance, and signal power. The classification performance was evaluated with a dataset obtained by on-road experiments. It is shown that our method outperforms the conventional one and the proposed features significantly contribute to the accurate identification.