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Instantaneous frequency measurement devices are designated for very fast measurements of the current frequency value of microwave signals, even if they are very short in the time domain. Fast measurements of frequency temporary values may be based on the evaluation of the phase difference of signal propagating through the microwave transmission lines with unequal, but known, lengths. This paper presents the principle of determination of temporary values of the microwave signal frequency using the digitalized signals and the binary value of them eventually. In the purpose of increase the frequency discrimination resolution, additional tracks with lines with a larger length are proposed. For the system with elements with analytical model transmission characteristics it is typical that bands of ambiguity of frequency measurement occurs. To tackle this problem in addition to 4 x 4 Butler matrix implementation the method of using combination sine and cosine signals is proposed.

Instantaneous frequency measurement devices are useful for conducting extremely fast measurements of the current frequency value of microwave signals, even if their duration is extremely short. This paper presents the principle of determination of temporary values of the microwave signal phase and frequency using interferometer techniques, based on passive microwave components. Additionally, the structures and results of measurements of two novel versions of integrated microwave correlators for microwave frequency discriminators, made on a single printed circuit board, are shown. Three Wilkinson-type, single-stage power dividers, and two rat-race hybrids create the developed correlators. The developed devices were designed to work over a wide frequency range, i.e., of 1.6–3.1 GHz, and can be used to monitor Wi-Fi devices as well as pulse and CW radar systems operating in the S band. They can also be applied in passive radars and active Doppler radars. The view of the printed circuits boards and results of measurements are presented. Recommendations for improving the accuracy of measurement are proposed.

Resistive switching, a phenomenon in which the resistance of a device can be modified by applying an electric field 1 – 5 , is at the core of emerging technologies such as neuromorphic computing and resistive memories 6 – 9 . Among the different types of resistive switching, threshold firing 10 – 14 is one of the most promising, as it may enable the implementation of artificial spiking neurons 7 , 13 , 14 . Threshold firing is observed in Mott insulators featuring an insulator-to-metal transition 15 , 16 , which can be triggered by applying an external voltage: the material becomes conducting (‘fires’) if a threshold voltage is exceeded 7 , 10 – 12 . The dynamics of this induced transition have been thoroughly studied, and its underlying mechanism and characteristic time are well documented 10 , 12 , 17 , 18 . By contrast, there is little knowledge regarding the opposite transition: the process by which the system returns to the insulating state after the voltage is removed. Here we show that Mott nanodevices retain a memory of previous resistive switching events long after the insulating resistance has recovered. We demonstrate that, although the device returns to its insulating state within 50 to 150 nanoseconds, it is possible to re-trigger the insulator-to-metal transition by using subthreshold voltages for a much longer time (up to several milliseconds). We find that the intrinsic metastability of first-order phase transitions is the origin of this phenomenon, and so it is potentially present in all Mott systems. This effect constitutes a new type of volatile memory in Mott-based devices, with potential applications in resistive memories, solid-state frequency discriminators and neuromorphic circuits. Mott materials feature scale-less relaxation dynamics after the insulator-to-metal transition that make its electric triggering dependent on recent switching events.

We present an approach using INS-aided GPS tracking loop with FFT frequency discriminator for tracking of dynamic weak GPS signals. In this approach, the FFT carrier frequency discriminator is used to deal with the attenuated GPS signal, and the INS is used to handle the platform dynamics. With a tactical level INS aiding, when the CN0 of the GPS signal is as low as 19 dB-Hz and the platform dynamic is as high as 100 g, the tracking results indicate that the Doppler tracking error is <4 Hz, and the code loop bandwidth can be decreased to 0.05 Hz. The positioning results show that the positioning error is <5 m and the velocity error is <0.5 m/s.

The orthogonal frequency discrimination is achieved by utilizing waveform transformation in the phase-shift multiplier frequency discriminator. Firstly, the constraint relationship is derived between the circuit parameters of the phase shifter and the maximum frequency spectrum of the input FM signal. Subsequently, the demodulation system for the input FM signal is designed using a combination of a phase shifter and multiplier. Finally, the maximum frequency offset range of distortion-free output from the frequency discriminator is analyzed, and the influence of resistance and capacitance parameters in the phase-shift network on the input frequency offset range is researched using parameter scanning analysis. Theoretical derivation analysis and simulation testing confirm that this designed circuit can effectively demodulate FM signals with a wide range of non-distortion when the circuit parameters match those of input signals. This circuit simulation method has been proven to visually demonstrate different output waveforms under various parameter settings, thereby facilitating the visualization of abstract theoretical knowledge.

A frequency locked loop (FLL) for phase noise reduction of wideband voltage controlled oscillators is proposed. The key building block of the system is a low noise (−160 dBV/Hz) and high sensitivity (22 V/GHz) delay line frequency discriminator with 5–8 GHz coverage, which makes use of a high performance multilayer hybrid. The authors derive closed-form, universal design equations for the maximum noise reduction and stability of the FLL circuitry. Application of the proposed technique to a state-of-the-art voltage controlled oscillator operating in the 5–8 GHz band yields a phase noise reduction of 8–10 dB at 100 kHz and 5 dB at 1 MHz off the carrier, which shows the results are in good agreement with the simulated results; so phase noise better than −107 dBc/Hz at 100 kHz and better than −123.5 dBc/Hz at 1 MHz is obtained.

Deep learning-based computer vision technology significantly improves the accuracy and efficiency of crop disease detection. However, the scarcity of crop disease images leads to insufficient training data, limiting the accuracy of disease recognition and the generalization ability of deep learning models. Therefore, increasing the number and diversity of high-quality disease images is crucial for enhancing disease monitoring performance. We design a frequency-domain and wavelet image augmentation network with a dual discriminator structure (FHWD). The first discriminator distinguishes between real and generated images, while the second high-frequency discriminator is specifically used to distinguish between the high-frequency components of both. High-frequency details play a crucial role in the sharpness, texture, and fine-grained structures of an image, which are essential for realistic image generation. During training, we combine the proposed wavelet loss and Fast Fourier Transform loss functions. These loss functions guide the model to focus on image details through multi-band constraints and frequency domain transformation, improving the authenticity of lesions and textures, thereby enhancing the visual quality of the generated images. We compare the generation performance of different models on ten crop diseases from the PlantVillage dataset. The experimental results show that the images generated by FHWD contain more realistic leaf disease lesions, with higher image quality that better aligns with human visual perception. Additionally, in classification tasks involving nine types of tomato leaf diseases from the PlantVillage dataset, FHWD-enhanced data improve classification accuracy by an average of 7.25% for VGG16, GoogleNet, and ResNet18 models.Our results show that FHWD is an effective image augmentation tool that effectively addresses the scarcity of crop disease images and provides more diverse and enriched training data for disease recognition models.

To address the problems of existing 2D image-based imbalanced fault diagnosis methods for rolling bearings, which generate images with inadequate texture details and color degradation, this paper proposes a novel image enhancement model based on a dual-branch generative adversarial network (GAN) combining spatial and frequency domain information for an imbalanced fault diagnosis of rolling bearing. Firstly, the original vibration signals are converted into 2D time–frequency (TF) images by a continuous wavelet transform, and a dual-branch GAN model with a symmetric structure is constructed. One branch utilizes an auxiliary classification GAN (ACGAN) to process the spatial information of the TF images, while the other employs a GAN with a frequency generator and a frequency discriminator to handle the frequency information of the input images after a fast Fourier transform. Then, a shuffle attention (SA) module based on an attention mechanism is integrated into the proposed model to improve the network’s expression ability and reduce the computational burden. Simultaneously, mean square error (MSE) is integrated into the loss functions of both generators to enhance the consistency of frequency information for the generated images. Additionally, a Wasserstein distance and gradient penalty are also incorporated into the losses of the two discriminators to prevent gradient vanishing and mode collapse. Under the supervision of the frequency WGAN-GP branch, an ACWGAN-GP can generate high-quality fault samples to balance the dataset. Finally, the balanced dataset is utilized to train the auxiliary classifier to achieve fault diagnosis. The effectiveness of the proposed method is validated by two rolling bearing datasets. When the imbalanced ratios of the four datasets are 0.5, 0.2, 0.1, and 0.05, respectively, their average classification accuracy reaches 99.35% on the CWRU bearing dataset. Meanwhile, the average classification accuracy reaches 96.62% on the MFS bearing dataset.

Research on perceived discrimination has overwhelmingly focused on one form of discrimination, especially race discrimination, in isolation from other forms. The present article uses data from the Black Youth Culture Survey, a nationally representative, racially and ethnically diverse sample of 1,052 adolescents and young adults to investigate the prevalence, distribution, and mental and physical health consequences of multiple forms of perceived discrimination. The findings suggest that disadvantaged groups, especially multiply disadvantaged youth, face greater exposure to multiple forms of discrimination than their more privileged counterparts. The experience of multiple forms of discrimination is associated with worse mental and physical health above the effect of only one form and contributes to the relationship between multiple disadvantaged statuses and health. These findings suggest that past research may misspecify the discrimination-health relationship and fails to account for the disproportionate exposure to discrimination faced by multiply disadvantaged individuals.

Conclusion This study explores a new navigation method using multi-path solar panel-reflected solar oscillations. Considering the solar panels of BeiDou-3 M1–M24 and GPS satellites as examples, the simulations show that the mean position error of FY-1 using solar panel-reflected solar oscillations is only 20.61 m in 30 days. Compared with the existing autonomous navigation methods for the Earth satellites, the newly proposed method has two advantages. (1) It has the highest navigation accuracy. (2) It does not require any additional accurate geomagnetic map, gravity gradient map, or refraction model. While the proposed method requires at least two atomic frequency discriminators to obtain the measurements and its accuracy is affected by the geometric relationship between the Earth satellite, reflected satellite, and Sun, which are the inherent drawbacks of the method. It is notable that the influence of the relativistic effects on the measurement accuracy needs further research.