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This work develops a low-power up-conversion mixer. The developed mixer integrates the LC parallel resonant technique, the inverter amplifier technique, the resistive-feedback technique, and the resistive source degeneration technique. Therefore, it has higher conversion gain, low power consumption, a large IF bandwidth, and gain control functionality, which provides favorable linearity. The output radio frequency of the proposed mixer is between 1950 MHz and 2000 MHz, and the input frequencies are 50 MHz to 100 MHz. The measured conversion gains of the mixer in high-gain mode and low-gain mode are 9.5 dB and 1 dB, respectively; the measured input third-order intercept points (IIP3) in high-gain and low-gain modes are 0 dBm and 7 dBm, respectively. The DC operation point of the proposed mixer does not differ between the modes. The mixer dissipates 2 mW of power at a supply voltage of 1.2 V, and is used in a Taiwan Semiconductor Manufacturing Company 90 nm RF CMOS process. The area of the chip is 0.7 mm 2 . [ABSTRACT FROM AUTHOR]

In this paper, a differential multi-band CMOS low noise amplifier (LNA), operated in a range of 800–1700 MHz, is proposed. In this design, the LNA is integrated wide-band interference rejection (input band selection technology) and capacitive cross-coupling topologies, which can improve the interference rejection and noise figure preference. Moreover, the conventional notch filter technology only rejected the specified frequency. In this experiment, by using the proposed wide-band interference rejection technology, the LNA can reject unwanted signals (out-of-band signals) and image signals from different frequency. Thus, the LNA has good linearity and interference rejection performance. With the increasing use of frequency spectrum, the proposed technology is even more important. The post-simulation results of proposed LNA show that the voltage gain is 13–17.5 dB, the noise figure (NF) is less than 3.4 dB, and the third-order intercept point (IIP3) is 7.36 dBm. The LNA consumes 8.96 mW under 1.8 V supply voltage in TSMC 0.18-μm RF CMOS process. [ABSTRACT FROM AUTHOR]

Olfactory dysfunction significantly impedes the life quality of patients. Neuropeptide Y (NPY) is not only a neurotrophic factor in the rodent olfactory system but also an orexigenic peptide that regulates feeding behavior. NPY increases the olfactory receptor neurons (ORNs) responsivity during starvation; however, whether NPY can promote differentiation of human ORNs remains unexplored. This study investigates the effect of NPY on the differentiation of human olfactory neuroepithelial cells in vitro. Human olfactory neuroepithelium explants were cultured on tissue culture polystyrene dishes for 21 days. Then, cells were cultured with or without NPY at the concentration of 0.5 ng/mℓ for 7 days. The effects of treatment were assessed by phase contrast microscopy, immunocytochemistry and western blot analysis. The further mechanism was evaluated with NPY Y1 receptor-selected antagonist BIBP3226. NPY-treated olfactory neuroepithelial cells exhibited thin bipolar shape, low circularity, low spread area, and long processes. The expression levels of Ascl1, βIII tubulin, GAP43 and OMP were significantly higher in NPY-treated cells than in controls (p < 0.05). NPY-treated olfactory neuroepithelial cells expressed more components of signal transduction apparatuses, G olf and ADCY3, than those without NPY treatment. Western blot analysis also further confirmed these findings (p < 0.05). Additionally, the expression levels of Ascl1, βIII2 tubulin, GAP43, OMP, ADCY3, and Golf in BIBP3226 + NPY and controls were comparable (p > 0.05). NPY not only increases expressions of protein markers of human olfactory neuronal progenitor cells, but also promotes differentiation of ORN and enhances formation of components of olfactory-specific signal transduction pathway through Y1 receptors. • Neuropeptide Y increases expression of human olfactory neuronal progenitor cells and olfactory receptor neurons. • Neuropeptide Y enhances components of olfactory signal transduction pathway. • NPY Y1 receptor plays an important role in mediating growth of olfactory receptor neurons. [ABSTRACT FROM AUTHOR]

• Adaptive calibration in fault localization enhanced by semantic segmentation. • Precise fault localization with multiscale analysis at macro and micro levels. • Approach validated through extensive simulations and field tests for accuracy. • An industrial prototype demonstrating the superiority of our approach over existing methods. Fault localization is crucial for ensuring stability, particularly in high impedance faults (HIF) characterized by low current levels and prolonged transient processes (TP). Existing methods predominantly analyze differences in the fixed-length transient waveform, potentially causing delays in triggering or failure in HIF scenarios. To address these challenges, a novel AI application paradigm for HIF localization was introduced, incorporating both adaptive TP calibration and multiscale correlation analysis. Based on 1D-Unet, the TP of the zero-sequence voltage (ZSV) can be adaptively calibrated to maximize the utilization of transient information. Subsequently, the differential zero-sequence voltage (DZSV) and transient zero-sequence current (TZSC) can be acquired to facilitate multiscale correlation analysis. Combined with a sliding window strategy, the micro correlation between DZSV and TZSC is articulated through the local correlation degree (LCD). The comprehensive correlation degree (CCD) between DZSV and TZSC is then formulated to realize fault feeder/ section localization at the macro level. The 1D-Unet model achieved a classification accuracy of 99.2 % for sample points in test datasets and showed robustness with an accuracy exceeding 93.5 % in the presence of 20 dB noise interference. When integrated with the well-trained 1D-Unet, the proposed approach underwent further validation using simulation data and field recordings. These tests confirmed the model's resilience to noise interference up to 20 dB and its efficacy across networks of diverse topologies, such as the IEEE-13 and 34-node distribution networks. Additionally, an industrial prototype applying this framework identified all fault conditions without false positives or omissions, outperforming existing methods under various fault scenarios, including those involving high impedance materials and different resistance levels across multiple feeders. [ABSTRACT FROM AUTHOR]

The diagnosis of high-impedance fault (HIF) is a critical challenge due to the presence of faint signals that exhibit distortion and randomness. In this study, we propose a novel diagnostic approach for HIF based on semantic segmentation of the signal envelope (SE) and Hilbert marginal spectrum (HMS). The proposed approach uses 1D-UNet to identify the transient process of potential fault events in zero-sequence voltage to judge fault inception. Longer timescale zero-sequence voltage is then used to extract SE and HMS, representing HIF distortion and randomness characteristics. These features are transformed into images, and ResNet18 is employed to detect the presence of HIF. An industrial prototype of the proposed approach has been implemented and validated in a 10 kV test system. The experimental results indicate that the proposed approach outperforms the comparison by a significant margin regarding triggering deviation and detection accuracy, particularly in resonant distribution networks. [ABSTRACT FROM AUTHOR]

Abstract Because of the limited reflected energy and incoming illumination in an individual band, the reflected energy captured by a hyperspectral sensor might be low and there is inevitable noise that significantly decreases the performance of the subsequent analysis. Denoising is therefore of first importance in hyperspectral image (HSI) analysis and interpretation. However, most HSI denoising methods remove noise with the important spectral information being severely distorted. This paper presents an HSI denoising method using trainable spectral difference learning with spatial initialization (called HDnTSDL) aimed at preserving the spectral information. In the proposed HDnTSDL model, a key band is automatically selected and denoised. The denoised key band acts as a starting point to reconstruct the rest of the non-key bands. Meanwhile, a deep convolutional neural network (CNN) with trainable non-linearity functions is proposed to learn the spectral difference mapping. Then, the rest of the non-key bands are denoised under the guidance of the learned spectral difference with the key band as a starting point. Experiments have been conducted on five databases with both indoor and outdoor scenes. Comparative analyses validate that the proposed method: (i) presents superior performance in spatial recovery and spectral preservation, and (ii) requires less computational time than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents a new symmetrical SURF descriptor to detect vehicles on roads and then proposes a novel sparsity-based classification scheme to recognize their makes and models. First, for vehicle detection, this paper proposes a symmetry transformation on SURF points to detect all possible matching pairs of symmetrical SURF points. Then, each desired ROI of vehicle can be located very accurately from the set of symmetrical matching pairs through a projection technique. The advantages of this scheme are no need of background subtraction and its extreme efficiency in real-time detection tasks. After that, two challenges in vehicle make and model recognition (MMR) should be addressed, i.e ., the multiplicity and ambiguity problems. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem means vehicles even made from different companies often share similar shapes. To treat the two problems, a dynamic sparse representation scheme is proposed to represent a vehicle model in an over-complete dictionary whose base elements are the training samples themselves. With the dictionary, a novel Hamming distance classification scheme is proposed to classify vehicle makes and models to detailed classes. Because of the sparsity of the representation and the nature of Hamming code highly tolerant to noise, different vehicle makes and models can be recognized with high accuracy. [ABSTRACT FROM AUTHOR]