MFDNet: Towards Real-time Image Denoising On Mobile Devices

Deep convolutional neural networks have achieved great progress in image denoising tasks. However, their complicated architectures and heavy computational cost hinder their deployments on a mobile device. Some recent efforts in designing lightweight denoising networks focus on reducing either FLOPs (floating-point operations) or the number of parameters. However, these metrics are not directly correlated with the on-device latency. By performing extensive analysis and experiments, we identify the network architectures that can fully utilize powerful neural processing units (NPUs) and thus enjoy both low latency and excellent denoising performance. To this end, we propose a mobile-friendly denoising network, namely MFDNet. The experiments show that MFDNet achieves state-of-the-art performance on real-world denoising benchmarks SIDD and DND under real-time latency on mobile devices. The code and pre-trained models will be released.Comment: Under review at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

Evolution towards dispatchable PV using forecasting, storage, and curtailment: A review

The 2050 net-zero emission goal has pushed the global transition of power systems from fuel-powered to renewable-powered. Solar photovoltaic (PV) power is anticipated to contribute significantly to renewable generation. However, the intermittent nature of solar power hinders the growth of PV capacity during this global transition. Integrating PV into power systems usually requires abundant support resources. Typical facilities include dispatchable fuel-based generators and energy storage systems. However, newer PV systems should not assume sufficient support from fuel-based generators to facilitate the net-zero transition. Although the option to use energy storage, especially batteries, to replace fuel-based generators exists, scaling the capacity can have affordability issues. Despite the challenges, PV penetration is growing and needs to grow further. Overcoming the challenges means eliminating intermittency using minimum storage and negligible fuel. The solution is effectively converting PV to a dispatchable source. The research about forecasting and controlling PV power has centered on reducing the impact of PV power intermittency. However, the need for developing dispatchability out of PV power has yet to be sufficiently addressed. This paper is conducted to identify the research directions needed to facilitate dispatchable PV and, thus, global high PV penetration. To describe the dispatchability of PV power, uncertainty, variability, and flexibility are chosen as descriptors. As the PV power gains flexibility, uncertainty and variability reduce for a PV system. Eventually, PV power can become flexible enough to be dispatchable. Moreover, the support services needed by PV power can be undertaken mainly by itself, thus enabling high penetration. From the literature, PV forecasting, energy storage, and inverter-controlled curtailment are identified to be cornerstones of dispatchable PV power. Power system dispatch algorithms have used PV forecasts to compensate for uncertainty efficiently. Storage, especially batteries, and PV inverters, have been used to control PV power output against undesirable variation. In this review paper, the practice of utilizing PV in power systems is uniquely divided into four categories according to the descriptors. Unlike the convention that curtailment should be avoided, this review emphasizes the practicality of overbuilding PV capacity and curtailing PV power. It ultimately will be possible to effectively eliminate uncertainty and variability as the three cornerstone technologies evolve. By presenting the road map for reducing the impact of PV power intermittency, this paper elaborates on the motivation for researching dispatchable PV. From the past and low penetration to the contemporary situation, PV power evolved from being unconstrained to forecasted and constrained. Based on the literature about forecasting, energy storage, and curtailment, this paper concludes that dispatchable PV power will be needed and is achievable

Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube

In modern society, the oil industry has become the foundation of the world economy, and how to efficiently extract oil is a pressing problem. Among them, the accurate measurement of oil-gas two-phase parameters is one of the bottlenecks in oil extraction technology. It is found that through the experiment the flow patterns of the oil-gas two-phase flow will change after passing through the venturi tube with the same flow rates. Under the different oil-gas flow rate, the change will be diverse. Being motivated by the above experiments, we use the dual ECT sensors to collect the capacitance values before and after the venturi tube, respectively. Additionally, we use the linear projection algorithm (LBP) algorithm to reconstruct the image of flow patterns. This paper discusses the relationship between the change of flow patterns and the flow rates. Furthermore, a convolutional neural network (CNN) algorithm is proposed to predict the oil flow rate, gas flow rate, and GVF (gas void fraction, especially referring to sectional gas fraction) of the two-phase flow. We use ElasticNet regression as the loss function to effectively avoid possible overfitting problems. In actual experiments, we compare the Typical-ECT-imaging-based-GVF algorithm and SVM (Support Vector Machine) algorithm with CNN algorithm based on three different ECT datasets. Three different sets of ECT data are used to predict the gas flow rate, oil flow rate, and GVF, and they are respectively using the venturi front-based ECT data only, while using the venturi behind-based ECT data and using both these data

G9a Suppression Alleviates Corneal Neovascularization through Blocking Nox4-Mediated Oxidative Stress

Background. G9a, a well-known methyltransferase, plays a vital role in biological processes. However, its role in corneal neovascularization (CoNV) remains unclear. Methods. In vitro and in vivo models were assessed in hypoxia-stimulated angiogenesis and in a mouse model of alkali burn-induced CoNV. Human umbilical vein endothelial cells (HUVECs) were cultured under hypoxic conditions and different reoxygenation times to identify the molecular mechanisms involved in this process. Results. In this study, we found that G9a was positively related to corneal alkali burn-induced injury. Inhibition of G9a with BIX 01294 (BIX) alleviated corneal injury, including oxidative stress and neovascularization in vivo. Similarly, inhibition of G9a with either BIX or small interfering RNA (siRNA) exerted an inhibitory effect on hypoxia/reoxygenation (H/R)-induced oxidative stress and angiogenesis in HUVECs. Moreover, our study revealed that ablation of reactive oxygen species (ROS) with N-acetyl-cysteine suppressed angiogenesis in HUVECs exposed to H/R stimulation. Furthermore, NADPH oxidase 4 (Nox4), which was positively associated with ROS production and angiogenesis, was elevated during H/R. This effect could be reversed through suppression of the transcription activity of G9a with BIX or siRNA. In addition, the Nrf2/HO-1 pathway, upstream of Nox4, was activated in both BIX-treated mice and G9a-inhibited HUVECs. Collectively, our results demonstrated that inhibition of G9a-alleviated corneal angiogenesis by inhibiting Nox4-dependent ROS production through the Nrf2/HO-1 signaling pathway. These findings indicate that G9a may be a valuable therapeutic target for CoNV

Lidars for vehicles: from the requirements to the technical evaluation

Abstract— ADAS (Advanced Driver Assistance Systems) is a collective term of vehicle mounted sensors and devices aiming to improve traffic safety and realize high-level autonomous driving. Lidar systems are considered an indispensable part of ADAS to complement the other sensors like cameras and Radar. They realize these complements by providing a real-time high-resolution 3D representation of the environment of the vehicle, in which the positional information of each object area is included so that obstacles and potential hazards can be detected in advance by the ADAS. For this purpose, a Lidar must have the reliability of continuous work and provide the information accurately. In this paper, the requirements of Lidar systems in ADAS are firstly figured out by comparing them with other sensors applied in vehicles. Afterward, different types of Lidar systems regarding traffic safety and driver assistance are presented according to the stated Lidar function and driving condition on the road. Apart from the requirements, different working principles of Lidar products on the market are reviewed according to their scanning methods. Furthermore the results of this review are summed up in a technical evaluation to show the applicability of specific Lidar designs with respect to the requirements of vehicle applications