For azimuth multi-channel (AMC) high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) systems, non-uniformly sampled signals will lead to false targets appearing in the image, which has a great impact on image quality and target detection. Many spectrum reconstruction methods for stationary scenes have been proposed to obtain images without false targets, but it is ineffective for moving targets due to the phase error caused by radial velocity. To present the radial velocity effect on the reconstructed image clearly, we establish a precise relation between the characteristics (imaging position, residual RCM, and amplitude) of false targets and radial velocity from the Doppler domain perspective. According to the effect analysis, we propose an image-domain false target suppression and radial velocity estimation method for moving targets. First, obtain multiple images through imaging preprocessing. Second, estimate and compensate the phase error of false targets based on the leastL1-norm optimization model to suppress false targets. Third, estimate the radial velocity of real moving targets based on the cross-correlation method. Compared to the existing methods, the proposed method is processed in the image domain which has a high signal-to-noise ratio (SNR) with the advantages of not requiring recognition and extraction of targets, lower computational complexity, and applicability for slow targets, fast targets, and multiple targets of the same range cell. The simulated SAR data and GaoFen-3 SAR data are processed to demonstrate the effectiveness of the proposed method. Furthermore, the radial velocity estimation method is verified by automatic identification system (AIS) information.

Short video streaming has been increasingly popular in recent years. Due to its unique user behavior of watching and sliding, a critical technique issue is to design a preload algorithm deciding which video chunk to download next, bitrate selection and the pause time, in order to improve user experience while reducing bandwidth wastage. However, designing such a preload algorithm is non-trivial, especially taking into account conflicting goals of improving QoE and reducing bandwidth wastage. In this paper, we propose a deep reinforcement learning-based approach to simultaneously decide the aforementioned three decision variables via learning an optimal policy under a complex environment of varying network conditions and unpredictable user behavior. In particular, we incorporate domain knowledge into the decision procedure via action masking to make decisions more transparent, and accelerate the model training. Experimental results validate the proposed approach significantly outperforms baseline algorithms in terms of QoE metrics and bandwidth wastage.

Web real-time communication (WebRTC) employs congestion control to ensure the quality of experience (QoE). Different from congestion control schemes for TCP, WebRTC keeps a low-level playback buffer that considers excessively delayed packets as losses, which makes the congestion control for WebRTC more challenging. Existing heuristic schemes estimate the network conditions based on hand-crafted rules that may be suboptimal, leading to under-utilization or over-utilization of link capacity in many cases. On the other hand, the existing learning-based schemes train a model that acts in a large action space, which is hard to converge to a stable status and has low performance over unpredictable network conditions. In this paper, we propose a hybrid receiver-side congestion control (HRCC) framework, which combines a heuristic congestion control scheme with an RL-Agent that periodically generates a gain coefficient to tune the bandwidth estimated by the heuristic scheme. Extensive simulation experiments demonstrate that the HRCC's RL-Agent effectively tunes the bandwidth estimate of the heuristic scheme. The hybrid scheme achieves higher bandwidth utilization than the fully heuristic scheme with similar queuing delay and packet loss and outperforms the fully RL-based scheme on overall performance.

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotated objects/faces. We launched the UG2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.