Image super-resolution (ISR) is a classic and challenging problem in computer vision because of complex and unknown degradation patterns in the data collection process. Leveraging powerful generative priors, diffusion-based methods have recently established new state-of-the-art ISR performance, but their characteristics in the frequency domain are still underexplored. In this paper, we innovatively investigate their frequency-domain behaviors from a sampling timestep perspective. Experimentally, we find that current diffusion-based ISR algorithms exhibit insufficiency in different frequency components in distinct groups of timesteps during the sampling. To address this, we first propose a Timestep Division Controller that is able to adaptively divide the timesteps into groups based on the performance gradient across different components. Next, we design two dedicated modules --- the Amplitude and Phase Enhancement Module (APEM) and the High- and Low-Frequency Enhancement Module (HLEM), to regulate the information flow of distinct frequency-domain features. By adaptively enhancing specific frequency components at different stages of the sampling process, the two modules effectively compensate for the insufficient frequency-domain perception of diffusion-based ISR models. Extensive experiments on three benchmark datasets verify the superior ISR performance of our method, e.g., achieving an average 5.40% improvement on CLIP-IQA compared to the best diffusion-based ISR baseline.

The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel approach to inspect the insulators with Deep Convolutional Neural Networks (CNN). A CNN model with multi-patch feature extraction method is applied to represent the status of insulators and a Support Vector Machine (SVM) is trained based on these features. A thorough evaluation is conducted on our insulator status dataset of six classes from real inspection videos. The experimental results show that a pre-trained model for classification is more accurate than the shallow features by hand-crafted. Our approach achieves 98.7095% mean Average Precision (mAP) in status classification. We also study the behavior of the neural activations of the convolutional layers. Different results vary with different fully connected layers, and interesting findings are discussed.

ABSTRACT To estimate the system performance of RTS/CTS scheme in an error‐prone WLAN channel, this paper proposes a new three‐dimensional Markov model, which considers the impact of bit error on all kinds of frames, and both station short retry limit and long retry limit. The system performance parameters including saturation throughput, packet drop probability, drop time, and average delay are derived using the new Markov model. By comparing the numerical results with simulation results, the model is proved to be more accurate than previous work. The numerical results show that the channel bit error rate has a significant impact not only on system performance, but also on the sensitivity of other system parameters including network scale, channel data rate, and packet length. Copyright © 2010 John Wiley & Sons, Ltd.

The Intel Cup Undergraduate Embedded System Electronic Design Contest is an embedded system design invitational contest in China. This contest is held every two years since 2002. The contest focuses on the embedded system design and implementation. And it gives undergraduates a chance to show their creativity and learn teamwork. The influence of the contest is keeping increasing. Now it is an international invitational contest. And some universities from the countries which are advanced in computing begin to take part in the contest. In this paper we describe the experiences in such a contest for embedded system.

A covariance matrix based anti-interference algorithm is proposed for SIMO OFDM systems. This algorithm constructs the interference-blocking filter without the information of the inferences. This algorithm is insensitive to the time synchronization error and to the carrier frequency synchronization error. Therefore, it is especially useful for the case when interferences are strong and unknown, such as the case in the unlicensed spectrum.

A covariance-based algorithm is proposed to find the interference blocking filter for single-input multiple-output orthogonal frequency division multiplexing (SIMO OFDM) systems. The algorithm uses only the auto-covariance matrix of the received signal. It does not require precise carrier synchronization and time synchronization, so it is applicable to an environment with uncooperative high power interferences