Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background. With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released.

Jitter is one of the key factors affecting bit error rate (BER) on high-speed links. A novel method of jitter decomposition by PointNet using 2D point cloud of jitter histogram is proposed for decomposing the time interval error (TIE) jitter into deterministic jitter (DJ) and random jitter (RJ). The proposed method uses transformNet (T-Net) and multi-layer perceptron (MLP) to learn global point cloud features and uses average pooling to aggregate information from all the points. Experimental results show that the proposed approach can decompose jitter into DJ and RJ, and is better than the traditional jitter decomposition method i.e. time lag correlation (TLC) and Time-Domain PLL Jitter Decomposition (T-D PLL). In addition, results show that the performance of the proposed method is better than CNN, PointRNN, and PointANN.