Swallowable Wireless Capsule Endoscopy: Progress and Technical Challenges

Wireless capsule endoscopy (WCE) offers a feasible noninvasive way to detect the whole gastrointestinal (GI) tract and revolutionizes the diagnosis technology. However, compared with wired endoscopies, the limited working time, the low frame rate, and the low image resolution limit the wider application. The progress of this new technology is reviewed in this paper, and the evolution tendencies are analyzed to be high image resolution, high frame rate, and long working time. Unfortunately, the power supply of capsule endoscope (CE) is the bottleneck. Wireless power transmission (WPT) is the promising solution to this problem, but is also the technical challenge. Active CE is another tendency and will be the next geneion of the WCE. Nevertheless, it will not come true shortly, unless the practical locomotion mechanism of the active CE in GI tract is achieved. The locomotion mechanism is the other technical challenge, besides the challenge of WPT. The progress about the WPT and the active capsule technology is reviewed

Diverse Cotraining Makes Strong Semi-Supervised Segmentor

Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it. In this work, we revisit the core assumption that supports co-training: multiple compatible and conditionally independent views. By theoretically deriving the generalization upper bound, we prove the prediction similarity between two models negatively impacts the model's generalization ability. However, most current co-training models are tightly coupled together and violate this assumption. Such coupling leads to the homogenization of networks and confirmation bias which consequently limits the performance. To this end, we explore different dimensions of co-training and systematically increase the diversity from the aspects of input domains, different augmentations and model architectures to counteract homogenization. Our Diverse Co-training outperforms the state-of-the-art (SOTA) methods by a large margin across different evaluation protocols on the Pascal and Cityscapes. For example. we achieve the best mIoU of 76.2%, 77.7% and 80.2% on Pascal with only 92, 183 and 366 labeled images, surpassing the previous best results by more than 5%.Comment: ICCV2023, Camera Ready Version, Code: \url{https://github.com/williamium3000/diverse-cotraining

Fabrication of periodically micropatterned magnetite nanoparticles by laser-interference-controlled electrodeposition

This paper introduces a laser-interference-controlled electrochemical deposition method for direct fabrication of periodically micropatterned magnetite (Fe3O4) nanoparticles (NPs). In this work, Fe3O4 NPs were controllably synthesized on the areas where the photoconductive electrode was exposed to the periodically patterned interferometric laser irradiation during the electrodeposition. Thus, the micropattern of Fe3O4 NPs was controlled by interferometric laser pattern, and the crystallization of the particles was controlled by laser interference intensity and electrochemical deposition conditions. The bottom-up electrochemical approach was combined with a top-down laser interference methodology. This maskless method allows for in situ fabrication of periodically patterned magnetite NPs on the microscale by electrodeposition under room temperature and atmospheric pressure conditions. In the experiment, Fe3O4 NPs with the mean grain size below 100 nm in the pattern of 5-lm line array were achieved within the deposition time of 100 s. The experiment results have shown that the proposed method is a one-step approach in fabricating large areas of periodically micropatterned magnetite NPs

Micro and nano dual-scale structures fabricated by amplitude modulation in multi-beam laser interference lithography

© 2017 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reservedIn this work, an effective method was presented to obtain a specific micro and nano dual-structures by amplitude modulation in multi-beam laser interference lithography (LIL). Moiré effect was applied to generate the amplitude modulation. The specific intensity modulation patterns can be obtained by the control of the parameter settings of incident laser beams. Both the incident angle and azimuth angle asymmetric configurations can cause the amplitude modulation in the interference optic field and the modulation period is determined by the angle offset. A four-beam LIL system was set up to fabricate patterns on photoresist and verify the method. The experimental results are in good agreement with the theoretical analysis

SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation

Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior knowledge of semantic consistency or priori concepts from self-supervised learning methods, which often overlook the coherence property of image segments. In this paper, we demonstrate that the smoothness prior, asserting that close features in a metric space share the same semantics, can significantly simplify segmentation by casting unsupervised semantic segmentation as an energy minimization problem. Under this paradigm, we propose a novel approach called SmooSeg that harnesses self-supervised learning methods to model the closeness relationships among observations as smoothness signals. To effectively discover coherent semantic segments, we introduce a novel smoothness loss that promotes piecewise smoothness within segments while preserving discontinuities across different segments. Additionally, to further enhance segmentation quality, we design an asymmetric teacher-student style predictor that generates smoothly updated pseudo labels, facilitating an optimal fit between observations and labeling outputs. Thanks to the rich supervision cues of the smoothness prior, our SmooSeg significantly outperforms STEGO in terms of pixel accuracy on three datasets: COCOStuff (+14.9%), Cityscapes (+13.0%), and Potsdam-3 (+5.7%).Comment: Accepted by NeurIPS 2023. Code available: https://github.com/mc-lan/SmooSe

Hierarchical Masked 3D Diffusion Model for Video Outpainting

Video outpainting aims to adequately complete missing areas at the edges of video frames. Compared to image outpainting, it presents an additional challenge as the model should maintain the temporal consistency of the filled area. In this paper, we introduce a masked 3D diffusion model for video outpainting. We use the technique of mask modeling to train the 3D diffusion model. This allows us to use multiple guide frames to connect the results of multiple video clip inferences, thus ensuring temporal consistency and reducing jitter between adjacent frames. Meanwhile, we extract the global frames of the video as prompts and guide the model to obtain information other than the current video clip using cross-attention. We also introduce a hybrid coarse-to-fine inference pipeline to alleviate the artifact accumulation problem. The existing coarse-to-fine pipeline only uses the infilling strategy, which brings degradation because the time interval of the sparse frames is too large. Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames. Experiments show that our method achieves state-of-the-art results in video outpainting tasks. More results are provided at our https://fanfanda.github.io/M3DDM/.Comment: ACM MM 2023 accepte

Fast Fourier transport analysis of surface structures fabricated by laser interference lithography

This paper presents an FFT (fast Fourier transform) analytical method for the study of surface structures fabricated by laser interference lithography (LIL). In the work, the FFT analytical method combined with Gaussian fitting is used to determine the periods and pattern distributions of surface structures from frequency spectra. For LIL, the processing parameters of incident and azimuth angles can be obtained corresponding to the period and pattern distribution. This work facilitates the detection of micro- and nano-structures, the analysis of pattern distribution in engineering, and the processing error analysis of LIL