A dual-grating InGaAsP/InP DFB laser integrated with an SOA for THz generation

We report a dual-mode semiconductor laser that has two gratings with different periods below and above the active layer. A semiconductor optical amplifier (SOA), which is integrated with the dual-mode laser, plays an important role in balancing the optical power and reducing the linewidths of the emission modes. A stable two mode emission with the 13.92-nm spacing can be obtained over a wide range of distributed feedback and SOA injection currents. Compared with other types of dual-mode lasers, our device has the advantages of simple structure, compact size, and low fabrication cost

Fully integrated multi-optoelectronic synthesizer for THz pumping source in wireless communications with rich backup redundancy and wide tuning range

We report a monolithic photonic integrated circuit (PIC) for THz communication applications. The PIC generates up to 4 optical frequency lines which can be mixed in a separate device to generate THz radiation, and each of the optical lines can be modulated individually to encode data. Physically, the PIC comprises an array of wavelength tunable distributed feedback lasers each with its own electro-absorption modulator. The lasers are designed with a long cavity to operate with a narrow linewidth, typically <4 MHz. The light from the lasers is coupled via an multimode interference (MMI) coupler into a semiconductor optical amplifier (SOA). By appropriate selection and biasing of pairs of lasers, the optical beat signal can be tuned continuously over the range from 0.254 THz to 2.723 THz. The EAM of each channel enables signal leveling balanced between the lasers and realizing data encoding, currently at data rates up to 6.5 Gb/s. The PIC is fabricated using regrowth-free techniques, making it economic for volume applications, such for use in data centers. The PIC also has a degree of redundancy, making it suitable for applications, such as inter-satellite communications, where high reliability is mandatory

The Quantitative Diagnosis Method of Rubbing Rotor System

The dynamics of the rubbing rotor system is analyzed by applying harmonic balance method. The relationship between harmonic components in the response of the rubbing rotor system and the dynamic stiffness matrix of the fault free rotor system is revealed, based on which a new model based method for rubbing identification is presented. By applying this method, the fault location and rubbing forces of the single rubbing rotor system can be identified by using vibration data of only two nodes, the rubbing locations and rubbing forces of the double rubbing rotor system can be identified by using vibration data of three nodes. The numerical simulations and experiments on the rotor test-rig are carried out to verify the efficiency of the present method

Graphs determined by their generalized characteristic polynomials

AbstractFor a given graph G with (0,1)-adjacency matrix AG, the generalized characteristic polynomial of G is defined to be ϕG=ϕG(λ,t)=det(λI-(AG-tDG)), where I is the identity matrix and DG is the diagonal degree matrix of G. In this paper, we are mainly concerned with the problem of characterizing a given graph G by its generalized characteristic polynomial ϕG. We show that graphs with the same generalized characteristic polynomials have the same degree sequence, based on which, a unified approach is proposed to show that some families of graphs are characterized by ϕG. We also provide a method for constructing graphs with the same generalized characteristic polynomial, by using GM-switching

Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis

Despite their impressive performance in various surgical scene understanding tasks, deep learning-based methods are frequently hindered from deploying to real-world surgical applications for various causes. Particularly, data collection, annotation, and domain shift in-between sites and patients are the most common obstacles. In this work, we mitigate data-related issues by efficiently leveraging minimal source images to generate synthetic surgical instrument segmentation datasets and achieve outstanding generalization performance on unseen real domains. Specifically, in our framework, only one background tissue image and at most three images of each foreground instrument are taken as the seed images. These source images are extensively transformed and employed to build up the foreground and background image pools, from which randomly sampled tissue and instrument images are composed with multiple blending techniques to generate new surgical scene images. Besides, we introduce hybrid training-time augmentations to diversify the training data further. Extensive evaluation on three real-world datasets, i.e., Endo2017, Endo2018, and RoboTool, demonstrates that our one-to-many synthetic surgical instruments datasets generation and segmentation framework can achieve encouraging performance compared with training with real data. Notably, on the RoboTool dataset, where a more significant domain gap exists, our framework shows its superiority of generalization by a considerable margin. We expect that our inspiring results will attract research attention to improving model generalization with data synthesizing.Comment: First two authors contributed equally. Accepted by IROS202

Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation

Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems. Medical data collection normally involves different scanners, protocols, and populations, making domain adaptation (DA) a highly demanding research field to alleviate model degradation in the deployment site. To preserve the model performance across multiple testing domains, this work proposes the Curriculum-based Augmented Fourier Domain Adaptation (Curri-AFDA) for robust medical image segmentation. In particular, our curriculum learning strategy is based on the causal relationship of a model under different levels of data shift in the deployment phase, where the higher the shift is, the harder to recognize the variance. Considering this, we progressively introduce more amplitude information from the target domain to the source domain in the frequency space during the curriculum-style training to smoothly schedule the semantic knowledge transfer in an easier-to-harder manner. Besides, we incorporate the training-time chained augmentation mixing to help expand the data distributions while preserving the domain-invariant semantics, which is beneficial for the acquired model to be more robust and generalize better to unseen domains. Extensive experiments on two segmentation tasks of Retina and Nuclei collected from multiple sites and scanners suggest that our proposed method yields superior adaptation and generalization performance. Meanwhile, our approach proves to be more robust under various corruption types and increasing severity levels. In addition, we show our method is also beneficial in the domain-adaptive classification task with skin lesion datasets. The code is available at https://github.com/lofrienger/Curri-AFDA. Note to Practitioners —Medical image segmentation is key to improving computer-assisted diagnosis and intervention autonomy. However, due to domain gaps between different medical sites, deep learning-based segmentation models frequently encounter performance degradation when deployed in a novel domain. Moreover, model robustness is also highly expected to mitigate the effects of data corruption. Considering all these demanding yet practical needs to automate medical applications and benefit healthcare, we propose the Curriculum-based Fourier Domain Adaptation (Curri-AFDA) for medical image segmentation. Extensive experiments on two segmentation tasks with cross-domain datasets show the consistent superiority of our method regarding adaptation and generalization on multiple testing domains and robustness against synthetic corrupted data. Besides, our approach is independent of image modalities because its efficacy does not rely on modality-specific characteristics. In addition, we demonstrate the benefit of our method for image classification besides segmentation in the ablation study. Therefore, our method can potentially be applied in many medical applications and yield improved performance. Future works may be extended by exploring the integration of curriculum learning regime with Fourier domain amplitude fusion in the testing time rather than in the training time like this work and most other existing domain adaptation works

Electronic, optical and transport properties of van der Waals Transition-metal Dichalcogenides Heterostructures: A First-principle Study

Two-dimensional (2D) transition-metal dichalcogenide (TMD) MX$_2$ (M = Mo, W; X= S, Se, Te) possess unique properties and novel applications. In this work, we perform first-principles calculations on the van der Waals (vdW) stacked MX$_2$ heterostructures to investigate their electronic, optical and transport properties systematically. We perform the so-called Anderson's rule to classify the heterostructures by providing the scheme of the construction of energy band diagrams for the heterostructure consisting of two semiconductor materials. For most of the MX$_2$ heterostructures, the conduction band maximum (CBM) and valence band minimum (VBM) reside in two separate semiconductors, forming type II band structure, thus the electron-holes pairs are spatially separated. We also find strong interlayer coupling at $\Gamma$ point after forming MX$_2$ heterostructures, even leading to the indirect band gap. While the band structure near $K$ point remain as the independent monolayer. The carrier mobilities of MX$_2$ heterostructures depend on three decisive factors, elastic modulus, effective mass and deformation potential constant, which are discussed and contrasted with those of monolayer MX$_2$, respectively.Comment: 7 figure

Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding

Curriculum learning and self-paced learning are the training strategies that gradually feed the samples from easy to more complex. They have captivated increasing attention due to their excellent performance in robotic vision. Most recent works focus on designing curricula based on difficulty levels in input samples or smoothing the feature maps. However, smoothing labels to control the learning utility in a curriculum manner is still unexplored. In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner. In ULS and SVLS, a bigger smoothing factor value enforces a heavy smoothing penalty in the true label and limits learning less information. Therefore, we design the curriculum by label smoothing (CBLS). We set a bigger smoothing value at the beginning of training and gradually decreased it to zero to control the model learning utility from lower to higher. We also designed a confidence-aware pacing function and combined it with our CBLS to investigate the benefits of various curricula. The proposed techniques are validated on four robotic surgery datasets of multi-class, multi-label classification, captioning, and segmentation tasks. We also investigate the robustness of our method by corrupting validation data into different severity levels. Our extensive analysis shows that the proposed method improves prediction accuracy and robustness. The code is publicly available at https://github.com/XuMengyaAmy/P-CBLS. Note to Practitioners —The motivation of this article is to improve the performance and robustness of deep neural networks in safety-critical applications such as robotic surgery by controlling the learning ability of the model in a curriculum learning manner and allowing the model to imitate the cognitive process of humans and animals. The designed approaches do not add parameters that require additional computational resources