NONLINEAR ADAPTIVE HEADING CONTROL FOR AN UNDERACTUATED SURFACE VESSEL WITH CONSTRAINED INPUT AND SIDESLIP ANGLE COMPENSATION

In this paper, a nonlinear adaptive heading controller is developed for an underactuated surface vessel with constrained input and sideslip angle compensation. The controller design is accomplished in a framework of backstepping technique. First, to amend the irrationality of the traditional definition of the desired heading, the desired heading is compensated by the sideslip angle. Considering the actuator physical constrain, a hyperbolic tangent function and a Nussbaum function are introduced to handle the nonlinear part of control input. The error and the disturbance are estimated and compensated by an adaptive control law. In addition, to avoid the complicated calculation of time derivatives of the virtual control, the command filter is introduced to integrate with the control law. It is analysed by the Lyapunov theory that the closed loop system is guaranteed to be uniformly ultimately bounded stability. Finally, the simulation studies illustrate the effectiveness of the proposed control method

High performance position-sensitive-detector based on graphene-silicon heterojunction

Position-sensitive-detectors (PSDs) based on lateral photoeffect have been widely used in diverse applications, including optical engineering, aerospace and military fields. With increasing demands in long working distance, low energy consumption, and weak signal sensing systems, the poor responsivity of conventional Silicon-based PSDs has become a bottleneck limiting their applications. Herein, we propose a high-performance passive PSD based on graphene-Si heterostructure. The graphene is adapted as a photon absorbing and charge separation layer working together with Si as a junction, while the high mobility provides promising ultra-long carrier diffusion length and facilitates large active area of the device. A PSD with working area of 8 mm x 8 mm is demonstrated to present excellent position sensitivity to weak light at nWs level (much better than the limit of ~{\mu}Ws of Si p-i-n PSDs). More importantly, it shows very fast response and low degree of non-linearity of ~3%, and extends the operating wavelength to the near infrared (IR) region (1319 and 1550 nm). This work therefore provides a new strategy for high performance and broadband PSDs.Comment: 25 pages, 13 figures, to appear in Optic

Synergistic enhancement of cancer therapy using a combination of docetaxel and photothermal ablation induced by single-walled carbon nanotubes

Lei Wang1, Mingyue Zhang1, Nan Zhang1, Jinjin Shi1, Hongling Zhang1, Min Li1, Chao Lu2, Zhenzhong Zhang1 1School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, People’s Republic of China; 2University of Maryland, College Park, MD, USA Background: Single-walled carbon nanotubes (SWNT) are poorly soluble in water, so their applications are limited. Therefore, aqueous solutions of SWNT, designed by noncovalent functionalization and without toxicity, are required for biomedical applications. Methods: In this study, we conjugated docetaxel with SWNT via p-p accumulation and used a surfactant to functionalize SWNT noncovalently. The SWNT were then conjugated with docetaxel (DTX-SWNT) and linked with NGR (Asn-Gly-Arg) peptide, which targets tumor angiogenesis, to obtain a water-soluble and tumor-targeting SWNT-NGR-DTX drug delivery system. Results: SWNT-NGR-DTX showed higher efficacy than docetaxel in suppressing tumor growth in a cultured PC3 cell line in vitro and in a murine S180 cancer model. Tumor volumes in the S180 mouse model decreased considerably under near-infrared radiation compared with the control group. Conclusion: The SWNT-NGR-DTX drug delivery system may be promising for high treatment efficacy with minimal side effects in future cancer therapy. Keywords: single-walled carbon nanotubes, docetaxel, NGR peptide, tumor-targeting, near-infrared radiatio

Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Effective Quantization for Diffusion Models on CPUs

Diffusion models have gained popularity for generating images from textual descriptions. Nonetheless, the substantial need for computational resources continues to present a noteworthy challenge, contributing to time-consuming processes. Quantization, a technique employed to compress deep learning models for enhanced efficiency, presents challenges when applied to diffusion models. These models are notably more sensitive to quantization compared to other model types, potentially resulting in a degradation of image quality. In this paper, we introduce a novel approach to quantize the diffusion models by leveraging both quantization-aware training and distillation. Our results show the quantized models can maintain the high image quality while demonstrating the inference efficiency on CPUs. The code is publicly available at: https://github.com/intel/intel-extension-for-transformers