Bring Consciousness to Mobile Robot Being Localized

Mobile robot localization is one of the most important problems in robotics research. A number of successful localization solutions have been proposed, among them, the well-known and popular Monte Carlo Localization (MCL) method. However, in all these methods, the robot itself does not carry a notion whether it has or has not been localized, and the success or failure of localization is judged by normally a human operator of the robot. In this paper, we put forth a novel method to bring consciousness to a mobile robot so that the robot can judge by itself whether it has been localized or not without any intervention from human operator. In addition, the robot is capable to notice the change between global localization and position tracking, hence, adjusting itself based on the status of localization. A mobile robot with consciousness being localized is obviously more autonomous and intelligent than one without

From imitation to innovation: A study of China's drug R&D and relevant national policies

Research & Development (R&D) plays an increasingly important role in China's pharmaceutical industry. To gain a competitive edge in the global pharmaceutical market, the current national strategy of China forcefully pushes for independent drug innovations. This article investigates the historical, legal, and institutional contexts in which China's drug R&D has evolved. Based on an analysis of the drug R&D evolution and national policies in China, it predicts the future trend of China's policies relevant to drug innovations. This paper helps to understand the impact of national policies on drug R&D in China, which can be used to inform decision-making on investments in China's pharmaceutical market or conducting technology trade and international cooperation with Chinese partners

Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo

In this paper, we build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations based on an arbitrary number of images captured under different light directions, performing accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules for the photometric stereo problem. Moreover, we propose to utilize the easily obtained object mask for eliminating adverse interference from invalid background regions in intra-frame spatial convolutions, thus effectively improve the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.Comment: 9 pages,8 figure

Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor

Terahertz waves offer numerous advantages for the nondestructive detection of hidden objects/defects in materials, as they can penetrate through most optically-opaque materials. However, existing terahertz inspection systems are restricted in their throughput and accuracy (especially for detecting small features) due to their limited speed and resolution. Furthermore, machine vision-based continuous sensing systems that use large-pixel-count imaging are generally bottlenecked due to their digital storage, data transmission and image processing requirements. Here, we report a diffractive processor that rapidly detects hidden defects/objects within a target sample using a single-pixel spectroscopic terahertz detector, without scanning the sample or forming/processing its image. This terahertz processor consists of passive diffractive layers that are optimized using deep learning to modify the spectrum of the terahertz radiation according to the absence/presence of hidden structures or defects. After its fabrication, the resulting diffractive processor all-optically probes the structural information of the sample volume and outputs a spectrum that directly indicates the presence or absence of hidden structures, not visible from outside. As a proof-of-concept, we trained a diffractive terahertz processor to sense hidden defects (including subwavelength features) inside test samples, and evaluated its performance by analyzing the detection sensitivity as a function of the size and position of the unknown defects. We validated its feasibility using a single-pixel terahertz time-domain spectroscopy setup and 3D-printed diffractive layers, successfully detecting hidden defects using pulsed terahertz illumination. This technique will be valuable for various applications, e.g., security screening, biomedical sensing, quality control, anti-counterfeiting measures and cultural heritage protection.Comment: 23 Pages, 5 Figure

All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.Comment: 21 Pages, 7 Figure

Unveiling the neuroprotective potential of dietary polysaccharides: a systematic review

Central nervous system (CNS) disorders present a growing and costly global health challenge, accounting for over 11% of the diseases burden in high-income countries. Despite current treatments, patients often experience persistent symptoms that significantly affect their quality of life. Dietary polysaccharides have garnered attention for their potential as interventions for CNS disorders due to their diverse mechanisms of action, including antioxidant, anti-inflammatory, and neuroprotective effects. Through an analysis of research articles published between January 5, 2013 and August 30, 2023, encompassing the intervention effects of dietary polysaccharides on Alzheimer’s disease, Parkinson’s disease, depression, anxiety disorders, autism spectrum disorder, epilepsy, and stroke, we have conducted a comprehensive review with the aim of elucidating the role and mechanisms of dietary polysaccharides in various CNS diseases, spanning neurodegenerative, psychiatric, neurodevelopmental disorders, and neurological dysfunctions. At least four categories of mechanistic bases are included in the dietary polysaccharides’ intervention against CNS disease, which involves oxidative stress reduction, neuronal production, metabolic regulation, and gut barrier integrity. Notably, the ability of dietary polysaccharides to resist oxidation and modulate gut microbiota not only helps to curb the development of these diseases at an early stage, but also holds promise for the development of novel therapeutic agents for CNS diseases. In conclusion, this comprehensive review strives to advance therapeutic strategies for CNS disorders by elucidating the potential of dietary polysaccharides and advocating interdisciplinary collaboration to propel further research in this realm

Between here & there—walking is thinking

Walking is commonly defined as getting from point A to point B. It is a transitory space we go through to get to where we want to go. However, is there a reason why we do some of our best creative thinking when we are up and about on our two feet? Why do some of the world’s greatest minds have one habit in common, that is, walking? In my own personal life, I have found that the best remedy for a creative block or a bad day is a long, solitary walk. I walk home every single day. On weekends, I walk for hours to clear my mind. Something amazing takes place when walking no longer becomes a way to get from Point A to Point B. When we walk, we think and when we think, we walk. This project aims to provide a new perspective on easily the most common and mundane thing we do every single day and bring new and fresh perspective to what walking is and what walking can be. It takes on a metaphorical approach to redefine what it means to walk and specifically how it relates to thinking. The objective of the project is to offer a new definition of walking through design.Bachelor of Fine Art

ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation

In a typical path planning pipeline for a ground robot, we build a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating indoors, a ground robot's knowledge about the environment may be limited due to occlusions. Therefore, the map will have many as-yet-unknown regions that may need to be avoided by a conservative planner. Instead, if a robot is able to correctly predict what its surroundings and occluded regions look like, the robot may be more efficient in navigation. In this work, we focus on predicting occupancy within the reachable distance of the robot to enable faster navigation and present a self-supervised proximity occupancy map prediction method, named ProxMaP. We show that ProxMaP generalizes well across realistic and real domains, and improves the robot navigation efficiency in simulation by \textbf{$12.40\%$} against the traditional navigation method. We share our findings on our project webpage (see https://raaslab.org/projects/ProxMaP ).Comment: This is an incremental work over an existing arxiv submission of the author. It will be re-uploaded as a version of that wor