Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots

Current state-of-the-art crowd navigation approaches are mainly deep reinforcement learning (DRL)-based. However, DRL-based methods suffer from the issues of generalization and scalability. To overcome these challenges, we propose a method that includes a Collision Probability (CP) in the observation space to give the robot a sense of the level of danger of the moving crowd to help the robot navigate safely through crowds with unseen behaviors. We studied the effects of changing the number of moving obstacles to pay attention during navigation. During training, we generated local waypoints to increase the reward density and improve the learning efficiency of the system. Our approach was developed using deep reinforcement learning (DRL) and trained using the Gazebo simulator in a non-cooperative crowd environment with obstacles moving at randomized speeds and directions. We then evaluated our model on four different crowd-behavior scenarios. The results show that our method achieved a 100% success rate in all test settings. We compared our approach with a current state-of-the-art DRL-based approach, and our approach has performed significantly better, especially in terms of social safety. Importantly, our method can navigate in different crowd behaviors and requires no fine-tuning after being trained once. We further demonstrated the crowd navigation capability of our model in real-world tests.Comment: 6 pages, 7 figure

DSCOT: An NFT-Based Blockchain Architecture for the Authentication of IoT-Enabled Smart Devices in Smart Cities

Smart city architecture brings all the underlying architectures, i.e., Internet of Things (IoT), Cyber-Physical Systems (CPSs), Internet of Cyber-Physical Things (IoCPT), and Internet of Everything (IoE), together to work as a system under its umbrella. The goal of smart city architecture is to come up with a solution that may integrate all the real-time response applications. However, the cyber-physical space poses threats that can jeopardize the working of a smart city where all the data belonging to people, systems, and processes will be at risk. Various architectures based on centralized and distributed mechanisms support smart cities; however, the security concerns regarding traceability, scalability, security services, platform assistance, and resource management persist. In this paper, private blockchain-based architecture Decentralized Smart City of Things (DSCoT) is proposed. It actively utilizes fog computing for all the users and smart devices connected to a fog node in a particular management system in a smart city, i.e., a smart house or hospital, etc. Non-fungible tokens (NFTs) have been utilized for representation to define smart device attributes. NFTs in the proposed DSCoT architecture provide devices and user authentication (IoT) functionality. DSCoT has been designed to provide a smart city solution that ensures robust security features such as Confidentiality, Integrity, Availability (CIA), and authorization by defining new attributes and functions for Owner, User, Fog, and IoT devices authentication. The evaluation of the proposed functions and components in terms of Gas consumption and time complexity has shown promising results. Comparatively, the Gas consumption for minting DSCoT NFT showed approximately 27%, and a DSCoT approve() was approximately 11% more efficient than the PUF-based NFT solution.Comment: 18 pages, 15 figures, 5 tables, journa

Validation of a new prognostic index score for disseminated nasopharyngeal carcinoma

Patients with metastatic nasopharyngeal carcinoma have variable survival outcomes. We previously designed a scoring system to better prognosticate these patients. Here, we report results on validation of this new prognostic index score in a separate cohort of patients. Clinical features and laboratory parameters were examined in 172 patients with univariate and multivariate analyses and a numerical score was derived for each independent prognostic variable. Significant independent prognostic variables and their scores assigned included poor performance status (score 5), haemoglobin <12 g dl−1 (score 4) and disease-free interval (DFI) (DFI⩽6 months (score 10) or metastases at initial diagnosis (score 1)). Maximum score was 19 and patients stratified into three prognostic groups: good, 0–3; intermediate, 4–8; poor, ⩾9. When applied to a separate cohort of 120 patients, 59 patients were good, 43 intermediate and 18 poor prognosis, with median survivals of 19.6 (95% CI 16.1, 23.1), 14.3 (95% CI 12.3, 16.2) and 7.9 (95% CI 6.6, 9.2) months, respectively. (logrank test: P=0.003). We have validated a new prognostic score with factors readily available in the clinics. This simple score will prove useful as a method to prognosticate and stratify patients as well as to promote consistent reporting among clinical trials

A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore

10.1371/journal.pone.0017468PLoS ONE63

Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies

Background: The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. Methods: ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. Results: At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. Conclusions: The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®

Object detection via convolutional neural network

Machine Learning and Artificial Intelligence are starting to gain attention around the world. Companies has begun to use it to improve lives around the world. One of the famous method is known as the object detection. This report will be covering the various object detection system that uses convolutional neural networks available. This report will also be covering the process of training a new dataset not found in pre- trained model. Starting from the pre-process of collecting or generating own datasets, creating a ground truth and increasing the count of dataset to be used for training. Uncommon objects are not easy to train without some huge datasets. Sometimes, the datasets offered are not sufficient enough to fine-tune the accuracy of the model. To make it up, simple tweak of image processing techniques could be applied with discretion. For this project, the dataset was flipped across the y-axis to increase the dataset two- folds and annotated images were chosen more meticulously to ensure that it contains variety data. This variety will make the model more robust towards unforeseen image being brought forward for object detection.Bachelor of Engineerin