Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar

State estimation is a crucial component for the successful implementation of robotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However, in real-world scenarios, the performance of these sensors is degraded by challenging environments, e.g. adverse weather conditions and low-light scenarios. The emerging 4D imaging radar technology is capable of providing robust perception in adverse conditions. Despite its potential, challenges remain for indoor settings where noisy radar data does not present clear geometric features. Moreover, disparities in radar data resolution and field of view (FOV) can lead to inaccurate measurements. While prior research has explored radar-inertial odometry based on Doppler velocity information, challenges remain for the estimation of 3D motion because of the discrepancy in the FOV and resolution of the radar sensor. In this paper, we address Doppler velocity measurement uncertainties. We present a method to optimize body frame velocity while managing Doppler velocity uncertainty. Based on our observations, we propose a dual imaging radar configuration to mitigate the challenge of discrepancy in radar data. To attain high-precision 3D state estimation, we introduce a strategy that seamlessly integrates radar data with a consumer-grade IMU sensor using fixed-lag smoothing optimization. Finally, we evaluate our approach using real-world 3D motion data

Maxwell's two-demon engine under pure dephasing noise

The interplay between thermal machines and quantum correlations is of great interest in both quantum thermodynamics and quantum information science. Recently, a quantum Szil\'ard engine has been proposed, showing that the quantum steerability between a Maxwell's demon and a work medium can be beneficial to a work extraction task. Nevertheless, this type of quantum-fueled machine is usually fragile in the presence of decoherence effects. We provide an example of the pure dephasing process, showing that the engine's quantumness can be degraded. Therefore, in this work, we tackle this question by introducing a second demon who can access a control system and make the work medium pass through two dephasing channels in a manner of quantum superposition. Furthermore, we provide a quantum circuit to simulate our proposed concept and test it on IBMQ and IonQ quantum computers.Comment: 10 pages, 7 figures, 2 table

Effect of Influenza Vaccination on Mortality and Risk of Hospitalization in Elderly Individuals with and without Disabilities: A Nationwide, Population-Based Cohort Study

Purpose: The effects of influenza vaccines are unclear for elderly individuals with disabilities. We use a population-based cohort study to estimate the effects of influenza vaccines in elderly individuals with and without disabilities. Methods: Data were taken from the National Health Insurance Research Database and Disabled Population Profile of Taiwan. A total of 2,741,403 adults aged 65 or older were identified and 394,490 were people with a disability. These two groups were further divided into those who had or had not received an influenza vaccine. Generalized estimating equations (GEE) were used to compare the relative risks (RRs) of death and hospitalization across the four groups. Results: 30.78% elderly individuals without a disability and 34.59% elderly individuals with a disability had vaccinated for influenza. Compared to the unvaccinated elderly without a disability, the vaccinated elderly without a disability had significantly lower risks in all-cause mortality (RR = 0.64) and hospitalization for any of the influenza-related diseases (RR = 0.91). Both the unvaccinated and vaccinated elderly with a disability had significantly higher risks in all-cause mortality (RR = 1.81 and 1.18, respectively) and hospitalization for any of the influenza-related diseases (RR = 1.73 and 1.59, respectively). Conclusions: The elderly with a disability had higher risks in mortality and hospitalization than those without a disability; however, receiving influenza vaccinations could still generate more protection to the disabled elderl

Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving

Sensor fusion is crucial for an accurate and robust perception system on autonomous vehicles. Most existing datasets and perception solutions focus on fusing cameras and LiDAR. However, the collaboration between camera and radar is significantly under-exploited. The incorporation of rich semantic information from the camera, and reliable 3D information from the radar can potentially achieve an efficient, cheap, and portable solution for 3D object perception tasks. It can also be robust to different lighting or all-weather driving scenarios due to the capability of mmWave radars. In this paper, we introduce the CRUW3D dataset, including 66K synchronized and well-calibrated camera, radar, and LiDAR frames in various driving scenarios. Unlike other large-scale autonomous driving datasets, our radar data is in the format of radio frequency (RF) tensors that contain not only 3D location information but also spatio-temporal semantic information. This kind of radar format can enable machine learning models to generate more reliable object perception results after interacting and fusing the information or features between the camera and radar

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)–based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed

A 64-week, multicenter, open-label study of aripiprazole effectiveness in the management of patients with schizophrenia or schizoaffective disorder in a general psychiatric outpatient setting

<p>Abstract</p> <p>Objective</p> <p>To evaluate the overall long-term effectiveness of aripiprazole in patients with schizophrenia in a general psychiatric practice setting in Taiwan.</p> <p>Methods</p> <p>This was a prospective, open-label, multicenter, post-market surveillance study in Taiwanese patients with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnosis of schizophrenia or schizoaffective disorder requiring a switch in antipsychotic medication because current medication was not well tolerated and/or clinical symptoms were not well controlled. Eligible patients were titrated to aripiprazole (5-30 mg/day) over a 12-week switching phase, during which their previous medication was discontinued. Patients could then enter a 52-week, long-term treatment phase. Aripiprazole was flexibly dosed (5-30 mg/day) at the discretion of the treating physicians. Efficacy was assessed using the Clinical Global Impression scale Improvement (CGI-I) score, the Clinical Global Impression scale Severity (CGI-S) score, The Brief Psychiatry Rating Scale (BPRS), and the Quality of Life (QOL) scale, as well as Preference of Medicine (POM) ratings by patients and caregivers. Safety and tolerability were also assessed.</p> <p>Results</p> <p>A total of 245 patients were enrolled and switched from their prior antipsychotic medications, and 153 patients entered the 52-week extension phase. In all, 79 patients (32.2%) completed the study. At week 64, the mean CGI-I score was 3.10 and 64.6% of patients who showed response. Compared to baseline, scores of CGI-S, QOL, and BPRS after 64 weeks of treatment also showed significant improvements. At week 12, 65.4% of subjects and 58.9% of caregivers rated aripiprazole as better than the prestudy medication on the POM. The most frequently reported adverse events (AEs) were headache, auditory hallucinations and insomnia. A total of 13 patients (5.3%) discontinued treatment due to AEs. No statistically significant changes were noted with respect to fasting plasma glucose, lipid profile, body weight, and body mass index after long-term treatment with aripiprazole.</p> <p>Conclusions</p> <p>Although the discontinuation rate was high, aripiprazole was found to be effective, safe and well tolerated in the long-term treatment of Taiwanese patients with schizophrenia who continued to receive treatment for 64 weeks.</p