An experiment with RTEMS

The Real-Time Executive for Multiprocessor Systems (RTEMS) is an open source real-time executive used in many embedded systems. This report describes our effort to gain hands-on experience with RTEMS and provides instructions on how to build and use RTEMS in two different operating environments.Approved for public release; distribution is unlimited

Stability and invariant measure asymptotics in a model for heavy particles in rough turbulent flows

We study a system of Skorokhod stochastic differential equations (SDEs) modeling the pairwise dispersion (in spatial dimension $d=2$) of heavy particles transported by a rough self-similar, turbulent flow with H\"{o}lder exponent $h\in (0,1)$. Under the assumption that $h>0$ is sufficiently small, we use Lyapunov methods and control theory to show that the Markovian system is nonexplosive and has a unique, exponentially attractive invariant probability measure. Furthermore, our Lyapunov construction is radially sharp and gives partial confirmation on a predicted asymptotic behavior with respect to the H\"{o}lder exponent $h$ of the invariant probability measure. A physical interpretation of the asymptotics is that intermittent clustering is weakened when the carrier flow is sufficiently rough

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions

In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios. Current deep learning techniques to address multiple-output problems are based on two main methodologies: (1) mixture density networks, which suffer from poor stability at high dimensions, or (2) multiple choice learning (MCL), an approach that uses $M$ single-output functions, each only producing a point estimate hypothesis. This paper presents a Mixture of Multiple-Output functions (MoM) approach using a novel variant of dropout, Multiple Hypothesis Dropout. Unlike traditional MCL-based approaches, each multiple-output function not only estimates the mean but also the variance for its hypothesis. This is achieved through a novel stochastic winner-take-all loss which allows each multiple-output function to estimate variance through the spread of its subnetwork predictions. Experiments on supervised learning problems illustrate that our approach outperforms existing solutions for reconstructing multimodal output distributions. Additional studies on unsupervised learning problems show that estimating the parameters of latent posterior distributions within a discrete autoencoder significantly improves codebook efficiency, sample quality, precision and recall.Comment: To appear in Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24). 13 pages (9 main, 4 appendix

Two Weeks of Ischemic Conditioning Improves Walking Speed and Reduces Neuromuscular Fatigability in Chronic Stroke Survivors

This pilot study examined whether ischemic conditioning (IC), a noninvasive, cost-effective, and easy-to-administer intervention, could improve gait speed and paretic leg muscle function in stroke survivors. We hypothesized that 2 wk of IC training would increase self-selected walking speed, increase paretic muscle strength, and reduce neuromuscular fatigability in chronic stroke survivors. Twenty-two chronic stroke survivors received either IC or IC Sham on their paretic leg every other day for 2 wk (7 total sessions). IC involved 5-min bouts of ischemia, repeated five times, using a cuff inflated to 225 mmHg on the paretic thigh. For IC Sham, the cuff inflation pressure was 10 mmHg. Self-selected walking speed was assessed using the 10-m walk test, and paretic leg knee extensor strength and fatigability were assessed using a Biodex dynamometer. Self-selected walking speed increased in the IC group (0.86 ± 0.21 m/s pretest vs. 1.04 ± 0.22 m/s posttest, means ± SD; P\u3c 0.001) but not in the IC Sham group (0.92 ± 0.47 m/s pretest vs. 0.96 ± 0.46 m/s posttest; P= 0.25). Paretic leg maximum voluntary contractions were unchanged in both groups (103 ± 57 N·m pre-IC vs. 109 ± 65 N·m post-IC; 103 ± 59 N·m pre-IC Sham vs. 108 ± 67 N·m post-IC Sham; P = 0.81); however, participants in the IC group maintained a submaximal isometric contraction longer than participants in the IC Sham group (278 ± 163 s pre-IC vs. 496 ± 313 s post-IC, P = 0.004; 397 ± 203 s pre-IC Sham vs. 355 ± 195 s post-IC Sham; P = 0.46). The results from this pilot study thus indicate that IC training has the potential to improve walking speed and paretic muscle fatigue resistance poststroke