Optimizing Data Processing in Space for Object Detection in Satellite Imagery

With the cost of launch plummeting, it is now easier than ever to get a satellite to orbit. This has led to a proliferation of the number of satellites launched each year, resulting in the downlinking of terabytes of data each day. The data received by ground stations is often unprocessed, making it an expensive process considering only a small amount of it is useful. This, coupled with the increasing demand for real-time data, has led to a growing need for on-orbit processing solutions. In this work, we investigate the performance of CNN-based object detectors on constrained devices by applying different image compression techniques to satellite data. We examine the capabilities of the NVIDIA Jetson Nano and NVIDIA Jetson AGX Xavier; low-power, high-performance computers, with integrated GPUs, small enough to fit on-board a nanosatellite. We take a closer look at object detection networks, including the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) models that are pre-trained on DOTA – a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of execution time, memory consumption, and accuracy, and are compared against a baseline containing a powerful GPU cluster. The results from the initial experiments show that by applying image compression techniques, we are able to improve the execution time and memory consumption. A lossless compression technique achieves roughly a 10% reduction in execution time and about a 3% reduction in memory consumption, with no impact on the accuracy. While a lossy compression technique improves the execution time by up to 144% and the memory consumption is reduced to as much as 97%. However, it has a significant impact on accuracy, varying depending on the compression ratio

Accelerating Deep Learning Applications in Space

Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of computer vision have led to a rise in demand for fast and reliable deep learning applications. In recent years, the industry has introduced devices with impressive processing power to perform various object detection tasks. However, with real-time detection, devices are constrained in memory, computational capacity, and power, which may compromise the overall performance. This could be solved either by optimizing the object detector or modifying the images. In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques. We examine the capabilities of a NVIDIA Jetson Nano; a low-power, high-performance computer, with an integrated GPU, small enough to fit on-board a CubeSat. We take a closer look at the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) that are pre-trained on DOTA – a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of inference time, memory consumption, and accuracy. By applying image compression techniques, we are able to optimize performance. The two techniques applied, lossless compression and image scaling, improves speed and memory consumption with no or little change in accuracy. The image scaling technique achieves a 100% runnable dataset and we suggest combining both techniques in order to optimize the speed/memory/accuracy trade-off

Interarticulator phasing and locus equations

Abstract: A locus equation plots the frequency of the second formant at vowel onset against the target frequency of the same formant for the vowel in a consonant-vowel sequence. The slope of the equation has been assumed to reflect the degree of coarticulation between the consonant and the vowel, with higher slopes associated with more coarticulation. This study examines the articulatory basis for this assumption, using VCV sequences where the consonant is a bilabial stop /bl and the vowels one of /i, a, u/. Articulatory movements were recorded using a magnetometer system. One articulatory measure was the temporal phasing between the onset of the lip closing movement and the onset of the tongue body movement from the first to the second vowel. Another was the magnitude of the tongue movement between the onset of the second vowel and the tongue position for the vowel, averaged across four receivers placed on the tongue. When compared with the corresponding locus equation slopes, neither measure showed support for the assumption that the slope serves as an index of the degree of coarticulation between the consonant and the vowel

Accelerating Deep Learning Applications in Space

Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of computer vision have led to a rise in demand for fast and reliable deep learning applications. In recent years, the industry has introduced devices with impressive processing power to perform various object detection tasks. However, with real-time detection, devices are constrained in memory, computational capacity, and power, which may compromise the overall performance. This could be solved either by optimizing the object detector or modifying the images. In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques. We examine the capabilities of a NVIDIA Jetson Nano; a low-power, high-performance computer, with an integrated GPU, small enough to fit on-board a CubeSat. We take a closer look at the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) that are pre-trained on DOTA -- a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of inference time, memory consumption, and accuracy. By applying image compression techniques, we are able to optimize performance. The two techniques applied, lossless compression and image scaling, improve speed and memory consumption with no or little change in accuracy. The image scaling technique achieves a 100% runnable dataset and we suggest combining both techniques in order to optimize the speed/memory/accuracy trade-off

Recipients of electric-powered indoor/outdoor wheelchairs provided by a National Health Service: A cross-sectional study

This is the post-print version of the final paper published in Archives of Physical Medicine and Rehabilitation. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 by the American Congress of Rehabilitation Medicine.OBJECTIVE: To describe the characteristics, across all ages, of powered wheelchair users and the assistive technology prescribed by a regional specialist wheelchair service DESIGN: Cross-sectional study SETTING: Regional wheelchair service provided to those fulfilling strict eligibility criteria by a National Health Service serving a population of 3 million. PARTICIPANTS: 544 Electric Powered Indoor/outdoor wheelchair (EPIOC) users. INTERVENTIONS: Not applicable MAIN OUTCOME MEASURES: Demographic, clinical/diagnostic details of EPIOC recipients including pain, (kypho)scoliosis and ventilators. Technical features including specialised (adaptive) seating (SS), tilt in space (TIS), and modified control systems. Factors were related to age groups: 1 (0-15), 2 (16-24), 3 (25-54), 4 (55-74) and 5 (75+). RESULTS: 262 men mean age 41.7 (range 8-82, sd 20.7) and 282 women mean age 47.2 (range 7-92, sd 19.7) years were studied. Neurological/neuromuscular conditions predominated (81%) with cerebral palsy (CP) (18.9%) and multiple sclerosis (16.4%). Conditions presenting at birth or during childhood constituted 39%. 99 had problematic pain, 83 a (kypho)scoliosis and 11 used ventilators. SS was provided to 169 users (31%), the majority had CP or muscular dystrophy. TIS was used by 258 (53%). Younger people were more likely to receive TIS than older ones. Only 92 had SS and TIS, mean age 29 (range 8-72, sd 17.8) years. 52 used modified control systems. CONCLUSIONS: The diversity of EPIOC users across age and diagnostic groups is shown. Their complex interrelationships with these technical features of EPIOC prescription are explored. Younger users were more complex due to age-related changes. This study provides outcomes of the EPIOC prescription for this heterogeneous group of very severely disabled people

A predictive score for retinopathy of prematurity in very low birth weight preterm infants

Aims This study describes the development of a score based on cumulative risk factors for the prediction of severe retinopathy of prematurity (ROP) comparing the performance of the score against the birth weight (BW) and gestational age (GA) in order to predict the onset of ROP.Methods A prospective cohort of preterm infants with BWp1500 g and/or GAp32 weeks was studied. the score was developed based on BW, GA, proportional weight gain from birth to the 6th week of life, use of oxygen in mechanical ventilation, and need for blood transfusions from birth to the 6th week of life. the score was established after linear regression, considering the impact of each variable on the occurrences of any stage and severe ROP. Receiver operating characteristic (ROC) curves were used to determine the best sensitivity and specificity values for the score. All variables were entered into an Excel spreadsheet (Microsoft) for practical use by ophthalmologists during screening sessions.Results the sample included 474 patients. the area under the ROC curve for the score was 0.77 and 0.88 to predict any stage and severe ROP, respectively. These values were significantly higher for the score than for BW (0.71) and GA (0.69) when measured separately.Conclusions ROPScore is an excellent index of neonatal risk factors for ROP, which is easy to record and more accurate than BW and GA to predict any stage ROP or severe ROP in preterm infants. the scoring system is simple enough to be routinely used by ophthalmologists during screening examination for detection of ROP. Eye (2012) 26, 400-406; doi: 10.1038/eye. 2011.334; published online 23 December 2011Hosp Clin Porto Alegre, Dept Ophthalmol, BR-90035903 Porto Alegre, RS, BrazilUniv Fed Rio Grande do Sul, Dept Ophthalmol, Sch Med, Porto Alegre, RS, BrazilUniversidade Federal de São Paulo, Dept Ophthalmol, Sch Med, São Paulo, BrazilUniv Fed Rio Grande do Sul, Dept Paediat, Newborn Sect, Sch Med, Porto Alegre, RS, BrazilUniversidade Federal de São Paulo, Dept Ophthalmol, Sch Med, São Paulo, BrazilWeb of Scienc

Models predicting the growth response to growth hormone treatment in short children independent of GH status, birth size and gestational age

<p>Abstract</p> <p>Background</p> <p>Mathematical models can be used to predict individual growth responses to growth hormone (GH) therapy. The aim of this study was to construct and validate high-precision models to predict the growth response to GH treatment of short children, independent of their GH status, birth size and gestational age. As the GH doses are included, these models can be used to individualize treatment.</p> <p>Methods</p> <p>Growth data from 415 short prepubertal children were used to construct models for predicting the growth response during the first years of GH therapy. The performance of the models was validated with data from a separate cohort of 112 children using the same inclusion criteria.</p> <p>Results</p> <p>Using only auxological data, the model had a standard error of the residuals (SD_res), of 0.23 SDS. The model was improved when endocrine data (GH_maxprofile, IGF-I and leptin) collected before starting GH treatment were included. Inclusion of these data resulted in a decrease of the SD_resto 0.15 SDS (corresponding to 1.1 cm in a 3-year-old child and 1.6 cm in a 7-year old). Validation of these models with a separate cohort, showed similar SD_resfor both types of models. Preterm children were not included in the Model group, but predictions for this group were within the expected range.</p> <p>Conclusion</p> <p>These prediction models can with high accuracy be used to identify short children who will benefit from GH treatment. They are clinically useful as they are constructed using data from short children with a broad range of GH secretory status, birth size and gestational age.</p

Relationship between Exercise Capacity and Brain Size in Mammals

A great deal of experimental research supports strong associations between exercise, cognition, neurogenesis and neuroprotection in mammals. Much of this work has focused on neurogenesis in individual subjects in a limited number of species. However, no study to date has examined the relationship between exercise and neurobiology across a wide range of mammalian taxa. It is possible that exercise and neurobiology are related across evolutionary time. To test this hypothesis, this study examines the association between exercise and brain size across a wide range of mammals.Controlling for associations with body size, we examined the correlation between brain size and a proxy for exercise frequency and capacity, maximum metabolic rate (MMR; ml O(2) min(-1)). We collected brain sizes and MMRs from the literature and calculated residuals from the least-squares regression line describing the relationship between body mass and each variable of interest. We then analyzed the correlation between residual brain size and residual MMR both before and after controlling for phylogeny using phylogenetic independent contrasts. We found a significant positive correlation between maximum metabolic rate and brain size across a wide range of taxa.These results suggest a novel hypothesis that links brain size to the evolution of locomotor behaviors in a wide variety of mammalian species. In the end, we suggest that some portion of brain size in nonhuman mammals may have evolved in conjunction with increases in exercise capacity rather than solely in response to selection related to cognitive abilities