33 research outputs found
Asynchronous Corner Tracking Algorithm based on Lifetime of Events for DAVIS Cameras
Event cameras, i.e., the Dynamic and Active-pixel Vision Sensor (DAVIS) ones,
capture the intensity changes in the scene and generates a stream of events in
an asynchronous fashion. The output rate of such cameras can reach up to 10
million events per second in high dynamic environments. DAVIS cameras use novel
vision sensors that mimic human eyes. Their attractive attributes, such as high
output rate, High Dynamic Range (HDR), and high pixel bandwidth, make them an
ideal solution for applications that require high-frequency tracking. Moreover,
applications that operate in challenging lighting scenarios can exploit the
high HDR of event cameras, i.e., 140 dB compared to 60 dB of traditional
cameras. In this paper, a novel asynchronous corner tracking method is proposed
that uses both events and intensity images captured by a DAVIS camera. The
Harris algorithm is used to extract features, i.e., frame-corners from
keyframes, i.e., intensity images. Afterward, a matching algorithm is used to
extract event-corners from the stream of events. Events are solely used to
perform asynchronous tracking until the next keyframe is captured. Neighboring
events, within a window size of 5x5 pixels around the event-corner, are used to
calculate the velocity and direction of extracted event-corners by fitting the
2D planar using a randomized Hough transform algorithm. Experimental evaluation
showed that our approach is able to update the location of the extracted
corners up to 100 times during the blind time of traditional cameras, i.e.,
between two consecutive intensity images.Comment: Accepted to 15th International Symposium on Visual Computing
(ISVC2020
Energy-Efficient Formation Morphing for Collision Avoidance in a Swarm of Drones
Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes' states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm's formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions
Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches
Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs
Emerging landscape of oncogenic signatures across human cancers.
Cancer therapy is challenged by the diversity of molecular implementations of oncogenic processes and by the resulting variation in therapeutic responses. Projects such as The Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail. The interpretation of these maps remains a major challenge. Here we distilled thousands of genetic and epigenetic features altered in cancers to ∼500 selected functional events (SFEs). Using this simplified description, we derived a hierarchical classification of 3,299 TCGA tumors from 12 cancer types. The top classes are dominated by either mutations (M class) or copy number changes (C class). This distinction is clearest at the extremes of genomic instability, indicating the presence of different oncogenic processes. The full hierarchy shows functional event patterns characteristic of multiple cross-tissue groups of tumors, termed oncogenic signature classes. Targetable functional events in a tumor class are suggestive of class-specific combination therapy. These results may assist in the definition of clinical trials to match actionable oncogenic signatures with personalized therapies
Evaluation of appendicitis risk prediction models in adults with suspected appendicitis
Background
Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis.
Methods
A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis).
Results
Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent).
Conclusion
Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified
Ovarian steroid hormones: Effects on immune responses and Chlamydia trachomatis infections of the female genital tract
Female sex hormones are known to regulate the adaptive and innate immune functions of the female reproductive tract. This review aims to update our current knowledge of the effects of the sex hormones estradiol and progesterone in the female reproductive tract on innate immunity, antigen presentation, specific immune responses, antibody secretion, genital tract infections caused by Chlamydia trachomatis, and vaccine-induced immunity