44,176 research outputs found
Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
Many systems are partially stochastic in nature. We have derived data driven
approaches for extracting stochastic state machines (Markov models) directly
from observed data. This chapter provides an overview of our approach with
numerous practical applications. We have used this approach for inferring
shipping patterns, exploiting computer system side-channel information, and
detecting botnet activities. For contrast, we include a related data-driven
statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems
and Securit
Multiparametric magnetic resonance imaging of the prostate-a basic tutorial.
Prostate cancer is the second most common cause of cancer related death in the United States and the most commonly diagnosed malignancy in men. In general, prostate cancer is slow growing, though there is a broad spectrum of disease that may be indolent, or aggressive and rapidly progressive. Screening for prostate is controversial and complicated by lack of specificity and over diagnosis of clinically insignificant cancer. Imaging has played a role in diagnosis of prostate cancer, primarily through systemic transrectal ultrasound (TRUS) guided biopsy. While TRUS guided biopsy radically changed prostate cancer diagnosis, it still remains limited by low resolution, poor tissue characterization, relatively low sensitivity and positive predictive value. Advances in multiparametric magnetic resonance imaging (mpMRI) have allowed more accurate detection, localization, and staging as well as aiding in the role of active surveillance (AS). The use of mpMRI for the evaluation of prostate cancer has increased dramatically and this trend is likely to continue as the technique is rapidly improving and its applications expand. The purpose of this article is to review the basic principles of mpMRI of the prostate and its clinical applications, which will be reviewed in greater detail in subsequent chapters of this issue
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
Passive detection of moving aerial target based on multiple collaborative GPS satellites
Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance−velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite
Marker based Thermal-Inertial Localization for Aerial Robots in Obscurant Filled Environments
For robotic inspection tasks in known environments fiducial markers provide a
reliable and low-cost solution for robot localization. However, detection of
such markers relies on the quality of RGB camera data, which degrades
significantly in the presence of visual obscurants such as fog and smoke. The
ability to navigate known environments in the presence of obscurants can be
critical for inspection tasks especially, in the aftermath of a disaster.
Addressing such a scenario, this work proposes a method for the design of
fiducial markers to be used with thermal cameras for the pose estimation of
aerial robots. Our low cost markers are designed to work in the long wave
infrared spectrum, which is not affected by the presence of obscurants, and can
be affixed to any object that has measurable temperature difference with
respect to its surroundings. Furthermore, the estimated pose from the fiducial
markers is fused with inertial measurements in an extended Kalman filter to
remove high frequency noise and error present in the fiducial pose estimates.
The proposed markers and the pose estimation method are experimentally
evaluated in an obscurant filled environment using an aerial robot carrying a
thermal camera.Comment: 10 pages, 5 figures, Published in International Symposium on Visual
Computing 201
- …