944 research outputs found
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
This paper focuses on an examination of an applicability of Recurrent Neural
Network models for detecting anomalous behavior of the CERN superconducting
magnets. In order to conduct the experiments, the authors designed and
implemented an adaptive signal quantization algorithm and a custom GRU-based
detector and developed a method for the detector parameters selection. Three
different datasets were used for testing the detector. Two artificially
generated datasets were used to assess the raw performance of the system
whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets
was intended for real-life experiments and model training. Several different
setups of the developed anomaly detection system were evaluated and compared
with state-of-the-art OC-SVM reference model operating on the same data. The
OC-SVM model was equipped with a rich set of feature extractors accounting for
a range of the input signal properties. It was determined in the course of the
experiments that the detector, along with its supporting design methodology,
reaches F1 equal or very close to 1 for almost all test sets. Due to the
profile of the data, the best_length setup of the detector turned out to
perform the best among all five tested configuration schemes of the detection
system. The quantization parameters have the biggest impact on the overall
performance of the detector with the best values of input/output grid equal to
16 and 8, respectively. The proposed solution of the detection significantly
outperformed OC-SVM-based detector in most of the cases, with much more stable
performance across all the datasets.Comment: Related to arXiv:1702.0083
Algorithms and Systems for IoT and Edge Computing
The idea of distributing the signal processing along the path that starts with the acquisition and ends with the final application has given light to the Internet of Things and Edge Computing, which have demonstrated several advantages in terms of scalability, costs, and reliability. In this dissertation, we focus on designing and implementing algorithms and systems that allow performing a complex task on devices with limited resources.
Firstly, we assess the trade-off between compression and anomaly detection from both a theoretical and a practical point of view. Information theory provides the rate-distortion analysis that is extended to consider how information content is processed for detection purposes. Considering an actual Structural Health Monitoring application, two corner cases are analysed: detection in high distortion based on a feature extraction method and detection with low distortion based on Principal Component Analysis.
Secondly, we focus on streaming methods for Subspace Analysis. In this context, we revise and study state-of-the-art methods to target devices with limited computational resources. We also consider a real case of deployment of an algorithm for streaming Principal Component Analysis for signal compression in a Structural Health Monitoring application, discussing the trade-off between the possible implementation strategies.
Finally, we focus on an alternative compression framework suited for low-end devices that is Compressed Sensing. We propose a different decoding approach that splits the recovery problem into two stages and effectively adopts a deep neural network and basic linear algebra to reconstruct biomedical signals. This novel approach outperforms the state-of-the-art in terms of quality of reconstruction and requires lower computational resources
Forensic Video Analytic Software
Law enforcement officials heavily depend on Forensic Video Analytic (FVA)
Software in their evidence extraction process. However present-day FVA software
are complex, time consuming, equipment dependent and expensive. Developing
countries struggle to gain access to this gateway to a secure haven. The term
forensic pertains the application of scientific methods to the investigation of
crime through post-processing, whereas surveillance is the close monitoring of
real-time feeds.
The principle objective of this Final Year Project was to develop an
efficient and effective FVA Software, addressing the shortcomings through a
stringent and systematic review of scholarly research papers, online databases
and legal documentation. The scope spans multiple object detection, multiple
object tracking, anomaly detection, activity recognition, tampering detection,
general and specific image enhancement and video synopsis.
Methods employed include many machine learning techniques, GPU acceleration
and efficient, integrated architecture development both for real-time and
postprocessing. For this CNN, GMM, multithreading and OpenCV C++ coding were
used. The implications of the proposed methodology would rapidly speed up the
FVA process especially through the novel video synopsis research arena. This
project has resulted in three research outcomes Moving Object Based Collision
Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and
Tampering Detection Inter-Frame Forgery.
The results include forensic and surveillance panel outcomes with emphasis on
video synopsis and Sri Lankan context. Principal conclusions include the
optimization and efficient algorithm integration to overcome limitations in
processing power, memory and compromise between real-time performance and
accuracy.Comment: The Forensic Video Analytic Software demo video is available
https://www.youtube.com/watch?v=vsZlYKQxSk
e-TLD: Event-based Framework for Dynamic Object Tracking
This paper presents a long-term object tracking framework with a moving event
camera under general tracking conditions. A first of its kind for these
revolutionary cameras, the tracking framework uses a discriminative
representation for the object with online learning, and detects and re-tracks
the object when it comes back into the field-of-view. One of the key novelties
is the use of an event-based local sliding window technique that tracks
reliably in scenes with cluttered and textured background. In addition,
Bayesian bootstrapping is used to assist real-time processing and boost the
discriminative power of the object representation. On the other hand, when the
object re-enters the field-of-view of the camera, a data-driven, global sliding
window detector locates the object for subsequent tracking. Extensive
experiments demonstrate the ability of the proposed framework to track and
detect arbitrary objects of various shapes and sizes, including dynamic objects
such as a human. This is a significant improvement compared to earlier works
that simply track objects as long as they are visible under simpler background
settings. Using the ground truth locations for five different objects under
three motion settings, namely translation, rotation and 6-DOF, quantitative
measurement is reported for the event-based tracking framework with critical
insights on various performance issues. Finally, real-time implementation in
C++ highlights tracking ability under scale, rotation, view-point and occlusion
scenarios in a lab setting.Comment: 11 pages, 10 figure
Detection algorithms for spatial data
This dissertation addresses the problem of anomaly detection in spatial data. The problem of landmine detection in airborne spatial data is chosen as the specific detection scenario. The first part of the dissertation deals with the development of a fast algorithm for kernel-based non-linear anomaly detection in the airborne spatial data. The original Kernel RX algorithm, proposed by Kwon et al. [2005a], suffers from the problem of high computational complexity, and has seen limited application. With the aim to reduce the computational complexity, a reformulated version of the Kernel RX, termed the Spatially Weighted Kernel RX (SW-KRX), is presented. It is shown that under this reformulation, the detector statistics can be obtained directly as a function of the centered kernel Gram matrix. Subsequently, a methodology for the fast computation of the centered kernel Gram matrix is proposed. The key idea behind the proposed methodology is to decompose the set of image pixels into clusters, and expediting the computations by approximating the effect of each cluster as a whole. The SW-KRX algorithm is implemented for a special case, and comparative results are compiled for the SW-KRX vis-à-vis the RX anomaly detector. In the second part of the dissertation, a detection methodology for buried mine detection is presented. The methodology is based on extraction of color texture information using cross-co-occurrence features. A feature selection methodology based on Bhattacharya coefficients and principal feature analysis is proposed and detection results with different feature-based detectors are presented, to demonstrate the effectiveness of the proposed methodology in the extraction of useful discriminatory information --Abstract, page iii
Apollo guidance, navigation, and control: Candidate configuration trade study, Stellar-Inertial Measurement System (SIMS) for an Earth Observation Satellite (EOS)
The ten candidate SIMS configurations were reduced to three in preparation for the final trade comparison. The report emphasizes subsystem design trades, star availability studies, data processing (smoothing) methods, and the analytical and simulation studies at subsystem and system levels from which candidate accuracy estimates will be presented
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