- author = "Pascal Libuschewski",
- title = "Exploration of cyber-physical systems for {GPGPU} computer vision-based detection of biological viruses",
- school = "LS07, Fakultaet fuer Informatik der Technischen Universitaet Dortmund",
- year = "2017",
- address = "Dortmund, Germany",
- month = "22 " # mar,
- keywords = "genetic algorithms, genetic programming, JGAP, GPU, Design space exploration, DSE, Virus detection, Biological viruses, Medical image processing, Computer vision, GPGPU, GPU, Cyber-physical systems, Energy-aware Multi-objective Optimization, Embedded systems, Mobile sensor",
-
URL = "
https://eldorado.tu-dortmund.de/bitstream/2003/35929/1/Dissertation_Libuschewski.pdf",
-
URL = "
http://hdl.handle.net/2003/35929",
-
URL = "https://eldorado.tu-dortmund.de/handle/2003/35929",
-
DOI = "
doi:10.17877/DE290R-17952",
- size = "290 pages",
-
abstract = "This work presents a method for a computer
vision-based detection of biological viruses in PAMONO
sensor images and, related to this, methods to explore
cyber-physical systems such as those consisting of the
PAMONO sensor, the detection software, and processing
hardware. The focus is especially on an exploration of
Graphics Processing Units (GPU) hardware for
General-Purpose computing on Graphics Processing Units
(GPGPU) software and the targeted systems are high
performance servers, desktop systems, mobile systems,
and hand-held systems. The first problem that is
addressed and solved in this work is to automatically
detect biological viruses in PAMONO sensor images.
PAMONO is short for Plasmon Assisted Microscopy Of
Nano-sized Objects. The images from the PAMONO sensor
are very challenging to process. The signal magnitude
and spatial extension from attaching viruses is small,
and it is not visible to the human eye on raw sensor
images. Compared to the signal, the noise magnitude in
the images is large, resulting in a small
Signal-to-Noise Ratio (SNR). With the VirusDetectionCL
method for a computer vision-based detection of
viruses, presented in this work, an automatic detection
and counting of individual viruses in PAMONO sensor
images has been made possible. A data set of 4000
images can be evaluated in less than three minutes,
whereas a manual evaluation by an expert can take up to
two days. As the most important result, sensor signals
with a median SNR of two can be handled. This enables
the detection of particles down to 100 nm. The
VirusDetectionCL method has been realized as a GPGPU
software. The PAMONO sensor, the detection software,
and the processing hardware form a so called
cyber-physical system. For different PAMONO scenarios,
e.g., using the PAMONO sensor in laboratories,
hospitals, airports, and in mobile scenarios, one or
more cyber-physical systems need to be explored.
Depending on the particular use case, the demands
toward the cyber-physical system differ. This leads to
the second problem for which a solution is presented in
this work: how can existing software with several
degrees of freedom be automatically mapped to a
selection of hardware architectures with several
hardware configurations to fulfil the demands to the
system? Answering this question is a difficult task.
Especially, when several possibly conflicting
objectives, e.g., quality of the results, energy
consumption, and execution time have to be optimized.
An extensive exploration of different software and
hardware configurations is expensive and
time-consuming. Sometimes it is not even possible,
e.g., if the desired architecture is not yet available
on the market or the design space is too big to be
explored manually in reasonable time. A Pareto optimal
selection of software parameters, hardware
architectures, and hardware configurations has to be
found. To achieve this, three parameter and design
space exploration methods have been developed. These
are named SOG-PSE, SOG-DSE, and MOGEA-DSE. MOGEA-DSE is
the most advanced method of these three. It enables a
multi-objective, energy-aware, measurement-based or
simulation-based exploration of cyber-physical systems.
This can be done in a hardware/software co-design
manner. In addition, offloading of tasks to a server
and approximate computing can be taken into account.
With the simulation-based exploration, systems that do
not exist can be explored. This is useful if a system
should be equipped, e.g., with the next generation of
GPUs. Such an exploration can reveal bottlenecks of the
existing software before new GPUs are bought. With
MOGEA-DSE the overall goal, to develop a method to
automatically explore suitable cyber-physical systems
for different PAMONO scenarios, could be achieved. As a
result, a rapid, reliable detection and counting of
viruses in PAMONO sensor data using high-performance,
desktop, laptop, down to hand-held systems has been
made possible. The fact that this could be achieved
even for a small, hand-held device is the most
important result of MOGEA-DSE. With the automatic
parameter and design space exploration 84% energy could
be saved on the hand-held device compared to a baseline
measurement. At the same time, a speed-up of four and
an F-1 quality score of 0.995 could be obtained. The
speedup enables live processing of the sensor data on
the embedded system with a very high detection
quality.
With this result, viruses can be detected and counted on a mobile, hand-held device in less than three minutes and with real-time visualization of results. This opens up completely new possibilities for biological virus detection that were not possible before.",
-
notes = "page 123 Figure 7.3
Supervisors Prof. Dr. Mueller and Prof. Dr. Marwedel
In English",
