6,887 research outputs found
Automated Netlist Generation for 3D Electrothermal and Electromagnetic Field Problems
We present a method for the automatic generation of netlists describing
general three-dimensional electrothermal and electromagnetic field problems.
Using a pair of structured orthogonal grids as spatial discretisation, a
one-to-one correspondence between grid objects and circuit elements is obtained
by employing the finite integration technique. The resulting circuit can then
be solved with any standard available circuit simulator, alleviating the need
for the implementation of a custom time integrator. Additionally, the approach
straightforwardly allows for field-circuit coupling simulations by
appropriately stamping the circuit description of lumped devices. As the
computational domain in wave propagation problems must be finite, stamps
representing absorbing boundary conditions are developed as well.
Representative numerical examples are used to validate the approach. The
results obtained by circuit simulation on the generated netlists are compared
with appropriate reference solutions.Comment: This is a pre-print of an article published in the Journal of
Computational Electronics. The final authenticated version is available
online at: https://dx.doi.org/10.1007/s10825-019-01368-6. All numerical
results can be reproduced by the Matlab code openly available at
https://github.com/tc88/ANTHE
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Trick or Heat? Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks
Temperature sensing and control systems are widely used in the closed-loop
control of critical processes such as maintaining the thermal stability of
patients, or in alarm systems for detecting temperature-related hazards.
However, the security of these systems has yet to be completely explored,
leaving potential attack surfaces that can be exploited to take control over
critical systems.
In this paper we investigate the reliability of temperature-based control
systems from a security and safety perspective. We show how unexpected
consequences and safety risks can be induced by physical-level attacks on
analog temperature sensing components. For instance, we demonstrate that an
adversary could remotely manipulate the temperature sensor measurements of an
infant incubator to cause potential safety issues, without tampering with the
victim system or triggering automatic temperature alarms. This attack exploits
the unintended rectification effect that can be induced in operational and
instrumentation amplifiers to control the sensor output, tricking the internal
control loop of the victim system to heat up or cool down. Furthermore, we show
how the exploit of this hardware-level vulnerability could affect different
classes of analog sensors that share similar signal conditioning processes.
Our experimental results indicate that conventional defenses commonly
deployed in these systems are not sufficient to mitigate the threat, so we
propose a prototype design of a low-cost anomaly detector for critical
applications to ensure the integrity of temperature sensor signals.Comment: Accepted at the ACM Conference on Computer and Communications
Security (CCS), 201
Statistical Techniques and Artificial Neural Networks for Image Analysis
The main topic of this PhD thesis is image analysis. The subject was investigated from different perspectives, starting from different image types and with different goals.
At the beginning x-ray hazelnuts images were analyzed. The target of this analysis was to determine whether a hazelnut was good or not. In order to do this an Artificial Neural Network was used, whose features were statistical variables. At a later stage a SVM was implemented to try to get better results.
The second kind of images were still x-ray ones; they were, however, images coming from a PCB productive process. What we were asked to do was to highlight the air bubbles trapped into a solder joint (particularly those inside the thermal pads). In this case filters and morphological operations were used.
The third case were ulcers photographs: the goal of the collaboration with SIF (SocietĂ Italiana di Flebologia, Italian society of phlebology) is to give doctors a way to evaluate ulcers remotely, in order to customize the treatments according to how the healing behaves.
A small digression was the development of a small and cheap Arduino-based robot for an educational laboratory (Xkè?, in collaboration with prof. Angelo Raffaele Meo from DAUIN, Politecnico di Torino). This should have been the first step towards the development of an evolved robot for agricultural purposes, but the project didn’t start
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