2,110 research outputs found
Defeating jamming with the power of silence: a game-theoretic analysis
The timing channel is a logical communication channel in which information is
encoded in the timing between events. Recently, the use of the timing channel
has been proposed as a countermeasure to reactive jamming attacks performed by
an energy-constrained malicious node. In fact, whilst a jammer is able to
disrupt the information contained in the attacked packets, timing information
cannot be jammed and, therefore, timing channels can be exploited to deliver
information to the receiver even on a jammed channel.
Since the nodes under attack and the jammer have conflicting interests, their
interactions can be modeled by means of game theory. Accordingly, in this paper
a game-theoretic model of the interactions between nodes exploiting the timing
channel to achieve resilience to jamming attacks and a jammer is derived and
analyzed. More specifically, the Nash equilibrium is studied in the terms of
existence, uniqueness, and convergence under best response dynamics.
Furthermore, the case in which the communication nodes set their strategy and
the jammer reacts accordingly is modeled and analyzed as a Stackelberg game, by
considering both perfect and imperfect knowledge of the jammer's utility
function. Extensive numerical results are presented, showing the impact of
network parameters on the system performance.Comment: Anti-jamming, Timing Channel, Game-Theoretic Models, Nash Equilibriu
Trade-Offs between Energy Saving and Reliability in Low Duty Cycle Wireless Sensor Networks Using a Packet Splitting Forwarding Technique
One of the challenging topics and design constraints in Wireless Sensor Networks (WSNs) is the reduction of energy consumption because, in most application scenarios, replacement of power resources in sensor devices might be unfeasible. In order to minimize the power consumption, some nodes can be put to sleep during idle times and wake up only when needed. Although it seems the best way to limit the consumption of energy, other performance parameters such as network reliability have to be considered. In a recent paper, we introduced a new forwarding algorithm for WSNs based on a simple splitting procedure able to increase the network lifetime. The forwarding technique is based on the Chinese Remainder Theorem and exhibits very good results in terms of energy efficiency and complexity. In this paper, we intend to investigate a trade-off between energy efficiency and reliability of the proposed forwarding scheme when duty-cycling techniques are considered too
A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multi-Carrier Wireless Networks
This paper proposes computationally efficient algorithms to maximize the
energy efficiency in multi-carrier wireless interference networks, by a
suitable allocation of the system radio resources, namely the transmit powers
and subcarrier assignment. The problem is formulated as the maximization of the
system Global Energy-Efficiency subject to both maximum power and minimum rate
constraints. This leads to a challenging non-convex fractional problem, which
is tackled through an interplay of fractional programming, learning, and game
theory. The proposed algorithmic framework is provably convergent and has a
complexity linear in both the number of users and subcarriers, whereas other
available solutions can only guarantee a polynomial complexity in the number of
users and subcarriers. Numerical results show that the proposed method performs
similarly as other, more complex, algorithms
Videolaparo-assisted subtotal colectomy with cecorectal anastomosis in the treatment of chronic slow transit constipation
Mechanical cecorectal anastomosis after subtotal colectomy, in the treatment of slow transit constipation, probably represents the most attractive surgical alternative to total colectomy and ileorectal anastomosis. In fact the operation allows better results in terms of postoperative diarrhoea, fecal incontinence and postoperative adherential syndrome. Literature data have demonstrated the feasibility of the laparoscopic approach with tipically advantages of less invasive surgery respect of parietal integrity,less postoperative pain and ileus, fewer postoperative adhesions, a reduced hospitalitation and finally, a better cosmesis.
The Authors report a case of mechanical end to end cecorectal anastomosis after laparo-assisted subtotal colectomy (by four trocars) preserving superior rectal and ilecolic vessels, for the treatment of slow transit constipation in a 20 years old male patient .The reported operative approach which links tipical laparoscopic advantages to a more “safety” and “accurate” extracorporeal mechanical anastomosis
An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans
COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic
outbreak all over the world with exponential increasing of confirmed cases and,
unfortunately, deaths. In this work we propose an AI-powered pipeline, based on
the deep-learning paradigm, for automated COVID-19 detection and lesion
categorization from CT scans. We first propose a new segmentation module aimed
at identifying automatically lung parenchyma and lobes. Next, we combined such
segmentation network with classification networks for COVID-19 identification
and lesion categorization. We compare the obtained classification results with
those obtained by three expert radiologists on a dataset consisting of 162 CT
scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for
COVID-19 detection, outperforming those yielded by the expert radiologists, and
an average lesion categorization accuracy of over 84%. Results also show that a
significant role is played by prior lung and lobe segmentation that allowed us
to enhance performance by over 20 percent points. The interpretation of the
trained AI models, moreover, reveals that the most significant areas for
supporting the decision on COVID-19 identification are consistent with the
lesions clinically associated to the virus, i.e., crazy paving, consolidation
and ground glass. This means that the artificial models are able to
discriminate a positive patient from a negative one (both controls and patients
with interstitial pneumonia tested negative to COVID) by evaluating the
presence of those lesions into CT scans. Finally, the AI models are integrated
into a user-friendly GUI to support AI explainability for radiologists, which
is publicly available at http://perceivelab.com/covid-ai
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