3,683 research outputs found
Medical Ultrasound Imaging and Interventional Component (MUSiiC) Framework for Advanced Ultrasound Image-guided Therapy
Medical ultrasound (US) imaging is a popular and convenient medical imaging
modality thanks to its mobility, non-ionizing radiation, ease-of-use, and real-time data
acquisition. Conventional US brightness mode (B-Mode) is one type of diagnostic
medical imaging modality that represents tissue morphology by collecting and displaying
the intensity information of a reflected acoustic wave. Moreover, US B-Mode imaging is
frequently integrated with tracking systems and robotic systems in image-guided therapy
(IGT) systems. Recently, these systems have also begun to incorporate advanced US
imaging such as US elasticity imaging, photoacoustic imaging, and thermal imaging.
Several software frameworks and toolkits have been developed for US imaging research
and the integration of US data acquisition, processing and display with existing IGT
systems. However, there is no software framework or toolkit that supports advanced US
imaging research and advanced US IGT systems by providing low-level US data (channel
data or radio-frequency (RF) data) essential for advanced US imaging.
In this dissertation, we propose a new medical US imaging and interventional
component framework for advanced US image-guided therapy based on networkdistributed
modularity, real-time computation and communication, and open-interface
design specifications. Consequently, the framework can provide a modular research
environment by supporting communication interfaces between heterogeneous systems to
allow for flexible interventional US imaging research, and easy reconfiguration of an
entire interventional US imaging system by adding or removing devices or equipment
specific to each therapy. In addition, our proposed framework offers real-time
synchronization between data from multiple data acquisition devices for advanced
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interventional US imaging research and integration of the US imaging system with other
IGT systems. Moreover, we can easily implement and test new advanced ultrasound
imaging techniques inside the proposed framework in real-time because our software
framework is designed and optimized for advanced ultrasound research. The system’s
flexibility, real-time performance, and open-interface are demonstrated and evaluated
through performing experimental tests for several applications
Nanoscale Heat Transfer from Magnetic Nanoparticles and Ferritin in an Alternating Magnetic Field
Recent suggestions of nanoscale heat confinement on the surface of synthetic and biogenic magnetic nanoparticles during heating by radio frequency-alternating magnetic fields have generated intense interest because of the potential utility of this phenomenon for noninvasive control of biomolecular and cellular function. However, such confinement would represent a significant departure from the classical heat transfer theory. Here, we report an experimental investigation of nanoscale heat confinement on the surface of several types of iron oxide nanoparticles commonly used in biological research, using an all-optical method devoid of the potential artifacts present in previous studies. By simultaneously measuring the fluorescence of distinct thermochromic dyes attached to the particle surface or dissolved in the surrounding fluid during radio frequency magnetic stimulation, we found no measurable difference between the nanoparticle surface temperature and that of the surrounding fluid for three distinct nanoparticle types. Furthermore, the metalloprotein ferritin produced no temperature increase on the protein surface nor in the surrounding fluid. Experiments mimicking the designs of previous studies revealed potential sources of the artifacts. These findings inform the use of magnetic nanoparticle hyperthermia in engineered cellular and molecular systems
Synthesizing Photorealistic Virtual Humans Through Cross-modal Disentanglement
Over the last few decades, many aspects of human life have been enhanced with
virtual domains, from the advent of digital assistants such as Amazon's Alexa
and Apple's Siri to the latest metaverse efforts of the rebranded Meta. These
trends underscore the importance of generating photorealistic visual depictions
of humans. This has led to the rapid growth of so-called deepfake and
talking-head generation methods in recent years. Despite their impressive
results and popularity, they usually lack certain qualitative aspects such as
texture quality, lips synchronization, or resolution, and practical aspects
such as the ability to run in real-time. To allow for virtual human avatars to
be used in practical scenarios, we propose an end-to-end framework for
synthesizing high-quality virtual human faces capable of speaking with accurate
lip motion with a special emphasis on performance. We introduce a novel network
utilizing visemes as an intermediate audio representation and a novel data
augmentation strategy employing a hierarchical image synthesis approach that
allows disentanglement of the different modalities used to control the global
head motion. Our method runs in real-time, and is able to deliver superior
results compared to the current state-of-the-art
Andro-Simnet: Android Malware Family Classification Using Social Network Analysis
While the rapid adaptation of mobile devices changes our daily life more
conveniently, the threat derived from malware is also increased. There are lots
of research to detect malware to protect mobile devices, but most of them adopt
only signature-based malware detection method that can be easily bypassed by
polymorphic and metamorphic malware. To detect malware and its variants, it is
essential to adopt behavior-based detection for efficient malware
classification. This paper presents a system that classifies malware by using
common behavioral characteristics along with malware families. We measure the
similarity between malware families with carefully chosen features commonly
appeared in the same family. With the proposed similarity measure, we can
classify malware by malware's attack behavior pattern and tactical
characteristics. Also, we apply a community detection algorithm to increase the
modularity within each malware family network aggregation. To maintain high
classification accuracy, we propose a process to derive the optimal weights of
the selected features in the proposed similarity measure. During this process,
we find out which features are significant for representing the similarity
between malware samples. Finally, we provide an intuitive graph visualization
of malware samples which is helpful to understand the distribution and likeness
of the malware networks. In the experiment, the proposed system achieved 97%
accuracy for malware classification and 95% accuracy for prediction by K-fold
cross-validation using the real malware dataset.Comment: 13 pages, 11 figures, dataset link:
http://ocslab.hksecurity.net/Datasets/andro-simnet , demo video:
https://youtu.be/JmfS-ZtCbg4 , In Proceedings of the 16th Annual Conference
on Privacy, Security and Trust (PST), 201
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