521 research outputs found
Augmented breast tumor classification by perfusion analysis
Magnetic resonance and computed tomography imaging aid in the diagnosis and analysis of pathologic conditions. Blood flow, or perfusion, through a region of tissue can be computed from a time series of contrast-enhanced images. Perfusion is an important set of physiological parameters that reflect angiogenesis. In cancer, heightened angiogenesis is a key process in the growth and spread of tumorous masses. An automatic classification technique using recovered perfusion may prove to be a highly accurate diagnostic tool. Such a classification system would supplement existing histopathological tests, and help physicians to choose the most optimal treatment protocol. Perfusion is obtained through deconvolution of signal intensity series and a pharmacokinetic model. However, many computational problems complicate the accurate-consistent recovery of perfusion. The high time-resolution acquisition of images decreases signal-to-noise, producing poor deconvolution solutions. The delivery of contrast agent as a function of time must also be determined or sampled before deconvolution can proceed. Some regions of the body, such as the brain, provide a nearby artery to serve as this arterial input function. Poor estimates can lead to an over or under estimation of perfusion. Breast tissue is an example of one tissue region where a clearly defined artery is not present. This proposes a new method of using recovered perfusion and spatial information in an automated classifier. This classifier grades suspected lesions as benign or malignant. This method can be integrated into a computer-aided diagnostic system to enhance the value of medical imagery
Water and Wastewater Pipe Nondestructive Evaluation and Health Monitoring: A Review
Civil infrastructures such as bridges, buildings, and pipelines ensure society's economic and industrial prosperity. Specifically, pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. The quantitative and early detection of defects in pipes is critical in order to avoid severe consequences. As a result of high-profile accidents and economic downturn, research and development in the area of pipeline inspection has focused mainly on gas and oil pipelines. Due to the low cost of water, the development of nondestructive inspection (NDI) and structural health monitoring (SHM) technologies for fresh water mains and sewers has received the least attention. Moreover, the technical challenges associated with the practical deployment of monitoring system demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper presents an overview of the most used NDI/SHM technologies for freshwater pipes and sewers. The challenges that said infrastructures pose with respect to oil and natural gas pipeline networks will be discussed. Finally, the methodologies that can be translated into SHM approaches are highlighted
Magnetoresistance sensor-based rotor fault detection in induction motor using non-decimated wavelet and streaming data
In this paper, the giant magnetoresistance broken rotor (GBR) method is used to diagnose the induction motor (IM) rotor bar fault at an early stage from outward magnetic flux developed by IM.The outward magnetic field signal has anti-clockwise radiation due to broken rotor bar current.In this paper, the outward magnetic signal is acquired using a giant magnetoresistance (GMR) sensor. In the GBR method, IM rotor fault is analysed with a non-decimated wavelet transform (NDWT)-based outward magnetic signal. Experimental result shows the difference in statistical features and energy levels of sub-bands of NDWT for healthy and faulty IM. Least square-support vector machine(LS-SVM)-based classification results are verified by confusion matrix based on 150 outward magnetic signals from a healthy and damaged rotor (broken rotor). The proposed method identifies IM rotor faults with 95% sensitivity, 90% specificity and 92.5% classification accuracy. Furthermore, run-time IM condition monitoring is performed through the ThinkSpeak internet of things (IoT) platform for collecting outer magnetic signal data. ThinkSpeak streaming data of outward magnetic field help detect rotor fault at the initial stage and understand the growth of rotor fault in the motor. The proposed GBR method overcomes sensitivity, translation-invariance limitations of existing IM rotor fault diagnosis methods
Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest Classifiers
Radio Frequency (RF) emissions from electronic devices expose security vulnerabilities that can be used by an attacker to extract otherwise unobtainable information. Two realms of study were investigated here, including the exploitation of 1) unintentional RF emissions in the field of Side Channel Analysis (SCA), and 2) intentional RF emissions from physical devices in the field of RF-Distinct Native Attribute (RF-DNA) fingerprinting. Statistical analysis on the linear model fit to measured SCA data in Linear Regression Attacks (LRA) improved performance, achieving 98% success rate for AES key-byte identification from unintentional emissions. However, the presence of non-Gaussian noise required the use of a non-parametric classifier to further improve key guessing attacks. RndF based profiling attacks were successful in very high dimensional data sets, correctly guessing all 16 bytes of the AES key with a 50,000 variable dataset. With variable reduction, Random Forest still outperformed Template Attack for this data set, requiring fewer traces and achieving higher success rates with lower misclassification rate. Finally, the use of a RndF classifier is examined for intentional RF emissions from ZigBee devices to enhance security using RF-DNA fingerprinting. RndF outperformed parametric MDA/ML and non-parametric GRLVQI classifiers, providing up to GS =18.0 dB improvement (reduction in required SNR). Network penetration, measured using rogue ZigBee devices, show that the RndF method improved rogue rejection in noisier environments - gains of up to GS =18.0 dB are realized over previous methods
Systematic Literature Review of EM-SCA Attacks on Encryption
Cryptography is vital for data security, but cryptographic algorithms can
still be vulnerable to side-channel attacks (SCAs), physical assaults
exploiting power consumption and EM radiation. SCAs pose a significant threat
to cryptographic integrity, compromising device keys. While literature on SCAs
focuses on real-world devices, the rise of sophisticated devices necessitates
fresh approaches. Electromagnetic side-channel analysis (EM-SCA) gathers
information by monitoring EM radiation, capable of retrieving encryption keys
and detecting malicious activity. This study evaluates EM-SCA's impact on
encryption across scenarios and explores its role in digital forensics and law
enforcement. Addressing encryption susceptibility to EM-SCA can empower
forensic investigators in overcoming encryption challenges, maintaining their
crucial role in law enforcement. Additionally, the paper defines EM-SCA's
current state in attacking encryption, highlighting vulnerable and resistant
encryption algorithms and devices, and promising EM-SCA approaches. This study
offers a comprehensive analysis of EM-SCA in law enforcement and digital
forensics, suggesting avenues for further research
A Review of Polymer Electrolyte Fuel Cells Fault Diagnosis: Progress and Perspectives
Polymer electrolyte fuel cells (PEFCs) are regarded as a substitution for the combustion engine with high energy conversion efficiency and zero CO2 emissions. Stable system operation requires control within a relatively narrow range of operating conditions to achieve the optimal output, leading to faults that can easily cause accelerated degradation when operating conditions deviate from the control targets. Performance recovery of the system can be realized through early fault diagnosis; therefore, accurate and effective diagnostic characterisation is vital for long-term serving. A review of off-line and on-line techniques applied to the fault diagnosis of fuel cells is presented in this work. Off-line approaches include electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), galvanostatic charge (GSC), visualisation-based and image-based techniques; the on-line methods can be divided into model-based, data-driven, signal-based and hybrid methods. Since each methodology has advantages and drawbacks, its effectiveness is analysed, and limitations are highlighted
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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