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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Multi-Column Multi-Layer Computational Model Of Neocortex, Beata Strack
Multi-Column Multi-Layer Computational Model Of Neocortex, Beata Strack
Theses and Dissertations
We present a multi-layer multi-column computational model of neocortex that is built based on the activity and connections of known neuronal cell types and includes activity-dependent short term plasticity. This model, a network of spiking neurons, is validated by showing that it exhibits activity close to biology in terms of several characteristics: (1) proper laminar flow of activity; (2) columnar organization with focality of inputs; (3) low-threshold-spiking (LTS) and fast-spiking (FS) neurons function as observed in normal cortical circuits; and (4) different stages of epileptiform activity can be obtained with either increasing the level of inhibitory blockade, or simulation of …
A Novel Spam Campaign In Online Social Networks, Yufeng Zhen
A Novel Spam Campaign In Online Social Networks, Yufeng Zhen
Theses and Dissertations
The increasing popularity of the Online Social Networks (OSNs)\nomenclature{$OSNs$}{Online Social Networks} has made the OSNs major targets of spammers. They aim to illegally gather private information from users and spread spam to them. In this paper, we propose a new spam campaign that includes following key steps: creating fake accounts, picking legitimate accounts, forming friendships, earning trust, and spreading spam. The unique part in our spam campaign is the process of earning trust. By using social bots, we significantly lower the cost of earning trust and make it feasible in the real world. By spreading spam at a relatively low …
Assessment And Prediction Of Cardiovascular Status During Cardiac Arrest Through Machine Learning And Dynamical Time-Series Analysis, Sharad Shandilya
Assessment And Prediction Of Cardiovascular Status During Cardiac Arrest Through Machine Learning And Dynamical Time-Series Analysis, Sharad Shandilya
Theses and Dissertations
In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viability for decision-support in clinical settings. The approach, which focuses on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic …
Motion Artifact Reduction In Impedance Plethysmography Signal, Sardar Ansari
Motion Artifact Reduction In Impedance Plethysmography Signal, Sardar Ansari
Theses and Dissertations
The research related to designing portable monitoring devices for physiological signals has been at its peak in the last decade or two. One of the main obstacles in building such devices is the effect of the subject's movements on the quality of the signal. There have been numerous studies addressing the problem of removing motion artifact from the electrocardiogram (ECG) and photoplethysmography (PPG) signals in the past few years. However, no such study exists for the Impedance Plethysmography (IP) signal. The IP signal can be used to monitor respiration in mobile devices. However, it is very susceptible to motion artifact. …
Complex Network Growing Model Using Downlink Motifs, Ahmad Al-Musawi Jr.
Complex Network Growing Model Using Downlink Motifs, Ahmad Al-Musawi Jr.
Theses and Dissertations
Understanding the underlying architecture of gene regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of GRNs (and other complex networks) and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to …
The Use Of Relation Valued Attributes In Support Of Fuzzy Data, Larry Ritchie Williams Jr.
The Use Of Relation Valued Attributes In Support Of Fuzzy Data, Larry Ritchie Williams Jr.
Theses and Dissertations
In his paper introducing fuzzy sets, L.A. Zadeh describes the difficulty of assigning some real-world objects to a particular class when the notion of class membership is ambiguous. If exact classification is not obvious, most people approximate using intuition and may reach agreement by placing an object in more than one class. Numbers or ‘degrees of membership’ within these classes are used to provide an approximation that supports this intuitive process. This results in a ‘fuzzy set’. This fuzzy set consists any number of ordered pairs to represent both the class and the class’s degree of membership to provide a …
Geometric Approach To Support Vector Machines Learning For Large Datasets, Robert Strack
Geometric Approach To Support Vector Machines Learning For Large Datasets, Robert Strack
Theses and Dissertations
The dissertation introduces Sphere Support Vector Machines (SphereSVM) and Minimal Norm Support Vector Machines (MNSVM) as the new fast classification algorithms that use geometrical properties of the underlying classification problems to efficiently obtain models describing training data. SphereSVM is based on combining minimal enclosing ball approach, state of the art nearest point problem solvers and probabilistic techniques. The blending of the three speeds up the training phase of SVMs significantly and reaches similar (i.e., practically the same) accuracy as the other classification models over several big and large real data sets within the strict validation frame of a double (nested) …
Automated Midline Shift Detection On Brain Ct Images For Computer-Aided Clinical Decision Support, Xuguang Qi
Automated Midline Shift Detection On Brain Ct Images For Computer-Aided Clinical Decision Support, Xuguang Qi
Theses and Dissertations
Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumptions. For the actual midline detection algorithm, a window …
Exploiting Rogue Signals To Attack Trust-Based Cooperative Spectrum Sensing In Cognitive Radio Networks, David Jackson
Exploiting Rogue Signals To Attack Trust-Based Cooperative Spectrum Sensing In Cognitive Radio Networks, David Jackson
Theses and Dissertations
Cognitive radios are currently presented as the solution to the ever-increasing spectrum shortage problem. However, their increased capabilities over traditional radios introduce a new dimension of security threats. Cooperative Spectrum Sensing (CSS) has been proposed as a means to protect cognitive radio networks from the well known security threats: Primary User Emulation (PUE) and Spectrum Sensing Data Falsification (SSDF). I demonstrate a new threat to trust-based CSS protocols, called the Rogue Signal Framing (RSF) intrusion. Rogue signals can be exploited to create the illusion of malicious sensors which leads to the framing of innocent sensors and consequently, their removal from …
Actual Entities: A Control Method For Unmanned Aerial Vehicles, Erica Absetz
Actual Entities: A Control Method For Unmanned Aerial Vehicles, Erica Absetz
Theses and Dissertations
The focus of this thesis is on Actual Entities, a concept created by the philosopher Alfred North Whitehead, and how the concept can be applied to Unmanned Aerial Vehicles as a behavioral control method. Actual Entities are vector based, atomic units that use a method called prehension to observe their environment and react with various actions. When combining multiple Actual Entities a Colony of Prehending Entities is created; when observing their prehensions an intelligent behavior emerges. By applying the characteristics of Actual Entities to Unmanned Aerial Vehicles, specifically in a situation where they are searching for targets, this emergent, intelligent …
Multiple-Instance And One-Class Rule-Based Algorithms, Dat Nguyen
Multiple-Instance And One-Class Rule-Based Algorithms, Dat Nguyen
Theses and Dissertations
In this work we developed rule-based algorithms for multiple-instance learning and one-class learning problems, namely, the mi-DS and OneClass-DS algorithms. Multiple-Instance Learning (MIL) is a variation of classical supervised learning where there is a need to classify bags (collection) of instances instead of single instances. The bag is labeled positive if at least one of its instances is positive, otherwise it is negative. One-class learning problem is also known as outlier or novelty detection problem. One-class classifiers are trained on data describing only one class and are used in situations where data from other classes are not available, and also …