Detection and Classification of Epileptiform Transients in EEG Signals Using Convolution Neural Network

EEG is the most common test done by neurologists to study a patient’s brainwaves for pre-epileptic conditions. This thesis explains an end-to-end deep learning approach for detect-ing segments of EEG which display abnormal brain activity (Yellow-Boxes) and further classifying them to AEP (Abnormal Epileptiform Paroxysmals) and Non-AEP. It is treated as a binary and a multi-class problem. 1-D Convolution Neural Networks are used to carry out the identification and classification. Detection of Yellow-Boxes and subsequent analysis is a tedious process which can be fre-quently misinterpreted by neurologists without neurophysiology fellowship training. Hence, an au-tomated machine learning system to detect and classify will greatly enhance the quality of diagnosis. Two convolution neural network architectures are trained for the detection of Yellow-Boxes as well as their classification. The first step is detecting the Yellow-Boxes. This is done by training convolution neural networks on a training set containing both Yellow-Boxed and Non-Yellow Boxed segments treated as a 2 class problem, and is also treated as a class extension to the classification of the Yellow-Boxes problem. The second step is the classification of the Yellow-Boxes, where 2 different architectures are trained to classify the Yellow-Boxed data to 2 and 4 classes. The over-all system is validated with the entire 30s EEG segments of multiple patients, which the system classifies as Yellow-Boxes or Non-Yellow Boxes and subsequent classification to AEP or Non-AEP, and is compared with the annotated data by neurologists

Evaluation of the effectiveness of smart inverters in mitigating voltage variations and fluctuations

With the increasing integration level of photovoltaics (PV) generation into the distribution grid, there would be detrimental impacts on residential customers in the form of rapid voltage variations and overvoltages. These voltage disturbances are caused by intermittent and varying power injections of PV generation. They can be reduced with the installation of smart inverters alongside PV with appropriate control settings. The smart inverters monitor and manage the reactive power exchange between the distribution circuit and the PV system so as to mitigate the adversity of voltage impacts. This thesis analyzes the effects of PV generation in regards to range and variation of voltage in a real-world distribution system. It also evaluates the effectiveness of PV generation equipped with smart inverters in mitigating these issues. A detailed study is conducted on few control strategies that would mitigate increasing range and variability of voltages. This study helps determine suitable control settings that increase the effectiveness of the discussed control functions and improve the overall performance of the distribution systemElectrical and Computer Engineerin

Updating Education: An Analysis of the Past and Present to Direct the Future

This thesis analyzes the education system in the United States from historical, cultural, philosophical and scientific perspectives to identify areas of improvement and proposes reforms in an attempt to shift the focus of education towards developing the ability to reason. The proposal for a modified education system considers the deficiencies within the current system, the educational practices of other countries and the biological processes that occur while learning to increase efficacy of the recommendations if implemented. The evidence and information for these recommendations were mainly derived from historical texts, archived government webpages, newspaper articles, and scientific studies. The conclusion finds that interdisciplinary teaching methods and writing practice will improve critical thinking and learning abilities, and that eliminating standardized tests is the first step for systemic improvement.Plan II Honors Progra

Relay Assisted Device to Device Communication Underlaying Cellular Networks

Device-to-Device (D2D) communication underlaying cellular networks is a latest technology of advanced wireless communication which allows two nearby devices to communicate without assistance of Base Station (BS) in cellular network. Device-to-Device (D2D) communication improves Spectral Eciency , Energy Eciency ,link reliability and overall system throughput by permitting nearby devices to communicate directly in licensed spectrum.In this thesis , two device discovery protocols are presented ,one reactive protocol and other proactive protocol which helps in discovering the D2D pairs which intend to communicate with each other.In addition, we propose a mode selection algorithm that decides the mode in which the devices can communicate either through traditional cellular mode or D2D mode. This optimum mode selection maximizes the overall throughput. The benets of D2D communication are limited practically when the distance between D2D users is long and poor channel environment between the D2D users. To overcome these drawbacks, a relay-assisted D2D communication is introduced where additional relay mode is proposed along with existing modes (i.e) cellular mode and D2D mode. A joint mode and relay selection scheme based on Hungarian algorithm is proposed to improve the overall system throughput. The Hungarian algorithm proposed, selects a suitable communication mode for each transmission and also select the relay device that acts as a relay between transmitting user and receiving user for relay mode communication.D2D devices sharing the same spectrum with cellular users results in interference, which requires to be managed in the resource allocation algorithm. A graph theory based resource allocation method for D2D users is proposed to improve the overall system capacity and extend the network coverage area

Surface effects and gold-nanostructure surface coating of whispering-gallery microresonators

Scope and Method of Study:The purpose of this study is to explore the surface effects of high-quality-factor optical microsphere resonators and thin-film-coated microresonators in various ambient gases. In this work, we present a systematic study of the assembly and characterization of gold nanostructures. We employ a wet-chemical synthesis method for growing gold nanorods and a directed electrochemical method for assembly of gold nanowires. The adhesion methods of gold nanostructures on high-quality-factor optical microsphere resonators are also investigated.Findings and Conclusions:A novel method is employed for measuring thermal accommodation coefficients of various gases like nitrogen, helium and ambient air on several coated and uncoated surfaces of fused-silica microresonators, operating at room temperature. This method is further extended to measure the absorption coefficient of a surface film or water layer on a fused-silica microresonator, and provides a novel method to find the water layer desorption and adsorption rates on the surface of a microresonator in the presence of gases like ambient air and nitrogen. We have adapted methods for growing gold nanorods of different aspect ratios (AR), and developed a novel method of growing high-AR (20-400) gold nanowires from low-AR gold nanorods. Various methods were discovered to coat these gold nanostructures and carbon nanotubes on the fused-silica surface. The most successful method involves surface modification with MPMDMS (i.e., silanization) before coating with gold nanorods. These coating methods have made microresonators useful for plasmonic sensing applications

An Empirical and Theoretical Investigation of Random Reinforced Forests and Shallow Convolutional Neural Networks

For many years, the global population of honey bees has been decreasing due to inconclusive reasons resulting in the syndrome Colony Collapse Disorder (CCD). This syndrome has been plaguing bees and affecting commercial agriculture pollination since 1998. Many researchers have suggested that pesticides, in-hive chemicals, pathogens, etc., might be the causes of CCD. Researchers also believe that any changes in a beehive can disturb the bees, which may negatively affect their health. Honey bees are the most vital among all the animal pollinators contributing to approximately 30% of the world’s commercial pollination services. As they are of keystone importance to their respective ecosystems, monitoring their hives is crucial for understanding the effects of CCD and enabling beekeepers to maintain the health of their hives. As beekeepers cannot monitor their hives continuously, electronic beehive monitoring (EBM) can help them keep an eye on their hives. EBM extracts the videos, audios, temperature using cameras, microphones, sensors for observing the forager traffic (incoming and outgoing flow of the bees through the hive) to track food and nectar availability, following the sounds of the buzzing, and monitoring the abrupt temperature changes. EBM reduces the number of invasive inspections and transportation costs incurred for traveling to the beehive location. This research proposes a new technique using reinforcement learning, a method based on a reward/punishment strategy and aims at providing both accurate and energy efficient classification techniques to improve individual bee recognition in bee traffic videos

Contraintes a l’adoption de la methode de l’hygiene sanitaire des vergers pour la lutte contre les mouches nuisibles aux fruits (Diptera, Tephritidae) par les producteurs de mangues et d’agrumes au Benin

No AbstractMots-clés: Bénin, hygiène sanitaire des vergers, mouches de fruits, producteurs de mangues et d’agrumes 

Multi Objective Optimization of Hot Machining of 15-5PH Stainless Steel Using Grey Relation Analysis

AbstractIn this paper an experimental investigation was carried out to optimize the performance characteristics (surface roughness and Metal removal rate) simultaneously in hot machining process. The experiments were conducted on 15-5PH stainless steel using K313 carbide tool based on Taguchi L27 orthogonal array design. The work-piece material was heated using oxy-acetylene gas flame which is the cost effective method compare to other heating technique used in hot machining process. Analysis of variance is performed to get the contribution of each parameter on the performance characteristics and it was observed that cutting speed is most significantly affect the performance characteristics compare to feed, depth of cut and temperature. The optimal set of process parameters were found to be cutting speed at 31 m/min, feed rate at 0.4mm/rev, depth of cut at 0.4mm and workpiece temperature at 400°C to maximize material removal rate and minimize surface roughness