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HASS Cloud Dynamics
No full textIn this data driven decade, it is in high need for a highly available, secure and scalable resource, no matter who provides it. In that case, we tried our best to come up with a better solution that could be a great asset for an organization that completely relies on Cloud data. Moreover, we developed a platform where we can monitor the complete setup of our all cloud platforms and the infrastructure. This monitoring not only allows us to view the performance but also gives us more information on health of the servers and cloud storage. This setup ensures high availability of data and provides great security to the data and the communication channel. Also, this setup can be scalable to any extent. This cloud base is cost efficient and ensures high security of data. Thus we named it as, HASS Cloud Dynamics where HASS stands for Highly Available, Scalable and Secure
Microgreen Growth In Microgravity Versus Earth: A UB-Student Spaceflight Experimental Project Mission 12
No full textFaculty Research Day 2018: Student Spaceflight Experiments Program FinalistThe experiment will give us a better understanding of growing a plant in space versus Earth. The purpose of this experiment is to compare the growth rate of microgreens in gravity versus microgravity. We want to see the similarities and differences of growing microgreens in the different conditions. The Veggie system was launched in 2014 according to Herridge, L., astronauts were able to grow red romaine lettuce in 2015 at the ISS according to Foley, K.E. Instead of growing red romaine lettuce, microgreens will be used. Gardener’s Gold potting soil was chosen as the dirt for this experiment because it is all organic and plants will grow a lot faster with this brand, which also contains fertilizer. This was obtained at Ganim's Garden Center in Fairfield CT, which is also available at other garden centers as well. Microgreen seeds were used for this experiment because it’s very versatile, easy to grow, and it’s an edible plant. It was also obtained from Ganim’s Garden Center, but these seeds could be obtained almost any garden center
Size Dependent Graphene Quantum Dot (GQD) Interactions with Protein Biomarkers
No full textGamma-H2Ax protein is seen at the DNA damage sites and it is also incorporated randomly in to the histones throughout the DNA, which produce other necessary components for the DNA repair. It also contributes to the stable nucleosome formation when the histone molecules wrap the DNA. The histone complexes comprise of proteins called H2A ,H2B, H3 and H4. The H2A protein family further contains the highest number of variants, which are H2A1, H2A2, H2AX and H2AZ. This protein has a unique carboxyl tail which consist of a conserved reactive site of one serine residue at the position 139. H2Ax becomes phosphorylated at Serine 139, which is 4 residues from the C-terminus, in the presence of DNA damage. Gamma-H2Ax is further acetylated at Lys 5 and ubiquitinated on Lys 119. Graphene quantum dots (GQDs) have a size less than 10nm and they are about 1-10 of layers of graphene, which are used in the various biomedical applications. Some of their important properties include chemical stability and quantum confinement effect. In this study we investigate the effects of the different sizes of GQDs as they interact with H2AX to form a molecular basis of isolating different protein biomarkers based on their molecular weight
Media and Crime in Mexico
No full textMexico closed 2017 as the most violent in 20 years with over 26,000 murders. Mexico was ranked as the second most violent country. Crime rates don’t reflect the reality of the country. These made up numbers are driven by political motives. Mortality and crime rates have increased so much that crime has become a topic of vital importance and public concern. Statistics of crime and violence are inconsistent between the different governmental institutions. Three of every four citizens are victims of a crime that was not reported. Media outlets are influenced by political figures and powerful individuals. Crime rates reported are manipulated. The demands of the citizens are not reflected in the reality expressed by the media. The main source of news information presented by the media comes from government actors. People have the right to free information and free access to it. The government and the media have the obligation to enforce that right
Electrical Vehicle Battery Change System (EVBCS)
No full textFaculty Research Day 2018: Graduate Student Poster 2nd PlaceAlthough electric vehicles have many advantages, recharging the battery in these vehicles is time consuming. It takes a long time to fully charge the battery and often uses a special charging station. Battery swap systems have been attempted, but these systems typically have severe drawbacks. This research is based on designing a self-service removable battery. This battery design is a unique one. It is easy and comfortable to remove and insert the battery from both slots in the Electrical Vehicle and the charging station. The station is a special design trailer
Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition
No full textRecent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding an architecture to fit a given problem. A hand-crafted architecture is a challenging, time-consuming process that requires expert knowledge and effort, due to a large number of architectural design choices. In this dissertation, we present an efficient framework that automatically designs a high-performing CNN architecture for a given problem. In this framework, we introduce a new optimization objective function that combines the error rate and the information learnt by a set of feature maps using deconvolutional networks (deconvnet). The new objective function allows the hyperparameters of the CNN architecture to be optimized in a way that enhances the performance by guiding the CNN through better visualization of learnt features via deconvnet. The actual optimization of the objective function is carried out via the Nelder-Mead Method (NMM). Further, our new objective function results in much faster convergence towards a better architecture. The proposed framework has the ability to explore a CNN architecture’s numerous design choices in an efficient way and also allows effective, distributed execution and synchronization via web services. Empirically, we demonstrate that the CNN architecture designed with our approach outperforms several existing approaches in terms of its error rate. Our results are also competitive with state-of-the-art results on the MNIST dataset and perform reasonably against the state-of-the-art results on CIFAR-10 and CIFAR-100 datasets. Our approach has a significant role in increasing the depth, reducing the size of strides, and constraining some convolutional layers not followed by pooling layers in order to find a CNN architecture that produces a high recognition performance. Moreover, we evaluate the effectiveness of reducing the size of the training set on CNNs using a variety of instance selection methods to speed up the training time. We then study how these methods impact classification accuracy. Many instance selection methods require a long run-time to obtain a subset of the representative dataset, especially if the training set is large and has a high dimensionality. One example of these algorithms is Random Mutation Hill Climbing (RMHC). We improve RMHC so that it performs faster than the original algorithm with the same accuracy
A Review of Influenza Detection and Prediction Through Social Networking Sites
No full textEarly prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.https://doi.org/10.1186/s12976-017-0074-
Simulation of Transient Over-voltage from Energization of Unloaded Transmission Line
Get PDFIn power systems, energized transmission line when slightly loaded unloaded, or open-circuited (disconnected), the voltage at the receiving-end will often be greater than the voltage at the sending-end. This phenomenon is known as Ferranti Effect. This paper investigates transient overvoltage caused by energization of unloaded transmission line based on analysis, and EMTP-ATP modeling and simulation. Solution to reduce and control overvoltage is demonstrated using shunt reactor compensation technique
A New Cost Function Combining Deep Neural Networks (DNNs) and l2,1-Norm with Extraction of Robust Facial and Superpixels Features in Age Estimation
No full textAutomatic age estimation from unconstrained facial images is a challenging task and it recently has gained much attention due to its wide range of applications. In this paper, we propose a new model based on convolutional neural networks (CNNs) and l2,1-norm to select age-related features for the age estimation task. A new cost function is proposed. To learn and train the new model, we provide the analysis and the proof for the convergence of the new cost function to solve minimization problem of deep neural networks (DNNs) and the l2,1-norm. High-level features are extracted from the facial images by using transfer learning, since there are currently not enough large age databases that can be used to train a deep learning network. Then, the extracted features are fed to the proposed model to select the most efficient age-related features. In addition, a new system that is based on DNN to jointly fine-tune two different DNNs with two different feature sets is developed. Experimental results show the effectiveness of the proposed methods and achieved the state-of-art performance on a public database.http://dx.doi.org/10.3390/app810194
LGBTQ Representation in the Digital Age
No full textJen Cox's poster on LGBTQ representation in the digital age