The high theoretical capacity of lithium oxygen batteries (LOBs) made them an attractive target for extensive research over the past two decades. However, major issues related to battery performance need to be addressed before the wide deployment of this technology. One major source of enhancement would be in the air electrode. This research focuses on developing carbon-based air electrodes with optimized textural properties and enhanced catalytic performance. The work will take into consideration the carbon structure design and the catalyst type, loading amount and distribution on the carbon surface. The effect of the interactions of the carbon and the catalyst on the discharge/charge reactions will be studied through galvanostatic discharge-charge measurements, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and other characterization techniques. The proposed electrode is expected to facilitate the electrochemical reactions during the discharge and charge of the battery, reduce overpotential, and increase battery capacity and cyclability.
Since zeolites have been used for cracking for decades, this work aims to produce hierarchical faujasite with the house of cards morphology to be used for FCC through extensive synthesis methods. These zeolites will have better reaction rates, selectivity and a unique adsorption behavior as compared to conventional zeolites used for cracking. Faujasite has only been synthesized in zeolite X with Si/Al ratio less than 1.5, here for the first time the synthesis of zeolite Y with the house of cards morphology will be approached through direct synthesis for catalytic usage. The best Si/Al ratio achieved is 1.36 showing that it is theoretically possible to keep increasing the Si/Al ratio and achieve zeolite Y.
Colorectal Cancer (CRC) is a leading cause of death around the globe, and therefore, the analysis of tumor microenvironment in the CRC WSIs is important for the early detection of CRC. We propose to employ the hypergraph neural network to classify seven different CRC tissue types. Firstly, image deep features are extracted from input patches using the pre-trained VGG19 model. The hypergraph is then constructed whereby patch-level deep features represent the vertices of a hypergraph and hyperedges are assigned using pair-wise euclidean distance. The edges, vertices, and their corresponding patch-level features are passed through a feed-forward neural network to perform tissue classification in a transductive manner. Experiments are performed on an independent CRC tissue classification dataset and compared with existing state-of-the-art methods. Our results reveal that the proposed algorithm outperforms existing methods by achieving an overall accuracy of 95.46% and AvTP of 94.42%.
In recent years, vision-driven robotic grasping has seen widespread use in academia and industry due to its compatibility with contextualized tasks and its sensitivity to object geometries. In this paper, we assume that an event-driven, neuromorphic camera is used and that the mean-shift algorithm is applied to segment the visual field and identify objects. Compared with standard machine vision, the neuromorphic approach has the advantages of low latency, low power consumption, and high dynamic range. In this paper, we address the problem of accelerating meanshift clustering for neuromorphic vision by proposing a novel parallel implementation on an edge computing platform. The platform used is the recently released Jetson Nano GPU from NVIDIA. The proposed GPU parallel implementation is evaluated in comparison to a CPU sequential algorithm running on the same platform, which shows approximately 100 times faster than its sequential counterpart on a set of neuromporphic data sequences.
In this paper, the problem of categorizing aircrafts based on their broadcasted ADS-B messages is addressed. Machine learning is proposed for generating models that are able to categorize aircrafts based on their ADS-B messages and flight pattern. The models are trained using real data and tested to evaluate their performance. The results show that high performance is observed especially when balancing the dataset.
In this paper, we present our solution to the Traffic4cast2020 traffic prediction challenge. The information provided includes nine channels where the first eight represent the speed and volume for four different traffic directions, while the last channel indicates the presence of traffic incidents. The expected output should have the first 8 channels of the input at six future timing intervals, while a one-hour duration of past traffic data, in 5mins intervals, are provided as input. We solve the problem using a novel sedenion U-Net neural network. Sedenion networks provide the means for efficient encoding of correlated multimodal datasets. We use 12 of the 15 sedenion imaginary parts for the dynamic inputs and the real part is used for the static input. The sedenion output of the network is used to represent the multimodal traffic predictions. The proposed system achieved a validation MSE of 1.26e-3 and a test MSE of 1.22e-3.
Pedestrian detection is a challenging yet essential task in computer vision with many potential applications in the real world, such as autonomous driving, robotics and surveillance. However, there exit gaps between research and industrial deployment. To bridge these gaps, we perform an extensive evaluation and experiment of the state-of-the-art techniques in a unified framework.
This research project aims to study and analyze the sentiments and emotions of the public in the UAE relevant to online education during and post COVID-19 pandemic. This will provide a faster and more representative insight on the public's mental health during and post the pandemic relevant to online education. This project has the objective of designing and implementing spatiotemporal analytics models that will support the decision makers with detailed insights against a specific aspect during a certain period or at a specific temporal event, and in a specific location. This allows for better understanding of the public's emotional triggers and the impact of the new changes in the educational delivery mode on the mental health of the public. Consequently, informed-intervention plans can be made. we target Twitter as a source of public opinion and emptions due to its popularity in the UAE.
The complex trust domain of the internet entities has become more complex when dealing with Internet of Things (IoT) devices as entities. Trust and reputation systems were designed to establish trust between entities, however, not tailored to the special needs of IoT devices. To provide a general framework for IoT devices in a public network the blockchain technology and reputation systems are used in conjunction. The framework aims to provide a customizable, secure and scalable architecture that enables a service oriented platform for IoT devices and their clients. We provide a design that utilizes subsystems that are motivated with a reward-penalty scheme to aid the end users of the system.
Electrical Impedance Tomography (EIT) is a non-invasive and cheap imaging technology that infers conductivity distributions inside a conductor from surface measurements alone. However, it is infamous for the ill-posed and non-linear nature of its inverse problem which has driven research in machine learning for the implementation of a tactile sensing skin for robotic applications. This paper summarises some of the major contributions in the field, to help track the different approaches to incorporating machine learning for the inverse solution.
