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enteknograte · 4 years ago

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FEA & CFD Based Design and Optimization

Enteknograte use advanced CAE software with special features for mixing the best of both FEA tools and CFD solvers: CFD codes such as Ansys Fluent, StarCCM+ for Combustion and flows simulation and FEA based Codes such as ABAQUS, AVL Excite, LS-Dyna and the industry-leading fatigue Simulation technology such as Simulia FE-SAFE, Ansys Ncode Design Life to calculate fatigue life of Welding, Composite, Vibration, Crack growth, Thermo-mechanical fatigue and MSC Actran and ESI VA One for Acoustics.

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Fluid-Strucure Interaction (FSI)

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Finite Element Simulation of Crash Test

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Fatigue Simulation

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Combustion Simulation

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About Enteknograte

Advanced FEA & CFD Enteknograte Where Scientific computing meets, Complicated Industrial needs. About us. Enteknograte Enteknograte is a virtual laboratory supporting Simulation-Based Design and Engineering

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bme-girl · 5 years ago

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University asks

from this blog

How far along are you in your studies? About to start the 4th/5th year of my undergrad program. So close, yet so far lol

How far do you plan on pursuing academia? I want to get my PhD and maybe I’ll do some post-doc work after that?

What made you want to attend university? My mom wasn’t able to attend in her home country and my dad got his Bachelor’s on his dad’s GI Bill. They allowed me to see how there are some limits without it and how education in this country is your ticket to money and living comfortably.

What do you study? Biomedical Engineering + Chemical/Biological Engineering

What do you wish to accomplish by studying at university? The end goal is to contribute to the knowledge in the field and have an impact on the community by inspiring underrepresented youth like me

What do you want to do with your degree? Be a researcher and teach in some capacity

What’s your dream job? Being a researcher and influencer for Latinx youth in STEM

Has university been what you expected it to be? Tbh, I don’t think so even though I didn’t really know what to expect. I’ve learned SO much along the way that no one had the knowledge to tell me when before uni

What’s the most important thing you have learned about yourself at university? That I’m strong af, I can do whatever I want to in life–literally. And not to compare yourself to others

What surprised you the most about university? The types of friends I made, people I met, and places I went bc of opportunities through uni

What classes are you taking this/next semester? For fall 2020, I am taking Chemical Engineering Design 1 (not truly a design class. more like ethics, safety, etc.), Transport Phenomena in Biomedical Eng, Separation Processes, Biochemistry, and an Independent Study.

What has been the most interesting class you’ve taken? This past semester I took a biomedical eng project-based course and it was LIT af, very challenging but projects were super realistic and interesting

What has been your favourite class you’ve taken? Same course as 12^ like it made me realize my true style of learning and working and it was difficult but so so cool

What is your favourite professor like? Haha, same prof as 12 & 13^^ he was laid back, challenging, young and tech-savvy. More than anything, he was there if you just needed someone to talk to. He didn’t ‘bend’ rules but he knew what it’s like to be a student with limited resources and no super-human abilities. Just a cool guy I’d grab a drink with

If you have to write a thesis, what are you going to write it on? I will have to provide a written report for the independent study so I’m gonna ask if it can be like a thesis. It’ll probs be on something like improving multi-modal neuroimaging data fusion using machine learning

What is your weird academic niche? I’m not really sure. Sometimes I feel like I am my own niche? Lmao. I don’t really know how to answer this tbh

What’s your favourite thing about academia? That honesty and integrity are highly valued and people just like wondering about things on a deeper level and bigger scale and have conversations about it

What’s your least favourite thing about academia? That it’s toxic: not as diverse, kinda bureaucratic, research matters more than ability to teach (in the US)

Would you go to a different university if you had to choose again? Yes and no: instead of going to a different uni then transferring to where I am now, I would 100% just have gone to the one I’m at now in the first place

Would you choose a different subject if you had to choose again? I don’t think so

If you couldn’t study the subject you study now, what would you study? Software Engineering or Comp Sci

What is your favourite course style? More applied learning, like projects. Not as exam heavy.

Theoretical or practical? AHHH! Both

Best book you’ve had to read for a course? Numerical Methods in MATLAB for Engineers

Worst book you’ve had to read for a course? Molecular Physical Chemistry for Engineers by Yates and Johnson OMFG LITERAL TRASH, even our instructor hated it

Favourite online resource? YouTube lol– broad but legit the best way to learn ANYTHING

The topic of the best essay you’ve written? I have a problem where everything I wrote in the past I now see as really bad bc my writing is always evolving. Most recent essay–maternal mortality rates in the US and race gaps within

Would you ever consider getting a phd? I am indeed considering haha

Who is doing the most interesting research in your field at the moment? In my research, Vince Calhoun or Danilo Bzdok. In my major, I’m not sure

Do you have any minors? I haven’t declared it officially, but I think I can achieve a math minor my 5th year

What is subject you wish your university taught but doesn’t? I don’t blame them cause it’s just now gaining more popularity, but like a strictly computational biology/biostatistics subject would be amazing

What is an area of your subject you wish your university taught but doesn’t? Same as 31^

The best advice anyone has ever given you about university? Do everything, don’t hold back. An opportunity can come from the smallest of things

Do you care about your grades? Yeah, but it’s not everything

Do you think you study enough? Sometimes I do, sometimes I don’t

How did your attitude towards studying and school change between high school and university? Uni hit me like a truck bc my high school was not the best. I learned to live and breathe studying, how to learn, how to manage my time, and my attitude is more serious and business-minded, but also have crazy fun in the right time and place

What do you do to rewind? Like to unwind? exercise, make music, watch movies, hit the hot tub

Best tip for making friends at university? Be yourself and be honest :)

Are you involved in any clubs/societies/extracurriculars? President for a diversity student org and I do intramural sports!

Have you done or are you planning on doing any internships? I’ve completed one research internship and I hope to do another next summer.

What is an interesting subject that you would never study yourself? Physics probably lol

What has been your favourite thing about university so far? The people, the location of my uni, the learning

What is your plan B career? Work in industry after undergrad, probably in biotech

Do you ever regret your choice of subject or university? Not really

Do you ever regret going to university? Hell no

How do you study? I try to vary it– I’ll read, do a crap ton of practice problems, do a study group with a lot of talking and teaching each other concepts, watch videos from other sources

What do you wish you’d done differently in your first year? Not been a student-athlete

What things do you think you did right in your first year? Was honest with myself

What are your thoughts on Academia? It can be good and bad, and it’s up to us as the next generation to change the bad :)

Strangest university tip you have? Hmmmmm... Don’t assume all your advisers/administration/profs know what you’re truly capable of. (not strange, but I don’t think it’s that common)

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cranesvarsity · 3 years ago

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#embedded #engineering

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globalmediacampaign · 4 years ago

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Building a knowledge graph with topic networks in Amazon Neptune

This is a guest blog post by By Edward Brown, Head of AI Projects, Eduardo Piairo, Architect, Marcia Oliveira, Lead Data Scientist, and Jack Hampson, CEO at Deeper Insights. We originally developed our Amazon Neptune-based knowledge graph to extract knowledge from a large textual dataset using high-level semantic queries. This resource would serve as the backend to a simplified, visual web-based knowledge extraction service. Given the sheer scale of the project—the amount of textual data, plus the overhead of the semantic data with which it was enriched—a robust, scalable graph database infrastructure was essential. In this post, we explain how we used Amazon’s graph database service, Neptune, along with complementing Amazon technologies, to solve this problem. Some of our more recent projects have required that we build knowledge graphs of this sort from different datasets. So as we proceed with our explanation, we use these as examples. But first we should explain why we decided on a knowledge graph approach for these projects in the first place. Our first dataset consists of medical papers describing research on COVID-19. During the height of the pandemic, researchers and clinicians were struggling with over 4,000 papers on the subject released every week and needed to onboard the latest information and best practices in as short a time as possible. To do this, we wanted to allow users to create their own views on the data—custom topics or themes—to which they could then subscribe via email updates. New and interesting facts within those themes that appear as new papers are added to our corpus arrive as alerts to the user’s inbox. Allowing a user to design these themes required an interactive knowledge extraction service. This eventually became the Covid Insights Platform. The platform uses a Neptune-based knowledge graph and topic network that takes over 128,000 research papers, connects key concepts within the data, and visually represents these themes on the front end. Our second use case originated with a client who wanted to mine articles discussing employment and occupational trends to yield competitor intelligence. This also required that the data be queried by the client at a broad, conceptual level, where those queries understand domain concepts like companies, occupational roles, skill sets, and more. Therefore, a knowledge graph was a good fit for this project. We think this article will be interesting to NLP specialists, data scientists, and developers alike, because it covers a range of disciplines, including network sciences, knowledge graph development, and deployment using the latest knowledge graph services from AWS. Datasets and resources We used the following raw textual datasets in these projects: CORD-19 – CORD-19 is a publicly available resource of over 167,000 scholarly articles, including over 79,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. Client Occupational Data – A private national dataset consisting of articles pertaining to employment and occupational trends and issues. Used for mining competitor intelligence. To provide our knowledge graphs with structure relevant to each domain, we used the following resources: Unified Medical Language System (UMLS) – The UMLS integrates and distributes key terminology, classification and coding standards, and associated resources to promote creating effective and interoperable biomedical information systems and services, including electronic health records. Occupational Information Network (O*NET) – The O*NET is a free online database that contains hundreds of occupational definitions to help students, job seekers, businesses, and workforce development professionals understand today’s world of work in the US. Adding knowledge to the graph In this section, we outline our practical approach to creating knowledge graphs on Neptune, including the technical details and libraries and models used. To create a knowledge graph from text, the text needs be given some sort of structure that maps the text onto the primitive concepts of a graph like vertices and edges. A simple way to do this is to treat each document as a vertex, and connect it via a has_text edge to a Text vertex that contains the Document content in its string property. But obviously this sort of structure is much too crude for knowledge extraction—all graph and no knowledge. If we want our graph to be suitable for querying to obtain useful knowledge, we have to provide it with a much richer structure. Ultimately, we decided to add the relevant structure—and therefore the relevant knowledge—using a number of complementary approaches that we detail in the following sections, namely the domain ontology approach, the generic semantic approach, and the topic network approach. Domain ontology knowledge This approach involves selecting resources relevant to the dataset (in our case, UMLS for biomedical text or O*NET for documents pertaining to occupational trends) and using those to identify the concepts in the text. From there, we link back to a knowledge base providing additional information about those concepts, thereby providing a background taxonomical structure according to which those concepts interrelate. That all sounds a bit abstract, granted, but can be clarified with some basic examples. Imagine we find the following sentence in a document: Semiconductor manufacturer Acme hired 1,000 new materials engineers last month. This text contains a number of concepts (called entities) that appear in our relevant ontology (O*NET), namely the field of semiconductor manufacturers, the concept of staff expansion described by “hired,” and the occupational role materials engineer. After we align these concepts to our ontology, we get access to a huge amount of background information contained in its underlying knowledge base. For example, we know that the sentence mentions not only an occupational role with a SOC code of 17-2131.00, but that this role requires critical thinking, knowledge of math and chemistry, experience with software like MATLAB and AutoCAD, and more. In the biomedical context, on the other hand, the text “SARS-coronavirus,” “SARS virus,” and “severe acute respiratory syndrome virus” are no longer just three different, meaningless strings, but a single concept—Human Coronavirus with a Concept ID of C1175743—and therefore a type of RNA virus, something that could be treated by a vaccine, and so on. In terms of implementation, we parsed both contexts—biomedical and occupational—in Python using the spaCy NLP library. For the biomedical corpus, we used a pretrained spaCy pipeline, SciSpacy, released by Allen NLP. The pipeline consists of tokenizers, syntactic parsers, and named entity recognizers retrained on biomedical corpora, along with named entity linkers to map entities back to their UMLS concept IDs. These resources turned out to be really useful because they saved a significant amount of work. For the occupational corpora, on the other hand, we weren’t quite as lucky. Ultimately, we needed to develop our own pipeline containing named entity recognizers trained on the dataset and write our own named entity linker to map those entities to their corresponding O*NET SOC codes. In any event, hopefully it’s now clear that aligning text to domain-specific resources radically increases the level of knowledge encoded into the concepts in that text (which pertain to the vertices of our graph). But what we haven’t yet done is taken advantage of the information in the text itself—in other words, the relations between those concepts (which pertain to graph edges) that the text is asserting. To do that, we extract the generic semantic knowledge expressed in the text itself. Generic semantic knowledge This approach is generic in that it could in principle be applied to any textual data, without any domain-specific resources (although of course models fine-tuned on text in the relevant domain perform better in practice). This step aims to enrich our graph with the semantic information the text expresses between its concepts. We return to our previous example: Semiconductor manufacturer Acme hired 1,000 new materials engineers last month. As mentioned, we already added the concepts in this sentence as graph vertices. What we want to do now is create the appropriate relations between them to reflect what the sentence is saying—to identify which concepts should be connected by edges like hasSubject, hasObject, and so on. To implement this, we used a pretrained Open Information Extraction pipeline, based on a deep BiLSTM sequence prediction model, also released by Allen NLP. Even so, had we discovered them sooner, we would likely have moved these parts of our pipelines to Amazon Comprehend and Amazon Comprehend Medical, which specializes in biomedical entity and relation extraction. In any event, we can now link the concepts we identified in the previous step—the company (Acme), the verb (hired) and the role (materials engineer)—to reflect the full knowledge expressed by both our domain-specific resource and the text itself. This in turn allows this sort of knowledge to be surfaced by way of graph queries. Querying the knowledge-enriched graph By this point, we had a knowledge graph that allowed for some very powerful low-level queries. In this section, we provide some simplified examples using Gremlin. Some aspects of these queries (like searching for concepts using vertex properties and strings like COVID-19) are aimed at readability in the context of this post, and are much less efficient than they could be (by instead using the vertex ID for that concept, which exploits a unique Neptune index). In our production environments, queries required this kind of refactoring to maximize efficiency. For example, we can (in our biomedical text) query for anything that produces a body substance, without knowing in advance which concepts meet that definition: g.V().out('entity').where(out('instance_of').hasLabel('SemanticType').out('produces').has('name','Body Substance')). We can also find all mentions of things that denote age groups (such as “child” or “adults”): g.V().out('entity').where(out('instance_of').hasLabel('SemanticType').in('isa').has('name','Age Group')). The graph further allowed us to write more complicated queries, like the following, which extracts information regarding the seasonality of transmission of COVID-19: // Seasonality of transmission. g.V().hasLabel('Document'). as('DocID'). out('section').out('sentence'). as('Sentence'). out('entity').has('name', within( 'summer', 'winter', 'spring (season)', 'Autumn', 'Holidays', 'Seasons', 'Seasonal course', 'seasonality', 'Seasonal Variation' ) ).as('Concept').as('SemanticTypes').select('Sentence'). out('entity').has('name','COVID-19').as('COVID'). dedup(). select('Concept','SemanticTypes','Sentence','DocID'). by(valueMap('name','text')). by(out('instance_of').hasLabel('SemanticType').values('name').fold()). by(values("text")). by(values("sid")). dedup(). limit(10) The following table summarizes the output of this query. Concept Sentence seasonality COVID-19 has weak seasonality in its transmission, unlike influenza. Seasons Furthermore, the pathogens causing pneumonia in patients without COVID-19 were not identified, therefore we were unable to evaluate the prevalence of other common viral pneumonias of this season, such as influenza pneumonia and All rights reserved. Holidays The COVID-19 outbreak, however, began to occur and escalate in a special holiday period in China (about 20 days surrounding the Lunar New Year), during which a huge volume of intercity travel took place, resulting in outbreaks in multiple regions connected by an active transportation network. spring (season) Results indicate that the doubling time correlates positively with temperature and inversely with humidity, suggesting that a decrease in the rate of progression of COVID-19 with the arrival of spring and summer in the north hemisphere. summer This means that, with spring and summer, the rate of progression of COVID-19 is expected to be slower. In the same way, we can query our occupational dataset for companies that expanded with respect to roles that required knowledge of AutoCAD: g.V(). hasLabel('Sentence').as('Sentence'). out('hasConcept'). hasLabel('Role').as('Role'). where( out('hasTechSkill').has('name', 'AutoCAD') ). in('hasObject'). hasLabel('Expansion'). out('hasSubject'). hasLabel('Company').as('Company'). select('Role', 'Company', 'Sentence'). by('socCode'). by('name'). by('text'). limit(1) The following table shows our output. Role Company Sentence 17-2131.00 Acme Semiconductor manufacturer Acme hired 1,000 new materials engineers last month. We’ve now described how we added knowledge from domain-specific resources to the concepts in our graph and combined that with the relations between them described by the text. This already represents a very rich understanding of the text at the level of the individual sentence and phrase. However, beyond this very low-level interpretation, we also wanted our graph to contain much higher-level knowledge, an understanding of the concepts and relations that exist across the dataset as a whole. This leads to our final approach to enriching our graph with knowledge: the topic network. Topic network The core premise behind the topic network is fairly simple: if certain pairs of concepts appear together frequently, they are likely related, and likely share a common theme. This is true even if (or especially if) these co-occurrences occur across multiple documents. Many of these relations are obvious—like the close connection between Lockheed and aerospace engineers, in our employment data—but many are more informative, especially if the user isn’t yet a domain expert. We wanted our system to understand these trends and to present them to the user—trends across the dataset as a whole, but also limited to specific areas of interest or topics. Additionally, this information allows a user to interact with the data via a visualization, browsing the broad trends in the data and clicking through to see examples of those relations at the document and sentence level. To implement this, we combined methods from the fields of network science and traditional information retrieval. The former told us the best structure for our topic network, namely an undirected weighted network, where the concepts we specified are connected to each other by a weighted edge if they co-occur in the same context (the same phrase or sentence). The next question was how to weight those edges—in other words, how to represent the strength of the relationship between the two concepts in question. Edge weighting A tempting answer here is just to use the raw co-occurrence count between any two concepts as the edge weight. However, this leads to concepts of interest having strong relationships to overly general and uninformative terms. For example, the strongest relationships to a concept like COVID-19 are ones like patient or cell. This is not because these concepts are especially related to COVID-19, but because they are so common in all documents. Conversely, a truly informative relationship is between terms that are uncommon across the data as a whole, but commonly seen together. A very similar problem is well understood in the field of traditional information retrieval. Here the informativeness of a search term in a document is weighted using its frequency in that document and its rareness in the data as a whole, using a formula like TFIDF (term frequency by inverse document frequency). This formula however aims at scoring a single word, as opposed to the co-occurrence of two concepts as we have in our graph. Even so, it’s fairly simple to adapt the score to our own use case and instead measure the informativeness of a given pair of concepts: In our first term, we count the number of sentences, s, where both concepts, c1 and c2, occur (raw co-occurrence). In the second, we take the sum of the number of documents where each term appears relative to (twice) the total number of documents in the data. The result is that pairs that often appear together but are both individually rare are scored higher. Topics and communities As mentioned, we were interested in allowing our graph to display not only global trends and co-occurrences, but also those within a certain specialization or topic. Analyzing the co-occurrence network at the topic level allows users to obtain information on themes that are important to them, without being overloaded with information. We identified these subgraphs (also known in the network science domain as communities) as groups centered around certain concepts key to specific topics, and, in the case of the COVID-19 data, had a medical consultant curate these candidates into a final set—diagnosis, prevention, management, and so on. Our web front end allows you to navigate the trends within these topics in an intuitive, visual way, which has proven to be very powerful in conveying meaningful information hidden in the corpus. For an example of this kind of topic-based navigation, see our publicly accessible COVID-19 Insights Platform. We’ve now seen how to enrich a graph with knowledge in a number of different forms, and from several different sources: a domain ontology, generic semantic information in the text, and a higher-level topic network. In the next section, we go into further detail about how we implemented the graph on the AWS platform. Services used The following diagram gives an overview of our AWS infrastructure for knowledge graphs. We reference this as we go along in this section. Amazon Neptune and Apache TinkerPop Gremlin Although we initially considered setting up our own Apache TinkerPop service, none of us were experts with graph database infrastructure, and we found the learning curve around self-hosted graphs (such as JanusGraph) extremely steep given the time constraints on our project. This was exacerbated by the fact that we didn’t know how many users might be querying our graph at once, so rapid and simple scalability was another requirement. Fortunately, Neptune is a managed graph database service that allows storing and querying large graphs in a performant and scalable way. This also pushed us towards Neptune’s native query language, Gremlin, which was also a plus because it’s the language the team had most experience with, and provides a DSL for writing queries in Python, our house language. Neptune allowed us to solve the first challenge: representing a large textual dataset as a knowledge graph that scales well to serving concurrent, computationally intensive queries. With these basics in place, we needed to build out the AWS infrastructure around the rest of the project. Proteus Beyond Neptune, our solution involved two API services—Feather and Proteus—to allow communication between Neptune and its API, and to the outside world. More specifically, Proteus is responsible for managing the administration activities of the Neptune cluster, such as: Retrieving the status of the cluster Retrieving a summary of the queries currently running Bulk data loading The last activity, bulk data loading, proved especially important because we were dealing with a very large volume of data, and one that will grow with time. Bulk data loading This process involved two steps. The first was developing code to integrate the Python-based parsing we described earlier with a final pipeline step to convert the relevant spaCy objects—the nested data structures containing documents, entities, and spans representing predicates, subjects, and objects—into a language Neptune understands. Thankfully, the spaCy API is convenient and well-documented, so flattening out those structures into a Gremlin insert query is fairly straightforward. Even so, importing such a large dataset via Gremlin-language INSERT statements, or addVertex and addEdge steps, would have been unworkably slow. To load more efficiently, Neptune supports bulk loading from the Amazon Simple Storage Service (S3). This meant an additional modification to our pipeline to write output in the Neptune CSV bulk load format, consisting of two large files: one for vertices and one for edges. After a little wrangling with datatypes, we had a seamless parse-and-load process. The following diagram illustrates our architecture. It’s also worth mentioning that in addition to the CSV format for Gremlin, the Neptune bulk loader supports popular formats such as RDF4XML for RDF (Resource Description Framework). Feather The other service that communicates with Neptune, Feather, manages the interaction between the user (through the front end) and Neptune instance. It’s responsible for querying the database and delivering the results to the front end. The front-end service is responsible for the visual representation of the data provided by the Neptune database. The following screenshot shows our front end displaying our biomedical corpus. On the left we can see the topic-based graph browser showing the concepts most closely related to COVID-19. In the right-hand panel we have the document list, showing where those relations were found in biomedical papers at the phrase and sentence level. Lastly, the mail service allows you to subscribe to receive alerts around new themes as more data is added to the corpus, such as the COVID-19 Insights Newsletter. Infrastructure Owing to the Neptune security configuration, any incoming connections are only possible from within the same Amazon Virtual Private Cloud (Amazon VPC). Because of this, our VPC contains the following resources: Neptune cluster – We used two instances: a primary instance (also known as the writer, which allows both read and write) and a replica (the reader, which only allows read operations) Amazon EKS – A managed Amazon Elastic Kubernetes Service (Amazon EKS) cluster with three nodes used to host all our services Amazon ElastiCache for Redis – A managed Amazon ElastiCache for Redis instance that we use to cache the results of commonly run queries Lessons and potential improvements Manual provisioning (using the AWS Management Console) of the Neptune cluster is quite simple because at the server level, we only need to deal with a few primary concepts: cluster, primary instance and replicas, cluster and instance parameters, and snapshots. One of the features that we found most useful was the bulk loader, mainly due to our large volume of data. We soon discovered that updating a huge graph was really painful. To mitigate this, each time a new import was required, we created a new Neptune cluster, bulk loaded the data, and redirected our services to the new instance. Additional functionality we found extremely useful, but have not highlighted specifically, is the AWS Identity and Access Management (IAM) database authentication, encryption, backups, and the associated HTTPS REST endpoints. Despite being very powerful, with a bit of a steep learning curve initially, they were simple enough to use in practice and overall greatly simplified this part of the workflow. Conclusion In this post, we explained how we used the graph database service Amazon Neptune with complementing Amazon technologies to build a robust, scalable graph database infrastructure to extract knowledge from a large textual dataset using high-level semantic queries. We chose this approach since the data needed to be queried by the client at a broad, conceptual level, and therefore was the perfect candidate for a Knowledge Graph. Given Neptune is a managed graph database with the ability to query a graph with milliseconds latency it meant that the ease of setup if would afford us in our time-poor scenario made it the ideal choice. It was a bonus that we’d not considered at the project outset to be able to use other AWS services such as the bulk loader that also saved us a lot of time. Altogether we were able to get the Covid Insights Platform live within 8 weeks with a team of just three; Data Scientist, DevOps and Full Stack Engineer. We’ve had great success with the platform, with researchers from the UK’s NHS using it daily. Dr Matt Morgan, Critical Care Research Lead for Wales said “The Covid Insights platform has the potential to change the way that research is discovered, assessed and accessed for the benefits of science and medicine.” To see Neptune in action and to view our Covid Insights Platform, visit https://covid.deeperinsights.com We’d love your feedback or questions. Email [email protected] https://aws.amazon.com/blogs/database/building-a-knowledge-graph-with-topic-networks-in-amazon-neptune/

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evoldir · 4 years ago

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Fwd: Postdoc:JohnsHopkins:InfectiousDiseaseDynamics

Begin forwarded message: > From: [email protected] > Subject: Postdoc:JohnsHopkins:InfectiousDiseaseDynamics > Date: 25 November 2020 at 10:01:40 GMT > To: [email protected] > > > --_000_SA0SPR01MB00190F9841F8516E1A404EFAAAFA0SA0SPR01MB0019pr_ > Content-Type: text/plain; charset="Windows-1252" > Content-Transfer-Encoding: quoted-printable > > The research group of Dr. Alison Hill at Johns Hopkins University invites applicants for a postdoctoral fellow to contribute to the group's work developing mathematical models and computational tools to better understand, predict, and control infectious diseases. We are seeking a motivated and creative PhD-level scientist with experience applying mathematics to biological systems. The successful applicant will become a part of two highly-collaborative multi-PI groups at the university: the Institute for Computational Medicine (https://icm.jhu.edu/) and the Infectious Disease Dynamics Group (https://ift.tt/3nSbVPK). The position is expected to be virtual for the duration of university closures in response to COVID-19. > > Multiple positions are available to contribute to new and ongoing projects in the group, such as modeling a) within-host dynamics and evolution of HIV infection in response to antiretroviral therapy, b) cure strategies for HIV/AIDS including immunotherapy and latency-targeting therapies, c) strategies to slow the evolution of drug resistance for a wide range of pathogens, d) spread of COVID-19 and the efficacy of control policies. The group additionally studies the spread of bed bug infestations, antiviral immune responses, cancer-causing viruses, and the intersection of human behavior and infection spread. The work involves both deterministic and stochastic modeling, and working with a variety of data types and collaborators around the world. The successful candidate will also be welcome to pursue independent research topics of mutual interest. The lab is funded by grants from the National Institutes of Health and the Bill & Melinda Gates Foundation. More information about > our current and previous work can be found at https://ift.tt/3pZH6up > > The successful applicant should have a PhD in a quantitative field and some experience working on topics in biology, medicine, or public health. Example fields of training of former lab members include (but are not limited to) applied math, biophysics, epidemiology, systems biology, ecology/evolutionary biology, physics, and biomedical engineering. The ideal candidate would be able to follow projects through from conception to publication, be a strong writer, have experience presenting their work to scientific audiences from diverse fields, enjoy working collaborative, and be excited to mentor junior trainees. Programming skills (ideally in at least one of Python, R, or Matlab) are required. > > The start date of the position is flexible, and could be as early as Dec 2020 or as late as Spring 2021. Due to university regulations during the COVID-19 pandemic, the applicant must already be living in the US and must be able to work remotely. The duration of the position is also flexible based on the employee's performance and career goals, and can be renewed on an annual basis. Salary is competitive and commensurate with experience; comprehensive benefits including health insurance are provided to all postdoctoral fellows. > > Interested candidates should submit the following (in pdf form) to [email protected]: > > * > A cover letter describing the applicant's research interests, educational background and previous research experiences, and career goals > * > A CV, which includes a link to a Google Scholar profile > * > Contact information for 3 references (will only be contacted after initial meeting with applicant) > > Applications will be considered on a rolling basis and should be submitted as soon as possible. > > > --_000_SA0SPR01MB00190F9841F8516E1A404EFAAAFA0SA0SPR01MB0019pr_ > Content-Type: text/html; charset="Windows-1252" > Content-Transfer-Encoding: quoted-printable > > >

> > P {margin-top:0;margin-bottom:0;} > > >

The research group of Dr. Alison Hill at Johns Hopkins University invites applicants for a postdoctoral fellow to contribute to the group's work developing mathematical models and computational tools to better understand, predict, and control infectious > diseases. We are seeking a motivated and creative PhD-level scientist with experience applying mathematics to biological systems. The successful applicant will become a part of two highly-collaborative multi-PI groups at the university: the Institute for Computational > Medicine (https://icm.jhu.edu/) and the Infectious Disease Dynamics Group (http://www.iddynamics.jhsph.edu/). The position is expected to be virtual for the duration of university > closures in response to COVID-19.

Multiple positions are available to contribute to new and ongoing projects in the group, such as modeling a) within-host dynamics and evolution of HIV infection in response to antiretroviral therapy, b) cure strategies for HIV/AIDS including immunotherapy > and latency-targeting therapies, c) strategies to slow the evolution of drug resistance for a wide range of pathogens, d) spread of COVID-19 and the efficacy of control policies. The group additionally studies the spread of bed bug infestations, antiviral > immune responses, cancer-causing viruses, and the intersection of human behavior and infection spread. The work involves both deterministic and stochastic modeling, and working with a variety of data types and collaborators around the world. The successful > candidate will also be welcome to pursue independent research topics of mutual interest. The lab is funded by grants from the National Institutes of Health and the Bill & Melinda Gates Foundation. More information about our current and previous work can be > found at http://alsnhll.github.io/

The successful applicant should have a PhD in a quantitative field and some experience working on topics in biology, medicine, or public health. Example fields of training of former lab members include (but are not limited to) applied math, biophysics, > epidemiology, systems biology, ecology/evolutionary biology, physics, and biomedical engineering. The ideal candidate would be able to follow projects through from conception to publication, be a strong writer, have experience presenting their work to scientific > audiences from diverse fields, enjoy working collaborative, and be excited to mentor junior trainees. Programming skills (ideally in at least one of Python, R, or Matlab) are required. >

The start date of the position is flexible, and could be as early as Dec 2020 or as late as Spring 2021. Due to university regulations during the COVID-19 pandemic, the applicant must already be living in the US and must be able to work remotely. The duration > of the position is also flexible based on the employee's performance and career goals, and can be renewed on an annual basis. Salary is competitive and commensurate with experience; comprehensive benefits including health insurance are provided to all postdoctoral > fellows.

Interested candidates should submit the following (in pdf form) to [email protected]: >

A cover letter describing the applicant's research interests, educational background and previous research experiences, and career goals >

A CV, which includes a link to a Google Scholar profile

Contact information for 3 references (will only be contacted after initial meeting with applicant) >

Applications will be considered on a rolling basis and should be submitted as soon as possible. >

> >

> > > > --_000_SA0SPR01MB00190F9841F8516E1A404EFAAAFA0SA0SPR01MB0019pr_-- > via IFTTT

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matlabassignmenthelp-blog · 5 years ago

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juniperpublishers-etoaj · 6 years ago

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Juniper Publishers - Open Access Journal of Engineering Technology

Removal of the Power Line Interference from ECG Signal Using Different Adaptive Filter Algorithms and Cubic Spline Interpolation for Missing Data Points of ECG in Telecardiology System

Authored by : Shekh Md Mahmudul Islam

Abstract

Maintaining one's health is a fundamental human right although one billion people do not have access to quality healthcare services. Telemedicine can help medical facilities reach their previously inaccessible target community. The Telecardiology system designed and implemented in this research work is based on the use of local market electronics. In this research work we tested three algorithms named as LMS (Least Mean Square), NLMS (Normalized Mean Square), and RLS (Recursive Least Square). We have used 250 mV amplitude ECG signal from MIT- BIH database, 25mV (10% of original ECG signal) of random noise, white Gaussian noise and 100mV (40% of original ECG signal) of 50 Hz power signal noise is added with ECG signal in different combinations and Adaptive filter with three different algorithms have been used to reduce the noise that is added during transmission through the telemedicine system. Normalized mean square error was calculated and our MATLAB simulation results suggest that RLS performs better than other two algorithms to reduce the noise from ECG. During analog transmission of ECG signal through existing Telecommunication network some data points may be lost and we have theoretically used Cubic Spline interpolation to regain missing data points. We have taken 5000 data points of ECG Signal from MIT -BIH database. The normalized mean square error was calculated for regaining missing data points of the ECG signal and it was very less in all the conditions. Cubic Spline Interpolation function on MATLAB platform could be a good solution for regaining missing data points of original ECG signal sent through our proposed Telecardiology system but practically it may not be efficient one.

Keywords: Telemedicine; Power line interference (PLI); ECG; Adaptive filter; LMS; NLMS; RLS

Abbreviations: EMG: Electromyography; ECG: Electrocardiogram; EEG: Electroencephalogram; NLMS: Normalized Least Mean Square; RLS: Recursive Least Square; SD: Storage Card

Introduction

The ECG signal measured with an electrocardiograph, is a biomedical electrical signal occurring on the surface of the body related to the contraction and relaxation of the heart. This signal represents an extremely important measure for doctors as it provides vital information about a patient cardiac condition and general health. Generally the frequency band of the ECG signal is 0.05 to 100 Hz [1]. Inside the heart there is a specialized electrical conduction system that ensures the heart to relax and contracts in a coordinated and effective fashion. ECG recordings may have common artifacts with noise caused by factors such as power line interference, external electromagnetic field, random body movements, other biomedical signals and respiration. Different types of digital filters may be used to remove signal components from unwanted frequency ranges [2].

The Power line interference 50/60 Hz is the source of interference in bio potential measurement and it corrupt the biomedical signal's recordings such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG) which are extremely important for the diagnosis of patients. It is hard to find out the problem because the frequency range of ECG signal is nearly same as the frequency of power line interference. The QRS complex is a waveform which is most important in all ECG's waveforms and it comes into view in usual and unusual signals in ECG [3]. As it is difficult to apply filters with fixed coefficients to reduce biomedical signal noises because human behaviour is not exact depending on time, an adaptive filtering technique is required to overcome the problem. Adaptive filer is designed using different algorithms such as least mean square (LMS), Normalized least mean square (NLMS), Recursive least square (RLS) [4]. Least square algorithms aims at minimization of the sum of the squares of the difference between the desired signal and model filter output when new samples of the incoming signals are received at every iteration, the solution for the least square problem can be computed in recursive form resulting in the recursive least square algorithm. The goal for ECG signal enhancement is to separate the valid signal components from the undesired artifacts so as to present an ECG that facilitates an easy and accurate interpretation.

The basic idea for adaptive filter is to predict the amount of noise in the primary signal and then subtract noise from it. In this research work a Telecardiology system has been designed and implemented using instrumentation amplifier, band pass filter and Arduino interfacing between Smartphone and Arduino board. First of all raw ECG signal has been amplified and filtered by Band pass filter. Analog signal has been digitized using Arduino board and then interfacing between Arduino board and smart phone has been implemented and Digitized value of analog signal has been sent from Arduino board to smart phone and digitized value of analog signal has been stored in SD storage card of smart phone. Using Bluetooth or existing Telecommunication Network. As sinusoidal signals are known to be corrupted during transmission it is expected that similarly an ECG signal will be corrupted.

We have therefore designed an adaptive filter with three different algorithms and simulated in MATLAB platform to compare the performance of denoising of ECG signal. During transmission of ECG signals through existing Telecommunication networks some data pints may be lost. In this research work we have used cubic spline interpolation to regain missing data points. If more data points are missing then reconstruction of an ECG signal becomes impossible and doctor can not accurately interpret a patient's ECG in an efficient manner. Cubic spline interpolation has been implemented for various missing data points of original ECG signal taken from MIT-BIH database. The normalized mean square error of cubic spline interpolation was very low. Cubic Spline interpolation in Matlab platform could be a better solution for regaining missing data points of ECG signal theoretically.

Related Works and Literature Review

The extraction of high-resolution ECG signals from recordings contaminated with system noise is an important issue to investigate in Telecardiology system. The goal for ECG signal enhancement is to separate the valid signal components from the undesired artifacts, so as to present an ECG that facilitates easy and accurate interpretation.

The work of this research paper is the development of our previous research work "Denoising ECG Signal using Different Adaptive Filter Algorithms and Cubic Spline Interpolation for Missing data points of ECG in Telecardiology System” [5]. Many approaches have been reported in the literature to address ECG enhancement using adaptive filters [6-9], which permit to detect time varying potentials and to track the dynamic variations of the signals. In Md. Maniruzzaman et al [7,10,11] proposed wavelet packet transform, Least-Mean-Square (LMS) normalized least-mean-square (NLMS) and recursive-least-square (RLS), and the results are compared with a conventional notch filter both in time and frequency domain. In these papers, power line interference noise is denoised by NLMS or LMS or RLS algorithms and performed by MTLAB or LABVIEW. But in our research work we have developed our previous work. We denoised ECG signal by removing power line interference, Random noise and White Gaussian noise. In our previous research paper [12] we denoised ECG signal from random noise and white Gaussian noise. In our present research work we have added power line interference with Pure ECG signal individually and added in mixed of power line interference, random noise and white Gaussian noise in different combinations. Finally in our research work we have used cubic spline interpolation for regaining missing data points of ECG signal sent through telecommunication network.

There are certain clinical applications of ECG signal processing that require adaptive filters with large number of taps. In such applications the conventional LMS algorithm is computationally expensive to implement The LMS algorithm and NLMS (normalized LMS) algorithm require few computations, and are, therefore, widely applied for acoustic echo cancellers. However, there is a strong need to improve the convergence speed of the LMS and NLMS algorithms. The RLS (recursive least-squares) algorithm, whose convergence does not depend on the input signal, is the fastest of all conventional adaptive algorithms. The major drawback of the RLS algorithm is its large computational cost. However, fast (small computational cost) RLS algorithms have been studied recently In this paper we aim to obtain a comparative study of faster algorithm by incorporating knowledge of the room impulse response into the RLS algorithm. Unlike the NLMS and projection algorithms, the RLS algorithm does not have a scalar step size.

Therefore, the variation characteristics of an ECG signal cannot be reflected directly in the RLS algorithm. Here, we study the RLS algorithm from the viewpoint of the adaptive filter because

a. The RLS algorithm can be regarded as a special version of the adaptive filter and

b. Each parameter of the adaptive filter has physical meaning.

Computer simulations demonstrate that this algorithm converges twice as fast as the conventional algorithm. These characteristics may plays a vital role in biotelemetry, where extraction of noise free ECG signal for efficient diagnosis and fast computations, high data transfer rate are needed to avoid overlapping of pulses and to resolve ambiguities. To the best of our knowledge, transform domain has not been considered previously within the context of filtering artifacts in ECG signals.

In this paper we compare the performances of LMS, NLMS and RLS algorithms to remove the artifacts from ECG. This algorithm enjoys less computational complexity and good filtering capability. To study the performance of the algorithms to effectively remove the noise from the ECG signal, we carried out simulations on MIT-BIH database. During transmission of ECG signal through existing Telecommunication network it may be corrupted or some data points may be lost. Linear Spline interpolation was popular method for regaining missing data points of ECG signal [13]. Cubic Spline interpolation has gained popularity very recently [6]. In our previous research work we used cubic spline interpolation. The development of our previous research work i.e., in the present research work, in this research paper we have also used cubic spline interpolation for regaining missing data points of ECG signal sent through telecommunication network.

Adaptive Filter Algorithms

In this research work a Telecardiology system has been implemented and proposed for sending ECG signal through smart phone (Figure 1).

The raw ECG signal will be taken from patient electrode and passed through instrumentation amplifier and band pass filter to amplify the signal and to reduce the noise coming from electrodes. Then that amplified and filtered analog ECG signal will be converted into digital signal by using Arduino AVR microcontroller based system. Then that digital value of ECG signal will be sent to smart phone by using Arduino interfacing with smart phone and digitized signal values will be sent to smart phone SD card. After that digital value will be sent to another smart phone by using Bluetooth technology. Digitized ECG value will be received to smart phone via Bluetooth .During transmission of ECG signal through Telecommunication network it may be corrupted by random noise or white Gaussian noise of the network. Adaptive filter using different algorithms have been used to reduce noise of the transmitted ECG signal. A MATLAB coding has been done to reduce the noise of the ECG signal and for reducing noise of digitized ECG signal, transmitted noisy ECG signal needs to be loaded in MATLAB and then it is filtered suing adaptive filter with different algorithms and performances of different algorithms are measured based on their de-noising capabilities. During transmission of ECG signal some data points may be missing and MATLAB spline interpolation algorithm will get them back so that ECG signal can be transmitted reliably (Figure 2).

Least Mean Square (LMS), Normalized Mean Square Algorithm (NLMS) and Recursive Least Square Algorithm (RLS) has been designed and implemented for denoising ECG signal in MATLAB platform [4,12-14]. Cubic Spline Interpolation has been used for regaining missing data points of ECG signal during transmission through existing Telecommunication network. The normalized mean square error was calculated for regaining missing data points of ECG signal and it was very less and so Cubic spline interpolation could be a better solution in MATLAB platform for regaining missing data points of ECG signal.

Result

In this work we have taken pure ECG signal from MIT-BIH database. The amplitude of our taken ECG signal was 250 mV which is amplified from.5mV (2 % of original ECG signal), 10 mV (4% of original ECG) 15mV (6% of original ECG), 20 mV (8% of original ECG signal) and 25mV (10% of original ECG signal) of random noise and white Gaussian noise is added with ECG signal. Three different algorithms of Adaptive filter were implemented and tested their performances of denosing ECG signal. We have taken ECG signal with 250 mV amplitude and 5000 samples were taken from MIT-BIH database (Figure 3). In our simulation work we have denoised 100mV of 50 Hz power signal noise.

Recursive Least Square (RLS) Algorithms

We have taken 5000 data points of ECG signal from MIT- BIH database. In our simulation 11data points (from 689 to 699 of original data points of ECG), 201 data points (from 800 to 1000 of original data points of ECG), 300 data points (from 1600 to 1900 of original data points of ECG), 500 data points (from 2000 to 5000) and 6 data points (from 4095 to 5000) are made zero and cubic spline interpolation function was called in MATLAB platform and it could regain the original data points of ECG signal (Figure 15-20). The MATLAB coding result of Spline Interpolation is given below Table 2

The above simulation result suggests that Recursive algorithms. RLS could be the best option for Telemedicine system Least Square algorithm (RLS) performs better than other two to denoise ECG signal during transmission (Table 1).

Normalized mean square error calculation suggests that Cubic Spline performs satisfactorily for regaining missing data points of ECG signal.

Conclusion

During transmission of ECG signal it may be corrupted due to random noise and Gaussian noise. So we have tested the performances of LMS, NLMS and RLS algorithm of adaptive filter. Our simulation result suggest that RLS could be the best option for recovering ECG signal or denoising EEG signal during transmission through Telemedicine system.

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