scholarly journals Scalable IOT solutions with the Amazon Echo Flex Model for 3P integrations.

2020 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Water Level ◽  
Code Generation ◽  
Predictive Analytics ◽  
Observer Design ◽  
Raspberry Pi ◽  
Flood Prediction ◽  
Scalable Network

IaC is also CaC, Circuits as Code, we introduce a uniform framework for IOT based sensor fusion and automated persistence to AWS S3 using the per-observer design pattern defined in reactive streams. A uniform scalable IoT architecture is in the automated code generation of Alexa Skills with both AlexaPi and the flex echo. We introduce CaC using the TOMU board, an open source ARM v7 based architecture.Keywords: IaC, CloudFormation, CaC, Circuits as Code, AWS, stack, SaaS, AIaaS, IoT, ARMWhat:The Amazon Echo Flex, retailing at $24.99 is presented as an attractive scalable IOT platform for use with grid compatible IOT solutions, in conjunction with the open source Tomu and Fomu platforms for multi sensor integrated IOT with Alexa skills and AWS Lambda cloud functions. We compare this with the AlexPi solution, enabling IOT with inexpensive hardware like the raspberry pi zero W or orange pi, or any scalable browser based device, as an IOT solution with Alexa. The marketing pitch for the echo flex, remains as a power source for all the hardware supported by AlexaPi or any other computing needing a 5v USB-A bus and thus supplements the AlexaPi. A case study of flooding in the Lilydale Regional Park in St Paul is presented where a scalable network of Tomu based water level indicators is hypothesized to predict flooding for closure of the park.How:Simple resistive water level monitoring sensors are integrated with Tomu and Echo Flex boards using the Alexa Gadget API, USB function for an Rx formulation of Alexa IOT interactions. This allows periodic uploading of water level information to AWS S3 for data mining and predictive analytics. A hypothetical model to compute the operations of such a ‘N’ node grid is presented with a case study of lilydale park and the Mississippi river.Why:Simplicity , robustness and cost effective solutions , lead to the evolution of the IOT or things network, while Lora or SigFox are touted as solutions we present a simpler approach using WiFi and VUI based echo flex units for IOT.Applications:IoT for AI for Earth series, starring Early Bird Warning for Flood Prediction Analytics, an AIaaS data mining Lambda of S3 data from a network of IoT nodes with river level sensors.

scholarly journals Smart disaster prediction application using flood risk analytics towards sustainable climate action

2018 ◽  
Vol 189 ◽  
pp. 10006
Author(s):  
Michael M. Orozco ◽  
Jonathan M. Caballero
Keyword(s):  
Water Level ◽  
River Water ◽  
Statistical Modeling ◽  
Early Warning ◽  
Flood Risk ◽  
Predictive Analytics ◽  
Water Pressure ◽  
Warning System ◽  
Flood Prediction ◽  
Disaster Prediction

Disaster prediction devices for early warning system are used by many countries for disaster awareness. This study developed smart disaster prediction application using microcontrollers and sensors to analyze the river water level for flood using flood risk analytics. Specifically, it monitors the river water level, water pressure and rain fallusing microcontroller, applying statistical modeling algorithms for river flood prediction, and monitor flood in a web-based system with SMS notification and alarm to the community as an early warning. The researchers used the system development method to measure the prototype feasibility study. The researchers applied the statistical modeling algorithm as the data can be observed from time to time or on a daily basis for the predictive analytics. Based on the 7-days observation result, rainfall resulted in precipitation average of 10.96 mm, water pressure with an average of 40.92 pound per square inch (psi) and water level averaged 138.78 cm. The tropical depression during the 7 days’observation reflected the average data result from the sensors as the target of the study. The result of the prototype device used the City Disaster Risk and Reduction management office (CDRRMO) as history logs for a flood risk and it was proven accurate which makes a good use for disaster prediction.

The Effects of Bytecode Generation in XSLT and XQuery

2011 ◽  
Author(s):  
O'Neil Davion Delpratt ◽  
Michael Kay
Keyword(s):  
Open Source ◽  
Code Generation ◽  
Experimental Evaluation ◽  
Query Rewriting ◽  
Relative Importance ◽  
Development Team ◽  
Java Bytecode ◽  
High Level ◽  
Performance Gains

This paper attempts to analyze the performance benefits that are achievable by adding a code generation phase to an XSLT or XQuery engine. This is not done in isolation, but in comparison with the benefits delivered by high-level query rewriting. The two techniques are complementary and independent, but can compete for resources in the development team, so it is useful to understand their relative importance. We use the Saxon XSLT/XQuery processor as a case study, where we can now translate the logic of queries into Java bytecode. We provide an experimental evaluation of the performance of Saxon with the addition of this feature compared to the existing Saxon product. Saxon's Enterprise Edition already delivers a performance benefit over the open source product using the join optimizer and other features. What can we learn from these to achieve further performance gains through direct byte code generation?

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

Performance Comparison of Web Backend And Database: A Case Study Of Node.JS, Golang and MySQL, MongoDB

2019 ◽  
Vol 13 ◽  
Author(s):  
Faried Effendy ◽  
Taufik ◽  
Bramantyo Adhilaksono
Keyword(s):  
Response Time ◽  
Open Source ◽  
Poisson Distribution ◽  
Mobile Application ◽  
Web Applications ◽  
Performance Comparison ◽  
Memory Usage ◽  
Data Traffic ◽  
Cpu Utilization

: Substantial research has been conducted to compare web servers or to compare databases, but very limited research combines the two. Node.js and Golang (Go) are popular platforms for both web and mobile application back-ends, whereas MySQL and Go are among the best open source databases with different characters. Using MySQL and MongoDB as databases, this study aims to compare the performance of Go and Node.js as web applications back-end regarding response time, CPU utilization, and memory usage. To simulate the actual web server workload, the flow of data traffic on the server follows the Poisson distribution. The result shows that the combination of Go and MySQL is superior in CPU utilization and memory usage, while the Node.js and MySQL combination is superior in response time.

scholarly journals Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4349
Author(s):  
Niklas Wulff ◽  
Fabia Miorelli ◽  
Hans Christian Gils ◽  
Patrick Jochem
Keyword(s):  
Open Source ◽  
Electric Vehicle ◽  
Full Range ◽  
Mobility Patterns ◽  
Electric Vehicle Charging ◽  
Survey Results ◽  
Vehicle Demand ◽  
Vehicle Fleet ◽  
Electric Loads

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

scholarly journals Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain

2021 ◽  
Vol 11 (15) ◽  
pp. 7169
Author(s):  
Mohamed Allouche ◽  
Tarek Frikha ◽  
Mihai Mitrea ◽  
Gérard Memmi ◽  
Faten Chaabane
Keyword(s):  
Load Balancing ◽  
Relative Error ◽  
Execution Time ◽  
General Purpose ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Embedded Platform ◽  
Memory Resources ◽  
Processing Solution

To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).

PVDP: A portable open source pipeline for detection of plant viruses in RNAseq data. A case study on potato viruses in Antioquia (Colombia)

2021 ◽  
Vol 113 ◽  
pp. 101604
Author(s):  
Pablo Gutiérrez ◽  
Ary Rivillas ◽  
Daniel Tejada ◽  
Susana Giraldo ◽  
Andrea Restrepo ◽  
...  
Keyword(s):  
Open Source ◽  
Plant Viruses ◽  
Potato Viruses ◽  
Rnaseq Data
Sign in / Sign up

Export Citation Format

Share Document