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EBAPy: A Python framework for analyzing the factors that have an influence in the performance of EEG-based applications
EBAPy: یک چارچوب پایتون برای تجزیه و تحلیل عوامل موثر بر عملکرد برنامه های مبتنی بر EEG-2021 EBAPy is an easy-to-use Python framework intended to help in the development of EEG-based applications. It allows performing an in-depth analysis of factors that influence the performance of the system and its computational cost. These factors include recording time, decomposition level of Discrete Wavelet Transform, and classification algorithm. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and evaluating new ideas in developing biometric systems using EEGs. Furthermore, different applications that classify EEG signals can use EBAPy because of the generality of its functions. These new applications will impact human–computer interaction in the near future.Code metadataCurrent code version v1.1Permanent link to code/repository used for this code version https://github.com/SoftwareImpacts/SIMPAC-2021-2Permanent link to Reproducible Capsule https://codeocean.com/capsule/4497139/tree/v1Legal Code License MITCode versioning system used gitSoftware code languages, tools, and services used Python Compilation requirements, operating environments & dependencies If available Link to developer documentation/manualSupport email for questions dustin.carrion@gmail.com Keywords: EEG-based applications | Recording time | Discrete wavelet transform |
مقاله انگلیسی |
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Design and analysis of gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using pythons
طراحی و تجزیه و تحلیل ربات دروازه ای برای مکانیزم انتخاب و مکان با میکروکنترلر آردوینو مگا 2560 و پردازش با استفاده از پایتون-2021 Robots are extensively used in industries for their precision work and amount of work that one can obtain without any defects. In this paper we are using a gantry robot for as it does not occupy the floor space therefore reducing the distance for reachability of the parts and hence reducing unnecessary material for guide way. Robots work in strictly defined path and there is no or very little change in such systems in order to overcome this we are using a vision based control system to make the system dynamic in nature the images are picked by using a USB camera processed images of the object is transmitted via serial communication to the Arduino Mega 2560 microcontroller and processed using pythons open source computer vision (Open CV) image to process the image captured by the USB camera to find the exact col- our and to pick the object and sort it.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on Advanced Research in Mechanical, Materials and Manufacturing Engineering-2020. Keywords: Gantry robot | Machine vision | Image processing | Arduino microcontroller | Open CV | Python |
مقاله انگلیسی |
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Lane detector for driver assistance systems
آشکارساز خط برای سیستم های کمک راننده-2021 The challenging problem in the traffic system is lane detection. This Lane detection which attracts the computer vision community’s attention. For computer vision and machine learning techniques, the Lane detection which acts as the multi-feature detection problem. Many machine learning techniques are used for lane detection. Driver support system is one of the most important features in the modern vehicles to ensure the safety of the driver and decrease the vehicle accidents on road. Road Lane detection is the most challenging task and complex tasks now-a-days. Road localization and relative position between vehicle and roads which also includes with this. But in this journal, we propose a new method. Here, an on- board camera to be used which is looking outwards are presented here in this work. This proposed technique which can be used for all types of roads like painted, unpainted, curved, straight roads etc in different weather conditions. No need for cam- era calibration and coordination of the transform, may be any changing illumination situation, shadow effects, various road types. No representation for speed limits. This includes that the system acquires the front view using a camera mounted on the vehicles and detect the Lane by applying the code from the Python Programming process. This proposed system does not require any more information about roads. This system which demonstrates a robust performance for Lane detection.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Virtual Conference on Sustainable Materials (IVCSM-2k20). Keywords: Lane detection | Computer vision | ITS | Driver support system | Machine learning techniques | Python programming | ADAS |
مقاله انگلیسی |
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Smart Technologies for Visually Impaired: Assisting and conquering infirmity of blind people using AI Technologies
فناوری های هوشمند برای افراد دارای اختلال بینایی : کمک و تسخیر ناتوانی افراد نابینا با استفاده از فناوری های هوشمند مصنوعی-2020 Physical disability has affected many people’s
lives across the world. One of these disabilities that strongly
affected some large category of people is visual lose. Blind
people often face difficulties in moving around freely such
as: in crossing the street, in reading, driving or socializing.
They often rely on using certain aid devices to reach certain
places or perform any other daily activities such as walking
sticks. There are ongoing scientific researches in the area of
rectifying blindness, but it has to go long way to achieve the
solution. Also, there are research unleashes the ideas of
assisting the blind people deficiency but lacks in
technological aspects of implementation. This research
project aims at helping blind people of all categories to
achieve their day to day tasks easier through the use of a
smart device. By using artificial intelligent and image
processing, this smart device is able to detect faces, colors
and deferent objects. The detection process is manifested by
notifying the visually impaired person through either a
sound alert or vibration. Additionally, this study presents a
palpable survey that entails visually impaired people from
the local community. Subsequently, the project uses both
Open CV and Python for programming and
implementation. The exertion of this project prototype
investigates the algorithms which are used for detecting the
objects. Also, it demonstrates how this smart device could
detects certain physical object and how it could send a
warning signal when faced by any obstacles. Overall, this
research will be a positive addition in the world of health
care sector by supporting blind people with the use of smart
technology. Keywords: Artificial Intelligent | Open CV | Python | Face Recognition | Object Detection | Health Care Introduction |
مقاله انگلیسی |
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AI and IoT Based Monitoring System for Increasing the Yield in Crop Production
سیستم مانیتورینگ مبتنی بر هوش مصنوعی و اینترنت اشیا برای افزایش عملکرد در محصولات زراعی-2020 Artificial Intelligence (AI) and Internet of things
(IoT) based monitoring systems are in great demand and gives
a precise extraction and analysis of data. In this paper, the
research is performed on a marigold plant to detect the most
suitable conditions for plant growth. The philosophy behinds
this work is to reduce the risks in agriculture and to promote
smart farming practices. The effect of physical conditions like
humidity, temperature, soil temperature and moisture and
light intensity on the plant growth, is monitored using IoT
based monitoring system. The data responsible for the plant
growth is obtained using different sensors units like DHT11,
LDR, DS18B20, Soil Moisture sensors, Noir camera, singleboard
microcontrollers and Application Programming
Interfaces (APIs). The variation of plant growth rate w.r.t. the
intensity of sunlight was observed within the range of 1000 lx-
1200 lx, category-2 (best). The further analysis of the extracted
parameters is done using different Machine Learning (ML)
algorithms. Logistic Regression, Gradient Boosting Classifier
and Linear Support Vector Classifier (SVC) algorithms are
found best for analysis of physical parameters responsible for
the marigold plant growth. Keywords: Machine Learning | Internet of Things | Smart farming | Agriculture | Artificial Intelligence | OpenCV | Python | Thingspeak |
مقاله انگلیسی |
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Assessment of weather-based influent scenarios for a WWTP: Application of a pattern recognition technique
ارزیابی سناریوهای تأثیرگذار بر اساس آب و هوا برای WWTP: استفاده از یک روش تشخیص الگو-2019 This study proposes an integrated approach by combining a pattern recognition technique and a process simulation
model, to assess the impact of various climatic conditions on influent characteristics of the largest
Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009–2016) of historical
influent data namely influent flow rate (Qin), chemical oxygen demand (COD), ammonium (N-NH4) and total
suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation
rates (PI) from the plant catchment area, are evaluated in this study. Following the outlier removal and missingdata
imputation, five influent climate-based scenarios are identified by K-means clustering approach. Statistical
characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed
approach could improve the process control and efficiency, a process simulation model was developed and
calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five
influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption
of the plant while meeting effluent requirements. The results indicate that with the adaptation of
suitable aeration strategies for each of the influent scenarios, 10–40% energy saving can be achieved while
meeting effluent requirements. Keywords: Wastewater treatment plant (WWTP) | Influent data | K-means clustering | Climatic data | Python |
مقاله انگلیسی |
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How to build a better database: When python programming meets Bloombergs Open API
چگونگی ساخت یک پایگاه داده ای بهتر: چه موقع برنامه نویسی با پایتون API آزاد بلومبرگ را برآورده می کند-2018 The need to hand collect data from SEC filings, among other sources, has long constituted a significant obstacle when conducting research in the area of finance (more specifically corporate finance) – and, indeed, business broadly defined. We propose a novel data collection alternative; using the Python programming language and Bloombergs Open API we show how to automate the data collection process and generate databases of indefinite size. This is particularly useful when collecting data generally thought to be available only in proxy statements. Such variables include, but are not limited to: CEO age, CEO tenure, board size, number of independent and female directors on the board, and number of shares held by insiders. The approach described in our paper has significant implications for research, academia and in areas heretofore limited by the need to hand collect data.
keywords: Bloomberg Open API| Python| Hand-collected data| SEC filings| Databases |
مقاله انگلیسی |
8 |
ترکیب دانش تخصصی با فراگیری ماشین براساس آموزش فازی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 18 در این مقاله یک رویکرد آموزش فازی مبتنی بر تنظیم غیر خطی که تلاش آن به منظور ممانعت در طول آموزش است، معرفی می کند. ایده اصلی به منظور محدود کردن آموزش بدین منظور است که دانش تخصصی مبنا برای ساخت مدلی که هنوز هم قابل مشاهده است، بکار برده شود. اجرای این ایده با یک روش جدید تنظیم غیر خطی که برای هر نوع مجموعه داده¬ی آموزشی قابل اجراست، صورت گرفت. این روش با استفاده از مجموعه داده¬ی عملکرد محصول بزرگ (> 4500 محصول زراعی) برای چغندرقند که در مزارع کشاورزی در طی یک دوره 14 ساله (1976-1989) در شرق آلمان جمع آوری شده، اثبات است. این نرم افزار در SAMT2، نرم افزار رایگان و منبع گسترده، با استفاده از زبان برنامه نویسی پایتون اجرا گردید.
کلید واژه ها: مدل سازی فازی | دانش تخصصی | فراگیری ماشین | تنظیم غیر خطی | بهينه سازي | مدل سازی عملکرد |
مقاله ترجمه شده |
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Performance Analysis of Parallel Python Applications
تحلیل کارایی برنامه های کاربردی پایتون موازی-2017 Python is progressively consolidating itself within the HPC community with its simple syntax,
large standard library, and powerful third-party libraries for scientific computing that are es
pecially attractive to domain scientists. Despite Python lowering the bar for accessing parallel
computing, utilizing the capacities of HPC systems efficiently remains a challenging task, after
all. Yet, at the moment only few supporting tools exist and provide merely basic information in
the form of summarized profile data. In this paper, we present our efforts in developing event
based tracing support for Python within the performance monitor Extrae to provide detailed
information and enable a profound performance analysis. We present concepts to record the
complete communication behavior as well as to capture entry and exit of functions in Python
to provide the according application context. We evaluate our implementation in Extrae by
analyzing the well-established electronic structure simulation package GPAW and demonstrate
that the recorded traces provide equivalent information as for traditional C or Fortran applica
tions and, therefore, offering the same profound analysis capabilities now for Python, as well.
Keywords: Performance Analysis | Tracing | Tools | HPC | Parallel | Python | Extrae, Paraver |
مقاله انگلیسی |
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How to build a better database: When python programming meets Bloombergs Open API
چگونه یک پایگاه داده بهتر ایجاد کنیم: زمانی که برنامه نویسی پایتون با API Bloombergs Open باز می شود-2017 The need to hand collect data from SEC filings, among other sources, has long constituted
a significant obstacle when conducting research in the area of finance (more specifically
corporate finance) – and, indeed, business broadly defined. We propose a novel data col
lection alternative; using the Python programming language and Bloomberg’s Open API
we show how to automate the data collection process and generate databases of indefi
nite size. This is particularly useful when collecting data generally thought to be available
only in proxy statements. Such variables include, but are not limited to: CEO age, CEO
tenure, board size, number of independent and female directors on the board, and number
of shares held by insiders. The approach described in our paper has significant implica
tions for research, academia and in areas heretofore limited by the need to hand collect
data.
Keywords: Bloomberg Open API | Python | Hand-collected data | SEC filings | Databases |
مقاله انگلیسی |