Machine Learning
This repository contains code to apply the ESPER method to quasi-continuum models of biomolecules exhibiting multiple degrees of freedom, as described in Seitz et al. (2022, IEEE TCI). As inputs into ESPER, detailed instructions are also provided for generating custom synthetic datasets with increasing complexity to mirror known cryo-EM image attributes.
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Currency recognition and classification is one essential task to do. Both paper and coin currency play important role in transactions in everyday life. But provided there are many datasets available of paper currency, and very less datasets are available of coin currency. Coin currency recognition becomes important because even though the amount for which people do coin transactions is small but inaccuracy in recognition can lead to huge loss. Following are the objectives to create this dataset:
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This dataset is gathered by using Inertial Measurement Unit Sensor (IMU) (MPU-9250) Positioned on the seat of Vehicle (like Bus, car bike, cycle). This Dataset is record with the help of IMU Sensor which gather only accelerometer data. Currently, collecting plain and rutty surface data through IMU Sensor by travelling in Bus, car, bike and cycle in different places in Haryana.
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Two electric vehicles were used in this study, namely the Renault Zoe Q210 2016 and the Renault Kangoo ZE 2018. The EVs were equipped with data loggers connected to the CAN bus recording data on the HV battery current, voltage, SoC, and instantaneous speeds. We also used a GPS logger mobile application to record GPS tracks and altitudes. Data were collected from six drivers (four men and two women) with varying levels of driving experience (from less than two months to more than 10 years) on a variety of roads and driving conditions for nearly 200 kilometers
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This dataset contains the data of three different positions of persons. The main focus of this dataset is on three positions those are Sit, Stand and Sleep. This dataset is collected by using a 3-axis accelerometer sensor value using the Inertial Measurement Unit (IMU) (MPU-9250) Sensor. We collected this data by positioning this instrument on the arm of the person.
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This dataset is gathered by using Inertial Measurement Unit Sensor (IMU) (MPU-9250) positioned on the seat of vehicle (Van). This dataset represents the real time sensory data collected with the help of vehicle i.e. School Van on a road at different places in Punjab. The objective of this dataset is to provide an accurate data for plain road and a road with pits.
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Task prioritization is one of the most researched areas in software development. Given the huge amount of papers written on the topic, it might be challenging for IT practitioners to find the most appropriate tools or methods developed to date to deal with this important issue. To overcome this problem, we conducted a systematic literature review. The main goal of this work is to review the current state of research and practice on task prioritization among IT practitioners and to individuate the most effective ranking tools and techniques used in the industry.
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This dataset curbs real time sensory data collected through different vehicles such as Cycle, Car, Bike and Bus on the humpty-dumpty road. This dataset is collected by using Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the seats of vehicle. Through some vehicles (Cycle and Bike) are not having place to keep sensor, but it was designed to handle all the hurdles of road having potholes. The dataset aims to tell the exact accuracy of pothole and plane road. This dataset can be used in future for government to allocate budget to repair the rough road.
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This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).
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