Pymote 2.0: Development of an Interactive Python Framework for Wireless Network Simulations

Shahzad, Farrukh

doi:10.1109/jiot.2016.2570220

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…A high-level python library Pymote 2.0 (Shahzad, 2016) is used for simulation and Tensorflow to implement our CNN model. Seventy percent of dataset was used for training and thirty percent used for…”

Section: Methodsmentioning

confidence: 99%

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

Sarangi

Mahapatro

Tripathy³

2021

International Journal of Business Data Communications and Networking

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Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

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Section: Methodsmentioning

confidence: 99%

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

Sarangi

Mahapatro

Tripathy³

2021

International Journal of Business Data Communications and Networking

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“…Python SV facilitates the development of verification environments by allowing users to write structures and functions that can be used directly in SystemVerilog. Python-implemented UVM classes are used to construct verification environments, and the SystemVerilog simulator Simulation [3] is used to execute the verification process.…”

Section: Python Sv Overviewmentioning

confidence: 99%

Python SystemVerilog (Python SV)

Nag

2023

WJET

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This paper discusses Python SystemVerilog (Python SV), a simulation-based verification approach leveraging the power of Python and SystemVerilog. The use of Python-implemented UVM classes in SystemVerilog enables users to write less code, minimize errors and reduce the verification time. This paper evaluates the use of Python SV in the verification of digital designs, its benefits, limitations, and future prospects. Python-SystemVerilog (Python-SV) is a research area that investigates the feasibility of building a high-level verification environment using Python and SystemVerilog. Python-SV aims to provide a unified framework for the design, simulation, and verification of digital systems, with an emphasis on ease of use and productivity. SystemVerilog is a hardware description and verification language that is widely used for designing digital systems. On the other hand, Python is a powerful, high-level programming language that is widely used in various fields, including software engineering, scientific computing, and data analysis. Python's popularity has grown in recent years, primarily due to its simplicity, ease of use, and wide range of libraries and frameworks. Python-SV research primarily focuses on the following areas: 1) Integration of Python and Sys-temVerilog: Python-SV aims to seamlessly integrate SystemVerilog and Python, allowing designers to write test benches and verification code in Python and interface them with SystemVerilog modules. This integration simplifies the development process, making it easier to write and maintain large and complex verification environments. 2) Development of Python libraries for verification: Python-SV research focuses on developing Python libraries specifically for digital system verification. These libraries provide a higher-level interface for writing test benches and other functions, such as analysis and visualization of simulation results. 3) Implementation of verification methodologies: Python-SV research investigates the implementation of various industry-standard verification methodologies, such as the Universal Verification Methodology (UVM), in Python. This implementation aims to enable designers to use Python to develop and simulate UVM-compliant test benches. 4

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“…Agrawal et al [9] offered a training method and algorithm, using a force-feedback joystick, with an "assist-as-needed" model for training vehicle drivers. [10] proposed a software structure, ASSOCIATES-ROS, for development of unresponsive, collaborative, multi-robot systems with copious software resources Shahzad [11] enlarged the Pymote structure to simulate packet-level performance. The extensions included radio propagation, energy consumption, and mobility.…”

Section: Motivementioning

confidence: 99%

ROS Based Multimode Control of Wheeled Robot

Megalingam¹,

Tammana²,

Thokala³

et al. 2020

Adv. sci. technol. eng. syst. j.

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This research work mainly presents the design and development of a small-scaled wheeled robot, which can be controlled using multiple controlling interfaces using some new technological trends. Raspberry Pi 3 as the main controller, Python as the programming language integrated with the Robot Operating System (ROS) and Virtual Network Computing (VNC) for screen sharing are utilized in order to control the robot. Laptop Keys, Graphical User Interface (GUI) on PC, Virtual Network Computing on smart phones, Joystick connected to Laptop using python integrated with Robot Operating System, Voice commanding through an android app integrated with python and ROS and Internet of Things using a customized webpage with Node MCU as the controller are the dominant control interfaces. A Local Area Network is established in order to communicate with the micro-controllers wirelessly. The robot can be controlled with multiple interfaces by using its corresponding commands. The robot is made to survive in five different paths, controlled by all the six methods and the time for each response obtained was appraised and illustrated properly.

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Pymote 2.0: Development of an Interactive Python Framework for Wireless Network Simulations

Cited by 10 publications

References 15 publications

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

Python SystemVerilog (Python SV)

ROS Based Multimode Control of Wheeled Robot

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