PhD is the highest course of action now studied. By doing PhD studies, a student can become familiar with investigating, coding, implementing, support with implementation tools for phd research and many more. In this sense, scholars’ analytical and logical skillsets are being enriched with or without their knowledge.
“Most of the upcoming researchers have educated themselves with our excellent articles & this is one of the latest articles which is arrived recently to expound about project implementation tools”
The main intention behind doctorate studies is to bring newfangled ideas from the existing and improving technologies. A researcher is instructed to present his or her notions through a research proposal and then they are suggested to implement their research works after getting approvals from concerned juries.
At the same time, students are getting dilemmas in cherry-picking implementation tools for PhD research. Hence, we have requested our technical experts to post blogs on the same. Treat this article as our small contribution to eradicating your tribulations. Come let us have further elucidations through this handout’s flow.
How to do Research for PhD?
In general, research investigations necessitate up-to-date toolkits, techniques, and other software-oriented requirements. Concerning the nature of undertaken research, one has to opt for correct technical protocols.
For example, if the project is related to data science then handpicking the algebraic-based tools will fit with the field exactly. R is one of the greatest programming languages used in statistical computing where interpreted data gets cleaned, analyzed, and represented in terms of graphical representations.
A research project is implemented by using more than 4 programming languages. It may get fluctuated according to the clients’ requirements.
While doing research, a PhD student or a researcher has to be familiar with coding. It is very essential for proving that the proposed idea will work under different scenarios. Coding is the process used to visualize the written concepts. In the forthcoming passage, we have listed you the bunch of programming languages used for PhD studies.
Which Programming Language is required during PhD?
In general, developer communities are preferring different sorts of programming languages such as,
- Constraint Programming Languages
- AIMMS
- AMPL
- ALMA-0
- Wolfram
- Descriptive Programming Languages
- JSON
- LATEX
- XML
- HTML
- Functional Programming Languages
- ERLANG
- Scala
- OCAML
- Haskell
- Logical Programming Languages
- CLIPS
- PyPi
- Python
- Mercury
- Oz
- Prolog
- Declarative Programming Languages
- ML
- Common Lisp
- SQL
These are the various programming languages used during PhD related projects. In that, scientific derivations are realized by modest programming languages which have diverse conceptions and syntaxes.
If python language is handpicked for data analyzing concepts, opting for Numpy and Pandas will help a researcher in experiencing the determined end suppositions. Matlab also offers specialized toolkits, especially for data analytics-related investigations.
To represent the results in graphical replicas, make use of MATPLOTLIB, GNUPLOT, and RSTUDIO. As this article is titled with implementation tools for PhD research, our technical folklores have illustrated to you the top 4 toolkits for project works.
Top 4 Research Tools for PhD
- NS3
- Matlab Simulink
- Matlab
- Java
These are the top 4 tools used in PhD projects. These tools are playing a vital role in code implementations. According to the nature of the research, choose the appropriate tool for project executions. Let us have detailed explanations through the ensuing arguments.
- NS3
The nomenclature NS stands for network simulator. It is one of the best tools available as open source. It is most likely used to program developments and constant investigations.
Every version in NS has unique characteristics. NS is a licensed tool. In other words, NS3 is the raised for network simulation. Network data packets moving from one node to another node are visualized by the simulation locomotives.
Here, our technical experts have exemplified to you the characteristics of version NS 3.33 for your better understanding,
- IPv6 addresses are now being recognized by LR WPAN prototypes & it also predicting the variations among RFC6282 & RFC4944
- Wi-Fi OFDM is now added as the feature to configure spectral data transmission over networks
- The physical layer is incorporated with the features like PCAP, High-Efficiency Resource Units, and Multi Users
- The position of the node could also be altered by uploading CSV Reader files
- Data packet losses in vehicular communication are now fixed up by improved channel promulgation models
- Distance measurements among different nodes are now possible by the length class feature
- Wi-Fi error discovering models are incorporated as a default feature in this feature and it is considering the simulation domino effects
- BCC is now added with LDPC FEC programming interfaces
- According to the Linux operations, NS3 has TCP and ECN (Electronic communication networks) integrated facilities
- As well as transmission control protocol has also been incorporated in NS3 for collision management
A centralized simulation aid called MPI permits the multi-users to make use of MPI communicators and its uncompelled apportionments. On the other hand, NS 3.35 is the latest version prevailing to date.
Compatible Operating Systems for NS3
- Linux
- Ubuntu
- Kali
Linux, Ubuntu, and kali operating systems are commonly used for network simulation.
NS3 based Research Areas
- Optical Networking Concepts
- Fused Beamforming
- Underwater Wireless Sensor Networks
- Vertical Transference
- Multiple Input and Multiple Output
- Routing Protocol for Low Power and Lossy Networks
NS3 – Modules
- Traffic Controller
- BRITE
- Virtual Net Expedients
- Flow Monitoring
- Bridge Network Maneuvers
NS3 – Plugins
- PYVIZ – Used for correcting or debugging
- Eclipse – Used for amalgamation
- Mesh Wi-Fi Interface Mac – Acting as the public interface
- Matlab Simulink
It is the collection of various block libraries. It is a block generating toolkit and it is acting as the preliminary interface. It facilitates integration with Matlab platforms. This is most likely used in computerized scenarios & signal processing procedures.
Prerequisites for Matlab Simulink
- Windows 7 – Service Pack (1)
- Windows 8
- Windows 8.1
- Windows 10
Matlab R2020A is the updated version in Simulink. Other advanced features of the Matlab Simulink are listed below,
- Earth Magnetic Model (EMM)
- AUTOSAR & Spotlights
- Motor Controlling Procedures
- Functional Mockup Units (FMU)
Matlab Simulink – Modules
Simscape is one of the best modules used in Simulink platforms to generate the physical prototypes’ models within the same. Simscape enables to interconnect with designing patterns and block illustrations.
Matlab Simulink based Research Areas
- Wind Energy
- Embedded Coding
- Physical Modeling
- Energy Harvesting
- Digital Signal Processing (DSP)
- Power Systems
- Rapid Prototyping
- Matlab
The array is the rudimentary component of the Matlab tool. It doesn’t necessitate dimensional altering procedures. Many complex derivations and originations are getting done with this tool. To be more specific, trajectory and matrix-oriented computing disputes-based programs are prepared within little time. Matlab is widely used for big projects like EISPACK & LINPACK. The main intention behind this tool is to give access to matrix-related software.
Installation requirements for Matlab are similar to Matlab Simulink and the updated version of Matlab is R2021A.
Matlab based Research Areas
- Artificial Neural Network (ANN)
- Convolutional Neural Network (CNN)
- Image Processing
- User Authentication using Biometrics
- Facial Recognition
- Satellite & Digital Communications
Matlab – Modules
Matlab is widely used for image processing-related projects. It has an inbuilt template model with different names such as,
- RGB2GRAY
- IMWRITE
- IMSHOW
- IMREAD
Matlab – Plugins
- Unit Test – Used to tailor the objects
- Data Plugin – For adapting to importing, exporting, reading, and writing
- Test Run Progress – Projects the notifications related to the test case
- Java
Java programming language is compatible with all operating systems. It is s open-source language which is somewhat relevant to the C++ language. Yet it has improved and abridged features. Java is the self-directed coding language that can synchronize events independently.
It is often denoted as OOP (Object Oriented Program). It has multiple properties in terms of classes. Java is the only language that doesn’t require further alterations to run on other platforms because coding once is enough.
Prerequisites for Java Implementation
- Microsoft Windows
- 7 SP1
- Vista SP2
- x for Desktop
- 10 8u51
- Ubuntu 14.10 LTS
- Windows Server
- 2012 R2 64 Bits
- 2008 R2 SP1 64 Bits
Java 10 and 11 are the most recent versions introduced. Java 10 facilitates us by offering junk collecting interfaces and confined inferences. These improved features are lessening the coding works. Java has the largest developer communities hence its progression will not be restricted within these versions whereas Java 11 gives aid with LTS features.
Java-based Research Areas
- Forensics Technologies
- Iris & Biometric Recognition
- Deep Learning (DL)
- Machine Learning (ML)
- Steganography Processes
- Intrusion Detection & Prevention System (IDS & IPS)
- Blockchain Security Techniques
Java – Modules
Java Runtime Environment is facilitating different Java plugins. The web applications with interactive features are possible by the Java and applets combinations. A developer can install the Java plug-ins online.
Web application and server interactions are supported by Java virtual machines. In addition to that, it helps to run all the Java-based apps. This virtual machine is also aiding with diverse plugins to locate the module paths.
Java – Plugins
- JMOD – To create files
- JLINK – To create java run time images
- DOAP – To render project descriptions
- SOURCE – To create source JAR from the existing plan
- SHADE – To construct UBER JAR
- APP CLIENT or ACR- To create a Java-EE application client
These are the 4 major implementation tools for PhD research. Concerning your research work, go with the right programming languages, techniques, tools, and other technical requirements. As we are having so many years of experience, we can help you incredibly in every phase of researching, implementing, publishing and writing thesis paper. At last, we just wanted to conclude this article by listing down some emerging trends for PhD research.
Latest Trends for PhD Research
- Smart Cities and Automated Domestic Infrastructures
- Bioinformatics and Medical Apparatus
- E-Vehicles and Computerized Transportations
- Intelligent Transport Systems & Vehicular Communications
- Industrial Internet of Things and Industry 4.0
- Machine to Machine Communications (M2M)
- Internet of Vehicles (IoV)
- Power Systems & Energy Harvesting
- Augmented Reality (AR) and Virtual Reality (VR)
- Unmanned Vehicles and Self-Directed Cars
The listed trends are actively investigated by the student majority. It is time to examine your interested fields to prove your capabilities. If any assistance is required, feel free to visit our digital and offline platforms.
“Revolute the existing technologies by your different perceptions and technical handlings”
