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Jasleen Bedi
Chandigarh, India0 followers0 following
- About
- High School
- Bachelor's
- Master's
- Target
- IELTS
Female
Chandigarh, India
Work ExperienceSoftware Engineer
Bedi IT Services - Full Time
Jun 7, 2022 - Present · 3 yrs 11 mos
Chandigarh, India
1. Requirement understanding (both functional and non-functional) by going through
the specifications located at Jira, Confluence, and with the inputs from business
analysts
2. Develop algorithms and flowcharts for a better understanding of the system flow
and working, Also helped in improving the system quality through unit testing and
finding defects and escalating the ticket to the concerned team
3. Helped in automating end-to-end flows by building automation scripts and
frameworks using SQL, Python, and shell scripting language, which helped in
reducing time, cost, and manual intervention in applications
4. Performing the release builds and other deployment-related activities by
coordinating with the QA (Quality assurance) team and the production environment
setup
5. Developing knowledge transfer documents and SOP (Standard operating
procedures) for a better understanding of the overall project and easy learning of
new joiners
6. Practiced Agile methodology as a part of Software Development Lifecycle from
planning project backlogs to sprints, and estimating time on issues by prioritizing
backlogs
7. Implemented IT standards and procedures to aid in the development of high-quality
software. Ensured that all existing IT standards were followed clearly.
8. Proactively report to management people, such as team lead and project manager,
so that they are well informed of the work currently under development
ProjectsSynchronisation and Receiver Design
Role - Project researcher
for Personal
Aug 10, 2022 - Present
Communication: Synchronisation and Receiver Design
I am doing a Project on communications. Using Matlab and Simulink models, I am implementing communication subtopics like QPSK Transmitter and Receiver, RF Sattelite link, RF receiver top-down design, OFDM synchronization, RF Noise modeling, architectural design of low IF Receiver system, transmitter and receiver, cardiac based QPSK carrier synchronization, coarse and fine compensation, MSK signal recovery, OFDM, the impact of thermal noise on communication system performance, RF noise modeling, adjusting damping factor to frequency offset under Dr. Brijesh Kumbhani. IIT Ropar. After this models will be installed in various hardware of communication
Industrial Automation using SIEMENS PLC, HMI & SCADA (M.Sc Final year project)
Role - Project worker and trainee (PEC Center of Excellence)
for Industrial
Jan 15, 2021 - Jun 7, 2021
• Tools used: Siemens PLC, Human-machine touch screen interfaces, SCADA WinCC and TIA portal software, and Ladder-Logic software.
Applications of various Electronic devices
Role - Researcher ( M.S Final Year Project)
for Academic
Jan 2, 2021 - Jun 15, 2021
• Worked on applications of electronics devices and concepts like infrared sensors, Arduino, Microcontrollers
pt100 temperature sensors, smart light control system, ADCs,mosfet as switch, power supply modules, optoisloators, diode bridge based regulated power supply for Aurdino part that involves ADCTSOP 1738 with 555 based IR LED pair. instrumentation amplifiers, optocouplers, power
supplies, constant current ICs, switching circuits, converters, traffic lights, LASCR,
PNPN, MOSFETs, crystal oscillator, superconductivity and flyback diodes. wireless sensing network(sno avalanche detection)
Jan 2021 - June 2021
Research PapersMusic Genre classification using Machine Learning and Deep Learning
Sep 12, 2022 - ...
These are the topics I learnt and publishing.
PROJECT A- AUDIO CLASSIFICATION USING DEEP LEARNING 7
GitHub link Jasleen77/AudioClassification (github.com) 7
Heroku Link: W3.CSS Template (audioclassification77.herokuapp.com) 7
PROJECT B- MUSIC GENRE CLASSIFICATION USING MACHINE LEARNING AND DEEP LEARNING 7
GitHub link: Jasleen77/MusicGenreClassification (github.com) 7
Heroku LinkW3.CSS Template (musicgenre-classification.herokuapp.com) 7
CHAPTER 1: INTRODUCTION ABOUT PROJECT 8
1.1 REAL WORLD AUDIO DOMAIN 8
1.2 ARTIFICIAL INTELLIGENCE 10
1.3 TYPES OF AI: 0
1.4 HOW AI WORKS?? 1
1.5 FAMOUS EXAMPLES OF AI: 2
CHATBOTS 2
MAPS AND NAVIGATION: 2
FACIAL DETECTION / RECOGNITION & AUTOCORRECT: 2
SEARCH AND RECOMMENDATION ALGORITHMS. 3
FRAUD DETECTION: 3
SOCIAL MEDIA: 3
E-PAYMENTS: 3
SELF DRIVING CARS: 4
1.6 ROLE OF AI TODAY: 4
1.7 AI APPLICATIONS IN HEALTHCARE: 5
1.8 AI APPLICATIONS IN GOVERNMENT: 6
1.9 AI APPLIATIONS IN DEFENCE 6
1.10 AI APPLICATIONS IN AGRICULTURE: 7
1.11 AI IN IMAGE RECOGNITION: 8
CHAPTER 2: MACHINE LEARNING 9
CHAPTER 3 - DEEP LEARNING: 12
How deep learning works 12
ARTIFICIAL NEURAL NETWORKS 13
Chapter 4 : AI TOOLS 15
4.1 ABOUT JUPYTER 15
INSTALLATION 15
4.2 ABOUT GOOGLE COLLABORATORY: 16
CHAPTER 5: AUDIO PROCESSING USING MACHINE LEARNING 19
5.1 DIGITAL SIGNAL PROCESSING 19
5.2 AUDIO SIGNAL PROCESSING 20
5.2.1 SOUND: 21
5.2.2 WAVEFORM: 22
5.2.3 FREQUENCY 23
5.2.4 AMPLITUDE & PITCH: 23
5.2.5 HEARING RANGE 24
5.2.6 DECIBEL: 25
5.2.7 MIDI NOTES: 25
5.2.8 PERCUSSION INSTRUMENTS: 26
5.2.9 MUSIC ELEMENTS: 27
5.2.10 SOUND POWER: 27
5.2.11 SOUND INTENSITY: 27
5.2.12 LOUDNESS: 28
5.2.13 EQUAL LOUDNESS CONTOURS: 28
5.2.14 TIMBRE 28
5.2.15 TEMPO 29
5.2.16 SOUND ENVELOPE 29
5.2.16 HARMONIC COMPONENT 29
5.2.17 FREQUENCY MODULATION: 30
5.3 TOWARDS MACHINE LEARNING 31
5.3.1 SAMPLING: 31
5.3.2 SAMPLING ERROR AND INFO LOSS: 32
5.3.4 QUANTISATION: 33
5.3.5 DYNAMIC RANGE 33
5.3.6 SNR/SIGNAL TO NOISE RATIO 33
5.4 AUDIO FEATURES 34
5.4.1 TIME DOMAIN FEATURES: 34
5.4.2 FREQUENCY DOMAIN FEATURES 35
5.4.3 TIME-FREQUENCY DOMAIN FEATURES 35
5.4.4 ML APPROACH 35
5.4.5 DEEP LEARNING APPROACH 36
5.4.6 TIME DOMAIN FEATURE PIPELINE 36
5.4.7 FREQUENCY DOMAIN EXTRACTION PIPELINE 37
5.4.8 SPECTRAL LEAKAGE 37
5.5 WINDOWING: 38
5.5.1 HOPLENGTH: 39
CHAPTER 6: AUDIO FEATURES EXTRACTION WITH MACHINE LEARNING 40
6.1 AMPLITUDE ENVELOPE 40
AE EXTRACTION IN JUPITER 40
6.2 ROOT MEAN SQUARE ENERGY 44
6.2.1 RMSE FROM SCRATCH 45
6.3 ZERO CROSSING RATE 46
6.3.1 ZCR WITH JUPITER 47
6.4 FOURIER TRANSFORM 49
6.4.1 FT WITH LIBROSA 50
6,5 INVERSE FOURIER TRANSFORM 55
6.7 COMPLEX NUMBERS ] 57
6.7.1 EULER FORMULA: 58
6.7.2 FT & COMPLEX NO 59
6.7.3 COMPUTING FOURIER TRANSFORM 59
6.8 COG / CENTER OF GRAVITY OR INTEGRAL: 62
6.9 SUM 64
6.10 DESCRETE FOURIER TRANSFORM 67
6.11 NYQUIST FREQUENCY 68
6.12 FFT / FAST FOURIER TRANSFORM 69
6.13 SHORT TIME FOURIER TRANSFORM 69
6.13.1 WINDOWING TECHNIQUE 69
6.13.2 HOP SIZE 71
6.13.3 HANN WINDOW 73
6.14 SPECTROGRAM 74
6.15 MEL
Audio Classification using Deep Learning
May 7, 2022 - ...
I have added my project report here. The research paper will be published soon. I am working on it. Date is wrong.
IMPORTANT LINKS
PROJECT A- AUDIO CLASSIFICATION USING DEEP LEARNING
GitHub link Jasleen77/AudioClassification (github.com)
Heroku Link: W3.CSS Template (audioclassification77.herokuapp.com)
PROJECT B- MUSIC GENRE CLASSIFICATION USING MACHINE LEARNING AND DEEP LEARNING
GitHub link: Jasleen77/MusicGenreClassification (github.com)
Heroku LinkW3.CSS Template (musicgenre-classification.herokuapp.com)
DOCX
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possibility of forming Z=120 via Ni and U reaction using (DCM) Dynamical Cluster Decay model in the International Journal (PHYSICAL REVIEW C 105 014610 (2022)).
Jan 14, 2022 - PHYSICAL REVIEW C 105, 014610 (2022)
I published a research paper on Nuclear Physics as a Co-author on the possibility of forming Z=120 via Ni and U reaction using (DCM) Dynamical Cluster Decay model in the International Journal (PHYSICAL REVIEW C 105 014610 (2022)).
According to different theoretical studies, the next magicity for the proton number should occur at
Z
=
114
, 120, or 126 and for the neutron number
N
=
184
. Superheavy nuclei of interest for the forthcoming synthesis of the isotopes with
Z
=
119
, 120 are investigated. Many reactions have yet to be studied in order to find the possible incoming channels for the formation of
Z
=
120
. In this work, synthesis of superheavy element 120 in terms of the fission and quasifission cross sections via
64
Ni
+
238
U
reaction is evaluated and discussed. We have explored the possibility of formation of
Z
=
120
via
64
Ni
+
238
U
reaction, using the experimental data from Kozulin , Phys. Lett. B 686, 227 (2010). Some experimental efforts have been made to synthesize
302
120
, via Ni-induced and Cr-induced reaction, i.e.,
64
Ni
+
238
U
reaction at five
E
∗
's (excitation energies) and the
54
Cr
+
248
Cm
reaction at only one energy, respectively. In this work, we have studied the Ni-induced reaction at given energies by including higher multipole deformations
β
λ
i
(
λ
=
2
,
3
,
4
;
i
=
1
,
2
)
, and compact orientations
θ
c
i
using the coplanar degree of freedom
(
Φ
=
0
∘
)
within the framework of the dynamical cluster-decay model (DCM). The neck-length parameter is the only one, which is fixed in reference to the observed data for fission cross section
(
σ
ff
)
calculated for mass region
A
/
2
±
20
, and quasifission cross section
(
σ
qf
)
for the incoming channel of
64
Ni
+
238
U
reaction. Our calculations for Ni-induced reaction have shown a good concurrence with the experimental data for quasifission and fission cross sections along with the DCM-calculated estimated and predicted cross sections for evaporation residues (ERs), which can be used for future references. Our results demonstrate the insignificant compound nucleus formation possibility
P
CN
(
≪
1
)
for
Z
=
120
from Ni-induced reaction because of the very low order of evaporation residues; thus, this work is not supporting this incoming channel for the formation of
Z
=
120
.
PDF
Research_Paper_-Nuclear_Physics.pdf
VolunteeringJasleen Bedi has not added any volunteering detail yet.