Du følger nu
Fejl under følg af bruger.
Denne bruger har ikke tilladt andre bruger at følge dem.
Du følger allerede denne bruger.
Din medlemsskabsplan tillader kun at følge 0. Opgrader her.
Du følger nu ikke længere
Kunne ikke stoppe med at følge bruger.
Du har nu anbefalet
Kunne ikke anbefale bruger.
Noget gik galt. Forny venligst siden og prøv igen.
Email verificeret.
lahore,
pakistan
Det er i øjeblikket 6:42 AM her
Tilmeldt juni 10, 2022
0
Anbefalinger
Fatima H.
@fhkhan1995
0,6
0,6
100%
100%
lahore,
pakistan
100 %
Jobs færdiggjort
100 %
Indenfor budgettet
100 %
Til tiden
N/A
Genansættelsesrate
Electrical Engineer/Machine Learning Expert (PhD)
Kontakt Fatima H. omkring dit job
Log ind for at diskutere detaljerne via chat.
Portfolio-mappe
Portfolio-mappe
Closed-loop anesthesia dosage control system based on the de
Closed-loop anesthesia dosage control system based on the de
Anmeldelser
Ændringer gemt
Viser 1 - 1 ud af 1 anmeldelser
$20,00 USD
Python
Coding
Deep Learning
•
Erfaring
Lecturer
jan. 2020 - okt. 2021 (1 år, 9 måneder)
I taught C and Python language to BS EE students.
Uddannelse
PhD
(2 år)
MS EE
(2 år)
BS EE
(4 år)
Publikationer
Advancements in Microprocessor Architecture for Ubiquitous AI
Micromachines
This paper presents an overview on the evolution of AI and how the increasing capabilities of microprocessors have fueled the adoption of AI in a plethora of application domains. The paper also discusses the upcoming trends in microprocessor architectures and how they will further propel the assimilation of AI in our daily lives.
A Patient-Specific Machine Learning based EEG Processor for Accurate Estimation of DoA
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
To enable a DoA to monitor the correct estimation across a range of patients, a novel feature extraction along with machine learning processor is utilized. The decisions are solely based on seven features extracted from EEG along with the EMG signal for motion artifacts rejection. To extract the features efficiently on hardware, a 128-point FFT is proposed that achieves an area reduction and energy/FFT-operation by 39% and 58%, respectively
Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of DoA
IEEE Transactions on Biomedical Circuits and Systems
his paper presents a machine learning classification processor for accurate DoA estimation irrespective of the patient's age and anesthetic drug. The classification is solely based on six features extracted from EEG signal, i.e., spectral edge frequency (SEF), beta ratio, and four bands of spectral energy (FBSE). A machine learning fine decision tree classifier is adopted to achieve a four-class DoA classification (deep, moderate, and light DoA versus awake state)
An EEG-Based Hypnotic State Monitor for Patients During General Anesthesia
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
this work implements an accurate EEG-based LoH monitoring processor using a bagged tree machine-learning (BTML) classifier. It is based on 12 temporal and spectral features to incorporate robustness against age variation and achieve high classification accuracy. Spectral features are computed using discrete wavelet transform (DWT) that uses time-multiplexed filter (TMF) architecture.
Kontakt Fatima H. omkring dit job
Log ind for at diskutere detaljerne via chat.
Verifikationer
Bedste færdigheder
Gennemse lignende freelancere
Gennemse lignende fremvisninger
Invitationen blev sendt!
Tak! Vi har sendt dig en e-mail med et link, så du kan modtage din kredit.
Noget gik galt, da vi forsøgte at sende din mail. Prøv venligst igen.
Kopiering til clipboard fejlede. Prøv venligst igen efter justering af dine tilladelser.
Kopieret til udklipsholder.
Indlæser forhåndsvisning
Geolokalisering er tilladt.
Din session er udløbet, og du er blevet logget ud. Log venligst ind igen.