Fatima H. Profile

5.0

(1 review)

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$10 USD / hour

lahore,

pakistan

$10 USD / hour

It's currently 3:35 PM here

Joined June 10, 2022

0 Recommendations

Fatima H.

@fhkhan1995

5.0

(1 review)

0.6

100%

$10 USD / hour

lahore,

pakistan

$10 USD / hour

100%

Jobs Completed

100%

On Budget

100%

On Time

N/A

Repeat Hire Rate

Electrical Engineer/Machine Learning Expert (PhD)

I am currently working as a PhD scholar in Department of Electrical Engineering at the Syed Babar Ali School of Science and Engineering, LUMS. My research work focuses on Smart Embedded Processors and AI-Enhanced Processing. Recently, i am working on FPGA accelerators for AI algorithms. I am also a MS-EE graduate with DHL from LUMS. In my MS program,I worked on machine learning based energy efficient and portable device for accurate estimation of Depth of Anesthesia

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Portfolio

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, compared to the conventional. A simple decision tree is used to perform a multiclass DoA classification.

Closed-loop anesthesia dosage control system based on the de

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5.0

$20.00 USD

Have to run mask RCNN based code in our sysyem

She is very good and cooperative to deal with problems

Python

Coding

Deep Learning

Ali F.

@alijcscc

•

2 years ago

Experience

Univesity of Engineering and Technology, Lahore

Jan 2020 - Oct 2021 (1 year, 9 months)

I taught C and Python language to BS EE students.

Education

PhD

Lahore University of Management Sciences,

Pakistan

2020 - 2022

(2 years)

MS EE

Lahore University of Management Sciences,

Pakistan

2017 - 2019

(2 years)

BS EE

University of Engineering and Technology, Lahore,

Pakistan

2013 - 2017

(4 years)

Publications

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.