Anique Akhtar      

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Anique Akhtar
Ph.D. Student
Department of Computer and Electrical Engineering
University of Missouri - Kansas City, Missouri, USA
Advisor: Dr. Zhu Li

Resume: (last updated: February 2020) PDF
Email: aniqueakhtar AT mail DOT umkc DOT edu


I am a Ph.D. student in the Computer and Electrical Engineering department at University of Missouri - Kansas City. I am currently working under Dr. Zhu Li in the Multimedia Computing & Communication Lab.

I did my M.S. in Electrical Engineering from Koc University, Istanbul, Turkey, where I worked under Dr. Sinem Coleri Ergen in the Wireless Networks Laboratory on 60 GHz directional wireless Communication.

Prior to coming to Koc University, I did my B.Sc in Electrical Engineering at Lahore University of Management Sciences (LUMS), Lahore, Pakistan.

Research

  • 3D Point Cloud Semantic Segmentation.

  • 3D Point Cloud Denoising and Outlier Removal.

  • Point Cloud Deep Learning Solutions.

  • Low Latency Visual Communication.

  • Point Cloud Capture and Compression (PCC).

  • Neural Networks and Deep Learning.

  • Wireless Networks (LTE and 5G).

  • OFDM and Waveform design.

  • 3GPP RAN.

  • MAC Protocols for Wireless Communication.

  • mmWave Directional Communication.

Project Work

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2D Penoptic Segmentation on street level imagery (SLI) from HERE True Drives.

Summer Internship at HERE Technologies.

  • 2D Building Facade Segmentation and Portal Detection.

  • 2D Building Tracking, Segmentation, and Instant Segmentation.

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Point Cloud Denoising.

Point clouds obtained from 3D scanners or by image-based reconstruction techniques are often corrupted with a significant amount of non-negligible noise.

  • We propose a two-stage deep neural network that takes in 3D point cloud data and outputs a denoised point cloud.

  • 1st stage: Outlier removal.

  • 2nd stage: Denoising surface noise.

  • We achieve state-of-the-art point cloud denoising results.

(Noisy point cloud on the left, Denoised point cloud on the right. JPEG 8i dataset)

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3D Semantic Segmentation on HERE True LiDAR Data.

Summer Internship at HERE Technologies.

  • Annotation of large scale outdoor LiDAR point cloud data.

  • Building Deep Learning Architecture for 3D Semantic Segmentation.

  • Feature abstraction from segmented 3D Point Cloud Data.

(Image on the left is from Semantic3D Dataset.)

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Realtime 3D Point Cloud Communication.

  • Joint source-channel coding for robustness to different channel conditions. (pdf)

  • Adaptive Modulation and Coding schemes for point cloud broadcasting.

  • Adaptive Random Network Coding (ARNC) for scalable point cloud multicasting. (pdf)

  • Low latency support for V2V as well as V2I communication

(Outdoor LiDAR data from Hesai shown on the left.)

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Mobile Edge Point Cloud Computing.

  • Registering infrastructure based and vehicle based point cloud submaps into one big point cloud.

  • Differentiating the static background from the dynamic live map.

(Google car collecting LiDAR data shown on the left.)

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3D Point Cloud Compression.

  • Binary Tree embedded Quad Tree (BTQT) source encoding. (pdf) & (pdf)

  • Lossless point cloud geometry compression.

  • Error Resilient and Scalable point cloud source coding that is layered for different quality of service requirements.

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Video Deduplication with Scalable Hash using Deep Learning Feature Aggregation with Triplet Loss.

  • Content based cache service for DASH video streaming.

  • Deep learning with triplet loss for feature map creation and aggregation.

  • Creating a scalable index/hash of frames of a very long video.

  • Video content retrieval based on the few frame of a hundred hour long video.

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Deep Learning

  • Deep Super Resolution Networks for SIFT point repeatability.

    • Novel technique to super-resolve low resolution images into high resolution images while maintaining SIFT key points features.

  • Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks.

    • Designed a Convolutional Neural Network for Human Activity Detection using accelerometers and gyroscopes sensors from your cell-phone or wearable device. The results achieve 95% classification accuracy over 10 classes.

  • Market Trend Prediction for Cryptocurrency using Machine Learning.

    • Using traditional financial Technical Analysis (TA) coupled with deep learning techniques to predict Bitcoin price. (pdf draft)

  • Machine learning applications in Wireless Communication and Network Science.

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5G and Beyond.

  • Resource allocation schemes for 5G heterogenous multi-numerology network. (pdf)

  • Flexible waveform and numerology design for future cellular systems.

  • Adaptive CP size optimization in OFDM waveform.

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Directional mmWave Communication.

  • Directional MAC Protocol for IEEE 802.11ad WLANs. (pdf) (WEB LINK)

  • Efficient Network Level Beamforming Training for IEEE 802.11ad WLANs. (pdf)

  • Energy Efficient MAC Protocol with Localization scheme for Wireless Sensor Networks using Directional Antennas. (pdf draft)

  • Multi-hop network neighbor discovery and beamforming using directional antennas in 802.11ad WLANs. (pdf draft)

  • Optimization of link scheduling in directional wireless networks using Heuristic methods. (pdf draft)

Publications

  • A. Akhtar, B. Kathariya, Z. Li, “Low Latency Scalable Point Cloud Communication”
    IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan. 2019. (pdf)

  • A. Akhtar, J. Ma, R. Shafin, J. Bai, L. Li, Z. Li, L. Liu, “Low Latency Scalable Point Cloud Communication in VANETs using V2I Communication”
    IEEE International Conference on Communications (ICC), Shanghai, China. 2019. (pdf)

  • A. Akhtar, H. Arslan, “Downlink Resource Allocation and Packet Scheduling in Multi-Numerology Wireless Systems”
    IEEE Wireless Communications and Networking Conference (IEEE WCNC), 2018. (pdf)

  • A. Akhtar, S. Coleri Ergen, “Directional MAC Protocol for IEEE 802.11ad WLANs”
    Ad Hoc Networks, 2018. (pdf) (Protocol's website with explanation and open source code)

  • A. Akhtar, S. Coleri Ergen, “Efficient Network Level Beamforming Training for IEEE 802.11ad WLANs”
    International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2015), Chicago, Illinois, US. July 2015 (pdf)

Unpublished

  • A. Akhtar, Z. Li, “Point Cloud Denoising using Deep Neural Networks”
    Outlier removal as well as surface denoising to achieve state-of-the-art denoising on point cloud data.

  • A. Akhtar, Y. Yilmaz, “Machine Learning for Market Trend Prediction in Bitcoin”
    Using traditional financial Technical Analysis (TA) coupled with deep learning techniques to predict Bitcoin price. (pdf draft)

  • A. Akhtar, S. Coleri Ergen, “Energy Efficient MAC Protocol with Localization scheme for Wireless Sensor Networks using Directional Antennas.”
    Energy efficient MAC protocol for with localization scheme using beamforming. (pdf draft)

  • A. Akhtar, O. Ozkasap, “Multi-hop network neighbor discovery and beamforming using directional antennas in 802.11ad WLANs”
    Unpublished. (pdf draft)

  • A. Akhtar, Ceyda Oguz, “Optimization of link scheduling in directional wireless networks using Heuristic methods”
    We propose multiple heuristic methods to solve the problem at hand and discuss how the problem behaves in different scenarios. (pdf draft)

Education

  • Ph.D. Computer and Electrical Engineering. - August 2016 - present

    • University of Missouri - Kansas City, Missouri, USA. - January 2018 - present
      Expected Graduation: January 2021

      • Advisor: Dr. Zhu Li

      • Research: Multimedia Computing & Communication.

    • University of South Florida, Tampa, Florida, USA. - August 2016 - December 2017

      • Research: 5G and Beyond, Machine Learning & Data Science.

  • Master of Science, Electrical Engineering - 2013-2015
    Koc University, Istanbul. Turkey
    Graduated: July 2015

  • Bachelor of Science, Electrical Engineering - 2008-2013
    Lahore University of Management Science, Lahore, Pakistan
    Graduated: June 2013

Teaching Experience

  • Koc University Aug. 2013 - July 2016
    Department of Electrical Engineering

    • ELEC 317, Microprocessors - Fall 2013

    • ENG 200, Probability for Engineers - Spring 2014

    • SCI 100, Natural Sciences - Fall 2014

    • ENG 200, Probability for Engineers - Spring 2015

    • SCI 100, Natural Sciences - Fall 2015

    • ENG 200, Probability for Engineers - Spring 2016

  • Lahore University of Management Science Aug. 2012- Jan. 2013
    Department of Electrical Engineering

    • EE 421, Digital System Design - Fall 2012