Search Results for author: Aly El Gamal

Found 20 papers, 8 papers with code

Knowledge Distillation For Wireless Edge Learning

1 code implementation3 Apr 2021 Ahmed P. Mohamed, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal

In this paper, we propose a framework for predicting frame errors in the collaborative spectrally congested wireless environments of the DARPA Spectrum Collaboration Challenge (SC2) via a recently collected dataset.

Federated Learning Knowledge Distillation

Gradient-based Adversarial Deep Modulation Classification with Data-driven Subsampling

no code implementations3 Apr 2021 Jinho Yi, Aly El Gamal

Automatic modulation classification can be a core component for intelligent spectrally efficient wireless communication networks, and deep learning techniques have recently been shown to deliver superior performance to conventional model-based strategies, particularly when distinguishing between a large number of modulation types.

Classification General Classification

Mitigating Gradient-based Adversarial Attacks via Denoising and Compression

no code implementations3 Apr 2021 Rehana Mahfuz, Rajeev Sahay, Aly El Gamal

To reduce the training time of the defense for a small trade-off in performance, we propose the hidden layer defense, which involves feeding the output of the encoder of a denoising autoencoder into the network.

Denoising Dimensionality Reduction

A Provably Convergent Information Bottleneck Solution via ADMM

no code implementations9 Feb 2021 Teng-Hui Huang, Aly El Gamal

Conventionally, it resorts to characterizing the information plane, that is, plotting $I(Y;Z)$ versus $I(X;Z)$ for all solutions obtained from different initial points.

Information Plane Unsupervised Representation Learning

Data-driven Analysis of Turbulent Flame Images

no code implementations2 Dec 2020 Rathziel Roncancio, Jupyoung Kim, Aly El Gamal, Jay P. Gore

Pockets or islands of unburned material are features of turbulent flames during these events.

Deep Learning for DOA Estimation in MIMO Radar Systems via Emulation of Large Antenna Arrays

no code implementations27 Jul 2020 Aya Mostafa Ahmed, Udaya Sampath K. P. Miriya Thanthrige, Aly El Gamal, Aydin Sezgin

We further analyze the best choice for the training SNR as a function of the test SNR, and observe dramatic changes in the behavior of this function for different angle ranges.

Direction of Arrival Estimation

Ensemble Wrapper Subsampling for Deep Modulation Classification

1 code implementation10 May 2020 Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu, Aly El Gamal, Yonina C. Eldar

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples.

Classification Feature Selection +1

Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Dataset

1 code implementation22 Mar 2020 Abu Shafin Mohammad Mahdee Jameel, Ahmed P. Mohamed, Xiwen Zhang, Aly El Gamal

We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge.

Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks

no code implementations22 Feb 2020 Kirthi Shankar Sivamani, Rajeev Sahay, Aly El Gamal

In this letter, we propose a novel method to detect adversarial inputs, by augmenting the main classification network with multiple binary detectors (observer networks) which take inputs from the hidden layers of the original network (convolutional kernel outputs) and classify the input as clean or adversarial.

Classification General Classification

Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks

no code implementations26 Jan 2020 Rehana Mahfuz, Rajeev Sahay, Aly El Gamal

Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architecture used for crafting the attack, or both.


Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimates

no code implementations26 Dec 2019 Xingchen Wang, Shengtai Ju, Xiwen Zhang, Sharan Ramjee, Aly El Gamal

We study efficient deep learning training algorithms that process received wireless signals, if a test Signal to Noise Ratio (SNR) estimate is available.

Efficient Wrapper Feature Selection using Autoencoder and Model Based Elimination

1 code implementation28 May 2019 Sharan Ramjee, Aly El Gamal

We propose a computationally efficient wrapper feature selection method - called Autoencoder and Model Based Elimination of features using Relevance and Redundancy scores (AMBER) - that uses a single ranker model along with autoencoders to perform greedy backward elimination of features.

Feature Selection General Classification

Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection

1 code implementation16 May 2019 Xiwen Zhang, Tolunay Seyfi, Shengtai Ju, Sharan Ramjee, Aly El Gamal, Yonina C. Eldar

We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi.

Classification General Classification

Fast Deep Learning for Automatic Modulation Classification

1 code implementation16 Jan 2019 Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu, Aly El Gamal, Yonina C. Eldar

We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy.

Classification General Classification

Deep Neural Network Architectures for Modulation Classification

1 code implementation1 Dec 2017 Xiaoyu Liu, Diyu Yang, Aly El Gamal

Finally, we introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to achieve an accuracy of approximately 88. 5% at high SNR.

Classification General Classification

A Sampling Theory Perspective of Graph-based Semi-supervised Learning

no code implementations26 May 2017 Aamir Anis, Aly El Gamal, Salman Avestimehr, Antonio Ortega

In this work, we reinforce this connection by viewing the problem from a graph sampling theoretic perspective, where class indicator functions are treated as bandlimited graph signals (in the eigenvector basis of the graph Laplacian) and label prediction as a bandlimited reconstruction problem.

Graph Sampling

Asymptotic Justification of Bandlimited Interpolation of Graph signals for Semi-Supervised Learning

no code implementations14 Feb 2015 Aamir Anis, Aly El Gamal, A. Salman Avestimehr, Antonio Ortega

Graph-based methods play an important role in unsupervised and semi-supervised learning tasks by taking into account the underlying geometry of the data set.

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