24 citations · 99 across the 33 of their papers we have counts for
38 papers
SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
Nazmul Karim, Umar Khalid, Mohsen Joneidi +2
Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts. Recent methods have tried to replicate the su…
CSI-Based Data-driven Localization Frameworking using Small-scale Training Datasets in Single-site MIMO Systems
Katarina Vuckovic, Farzam Hejazi, Nazanin Rahnavard
This work presents a date-driven user localization framework for single-site massive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel…
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
Nazmul Karim, Niluthpol Chowdhury Mithun, Abhinav Rajvanshi +3
Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a labeled source domain to an unlabeled target domain. UDA methods have a strong assumption that…
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning
Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard +2
Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization…
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning
Nazmul Karim, Umar Khalid, Ashkan Esmaeili +1
The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario…
CoDGraD: A Code-based Distributed Gradient Descent Scheme for Decentralized Convex Optimization
Elie Atallah, Nazanin Rahnavard, Qiyu Sun
In this paper, we consider a large network containing many regions such that each region is equipped with a worker with some data processing and communication capability. For such…
RF Signal Transformation and Classification using Deep Neural Networks
Umar Khalid, Nazmul Karim, Nazanin Rahnavard
Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this chal…
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
Umar Khalid, Ashkan Esmaeili, Nazmul Karim +1
Recent studies have addressed the concern of detecting and rejecting the out-of-distribution (OOD) samples as a major challenge in the safe deployment of deep learning (DL) models.…
MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI
Katarina Vuckovic, Farzam Hejazi, Nazanin Rahnavard
This paper presents a new map-assisted localization approach utilizing Chanel State Information (CSI) in Massive Multiple-Input Multiple-Output (MIMO) systems. Map-assisted localiz…
Straggler-Robust Distributed Optimization in Parameter-Server Networks
Elie Atallah, Nazanin Rahnavard, Chinwendu Enyioha
Optimization in distributed networks plays a central role in almost all distributed machine learning problems. In principle, the use of distributed task allocation has reduced the…
Spectrum Shaping For Multiple Link Discovery in 6G THz Systems
Farzam Hejazi, Katarina Vuckovic, Nazanin Rahnavard
This paper presents a novel antenna configuration to measure directions of multiple signal sources at the receiver in a THz mobile network via a single channel measurement. Directi…
Phase Spectrometry For High Precision mm-Wave DoA Estimation In 5G Systems
Farzam Hejazi, Nazanin Rahnavard
In this paper, we introduce a direction of arrival (DoA) estimation method based on a technique named phase spectrometry (PS) that is mainly suitable for mm-Wave and Tera-hertz app…
SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network
Nazmul Karim, Nazanin Rahnavard
Single-pixel imaging is a novel imaging scheme that has gained popularity due to its huge computational gain and potential for a low-cost alternative to imaging beyond the visible…
RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing
Nazmul Karim, Alireza Zaeemzadeh, Nazanin Rahnavard
A reinforcement-learning-based non-uniform compressed sensing (NCS) framework for time-varying signals is introduced. The proposed scheme, referred to as RL-NCS, aims to boost the…
Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection
Ashkan Esmaeili, Mohsen Joneidi, Mehrdad Salimitari +2
The problem of simultaneous column and row subset selection is addressed in this paper. The column space and row space of a matrix are spanned by its left and right singular vector…
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack
Ashkan Esmaeili, Marzieh Edraki, Nazanin Rahnavard +2
We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) to dramatically reduce the number of queries and achieve s…
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks
Farzam Hejazi, Katarina Vuckovic, Nazanin Rahnavard
This paper presents a data-driven localization framework with high precision in time-varying complex multipath environments, such as dense urban areas and indoors, where GPS and mo…
Cassandra: Detecting Trojaned Networks from Adversarial Perturbations
Xiaoyu Zhang, Ajmal Mian, Rohit Gupta +2
Deep neural networks are being widely deployed for many critical tasks due to their high classification accuracy. In many cases, pre-trained models are sourced from vendors who may…
Straggler-Robust Distributed Optimization with the Parameter Server Utilizing Coded Gradient
Elie Atallah, Nazanin Rahnavard, Chinwendu Enyioha
Optimization in distributed networks plays a central role in almost all distributed machine learning problems. In principle, the use of distributed task allocation has reduced the…
Odyssey: Creation, Analysis and Detection of Trojan Models
Marzieh Edraki, Nazmul Karim, Nazanin Rahnavard +2
Along with the success of deep neural network (DNN) models, rise the threats to the integrity of these models. A recent threat is the Trojan attack where an attacker interferes wit…
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks
Mojtaba Taherisadr, Mohsen Joneidi, Nazanin Rahnavard
A new deep learning-based electroencephalography (EEG) signal analysis framework is proposed. While deep neural networks, specifically convolutional neural networks (CNNs), have ga…
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas
Mohsen Joneidi, Hassan Yazdani, Azadeh Vosoughi +1
Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional…
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks
Mohsen Joneidi, Ismail Alkhouri, Nazanin Rahnavard
A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum usage is a…
Primary User Localization and Online Radio Cartography via Structured Tensor Decomposition
Mohsen Joneidi, Nazanin Rahnavard
Source localization and radio cartography using multi-way representation of spectrum is the subject of study in this paper. A joint matrix factorization and tensor decomposition pr…