539 citations · 1.1k across the 21 of their papers we have counts for
25 papers
Machine learning empowered Modulation detection for OFDM-based signals
Ali Pourranjbar, Georges Kaddoum, Verdier Assoume Mba +2
We propose a blind ML-based modulation detection for OFDM-based technologies. Unlike previous works that assume an ideal environment with precise knowledge of subcarrier count and…
Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles
Siyuan Liang, Hao Wu
Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasin…
Structural block driven - enhanced convolutional neural representation for relation extraction
Dongsheng Wang, Prayag Tiwari, Sahil Garg +2
In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning. Specifically, we detect the essential sequent…
Collaborative Coded Computation Offloading: An All-pay Auction Approach
Jer Shyuan Ng, Wei Yang Bryan Lim, Sahil Garg +4
As the amount of data collected for crowdsensing applications increases rapidly due to improved sensing capabilities and the increasing number of Internet of Things (IoT) devices,…
The Shift to 6G Communications: Vision and Requirements
Muhammad Waseem Akhtar, Syed Ali Hassan, Rizwan Ghaffar +3
The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more rel…
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Yi Liu, Sahil Garg, Jiangtian Nie +4
Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming…
An RNN-Survival Model to Decide Email Send Times
Harvineet Singh, Moumita Sinha, Atanu R. Sinha +2
Email communications are ubiquitous. Firms control send times of emails and thereby the instants at which emails reach recipients (it is assumed email is received instantaneously f…
Nearly-Unsupervised Hashcode Representations for Relation Extraction
Sahil Garg, Aram Galstyan, Greg Ver Steeg +1
Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relat…
LiSA: A Lightweight and Secure Authentication Mechanism for Smart Metering Infrastructure
Sahil Garg, Kuljeet Kaur, Georges Kaddoum +3
Smart metering infrastructure (SMI) is the core component of the smart grid (SG) which enables two-way communication between consumers and utility companies to control, monitor, an…
A Lightweight and Privacy-Preserving Authentication Protocol for Mobile Edge Computing
Kuljeet Kaur, Sahil Garg, Georges Kaddoum +2
With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for real-time data processing and analysis has emerged as an essential pre-…
An Efficient Blockchain-based Hierarchical Authentication Mechanism for Energy Trading in V2G Environment
Sahil Garg, Kuljeet Kaur, Georges Kaddoum +2
Vehicle-to-grid (V2G) networks have emerged as a new technology in modern electric power transmission networks. It allows bi-directional flow of communication and electricity betwe…
Blockchain-based Lightweight Authentication Mechanism for Vehicular Fog Infrastructure
Kuljeet Kaur, Sahil Garg, Georges Kaddoum +2
With the increasing development of advanced communication technologies, vehicles are becoming smarter and more connected. Due to the tremendous growth of various vehicular applicat…
An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks
Kuljeet Kaur, Sahil Garg, Georges Kaddoum +3
Network Functions Virtualization (NFV) in Software Defined Networks (SDN) emerged as a new technology for creating virtual instances for smooth execution of multiple applications.…
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning
Poulmanogo Illy, Georges Kaddoum, Christian Miranda Moreira +2
The growing interest in the Internet of Things (IoT) applications is associated with an augmented volume of security threats. In this vein, the Intrusion detection systems (IDS) ha…
Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm
Jung-yeon Baek, Georges Kaddoum, Sahil Garg +2
The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service…
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots
Sahil Garg, Nora Ayanian
This paper presents a solution for persistent monitoring of real-world stochastic phenomena, where the underlying covariance structure changes sharply across time, using a small nu…
Learning Non-Stationary Space-Time Models for Environmental Monitoring
Sahil Garg, Amarjeet Singh, Fabio Ramos
One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both sp…
Adaptive Sensing for Learning Nonstationary Environment Models
Sahil Garg, Amarjeet Singh, Fabio Ramos
Most environmental phenomena, such as wind profiles, ozone concentration and sunlight distribution under a forest canopy, exhibit nonstationary dynamics i.e. phenomenon variation c…
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis
Emilia M. Wysocka, Valery Dzutsati, Tirthankar Bandyopadhyay +2
An important task for many if not all the scientific domains is efficient knowledge integration, testing and codification. It is often solved with model construction in a controlla…
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact
Sahil Garg
Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation cha…
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
Sahil Garg, Irina Rish, Guillermo Cecchi +5
We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as compressed…
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling
Sahil Garg, Greg Ver Steeg, Aram Galstyan
Natural language processing often involves computations with semantic or syntactic graphs to facilitate sophisticated reasoning based on structural relationships. While convolution…
Kernelized Hashcode Representations for Relation Extraction
Sahil Garg, Aram Galstyan, Greg Ver Steeg +3
Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kern…
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
Sahil Garg, Irina Rish, Guillermo Cecchi +1
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture. We propose a novel online di…