Keyword search (4,163 papers available)

"Mohammadi A" Authored Publications:

Title Authors PubMed ID
1 Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module Khademi S; Heidarian S; Afshar P; Mohammadi A; Sidiqi A; Nguyen ET; Ganeshan B; Oikonomou A; 41150036
ENCS
2 Energy-delay analysis in advection-diffusion-based wireless body area networks Kianfar G; Hosseini P; Azadi M; Abouei J; Mohammadi A; 40880450
ENCS
3 Differentiation of COVID-19 from other types of viral pneumonia and severity scoring on baseline chest radiographs: Comparison of deep learning with multi-reader evaluation Enshaei N; Mohammadi A; Naderkhani F; Daneman N; Abu Mughli R; Anconina R; Berger FH; Kozak RA; Mubareka S; Villanueva Campos AM; Narang K; Vivekanandan T; Chan AK; Lam P; Andany N; Oikonomou A; 40729327
ENCS
4 Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals Montazerin M; Rahimian E; Naderkhani F; Atashzar SF; Yanushkevich S; Mohammadi A; 37419881
ENCS
5 Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans Khademi S; Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Oikonomou A; Shafiee A; Babaki Fard F; Plataniotis KN; Mohammadi A; 36862633
ENCS
6 DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis Karimi R; Mohammadi A; Asif A; Benali H; 35408182
ENCS
7 Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network Afshar P; Rafiee MJ; Naderkhani F; Heidarian S; Enshaei N; Oikonomou A; Babaki Fard F; Anconina R; Farahani K; Plataniotis KN; Mohammadi A; 35318368
ENCS
8 COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images Enshaei N; Oikonomou A; Rafiee MJ; Afshar P; Heidarian S; Mohammadi A; Plataniotis KN; Naderkhani F; 35217712
ENCS
9 COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A; 34113843
ENCS
10 COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images. Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A 32958971
ENCS
11 PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models. Shahtalebi S; Atashzar SF; Samotus O; Patel RV; Jog MS; Mohammadi A; 32042111
ENCS

 

Title:Energy-delay analysis in advection-diffusion-based wireless body area networks
Authors:Kianfar GHosseini PAzadi MAbouei JMohammadi A
Link:https://pubmed.ncbi.nlm.nih.gov/40880450/
DOI:10.1371/journal.pone.0330744
Publication:PloS one
Keywords:
PMID:40880450 Category: Date Added:2025-08-29
Dept Affiliation: ENCS
1 Concordia Institute of Information Systems Engineering (CIISE), Concordia University, Montreal, Canada.
2 Department of Electrical Engineering, Yazd University, Yazd, Iran.

Description:

Molecular communication (MC) emerges as an encouraging concept in wireless body area nanonetworks ([Formula: see text]), which utilizes molecules as information carriers for communication between nanomachines. In this paper, we aim to define an electrical model of a molecular-based nano-transmitter to analyze the effect of the remained transmitted molecules in a fluidic medium. To this end, we will address an advection-diffusion equation with a non-zero initial condition to analyze the residual molecules' influence the medium. Moreover, considering the energy consumption limitations of nanomachines, we will employ the derived electrical model to further investigate how nanomachines consume the energy in presence of residual molecules. Following this, to enhance the energy consumption of the nano-transmitters, the settle-time method will be proposed to tackle the negative impact of the residual molecules on energy consumption. Nevertheless, since the proposed method increases the delay at nano-transmitters, the energy-delay trade-off relation at nano-transmitters will be investigated. Then, by introducing an interruption period and a control coefficient, we control the trade-off between the energy consumption and the created delay. Finally, by considering insulin molecules as messenger molecules in our simulations, we will demonstrate that implementing short interruption periods significantly enhances energy consumption, while introducing a small amount of delay to the system. Particularly, the energy consumption is reduced by 15% and the latency is increased by 2.2 ms when 1 ms interrupt period is used for 20 mol of insulin molecule.





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