Conferences Links
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Paper Review Claims |
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Paper ID |
Title |
Abstract |
Congress |
Claim |
| 1 |
69.Cnf-1213 |
Parametrically Efficient Conv‑TasNet for 44.1 kHz Speech Separation |
Speech separation is a fundamental task in audio processing, yet most state-of-the-art models are trained on low-frequency datasets (8–16
kHz) and are not readily adaptable to high-quality
audio (44.1 kHz). In this work, we propose
a modified Conv-TasNet architecture adapted
for high-fidelity speech separation at 44.1 kHz.
Our contributions include architectural modifications — replacing global layer normalization with
GroupNorm, introducing dynamic padding for
variable-length inputs, and systematically tuning
hyperparameters (kernel size, stride, TCN depth).
We generated a custom VCTK-2mix dataset comprising 30,000 training mixtures with controlled
SNR and implemented a fully reproducible pipeline
in PyTorch. Extensive experiments demonstrate
that our model achieves an SI-SNR of 16.88 dB,
SDR of 17.33 dB, SIR of 26.38 dB, and SAR of 17.92
dB, surpassing the original Conv-TasNet (15.3 dB
on WSJ0-2mix at 8 kHz). Our findings indicate
that increasing kernel size relative to the original
architecture is beneficial for 44.1 kHz processing,
while maintaining a compact model size suitable
for resource-constrained environments. The source
code is publicly available to ensure reproducibility. |
GC-ElecEng 2026 |
Claim |
| 2 |
45.Cnf-1214 |
Application Of Rüppell's Fox Optimizer for Solving Economic Dispatch in Power System |
Economic Dispatch (ED) allocates power generation among thermal units to minimize total fuel cost subject to operational constraints. The resulting optimization is made non-linear and non-convex by real-world factors such as valve-point loading effects, transmission losses, and emission penalties. In this paper, the Rüppell's Fox Optimizer (RFO), a metaheuristic inspired by the foraging behaviour of Rüppell's foxes, is applied to constrained ED problems. RFO uses a multi-sensory search mechanism that combines eyesight, hearing, and scent-driven strategies to transition between exploration and exploitation. Two standard test systems are used for validation: a 6-unit IEEE 30-bus system with quadratic cost functions and B-coefficient transmission losses, and a 10-unit system with valve-point effects, losses, and emission penalty cast as a single-objective Combined Economic Emission Dispatch (CEED) problem. On the 6-unit system, a best fuel cost of 800.9034 $/hr was achieved by RFO, compared to PSO (806.1893 $/hr) and WOA (809.3234 $/hr), with statistical significance confirmed by the Wilcoxon rank-sum test (p < 0.001). On the 10-unit CEED, the lowest fuel cost of 112,886.6560 $/hr was recorded by RFO. Over 30 independent runs, RFO is shown to be a competitive solver for ED problems in power systems. |
GC-ElecEng 2026 |
Claim |
| 3 |
42.Cnf-1215 |
An Efficient Operator-Splitting Implicit Finite Difference Time Domain Scheme for Modeling Magnetized Graphene Nano-Material in the THz-Band Optical Communication |
In this paper, an efficient operator-splitting implicit scheme, based on the matrix-exponential product formula, is presented for stable finite difference time domain implementation of magnetized graphene nano-material in the THZ-Band optical communication. The presented splitting mechanism decomposes the system's main operator into sub-operators to isolate different characteristics of the graphene layer. In addition, the splitting scheme is simple to implement, where higher dimensional problems are broken down into multiple one-dimensional problems. To
illustrate the validity of the given splitting scheme, numerical tests are also included in this study. |
GC-ElecEng 2026 |
Claim |
| 4 |
55.Cnf-1216 |
Wavelet-Based Multiresolution Analysis for Fault Detection in Nonlinear PMSM Drives A Finite Element Study |
Wavelet-based multiresolution analysis is utilized
for fault diagnosis in permanent magnet synchronous motors
operating under direct torque control. The inherent challenges of this control method, namely variable switching frequency and hysteresis tolerance, are addressed by applying the maximum overlap discrete wavelet transform to the estimated electromagnetic torque signal. This noninvasive approach extracts time-frequency characteristics of the torque signal while isolating
the switching and hysteresis impact in separate bands to ensure clear identification of fault related components. The RMS values of the wavelet detail coefficients serve as inputs to a quadratic discriminant analysis classifier that not only detects and differentiates common faults including turn-turn short circuits, mixed eccentricity, and partial demagnetization but also estimates their severity levels. Validation is performed through high fidelity simulations using ANSYS-MAXWELL involving a finite element machine model integrated with the drive system implemented in ANSYS-SIMPLORER, under both normal and faulty conditions. This method also supports the creation of fault detection databases in practical settings, mitigating the scarcity
of experimental fault data. |
EngiTek 2026 |
Claim |
| 5 |
65.Cnf-1217 |
VLost: An AI-Powered Campus Lost-and-Found Web Application with Image-Based Item Matching and Secure OTP-Verified Handover |
Most individuals, who have spent some time at a college know losing things is quite common. Some water bottles lost at the cafeteria, ID cards left behind at the library, chargers disappearing from labs are common issues. In this paper, we present a web application we created for our school, called VLost, created especially in order to automate lost and found process. VLost has a matching engine that takes the image of an lost item and description and compares it with the found item reports using OpenCV-based pixel comparison and word-overlap analysis. We also integrated Google Gemini in order to allow when someone uploads photo of the found item, the relevant form to be filled automatically with the title, description and type. Once a matching is made and the person expresses their interest in getting the item, there are four steps needed to be done, including OTP verification. Additionally, we implemented user-specific QR codes, that can be printed and attached to someone’s item, permitting connection to be established with the owner in case someone found the lost thing and scanned the QR code. Our platform is built with the help of Flask and SQLite to become a Progressive Web App with a simple Android version. The determined functionality of VLost was examined and tested. |
GC-ElecEng 2026 |
Claim |
| 6 |
80.Cnf-1218 |
A Cross-Domain Platform for Urban Stress Monitoring
Using Traffic Prediction and Waste Management Data |
Many civic intelligence systems address traffic
congestion and solid waste management separately, making it
difficult to understand how these problems influence one
another. This paper presents a tested platform that brings
both domains together through a composite Urban Stress
Index. The platform uses a graph neural network to forecast
congestion across a road network containing 393,090 edges. It
also applies a grid-based model to estimate waste-related risks
in different zones, incorporating reports submitted by citizens.
These two scores are combined through a cross-domain
interaction mechanism. When an area experiences severe
traffic and waste problems at the same time, the system
increases its overall stress level by one full severity band rather
than simply adding the two scores. The resulting index powers
an alert system based on thresholds, cooldown periods, and
escalation overrides. It also helps prioritize waste-collection
routes. Evaluation using spatial and temporal holdouts
produced coefficients of determination of 0.622 and 0.741, with
mean absolute errors of 5.925 and 3.019, respectively. The
results show that the system generalizes better over time on
monitored road segments than across unfamiliar geographic
areas. All metrics were reproduced after reloading the model
checkpoint, and the complete platform passed end-to-end
testing with an average latency of approximately 252
milliseconds. This addresses a common gap in existing
research, where individual models are validated separately
rather than as complete, deployed systems. |
EngiTek 2026 |
Claim |
| 7 |
68.Cnf-1208 |
A Blockchain Framework for Enhancing Selection of Optimal Parameters at NB-IOT |
By 2030, wireless communications will have connected over thirty billion devices. Narrowband Internet of Things (NB-IOT) technology has grown in popularity in response to the rapid growth of the internet of things (IOT) sector. The main aim of this study is to supply overall survey of the design modifications transported in the NB-IoT standardization along with comprehensive study evolutions according to popular companies in many countries such as: Telia, Elisa, Orange, Telecom Italia, Telstra, Vodafone, On the other hand, because there is a lot of work in NB-IOT on optimizing parameters or improving optimization methods, Consequently, this work presents a Blockchain architecture that may be used to enhance security, authentication, and efficient data access while maintaining data integrity. We describe the optimization parameters for physical channel and signal transmission and reception. Using the Physical Downlink Control Channel, we devise an adaptation scheme for 200 KHz bandwidth in NB-IOT networks (PDCCH). Finally, we want to locate the following: optimal parameters: Number of frames, Doppler frequency and Diversity performance. |
GC-ElecEng 2026 |
Claim |
| 8 |
90.Cnf-1210 |
Supervised Neural Network Based Intrusion Detection Systems |
The magnificent problem for today’s computer networks is the internet attacks which is growing rapidly. Therefore, applying security methods to prevent such attacks on computer network is very important. Network attacks are challenging because they are continuously changing their patterns and techniques. Machine learning approaches play vital roles in detecting, and preventing attacks in different types of computer networks. The security is a critical issue whereas the security mechanism may affect the way of detecting, analysing, and preventing attacks. Therefore, security methods must modify their techniques to deal with. We have proposed Supervised Neural Network Based Intrusion Detection System (SNN-based IDS) to address a critical and timely issue in computer networking - the escalating threat of internet attacks. As these attacks continue to evolve, finding effective methods to detect and prevent them is of paramount importance. this paper leverages advanced technology to tackle the complex task of intrusion detection. This demonstrates the practical application of cutting-edge methods in cybersecurity. The paper suggests that the proposed model has the potential to predict and mitigate new types of attacks, even those not present in the training dataset. This feature is crucial in a rapidly evolving threat landscape. Our SNN is trained by KDDCUP’99 dataset which include 14 attack types in the test data, with an overall number of 24 training attack types. Experiments results show that the proposed system is capable to identifying the attacks and classifying them with high accuracy and reliability, regardless of the dataset's nonlinearity, size, or incompleteness. |
CompuSys 2027 |
Claim |
| 9 |
68.Cnf-1206 |
Design of Array Inverted F Antenna for IOT |
This work analyzes the performance and designs a rectangular Microstrip patch F antenna. 5.9 GHz is the antenna's resonance frequency range, making it suitable for Internet of Things (IOT) applications. Simulation software for this work was Computer Simulation Technology (CST) software. A rectangular Inverted F coplanar antenna array structure was used in the antenna's design. The bandwidth, gain, and return on loss of these antennas were evaluated to determine their respective performances. The main findings of this study shown that, in comparison to a conventional antenna, the optimized array-shaped antenna increased bandwidth, gain, and return on loss. Furthermore, the improved antenna attained an operating frequency of 5.9 GHz, making it appropriate for Internet of Things applications. |
GC-ElecEng 2026 |
Claim |
| 10 |
46.Cnf-1207 |
Design and analysis of off -grid PV/diesel system for small scale factory located in Wadi Rum, Jordan using Homer software |
The fuel prices are increasing nowadays, causing a burden on the power system. From this, diesel generators that are still in use, must be hybridized with renewable energy (RE) to levelized the overall projects costs. This study aims to schedule an operation of a hybrid system photovoltaic (PV)/ diesel in a remote area which located in Wadi Rum district in Jordan to serve factory load in addition to obtain the best economical system combination that can serve the needs of a continuous electrical load without any interruption. Based on the results of the analysis using HOMER software, the configuration in the system will be a combination between PV modules, and diesel generator. The hybrid system has met the target for electricity production which is documented in upcoming sections. |
GC-ElecEng 2026 |
Claim |
| 11 |
0.Cnf-1211 |
On the Scalability of PM-EDS Maglev Systems:From Laboratory Prototypes to Full-Scale Operation |
As a passive and energy-efficient alternative systemto the maglev technologies; Permanent Magnet ElectrodynamicSuspension (PM-EDS) systems was offered. However, scalabilityremains unclear. This paper present an analytical study of PM-EDS scalability using dimensionless parameters derived fromelectromagnetic relationships. Scaling conditions required topreserve system behavior across different sizes are established,showing that scalability depends on maintaining keydimensionless parameters. The results further reveal that systemperformance is constrained by the coupled effects of velocity,magnetic field strength, and system geometry, as well as regime-dependent behavior. |
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Claim |
| 12 |
42.Cnf-1212 |
NEUROFLOW: Early ICU Delirium Prediction via Temporal Convolutional Networks with FHIR-Native Intervention Orchestration |
Intensive care unit (ICU) delirium affects an estimated 7 million United States patients annually and is associated with 30-40% increases in one-year mortality, yet the evidence-based ABCDEF prevention bundle achieves only 0-1% worldwide compliance. Existing machine learning systems generate a risk score and stop, leaving the prediction-to-intervention gap unaddressed. This paper presents NEUROFLOW, a FHIR-native ICU delirium prediction and intervention orchestration system. A 56,065-parameter dilated causal Temporal Convolutional Network (TCN) with a learned pharmacodynamic embedding processes a 40-channel, 63-hour time-series tensor constructed directly from HL7 FHIR R4 resources. Trained on 74,829 MIMIC-IV ICU stays under strict temporal holdout validation, NEUROFLOW achieves AUROC 0.8125 and AUPRC 0.5638, warning 77.4% of eligible patients a median of 20 hours before clinical detection. When predicted risk exceeds a threshold, the system automatically writes a US Core CarePlan with six ABCDEF activities and scores bundle compliance hourly from existing FHIR Observations. Split conformal prediction intervals are encoded as structured FHIR extensions. Zero-shot external validation on 132,900 eICU admissions across 208 community hospitals yielded AUROC 0.6568; a label-noise analysis identified 11,405 clinically plausible undocumented cases, and an in silico sensitivity reclassification to the training prevalence recovered AUROC 0.8893, indicating the external gap reflects documentation incompleteness rather than model failure. |
GC-ElecEng 2026 |
Claim |
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