IECE Transactions on Sensing, Communication, and Control

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  ISSN:  3065-7431 (online)  |  3065-7423 (print)
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IECE Transactions on Sensing, Communication, and Control is a peer-reviewed international academic journal dedicated to exploring the latest advancements in sensing technologies, communication systems, and control methodologies.
E-mail:[email protected]  DOI Prefix: 10.62762/TSCC
1.13
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Recent Articles

Free Access | Review Article | 27 March 2025
Navigating Ethical Challenges in 6G-Enabled Smart Cities: Privacy, Equity, and Governance
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 48-65, 2025 | DOI: 10.62762/TSCC.2025.291581
Abstract
The rapid urbanization and technological advancements have driven the development of smart cities, envisioned as sustainable, efficient, and interconnected urban spaces. The integration of sixth-generation (6G) wireless technology in smart cities promises unprecedented opportunities in connectivity, low-latency communication, and data management, which transforms urban living. However, this evolution raises critical ethical concerns related to privacy, inclusion, transparency, accountability, and environmental sustainability. This paper explores the ethical considerations inherent in designing smart cities with 6G, emphasizing data governance, equity, and human-centric approaches. It delves... More >

Graphical Abstract
Navigating Ethical Challenges in 6G-Enabled Smart Cities: Privacy, Equity, and Governance

Free Access | Research Article | 25 March 2025
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 36-47, 2025 | DOI: 10.62762/TSCC.2025.378503
Abstract
Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99% training accuracy and 80% testing accuracy after 50 epochs, while the VGG16 model, after fine-tuning... More >

Graphical Abstract
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture

Free Access | Research Article | 20 March 2025
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 25-35, 2025 | DOI: 10.62762/TSCC.2024.964451
Abstract
Brain tumor detection (BTD) is a crucial task, as early detection can save lives. Medical professionals require visual intelligence assistance to efficiently and accurately identify brain tumors. Conventional methods often result in misrecognition, highlighting a critical research gap. To address this, a novel BTD system is proposed to predict the presence of a tumor in a given MRI image. The system leverages a convolutional neural network (CNN) architecture, combined with a multi-layer perceptron (MLP) for feature extraction and understanding complex pixel patterns. An extensive ablation study was conducted to empirically analyze and identify the optimal model for the task. The findings dem... More >

Graphical Abstract
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks

Free Access | Research Article | 05 March 2025
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 11-24, 2025 | DOI: 10.62762/TSCC.2025.435806
Abstract
Accurate and timely detection of wheat diseases remains crucial for sustainable agriculture, particularly in major wheat-producing regions. Wheat diseases pose a significant threat to global food security, need precise and timely detection to promote sustainable agriculture. Existing approaches consistently employ single-scale features with shallow-layered convolutional neural networks (CNNs). To bridge the research gaps, we introduce a novel Multi-Scale Wheat Disease Network (MSWDNet) with feature collaboration for wheat disease recognition supported by a comprehensive dataset collected from wheat fields. This study fills research gaps by introducing a novel technique to improve detection a... More >

Graphical Abstract
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization

Free Access | Research Article | 10 February 2025
High-Voltage Power Supply: Design Considerations and Optimization Techniques
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 1-10, 2025 | DOI: 10.62762/TSCC.2024.741277
Abstract
The main goal of this study is to design and develop a half-bridge inverter architecture specifically for high-voltage power supply applications. An effective, small, and affordable system that converts direct current (DC) to alternating current(AC) can be built, thanks to the IR2151 chip’s dependable characteristics and performance. To get the desired output voltage, the transformer first increases the voltage and then the voltage is increased with a voltage-doubling rectifier (VDR) circuit. The study emphasizes how crucial it is to choose components carefully and simulate the circuit design and implementation process to guarantee dependable performance. The experimental results validate... More >

Graphical Abstract
High-Voltage Power Supply: Design Considerations and Optimization Techniques

Free Access | Research Article | 31 December 2024
Vehicular Network Security Through Optimized Deep Learning Model with Feature Selection Techniques
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 136-153, 2024 | DOI: 10.62762/TSCC.2024.626147
Abstract
In recent years, vehicular ad hoc networks (VANETs) have faced growing security concerns, particularly from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks flood the network with malicious traffic, disrupting services and compromising resource availability. While various techniques have been proposed to address these threats, this study presents an optimized framework leveraging advanced deep-learning models for improved detection accuracy. The proposed Intrusion Detection System (IDS) employs Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN) alongside robust feature selection techniques, Random Projecti... More >

Graphical Abstract
Vehicular Network Security Through Optimized Deep Learning Model with Feature Selection Techniques

Free Access | Research Article | 18 December 2024 | Cited: 1
Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 126-135, 2024 | DOI: 10.62762/TSCC.2024.672831
Abstract
In engineering applications, high-precision tracking control is crucial for robotic manipulators to successfully complete complex operational tasks. To achieve this goal, this study proposes an adaptive tunable predefined-time backstepping control strategy for uncertain robotic manipulators with external disturbances and model uncertainties. By establishing a novel practical predefined-time stability criterion, a tunable predefined-time backstepping controller is systematically presented, allowing the upper bound of tracking error settling time to be precisely determined by adjusting only one control parameter. To accurately address lumped uncertainty, two updating laws are designed: a fuzz... More >

Graphical Abstract
Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators

Free Access | Review Article | 27 November 2024
Next-Generation Technologies for Secure Future Communication-based Social-Media 3.0 and Smart Environment
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 101-125, 2024 | DOI: 10.62762/TSCC.2024.322898
Abstract
Smart Environment is rapidly growing with the inclusion of Artificial Intelligence of Things (AIoT) when it connects to future communication and social media networks. Security and privacy are significant challenges, including data integrity, account hijacking, cybersecurity, and cyberbullying. To mitigate these challenges, Social Media 3.0 is utilized with advanced emerging technologies such as Blockchain, Federated Learning (FL), and others and offers solutions in existing research. This article comprehensively reviews and proposes Next-Generation Technologies for Secure Future Communication Service Scenario for Smart Environment and Social-Media 3.0. We discuss existing attacks with their... More >

Graphical Abstract
Next-Generation Technologies for Secure Future Communication-based Social-Media 3.0 and Smart Environment

Free Access | Research Article | 30 October 2024 | Cited: 1
Enhanced Recognition for Finger Gesture-Based Control in Humanoid Robots Using Inertial Sensors
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 89-100, 2024 | DOI: 10.62762/TSCC.2024.805710
Abstract
Humanoid robots have much weight in many fields. Their efficient and intuitive control input is critically important and, in many cases, requires remote operation. In this paper, we investigate the potential advantages of inertial sensors as a key element of command signal generation for humanoid robot control systems. The goal is to use inertial sensors to detect precisely when the user is moving which enables precise control commands. The finger gestures are initially captured as signals coming from the inertial sensor. Movement commands are extracted from these signals using filtering and recognition. These commands are subsequently translated into robot movements according to the attitud... More >

Graphical Abstract
Enhanced Recognition for Finger Gesture-Based Control in Humanoid Robots Using Inertial Sensors

Free Access | Review Article | 29 October 2024 | Cited: 4
Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 72-88, 2024 | DOI: 10.62762/TSCC.2024.211408
Abstract
Collective motion has been a pivotal area of research, especially due to its substantial importance in Unmanned Aerial Vehicle (UAV) systems for several purposes, including path planning, formation control, and trajectory tracking. UAVs significantly enhance coordination, flexibility, and operational efficiency in practical applications such as search-and-rescue operations, environmental monitoring, and smart city construction. Notwithstanding the progress in UAV technology, significant problems persist, especially in attaining dependable and effective coordination in intricate, dynamic, and unexpected settings. This study offers a comprehensive examination of the fundamental principles, mod... More >

Graphical Abstract
Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination

Free Access | Research Article | 25 October 2024
Spatio-temporal Feature Soft Correlation Concatenation Aggregation Structure for Video Action Recognition Networks
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 60-71, 2024 | DOI: 10.62762/TSCC.2024.212751
Abstract
The efficient extraction and fusion of video features to accurately identify complex and similar actions has consistently remained a significant research endeavor in the field of video action recognition. While adept at feature extraction, prevailing methodologies for video action recognition frequently exhibit suboptimal performance in the context of complex scenes and similar actions. This shortcoming arises primarily from their reliance on uni-dimensional feature extraction, thereby overlooking the interrelations among features and the significance of multi-dimensional fusion. To address this issue, this paper introduces an innovative framework predicated upon a soft correlation strategy... More >

Graphical Abstract
Spatio-temporal Feature Soft Correlation Concatenation Aggregation Structure for Video Action Recognition Networks

Free Access | Research Article | 21 October 2024 | Cited: 3
RF Planning And Optimization Of 5G On The City Campus (MUST) of Mirpur, Pakistan
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 52-59, 2024 | DOI: 10.62762/TSCC.2024.670663
Abstract
As we know, the world is rapidly moving towards 5G and B5G technology to achieve high data rates, massive communication capacity, connectivity, and low latency. 5G offers a latency of less than 1 ms and extremely high data volume compared to previous technologies. The main challenge is the complex nature of 5G network deployment, especially at high frequencies (3–300 GHz) on a university campus with varied building structures. In this paper, we will discuss a scenario for deploying 5G at the Mirpur University of Science and Technology (MUST) in Mirpur, Pakistan so that telecom operators and vendors who wish to deploy a 5G network on the campus in the future can draw on our research finding... More >

Graphical Abstract
RF Planning And Optimization Of 5G On The City Campus (MUST) of Mirpur, Pakistan

Free Access | Review Article | 15 October 2024 | Cited: 1
Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 30-51, 2024 | DOI: 10.62762/TSCC.2024.898503
Abstract
The proliferation of Recommender Systems (RecSys), driven by their expanding application domains, explosive data growth, and exponential advancements in computing capabilities, has cultivated a dynamic and evolving research landscape. This paper comprehensively reviews the foundational concepts, methodologies, and challenges associated with RecSys from technological and social scientific lenses. Initially, it categorizes personalized RecSys technical solutions into five paradigms: collaborative filtering, scenario-aware, knowledge & data co-driven approaches, large language models, and hybrid models integrating diverse data sources. Subsequently, the paper analyses the key challenges and fut... More >

Graphical Abstract
Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications

Free Access | Review Article | 12 October 2024 | Cited: 4
Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 3-29, 2024 | DOI: 10.62762/TSCC.2024.989358
Abstract
This review paper offers a thorough assessment of three-dimensional object recognition methods, an essential element in the perception frameworks of autonomous systems. This analysis emphasises the integration of LiDAR and camera sensors, providing a distinctive contrast with more economical alternatives like camera-only or camera-Radar combinations. This study objectively evaluates performance and practical implementation issues, such as cost and operational efficiency, thereby elucidating the limitations of existing systems and proposing avenues for further research. The insights provided render it a significant asset for enhancing 3D object recognition and autonomy in intelligent systems. More >

Graphical Abstract
Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions

Open Access | Editorial | 08 October 2024
Sensing, Communication, and Control: A New Transactions
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 1-2, 2024 | DOI: 10.62762/TSCC.2024.287867
Abstract
On behalf of the Editorial Board, I am very pleased to announce the launch of our new transactions, IECE Transitions on Sensing, Communication, and Control. This publication aims to serve as a premier platform for researchers, engineers, and scholars to share cutting-edge discoveries, methodologies, and applications in the rapidly evolving fields of sensing, communication, and control. More >
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IECE Transactions on Sensing, Communication, and Control

IECE Transactions on Sensing, Communication, and Control

eISSN: 3065-7431 | pISSN: 3065-7423

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