Electronic Communications, IOT and Big Data, 2nd Volume

Topic Information

Dear Colleagues,

The 2025 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data will be held at Tamkang University, Taipei, Taiwan from April 25 to 27, 2025. It will provide a platform for high-tech personnel and researchers in the topics of Electronic Communications, Internet of Things and Big Data to discuss their research and recent developments in the field The booming economic development in Asia, especially the advanced technology in the fields of Electronic Communications, Internet of Things and Big Data, has attracted significant attention from many universities, research institutions and companies. The focus of this conference will be on research that presents innovative ideas or results and practical applications. The topics of interest are as follows:

I. Big Data and Cloud Computing

1. Models and Algorithms of Big Data
2. Architecture of Big Data
3. Big Data Management
4. Big Data Analysis and Processing
5. Security and Privacy of Big Data
6. Big Data in Smart Cities
7. Search, Mining and Visualization of Big Data
8. Technologies, Services and Application of Big Data
9. Edge Computing
10. Architectures and Systems of Cloud Computing
11. Models, Simulations, Designs, and Paradigms of Cloud Computing
12. Management and Operations of Cloud Computing
13. Technologies, Services and Applications of Cloud Computing
14. Dynamic Resource Supply and Consumption
15. Management and Analysis of Geospatial Big Data
16. UAV Oblique Photography and Ground 3D Real-scene Modeling
17. Aerial Photography of UAV loaded with Multispectral Sensors
18. Medical Informatics and Healthcare Engineering

II. Technologies and Application of Artificial Intelligence

1. Basic Theory and Application of Artificial Intelligence
2. Knowledge Science and Knowledge Engineering
3. Machine Learning and Data Mining
4. Machine Perception and Virtual Reality
5. Natural Language Processing and Understanding
6. Neural Network and Deep Learning
7. Pattern Recognition Theory and Application
8. Rough Set and Soft Computing
9. Biometric Identification
10. Computer Vision and Image Processing
11. Evolutionary Calculation
12. Information Retrieval and Web Search
13. Intelligent Planning and Scheduling
14. Intelligent Control
15. Classification and Change Detection of Remote Sensing Images or Aerial Images
16. Computer and Software Application

III. Robotics Science and Engineering

1. Robot control
2. Mobile robotics
3. Intelligent pension robots
4. Mobile sensor networks
5. Perception systems
6. Micro robots and micro-manipulation
7. Visual serving
8. Search, rescue and field robotics
9. Robot sensing and data fusion
10. Indoor localization and navigation
11. Dexterous manipulation
12. Medical robots and bio-robotics
13. Human-centered systems
14. Space and underwater robots
15. Tele-robotics

IV. Internet of Things and Sensor Technology

1. Technology architecture of Internet of Things
2. Sensors in Internet of Things
3. Perception technology of Internet of Things information
4. Multi-terminal cooperative control and Internet of Things intelligent terminal
5. Multi-network resource sharing in the environment of Internet of Things
6. Heterogeneous fusion and multi-domain collaboration in the Internet of Things environment 
7. SDN and intelligent service network
8. Technology and its application in the Internet of Things
9. Cloud computing and big data in the Internet of Things
10. Information analysis and processing of Internet of Things
11. CPS technology and intelligent information system
12. Internet of Things technology standard
13. Internet of Things information security
14. Narrow Band Internet of Things (NB-IoT)
15. Smart cities
16. Smart farming
17. Smart grids
18. Digital health/telehealth/telemedicine

V. Electronic and Electrical Engineering

1. Bioengineering
2. Communication, Networking and Broadcasting
3. Components, Circuits, Devices and Systems
4. Computing and Processing
5. Engineered Materials, Dielectrics and Plasmas
6. Engineering Profession
7. Fields, Waves and Electromagnetics
8. General Topics for Engineers (Math, Science and Engineering)
9. Geoscience
10. Photonics and Electro-Optics
11. Power, Energy and Industry Applications
12. Signal Processing and Analysis

VI. Big Data and AI Technologies on Civil and Architecture Engineering

1. Green Architecture
2. Ecological Architecture
3. Building Energy Management
4. Building Information Modeling
5. Architectural Environment and Equipment Engineering
6. Green Building Materials
7. IOT/Smart Buildings
8. Digital Architecture
9. Civil Engineering and Engineering Management
10. Advanced Construction Technology
11. Innovative Construction and Development
12. Digital Construction

VII. Big Data and AI Technologies on Environmental Engineering and Geographic Information 

1. Environmental Monitoring and Control
2. Environmental Planning and Assessment
3. Environmental Protection
4. Environmental Sustainability
5. Environmental Safety and Health
6. Biomedical Engineering
7. Geographic Information and Remote Sensing Science
8. Geographic Information System Application

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Charles Tijus
Prof. Dr. Cheng-Chien Kuo
Prof. Dr. Kuei-Shu Hsu
Prof. Dr. Jih-Fu Tu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Big Data and Cognitive Computing BDCC	5.3	11.4	2017	23.3 Days	CHF 1800	Submit
Computers computers	5.2	9.1	2012	15.4 Days	CHF 1800	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Journal of Sensor and Actuator Networks jsan	4.8	11.3	2012	24.4 Days	CHF 2000	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Technologies technologies	5.2	6.7	2013	17 Days	CHF 1800	Submit
Telecom telecom	2.8	5.2	2020	22.8 Days	CHF 1400	Submit

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Published Papers (21 papers)

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22 pages, 47461 KB  

Open AccessArticle

Hybrid CPU–GPU Cutting Simulation Using Position-Based Dynamics and Internal Shape-Preserving Constraints in Unity 3D

by Lyudmila Khan, Yoo-Joo Choi and Min Hong

Electronics 2026, 15(17), 3796; https://doi.org/10.3390/electronics15173796 - 24 Aug 2026

Viewed by 205

Abstract

Cutting simulation is a key component of surgical simulators, requiring real-time performance, numerical stability, and physically plausible deformation. Achieving these properties is challenging because cutting combines collision detection, dynamic topology modification, and computationally intensive physical simulation. In this work, deformable objects are modeled [...] Read more.

Cutting simulation is a key component of surgical simulators, requiring real-time performance, numerical stability, and physically plausible deformation. Achieving these properties is challenging because cutting combines collision detection, dynamic topology modification, and computationally intensive physical simulation. In this work, deformable objects are modeled using Position-Based Dynamics (PBD), while volumetric behavior is approximated through internal shape-preserving constraints (ISPCs) applied to triangular surface meshes. The primary contribution is a hybrid CPU–GPU cutting architecture in Unity 3D, in which the computationally expensive triangle–triangle intersection test is parallelized using compute shaders, surface rendering is performed directly from GPU-resident geometry buffers, and CPU–GPU synchronization overhead is minimized. Compared with our previous implementation, which parallelized only deformation and collision handling, the proposed framework additionally accelerates the intersection detection stage of cutting. The method was evaluated with single- and multiple-cut experiments on eight triangular meshes of increasing complexity (20–30,000 triangles). Seven of the eight models sustained interactive frame rates, and the cutting stage contributed negligibly to steady-state cost—below 0.5 ms on average even under repeated free cutting—with frame time being dominated instead by deformation and inter-part collision handling. Relative to our previous work, average frame rates improved by up to 677% over the slicing-based approach and up to 395% over the CPU-based ISPC implementation. Volume was maintained to within 0.4% of the initial volume for models of low geometric complexity. Two limitations were observed: for the high-resolution Armadillo model, mean frame cost was dominated by deformation and collision, while rare cut-finalization frames produced large worst-case spikes, keeping it below interactive rates; the Octopus model exhibited markedly reduced volume preservation, stabilizing at 73.4% of its initial volume. These results indicate that the proposed hybrid method substantially improves the efficiency of cutting simulation, but scaling to large and geometrically complex models remains future work. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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24 pages, 8848 KB  

Open AccessArticle

Event-Triggered Resilient Control with High Communication Efficiency of Networked DC Microgrid Clusters Under Nodal DoS Attacks

by Zhen Liu and Dazhong Ma

J. Sens. Actuator Netw. 2026, 15(4), 64; https://doi.org/10.3390/jsan15040064 - 6 Aug 2026

Viewed by 329

Abstract

In DC microgrid (DC-MG) clusters, distributed generation units rely on electronic communication networks to exchange voltage measurements, current information, and coordination signals for voltage recovery and current sharing. As the degree of system clustering and communication coupling increases, nodal denial-of-service (DoS) attacks may [...] Read more.

In DC microgrid (DC-MG) clusters, distributed generation units rely on electronic communication networks to exchange voltage measurements, current information, and coordination signals for voltage recovery and current sharing. As the degree of system clustering and communication coupling increases, nodal denial-of-service (DoS) attacks may interrupt the information exchange of leaders and followers, resulting in communication topology switching and degraded cooperative control performance. Accordingly, this paper proposes an event-triggered (ET) resilient control scheme with high communication efficiency for networked DC-MG clusters under nodal DoS attacks. First, a distributed secondary control model with a cross-layer communication mechanism is constructed in accordance with the requirements of the system’s overall power distribution, which incorporates the two-layer node architecture of leaders and followers in DC-MG clusters. Second, a statistical multimode nodal DoS attack model is developed to characterize heterogeneous communication interruptions through topology-dependent attack modes and their occurrence probabilities. Finally, an exponential threshold ET mechanism based on bus-voltage recovery errors is designed within the distributed secondary control framework to reduce redundant information transmission while preserving resilience against nodal communication attacks. Simulation results demonstrate that the proposed method can maintain accurate voltage recovery and current sharing in networked DC-MG clusters under large-scale DoS attacks, while improving communication efficiency through ET updates. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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28 pages, 1408 KB  

Open AccessArticle

Parity-Based Error Correction Code with Crosstalk Avoidance and Link Power Reduction for NoC

by Srinidhi Kalyanaraman, Vinodhini Manickaraj, Nemanja Zdravković and Miloš Kostić

Electronics 2026, 15(14), 3075; https://doi.org/10.3390/electronics15143075 - 13 Jul 2026

Viewed by 432

Abstract

The performance of Network-on-Chip (NoC) architectures is severely limited by the high dynamic power consumption and interconnect crosstalk with the scaling of deep sub-micron (DSM) technology. The coupling capacitance between wires dominates the self-capacitance in dense interconnects, and the coupling transitions contribute the [...] Read more.

The performance of Network-on-Chip (NoC) architectures is severely limited by the high dynamic power consumption and interconnect crosstalk with the scaling of deep sub-micron (DSM) technology. The coupling capacitance between wires dominates the self-capacitance in dense interconnects, and the coupling transitions contribute the most to the link power dissipation. In this paper, we propose a new low-power error correction coding scheme called Parity-based Multi-bit Transient Error Correction (PMTEC), which can simultaneously deal with self-transitions and coupling transitions in NoC links. The proposed method reduces the effective switching activity by employing a balanced encoding strategy, unlike existing coding techniques, which typically degrade the signal integrity or impose significant power overheads for particular data patterns. Experimental analysis demonstrates that PMTEC achieves strong link power reductions on various data profiles, including text, image, and video traffic, without the negative overheads of cutting-edge techniques like Duplicated Two-Dimensional Parities (DTDPs) and Crosstalk-Aware Transient Error Correction (CATEC). Furthermore, the codec’s parametric synthesis reveals a very small footprint, taking up only 2472.018 $\mathsf{\mu }{\mathrm{m}}^2$ of silicon area and using a negligible 0.219 $\mathsf{\mu }$W of power, which are 87.57% and 99.20% reductions over CATEC, respectively. The suggested work offers a scalable and energy-efficient solution for dependable on-chip communication in power-constrained systems, despite requiring a 15.371 ns latency trade-off to correct 8-bit bursts. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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18 pages, 5789 KB  

Open AccessArticle

IoT Architecture Based on the OSI Model for Industrial Interconnection Using PLC and Modbus Gateway

by Adrian Benavides, Leonardo Banegas and Luigi O. Freire

Telecom 2026, 7(3), 77; https://doi.org/10.3390/telecom7030077 - 18 Jun 2026

Cited by 1 | Viewed by 762

Abstract

The industrial Internet of Things (IoT) allows traditional electromechanical systems to be connected to digital monitoring and control platforms, especially when field devices use industrial protocols that must be integrated into web services without modifying their main operation. This work implements an IoT [...] Read more.

The industrial Internet of Things (IoT) allows traditional electromechanical systems to be connected to digital monitoring and control platforms, especially when field devices use industrial protocols that must be integrated into web services without modifying their main operation. This work implements an IoT architecture based on the Open Systems Interconnection (OSI) model to interconnect two Variable Frequency Drives (VFDs) through a LOGO! Programmable Logic Controller (LOGO! PLC), a Human–Machine Interface (HMI), a ZLAN5143D gateway, Node-RED, Message Queuing Telemetry Transport (MQTT), and Adafruit IO. The communication integrates RS485/Modbus RTU at the field level and Modbus TCP/IP over Ethernet at the upper network level using the gateway as the protocol conversion element. The validation was performed through Modbus Poll, variable acquisition, MQTT publication, and web visualization. The results show local communication response, acquisition of frequency, voltage, current, and revolutions per minute (RPM), together with remote control of start, stop, frequency setpoint, and rotation direction. The architecture is presented as a modular solution for electromechanical applications with IoT projection. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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23 pages, 516 KB  

Open AccessArticle

Design and Experimental Evaluationof an Open-Architecture Multi-Sensor Telemetry System for Real-Time Motorcycle Dynamics Acquisition

by Andrei García Cuadra, Alberto Brunete González and Francisco Santos Olalla

Electronics 2026, 15(12), 2604; https://doi.org/10.3390/electronics15122604 - 12 Jun 2026

Viewed by 484

Abstract

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The [...] Read more.

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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36 pages, 2295 KB  

Open AccessReview

The Evolution of FTTH Networks in Europe and South Korea—Regulatory Power

by Jorge Duarte, Carlos Serôdio, Sílvia de Castro Pereira, Fernando Santos, António Valente, Sérgio Ramos and Sérgio Leitão

Telecom 2026, 7(3), 59; https://doi.org/10.3390/telecom7030059 - 26 May 2026

Cited by 2 | Viewed by 1405

Abstract

Regulations of next-generation networks (NGNs) have played a central role in the transition from copper networks to broadband networks in Fiber to The Home (FTTH), also allowing for a reduction in asymmetries between incumbent operators and their competitors. Despite common European Union directives, [...] Read more.

Regulations of next-generation networks (NGNs) have played a central role in the transition from copper networks to broadband networks in Fiber to The Home (FTTH), also allowing for a reduction in asymmetries between incumbent operators and their competitors. Despite common European Union directives, telecom infrastructure development varies across countries due to differences in regulation, investment models, legacy networks, operators’ behavior, and public policies. This study analyzes the evolution of Very High-Capacity Networks (VHCNs), focusing on the implementation of FTTH in four European countries (Portugal, Spain, France, and Germany), and in South Korea. The latter was included as a benchmark in government-driven broadband development. The analysis considers key factors influencing FTTH development, including infrastructure regulation, fiber investment incentives, incumbent strategies, infrastructure sharing and co-investment, rural coverage plans, and demographic differences of each country. The results show that regulatory measures directly influence the pace of FTTH installation; but its effectiveness also depends on investment incentives, market competition, and demand factors. Portugal, Spain, and France have high FTTH coverage despite different regulatory and investment models. In contrast, Germany relied on xDSL over copper networks for a long time, causing significant delays, with an FTTH coverage rate of 42.5% and a penetration rate of only 12.3%, putting the European Union’s 2030 goal of universal 1 Gbps coverage at risk. South Korea shows that long-term public policies, demand-side incentives, and high digital adoption accelerate mass FTTH adoption and turn telecommunications infrastructure into a key driver of economic and technological development. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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24 pages, 5366 KB  

Open AccessArticle

A Three-Tier Hybrid Architecture for an Admissions Dialogue Assistant with Graph-Aware Context Routing

by Nikita Stepanov, Anastasiya Radaeva, Peter Panfilov, Alexander Suleykin and Valery Pyatetsky

Big Data Cogn. Comput. 2026, 10(5), 156; https://doi.org/10.3390/bdcc10050156 - 15 May 2026

Viewed by 588

Abstract

University admissions services must answer large volumes of applicant questions that differ substantially in complexity, ranging from repetitive FAQ-type requests to multi-step questions involving programs, entrance exams, admission rules, passing scores, and temporal comparisons. Ungrounded large language model responses are risky in this [...] Read more.

University admissions services must answer large volumes of applicant questions that differ substantially in complexity, ranging from repetitive FAQ-type requests to multi-step questions involving programs, entrance exams, admission rules, passing scores, and temporal comparisons. Ungrounded large language model responses are risky in this domain because answers must be factually correct, source-based, and consistent with official institutional data. This paper presents a three-tier hybrid architecture for an admissions dialogue assistant that combines deterministic FAQ matching, hybrid retrieval-augmented generation, and graph-grounded retrieval for complex queries. The first tier, Hash-FAQ, returns verified answers for frequent intents using normalized keys, hash-based lookup, near-duplicate fingerprinting, and semantic similarity checks. The second tier applies hybrid RAG based on BM25 retrieval, vector search, rank fusion, and optional cross-encoder reranking. The third tier uses GraphRAG to extract a constrained k-hop subgraph from a Neo4j knowledge graph built from relational admissions data and document-derived facts. All tiers are synchronized through a versioned indexing pipeline with shadow collections and atomic switching across lexical, vector, FAQ, relational, and graph stores. The system was evaluated using real admissions-campaign traffic and a labeled subset of applicant queries. Tier 1 resolved 68.7% of requests with low latency, while the GraphRAG branch improved factual accuracy with attribution on multi-step queries from 0.55 to 0.91 compared with the non-graph baseline. The main contribution of the study is a production-oriented, cost-aware retrieval-and-generation architecture that links tiered routing, synchronized knowledge publication, source attribution, and operational evaluation for applicant-facing institutional dialogue systems. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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22 pages, 3487 KB  

Open AccessArticle

An Efficient Quantum-Dot Cellular Automata Memory Architecture for Internet of Things Systems

by B. S. Premananda, Mohsen Vahabi, Muhammad Zohaib, Seyed-Sajad Ahmadpour, M. Barath and K. R. Sreesha

Computers 2026, 15(5), 302; https://doi.org/10.3390/computers15050302 - 9 May 2026

Viewed by 891

Abstract

Internet of Things (IoT) nodes continuously acquire, buffer, and transmit sensor data under strict constraints on area, latency, and energy consumption. However, conventional complementary metal–oxide–semiconductor (CMOS)-based memory-access circuits face increasing power loss, parasitic effects, interconnect complexity, and sensitivity to process variations at the [...] Read more.

Internet of Things (IoT) nodes continuously acquire, buffer, and transmit sensor data under strict constraints on area, latency, and energy consumption. However, conventional complementary metal–oxide–semiconductor (CMOS)-based memory-access circuits face increasing power loss, parasitic effects, interconnect complexity, and sensitivity to process variations at the nanoscale. To address these limitations, this paper proposes a quantum-dot cellular automata (QCA)-based decoder-driven static random-access memory (SRAM)-access architecture for compact and energy-efficient IoT perception-layer memory. The proposed framework integrates three main components: a majority-logic RAM cell with feedback-based storage and non-destructive readout, a compact 2 × 4 decoder with enable and auxiliary asynchronous set/reset control, and a 1 × 4 SRAM array in which the decoder is embedded to reduce routing and clocking overhead. The circuit layouts were implemented and functionally verified using QCADesigner 2.0.3, while the energy behavior was evaluated using QCADesigner-E. Simulation results confirm correct write/read (W/R) and address-selection behavior. The proposed 2 × 4 decoder achieves 86 QCA cells, 0.08 µm² occupied area, and one clocking unit, reducing cell count, area, and clocking by 48.19%, 50.00%, and 20.00%, respectively, compared with the best selected decoder baseline. The integrated 1 × 4 SRAM array achieves 684 cells and 14 clocking units, improving timing by 30.00% compared with the closest SRAM-array baseline. These results demonstrate that the proposed QCA-based memory-access structure provides a compact and low-overhead solution for energy-constrained IoT communication systems. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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31 pages, 7577 KB  

Open AccessArticle

A Zero-Interaction, Cloud-Free Remote ECG Monitoring and Arrhythmia Screening System Using Handheld Leads and Email Transmission

by Wenjie Feng, Lingjun Meng, Tianxiang Yang, Hong Jin, Xinhao Liu and Pan Pei

Appl. Sci. 2026, 16(6), 2640; https://doi.org/10.3390/app16062640 - 10 Mar 2026

Viewed by 1206

Abstract

To address the challenges of complex operation, high server deployment costs, and insufficient automated identification capabilities in community-based centralized electrocardiogram (ECG) screening, a novel arrhythmia screening system based on handheld ECG leads and email transmission is proposed. The system is operated in a [...] Read more.

To address the challenges of complex operation, high server deployment costs, and insufficient automated identification capabilities in community-based centralized electrocardiogram (ECG) screening, a novel arrhythmia screening system based on handheld ECG leads and email transmission is proposed. The system is operated in a zero-interaction mode: ECG acquisition is initiated automatically upon skin contact with the electrodes, and upon completion, the ECG signal is automatically analyzed and the email transmission function is triggered—no user intervention being required. First, noise in the ECG signal is effectively suppressed by cascading a zero-phase high-pass filter with a sliding window and a zero-crossing-rate (ZCR) guided adaptive wavelet thresholding technique. Subsequently, RR interval sequences are extracted from the denoised signals and fed into a lightweight bidirectional long short-term memory (BiLSTM) network for automatic arrhythmia detection. In the final step, a 30 s standard ECG, screening status, and acquired image are automatically delivered to clinicians via standard IMAP/SMTP email protocols—eliminating the need for dedicated mobile applications or cloud platforms. Experimental results demonstrated that the relative signal-to-noise ratio (SNR_ECG) was improved by 2.36 dB. On the independent test set, a sensitivity of 97.98%, a specificity of 98.21%, and an AUC of 0.994 were achieved. Furthermore, an end-to-end email transmission latency of less than 7.68 s was recorded. These findings confirm the potential of the proposed system as a low-cost, easily deployable, and elderly-friendly remote ECG solution for primary healthcare settings. Finally, in a pilot screening involving 10 volunteers, one case of arrhythmia was successfully identified, which validated the feasibility of the system. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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26 pages, 2272 KB  

Open AccessArticle

A Reinforcement Learning Approach for Automated Crawling and Testing of Android Apps

by Chien-Hung Liu, Shu-Ling Chen and Kun-Cheng Chan

Appl. Sci. 2026, 16(2), 1093; https://doi.org/10.3390/app16021093 - 21 Jan 2026

Viewed by 1082

Abstract

With the growing global popularity of Android apps, ensuring their quality and reliability has become increasingly important, as low-quality apps can lead to poor user experiences and potential business losses. A common approach to testing Android apps involves automatically generating event sequences that [...] Read more.

With the growing global popularity of Android apps, ensuring their quality and reliability has become increasingly important, as low-quality apps can lead to poor user experiences and potential business losses. A common approach to testing Android apps involves automatically generating event sequences that interact with the app’s graphical user interface (GUI) to detect crashes. To support this, we developed ACE (Android Crawler), a tool that systematically generates events to test Android apps by automatically exploring their GUIs. However, ACE’s original heuristic-driven exploration can be inefficient in complex application states. To address this, we extend ACE with a deep reinforcement learning-based crawling strategy, called Reinforcement Learning Strategy (RLS), which tightly integrates with ACE’s GUI exploration process by learning to intelligently select GUI components and interaction actions. RLS leverages the Proximal Policy Optimization (PPO) algorithm for stable and efficient learning and incorporates an action mask to filter invalid actions, thereby reducing training time. We evaluate RLS on 15 real-world Android apps and compare its performance against the original ACE and three state-of-the-art Android testing tools. Results show that RLS improves code coverage by an average of 2.1% over ACE’s Nearest unvisited event First Search (NFS) strategy and outperforms all three baseline tools in terms of code coverage. Paired t-test analyses further confirm that these improvements are statistically significant, demonstrating its effectiveness in enhancing automated Android GUI testing. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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16 pages, 3209 KB  

Open AccessArticle

Real-Time Progressive Cutting of Deformable Objects in Unity 3D with Internal Shape-Preserving Constraints

by Lyudmila Dmitrievna Khan, Taeheon Kim and Min Hong

Appl. Sci. 2025, 15(24), 12984; https://doi.org/10.3390/app152412984 - 9 Dec 2025

Cited by 1 | Viewed by 1301

Abstract

This study discovers a real-time method for simulating progressive cutting in the Unity game engine. The proposed approach utilizes Position-Based Dynamics (PBD) to model deformable objects, making it suitable for applications such as surgical simulation training. Additionally, Unity’s compute buffers are employed to [...] Read more.

This study discovers a real-time method for simulating progressive cutting in the Unity game engine. The proposed approach utilizes Position-Based Dynamics (PBD) to model deformable objects, making it suitable for applications such as surgical simulation training. Additionally, Unity’s compute buffers are employed to enhance computational efficiency through parallel processing. The cutting simulation operates on the surface mesh of the object, while internal deformations and volume preservation are represented using internal shape-preserving constraints (ISPCs). A series of progressive cutting experiments were conducted on various 3D models to evaluate the performance and accuracy of the algorithm. The results demonstrate that the proposed method achieves visually plausible real-time simulations of cuts. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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21 pages, 1969 KB  

Open AccessArticle

A Software-Defined Networking-Based Computing-Aware Routing Path Selection Method

by Chang Lv, Xianzhi Cao, Jiali Li and Jian Wang

Electronics 2025, 14(22), 4418; https://doi.org/10.3390/electronics14224418 - 13 Nov 2025

Cited by 1 | Viewed by 1518

Abstract

With the rapid advancement of cloud computing, edge computing, and the Internet of Things, traditional routing protocols such as OSPF—which rely solely on network topology and link state while neglecting computing power resource status—struggle to meet the network-computing synergy demands of the Computing [...] Read more.

With the rapid advancement of cloud computing, edge computing, and the Internet of Things, traditional routing protocols such as OSPF—which rely solely on network topology and link state while neglecting computing power resource status—struggle to meet the network-computing synergy demands of the Computing Power Network (CPN). Existing reinforcement learning-based routing approaches, despite incorporating deep strategies, still suffer from issues such as resource imbalance. To address this, this study proposes a reinforcement learning-based computing-aware routing path selection method—the Computing-Aware Routing-Reinforcement Learning (CAR-RL) algorithm. This achieves coordination between network and computing power resources through multi-factor joint computation of “computational power + network”. The algorithm constructs a multi-factor weighted Markov Decision Process (MDP) to select the optimal computing-aware routing path by real-time perception of network traffic and computing power status. Experiments conducted on the GN4–3N network topology using Mininet and K8S simulations demonstrate that compared to algorithms such as Q-Learning, DDPG, and CEDRL, the CAR-RL algorithm achieves performance improvements of 24.7%, 35.6%, and 23.1%, respectively, in average packet loss rate, average latency, and average throughput. This research not only provides a reference technical implementation path for computing-aware routing selection and optimisation in computing power networks but also advances the efficient integration of network and computing power resources. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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16 pages, 1882 KB  

Open AccessArticle

A Hybrid GA–Digital Twin Strategy for Real-Time Nighttime Reactive Power Compensation in Utility-Scale PV Plants

by Yu-Ming Liu, Cheng-Chien Kuo and Hung-Cheng Chen

Appl. Sci. 2025, 15(20), 11282; https://doi.org/10.3390/app152011282 - 21 Oct 2025

Cited by 3 | Viewed by 1089

Abstract

This study proposes a hybrid method that combines a Genetic Algorithm (GA) with Digital Twin (DT) technology to address nighttime reactive power backfeed in large-scale photovoltaic (PV) power plants. First, the GA is employed to optimize the location and number of multitask inverters [...] Read more.

This study proposes a hybrid method that combines a Genetic Algorithm (GA) with Digital Twin (DT) technology to address nighttime reactive power backfeed in large-scale photovoltaic (PV) power plants. First, the GA is employed to optimize the location and number of multitask inverters to minimize line losses and eliminate the reactive power backfeed. Subsequently, the DT continuously monitored the grid conditions and performed rolling dispatch to mitigate the residual reactive power caused by nighttime voltage fluctuations. Simulation results show that GA-based optimization reduces line losses from 0.346 to 0.2818 kW (18.6% reduction) and helps alleviate inverter thermal stress. When integrated with DTs, the method further improves voltage stability and demonstrates a strong adaptive control capability. The proposed GA–DT strategy can also be regarded as a potential AIoT application in PV plants, with the potential to reduce operational and maintenance costs and enhance the system reliability in the future. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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16 pages, 2125 KB  

Open AccessArticle

A Multi-Model Machine Learning Framework for Daily Stock Price Prediction

by Bharatendra Rai and Leili Soltanisehat

Big Data Cogn. Comput. 2025, 9(10), 248; https://doi.org/10.3390/bdcc9100248 - 28 Sep 2025

Cited by 5 | Viewed by 6848

Abstract

Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features derived from technical indicators. Empirical analysis is conducted on data [...] Read more.

Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features derived from technical indicators. Empirical analysis is conducted on data from Apple, Tesla, and NVIDIA, employing nine classification algorithms, including support vector machines, random forests, extreme gradient boosting, and logistic regression. Results indicate that momentum-based indicators are the most influential predictors. While support vector machines achieve the highest accuracy for Apple, extreme gradient boosting performed best for NVIDIA and Tesla. In addition, explainable AI techniques are applied to interpret individual model predictions, thereby enhancing transparency and trust in the results. The study contributes to financial analytics research by providing a comparative evaluation of diverse machine learning methods and highlighting key indicators critical for short-term stock price forecasting. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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34 pages, 1807 KB  

Open AccessArticle

Moving Towards Large-Scale Particle Based Fluid Simulation in Unity 3D

by Muhammad Waseem and Min Hong

Appl. Sci. 2025, 15(17), 9706; https://doi.org/10.3390/app15179706 - 3 Sep 2025

Cited by 3 | Viewed by 6617

Abstract

Large-scale particle-based fluid simulations present significant computational challenges, particularly in achieving interactive frame rates while maintaining visual quality. Unity3D’s widespread adoption in game development, VR/AR applications, and scientific visualization creates a unique need for efficient fluid simulation within its ecosystem. This paper presents [...] Read more.

Large-scale particle-based fluid simulations present significant computational challenges, particularly in achieving interactive frame rates while maintaining visual quality. Unity3D’s widespread adoption in game development, VR/AR applications, and scientific visualization creates a unique need for efficient fluid simulation within its ecosystem. This paper presents a GPU-accelerated Smoothed Particle Hydrodynamics (SPH) framework implemented in Unity3D that effectively addresses these challenges through several key innovations. Unlike previous GPU-accelerated SPH implementations that typically struggle with scaling beyond 100,000 particles while maintaining real-time performance, we introduce a novel fusion of Count Sort with Parallel Prefix Scan for spatial hashing that transforms the traditionally expensive O(n²) neighborhood search into an efficient O(n) operation, significantly outperforming traditional GPU sorting algorithms in particle-based simulations. Our implementation leverages a Structure of Arrays (SoA) memory layout, optimized for GPU compute shaders, achieving 30–45% improved computation throughput over traditional Array of Structures approaches. Performance evaluations demonstrate that our method achieves throughput rates up to 168,600 particles/ms while maintaining consistent 5.7–6.0 ms frame times across varying particle counts from 10,000 to 1,000,000. The framework maintains interactive frame rates (>30 FPS) with up to 500,000 particles and remains responsive even at 1 million particles. Collision rates approaching 1.0 indicate near-optimal hash distribution, while the adaptive time stepping mechanism adds minimal computational overhead (2–5%) while significantly improving simulation stability. These innovations enable real-time, large-scale fluid simulations with applications spanning visual effects, game development, and scientific visualization. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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14 pages, 1202 KB  

Open AccessArticle

Optimization of Gabor Convolutional Networks Using the Taguchi Method and Their Application in Wood Defect Detection

by Ming-Feng Yeh, Ching-Chuan Luo and Yu-Cheng Liu

Appl. Sci. 2025, 15(17), 9557; https://doi.org/10.3390/app15179557 - 30 Aug 2025

Cited by 4 | Viewed by 1373

Abstract

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition [...] Read more.

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition model employing a Gabor Convolutional Network (GCN) that integrates convolutional neural networks (CNNs) with Gabor filters. To systematically optimize the network’s architecture and improve both detection accuracy and computational efficiency, the Taguchi method is employed to tune key hyperparameters, including convolutional kernel size, filter number, and Gabor parameters (frequency, orientation, and phase offset). Additionally, image tiling and augmentation techniques are employed to effectively increase the training dataset, thereby enhancing the model’s stability and accuracy. Experiments conducted on the MVTec Anomaly Detection dataset (wood category) demonstrate that the Taguchi-optimized GCN achieves an accuracy of 98.92%, outperforming a baseline Taguchi-optimized CNN by 2.73%. Results confirm that Taguchi-optimized GCNs enhance defect detection performance and computational efficiency, making them valuable for smart manufacturing. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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17 pages, 3738 KB  

Open AccessArticle

Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System

by Homari Matsumoto, Atomu Nakamura and Shun Nishide

Appl. Sci. 2025, 15(13), 7038; https://doi.org/10.3390/app15137038 - 23 Jun 2025

Cited by 3 | Viewed by 2589

Abstract

With rapid advances in image-generative AI, interest has grown in applying these technologies across diverse domains. While current models excel at producing high-quality final images, they rarely model the intermediate stages of the drawing process. This study proposes a drawing support system that [...] Read more.

With rapid advances in image-generative AI, interest has grown in applying these technologies across diverse domains. While current models excel at producing high-quality final images, they rarely model the intermediate stages of the drawing process. This study proposes a drawing support system that leverages generative AI to sequentially generate and modify images during the drawing process. To train the system, we constructed a custom dataset of time-series drawing images, which are typically unavailable in existing AI models. We developed an encoder–decoder model based on convolutional neural networks to predict the next frame from a current input image. To address error accumulation during recursive generation, we introduced a noise-augmented training method, incorporating noisy images into the dataset. Experimental results show that standard training suffers from image degradation over time, while the noise-augmented approach significantly improves stability and quality throughout the sequence. Averaged across all generated frames, the noise-augmented training achieved a PSNR of 20.48, SSIM of 0.81, and LPIPS of 0.13. As a benchmark, we compared this approach to PredRNN, a representative temporal model, which achieved a PSNR of 24.05, SSIM of 0.88, and LPIPS of 0.08. These results demonstrate the effectiveness of noise-augmented learning while also highlighting potential performance gains using temporal architectures. PredRNN also required more computation, with 6.41 M parameters and a 0.188 s inference time per sequence, compared to 5.20 M and 0.005 s for our model. Furthermore, in a sample-level analysis of 10 final images, the proposed model outperformed PredRNN in three cases across all metrics, suggesting its robustness in certain sequences despite architectural simplicity. Future directions include using more advanced models such as Variational Autoencoders and diffusion models, and enhancing consistency in long-term sequences. Our work confirms the feasibility of generating interactive drawing sequences using AI and sets the foundation for more robust and creative drawing support tools. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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Graphical abstract

26 pages, 5688 KB  

Open AccessArticle

Image-Based Nutritional Advisory System: Employing Multimodal Deep Learning for Food Classification and Nutritional Analysis

by Sheng-Tzong Cheng, Ya-Jin Lyu and Ching Teng

Appl. Sci. 2025, 15(9), 4911; https://doi.org/10.3390/app15094911 - 28 Apr 2025

Cited by 13 | Viewed by 5362

Abstract

Accurate dietary assessment is essential for effective health management and disease prevention. However, conventional methods that rely on manual food logging and nutritional lookup are often time consuming and error prone. This study proposes an image-based nutritional advisory system that integrates multimodal deep [...] Read more.

Accurate dietary assessment is essential for effective health management and disease prevention. However, conventional methods that rely on manual food logging and nutritional lookup are often time consuming and error prone. This study proposes an image-based nutritional advisory system that integrates multimodal deep learning to automate food classification, volume estimation, and dietary recommendation to address these limitations. The system employs a fine-tuned CLIP model for zero-shot food recognition, achieving high accuracy across diverse food categories, including unseen items. For volume measurement, a learning-based multi-view stereo (MVS) approach eliminates the need for specialized hardware, yielding reliable estimations with a mean absolute percentage error (MAPE) of 23.5% across standard food categories. Nutritional values are then calculated by referencing verified food composition databases. Furthermore, the system leverages a large language model (Llama 3) to generate personalized dietary advice tailored to individual health goals. The experimental results show that the system attains a top 1 classification accuracy of 91% on CNFOOD-241 and 80% on Food 101 and delivers high-quality recommendation texts with a BLEU-4 score of 45.13. These findings demonstrate the system’s potential as a practical and scalable tool for automated dietary management, offering improved precision, convenience, and user experience. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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17 pages, 428 KB  

Open AccessArticle

Dynamic UAV Task Allocation and Path Planning with Energy Management Using Adaptive PSO in Rolling Horizon Framework

by Zhen Han and Weian Guo

Appl. Sci. 2025, 15(8), 4220; https://doi.org/10.3390/app15084220 - 11 Apr 2025

Cited by 22 | Viewed by 3894

Abstract

Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments for applications such as surveillance, delivery, and data collection, where efficient task allocation and path planning are critical to minimizing mission completion time while managing limited energy resources. This paper proposes a novel [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments for applications such as surveillance, delivery, and data collection, where efficient task allocation and path planning are critical to minimizing mission completion time while managing limited energy resources. This paper proposes a novel approach that integrates energy management into a rolling horizon framework for dynamic UAV task allocation and path planning. We introduce an enhanced Particle Swarm Optimization (PSO) algorithm, incorporating adaptive perturbation strategies and a local search mechanism based on simulated annealing, to optimize UAV task assignments and routes. The rolling horizon framework enables the system to adapt to evolving task demands over time. Energy consumption is explicitly modeled, accounting for flight, computation, and recharging at designated stations, ensuring practical applicability. Extensive simulations demonstrate that the proposed method reduces the mission makespan significantly compared to conventional static planning approaches, while effectively balancing energy usage and recharging requirements. These results highlight the potential of our approach for real-world UAV operations in dynamic settings. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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21 pages, 5795 KB  

Open AccessArticle

Design and Implementation of a Tripod Robot Control System Using Hand Kinematics and a Sensory Glove

by Jakub Krzus, Tomasz Trawiński, Paweł Kielan and Marcin Szczygieł

Electronics 2025, 14(6), 1150; https://doi.org/10.3390/electronics14061150 - 14 Mar 2025

Cited by 3 | Viewed by 1951

Abstract

Current technological progress in automation and robotics allows human kinematics to be used to control any device. As part of this study, a sensory glove was developed that allows for a delta robot to be controlled using hand movements. The process of controlling [...] Read more.

Current technological progress in automation and robotics allows human kinematics to be used to control any device. As part of this study, a sensory glove was developed that allows for a delta robot to be controlled using hand movements. The process of controlling an actuator can often be problematic due to its complexity. The proposed system solves this problem using human–machine interactions. The sensory glove allows for easy control of the robot by detecting the rotation of the hand and pressing the control buttons. Conventional buttons have been replaced with SMART materials such as conductive thread and conductive fabric. The ESP32 microcontroller placed on the control glove collects data read from the MPU6050 sensor. It also facilitates wireless communication with the Raspberry Pi microcontroller supporting the Modbus TCP/IP protocol, which controls the robot’s movement. Due to the noise of the data read from the gyroscope, the signals were subjected to a filtering process using basic recursive filters and an advanced algorithm with Kalman filters. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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28 pages, 2405 KB  

Open AccessArticle

Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods

by Ahmed Khamees and Hüseyin Altınkaya

Appl. Sci. 2025, 15(4), 1956; https://doi.org/10.3390/app15041956 - 13 Feb 2025

Cited by 2 | Viewed by 1510

Abstract

Real-world power systems face challenges from demand fluctuations, system constraints, communication delays, and unmeasurable disturbances. This paper presents a real-time hybrid approach integrating Nonlinear Model Predictive Control (NLMPC) and data-driven methods for automatic generation control (AGC) of synchronous generators, particularly under cyber-physical attacks. [...] Read more.

Real-world power systems face challenges from demand fluctuations, system constraints, communication delays, and unmeasurable disturbances. This paper presents a real-time hybrid approach integrating Nonlinear Model Predictive Control (NLMPC) and data-driven methods for automatic generation control (AGC) of synchronous generators, particularly under cyber-physical attacks. Unlike previous studies, this work considers both technical and economic aspects of power system management. A key innovation is the incorporation of a detailed thermo-mechanical model of turbine and governor dynamics, enabling optimized control and effective management of power oscillations. The proposed NLMPC-based AGC strategy addresses governor saturation and generation rate constraints, ensuring stability. Extensive simulations in MATLAB/Simulink, including IEEE 11-bus and 9-bus test systems, validate the controller’s effectiveness in enhancing power system performance under various challenging conditions. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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