Original Research Paper
Analogue Integrated Circuits
Mostafa Khoshnoud; Seyed Mahmoud Anisheh; Mehdi Radmehr
Abstract
Background and Objectives: A Time-to-Digital Converter (TDC) is a fundamental electronic component that converts time intervals into digital representations. It plays a critical role in high-precision applications such as particle physics experiments, time-of-flight measurements, and the processing of ...
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Background and Objectives: A Time-to-Digital Converter (TDC) is a fundamental electronic component that converts time intervals into digital representations. It plays a critical role in high-precision applications such as particle physics experiments, time-of-flight measurements, and the processing of high-frequency signals in communication systems. This paper presents a comprehensive study on the design and simulation of two innovative low-power TDC architectures.Methods: The approach introduces a novel low-power D Flip-Flop (D-FF) circuit using transmission gates (TG) and CMOS inverters to reduce power consumption while maintaining high performance. Specialized low-power delay cells are proposed for Flash TDC implementation. Detailed simulations were conducted using Cadence software with a 0.18 μm CMOS fabrication process at a supply voltage of 1.8 V.Results: The results demonstrate significant improvements in power efficiency and performance metrics, indicating the potential of the proposed TDC designs for future applications requiring precise temporal measurements. The Figure of Merit (FOM) values of the two proposed structures are 0.033 and 0.020, respectively.Conclusion: Power consumption in TDCs is a critical factor, as it directly influences the overall efficiency of electronic systems. Reducing power consumption can lead to decreased energy use, improved thermal management, and an extended lifespan for devices. Conversely, higher power consumption can generate excessive heat, which can negatively impact the system's performance and reliability. Thus, it is vital to strike an optimal balance between accuracy and power consumption in TDCs to enhance the longevity of electronic devices. This paper presents the design of delay cell circuits and a D-FF using a 0.18 µm CMOS process with a 1.8 V supply voltage. The power consumption of the proposed delay cells has been minimized through the application of the body bias technique. The performance of the delay cell has been evaluated in flash TDC circuits, and the results demonstrate the effective performance of the proposed structures.
Original Research Paper
Natural Language Processing
Mohammad Javad Nasri-Lowshani; Javad Salimi Sartakhti; Hossein Ebrahimpour-Komole
Abstract
Background and Objectives: Developing efficient task-oriented dialogue systems capable of handling multilingual interactions is a growing area of research in natural language processing (NLP). In this paper, we propose SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, ...
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Background and Objectives: Developing efficient task-oriented dialogue systems capable of handling multilingual interactions is a growing area of research in natural language processing (NLP). In this paper, we propose SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, designed for multilingual conversations.Methods: The system utilizes a deep Q-network and the SBERT model to represent the dialogue environment. We introduce two variants, SenSimpleDS+ and SenSimpleDS-NSP, which incorporate modifications in the ε-greedy method and leverage next sequence prediction (NSP) using BERT to refine the reward function. These methods are evaluated on datasets in English, Persian, Spanish, and German, and compared with baseline methods such as SimpleDS and SCGSimpleDS.Results: Our experimental results demonstrate that the proposed methods outperform the baselines in terms of average collected rewards, requiring fewer learning steps to achieve optimal dialogue policies. Notably, the incorporation of NSP significantly improves performance by optimizing reward collection. The multilingual SenSimpleDS further showcases the system’s ability to function across languages using a random forest classifier for language detection and MPNet for environment construction. In addition to system evaluations, we introduce a new Persian dataset for task-oriented dialogue in the restaurant domain, expanding the resources available for developing dialogue systems in low-resource languages.Conclusion: SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, demonstrates superior performance over baseline methods by leveraging deep Q-networks, SBERT. The integration of next sequence prediction (NSP) significantly enhances reward optimization, enabling faster convergence to optimal dialogue policies. This work establishes a foundation for future research in multilingual dialogue systems, with potential applications across diverse service domains.
Original Research Paper
Power Electronics
Soghra Ebrahimzadeh; Farzad Sedaghati; Hadi Dolati
Abstract
Background and Objectives: To achieve zero carbon emissions, renewable energy sources have gained noteworthy regard due to their dependable performance, cost efficiency, and adaptability within systems. Increasing adoption of renewable energy sources and electric vehicle (EV) has led to a growing need ...
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Background and Objectives: To achieve zero carbon emissions, renewable energy sources have gained noteworthy regard due to their dependable performance, cost efficiency, and adaptability within systems. Increasing adoption of renewable energy sources and electric vehicle (EV) has led to a growing need for enhanced voltage boost capability. Nevertheless, most of DC sources such as solar cells have a restricted capacity for boosting power. Multilevel inverters can operate as interfaces. In this study, two topologies of switched-capacitor multilevel inverters (SC-MLI) is suggested to overcome the mentioned constraints.Methods: Each stage of the introduced SC-MLI comprises a capacitor, a DC voltage supply, a diode, and two power electronic switches. A comprehensive analysis of the operational principles, and the characteristics of the presented converter, including its charging and discharging behaviors, are provided. Furthermore, the phase-disposition pulse width modulation (PD-PWM) technique is employed to generate the output voltage waveform of the introduced multilevel SC inverter.Results: In the recommended topologies, the quantity of semiconductor power switches, isolated DC voltage supply, diodes, and so, volume and cost of the overall system are decreased in compare to similar SC-MLI topologies. The voltage across the capacitors is self-balanced accurately without using any auxiliary circuits or closed-loop systems. To validate the proposed SC-MLI's effective operation, the implemented topology's simulation and measurement results are presented. The total harmonic distortion for the 17-level inverter using the PD-PWM technique at a modulation index 1 obtained 6.97%. Conclusion: Comprehensive comparative analysis reveals that the introduced topologies have merits and superior performance compared to existing solutions regarding component number, voltage boost factor (BF), and voltage stress. Also, simulation and experimental test results verify theoretical analysis.
Original Research Paper
Image Processing
Ali Habibi; Mahlagha Afrasiabi; Moniba Chaparian
Abstract
Background and Objectives: Facial recognition technology has become a reliable solution for access control, augmenting traditional biometric methods. It primarily focuses on two core tasks: face verification, which determines whether two images belong to the same individual, and face identification, ...
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Background and Objectives: Facial recognition technology has become a reliable solution for access control, augmenting traditional biometric methods. It primarily focuses on two core tasks: face verification, which determines whether two images belong to the same individual, and face identification, which matches a face to a database. However, facial recognition still faces critical challenges such as variations in pose, illumination, facial expressions, image noise, and limited training samples per subject.Method: This study employs a Siamese network based on the Xception architecture within a transfer learning framework to perform one-shot face verification. The model is trained to compare image pairs rather than classify them individually, using deep feature extraction and Euclidean distance measurement, optimized through a contrastive loss function.Results: The proposed model achieves high verification accuracy on benchmark datasets, reaching 97.6% on the Labeled Faces in the Wild (LFW) dataset and 96.25% on the Olivetti Research Laboratory (ORL) dataset. These results demonstrate the model’s robustness and generalizability across datasets with diverse facial characteristics and limited training data.Conclusion: Our findings indicate that the Siamese-Xception architecture is a robust and effective approach for facial verification, particularly in low-data scenarios. This method offers a practical, scalable solution for real-world facial recognition systems, maintaining high accuracy despite data constraints.
Original Research Paper
Artificial Intelligence
Atefeh Mohammadi; Mohammad Reza Pajoohan; Ali Mohammad Zareh Bidoki
Abstract
Background and Objectives: In Natural Language Processing (NLP), sentiment analysis is crucial for understanding and extracting aspects and opinions expressed in textual data. Recent methods have emphasized determining polarity in multi-domain sentiment analysis while giving less attention to aspect ...
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Background and Objectives: In Natural Language Processing (NLP), sentiment analysis is crucial for understanding and extracting aspects and opinions expressed in textual data. Recent methods have emphasized determining polarity in multi-domain sentiment analysis while giving less attention to aspect and opinion extraction. Furthermore, the terms that convey aspects and opinions may have different importance in different domains, and this difference should be considered to enhance the extraction of aspect-opinion pairs. Methods: To address these challenges, we propose a Weighted Words Multi-Domain (WWMD) model for aspect-opinion pairs extraction, consisting of a self-attention mechanism and a dense network. The self-attention mechanism extracts each word's importance according to the sentence's overall meaning. The dense network is used for domain prediction. It assigns greater weight to words relevant to each domain, which leads to considering the different significance of terms across various contexts. Adding an attention mechanism to the domain module allows for a clearer understanding of different aspects and opinions across various domains. We utilize a two-channel approach, one channel extracts aspects and opinions, while the other extracts the relationships between them. The weighted words extracted by our model are simultaneously considered as the input for both channels.Results: Using weighted words specific to each domain, improves the model output.Conclusion: Evaluation results on benchmark datasets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.
Original Research Paper
Artificial Intelligence
Mahdi Shahbazi Khojasteh; Armin Salimi Badr
Abstract
Background and Objectives: Unmanned Aerial Vehicles (UAVs) face significant challenges in navigating narrow passages within GPS-denied environments due to sensor and computational limitations. While deep reinforcement learning (DRL) has improved navigation, many methods rely on costly sensors like depth ...
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Background and Objectives: Unmanned Aerial Vehicles (UAVs) face significant challenges in navigating narrow passages within GPS-denied environments due to sensor and computational limitations. While deep reinforcement learning (DRL) has improved navigation, many methods rely on costly sensors like depth cameras or LiDAR. This study addresses these issues using a vision-based DRL framework with a monocular camera for autonomous UAV navigation.Methods: We propose a DRL-based navigation system utilizing Proximal Policy Optimization (PPO). The system processes a stack of grayscale monocular images to capture short-term temporal dependencies, approximating the partially observable environment. A custom reward function encourages trajectory optimization by assigning higher rewards for staying near the passage center while penalizing further distances. The navigation system is evaluated in a 3D simulation environment under a GPS-denied scenario.Results: The proposed method achieves a high success rate, surpassing 97% in challenging narrow passages. The system demonstrates superior learning efficiency and robust generalization to new configurations compared to baseline methods. Notably, using stacked frames mitigates computational overhead while maintaining policy effectiveness.Conclusion: Our vision-based DRL approach enables autonomous UAV navigation in GPS-denied environments with reduced sensor requirements, offering a cost-effective and efficient solution. The findings highlight the potential of monocular cameras paired with DRL for real-world UAV applications such as search and rescue and infrastructure inspection. Future work will extend the framework to obstacle avoidance and general trajectory planning in dynamic environments.
Original Research Paper
Wireless Networks
Neda Sedighian; Abbas Karimi; Javad Mohammadzadeh; Faraneh Zarafshan
Abstract
Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. ...
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Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. However, traditional clustering methods such as K-Means and MFO, which mainly rely on simple metrics like distance or signal strength, fail to deliver optimal performance in dynamic environments with variable network density. The primary objective of this study is to design and evaluate an advanced clustering algorithm called AI_MCA (Artificial Intelligence Multi Clustering Algorithm), leveraging artificial intelligence and multi-criteria decision-making. By considering factors such as signal strength, relative speed, node density, and vehicle movement direction, the proposed algorithm forms clusters with higher stability and efficiency in dynamic and high-density environments.Methods: This study uses simulations to evaluate AI_MCA in VANETs, which facilitate vehicle-to-vehicle communication and are characterized by high mobility and rapid position changes.Results: Simulations in NS3 and SUMO show that AI_MCA reduces latency by 20% (12ms vs. 15ms in MFO) and improves cluster stability by 30% (lifetime of 45s vs. 33s in K-Means) within a 600m range. At a 1000m range with 300 nodes, delay increases to 14ms and PDR drops to 88%.Conclusion: AI_MCA outperforms traditional methods like K-Means and MFO, offering a scalable solution for VANET clustering.
Original Research Paper
Software
Seyyed AmirHossein Eshghazadi; Einollah Pira; Mohammad Khodizadeh-Nahari; Alireza Rouhi
Abstract
Background and Objectives: Software testing plays a vital role in software development, aimed at verifying the reliability and stability of software systems. The generation of an effective test suite is key to this process, as it directly impacts the detection of defects and vulnerabilities. However, ...
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Background and Objectives: Software testing plays a vital role in software development, aimed at verifying the reliability and stability of software systems. The generation of an effective test suite is key to this process, as it directly impacts the detection of defects and vulnerabilities. However, for software systems with numerous input parameters, the combinatorial explosion problem hinders the creation of comprehensive test suites. This research introduces a novel approach using the β-Hill Climbing optimizer, an advanced variant of the traditional hill climbing algorithm, to efficiently generate optimal test suites.Methods: The β-Hill Climbing optimizer introduces a dynamic parameter, β, which facilitates a precise balance between exploration and exploitation throughout the search process. To evaluate the performance of this proposed strategy (referred to as BHC), it is compared with TConfig as a mathematical approach, PICT and IPOG as greedy algorithms, and GS, GALP, DPSO, WOA, BAPSO, and GSTG as meta-heuristic methods. These strategies are tested across a variety of configurations to assess their relative efficiency.Results: The reported results confirm that BHC outperforms the others in terms of the size of generated test suites and convergence speed. The statistical analysis of the experimental results on several different configurations shows that BHC outperforms TConfig as a mathematical strategy, PICT and IPOG as greedy strategies, GS, GALP, DPSO, WOA, BAPSO, and GSTG as meta-heuristics by 83%, 88%, 87%, 61%, 61%, 46%, 61%, 62%, and 70%, respectively.Conclusion: The BHC strategy presents a novel and effective approach to optimization, inspired by β-Hill Climbing optimizer for the generation of optimal test suite. Its superior performance in the generation of test suites with smaller size and higher convergence speed compared to other strategies.
Original Research Paper
Control
Marziyeh Barootkar
Abstract
Background and Objectives: While intelligent vehicle teleoperation systems prioritize operational performance, their vulnerability to cyber-physical attacks—such as sensor spoofing and latency exploitation—remains a critical unsolved challenge. Existing solutions predominantly focus on attack ...
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Background and Objectives: While intelligent vehicle teleoperation systems prioritize operational performance, their vulnerability to cyber-physical attacks—such as sensor spoofing and latency exploitation—remains a critical unsolved challenge. Existing solutions predominantly focus on attack prevention, leaving systems defenseless during active attacks that threaten stability and collision avoidance. This study addresses the unmet need for real-time resilience by introducing an adaptive control framework that dynamically mitigates attack-induced disruptions without relying on predefined vehicle models. Methods: We propose a novel adaptive LQR-based optimal controller that compensates for multi-vector attacks (e.g., false data injection, GPS spoofing) by estimating disturbed signals in real time. Unlike static models, our data-driven approach eliminates dependency on fixed dynamics. A rigorous case study evaluates performance under simultaneous command injection and DoS attacks, measuring trajectory deviation and recovery time. Results: The framework achieves ≤12% trajectory deviation (35% improvement over benchmarks) and 40% faster recovery from destabilizing attacks. It outperforms conventional controllers by adapting to model uncertainties and multi-vector threats without prior knowledge of system parameters. Conclusion: This work pioneers a model-agnostic, real-time resilience paradigm for teleoperated vehicles, merging human oversight with autonomous adaptability. Beyond immediate safety gains, it underscores the necessity of embedding cybersecurity-aware control mechanisms in connected vehicles, shifting from passive prevention to active threat mitigation.
Original Research Paper
VLSI
Seyede Mahboobeh Mousavi Monazah; Nabiollah Shiri; Mahmood Rafiee; Ayoub Sadeghi
Abstract
Background and Objectives: In modern digital design, ternary logic gives simplicity and efficiency by reducing connectivity and chip area. This paper presents a new ternary decoder with only two ternary inverters and one binary NOR gate. One of the inverters is used simultaneously as a negative ternary ...
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Background and Objectives: In modern digital design, ternary logic gives simplicity and efficiency by reducing connectivity and chip area. This paper presents a new ternary decoder with only two ternary inverters and one binary NOR gate. One of the inverters is used simultaneously as a negative ternary inverter (NTI), and a positive ternary inverter (PTI) to attain circuit area reduction. Also, using the binary NOR instead of the ternary NOR eliminates don’t care states (middle voltage mode). The proposed decoder is implemented with complementary metal-oxide-semiconductor (CMOS), double pass logic (DPL), gate diffusion input (GDI), and pass transistor logic (PTL). In the proposed ternary decoder, the four mentioned technologies show an appropriate power delay product (PDP) and a smaller occupied area compared to the literature. Methods: In this paper, all simulations are performed using the 90 nm model, BSIM4 (level 54) version 4.4 by the HSPICE tool. The CMOS, DPL, PTL, and GDI techniques are used in the presented ternary decoder, and the results are extracted. The decoder shows good functionality compared to the previous research when implemented by these four circuits, but the best performance in terms of PDP results is from the CMOS.Results: The CMOS-based ternary decoder has only 10 transistors and shows the best results, its power consumption, and propagation delay are 25 μW and 0.07 ns, respectively. Besides, the number of transistors is reduced by 16.66% while it has 2 times increase in speed compared to the best decoders in previous research. The proposed high-speed and low-complexity decoder can be used in full adders (FAs) and digital signal processors (DSPs). Conclusion: Due to the application and advantage of ternary logic over binary, a ternary decoder with CMOS technique is designed that has fewer elements, a smaller area, and high speed compared to the existing ternary decoders. This new decoder includes only two ternary inverters and one binary NOR gate one of the inverters is used as a negative ternary inverter (NTI), and a positive ternary inverter (PTI), simultaneously. Also, the use of binary NOR gate eliminates don’t care states and reduces the circuit area.
Original Research Paper
Artificial Intelligence
Oveis Dehghantanha; Nasser Mehrshad; Roksana Bakhshali; Ahmad Reza Sebzari
Abstract
Background and Objectives: Understanding the heterogeneity of breast cancer is crucial for improving treatment strategies. This study investigates the application of K-Means and Hierarchical Clustering to a local dataset of breast cancer patients from Iranmehr Hospital, Birjand, Iran, with the primary ...
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Background and Objectives: Understanding the heterogeneity of breast cancer is crucial for improving treatment strategies. This study investigates the application of K-Means and Hierarchical Clustering to a local dataset of breast cancer patients from Iranmehr Hospital, Birjand, Iran, with the primary goal of identifying potential patient subgroups based on their clinical and treatment characteristics for knowledge discovery. The potential of these subgroups to inform future research on personalized treatment approaches is explored.Methods: A retrospective dataset comprising pathological and clinical information was analyzed using K-Means and Agglomerative Hierarchical Clustering to identify patient subgroups. The optimal number of clusters was consistently determined to be two (k=2) for both methods based on rigorous internal validation metrics (Elbow Method, Silhouette Analysis, Calinski-Harabasz Index, and Largest Jump Analysis for Hierarchical Clustering). Statistical tests (ANOVA and Chi-squared) were employed to assess significant differences in features across the identified clusters from both K-Means and Hierarchical analyses, providing insights into the key factors differentiating these groups. Internal cluster validity was assessed using Silhouette Score and Calinski-Harabasz Index.Results: The K-Means analysis identified two clusters exhibiting significant differences in characteristics such as age, chemotherapy session intensity, menopausal status, nodal involvement, and biomarker expression (ER, PR, HER2, Ki67). The Hierarchical Clustering also yielded two clusters with varying characteristics, and a comparison between the two methods highlighted both similarities and differences in the identified patient stratifications. The overall agreement between K-Means and Hierarchical Clustering was quantified by an Adjusted Rand Index (ARI) of 0.4697.Conclusion: Both K-Means and Hierarchical Clustering effectively revealed potential patient subgroups within the studied dataset, highlighting the heterogeneity of breast cancer presentation and treatment at a local level These clusters exhibited statistically significant differences across key clinical and treatment features. Future research is needed to validate these findings in larger, multi-center studies, explore the clinical significance of these subgroups in terms of treatment outcomes, and compare the effectiveness of different clustering methodologies for this purpose.
Original Research Paper
Classification
Mujtaba Sultani; Negin Daneshpour
Abstract
Background and Objectives: Massive Open Online Courses (MOOCs) face unique challenges in extracting student feedback from large, asynchronous student discussion forums. While traditional survey methods are commonly used, they struggle with scalability and real-time analysis in the MOOC context. This ...
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Background and Objectives: Massive Open Online Courses (MOOCs) face unique challenges in extracting student feedback from large, asynchronous student discussion forums. While traditional survey methods are commonly used, they struggle with scalability and real-time analysis in the MOOC context. This study aims to address these limitations and focus on automated extraction and classification of student opinions and their urgency. The study bridges the gap between suggestion mining in commercial applications and educational domains.Methods: We presented a novel deep learning approach using a BERT-based hybrid Convolutional Neural Network (CNN) – Bidirectional Long Short-term Memory (BiLSTM) multi-output model, named CBiLSTM. The model was trained to classify student posts into opinions and further categorize them by urgency. Performance metrics such as F1-weighted scores, Precision-Recall curves, and Area Under the Curve (AUC) were used to evaluate the model's efficacy, particularly in handling imbalanced datasets.Results: The presented CBiLSTM model got F1-weighted score of 87.3% for opinion classification and 81.1% for urgency classification which represents an improvement of 1.3% and 1.8% over the best-performing baseline model. Precision-Recall curves and AUC metrics highlights the model's strength in balancing precision and recall. These findings demonstrate the model's capacity to accurately classify and prioritize student feedback in the educational domain.Conclusion: This study offers a robust framework to enhance decision-making processes in MOOCs through effective feedback analysis. The CBiLSTM model provides a scalable, data-driven solution that empowers instructors, course designers, and policymakers to make targeted improvements, and improves student engagement and course quality.
Original Research Paper
Hardware
Shahrbanoo Zangaraki; S. Hossein Erfani; Amir Sahafi
Abstract
Background and Objectives: The Internet of Things (IoT) serves as a fundamental communication model, enabling objects to deliver data and services to users. With the rapid expansion of IoT, ensuring privacy and preventing the disclosure of sensitive data during message exchanges between objects has become ...
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Background and Objectives: The Internet of Things (IoT) serves as a fundamental communication model, enabling objects to deliver data and services to users. With the rapid expansion of IoT, ensuring privacy and preventing the disclosure of sensitive data during message exchanges between objects has become increasingly challenging. This paper presents an attribute-based framework designed to enhance privacy protection in IoT environments by leveraging software-defined networking (SDN) technology.Methods: By leveraging the SDN and the Attribute-Based Privacy Preserving (ABPP) model, our proposed framework employs an advanced algorithm to enhance privacy for client requests accessing IoT services. It focuses on protecting sensitive information during message transmission by implementing techniques for anonymity, unlinkability, and untraceability, tailored to the sensitivity level of each message. To further enhance message privacy within the IoT network, our framework incorporates IP aliasing, dynamic channel switching, and payload encryption.Results: Our proposed framework significantly enhances privacy protection in IoT networks by dynamically applying anonymity and concealment techniques tailored to the sensitivity of CoAP messages. Simulation results using CloudSimSDN confirm the framework's effectiveness in safeguarding sensitive information while maintaining optimal communication performance. Employing three privacy-preserving techniques results in an average CPU utilization that is 0.14 units higher compared to using a single technique. We provide a security evaluation that includes formal verification techniques and informal analysis, and show that the proposed framework is secure against anonymity and MITM attacks, replay attacks, Sybil, and IP spoofing.Conclusion: In this paper, we present a four-layer SDN-based framework designed to enhance privacy in IoT networks through the use of the Attribute-Based Privacy Preserving (ABPP) model. The framework employs IP aliasing, dynamic routing, and content encryption techniques tailored to the sensitivity of CoAP messages to ensure data protection. Our implementation and experiments conducted with CloudSimSDN validate the framework's effectiveness in safeguarding sensitive information.
Original Research Paper
Power Electronics
Seyed Reza Mousavi-aghdam; Milad Purhasan
Abstract
Background and Objectives: The objective of this study is to achieve fast finite-time control of DC-DC buck converters by designing a robust-adaptive terminal sliding-mode controller utilizing a fractional-order control strategy combined with a state-disturbance observer. The work begins by considering ...
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Background and Objectives: The objective of this study is to achieve fast finite-time control of DC-DC buck converters by designing a robust-adaptive terminal sliding-mode controller utilizing a fractional-order control strategy combined with a state-disturbance observer. The work begins by considering that all dynamic parameters of the converter circuit, such as resistance, capacitance, and inductance, are completely unknown due to significant uncertainties.Methods: To formulate the stabilizing fractional-order sliding mode controller, the fractional-order sliding surface is initially presented. Given the absence of knowledge about the converter's dynamic parameters, an adaptive fractional-order finite-time sliding mode controller is developed to ensure the finite-time stability of the system and to achieve rapid convergence of the converter voltage to the prescribed reference value in the presence of parameters uncertainties and external disturbances. To estimate unknown varying parameters in the converter, an adaptive law is additionally designed. Furthermore, a high-speed observer is incorporated to estimate both external disturbances and the state variables affecting the system. An analysis is also presented to find the maximum allowable sampling time based on the controller parameters.Results: The closed-loop system stability is validated through the application of the extended Lyapunov method. Importantly, the proposed method ensures that the output voltage of the converter reaches its desired value within a finite time taking external disturbances into account as well as operating with unknown parameters, and also, without direct feedback from voltage or current measurements. Ultimately, simulation results along with some enhanced robustness analysis are presented to demonstrate the effectiveness of the proposed control approach. Conclusion: The innovative controller based on fractional-order sliding mode control in a sensor less DC-DC buck converters is considered. The goal is to converge the output voltage to the desired value in finite time considering that all of the converter dynamic parameters are unknown and system is exposed to external disturbances. Chattering is reduced using a new technique and the maximum allowable sampling time is also obtained. The results validate the suitability of the proposed technique especially in overcoming against uncertainties and disturbances without requiring voltage/current measurements.
Original Research Paper
Electronic Circuits
Bahram Rashidi; Ali Mirzajani Nooshabadi
Abstract
Background and Objectives: This paper presents a high-performance and efficient circuit based on low-cost coils for induction heating applications. We design the coils specifically for induction sealing and induction cooker applications. The proposed circuit utilizes two parallel sets of MOSFET transistors ...
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Background and Objectives: This paper presents a high-performance and efficient circuit based on low-cost coils for induction heating applications. We design the coils specifically for induction sealing and induction cooker applications. The proposed circuit utilizes two parallel sets of MOSFET transistors to increase the current flow and output power.Methods: Three types of coils have been developed in square and rectangular designs on the printed circuit board (PCB). In this case, the construction process is simple and requires minimal time. Induction coils designed for induction sealing applications have a rectangular structure that effectively seals a wide range of bottles.Results: The flexibility of the proposed circuit is one of its advantages; the output frequency can be adjusted by increasing or decreasing the number of capacitors in the capacitor bank. Performance comparisons (e.g., efficiency, power density, cost) between the proposed method and other studies show that the implementation cost of the proposed circuit is lower than that of others. The proposed circuit achieves 95% efficiency. Thermal imaging confirms the circuit's performance. Based on the electromagnetic interference results, the circuit's performance is not affected by an external magnetic field.Conclusion: The proposed circuit has been tested with different capacitor banks using two power supplies of 24 V and 12 V. The peak-to-peak output voltage is 181 V and 92 V for the 24 V and 12 V power supplies, respectively. The results demonstrate that the circuit and coils are suitable for induction heating applications.
Original Research Paper
Data Preprocessing
Sajjad Mahmoudikhah; Seyed Hamid Zahiri; Iman Behravan
Abstract
Background and Objectives: Sonar data processing helps in identifying and tracking targets with unstable echoes, which conventional tracking methods often misidentify. Recently, RLA has significantly improved the accuracy of undersea target detection compared to traditional sonar object recognition techniques ...
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Background and Objectives: Sonar data processing helps in identifying and tracking targets with unstable echoes, which conventional tracking methods often misidentify. Recently, RLA has significantly improved the accuracy of undersea target detection compared to traditional sonar object recognition techniques that tend to lack robustness and precision.Methods: This research utilizes a combination of classifiers to improve the accuracy of Sonar data classification for complex tasks like identifying marine targets. Each classifier creates its own data pattern and maintains a model. Ultimately, a weighted voting process is carried out by the fuzzy learning automata algorithm among these classifiers, with the one receiving the highest votes being the most impactful on performance improvement.Results: We compared the performance of SVM, RF, DT, XGBoost, ensemble methods, R-EFMD, T-EFMD, R-LFMD, T-LFMD, ANN, CNN, TIFR-DCNN+SA, and joint models against the proposed model. Given the differences in objectives and databases, we focused on benchmarking the average detection rate. This comparison examined key parameters including Precision, Recall, F1_Score, and Accuracy to highlight the superior performance of the proposed method compared to the others.Conclusion: The results obtained with the analytical parameters Precision, Recall, F1_Score and Accuracy have been examined and compared with the latest similar research and the values of 88.6%, 90.2%, 89.02% and 88.6% have been obtained for each of these parameters in the proposed method, respectively. Also, in this research, the impressive performance of the new method compared to the Sonar data fusion by the conventional learning automata method is evident.
Original Research Paper
Control
Zahra Hassani; Vahab Nekoukar
Abstract
Background and Objectives: Currently, the control engineering community is increasingly focusing on research related to Unmanned Aerial Vehicles (UAVs) due to their versatile capabilities. Among the various applications, target detection and tracking stand out as crucial. Recent advancements in Artificial ...
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Background and Objectives: Currently, the control engineering community is increasingly focusing on research related to Unmanned Aerial Vehicles (UAVs) due to their versatile capabilities. Among the various applications, target detection and tracking stand out as crucial. Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL) have the potential to enhance the synergy between vision and control in UAV operations. By integrating AI algorithms with control methods, the accuracy of target information can be significantly improved in UAVs. This research introduces an autopilot system for quadcopters to search for and track a predetermined target.Methods: The autopilot system utilizes the YOLO network, a robust convolutional neural network-based system, for real-time target detection. To enhance object tracking robustness, the Kalman filter is integrated into the system. Furthermore, Proportional-Derivative (PD) controllers are utilized to calculate suitable control commands, enabling the quadcopter to effectively track both stationary and moving targets. Additionally, an object retrieval strategy is proposed to locate and recover lost objects during the tracking phase. Results: The effectiveness of the proposed system was evaluated through real-time experimental trials involving diverse scenarios encompassing both stationary and moving targets. The integration of the YOLOv5 network with the Kalman filter substantially improved detection accuracy and stability. Furthermore, the object retrieval mechanism demonstrated high reliability in recovering lost targets, thereby increasing overall system resilience. The PD-based control scheme enabled responsive and precise trajectory adjustments, contributing to consistent target tracking performance across all test cases.Conclusion: Integration of a YOLOv5-based detection module, a Kalman filter for robust tracking, and PD controllers for flight control provides an autonomous quadcopter system capable of detecting and tracking both stationary and moving targets with unknown dynamics. The proposed approach shows promise for real-time autonomous tracking applications and offers a foundation for future development in more complex, outdoor scenarios.
Original Research Paper
Micro Sensors
Abolfazl Bijari; Sara Nooki
Abstract
Background and Objectives: Accurate measurement of moisture content (MC) in wheat grains is vital for quality control and storage management. This study presents the development of a differential microwave sensor with enhanced sensitivity for detecting MC in wheat grains, leveraging resonant techniques ...
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Background and Objectives: Accurate measurement of moisture content (MC) in wheat grains is vital for quality control and storage management. This study presents the development of a differential microwave sensor with enhanced sensitivity for detecting MC in wheat grains, leveraging resonant techniques to improve detection precision.Methods: The proposed sensor utilizes two identical half-wavelength (λ/2) microstrip resonators symmetrically coupled to a standard 50Ω transmission line. One resonator serves as a reference with a relative permittivity (έr) of 1, while the second is exposed to the sample under test (SUT), where έr > 1. This differential structure enables the identification of dielectric property changes due to moisture variation. The operating principles are theoretically analyzed using even- and odd-mode techniques.Results: The proposed sensor exhibits three distinct transmission zeros (TZs) within the 0.1–2 GHz frequency range, which arise from the harmonic behavior of the resonators. A prototype was fabricated on a low-cost, compact substrate with dimensions of 6 × 4.5 × 0.16 cm³ and was experimentally tested, confirming the simulation results. The sensor demonstrates a high normalized sensitivity of 7.63%.Conclusion: The developed differential microwave sensor demonstrates strong potential for precise and reliable MC detection in wheat grains. Its compact design, cost-effectiveness, and high sensitivity make it a suitable candidate for practical agricultural and food monitoring applications.
Original Research Paper
Power Electronics
Pezhman Bayat; Peyman Bayat; Seyed Mehdi Mousavi
Abstract
Background and Objectives: Impedance source networks have gained significant attention in electrical energy conversion due to their ability to overcome the limitations of conventional methods. While existing impedance-based converters offer various advantages, challenges such as voltage gain limitations ...
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Background and Objectives: Impedance source networks have gained significant attention in electrical energy conversion due to their ability to overcome the limitations of conventional methods. While existing impedance-based converters offer various advantages, challenges such as voltage gain limitations and component stress remain. This study introduces an advanced ultra-gain enhanced A-source (UGEA-S) DC/DC converter incorporating switched-capacitor technology to address these concerns and significantly improve voltage gain.Methods: The proposed UGEA-S converter is designed to enhance energy conversion efficiency while minimizing voltage stress on switching elements. The topology integrates switched-capacitor techniques to achieve superior voltage gain, reducing reverse recovery issues in diodes and maintaining a continuous input current. A thorough theoretical analysis is conducted to explore its operational principles and steady-state behavior. Comparative assessments with other recently developed converters further highlight its distinct performance attributes. Additionally, MATLAB/Simulink simulations and experimental results are performed to validate the converter’s functionality under practical operating conditions.Results: Experimental, simulation and numerical analysis confirm that the proposed UGEA-S converter achieves an ultra-high voltage gain of up to 8× (480 V output from a 60 V input) while maintaining low voltage stress across switching components. The MOSFET experiences a peak voltage of 230 V and a current of 28 A, well within safe operating limits. Diodes D1–D4 exhibit voltage stresses ranging from 230 V to 520V, with average currents between 2.65 A and 20.3 A. The input inductor sustains a continuous current of 19.5 A, validating the converter’s smooth current profile. Efficiency measurements show a peak of 96.93% at 230 W output, with performance remaining above 92% even at full 1 kW load. These results demonstrate the converter’s resilience under dynamic conditions and its suitability for high-performance applications such as electric vehicles and renewable energy systems.Conclusion: The UGEA-S converter offers a robust and innovative solution for high-gain DC/DC conversion, addressing key limitations of conventional designs. Its exceptional voltage gain, reduced voltage stress, and stable current regulation make it a promising candidate for advanced energy systems. The findings underscore the converter’s feasibility for real-world applications, particularly in electric vehicle power systems. Future research can further optimize its design for enhanced efficiency and broader scalability.
Original Research Paper
Power Electronics
Zahra Emami; Abolfazl Halvaei Niasar
Abstract
Background and Objectives: Multiphase electric motors are useful for industrial and military applications that need high power, smooth torque and the ability sharing power and torque in comparison to conventional three-phase electric motors. Also, these motors a more suitable substitute than three-phase ...
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Background and Objectives: Multiphase electric motors are useful for industrial and military applications that need high power, smooth torque and the ability sharing power and torque in comparison to conventional three-phase electric motors. Also, these motors a more suitable substitute than three-phase motors because of their ability to manage fault condition, guaranteeing the postfault operation of the drive. One type of Multiphase electric machines is Permanent Magnet Brushless Motors (PMBLM) that due to the inevitable limitations in their construction, back-EMF voltages are neither sinusoidal nor trapezoidal. Using traditional control strategies of and Brushless DC Motors (BLDCM) and Permanent Magnet Synchronous Motors (PMSM) results high electromagnetic torque ripple, vibrations and noises that are undesirable for medium voltage applications. Methods: This paper suggests a new finite control set model predictive control (FCS-MPC) method for two diode-clamped three-level (DC3L) inverters fed non-sinusoidal dual three phase PMBLM (DTP-PMBLM) with the capability to manage pre-fault conditions for reduction torque ripple and withstand postfault situations. The suggested MPC method removes requirement of weighting factor in the cost function for neutral point voltages in both DC3L inverters with a simple scheme balancing of capacitive voltages. Also, the fault tolerant control (FTC) schemes open phase fault and open switch fault are considered.Results: To study the effectiveness of the suggested MPC method, simulation results non-sinusoidal DTP-PMBLM drive are investigated and compared to with multiband hysteresis current (MHC) controller. Simulations have been carried out using MATLAB/Simulink with specifications 4125-V/2.7MW/350-RPM. Conclusion: Simulation results validate that the suggested MPC method has great dynamic responses such as lower torque ripple than the MHC controller. The FTC schemes are implemented without complexity changing the mathematical model and control framework.
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