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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350355079

The utilization of sensor networks for collecting climate-related data, encompassing temperature, rainfall, pressure, and wind measurements, has become widespread in endeavors to understand climate change. Typically, these data are depicted through an irregular graph model, where each sensor corresponds to a vertex, and edges denote similarity relations between sensors, with the measured data at each vertex represented as graph signals. However, a notable challenge in sensor networks stems from potential data gaps due to sensor malfunctions or communication failures, necessitating the development of methods to accurately estimate missing signal values. This paper addresses this challenge by introducing a method for recovering missing data. Initially, it defines the fractional graph Fourier transform (FrGFT) of irregular data and utilizes the bandlimitedness property of FrGFT to devise a missing data recovery approach. Subsequently, it investigates the relationship between recovery error and fractional order to pinpoint the optimal fractional order that minimizes recovery error. Finally, the efficacy of the proposed FrGFT method is demonstrated using temperature data collected by a sensor network in the USA. Additionally, a performance comparison with the conventional graph Fourier transform (GFT) method is conducted to highlight the superiority of the proposed approach. © 2024 IEEE.

关键词： graph Fourier transform graph signal processing missing data recovery sensor network

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Workload Pattern Learning-based Cloud Resource Management Models: Concepts and Meta-analysis

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 418-438页

作者： Saxena, Deepika Singh, Ashutosh Kumar The University of Aizu Department of Computer Science and Engineering Japan Director with Indian Institute of Information Technology Bhopal India

Workload pattern learning-based resource management is crucial for cloud computing environments for achieving higher performance, sustainability, fault-tolerance, and quality of service. The existing literature lacks a comprehensive discussion and metaanalysis of workload pattern learning centered cloud resource management. In this context, this paper presents a first comprehensive study about five pattern learning and analysis-driven techniques applied for achieving higher efficiency and performance during multi-constrained cloud resource management. The paper manifests utility and significance of workload pattern learning-based resource management as compared with traditional resource management. The five principle techniques are thoroughly discussed with coherent depiction of intended concept alongwith numerical illustration. The most prominent state-of-the-art models belonging to each technique are further distinguished based on distinct objectives conferring an extensive survey and comparison. Besides, conceptual and theoretical analysis, the leading models underlying the major resource management techniques are implemented on a common platform and thoroughly examined using real-world Google Cluster workload traces. Based on the all-inclusive study and performance evaluation, trade-off discussion among these techniques are capsuled to put forward imperative concluding remarks with concrete open issues and insightful future research directions. © 2024 IEEE.

关键词： Quality of service

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Robust Deep Convolutional Solutions for Identifying Biotic Crop Stress in Wild Environments

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 497-508页

作者： Pal, Chiranjit Mukherjee, Imon Chatterji, Sanjay Pratihar, Sanjoy Mitra, Pabitra Chakrabarti, Partha Pratim Indian Institute of Information Technology Kalyani Department of Computer Science and Engineering Kalyani741235 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

In the realm of agricultural automation, the precise identification of crop stress holds immense significance for enhancing crop productivity. Existing methods primarily focus on controlled environments, which may not accurately reflect field conditions. Field-based leaf image analysis poses challenges due to varying image quality and sunlight intensity. Moreover, the complexity of crop stress images, with their random lesion distribution, diverse symptoms, and complex backgrounds, further complicates the analysis. To overcome these limitations, a lightweight hybrid convolutional neural network has been developed. This system integrates the powerful three-deep blocks model with an autoencoder running in parallel to highlight regions of crop stress effectively. To support this approach, we have introduced the Indian Rice Disease Dataset (IRDD) with labeled images. The proposed system reports an average true positive rate (TPR) of 0.8766 and an average positive predicted value of 0.8720 on IRDD, which are higher than other state-of-the-art crop disease detection models. The system is validated on benchmark datasets, yielding significant results: TPR of 0.9870 (rice), 0.9985 (tomato), and 0.8559 (corn). Furthermore, the proposed model outperforms recent state-of-the-art works on the benchmark PlantDoc dataset, showing its effectiveness in generalizing plant disease identification tasks. Finally, an ablation study has been carried out to explore the importance of the two parallel branches. Overall, this study acts as a bridge between advanced science and practical application, showcasing how interdisciplinary automation could revolutionize crop disease identification, improve agricultural efficiency, and reshape broader industrial practices. © 2024 IEEE.

关键词： Convolutional neural networks

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Developing Explainable Deep Learning Models for Android Malware Detection 2

Developing Explainable Deep Learning Models for Android Malw...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Nisa, Sabrina Subah Al Asad, Nafiz Rahaman, Abu Sayed Md. Mostafizur Bangladesh University of Professionals Department of Information and Communication Technology Dhaka Bangladesh Jahangirnagar University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

The Android operating system is becoming increasingly popular among mobile users due to its smooth handling and versatile features. But in recent years, Android malware has also increased due to its popularity. Cybercriminals deploy unwanted or dangerous software that might damage the device or steal personal data. Identifying and preventing malware threats in real-time is crucial to ensure security. In order to address this issue, we have created a framework that can continuously adjust to new mal ware threats while maintaining a high level of detection accuracy. We used a deep learning technique that can efficiently learn from both static and dynamic analytic character-istics of Android apps in order to accomplish this. Additionally, in order to determine which features are most relevant in identifying Android malware, we performed a feature importance analysis. This analysis gives security analysts and developers operative insights. Our experimental findings demonstrate that, with an accuracy of 95.87 %, our deep learning system performs better than several other methods. © 2024 IEEE.

关键词： Android malware

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Seamless V2I Connectivity: How THz Waves and Base Station Density Contribute 18

Seamless V2I Connectivity: How THz Waves and Base Station De...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Singh, Sameer Kumar Gupta, Nishant Singh, Rohit Kumar, Shiv Kumbhani, Brijesh Indian Institute of Technology Electrical Engineering Department Ropar India LNM Institute of Information Technology Communication and Computer Engineering Department Jaipur India Dr B R Ambedkar National Institute of Technology Electronics and Communication Department Jalandhar India

ISBN: (纸本)9798350391725

Higher frequencies, such as Terahertz waves (THz waves), are gaining widespread attention and emerging as the foundational framework for next-generation communication systems (e.g., 6G). These frequencies offer a larger bandwidth, unlocking new opportunities, including applications like vehicular communication. Due to significant traffic variations, vehicles frequently experience outages. Therefore, this scenario requires ultra-reliability and improved coverage. To meet these requirements, we introduced an architecture that uses joint transmission from two distinct Terahertz Stations (TSs), which offers several benefits. Primarily, this collaborative approach is expected to enhance network connectivity by reducing outage probability. Additionally, the use of THz waves can support higher data rates and lower latency, which are crucial for real time communication. © 2024 IEEE.

关键词： Gravity waves

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A Networked Virtual Reality System for Real-time Immersive Learning 13

A Networked Virtual Reality System for Real-time Immersive L...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Wataru, Iwai Tesuhito, Oorui Nguyen, Duc V. Tohoku Insitute of Technology Department of Communication and Information Engineering Sendai Japan

ISBN: (纸本)9798350355079

In this paper, we develop a Virtual Reality-based immersive learning environment that allows teachers to conduct a lesson in a virtual space. The proposed system allows teachers and students to be present in the same virtual space regardless of their actual physical locations. The teachers can verbally communicate with students in real-time, sharing/collecting learning materials, and conducting in-class assignments. The students can interactively learn about real-world objects by touching and manipulating 3D models with hand tracking. Evaluation results demonstrate the effectiveness of the proposed system. © 2024 IEEE.

关键词： Virtual environments

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Trust and QoS-Driven Query Service Provisioning Using Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 1827-1844页

作者： K.Narmatha K.Karthikeyan Department of Computer Science and Engineering SRM Institute of Engineering and TechnologyRamapuramChennaiIndia Department of Information Technology Coimbatore Institute of TechnologyCoimbatoreIndia

The growing advancements with the Internet of Things(IoT)devices handle an enormous amount of data collected from various applications like healthcare,vehicle-based communication,and smart *** research analyses cloud-based privacy preservation over the smart city based on query ***,there is a lack of resources to handle the incoming data and maintain them with higher privacy and ***,a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment.A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards *** handling privacy,other inter-connected metrics like efﬁciency are also essential,which must be considered to fulﬁl the privacy ***,this work provides a query-based service model and clusters the query to measure the relevance of frequently generated ***,a Bag of Query(BoQ)model is designed to collect the query from various *** is done with a descriptive service provisioning model to cluster the query and extract the query’s summary to get theﬁnal *** processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Opti-mizer(GWO).It is used to attain global and local solutions regarding privacy *** iterative data is evaluated without any over-ﬁtting issues and computational complexity due to the tremendous data handling *** on this analysis,metrics like privacy,efﬁciency,computational complexity,the error rate is *** simulation is done with a MATLAB 2020a *** proposed model gives a better trade-off in contrast to existing approaches.

关键词： Cloud computing IoT privacy security grey wolf optimization

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Asymmetrical Fault Current Calculation Method and Influencing Factors Analysis of Droop-Controlled Inverters

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 562-573页

作者： Huimin Zhao Zhikang Shuai Jun Ge An Luo Weimin Wu Z.John Shen College of Electrical and Information Engineering Hunan UniversityChangsha 410006China Electrical Engineering Department Shanghai Maritime UniversityShanghai 201306China Department of Electrical and Computer Engineering Ilinois Institute of TechnologyChicagoIL 60616 USA

Since the fault dynamic of droop-controlled inverter is different from synchronous generators (SGs), protection devices may become invalid, and the fault overcurrent may damage power electronic devices and threaten the safety of the microgrid. Therefore, it is imperative to conduct a comprehensive fault analysis of the inverter to guide the design of protection schemes. However, due to the complexity of droop control strategy, existing literatures have simplified asymmetric fault analysis of droop-controlled inverters to varying degrees. Therefore, accurate fault analysis of a droop-controlled inverter is needed. In this paper, by analyzing the control system, an accurate fault model is established. Based on this, a calculation method for instantaneous asymmetrical fault current is proposed. In addition, the current components and current characteristics are analyzed. It was determined that fault currents are affected by control loops, fault types, fault distance and nonlinear limiters. In particular, the influences of limiters on the fault model, fault current calculation and fault current characteristics were analyzed. Through detailed analysis, it was found that dynamics of the control loop cannot be ignored, the fault type and fault distance determine fault current level, and part of the limiters will totally change the fault current trend. Finally, calculation and experimental results verify the correctness of the proposed method.

关键词： Asymmetrical fault analysis droop control fault current calculation method limiter transient fault characteristics

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Secure probe-based latency measurement using NTP and PTP for optimized gateway placement in LoRaWAN

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International Journal of information technology (Singapore) 2025年 1-10页

作者： Bhatnagar, Kumar Vaibhav Kushwah, Rashmi Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida 201304 India

The paper presents a novel framework for optimizing LoRaWAN gateway placement to enhance network performance and reliability. By integrating Network Time Protocol (NTP) for global synchronization and Precision Time Protocol (PTP) for local precision, the approach ensures accurate and secure latency measurements. A robust hash algorithm is employed to safeguard the integrity of latency data. The Performance Efficiency Index (PEI) is introduced as a comprehensive metric to evaluate key performance indicators such as latency, throughput, energy efficiency, and packet delivery ratio (PDR). The model incorporates factors like geographical distribution, signal strength, and gateway density to achieve optimal placement. Simulation results demonstrate the framework’s effectiveness in improving network latency, coverage, energy efficiency, and security, providing actionable insights for LoRaWAN-based IoT network design. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Gateway placement LoRaWAN Network performance NTP PTP Timestamp

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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