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A 0.75 to 1.5 GHz Reconfigurable Diplexer With Independently Controllable Center Frequency and Insertion Phase

IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第4期71卷 1924-1928页

作者： Yi, Chengfei Chen, Xiong Chi, Pei-Ling Yang, Tao The School of Electronic Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China The Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan

A novel reconfigurable diplexer with independently controllable center frequency and insertion phase is proposed in this brief. It simply consists of six coupled resonators and two non-resonating-nodes (NRNs). These six resonators are arranged in two triplet topologies and construct two typical third-order filters, forming the lower and upper channels in the diplexer, respectively. The NRNs are loaded at the receiver (RX) and transmitter (TX) ports. With the utilization of NRNs, the insertion phases of the upper and lower channels in the diplexer can be independently controlled. Optimization technique is used to obtain the coupling matrix of the diplexer, in which the NRN is considered to achieve a prespecified insertion phase. To validate the proposed idea, a microstrip prototype is fabricated and measured. Experiment results show that the center frequencies of lower and upper channels can be tuned from 0.75 GHz to 0.98 GHz, and from 1.23 GHz to 1.5 GHz, respectively. The insertion phases of both channels can be independently tuned from 0 to 120 degrees at each frequency tuning state. © 2023 IEEE.

关键词： Resonators

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A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

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SN computer science 2024年第8期5卷 1-15页

作者： Sethupathi, M. Gowtham Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India

With the rapid increase in Electric Vehicles (EVs), the issue of charging them has received a lot of attention. Since one EV’s charging time cannot be cut quickly and the number of charging stations will not expand quickly, scheduling EV charging operations in metropolitan areas becomes vital as it can boost charging efficiency and minimize EV users’ concern over charging. There isn’t currently a useful scheduling software product available in this industry. Effective trip time prediction is essential for charging stop scheduling and route optimization, especially for EVs attempting to maneuver through intricate metropolitan *** order to precisely predict trip durations for EVs, this research presents a unique hybrid model that combines the Reinforcement Learning (RL) and Improved Weighted Wolf Optimization (IWWO) algorithms. The model's capacity to identify complex patterns in traffic data is improved by optimizing the selection of pertinent features and parameters via the use of the IWWO algorithm. The model's performance is then adjusted using RL approaches, which use iterative learning and adaptability to changing traffic circumstances. The suggested hybrid approach provides reliable and flexible EV travel time estimate by using the advantages of both optimization and machine learning paradigms. The outcomes of the experiments show how well the suggested method works in estimating trip times, especially in situations with erratic traffic patterns and variable ambient factors. With the use of this hybrid model, EV travel time prediction has advanced significantly, enabling more effective route planning and charge management to promote sustainable urban transportation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Charging management Electric vehicle Improved weighted wolf optimization Reinforcement learning Route optimization Sustainable transportation Traffic patterns Travel time estimation Urban mobility

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Texture and Deep Feature Extraction in Brain Tumor Segmentation Using Hybrid Ensemble Classifier 1st

Texture and Deep Feature Extraction in Brain Tumor Segmentat...

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1st International Conference on Computational Intelligence for engineering and Management Applications, CIEMA 2022

作者： Mohan, Divya Ulagamuthalvi, V. Joseph, Nisha Department of Computer Science and Engineering Sathyabama University Chennai India Computer Science and Engineering SAINTGITS College of Engineering Kerala Kottayam India

ISBN: (纸本)9789811984921

The abnormal cell development in the brain is referred to as a tumor. Brain tumors are treated by physicians using radiation and surgery. The brain tumor is categorized as benign or malignant. The benign tumor can be treated and cured using the appropriate medication suggested. A malignant tumor is an abnormal tissue that affects the nearby tissues, and it can be cured only through proper surgery by a physician. Manual identification process of malignant and benign tumors is very time taking and has a good chance of occurrence error in the process. To overcome this limitation, automatic brain tumor classification technique is proposed. An efficient methodology for the detection of brain tumors is done. Initially, the brain MRI image is smoothed and enhanced by a Gaussian filter. Then, deep and texture features are extracted. In the proposed work, an ensemble technique using three different classifiers based on majority voting method is used. The research work proposed in this paper is tested using BRATS 2017, 2018 datasets, and the obtained results are compared with recent methods and proved its efficacy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Textures

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Efficient data visualization through novel recurrent neural network-based dimension reduction 2nd

Efficient data visualization through novel recurrent neural ...

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2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Sharma, Saurabh Vijyal, Shafalika Upadhyay, Navin Mani Department of Computer Science & Engineering Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

Data visualization can be a challenging task in our everyday lives, especially when dealing with large and complex datasets. Multiple researchers have dedicated their efforts to this topic and have put forth various solutions, such as t-SNE, UMAP, and the Tramp technique. These techniques maintain the overall structure, but they do not accurately depict the local structure in the visualization. Therefore, many of them are not suitable for all types of data sets. Because of these issues, the demand for other novel approaches for data visualization will be high. In this paper, the proposed work is based on a novel Recurrent Neural Network (RNN) based dimension reduction technique to correctly visualize the selected real-world data set. The RNN models can reliably visualize and forecast the numeric values for a general-purpose machine architecture, but for special-purpose machines, the construction of RNN architecture construction has yet to be devised. The proposed model is based on a trial and error-based iteration technique for multiple input parameters. The major challenge for the selection of such input parameters is: that when there are many input factors, it is hard to determine which ones affect model prediction. This problem is reflected as the "curse of dimensionality." The proposed method shows a significant improvement of up to 3% compared to all three compared models (t-SNE, UMAP, & Tramp). © 2025 The Author(s).

关键词： Data visualization

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An Analytical framework for Classification of ElectroEncephaloGram (EEG) Motor Imagery tasks using Deep Learning Techniques 2

An Analytical framework for Classification of ElectroEncepha...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Sathish, M. Geetha Devasena, M.S. Saravanan, B. Sudharsan Kumar, S. Kumaraguru College Of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College Of Technology Information Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331534950

Motor Imagery (MI) on Electroencephalogram (EEG) signals is a common approach within Brain-Machine Interface (BMI) models, extensively applied in medical rehabilitation and artificial machinery. BMI serves as a direct link between the brain's electrical activity and external devices, such as computers or artificial limbs. BMIs are regularly expressed at researching, directing, helping, extending, augmenting, and correcting the people cognitive or motor sensory devices. The accomplishment of BMI's ranges from non-invasive to invasive device based on the tight electrodes gets into brain tissue. Now a days, Deep Learning (DL) is playing a crucial role in MI EEG based BMI. The framework focuses on how Deep Learning (DL) is enhancing MI-based BMIs by improving signal processing and classification. Key aspects covered include preprocessing, feature extraction, classification techniques, and performance evaluation. The analysis highlights the strengths and challenges of various DL approaches, providing insights to advance the development of reliable and efficient BMI systems. The models were analyzed with respect to the domains of undirected and discriminated analysis using belief networks. © 2024 IEEE.

关键词： Deep learning

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Android offloading computation over the cloud 1st

Android offloading computation over the cloud

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

This paper analyzes whether Android apps may outsource computational activities to cloud servers. Due to the complexity of mobile apps, shifting computing operations to the cloud has become popular to improve performance and reduce energy consumption. This paper explains how to deploy an Android app on Cloudflare and tests it under various settings. The testing showed that cloud computing can boost Android app efficiency. The findings show that shifting computing workloads to the cloud reduces mobile device CPU utilization. This frees up device resources, allowing it to perform other tasks. However, cloud processing has limitations. Network latency between the mobile device and the cloud server may affect application performance. In addition, the cost of offloading computation to the cloud can be a concern for some users, especially in high data consumption scenarios. Our findings can be helpful for developers who want to optimize the performance of their Android applications and for researchers who wish to investigate this topic further. Future research could explore ways to minimize the impact of network latency on offloading computation and investigate the cost-effectiveness of this technique for different types of applications. © 2025 the Author(s).

关键词： Cloud security

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Comprehensive Vulnerability Detection and Malware Infection Testing Strategies for IoT Devices

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IEEE Internet of Things Journal 2025年第12期12卷 20556-20571页

作者： Liang, Bo-Hao Hwang, Ren-Hung Lin, Jia-You Chen, Hsiao-Hwa National Chung Cheng University Department of Computer Science and Information Engineering Taiwan National Yang Ming Chiao Tung University College of Artificial Intelligence Taiwan National Cheng Kung University Department of Engineering Science Taiwan

With the increasing prevalence of IoT devices, security vulnerabilities and malware infections have emerged as significant risks. To address these challenges, advanced vulnerability detection tools are essential for enhancing IoT security assessments. In this study, we analyzed common vulnerabilities and evolving attack methodologies to develop improved detection techniques. Our research focuses on two key areas: comprehensive vulnerability detection and malware infection testing strategies. Through on-site testing and detailed analysis, we identified prevalent security flaws in IoT devices and developed a suite of tools tailored for detecting these vulnerabilities. Additionally, we discovered that some devices exhibit inherent immunity to specific malware strains, emphasizing the need for novel malware infection detection strategies. Real-world evaluations uncovered previously unknown vulnerabilities and weaknesses, revealed widespread susceptibility to DoS attacks, and demonstrated that not all devices are vulnerable to malware infections. These findings confirm the effectiveness of our approach in identifying risks and enhancing IoT security. © 2014 IEEE.

关键词： Internet of Things Security Malware Object recognition Protocols Codes Testing Passwords Botnet Vectors

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Sensing the Future: Challenges and Trends in IoT Sensor Technology 4

Sensing the Future: Challenges and Trends in IoT Sensor Tech...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350307757

In the ever-evolving world of the Internet of Things (IoT), sensors play a crucial role as essential parts, linking and merging different elements to create a smart worldwide network. This study examines the crucial function of sensors in the Internet of Things (IoT) ecosystem, where they serve as sensory organs that translate the intricacies of the physical world into a uniform language for networked systems. The main emphasis is on their ability to gather and evaluate factual data from the surroundings, which impacts the performance and effectiveness of IoT applications in many industries. This research explores the uses and relevance of many sensor types, including temperature, humidity, proximity, motion, gas, biometric, vibration, image, infrared (IR), occupancy, water quality, pressure, light, and sound sensors. Although sensors contribute to the improvement of automation, control systems, and predictive analysis, they also give rise to issues in terms of security, interoperability, and power efficiency. The convergence of edge computing, artificial intelligence (AI), quantum sensors, 5G connection, and improved sensor integration has the potential to bring about significant changes in the Internet of Things (IoT) environment. The study provides a thorough survey of sensor applications in several domain including healthcare, agriculture, smart cities, and automotive sectors. Lastly, it examines the difficulties and potential opportunities, highlighting the vital significance of sensors in influencing the future of interconnection and data-driven decision-making. © 2024 IEEE.

关键词： Internet of things

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Insider Threat Detection and its Behavior with Excessive Access Privileges 1

Insider Threat Detection and its Behavior with Excessive Acc...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Bolukonda, Deepthi Bolukonda, Deepthi Mishra, Rupesh Kumar Ranjan, Ratnesh Sr University Department of Computer Science and Engineering Warangal India Chaitanya Bharathi Institute of Technology Gandipet Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350352931

Insider threats, one of the most challenging cyber threats, often result in significant organizational losses. This study explores the correlation between suspicious users and job roles vulnerable to insider threats. The research highlights that users with higher access privileges are more exposed to potential insider attacks. K-Nearest Neighbors (KNN) and hierarchical clustering, implemented using Weka, were employed to detect suspicious activity and group users by their roles. Based on a comparison of successive months, an overall accuracy value of 0.85 was obtained. By monitoring for suspicious changes in their cluster over time, we were able to identify 80% of insider threats with the ITAdmin job. The results revealed that many users exhibiting abnormal activities held IT admin roles, indicating that those with the highest access permissions are more prone to insider threats. This study addresses a gap in understanding the relationship between excessive access permissions and security risks. © 2024 IEEE.

关键词： Cyber attacks

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A Linear-Time Optimal Broadcasting Algorithm in Stars of Cliques

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Journal of Graph Algorithms and Applications 2024年第1期28卷 385-388页

作者： Damaschke, Peter Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg GothenburgSE-41296 Sweden

For the telephone broadcast model, an O(n log n)-time algorithm for constructing an optimal broadcasting scheme in a star of cliques with a total of n vertices was recently presented by Ambashankar and Harutyunyan at IWOCA 2024. In the present note we give a considerably shorter and purified algorithm description and correctness proof. Moreover, we improve the time complexity to O(n). © 2024, Brown University. All rights reserved.

关键词： Consensus algorithm

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