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Investigations of PAPR reduction methods in FBMC System

Investigations of PAPR reduction methods in FBMC System

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Rashmi Rani Samantaray Siddappaji Department of Electronics and Communication VTU HKBK College Of Engineering Bengaluru India Department of Electronics and Communication BMS College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

the primary concern of the 5G radio network has sparked interest in the development of new waveforms beyond 4G (5th generation networks). Filter Bank Multicarrier (FBMC) waveforms have recently been suggested as the desired one for 5G networks. It has both advantages and disadvantages. One of the biggest drawback of the FBMC broadcast signal is its high Peak to Average Power Ratio (PAPR). It has been widely used in wireless communication to achieve high speed and data rates. A high PAPR system results in substantial distortion of the signal because of the nonlinear power amplifier. therefore, methods that effectively reduce PAPR are necessary for the realization of the FBMC system. the FBMC system uses the some common techniques as the OFDM system. Comparative analysis of various approaches such as PTS, SLM, Companding, etc are performed by considering their simulation outcomes when it comes to effective spectrum utilization, improved PAPR, along with minimum complexity.

关键词： Wireless communication 5G mobile communication Nonlinear distortion Metaheuristics Power amplifiers Peak to average power ratio Whale optimization algorithms complexity theory Radio networks Particle swarm optimization

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FPGA Verification Method Based on Test Coverage Analysis

FPGA Verification Method Based on Test Coverage Analysis

引用

Machine Learning and Computer Application (ICMLCA), International conference on

作者： Lanlai Wang Wei Liu Li Xu Daojin Xu Xiaoming Zhang Shanghai Institute of Aerospace Technonical Foundation Shanghai China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

In modern FPGA (Field Programmable Gate Array) design, due to the increase in complexity, traditional verification methods are difficult to achieve high test coverage, resulting in limited verification efficiency and quality. this study proposed an FPGA verification method based on test coverage analysis, aiming to improve coverage and verification efficiency. Firstly, this article established a coverage analysis framework by analyzing the existing FPGA verification process and the reasons for insufficient testing coverage. Next, it utilized automated test generation tools to increase the diversity and complexity of testing scenarios, thereby expanding the scope of testing coverage. then, this article introduced machine learning algorithms based on Long Short-Term Memory (LSTM) networks to adjust test parameters to meet different design requirements. Finally, changes in coverage can be monitored through dynamic tracking and analysis tools, and validation strategies can be adjusted accordingly. the experimental results show that compared to random test generation, manual test case design, and equal probability coverage testing, the method proposed in this study achieved a test coverage rate of 95% in FPGA projects and significantly shortened the verification cycle. Research has shown that this method based on test coverage analysis can effectively solve key problems in FPGA verification, and has good practical value and promotion prospects.

关键词： Machine learning algorithms Heuristic algorithms Real-time systems complexity theory Test pattern generators Field programmable gate arrays Long short term memory Monitoring Optimization Testing

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Enhancing DarkWebActivities Classification Using Embedding Methods

Enhancing DarkWebActivities Classification Using Embedding M...

引用

Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Meriem Sennad Zineb Ellaky Faouzia Benabbou Laboratory of Modeling and Information Technology University Hassan II Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the Dark Web is frequently associated with illegal operations, including drug trafficking, arms dealing, cybercrime, and hacking-related services. Mitigating these threats remains a major challenge for cybersecurity professionals and law enforcement agencies, given the complexity and evolving nature of cybercriminal activities. However, the recent progress in Artificial Intelligence and machine learning has introduced new approaches to analyse the Dark Web content and activities. this study proposes a novel classification approach capable of identifying ten types of illicit activities. the system is built upon the publicly accessible DUTA dataset, which offers a structured corpus for such analysis. To classify the Dark Web activities, ensemble learning algorithms were employed, including Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting, Gradient Boosting, and CatBoost. To convert text-based data into numerical vector formats suitable for computational processing, five distinct word embedding techniques were utilized. the results of this study show that the combination of the RoBERTa embedding with the XGB classifier achieved promising results, with a precision of 99.98%. these results underscore the capability of the proposed methodology to detect and categorize illicit Dark Web activities.

关键词： Drugs Dark Web Law enforcement Boosting Vectors complexity theory Classification algorithms Ensemble learning Computer crime Random forests

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A Key Generation Algorithm for Cryptographic algorithms to Improve Key complexity and Efficiency

A Key Generation Algorithm for Cryptographic Algorithms to I...

引用

International conference on Smart Systems and Inventive Technology (ICSSIT)

作者： B. Umapathy G. Kalpana Department of Computer Science College of Science and Humanities SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India

In the recent days people use a variety of online software applications to move data from one location to another. To secure personal data from hackers, software or applications use cryptographicbased algorithms. the encryption and decryption processes are the foundation of the cryptographic algorithm, which will be carried out with the help of a key. Cryptographic algorithms employ automatic key generation that starts with the user’s password, whereas another generating key directly interprets the password. A symmetric algorithm uses the same key for both encryption and decryption. For key generation, symmetric algorithms use a second or subsidiary method. the password is protected using this key generation process from various key attacks. the key generation algorithm functions as an interpreter, transforming a password from a human-readable form to a machine-readable form. An increase in permutations and combinations helps to protect the password; additional characteristics are being added to the algorithm. To get around the restricted key bit size, this paper proposed the KEY SECURE-KEY (KSKEY) algorithm. However, increasing the key size is not a suitable way to encrypt data because multi-core processors in modern microprocessors are significantly increasing processingspeed.

关键词： Multicore processing Microprocessors Software algorithms Force Passwords Metadata Prediction algorithms

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Reduce Deblurring Generative Networks Training complexity with Encoder-Decoder Model

Reduce Deblurring Generative Networks Training Complexity wi...

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Artificial Intelligence and Electromechanical Automation (AIEA), International conference on

作者： Jiangmai Cheng Bo Jiang Hong Xu The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

We propose a deblurring method that integrates a combination of Generative Adversarial Networks (GANs) and encoder-decoder models. Our approach offers higher efficiency and reduced training complexity compared to generative deblurring models which utilizing GANs as the backbone network. In this approach, we employ the encoder as the feature extraction module, and then passes the features to the GANs for deblurring, followed by image reconstruction using the decoder as the last module. To ensure image fidelity, skip connections are introduced between the encoder and decoder. Meanwhile, we introduce a targeted training methodology designed to mitigate fluctuations in network parameters, thereby maximizing the efficacy of the model’s training velocity. We additionally conducted comparative evaluations based on the GoPro and Kohler datasets, contrasting our model against previously proposed algorithms to approve its exceptional performance in deblurring efficacy and efficiency.

关键词： Training Fluctuations Automation Image synthesis Feature extraction Generative adversarial networks Decoding complexity theory Artificial intelligence Image reconstruction

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Analysis of Maze Generation algorithms

Analysis of Maze Generation Algorithms

引用

Innovative Trends in Information Technology (ICITIIT), International conference on

作者： J. Vijaya Arunkumar Gopu K V S G Vinayak thakur Jaideep Singh Kanika Malhotra Department of DSAI) IIIT Naya Raipur Raipur Chattisgarh India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Procedural content generation plays a crucial role in modern game development, offering efficient methods for creating intricate mazes, a prime example of its utility. However, when it comes to homemade maze generation, there’s often a limitation in providing the necessary diversity of mazes to maintain player engagement. Predictability can creep in, diminishing the player’s experience. In this composition, we embark on a comprehensive exploration of three introductory 2-D maze-generation algorithms, firstly, DFS (Depth-First Search), secondly, Recursive description, and lastly Prim’s-Method algorithm. Rather than merely analyzing these algorithms, our focus is on addressing their inherent drawbacks. Specifically, we aim to find solutions to enhance their utility in game development, particularly in terms of improving path lengths and increasing the diversity of generated mazes. In essence, procedural content generation, as exemplified by maze generation algorithms, serves as an indispensable tool for game designers seeking not only to understand the limitations but also to find creative solutions that strike a balance between generating engaging content and maintaining gameplay diversity.

关键词： Measurement Procedural generation Scalability Layout Prediction algorithms Market research complexity theory

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A hybrid approach for Capacitated Vehicle Routing Problem with Time Windows based on Conflict-Driven Clause Learning and Reinforcement Learning

A hybrid approach for Capacitated Vehicle Routing Problem wi...

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Innovative Trends in Information Technology (ICITIIT), International conference on

作者： A Christy S Prayla Shyry M D Anto Praveena L Suji Helen Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

In the present scenario, the major applications of Vehicle Routing Problem (VRP) is food delivery. Many algorithms are available to solve the variants of problems related to VRP in minimizing the service time. In this paper, an algorithm is proposed for Vehicle Routing Problem with Time windows (VRPTW) considering traffic prediction. this algorithm is constructed based on Conflict-Driven Clause Learning and Reinforcement learning(CLRL). Experiments were constructed for inter-distance route optimization and intra-distance route optimization. the proposed algorithm shows that the CLRL algorithm is more flexible and scalable in solving multi-path and multi-driver problem under VRPTW compared to Dijkstra’s algorithm and Google’s operations research tool.

关键词： Training Operations research Vehicle routing Reinforcement learning Prediction algorithms Market research Time complexity

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A Feature Fusion-Based Lightweight Algorithm for Small Object Detection in UAV Aerial Imagery

A Feature Fusion-Based Lightweight Algorithm for Small Objec...

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International conference on

作者： Guang Wang Yanming Zhang Qiang Ai Software College Liaoning Technical University Huludao China College of Computer Qinghai Normal University Xining China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

To address the issue of low detection accuracy for small objects in drone aerial imagery, this paper improves the YOLOv8n framework. the backbone network is replaced with an improved EfficientNetV2, and a Multi-Scale Feature Aggreg-ation Pyramid is introduced to enhance semantic understanding of features. Validation on the VisDrone-2019 dataset demons-trates that the proposed improvements significantly increase the mAP, proving that the model’s ability to detect small objects in aerial images has been enhanced.

关键词： Accuracy Smoothing methods Pedestrians Semantics Software algorithms Object detection Feature extraction Computational complexity Drones Software engineering

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Token Embedding Graph Neural Network for Paper Classification

Token Embedding Graph Neural Network for Paper Classificatio...

引用

Artificial Intelligence and Computer Engineering (ICAICE), International conference on

作者： Shuchen Cai Xiaoying Gan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

the topic classification task on academic papers significantly contributes to the analysis of valuable papers. Classic methods first build a paper citation graph and then classify each paper node with a graph neural network (GNN). Some successors notice the rich semantics within the text features, namely the title and abstract of one paper, and start to extract the node representation from a text attributed graph (TAG). But the heterogeneity between graph topology and text sequence requires the representation learning algorithm to aggregate knowledge from both sides. Existing works embed the text feature in the corresponding node directly with a separate language model, so as to couple the knowledge in a cascading style. However, this text embedding algorithm faces a severe problem on calculation complexity because of the coupling. Given that the complexity of GNN grows exponentially as the number of layers increases, the high cost of the language model is further amplified to an intolerable degree. To conquer this challenge, we propose the token embedding graph neural network (TEGNN). Instead of cascading the language model and GNN, a shared token embedding layer is introduced to serve as the knowledge link between them. this structure decouples two modules without any knowledge loss, decreasing the complexity considerably. Experiments on the large-scale dataset prove the enhancement of efficiency. Compared with baseline algorithms, TEGNN decreases the training time by 70.17% on condition that the classification accuracy is increased by 1.04%.

关键词： Training Costs Accuracy Computational modeling Feature extraction Graph neural networks complexity theory Classification algorithms Time-domain analysis Time-varying systems

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Unraveling Genetic complexity: Fine-tuning Machine Learning Models for DNA Sequence Analysis with K-mer Size

Unraveling Genetic Complexity: Fine-tuning Machine Learning ...

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International conference on Cybernetics and Intelligent System (ICORIS)

作者： Lukman I Made Anggar Dwi Adnyana Fajar Purnama Luther Alexander Latumakulita Ayang Armelita Rosalia I Wayan Adi Juliawan Pawana Dept. of Marine Information System Indonesia University of Education Bandung Indonesia Dept. of Electrical Engineering Udayana University Denpasar Indonesia Dept. of Information Technology Udayana University Denpasar Indonesia Dept. of Information System Sam Ratulangi University Manado Indonesia

In the realm of genetic analysis, the utilization of machine learning models holds great potential for unraveling the intricate complexities of DNA sequence data. this study aims to address the pressing need for advanced DNA sequence analysis methods by leveraging K-mer representations and fine-tuning machine learning algorithms to achieve optimal performance. By elucidating the reasons behind the study and highlighting the significance of these approaches, this research contributes to the broader understanding of DNA sequence intricacies.

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