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检索条件"主题词=Signal Processing Algorithms"

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Loan Approval Prediction Based on Tabu Search Optimization Technique

Loan Approval Prediction Based on Tabu Search Optimization T...

引用

signal processing and Advance Research in Computing (SPARC), International Conference on

作者： Rishika Chittimalla Alekhya Cholleti Sanjana Gunda Vyshnavi Manthurthi Anshu Kumar Dwivedi CSE(AIML) SR University Warangal India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

The banks and their customers know loan approval methods. Although traditional methods like taking all the relevant information are time-consuming, there is a need for technology, which can result in machine learning algorithms being applied to the data sets that contain the past data of customers to help in predicting whether the loan is being approved or not. Now the question is how much these algorithms predict accurate results, our research paper tells how the optimization we applied will maximize the number of loan approvals and minimize the risk analysis. We applied the Tabu search optimization along with a few machine-learning algorithms on the loan data set, and our accuracy is about ${9 8 \%}$.

关键词： Support vector machines Accuracy Machine learning algorithms signal processing algorithms signal processing Prediction algorithms Risk analysis Optimization Regression tree analysis Random forests

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Efficient Optimal Path-Planning for Congestion Mitigation in Large-Scaled Autonomous Driving

Efficient Optimal Path-Planning for Congestion Mitigation in...

引用

IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Yeongwon Jeon Daejin Park School of Electronics Engineering Kyungpook National University Daegu Republic of Korea

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

This paper reveals that there is a greedy characteristic in the pathfinding algorithm that finds the optimal route based on individual vehicles, and because of that characteristic, unexpected congestion can occur when several vehicles search for a route in their optimal way, resulting in a loss in the overall travel time. To solve this problem, we will modify the $\mathrm{A}^{*}$ algorithm, a well-known algorithm in pathfinding. The heuristic function of $\mathrm{A}^{*}$ is a function that determines the performance of the algorithm, and if information about other vehicles is included in this function, the greedy characteristic decreases. The resulting path may not be optimal for each vehicle. But overall, the path can be determined to mitigate congestion. Finally, we argue for an arbiter who collects information about other vehicles and assigns heuristics to each vehicle’s pathfinding algorithm.

关键词： Costs Communication systems Heuristic algorithms Prevention and mitigation signal processing algorithms Interference signal processing Autonomous vehicles

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HRRP Synthesis of Sparse Frequency Stepped Waveform with Time-Varying Amplitude Errors

HRRP Synthesis of Sparse Frequency Stepped Waveform with Tim...

引用

International Conference on Vision, Image and signal processing (ICVISP)

作者： Shuang Cui Shuai Shao Hongwei Liu National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Compared to narrowband signals, the high-resolution range profile (HRRP) images of non-cooperative targets offer enhanced distance resolution and richer target information, which is crucial in the field of radar automatic target recognition. To alleviate the stringent hardware demands linked to wideband signal processing and minimize sampling point requirements, employing sparsely spaced frequency-hopping pulse trains presents an effective approach for HRRP generation. However, when the radar system is unstable, it can lead to parameter estimation errors. The amplitude of the radar echoes may vary over time, resulting in degraded image quality from traditional HRRP synthesis methods. To tackle this issue, this paper proposes an algorithm designed to mitigate the effects of time-varying amplitude (TVA) errors on synthesized HRRP, employing sparse frequency stepped waveform (SFSW). Extensive experimental results based on simulations demonstrate the effectiveness and robustness of this approach.

关键词： Frequency synthesizers Time-frequency analysis Target recognition Simulation signal processing algorithms Radar Radar imaging Wideband Time-varying systems signal resolution

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"Next-Gen Vehicle Safety: The Futuristic Approach to Auto-Stop in Modern Vehicles"

"Next-Gen Vehicle Safety: The Futuristic Approach to Auto-St...

引用

International Conference on Communications and signal processing

作者： Anil Kumar Thandlam T. Vignesh A Saravanan Prakash Subramani Ramaganesh Marimuthu Sandeep Gupta Department of Petroleum Technology Aditya University Surampalem India Department of Mechanical & Mechatronics Engineering (AM) SNS College of Engineering Coimbatore India Department of Mechanical Engineering Kongu Engineering College Perundurai Erode India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnan Kovil India Department of Electrical Engineering Graphic Era (Deemed to be University) Dehradun (Uttarakhand) India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In the realm of next-generation vehicle safety, this project introduces a cutting-edge approach to auto-stop functionality in modern vehicles. Leveraging the state-of the-art YOLO (You Only Look Once) algorithm, the system focuses on enhancing driver safety through two critical aspects: facial identification and drowsiness monitoring. The YOLO algorithm efficiently detects and analyses facial features, providing real-time identification of the driver. Simultaneously, the system employs advanced drowsiness monitoring techniques, utilizing facial cues to gauge the driver's alertness levels. This futuristic safety initiative aims to mitigate potential accidents caused by driver fatigue or distraction by implementing an automatic auto-stop mechanism. The YOLO algorithm's speed and accuracy play a pivotal role in enabling swift and reliable detection of facial attributes, ensuring a seamless integration with the vehicle's safety system. By seamlessly integrating facial identification and drowsiness monitoring, the project presents a comprehensive solution to enhance road safety. The innovation lies in the project's ability to interpret facial expressions, track eye movements, and discern signs of drowsiness, enabling the vehicle to proactively intervene when a compromised driver state is detected. With an emphasis on real-time responsiveness, the system strives to revolutionize auto-stop technology, creating a safer driving environment for both the driver and other road users. This abstract encapsulates a groundbreaking fusion of advanced computer vision, machine learning, and automotive safety, ushering in a new era of proactive accident prevention in next-gen vehicles.

关键词： YOLO Technological innovation Vehicle safety signal processing algorithms Prediction algorithms Real-time systems Safety

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Optimal Placement of Battery Swapping Stations in Power System Using Genetic Algorithm

Optimal Placement of Battery Swapping Stations in Power Syst...

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： S Murugesan Geetha Sai Sree Palla Chaganti Bhavya Harshita Samvith Varadkar C H Ram Jethmalani Department of EEE National Institute of Technology Puducherry India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

There has been an increase in electric vehicle (EV) sales in recent years. The prime disadvantage of using EV s is the availability of battery charging stations(BCS) and the long waiting time for battery charging. Battery swapping stations (BSS) are alternatives to BCS and do not have the waiting time associated with charging a battery. The increased use of EV s will increase the burden on the power system network. Placement of BSS at optimal locations will lead to reduced transmission losses and improved voltage profile. However, they may also require the provision of additional transmission corridors. In this context, this paper explores the strategic placement of BSS within a power system. In addition, prior to the strategic placement, the effect of placement of BSS on the reinforcement cost(RFC), transmission 10ss(PL), and voltage deviation index(VDI) are investigated. The optimal strategic placement is carried out considering the minimum RFC, minimum PL, and maximum VDI using the Genetic Algorithm.

关键词： Costs Voltage fluctuations signal processing algorithms signal processing Propagation losses Batteries Power systems

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Modulation Classification for OTFS-NOMA in Heterogeneous User Mobility Profile

Modulation Classification for OTFS-NOMA in Heterogeneous Use...

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International Conference on signal processing and Communications, SPCOM

作者： Pawan Maurya Kamal Captain Ashok Parmar Prashant K. Shah Department of Electronics Engineering Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

An Orthogonal Time Frequency Space (OTFS) is particularly proposed for challenging communication scenarios where the users are moving at high speeds. Traditionally, the bandwidth resources are entirely utilized by the high mobility users. To overcome this issue, a new non-orthogonal multiple access (NOMA) protocol that utilizes OTFS called OTFS-NOMA is proposed for users with heterogeneous mobility profiles. In OTFS-NOMA, the signal for high and low mobility users are combined in power domain that utilize the same spectrum. In order to effectively utilize the available bandwidth, an adaptive modulation is used by the transmitter based on their channel conditions. At the low mobility user, the signal of high mobility user needs to be demodulated in order to perform successive interference cancellation (SIC). We generate the dataset for downlink OTFS-NOMA system considering four possible modulations for high and low mobility users and two power ratios for signal to Noise Ratio (SNR) ranging from −10 dB to 20 dB. In this paper, we extract multiple features from the data and explore multiple machine learning based algorithms for AMC in OTFS-NOMA system. The classification performance of different machine learning models are evaluated for different scenarios. Among the various ML models, the Random Forest classifier performs the best and achieves more than 90% classification accuracy at 8 dB or higher SNRs.

关键词： NOMA Machine learning algorithms Interference cancellation Transmitters Modulation signal processing algorithms Bandwidth

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Infrared small target detection based on long-short term frame difference algorithm(LSTFD)

Infrared small target detection based on long-short term fra...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Bing Zhu Jiahui Shi Ruixing Yu Weixin Gao Siyu Wang School of Electronic Engineering Xi'an Shiyou University Xi'an China School of Astronautics Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time domain information is proposed, called the Long-Short Term Frame Difference (LSTFD) algorithm. The algorithm first adopts the SURF (Speeded Up Robust Feature) alignment strategy to eliminate the relative motion of background pixels caused by camera shake and achieve global motion compensation. Then, the image sequence is constructed based on the current frame, and the computational results of long-interval difference and short-interval difference are combined to detect the infrared small targets. The experimental results show that the LSTFD algorithm can accurately detect moving targets with high detection accuracy and low false alarm rate under the condition of low signal-to-noise ratio. And the average detection rate can reach over 90% on five sets of experimental data.

关键词： Accuracy signal processing algorithms Object detection Cameras Motion compensation Trajectory Image sequences Motion measurement Time-domain analysis signal to noise ratio

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Enhancing CNN Classification with Lamarckian Memetic algorithms and Local Search

Enhancing CNN Classification with Lamarckian Memetic Algorit...

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signal processing and Advance Research in Computing (SPARC), International Conference on

作者： Akhilbaran Ghosh Rama Sai Adithya Kalidindi Tata Consultancy Services Limited Mumbai India Shiv Nadar University Dadri Uttar Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Optimization is critical for achieving optimal performance in deep neural networks (DNNs). Traditional gradient-based methods, such as stochastic gradient descent (SGD) and ADAM, often face challenges like local minima entrapment and slow convergence rates. This paper explores population-based metaheuristic optimization algorithms for image classification networks. We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms and local search capabilities to enhance solution quality and convergence speed. In the first stage, a global search is conducted using a population-based algorithm to explore the solution space extensively, allowing the network to escape local minima and find promising regions. In the second stage, a local search fine-tunes the solutions from the global search, ensuring improved accuracy and stability. Our experiments show that the proposed method outperforms state-of-the-art gradient-based techniques like ADAM in accuracy and computational efficiency, particularly in scenarios with high computational complexity and numerous trainable parameters. The results suggest that our approach offers a robust and efficient alternative for weight optimization in convolutional neural networks (CNNs). Future work will explore integrating adaptive mechanisms for parameter tuning and extending the method to other types of neural networks and real-time applications.

关键词： Training Accuracy signal processing algorithms Feature extraction Classification algorithms Convolutional neural networks Optimization Genetic algorithms Convergence Image classification

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SecretPixel: Advanced Steganography with Seeded Random Embedding and AES-RSA Encryption

SecretPixel: Advanced Steganography with Seeded Random Embed...

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Aditya Barnwal Aryman Sah Rajkumar S Naman Umesh Khera Rohan Srivastav Achyuthan Sivasankar SCOPE Vellore Institure of Technology Vellore India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

This paper presents SecretPixel, a steganographic tool that integrates random embedding techniques and advanced encryption to securely hide data within images. SecretPixel ensures confidentiality and integrity of the embedded data using AES-2S6 and RSA-4096 encryption algorithms. The Pixel Scattered Embedding (PSE) method hides encrypted information across image pixels, using a seeded random generator, enhancing resistance against steganalysis detection. This combination of two-tiered encryption, efficient compression and embedding strategies makes SecretPixel an effective solution for data concealment in digital communication.

关键词： Resistance Visualization Steganography Fault tolerance Image coding signal processing algorithms signal processing Digital communication Generators Encryption

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Design of Cognitive Radar Orthogonal Subpulse Width against Multiple Jamming

Design of Cognitive Radar Orthogonal Subpulse Width against ...

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European signal processing Conference (EUSIPCO)

作者： Yi Wang Xianxiang Yu Tao Fan Guolong Cui School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper develops a Cognitive Radar Orthogonal Subpulse Width Design Framework (CROSWDF) to counter multiple mainlobe Interrupted Sampling Repeater Jamming (ISRJ). Specifically, this framework sequentially emits three subpulses, labeled 1, 2, and 3, in the time domain, where subpulses 1 and 3 are shielding signals designed to deceive the jammers, and subpulse 2 is the probing signal for target detection. The framework employs a greedy search algorithm with a Convolutional Neural Network (CNN) to acquire the Subpulse Widths (SW) of subpulses 1 and 3 online, pulse by pulse, to prevent subpulse 2 from being sampled. The CNN is utilized to check if subpulse 2 is disturbed and to measure the current SW value. Numerical simulations demonstrate that the proposed framework can effectively optimize the SW of subpulses 1 and 3 in the presence of multiple targets and multiple ISRJ with different jamming types and modulated parameters, achieving reliable detection performance compared to some existing methods.

关键词： Design methodology signal processing algorithms signal processing Cognitive radar Repeaters Numerical simulation Convolutional neural networks Reliability Jamming Time-domain analysis

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