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检索条件"主题词=Signal Processing Algorithms"

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Designing The Energy Efficient And Memory-Conserving Cryptographic Algorithm For Iot Environment

Designing The Energy Efficient And Memory-Conserving Cryptog...

引用

Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： S. Prabagaran Savinder Kaur X. Mercilin Raajini Neelesh Singh Sunitha B K N. N. Wasatkar Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

In secure, resource-constrained environments there are new challenges viewed due to the impact of Internet of Things (IoT) on data-oriented applications. Owing to physical constraints such as capacity, capacity and calculating capability, classical cryptography algorithms are often worthless. To resolve the IoT applications’ security issues, resource constraints, and low energy capabilities, this research proposes a novel, extremely lightweight block cipher. The technique enhances security whilst minimising computational requirements when generating the keys. As a result of repeated encoding with the varying degrees of keys, this it is versatile to a number of IoT applications. Lower round and operation demand for granting and revoking the encrypted messages make the algorithm more energy-friendly, it is ideal for application in environments that have minimal power supply. It is important in the current lightweight cryptographic encryption standards; the proposed cryptographic algorithm enhances the current one that tremendously improves memory optimization and energy conservation when tested. Two of the main directions for the future work are the decrease of latency in secure communication and the improvement of the algorithm usage in the multi-hop networks.

关键词： Ciphers Technological innovation Memory management signal processing algorithms Encoding Energy efficiency Encryption Decoding Internet of Things Standards

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Crop Recommendation And Disease Prediction Using IOT And AI

Crop Recommendation And Disease Prediction Using IOT And AI

引用

International Conference on Communications and signal processing

作者： K Sathya Priya J Ancy Jenifer Snowlin Preethi Janani M Shilpa Aarthi T Kavitha CSE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Although farmers are the backbone of this emerging country, they face challenges in their daily lives related to finances and finding a place to work. These farmers search far and wide for the ideal location, climate, crop, and so on. By anticipating possible illnesses through image processing and suggesting appropriate crops based on real-time sensor data, you may use IOT and AI technology to improve agricultural operations. Used Sensors: Use sensors for pH, temperature, and soil moisture to gather important information from the agricultural field. Data Transmission: Create a communication network so that sensor-generated data may be sent to an AI model that has already been trained for analysis. Training AI Models: In order to precisely estimate the ideal crop options based on temperature, sand moisture, pH levels, give priority to training the AI model with pertinent data. Crop Recommendation: Create algorithms in the AI model that take into account the sensor data that has been collected in order to suggest the best crops yield in a certain location. An innovative answer to the growing need for effective and sustainable farming methods is the fusion of Internet of Things (IOT) with artificial intelligence (AI) technology. Thus the result of the project is to find a resolution for cost loss issue. In order to improve agricultural operations, this research uses real-time sensor data to select appropriate crops and forecast probable illnesses.

关键词： Temperature sensors Training Crops signal processing algorithms Data models Real-time systems Internet of Things

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Research and Development of Wireless Sensor Network Detection Software

Research and Development of Wireless Sensor Network Detectio...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Yue Zhou Siming Huang Hong Wang Xiaotong Zhou Shenyang Ligong University Shenyang China New South Wales University Sydney Australia

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

This paper investigates the technology of sensor network node parameter monitoring software and designs a complete monitoring software solution in conjunction with a specific application scenario of greenhouse monitoring. The communication data verification algorithm is thoroughly studied in this study, and the discussion and research on the nonlinear calibration algorithm of sensor parameters in data processing are conducted. The CRC cyclic redundancy algorithm and the least squares method are employed in the subsequent software design for relevant data *** real-time monitoring system is based on sensor network technology, integrating measurement and control to achieve remote monitoring. The system formats the data and uploads it to the host computer via COM serial communication according to the set transmission mode and communication protocol. Upon receiving a series of data packets, the system analyzes the data, dynamically displays it in real-time, issues normal or alarm messages, and notifies the system through different means. The software of this system demonstrates good operational performance, meeting the basic requirements of the application scenario. It possesses excellent monitoring real-time capability, openness, and compatibility, providing comprehensive information and decision support for sensor network node parameter monitoring.

关键词： Wireless sensor networks Software design Software algorithms signal processing algorithms Termination of employment Software Real-time systems Calibration Software measurement Monitoring

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Identification of Quasi-Periodically Varying Systems Using the Local Basis Function Approach

Identification of Quasi-Periodically Varying Systems Using t...

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European signal processing Conference (EUSIPCO)

作者： Artur Gańcza Maciej Niedźwiecki Department of Signals and Systems Gdansk University of Technology Gdansk Poland

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In this paper we propose a solution to the problem of tracking quasi-periodically varying systems based on the local basis function (LBF) approach. Within this framework, parameter trajectories are locally approximated using linear combinations of specific functions of time known as basis functions. We derive both bias and variance characteristics of LBF estimators. Additionally, we demonstrate that the computational burden associated with LBF estimation algorithms can be significantly reduced, without sacrificing high estimation accuracy, by employing the computationally fast, approximate version of the LBF scheme.

关键词： Accuracy signal processing algorithms Estimation Europe signal processing Benchmark testing Approximation algorithms Trajectory Computational efficiency

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SRAM: Spectral Residual Attention Module for Enhancing Shallow Features

SRAM: Spectral Residual Attention Module for Enhancing Shall...

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6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Chenhu Xiao Yanzi Li Yongsheng Liang Qingyu Mao College of Electronics and Information Engineering Shenzhen University Shenzhen China College of Big Data And Internet Shenzhen Technology University Shenzhen China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Convolutional neural networks (CNNs) have achieved significant success in the field of computer vision by effectively identifying complex features within images. However, the shallow layers of CNNs are particularly susceptible to the influence of background noise, thereby compromising the quality of convolutional features. Integrating attention mechanisms into models can effectively enhance the recognition of crucial convolutional features. However, enhancing the entire feature map may inadvertently diminish the significance of certain features, potentially compromising the model's ability to accurately extract key information. To address this issue, we propose the Spectral Residual Attention Module (SRAM), which consists of the feature separation reconstruction unit (FSRU) and the salient feature detection unit (SFDU). The former separates features in the channel dimension and highlights foreground features, while the latter enhances salient features of important channels in the spatial dimension. Unlike attention mechanisms that rely on spatial domain for local assessment, SRAM achieves more effective global feature enhancement by identifying salient features in the frequency domain. In addition, SRAM incorporates traditional saliency detection algorithms, characterized by low computational costs and fewer parameters. The experimental results demonstrate that SRAM can effectively enhance shallow features, leading to superior performance in visual saliency visualization, computational complexity, and image classification compared to SIMAM.

关键词： Visualization Attention mechanisms Random access memory signal processing algorithms signal processing Feature extraction Classification algorithms Convolutional neural networks Saliency detection Residual neural networks

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Heart Disease Detection Using Different Machine Learning algorithms

Heart Disease Detection Using Different Machine Learning Alg...

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International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Baikani Akhila Jaitaram Thrisha Reddy Yarram Sai Ruthik Shanmugasundaram Hariharan Vinay Kukreja Murugaperumal Krishnamoorthy Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Centre for Research Impact & Outcome Chitkara University Rajpura Punjab India Electrical and Electronics Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Now a days, Heart Disease had became an significant issue world wide. Initial prediction of heart disease can be done using Machine learning(ML). The Primary objective of writing this paper is to develop a quick and easy method for predicting the heart disease using machine learning algorithms. ML can be used to improve the prognosis of heart *** article examines the results of different ML algorithms using different performance evaluation *** this paper, we used six(6) machine learning algorithms i.e, Support Vector Machine (SVM), Decision tree, Naive Bayes, Logistic Regression, Random forest and also K-Nearest Neighbors (KNN). In comparison to other ML algorithms, the Random forest classifier approach attained a heavy accuracy of 95.00%, according to the data obtained from the classification *** contain the effective techniques for making decisions. The medical industry makes extensive use of ML techniques. This study offers many machine learning methods for heart disease detection. The dataset used in this is taken from the kaggle. We developed an approach for heart disease prediction using actual patient data. This work uses different classification algorithms. This mixed technique is implemented using Google Colab.

关键词： Heart Support vector machines Machine learning algorithms Accuracy signal processing algorithms Nearest neighbor methods Prediction algorithms Classification algorithms Random forests Diseases

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Robust Framework for the detection of Alzheimer’s Disease via analysis of Spontaneous Speech signals

Robust Framework for the detection of Alzheimer’s Disease v...

引用

signal processing algorithms, Architectures, Arrangements and Applications (SPA)

作者： Adnan Elahi Danilo Comminiello Sara Marceglia Department of Engineering and Architecture University of Trieste Trieste Italy Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Italy

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

Alzheimer’s disease (AD) is a neurodegenerative disorder-characterized by the decline in cognitive functions of the brain. AD poses a life-risk and is one of the most common causes of death in the elderly population. Speech patterns are densely related to the state of brain providing non-invasive ways to track AD. In the context of AD, the existing literature lacks the (1) time series analysis of spontaneous speech, (2) availability of a framework to estimate speech consistency, and (3) availability and applicability of time series features. The target of this research is to fill these gaps. The training data from the ADReSS dataset composed of spontaneous speech signals from AD and non-AD subjects is used as a reference for this study [1]. The speech signals are processed as time series signals, and some analysis methods are introduced. To identify consistency in time series, notions of standing and averaged time series are proposed. Several new features such as- sleep frequency $f_{s}$, perception index ${\mathcal{P}}_{i}$, variant mean $\mu_{{\mathcal{S}}}$, minimum or maximum average Discrete Fourier Transform (DFT) coefficient contributor ${\mathcal{M}}_{\mathbf{m}}^{\mu}, {\mathcal{M}}_{\mathbf{M}}^{\mu}$, and normalized frequency $f_{n}$ are proposed, and are found helpful to achieve multi-modal representations. The concept of epochs is applied to extend single speech to multiple-speech(es) in reference to the heuristics of short time stationarity or meta-stability of time ordered data, and thus, improving the robustness, accuracy and precision scores. The best performing (or golden reference) model has achieved a global precision and accuracy of 100% for {Non-AD, AD} classes, respectively.

关键词： Accuracy Target tracking Time series analysis Discrete Fourier transforms signal processing algorithms Resonant frequency Training data Robustness Indexes Alzheimer's disease

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Multivariate Multiscale Sample Entropy for Multi-Channel Underwater Acoustic signal Analysis

Multivariate Multiscale Sample Entropy for Multi-Channel Und...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Jing Zhou Yaan Li Mingzhou Wang Lei Shi School of Marine Science and Technology Northwestern Polytechnical University Xi'an China National Key Laboratory of Underwater Information and Control Xi'an Precision Machinery Research Institute Xi'an China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Multi-channel time series collected by arrays are prevalent in underwater acoustic signal processing and are crucial for uncovering the intrinsic characteristics of the observed system. This paper introduces the multivariate multiscale sample entropy (MMSE) to assess the complexity information in multi-channel underwater acoustic signals. Simulation analysis is employed to validate the effectiveness of the MMSE algorithm in extracting both within-channel and cross-channel information from multi-channel signals. Various types of simulated ship-radiated noise are generated to illustrate the MMSE algorithm's capability to differentiate between signals. Additionally, we employ MMSE to analyze various real-world signals, including ship-radiated noise, ambient noise, and interface reverberation. The MMSE results demonstrate its proficiency in differentiating various underwater acoustic signals compared to traditional frequency domain methods.

关键词： Heuristic algorithms Delay effects Noise signal processing algorithms Feature extraction Entropy Classification algorithms Reverberation Data mining Underwater acoustics

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Study of a Real-Time Filtering Algorithm Suitable for LQR Control

Study of a Real-Time Filtering Algorithm Suitable for LQR Co...

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Artificial Intelligence and Automation Control (AIAC), International Conference on

作者： Xinpeng Yang Linghui Zhang Digital Manufacturing Laboratory Beijing Institute of Technology Beijing China China North Vehicle Research Institute Beijing China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Linear quadratic regulator (LQR) is crucial in modern optimal control theory. However, its application to nonlinear unstable systems suffers from poor robustness and instability. To address this, this paper proposes a self-optimizing filtering algorithm from a signal processing perspective. The algorithm samples signal values via recursive queue, calculates averages, and limits them. Iterations find the optimal limit interval, and the average of interval values is the filtering result. Experimental verification on a first-order linear inverted pendulum shows this method outperforms others in accuracy, filtering quality, and stability, while meeting real-time control requirements. This improves LQR's robustness and control accuracy.

关键词： Accuracy Regulators Filtering Noise signal processing algorithms Process control Optimal control Filtering algorithms Real-time systems Robustness

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Real-Time Monitoring of Three-Phase Unbalanced Devices and Real-Time Adjustment Analysis of Action Characteristics

Real-Time Monitoring of Three-Phase Unbalanced Devices and R...

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IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Yan Ming Gu Haifeng Gu Xiaojun Gao Jinhua Zhang Shuguang Liang Ji Yu Pengjie Rugao Power Supply Branch of State Grid Jiangsu Electric Power Co. Ltd. Nantong China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

This paper designs and implements a three-phase unbalanced monitoring and adjustment system based on advanced algorithms and model simulation. The system uses a variety of sensors and signal processing algorithms to achieve high-precision real-time monitoring of three-phase unbalanced parameters. By establishing a dynamic adjustment algorithm for action characteristics based on machine learning, the system can intelligently adjust the action characteristics of the three-phase unbalanced device according to real-time monitoring data to maintain the stability and efficiency of the system. The simulation platform uses MATLAB Simulink to establish a system model, and verifies the effectiveness and real-time response capability of the algorithm through multiple sets of simulation experiments. The experimental results show that the proposed system shows good adaptability under different loads and power grid environments, can effectively reduce the three-phase imbalance, and improve the power supply quality of the system. The real-time adjustment algorithm performs well in stability and response speed, and realizes the dynamic optimization of the action characteristics of the three-phase unbalanced device. This study provides innovative ideas and technical support for solving the three-phase imbalance problem in the power system.

关键词： Machine learning algorithms Heuristic algorithms Power system dynamics signal processing algorithms Power system stability Sensor phenomena and characterization Real-time systems Stability analysis Mathematical models Monitoring

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