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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

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Replay Attack Detection in Overcurrent Relays using Mathematical Morphology and LSTM Autoencoder

Replay Attack Detection in Overcurrent Relays using Mathemat...

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International Symposium on advanced Networks and Telecommunication Systems (ANTS)

作者： Jagendra Kumar Narang Baidyanath Bag Department of Electrical Engineering National Institute of Technology Raipur India

ISBN: (数字)9781665473408

ISBN: (纸本)9781665473415

Replay attacks on transmission protective relays installed at modern smart grid substations are the stealthiest and most challenging to detect. Since the pre-recorded actual fault events are played back at the sensor’s terminals, mimicking the real-time fault. To launch such attacks, the attackers take advantage of known vulnerabilities of protection algorithms and communication infrastructures in the smart grid. Thus, the existing information technology-based security mechanism is inadequate to counter such attacks. This work proposes a data-driven replay attack detection framework leveraging the power of mathematical morphology and unsupervised deep learning long short-term memory autoencoder. With the help of significantly crafted features of three-phase current signals provided by the mathematical morphology signal processing technique, the long-short-term memory autoencoder detects replay attacks. It coordinates with the relay to prevent the false tripping command to breakers. The result shows that the method achieved a high recall and precision rate. The performance assessment is based on the unsynchronized and partially synchronized replay injections. Alternatively, the analysis of the choice of threshold for optimal performance is also studied.

关键词： Substations Morphology signal processing algorithms Protective relaying signal processing Real-time systems Smart grids

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Research on User Action Recognition Method Based on parallel CNN-BiLSTM neural network 4

Research on User Action Recognition Method Based on parallel...

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4th IEEE advanced Information Management, Communicates, Electronic and Automation Control conference, IMCEC 2021

作者： Wu, Minjie Tang, Zhiling Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Broadband Communication Signal Processing Guilin China

ISBN: (纸本)9781728185347

With the maturity of artificial intelligence algorithms, the recognition of human actions is gradually used to detect human body conditions and other directions. Human activities and the movement of objects change the multipath characteristics of the wireless channel, leading to changes in the channel state information (CSI). Aiming at the problems of poor flexibility and low accuracy of traditional user action recognition methods, a user action recognition method based on parallel convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) is proposed. This method uses discrete wavelet transform to filter out the noise in the CSI action data, then extracts the amplitude characteristics of the CSI data, and finally uses a deep learning network to recognize the action. CNN can learn the spatial feature information of actions, while the BiLSTM network can learn information with complex temporal features. In the experiment, 7 actions of 5 people were collected and evaluated, including falling, picking up, walking, sitting, etc. By extracting the features of these actions and using the Softmax function to classify, the effect of user action recognition is achieved, with an average accuracy rate of 98.7%. The results are compared with a single CNN and a singal BiLSTM network respectively, indicating the parallel CNN-BiLSTM model has high classification accuracy, strong learning ability, and robustness under different users and multiple action categories. © 2021 IEEE.

关键词： Channel state information

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Analysis of ECG signal based on feature fusion and two-fold classification approach 1

Analysis of ECG signal based on feature fusion and two-fold ...

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1st IEEE International conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Sinha, Nabanita Das, Arpita Institute of Radio Physics and Electronics University of Calcutta India

ISBN: (纸本)9781728157900

Automated analysis of electrocardiogram (ECG) signal for home health care system can ensure early stage detection of cardiac arrhythmia. Most of the published works are based on advanced signal processing algorithms, large number of ECG data, complex classifiers for arrhythmia diagnosis. Hence it is difficult to implement these algorithms in portable health monitoring device. This study introduces two-fold feature selection and classification approach for easier and faster arrhythmia identification. The discrimination of different ECG data is employed by fusion of different non-linear, temporal and statistical parameters of ECG signals. The significances of individual feature in each stage of detection process are evaluated and ranked by introducing Euclidean score. Further, the eligible features are selected to construct of an integrated feature called fused feature. These fused features are efficient to accurately classify ECG beats with lesser computational burden. The proposed method is validated using MIT-BIH arrhythmia database. Experimental result of each stage classification shows better performance of the fused feature compare to the individual feature combinations. Furthermore, proposed work produces encouraging result comparing with existing work. © 2021 IEEE

关键词： Electrocardiograms

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Parasitic sorority of speech processing algorithms with an assortment of statistical toolkits 3

Parasitic sorority of speech processing algorithms with an a...

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3rd International conference on Smart and Intelligent Learning for Information Optimization, CONSILIO 2021

作者： Sudhakaran, Prathibha Yadav, Ashwani Kumar Karamchandani, Sunil ASET Amity University Jaipur Rajasthan India DJ Sanghvi College of Engineering Maharashtra India

Speech is a one-dimensional quasi non-stationary time varying signal produced by a sequence of sounds. Speech signals are random in nature. Speech signals are easily corrupted by noise so recognition is an important role in speech processing. Many researches have designed recognition system with challenging parameters. Speech corpus can vary from environment, region, dialects, age, rate at which words are spoken. Pre-processing is the first step which includes framing, de-noisingand filtering. This paper focuses on speech techniques and statistical open source tools such as HTK, Julius, CMUSphinx and Kaldi. The word error rate obtained using all the toolkits on WSJ1 corpus gives us a clear understanding that Kaldi stands out as the most advanced recipes and scripts for speech recognition systems. An Indian English corpus by IITM was implemented in Kaldi yeilds WER of 6.41 and has been compared to other indian and international languages and well known corpuses. © Published under licence by IOP Publishing Ltd.

关键词： Speech processing

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Improved Cassava Plant Disease Classification with Leaf Detection

Improved Cassava Plant Disease Classification with Leaf Dete...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Ming Xuan Chai Yao Deng Fam Quinito Norman Octaviano Chih-Yang Pee Lai-Kuan Wong Mas Ira Syafila Mohd Hilmi Tan John See Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia School of Mathematical and Computer Science Heriot-Watt University (Malaysia) Putrajaya Malaysia

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Cassava (Manihot esculenta Crantz), a crucial food source for millions, has experienced significant crop yield losses due to various plant diseases. To address this issue, extensive research has been conducted on the Cassava 2020 dataset released by the Makerere AI Lab. Images in this dataset are captured in complex real-world environments, adding difficulty to the classification process. While several deep learning models have shown promising results, we believe that reducing interference from overlapping leaves and complex backgrounds could further enhance classification performance. To accomplish this, up to five leaves of interest have been identified and annotated in each image. These annotated leaves are then used to train a detection model: Faster R-CNN with ResNet-101, designed to automatically detect leaves of interest in the images. The identified leaves are separated from the background through a masking process, producing masked images (Mi), where i indicates the maximum number of leaves in the masked images, with i ranging from 1 to 5. The masked images M1 to M5 are then trained on various CNN models; i.e. EfficientNetB1, DenseNet121, ResNet50, and Xception, for Cassava disease classification. The results show that classifiers trained with M3 improve the accuracy by 2.13% to 3.06% compared to models trained with original images. The main contribution of this work is a novel technique capable of identifying leaves of interest from complex backgrounds to produce masked images, which improve the accuracy of various CNN-based classification models, and a novel leaf annotation where up to 5 leaves of interest have been annotated for each image in Cassava 2020 dataset. Annotated images can serve as a valuable resource for advanced cassava disease analysis, detection, and classification.

关键词： Training Plant diseases Accuracy Annotations Interference Information processing Feature extraction Detection algorithms Faces Residual neural networks

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Space Time Adaptive processing for High signal to Clutter Ratio Target's Detection Radar System

Space Time Adaptive Processing for High Signal to Clutter Ra...

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Augmented Intelligence and Sustainable Systems (ICAISS), International conference on

作者： Ananjay Pandey Mahmud Hasan A.A.Bazil Raj Electronics department Radar System Laboratory Defence Institute of Advanced Technology Pune India

ISBN: (纸本)9781665489638

In this paper the various methods for clutter suppression are discussed, like SMI, DPCA and Matrix Manifold and reviewed MATLAB's study based on these algorithms. In the further section of this paper comparison between these algorithms based on simultaneous processing of clutter as well as Jammer can be seen based on MATLAB study. As we know In a radar system, transmitting antenna will transmit the RF signal after transmission of a radio frequency signal, the signal is received by the receiving antenna, which is composed of transmitted signal echo and several other undesired signals also. Among these signals, clutter signals as well as jammer signals may be present. The role of a jammer is to continuously transmit the wide spectrum of radio frequency signals towards radar so that the receiver section of radar gets random noise or false information or, in some cases, it may get saturated also. Hence, the received signal will be a weighted sum of target return echo, clutter and jammer signal. which makes it a 3-D signal. Suppression of clutter in non homogenous media is also a challenge. To separate the desired signal from other unwanted components, Space Time Adaptive processing techniques can be used. Simultaneous application of spatial as well as temporal filtering techniques can give better improvement in slow moving target detection by cancelling the clutter and jammer signal. In STAP, the signal is filtered in both angular as well as the Doppler domain by suppressing the clutter and jammer signals.

关键词： Manifolds Spaceborne radar Transmitting antennas Receiving antennas Radar antennas Doppler radar Doppler effect

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ECG signal Analysis with QRS Complex Detection for Accurate Cardiac Abnormality Diagnosis

ECG Signal Analysis with QRS Complex Detection for Accurate ...

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International conference on ICT in Business Industry & Government (ICTBIG)

作者： Juhi Jain Chandan Kumar Roy Rishab Pastariya Avita Fuskele V. Kishore Kumar Harshit Dubey Dept of CSE-DS Gyan Ganga Institute of Technology & Sciences Jabalpur Dept. of ECE CMR College of Engineering and Technology Hyderabad Telangana India Department of Computer Science & Engineering IES College of Technology Bhopal Madhya Pradesh India Information Technology Department Jabalpur Engineering College Jabalpur Department of ECE Chennai Institute of Technology Chennai GGITS Jabalpur MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

To improve the ECG signal interpretation helping to diagnose the indicated cardiac pathologies, the research concentrates on the formation and analysis of the QRS complex detection methods only. The QRS complex is widely used for diagnosing most cardiac disorders such as arrhythmias and myocardial infarction among others. Proposing a series of QRS complex classifications that enhance the existing solutions in terms of sensitivity and accuracy, the suggested approaches are built using advanced signal processing methodologies combined with logical focalization governed by the fuzzy formalism. This proposed approach showed better results than the benchmark fuzzy rule-based classifiers: classifications based on classical ICA and PCA algorithms in favor of accuracy, sensitivity, specificity, and the F1 score. Essential strengths of this method were higher accuracy due to the improved detection algorithm highlighted, paving way for real-time ECG to monitor and diagnose cardiac disorders. This research has also pointed out the strategies for the advancement of other non-invasive replacements for cardiac monitoring by utilizing wearable or portable ECG devices and constructed a technique for the efficient and accurate QRS complex differentiation in ECG. The future work also includes the fine tuning of the proposed model, expanding the applicability of the model in the hospitals and improving the diagnostic accuracy by implementing more complex machine learning techniques.

关键词： Time-frequency analysis Accuracy Sensitivity Embedded systems Fast Fourier transforms Noise Electrocardiography Real-time systems Hardware Monitoring

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Improvement in Available Methods for Simultaneous and Proportional Control using the Kernel Technique for Unsupervised Myoelectric Intention Estimation of Individual Fingers

Improvement in Available Methods for Simultaneous and Propor...

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IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Jamal, Muhammad Zahak Lee, Dong-Hyun Hyun, Dong Jin Hyundai Motor Co Articulated Robot Res Grp Hyundai Robot Lab Uiwang Res Ctr Uiwang Si 16082 Gyeonggi Do South Korea

ISBN: (纸本)9781728167947

Recent advances in the field of biomedical engineering has prompted modern research to focus on challenges of human machine interface. This paper provides an improvement in unsupervised learning methods already available for estimating myoelectric intention of individual fingers using the kernel technique. The unsupervised methods which have been improved upon for simultaneous and proportional intention estimation are NMF and NMF-HP. These methods are called semi unsupervised algorithms as models are evaluated blindly using only the target finger. The algorithms implemented with kernels are named as kNMF and kNMF-HP. The kernel technique increases the feature matrix for the NMF and NMF-HP models and improves the performance of these algorithms. The algorithms were analyzed in terms of signal to noise ratio using the strength of the signal of the activated finger and the levels of other fingers not activated. Significant improvements were seen through the implementation of the kernel matrix on the parameters analyzed. An in-house eight channel signal instrumentation scheme was used to acquire the EMG signals using dry electrodes. In addition, a comprehensive signal filtering scheme was designed in order to remove the acquired EMG signal of noise. Finally, we used the algorithms to successfully drive a robotic hand.

关键词： Electromyography NMF NMF-HP semi unsupervised kNMF kNMF-HP dry surface EMG EMG signal processing

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In-Band Full-Duplex Antenna Beamforming and Synchronization for Self-Interference Mitigation: Multi-Tile RFSoC Testbed Design and Deployment

In-Band Full-Duplex Antenna Beamforming and Synchronization ...

引用

European conference on Antennas and Propagation, EuCAP

作者： Mustafa Ayebe Rob Maaskant Marianna Ivashina Johan Malmström Sten E. Gunnarson Henrik Holter Simon Westergren Erik Johansson Electrical Engineering Department (E2) Chalmers University of Technology Göteborg Sweden Surveillance SAAB Järfälla Sweden Hardware Research Ericsson AB Stockholm Sweden

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper presents a newly developed in-band full-duplex (IBFD) communication and sensing testbed utilizing digital beamforming on the advanced Xilinx ZCU216 Radio Frequency System-on-Chip (RFSoC) platform, featuring 16 DACs and 16 ADCs with 14-bit resolution divided into four tiles. Key design and deployment challenges include achieving precise phase synchronization across multiple RFSoC tiles (due to independent clocks causing random phase shifts) and mitigating self-interference (SI) caused by strong mutual coupling effects between closely spaced transmitting (Tx) and receiving (Rx) antenna arrays. To address these challenges, we employed a Tx beamforming algorithm for maximum antenna gain and an Rx beamforming method to maximize the signal-to-self-interference ratio (SSIR) at the receiver output. Specifications of two 1 x 5 Vivaldi antenna arrays (3–6 GHz), integrated into our RFSoC-based testbed, and the multi-tile synchronization (MTS) framework were established to meet IBFD performance requirements. The deployment involved implementing the beam-forming algorithms, configuring the Tx and Rx arrays, and ensuring calibration and synchronization across RFSoC tiles. Experimental results demonstrate over 80 dB SI suppression with half-wavelength separation (@6 GHz) between the Tx and Rx arrays, highlighting the testbed's potential for applications in 6G integrated communication and radar sensing.

关键词： Phased arrays Interference cancellation Array signal processing Vivaldi antennas Receiving antennas Full-duplex system Radar antennas Sensors Radar applications Synchronization

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Research and Simulation of Millimeter Wave Radar Localization Method for Indoor Mobile Robots

Research and Simulation of Millimeter Wave Radar Localizatio...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Fuyu Zhu Hua Wang Yi Wang College of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781665479691

With the rapid development of robotics and sensor technology, indoor mobile robots have become important in the field of robotics, so it is especially important to study the positioning methods of mobile robots. In this paper, we first analyze the bottlenecks of indoor mobile robots and the localization problems that need to be solved; then, we design an online low-complexity algorithm for super-resolution localization using one frame of IF signal to address the shortcomings of localization technology, and verify the performance of the algorithm through numerical experiments. The relative motion trajectory of the object is calculated for one frame of data provided. The orthogonal matching pursuit algorithm requires a pseudo-inverse of the matrix, and the dictionary needs to be very large to ensure the reconstruction accuracy, resulting in a high complexity of matrix operations. To reduce the complexity, the most direct way is to reduce the size of the dictionary matrix, which can use the information of the previous frame to estimate the distance and angle range of the possible target in the next frame. Finally, the discrete data points are then linked together in a data association manner. Simulation results show that the proposed method has significant improvement in the probability of target detection as well as localization accuracy.

关键词： Location awareness Simulation Software algorithms Superresolution signal processing algorithms Matching pursuit algorithms Millimeter wave radar

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