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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chen Zhong Lan Tang Ying-Chang Liang School of Electronic Science and Engineering Nanjing University Nanjing China Center for Intelligent Networking and Communication (CINC) University of of Electronic Science and Technology of China Chendu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a passive multiple trajectories tracking system with multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals transmitted by the base station (BS). Firstly, we propose a Bayesian learning method to obtain the coarse estimations of targets and clutter, which are utilized for trajectories tracking. Considering the potential for a target to generate multiple measurements in overlapping areas of sweeping beams and non-Poisson clutter, we extend the trajectory probability hypothesis density (TPHD) filter to multi-detection TPHD (MD-TPHD) filter with non-Poisson clutter and provide its Gaussian mixture implementation. Simulation results show the performance of the algorithm.

关键词： Learning systems Target tracking Trajectory tracking Simulation filtering algorithms MIMO Trajectory Bayes methods Clutter Speech processing

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Radar-Based Tomographic Imaging of Inhomogeneous Structures with Filtered Backprojection Algorithm

Radar-Based Tomographic Imaging of Inhomogeneous Structures ...

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European Conference on Antennas and Propagation, EuCAP

作者： Irwin Barengolts Ali Al-Tayar Manuel Funk Kristof Dausien Dennis Pohle Christian Schulz Ilona Rolfes Jan Barowski *Institute of Microwave Systems Ruhr University Bochum Germany

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this contribution a tomographic imaging system using a broadband frequency modulated continuous wave (FMCW) radar system operating from 126–182 GHz is presented. The system consists of two parabolic mirrors to focus the beam and investigate objects in the area of the focal point. Moreover, the setup is based on the parallel-beam geometry, containing multiple electrical translation stages and a turntable to measure from various positions and angles. The tomographic reconstruction follows a monostatic approach, analyzing reflections from a metallic plate in terms of attenuation and time shift caused by the object itself. A 3D-printed cylinder made of polylactic acid (PLA) with an eccentrically arranged hole was used to investigate the utilization of the reconstruction algorithm on inhomogeneous objects. The image processing is based on the filtered backprojection algorithm, which is mainly used for computed tomography (CT). The results show promising potential and provide the basis for further investigations.

关键词： Translation Computed tomography Radar Radar imaging filtering algorithms Reconstruction algorithms Position measurement Nonhomogeneous media Reflection Testing

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A Sigmoid Filter Proportionate Normalized LMS Algorithm for Sparse Systems

A Sigmoid Filter Proportionate Normalized LMS Algorithm for ...

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International Conference on Signal Processing and Communication (ICSC)

作者： Ansuman Patnaik Rosalin Deepak Kumar Rout Satish Kumar Gannamaneni School of Electronics Engineering KIIT Deemed to be University Bhubaneswar India School of Electronics Science OUTR Bhubaneswar India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Formerly, a least mean square algorithm employing a sigmoid framework (SLMS) was developed to cope with the impulsive noise. However, the SLMS technique was sparse agnostic, and the LMS approach has a gradient noise amplification issue. To address the point, the SLMS algorithm is updated to sigmoid normalized LMS (SNLMS), and to adjust for sparsity, a filter proportionate adaptation principle is applied to the SNLMS method, yielding the SFPNLMS algorithm. Finally, simulation results show that the innovative SFPNLMS technique is more resistant in an impulsive environment and has a rapid convergence for identification of the sparse systems than other current algorithms.

关键词： Resistance Adaptive systems Simulation Noise Signal processing algorithms Interference filtering algorithms System identification Convergence

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The Novel Iterative Two-Stage Algorithm with Neighborhood Correlation for Removal the AAIN (Arbitrary Amplitude Impulse Noise)

The Novel Iterative Two-Stage Algorithm with Neighborhood Co...

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International Electrical Engineering Congress (iEECON)

作者： Vorapoj Patanavijit Darun Kesrarat Kornkamol Thakulsukanant Kanabadee Srisomboon Wilaiporn Lee Assumption University of Thailand Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Within outlier removal experimentation association, in the outlier revelation mechanism, the investigated figuring basis (which is located at the midpoint of the investigated window) and its neighborhood correlation (which is estimated from the investigated figuring basis and its encircling figuring basis) are theoretically elucidated as outlier correlation. One of the ultramodern and most energetic of noise removal algorithms is an original two-stage noise removal algorithm stand on the neighborhood correlation, which was introduced in 2020 for AAIN (Arbitrary Amplitude Impulse Noise). Thereby, this paper proposes the novel iterative two-stage algorithm with neighborhood correlation, which is progressed from the original two-stage noise removal algorithm, for removal the AAIN. In the simulated investigation, copious digital figures, which are compounded of F16 and Pepper under all random noise proportion, are engaged in the novel iterative two-stage algorithm in contemplating of the energetic of precedent state-of-art algorithms: MF, SMF, AMF and original 2-Stage system. From these simulated investigation products, the noise removal algorithm based on novel iterative 2-stage algorithm constitutes the exclusive energetic, which is contemplated with precedent state-of-art algorithms.

关键词： Electrical engineering Correlation Digital images Noise Noise reduction Adaptive filters filtering algorithms Iterative algorithms

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Multi-Frequency Filter Based Denoising Diffusion Model for Knowledge-Aware Recommendation

Multi-Frequency Filter Based Denoising Diffusion Model for K...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Yiran Shi Bangzuo Zhang Dongbing Pu Dept. School of Information Science and Technology Northeast Normal University Changchun China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

Knowledge Graph (KG) introduces fruitful domain information of structure and semantic to solve data sparsity. However, the presence of irrelevant entities as noise results in the inability to predict user true preference. Recently, diffusion models have been broadly applied to remove noise. In addition, filtering algorithms are current effective methods for filtering out noise signals. To fundamentally eliminate noise in KG, we propose a remarkable multi-frequency filter based denoising diffusion model (FDiff) for knowledge-aware recommendation. In our framework, we devise a diffusion with multi-frequency filter to generate a denoised KG. Specifically, the reverse process incorporates a multi-frequency filter, which divides frequencies with different weights to eliminate irrelevant signals. Meanwhile, the top k optimal values are returned to represent the knowledge data. For prediction results, the aggregation strategy consists of knowledge aggregation and collaborative knowledge graph aggregation to obtain user personalized preferences. Furthermore, we utilize a contrastive learning method to optimize the recommendation task. Extensive experiments are performed on three realistic datasets. The results consistently prove that our FDiff surpasses other competitive baselines.

关键词： filtering Noise Noise reduction Semantics Knowledge graphs Contrastive learning filtering algorithms Diffusion models Frequency conversion Recommender systems

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Speech Enhancement Using Compact Microphone Array and Applications in Distant Speech Acquisition

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Chinese Journal of Electronics 2023年第3期18卷 481-486页

作者： Heng Zhang Qiang Fu Yonghong Yan ThinkIT Speech Laboratory Institute of Acoustics Chinese Academy of Sciences Beijing China

In this paper, a novel two-element-microphone-array-based speech enhancement algorithm is proposed. This algorithm is designed to achieve better overall performance with relatively small array size. A frequency domain adaptive null-forming is used, in which adaptive noise cancelation is implemented in auditory subbands. And an OM-LSA based postfiltering stage further purifies the output. The algorithm also features interaction between the array processing and the postfilter to make the filter adaptation more robust. This approach achieves considerable improvement on Signal-to-noise ratio (SNR) and subjective quality of the desired speech. Experiments confirm the effectiveness of the proposed system.

关键词： Frequency-domain analysis Adaptive filters Speech enhancement filtering algorithms Hearing aids Microphone arrays Mobile handsets

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Performance with Feature Selection Based Machine Learning: Combination of Low Variance Filter Pearson Correlation for Bots and Brute Force

Performance with Feature Selection Based Machine Learning: C...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Andri Saputra Kalamullah Ramli Anto Satriyo Nugroho Department of Electrical Engineering Universitas Indonesia Depok Indonesia Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

This research examines the efficacy of selection feature selection based machine-learning by combining low variance filter, Pearson correlation low variance filter algorithms to detect Bot and Brute Force attacks. The research uses CSE-CIC-IDS2018 dataset and employs a systematic methodology that includes normalization, data cleaning, and equal-size sampling. Through the implementation of low variance filtering with a 0.016 threshold followed by Pearson correlation analysis, the research achieved significant feature reduction while maintaining high classification accuracy. The experimental results demonstrate that for Bot detection, reducing features from 80 to 21 yielded 100% accuracy on threshold 0.1 and threshold 0.2. In Brute Force-Web attack detection, utilizing 57 selected features achieved 99.3% accuracy on threshold 0.4, while SSH-Brute Force detection maintained overall 100% accuracy with only 15 features. Performance evaluation using Decision Tree classification revealed consistently high precision and recall values across all attack types. The findings confirm that the proposed feature selection methodology effectively optimizes intrusion detection systems by identifying the most relevant features while reducing computational complexity without compromising detection capabilities. Future work is recommended to experiment develop real-time predictions for combine ML models with deep learning in the future.

关键词： Accuracy Correlation Force Diversity reception Intrusion detection Predictive models filtering algorithms Feature extraction Chatbots Real-time systems

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Novel Method for FIR Notch Filter Design Using Firefly Algorithm

Novel Method for FIR Notch Filter Design Using Firefly Algor...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Haijiang Hu Yanchun Wang Cheng Ma Qiang Wang College of Electronic and Electrical Engineering Bengbu University Bengbu China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Notch filter is an important type of filter which is widely used in communication technology and other fields because it effectively filters out the interference signals with the specific extremely narrow frequency bands, and efficiently transmits signals outside these frequency bands. A novel optimization method for FIR notch filter design using Firefly Algorithm is proposed. This method uses the minimum magnitude error in stopband as the optimization object, uses the magnitude error in passband as the constraint conditions, and realizes the FIR notch filter design by Firefly Algorithm. The result of design has both small order and good performance. Therefore this method can be employed in the FIR notch filter design efficiently.

关键词： Finite impulse response filters Design methodology Signal processing algorithms Optimization methods Interference filtering algorithms Signal processing Power electronics Passband Notch filters

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Binary Learning Cooking Algorithm with Relief for Feature Selection of High-dimensional Gene Expression Data

Binary Learning Cooking Algorithm with Relief for Feature Se...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Bibhuprasad Sahu Swagatika Dalai Amrutanshu Panigrahi Abhilash Pati Chetan Aher JVR Ravindra Dept. of Information Technology Vardhaman College of Engineering Hyderabad India Department CSE KIIT Polytechnic Bhubaneswar Odisha India Department CSE Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Department CSE AISSMS Institute of Information Technology Pune Maharashtra India Dept. of ECE Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Feature selection (FS) has a significant impact on machine learning. The objective is to find the most important features while maintaining high classification accuracy by identifying the possible fewest features from the high-dimensional dataset. Researchers prefer to use the filter-wrapper model by considering different metaheuristic methods for accurate feature selection. But in some cases, the proposed algorithms become less productive and achieve high time complexity. This paper presents a hybrid model of Relief with Binary Learning Cooking Algorithm (BLCA) to select optimal features. The algorithm builds on the Learning cooking algorithm (BLCA), a new optimization method inspired by the learning process "How to learn cooking". We tested how well this method responds to FS challenges in high-dimensional datasets using six cancer datasets, many with different ranges of features. We compared different existing FS algorithms with R-BLCA using accuracy, precision, sensitivity, specificity, and F-Score. The results show that the proposed algorithm performs better than other FS methods with an accuracy of 99. 91% for the SRBCT dataset.

关键词： Support vector machines Accuracy Sensitivity Optimization methods filtering algorithms Feature extraction Classification algorithms Object recognition Time complexity Cancer

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Resampling from the Niching Genetic Algorithm Applicated in Extended Kalman Particle Filter*

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Chinese Journal of Electronics 2023年第3期20卷 553-559页

作者： Honglei QIN Ziyu LI Li CONG School of Electronic and Information Engineering Beihang University Beijing China Beijing Aerospace Control Center Beijing China

Two serious problems existing in Particle filter (PF) are the degeneracy phenomenon and the sample impoverishment caused by simple random resampling. In this paper, based on the Extended Kalman particle filter (EKPF) which selects the importance distribution of PF by the Extended Kalman filter (EKF), we propose a new resampling method from niching genetic algorithm to inhibit the degeneracy phenomenon and avoid the sample impoverishment problem, and name the improved particle filtering algorithm as Niching genetic algorithm Extended Kalman particle filter (NGA-EKPF). According to the theoretical analysis and computer simulation of three algorithms in the Global positioning system (GPS), i.e. EKF, EKPF with simple random resampling and NGA-EKPF, the performance of the proposed algorithm has improvement compared with other algorithms not only in positioning accuracy, but also by Cramér-Rao low bound (CRLB) which provides a theoretical bound on the filtering performance.

关键词： filtering Computer simulation filtering algorithms Particle filters Systems simulation Kalman filters Genetic algorithms

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