Article
Computer Science, Information Systems
Akash Kumar Mandal, Swades De
Summary: This letter proposes a novel statistical hybrid neural network (S-HNN) for estimating wireless communication channels contaminated by impulse noise. The S-HNN utilizes a convolutional neural network (CNN) to capture spatial fading characteristics and a long short-term memory (LSTM) network to extract temporal information. By employing finite lag samples and recycling the CNN-LSTM network, the proposed S-HNN framework outperforms existing channel estimation techniques in terms of reduced training length and time savings.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Yiting Zhao, Youming Li, Shoudong Shi, Jianding Yu
Summary: In this paper, a joint channel and impulse noise estimation method based on all subcarriers is proposed. It utilizes sparse Bayesian learning algorithm and forward-backward Kalman filter to tackle the performance degradation caused by impulse noise in OFDM systems. The method estimates unknown vectors using a SBL framework and applies a forward-backward joint system for data detection simultaneously.
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
Cong Lin, Youqiang Ye, Siling Feng, Mengxing Huang
Summary: This paper proposes an effective method of random-value impulse noise level estimation based on local similarity. The method quantifies the similarity between pixels by using Euclidean distance and gray difference to determine if a pixel is clean or contains impulse noise. The noise level of the entire image is estimated by detecting noise pixels in multiple flat regions and processing their levels with average operation. Experimental results show that the method is effective in scenarios with various noise levels, and it has higher robustness and accuracy compared to other estimation algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Xinqi Huang, Yingsong Li, Yuriy V. Zakharov, Yibing Li, Badong Chen
Summary: An adaptive estimation algorithm based on the Lorentzian norm, called the Affine-Projection Lorentzian (APL) algorithm, is proposed for echo cancellation in vehicle hands-free communication systems and video teleconferencing systems. The APL algorithm achieves robustness against impulsive disturbances and speeds up convergence for colored input signals through dynamic control parameters. Additionally, it outperforms other algorithms in various impulsive interference environments for channel estimation and in-car echo cancellation scenarios.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Automation & Control Systems
Viresh S. Patel, Saikat Chakrabarti, Ankush Sharma
Summary: This article proposes a method to process power system data, aiming to improve the quality of data for state estimation. It addresses issues such as noise, bias, and outliers in the data, and uses a weighted deep neural network to reduce noise and bias in the data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Grzegorz Dziwoki, Marcin Kucharczyk
Summary: The study introduces a new block-pilot-assisted channel reconstruction procedure based on the DFT-based approach and the Least Square impulse response estimation. It improves channel reconstruction accuracy, reduces computational complexity, and considers the compressibility feature of the channel impulse response.
Article
Computer Science, Information Systems
Asif Iqbal, Micheal Drieberg, Varun Jeoti, Azrina Abd Aziz, Goran M. Stojanovic, Mitar Simic, Nazabat Hussain
Summary: This work proposes a novel Iterative Multiband Spectrally Constrained Time-Domain technique to reduce the residual error of correlation caused by spectrally constrained waveforms. The performance of the technique is evaluated through numerical experiments and compared with known channel state information and conventional techniques. The results show that the proposed technique outperforms conventional techniques in low signal-to-noise ratio scenarios and requires fewer pilot signals.
Article
Mathematics, Interdisciplinary Applications
Xuelian Liu, Xuemei Li, Bo Xiao, Chunyang Wang, Bo Ma
Summary: This study proposes a method using the fastest tracking differentiator and fractional Fourier transform to address the problem of impulse noise in LFM signal parameter estimation. Experimental results demonstrate the good filtering performance and accurate parameter estimation ability of the proposed method.
FRACTAL AND FRACTIONAL
(2023)
Review
Engineering, Electrical & Electronic
Sowjanya Modalavalasa, Upendra Kumar Sahoo, Ajit Kumar Sahoo, Satyakam Baraha
Summary: Distributed estimation strategies over wireless sensor networks are widely used in various fields, but the presence of impulsive noise in practical scenarios requires the use of robust cost functions derived from robust statistics theory.
Article
Engineering, Mechanical
Yu-Ping Tian, Wenbo Zhu
Summary: This paper studies the evolution of detection error probability in target detection based on wireless sensor networks. The analysis reveals that the total error probability does not tend to zero as the network size grows to infinity, as long as the channel bit error probability is not zero. Furthermore, when the channel bit error probability is greater than 2-root 3/2, the total error probability will continue to increase with the increase in the network size.
NONLINEAR DYNAMICS
(2022)
Article
Computer Science, Information Systems
Zhengyang Hu, Jiang Xue, Feng Li, Qian Zhao, Deyu Meng, Zongben Xu
Summary: This paper introduces a novel self-adaptive channel estimation algorithm based on the maximum entropy principle, which can provide accurate estimation in real wireless communication situations and improve efficiency by reducing pilot consumption.
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Article
Audiology & Speech-Language Pathology
Meibian Zhang, Xiangjing Gao, William J. Murphy, Chucri A. Kardous, Xin Sun, Weijiang Hu, Wei Gong, Jingsong Li, Wei Qiu
Summary: The objective of this study was to find an optimal kurtosis-adjusted algorithm to evaluate hearing loss caused by both continuous noise and complex noise. The study used noise recordings and audiometry data collected from 2601 Chinese workers exposed to various industrial noises. The results showed that a kurtosis-adjusted L-Aeq,L-8h formula with an adjustment coefficient of 6.5 allowed for a more accurate prediction of high-frequency NIHL. Relying on a single value as a recommended exposure limit appeared to be insufficient to protect the hearing of workers exposed to complex noise.
Article
Engineering, Electrical & Electronic
Daxing Xu, Xinhao Yan, Bo Chen, Li Yu
Summary: This paper investigates the energy-constrained confidentiality fusion estimation problem in the presence of eavesdroppers. To prevent eavesdropping, an injection method based on artificial noise is proposed, along with stochastic sensor data triggers to reduce communication rate. By deriving sufficient conditions and selecting appropriate trigger thresholds, the effectiveness of noise insertion strategy and mitigation of eavesdroppers' estimation errors while maintaining user's expected error boundedness are demonstrated.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Automation & Control Systems
Dusan Jakovetic, Manojlo Vukovic, Dragana Bajovic, Anit Kumar Sahu, Soummya Kar
Summary: We propose a distributed recursive estimation method for an unknown vector parameter in the presence of impulsive communication noise. We introduce a nonlinearity in the consensus update to combat the effect of heavy-tailed or outlier-contaminated communication noise. We prove almost sure convergence, asymptotic normality, and evaluate the asymptotic covariance for the general nonlinear estimator and a class of additive communication noises.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2023)
Article
Engineering, Electrical & Electronic
Fawad Ud Din, Fabrice Labeau
Summary: This paper proposes a novel secure channel estimation technique using artificial noise and full-duplex transmissions to prevent leakage of channel estimates to malicious users. Through three stages, the legitimate nodes are able to transmit secure signals that cannot be decoded by eavesdroppers, ensuring robust communication.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Ankit Dubey, Ranjan K. Mallik
IEEE TRANSACTIONS ON COMMUNICATIONS
(2015)
Article
Engineering, Electrical & Electronic
Ankit Dubey, Ranjan K. Mallik, Robert Schober
IET COMMUNICATIONS
(2015)
Article
Engineering, Electrical & Electronic
Ankit Dubey, Ranjan K. Mallik
IET COMMUNICATIONS
(2016)
Article
Engineering, Electrical & Electronic
Ankit Dubey, Ranjan K. Mallik, Robert Schober
IET COMMUNICATIONS
(2014)
Article
Computer Science, Information Systems
Ankit Dubey, Chinmoy Kundu, Telex M. N. Ngatched, Octavia A. Dobre, Ranjan K. Mallik
IEEE SYSTEMS JOURNAL
(2019)
Article
Engineering, Electrical & Electronic
Yeduri Sreenivasa Reddy, Meenakshi Panda, Ankit Dubey, Abhinav Kumar, Trilochan Panigrahi, Khaled M. Rabie
IET COMMUNICATIONS
(2020)
Proceedings Paper
Telecommunications
K. P. Sumesh, Ankit Dubey, Trilochan Panigrahi
2019 25TH NATIONAL CONFERENCE ON COMMUNICATIONS (NCC)
(2019)
Article
Computer Science, Information Systems
Dushyant Sharma, Ankit Dubey, Sukumar Mishra, Ranjan K. Mallik
Proceedings Paper
Computer Science, Theory & Methods
Asha Anil, K. Manjunath, Trilochan Panigrahi, Ankit Dubey
2018 CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (CICT'18)
(2018)
Proceedings Paper
Computer Science, Hardware & Architecture
Rajprakash Bale, T. Sai Seetharamaiah, Y. Sreenivasa Reddy, Ankit Dubey, Trilochan Panigrahi
2018 10TH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS & NETWORKS (COMSNETS)
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Ankit Dubey, Chinmoy Kundu, Telex M. N. Ngatched, Octavia A. Dobre, Ranjan K. Mallik
2017 IEEE 86TH VEHICULAR TECHNOLOGY CONFERENCE (VTC-FALL)
(2017)
Proceedings Paper
Energy & Fuels
Dushyant Sharma, Ranjan K. Mallik, Sukumar Mishra, Ankit Dubey, Vishruti Ranjan
2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM)
(2016)
Proceedings Paper
Energy & Fuels
Ankit Dubey, Dushyant Sharma, Ranjan K. Mallik, Sukumar Mishra
2015 IEEE POWER & ENERGY SOCIETY GENERAL MEETING
(2015)
Proceedings Paper
Engineering, Electrical & Electronic
Ankit Dubey, Ranjan K. Mallik, Robert Schober
2012 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
(2012)
Article
Engineering, Electrical & Electronic
Zou Deyue, Gao Siyu, Li Xinyue, Zhao Wanlong
Summary: In this paper, an integrated navigation/communication signal is proposed by combining Cyclic Code Shift Keying (CCSK) as a communication signal with traditional Global Navigation Satellite System (GNSS) signal. A fuzzy judging algorithm and segmenting correlation domain are used to improve the transmission robustness of the integrated signal. Additionally, a strategy is proposed to solve the incompatibility problem between the integrated signal and binary communication system using interleaving coding and dual-thresholds optimization. The proposed algorithm significantly improves the fault-tolerant rate and reduces the Bit Error Rate (BER), enhancing the reliability of the communication system.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Guoxing Huang, Yunfei Xiang, Shibiao Deng, Yu Zhang, Jingwen Wang
Summary: This paper proposes a dual-channel cooperative sub-Nyquist sampling system for LFM-BPSK hybrid modulated signal. The system can solve the frequency ambiguity problem caused by low sampling rate and estimate the phases of binary symbols and the positions of discontinuities through self-mixing technology.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
A. Yousefian Darani, Y. Khedmati Yengejeh, G. Navarro, H. Pakmanesh, J. Sharafi
Summary: The development and use of network technology have brought society into the internet world, where communication through digital data takes place. However, the protection of digital resources has become a significant challenge, which steganography is seen as a possible solution for. This paper presents an image steganography algorithm that hides a grayscale secret image within an RGB color cover image, with a genetic algorithm used to optimize the selection of suitable hiding places. The proposed method is empirically demonstrated to be effective in terms of transparency, security, and resistance.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Xiao Tan, Zhiwei Yang, Xianghai Li, Yongfei Mao, Di Jiang
Summary: A novel gridless SR-STAP algorithm applicable to a non-ULA with AAPEs is proposed in this study. By establishing the ANM-STAP model and iteratively updating the clutter subspace and AAPEs, the performance of STAP is improved.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Irfan Manisali, Okyanus Oral, Figen S. Oktem
Summary: In this paper, a novel non-iterative deep learning-based reconstruction method for real-time near-field MIMO imaging is proposed. The method achieves high image quality with low computational cost at compressive settings through two stages of processing. A large-scale dataset is also developed for training the neural networks. The effectiveness of the method is demonstrated through experimental data and extensive simulations.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Haibin Li, Dengchao Wu, Wenming Zhang, Cunjun Xiao
Summary: Workplace safety accidents are a pervasive issue worldwide, with 67.95% of construction accidents attributed to the lack of helmet-wearing. Existing helmet detection algorithms suffer from underperformance in real-world scenarios due to various challenges. This study introduces a lightweight helmet detection algorithm, YOLO-PL, which achieves state-of-the-art performance by optimizing network structure and incorporating lightweight modules.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Fen Liu, Jianfeng Chen, Kemeng Li, Jisheng Bai, Weijie Tan, Chang Cai, Muhammad Saad Ayub
Summary: In this paper, a semi-tensor product-based multi-modal factorized multilinear (STP-MFM) pooling method is proposed for information fusion in sentiment analysis. Experimental results demonstrate that the proposed method outperforms baselines in terms of accuracy, training speed, and model complexity.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong, Wen Yao, Yunyang Zhang, Xiaohu Zheng
Summary: In this paper, a contrastive enhancement approach is proposed to mine latent prototypes from background regions and leverage latent classes to improve the utilization of similarity information between prototype and query features. The proposed modules, including a latent prototype sampling module and a contrastive enhancement module, significantly improve the performance of state-of-the-art methods for few-shot segmentation.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Xiaoyong Lyu, Baojin Liu, Wenbing Fan, Zhi Quan
Summary: This paper provides an in-depth analysis of the channel estimate model (CEM) in passive radar using orthogonal frequency division multiplexing (OFDM) waveforms. The authors propose a new CEM that takes into consideration the influence of inter-carriers interference (ICI). The theoretical analysis and simulation results support the effectiveness of the new CEM and the proposed method for canceling ICI.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Ruisheng Rana, Jinping Wang, Bin Fang, Weiming Yang
Summary: This paper introduces an improved neighborhood preserving embedding method (NPEAE), which utilizes a linear autoencoder to achieve more accurate and effective data projection from high-dimensional space to low-dimensional space. NPEAE performs better in recognition accuracy compared to other comparative methods.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Yuehao Guo, Xianpeng Wang, Jinmei Shi, Lu Sun, Xiang Lan
Summary: This article presents a fast real-valued tensor propagator method for FDA-MIMO radar. The method improves estimation accuracy by utilizing the original structural information of multidimensional data and eliminates the high computational complexity of high-order singular value decomposition. The proposed algorithm achieves parameter estimation at low snapshots and has lower computational complexity than other algorithms at high snapshots.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Bang Huang, Wen-Qin Wang, Weijian Liu, Shunsheng Zhang, Yizhen Jia
Summary: This paper studies the robust moving target detection problem in FDA-MIMO radar with an unknown covariance matrix in Gaussian clutter. The proposed approach adopts the subspace method and proposes three robust adaptive detectors, which are validated to be effective.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Asutosh Kar
Summary: In this paper, a class of variable step size adaptive algorithms is developed for hybrid narrow-band active noise control (HNANC) systems and compared with the existing state-of-the-art methods. Two algorithms are proposed for HNANC systems operating in multiple noise environments, and their performance is analyzed. The results demonstrate significant improvement in noise reduction compared to the counterparts.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Shouqi Wang, Zhigang Feng
Summary: With the rapid development of artificial intelligence and sensor technology, intelligent fault diagnosis methods based on deep learning are widely used in industrial production. In practical applications, complex noise environments and huge model parameters affect the performance and cost-effectiveness of diagnostic models. In this paper, a lightweight intelligent fault diagnosis model using multi-sensor data fusion is proposed to address these issues. By processing vibration signals from different sensors of rolling bearings using variational mode decomposition (VMD) and designing unique grayscale feature maps based on each intrinsic modal function (IMF) component, the proposed model achieves high accuracy diagnosis in noisy environments while meeting the requirements of small, light, and fast production. The ultra-lightweight GoogLeNet model (ULGoogLeNet) is constructed by adjusting the traditional GoogLeNet structure, and the ultra-lightweight subspace attention module (ULSAM) is introduced to reduce model parameters and enhance feature extraction capability. Experimental results on two datasets demonstrate the effectiveness and superiority of the proposed method.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Jichen Yang, Fangfan Chen, Yu Cheng, Pei Lin
Summary: Recently, there has been significant attention on multi-speaker multimedia speaker recognition (MMSR). This study explores innovative techniques for integrating audio and visual cues from the front-end representations of both speaker's voice and face. Experimental results show that these techniques achieve considerable improvements in the performance of MMSR.
DIGITAL SIGNAL PROCESSING
(2024)