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Ship License Plate Super-Resolution in the Wild

IEEE signal processing LETTERS 2023年 30卷 394-398页

作者： Wu, Huahua Chen, Jiagui Wang, Tianlei Lai, Xiaoping Cao, Jiuwen Hangzhou Dianzi Univ Machine Learning & I Hlth Int Cooperat Base Zhejia Zhejiang 310018 Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Zhejiang 310018 Peoples R China Hangzhou Zhichuang Informat Technol Co Ltd Zhejiang 310050 Peoples R China

Ship license plate (SLP) recognition plays an important role in ship supervision and harbour management. Practically, low-resolution (LR) SLP images are illegible and challenging to SLP recognition. Most existing super-resolution (SR) methods are not suitable for real-world LR SLP images with over smoothed reconstructions. To alleviate these deficiencies, in this letter, we propose a parallel enhanced SR generative adversarial network (PESRGAN) for SLP images. A novel degradation model is developed to construct a more feasible LR dataset. A parallel SR convolutional neural network (SRCNN) module based on ESRGAN is proposed for feature extraction. To characterize the difference between text foreground and background, a new gradient loss is developed in PESRGAN to sharpen the character boundary. Comparisons to many state-of-the-art (SOTA) SR methods are presented to show the effectiveness of the proposed algorithm.

关键词： Feature extraction Generators Degradation Marine vehicles Convolution Training signal processing algorithms Ship license plate super-resolution ESRGAN parallel SRCNN gradient loss

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signal processing in GPR Applications for Cave-in Prediction in Road Assessment: A Review

Signal Processing in GPR Applications for Cave-in Prediction...

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Digital signal processing and its Applications (DSPA), International Conference on

作者： Vladimir Shlykov Daniil Spitsyn Sergey Guzhov Vadim Tutubalin Sergey Vityazev Stepan Andreev Rui Fan Alexandr Dvorkovich Moscow Institute of Physics and Technology Dolgoprudny Moscow Region Russian Federation Ryazan state radio engineering university named after V.F.Utkin Ryazan Russian Federation Tongji University Shanghai P. R. China National Research University “Moscow Power Engineering Institute” Moscow Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The article focuses on the problem of searching for voids under the road surface using ground penetrating radar (GPR). Early detection of such voids allows for the prediction of road collapses and prevents the occurrence of hazards. The main emphasis is on a review of signal processing algorithms used for this purpose. First, basic signal processing steps common to all GPR applications are considered. Then some advanced GPR signal processing methods are discussed. Next, the techniques used specifically for cave-in prediction are highlighted. Finally, an experimental study is described. The article is a review and introduces an important area of research and development.

关键词： Three-dimensional displays Reviews Roads signal processing algorithms Radar imaging Media Search problems Recording Surface treatment Research and development

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Multi-Frame Compressed Video Quality Enhancement by Spatio-Temporal Information Balance

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IEEE signal processing LETTERS 2023年 30卷 105-109页

作者： Wang, Zeyang Ye, Mao Li, Shuai Li, Xue Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane 4072 Australia

In recent years, the performance of multi-frame quality enhancement algorithms for compressed videos has been greatly improved compared with single-frame based algorithms. However, the existing methods mainly focus on mining the temporal information of multiple frames. The large number of reference frames reduces the exploration of spatial information, although the existing single-frame based algorithms for enhancement, denoising, and super-resolution demonstrate the significance of the spatial information. To address this problem, we propose a plug-and-play module called Spatio-temporal Information Balance (STIB) to adaptively balance the spatial and temporal information. In our method, we use a feature extractor to exploit richer spatial information, and use a refinement module to refine the aligned temporal information, to be more conducive to the fusion of spatio-temporal information. Finally, we use the deformable convolution based re-alignment module to do alignment and fusion in feature space for balancing the spatio-temporal information. Experiments show that our module can significantly improve the performance of the existing multi-frame based enhancement algorithms.

关键词： Feature extraction Convolution signal processing algorithms Encoding Computational modeling Image coding Computer architecture Video quality enhancement compressed video deep learning spatio-temporal information

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Optimizing Neural Prosthetic Control Systems Using Genetic Algorithm for Adaptive signal processing

Optimizing Neural Prosthetic Control Systems Using Genetic A...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： S. Purnima A. Sathiya P. Poonkuzhali Minu Balakrishnan Saravanan M M. Saravanan Dept. of BME Jerusalem College of Engineering Chennai Department of ECE M. Kumarasamy College of Engineering (Autonomous) Karur Department of ECE R.M.D. Engineering College Chennai Department of Information Technology Sri Eshwar College of Engineering Coimbatore Department of ECE Sri Eshwar college of engineering Coimbatore Department of ECE Faculty of Engineering & Technology Annamalai University Chidambaram

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Neural prosthetic systems provide promising solutions for restoring motor functions in individuals with disabilities. However, optimizing the control systems that decode neural signals remains a key challenge due to the dynamic nature of neural activity. This paper proposes a novel approach using a Genetic Algorithm (GA) to optimize the parameters of adaptive signal processing algorithms in neural prosthetic systems. The GA enables continuous calibration and adjustment, improving the accuracy of neural signal decoding and control over time. Simulation results show significant improvements in the efficiency of signal decoding from EEG/EMG data, leading to smoother control of prosthetic devices. The proposed methodology offers an adaptive and user-specific solution, contributing to enhanced prosthetic functionality and user experience.

关键词： Accuracy signal processing algorithms Control systems Adaptive signal processing User experience Decoding Time factors Prosthetics Genetic algorithms Testing

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A Class of Online Censoring Based Quaternion-Valued Least Mean Square algorithms

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IEEE signal processing LETTERS 2023年 30卷 244-248页

作者： Menguc, Engin Cemal Acir, Nurettin Mandic, Danilo P. P. Kayseri Univ Dept Elect & Elect Engn TR-38280 Kayseri Turkiye Natl Def Univ Turkish Air Force Acad Dept Elect Engn TR-34149 Istanbul Turkiye Imperial Coll London Dept Elect & Elect Engn London SW7 2BT England

Streaming Big Data applications require the means to efficiently utilize large-scale data in an online manner. This issue becomes even more pressing when data are also multidimensional, as is the case with quaternion data streams. To this end, we first introduce the online censoring (OC) based quaternion least mean square (OC-QLMS) and OC-augmented QLMS (OC-AQLMS) algorithms, which censor less informative data in order to reduce computational complexity without severely affecting performance. Next, to censor both the outlier and noninformative data, we also propose the robust OC-QLMS (ROC-QLMS) and ROC-AQLMS. Fixed and adaptive threshold rules are introduced into the proposed OC algorithms to efficiently implement the desired censoring probability in the quaternion domain. The fundamental convergence analysis on the step size for all the proposed algorithms is also presented and the superior properties of the proposed algorithms are demonstrated in system identification scenarios.

关键词： signal processing algorithms Quaternions Calculus Cost function Convergence Filtering Technological innovation Quaternion-valued least mean square online censoring augmented statistics big data streams

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Research and Design of AIS Reception signal processing System Based on GNU Radio

Research and Design of AIS Reception Signal Processing Syste...

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International Conference on Advanced Communication Technology (ICACT)

作者： Zijiao Zhou Peng Gong Bin Tang Piao He Xiuming Peng Guangwei Zhang National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

The Automatic Identification System (AIS), as a standard communication and navigation device for ships, plays a significant role in ensuring the safety and efficiency of maritime traffic. The accuracy of the AIS receiver’s signal processing directly affects the bit error rate (BER) and packet error rate (PER) of the AIS system, drawing considerable attention to AIS signal reception and processing technologies. Current research on AIS reception signals mainly focuses on custom implementations using hardware platforms like FPGAs; however, the high cost of hardware implementation has become a limiting factor in the development of AIS signal processing technologies. Therefore, this paper designs an AIS reception signal processing system based on GNU Radio, developing demodulation algorithm and NRZI decoding modules, and testing them with typical demodulation algorithms. Experimental results demonstrate the feasibility and effectiveness of the designed reception signal processing system.

关键词： Costs Software algorithms Bit error rate signal processing algorithms signal processing Hardware Decoding Artificial intelligence Standards Demodulation

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A Bias-Compensated NMMCC Algorithm Against Noisy Input and Non-Gaussian Interference

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第9期70卷 3689-3693页

作者： Long, Xiaoqiang Zhao, Haiquan Hou, Xinyan Southwest Jiaotong Univ Sch Elect Engn Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China

This brief proposes a bias-compensated normalized adaptive filtering algorithm (BC-NMMCC) under the mixture maximum correntropy (MMCC), which can increase the flexibility of the correntropy by changing the weight of the mixture parameter, as well as change the mixture kernel function to a single kernel function when the mixture parameter takes endpoint value. In order to deal with the case of noisy input and non-Gaussian noise interference, a bias-compensated term is obtained by utilizing an unbiased criterion and some assumptions to increase the robustness of the NMMCC algorithm. To further improve the performance of the proposed algorithm, a modified parameter is added to the bias-compensated term. Then, the computational complexity of the proposed algorithm is analyzed. Finally, the numerical simulation results also show that the BC-NMMCC algorithm performs better in terms of convergence speed and steady-state performance than several other existing algorithms with better robustness under different noise disturbances.

关键词： signal processing algorithms Kernel Estimation Interference Robustness Noise measurement Filtering algorithms Adaptive filtering mixture maximum correntropy mixture parameter modified parameter

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Corner Detection via Scale-Space Behavior-Guided Trajectory Tracing

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IEEE signal processing LETTERS 2023年 30卷 50-54页

作者： Sun, Xun Zhong, Baojiang Yang, Jianyu Ma, Kai-Kuang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Suzhou Vocat Univ Sch Comp Engn Suzhou 215104 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Existing curvature scale-space (CSS) methods detect corners by tracing the CSS trajectories from a determined high scale toward the lowest one. For those images with sophisticated details, such approach could often yield unsatisfactory corner detection results;i.e., miss-detected true corners (false negatives) and round corners (false positives). In this letter, these two fundamental problems are investigated. To tackle them, a novel CSS-based corner detector is proposed by incorporating our mathematically derived scale-space properties of the planar curves and corner points into the developed trajectory tracing algorithm, called the scale-space behavior-guided trajectory tracing (SBTT). In view of lacking a benchmark dataset with the ground truth, another contribution from our work is on the establishment of an augmented test image dataset, containing 147 test images with manually-labelled ground truth and their augmented images up to 62,328 images in total. Based on the ground truth, four commonly-used metrics are exploited to conduct corner detection performance evaluation. The obtained simulation results show that our proposed corner detector yields the highest F-score, when compared with that of nine state-of-the-art methods.

关键词： Trajectory Behavioral sciences Detectors Corner detection Stairs Computational modeling signal processing algorithms scale space curvature corner tracing Gaussian smoothing

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Meta-Heuristic Approaches to Parallel processing in signal Representation and Real-Time Analysis

Meta-Heuristic Approaches to Parallel Processing in Signal R...

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Automation and Computation (AUTOCOM), International Conference on

作者： Harisha Naik N T Sudhanshu Dev Aniket Shivam Neelesh Singh Shreenidhi H S Awakash Mishra Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Meta-heuristic techniques have been popular in solving highly complex optimization problems. Parallel processing is very important in signal representation and real-time analysis, as it helps lower the time required for the data process so that algorithms can perform faster. The biggest challenge with the traditional parallel processing techniques is load balancing and higher communication overhead, which may need to scale up to a larger extent. Meta-heuristic techniques reduce the complexity of problems that are NP-complete or NP-hard by providing satisfactory solutions and approximate answers to difficult ones. This has helped in parallel processing for signal representation and real-time analysis. Parallel processing has also been useful in using meta-heuristic algorithms like genetic algorithms, ant colony optimization, and particle swarm optimization. Meta-heuristic algorithms produce flexible, adaptive mechanisms for solving complex problems.

关键词： Heuristic algorithms Metaheuristics signal processing algorithms Parallel processing signal processing Approximation algorithms Load management Real-time systems signal representation Particle swarm optimization

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Source Detection With Multi-Label Classification

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2023年 4卷 336-345页

作者： Vijayamohanan, Jayakrishnan Gupta, Arjun Noakoasteen, Oameed Goudos, Sotirios K. K. Christodoulou, Christos G. Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Aristotle Univ Thessaloniki Dept Phys ELEDIA AUTH Thessaloniki 54124 Greece

Radio source detection through conventional algorithms has been unreliable when trying to solve for large number of sources in the presence of low SINR and less number of snapshots. We address this by reformulating source detection as a multi-class classification problem solved using deep learning frameworks. Incoming waveforms are sampled using a centro-symmetric linear array with omni-directional elements and the normalized upper triangle of the autocorrelation matrix is extracted as the input feature to a modified convolutional neural network with uni-dimensional filters, trained to detect the sources in the presence of both uncorrelated and correlated signals. Two detection algorithms are introduced and referred to as CNNDetector and RadioNet, and subsequently benchmarked against the conventional source detection algorithms. By including pre-processing in forward backward spatial smoothing, RadioNet can also resolve the number of uncorrelated sources in the presence of correlated paths. Finally, the algorithms are stress tested under challenging operational conditions and extensive evaluations are presented showing the efficacy and contributions of the introduced predictive models. To the best of our knowledge, this is the first time the source detection problem has resolved L-1 sources, for an antenna array of L elements using a deep learning framework.

关键词： Autocorrelation Feature extraction Convolutional neural networks signal resolution signal processing algorithms Neural networks Computational modeling Array signal processing multi-label classification ResNet CNN direction of arrival residual learning signal source detection statistical signal processing

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