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13th International Symposium on Communication Systems, Networks and Digital signal processing (CSNDSP)

作者： Xiang, Yixun Jin, Xianqing Huang, Nuo Gong, Chen Univ Sci & Technol China Sch Informat Sci & Technol Key Lab Wireless Opt Commun Chinese Acad Sci Hefei 230026 Peoples R China

ISBN: (数字)9781665410441

ISBN: (纸本)9781665410441

For optical wireless communication, the laser needs to be accurately incident into the single-mode fiber (SMF) and further propagates in the fiber. However, the core diameter of SMF is generally about 10 microns, and any slight deviation will reduce the power coupled to the fiber end. To solve this issue, we propose a defocus method to increase the tolerance of deviation between the fiber core and coupling spot center. Then, a spot search method based on raster scanning is proposed to further increase the tolerance of deviation. In order to verify the defocus method, we adopt the pattern search method (PSM) to restore the alignment under a medium offset. Then, we propose a continuous variable hill-climbing (CVHC) method combined with raster scanning to restore alignment under a large offset. Both simulation and experimental results demonstrate the effectiveness of the proposed approach under medium and large offsets.

关键词： Optical fibers Couplings Wireless communication Micrometers Search methods Optical propagation signal processing algorithms

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UKF-FNN-RIC: A Highly Accurate UWB Localization Algorithm for TOA Scenario

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Zhang, Shuning Wang, Ershen Zhu, Zhanchen Yi, Jingyi Wang, Yongkang Kuai, Enze Shenyang Aerosp Univ Coll Aeronaut & Astronaut Shenyang 110136 Peoples R China Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110136 Peoples R China Monash Univ Dept Data Sci & AI Melbourne Vic 3800 Australia

UWB positioning has become an important branch of indoor localization technology, which has high accuracy in the ideal state but is easily affected by the environment and other factors. To address the accuracy enhancement challenges of indoor UWB positioning across various conditions, a versatile UKF-FNN-RIC algorithm is proposed, leveraging the unscented Kalman filter (UKF), feedforward neural network (FNN), and redundant information correction (RIC). First, various existing UWB positioning algorithms are analyzed, the redundant information in the UWB solution model is studied in depth, and the relationship between the redundant information and the measurement vectors is clarified, thus the error correction algorithm of gradient descent of redundant information is proposed, and the data meets the preconditions of its application through UKF and FNN transforms. The UKF-FNN-RIC algorithm is proposed in time of arrival (TOA) scenarios. Finally, simulation experiments and practical experiments are carried out for two-way ranging (TWR) TOA scenario, which show that the algorithm in the article can greatly improve the accuracy and robustness. The accuracy of fixed point in the practical experiment reaches the millimeter level, the accuracy of track determination reaches the centimeter level, and the algorithms can be easily migrated to other positioning systems.

关键词： Accuracy Location awareness signal processing algorithms Kalman filters Distance measurement Base stations Heuristic algorithms Robustness Clustering algorithms Synchronization Feedforward neural network (FNN) gradient descent method redundant information correction (RIC) time of arrival (TOA) unscented Kalman filter (UKF) UWB localization

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Complex Scenario-Oriented Fine-Grained Visual Classification Platform 24

Complex Scenario-Oriented Fine-Grained Visual Classification...

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IEEE 24th International Workshop on Multimedia signal processing (MMSP)

作者： Chang, Dongliang Chen, Junhan Wang, Xinran Du, Ruoyi Yu, Wenqing Liu, Yufan Tong, Yujun Liang, Kongming Song, Yi-Zhe Ma, Zhanyu Beijing Univ Posts & Telecommun Sch Artificial Intelligence Pattern Recognit & Intelligent Syst Lab Beijing 100876 Peoples R China Univ Surrey CVSSP SketchX Guildford GU2 7XH Surrey England

ISBN: (数字)9781665471893

ISBN: (纸本)9781665471893

In recent years, fine-grained visual classification (FGVC) algorithms have achieved excellent performance across a variety of datasets. However, it is still rare to see these algorithms applied in daily life. The main reasons for this are i) the algorithms are developed based on different design guidelines and cannot be deployed in the same environment;ii) there is not a simple and efficient platform to present the algorithm's results to the user - the accuracy is meaningless to the users. To address the above problem, we built a complex scenario-oriented fine-grained visual classification platform. The platform consists of a PyTorch-based fine-grained visual recognition algorithm library (FGL) and a WeChat applet-based user interaction module (WEM). We can quickly develop new algorithms or readily apply existing algorithms in the same environment through FGL. Driven by FGL, the WEM enables users to achieve fine-grained recognition of complex scenes interactively. In addition to showing the user the fine-grained labels of objects, we will also show how the model makes decisions to help the user master the ability to recognise the fine-grained object so that everyone can become a domain expert. A video demo shows an example of the proposed platform in a real-world scenario: https://***/rRZE7O.

关键词： Visualization Social networking (online) Design methodology Conferences signal processing algorithms Streaming media signal processing

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Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge 23

Status Updating with an Energy Harvesting Sensor under Parti...

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23rd International Workshop on signal processing Advances in Wireless Communication (SPAWC)

作者： Hatami, Mohammad Leinonen, Markus Codreanu, Marian Univ Oulu Ctr Wireless Commun Oulu Finland Linkoping Univ Dept Sci & Technol Linkoping Sweden

ISBN: (数字)9781665494557

ISBN: (纸本)9781665494557

We consider status updating under inexact knowledge of the battery level of an energy harvesting (EH) sensor that sends status updates about a random process to users via a cache-enabled edge node. More precisely, the control decisions are performed by relying only on the battery level knowledge captured from the last received status update packet. Upon receiving on demand requests for fresh information from the users, the edge node uses the available information to decide whether to command the sensor to send a status update or to retrieve the most recently received measurement from the cache. We seek for the best actions of the edge node to minimize the average AoI of the served measurements, i.e., average on-demand AoI. Accounting for the partial battery knowledge, we model the problem as a partially observable Markov decision process (POMDP), and, through characterizing its key structures, develop a dynamic programming algorithm to obtain an optimal policy. Simulation results illustrate the threshold-based structure of an optimal policy and show the gains obtained by the proposed optimal POMDP-based policy compared to a request-aware greedy (myopic) policy.

关键词： Wireless communication Simulation signal processing algorithms signal processing Performance gain Battery charge measurement Batteries

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High-Success-Rate and Fast ISAR Imaging of Nonstationary Moving Platform-Attitude Rapidly Changing Ship Target With DS Evidence and Minimum Entropy Theory

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Fan, Jiabo Shao, Shuai Liu, Hongwei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

The acquisition of a well-focused and high-resolution inverse synthetic aperture radar (ISAR) image of a ship target is crucial for accurate target classification and recognition. In practical scenarios, ship targets exhibit complex maneuverability, and radar platforms demonstrate nonstationary behavior, which poses a serious challenge to conventional ISAR imaging algorithms. To address this problem, this article proposes a high-success-rate and fast ISAR imaging algorithm of nonstationary moving platform-attitude rapidly changing ship target (NSMP-ARCST) with Dempster-Shafer (DS) evidence and minimum entropy theory. The proposed algorithm employs multiple metrics to evaluate the imaging results and leverages the DS evidence theory to fuse these metrics for optimal imaging time interval (OITI) selection, aiming to enhance the success rate and robustness of ISAR imaging. Furthermore, this article introduces an improved fixed-point iterative minimum entropy phase-adjustment (IFPI-MEPA) method to optimize the ISAR imaging quality and computational speed under low signal-to-noise ratio (SNR) conditions, which contributes to an increased success rate of OITI selection and reduced computational complexity, thereby endowing it with substantial practical applicability. Experimental results using both simulated and real measured data illustrate the effectiveness and robustness of the proposed algorithm.

关键词： Radar imaging Imaging Radar Marine vehicles Doppler effect signal processing algorithms Robustness Inverse synthetic aperture radar (ISAR) minimum entropy nonstationary moving platforms optimal imaging time interval (OITI) selection ship targets

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Fast Trajectory Forecasting With Automatic Identification System Broadcasts 11

Fast Trajectory Forecasting With Automatic Identification Sy...

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11th Sensor signal processing for Defence Conference (SSPD)

作者： Wang, Yicheng Uncy, Murat Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

This work proposes a fast trajectory forecasting algorithm to use with automatic identification system (AIS) broadcasts of vessels. The algorithm involves fast sub-optimal model parameter estimation from AIS messages and the computation of Gaussian location predictions for a series of future timestamps. The underlying trajectory model is a stochastic process that uses six parameters to generate near-constant velocity trajectories. These parameters include the desired cruise heading and speed of the vessel and velocity standard deviations along the heading direction and its perpendicular complement. We demonstrate the performance of our approach using a real AIS data set.

关键词： Computational modeling signal processing algorithms Stochastic processes signal processing Predictive models Prediction algorithms Trajectory

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FAIRNESS-AWARE SELECTIVE SAMPLING ON ATTRIBUTED GRAPHS 47

FAIRNESS-AWARE SELECTIVE SAMPLING ON ATTRIBUTED GRAPHS

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47th IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Kose, Oyku Deniz Shen, Yanning Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781665405409

Selective sampling is an online learning framework where the learner tries to detect the data samples whose labels can boost the performance maximally, and only the labels of chosen data samples are queried. While the design of selective sampling algorithms is extensively studied for independent data samples, the area is rather under-explored in the context of graphs. Furthermore, the limited number of existing graph-based approaches do not take into consideration the nodal attributes that are available in attributed graphs. In this study, existing online learning and selective sampling algorithms are modified to be used with graphs that have nodal features. Additionally, the bias in the results of original algorithms is investigated, and a novel bias reduction strategy is proposed that can be embedded into the dimensionality reduction step without incurring a significant complexity cost. Experiments for node classification are carried out on real social networks to showcase the advantages of incorporating nodal features, as well as the fairness-enhancement framework.

关键词： Dimensionality reduction Costs Social networking (online) Error analysis signal processing algorithms signal processing Approximation algorithms

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An Efficient Conflict-Free 2-D FFT for FMCW Radar Imaging on DSP

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Ye, Hong Dai, Yiming Zheng, Hui Jia, Zhaohong Liang, Dong Tang, Jun Wang, Ke Anhui Univ Sch Internet Hefei 230039 Peoples R China Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China Anhui Univ Sch Elect Informat Hefei 230601 Peoples R China

This article proposes an efficient 2D-FFT method based on the digital signal processing (DSP) platform, suitable for frequency-modulated continuous-wave (FMCW) radar systems. Given the real-time detection and imaging requirements of FMCW radar, enhancing system efficiency is of paramount importance. As a crucial step in fast algorithms for FMCW radar imaging, improving the calculation speed of 2D-FFT can significantly optimize system performance. As 2D-FFT is a data-intensive application with strided memory access patterns, traditional schemes cannot fully exploit the computing and storage performance of the DSP processor. Moreover, instruction and data parallelization often lead to memory access conflicts. This article introduces a multistage merged and conflict-free modulo addressing scheme called the CF2D-FFT algorithm, which reduces memory access operations and effectively resolves parallel memory conflicts. The proposed scheme is implemented and optimized on the HXDSP104x DSP platform, and the enhanced algorithm achieves a 1.24x improvement over the theoretical runtime. The performance improvement of the proposed algorithm was validated through comparative experiments and imaging experiments.

关键词： Runtime System performance signal processing algorithms Imaging Radar detection Digital signal processing Radar imaging Real-time systems Radar signal processing VLIW 2D-FFT digital signal processing (DSP) frequency-modulated continuous-wave (FMCW) millimeter-wave (MMW) modulus addressing single-instruction multiple-data (SIMD) very long instruction word (VLIW)

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Fast Type-II Hartley Transform algorithms for Short-Length Input Sequences

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10719页

作者： Polyakova, Marina Cariow, Aleksandr Odesa Polytech Natl Univ Inst Comp Syst Shevchenko Ave 1 UA-65044 Odesa Ukraine West Pomeranian Univ Technol Szczecin Fac Comp Sci & Informat Technol Zolnierska 49 PL-71210 Szczecin Poland

This paper presents the type-II fast discrete Hartley transform (DHT-II) algorithms for input data sequences of lengths from 2 to 8. The starting point for developing the eight algorithms is the representation of DHT-II as a matrix-vector product. The underlying matrices usually have a good block structure. These matrices must then be successfully factorized to obtain a computational procedure that reduces the number of operations in computing the matrix-vector product. In some cases, it is necessary to pre-decompose the original matrices into submatrices and rearrange the rows and/or columns of the resulting matrices to find the factorizations that would substantially save the arithmetic operations. As a result of applying the pointed transformations, we synthesized the final algorithms with reduced computational complexity. The correctness of the obtained algorithmic solutions was theoretically justified using the rigorous mathematical background of each of them. Then, the complex algorithms were further tested using the MATLAB R2023b software to confirm their performance. Finally, an evaluation of the computational complexity for each obtained solution was compared with the computational complexity of the direct calculation of the matrix-vector product and existing fast DHT-II algorithms. The obtained factorizations of the DHT-II transformation matrices on average reduce the number of additions by 5% and the number of multiplications by 73% compared with the direct calculation of the matrix-vector product.

关键词： complexity theory digital signal processing discrete Hartley transform matrix factorization signal processing algorithms

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Frame Synchronization algorithms for Satellite Internet of Things Scenarios 11

Frame Synchronization Algorithms for Satellite Internet of T...

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11th Advanced Satellite Multimedia Systems Conference / 17th signal processing for Space Communications Workshop (ASMS/SPSC)

作者： Liess, Marco Lazaro, Francisco Munari, Andrea Tech Univ Munich Chair Integrated Syst Munich Germany German Aerosp Ctr Inst Communicat & Nav DLR Wessling Germany

ISBN: (数字)9781665473774

ISBN: (纸本)9781665473774

Non-terrestrial networks (NTNs) such as satellite constellations offer a large coverage area and the potential to serve vast populations of devices, and they are hence a promising solution for multiple Internet of Things (IoT) applications. Given the power constraints of typical IoT devices, low earth orbit (LEO) satellites have to be considered due to the improved link budget. A common characteristic of such satellite links is a large Doppler shift, which introduces additional challenges to the problem of frame synchronization. In this work we consider several frame synchronization algorithms, comparing their robustness against frequency offsets in different scenarios. The main finding of this paper is that a commonly considered algorithm, which is derived as a low-complexity approximation of the optimal likelihood-ratio test, exhibits a poor performance for low signal-to-noise ratio (SNR) values typical of satellite scenarios. In such realistic scenarios, a technique known as bank of correlators reaches the best performance among the considered algorithms but has a very high complexity. An FFT-based technique commonly known as swiveled correlation seems to provide the best tradeoff between performance and complexity.

关键词： Satellites Space communications Sociology signal processing algorithms Low earth orbit satellites Approximation algorithms Complexity theory

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