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IEEE Pacific Rim Conference on Communications, Computers and signal processing

作者： S. Mabbs High-Frequency Radar Division Defence Science and Technology Organisation Salisbury SA Australia

The author outlines the performance assessment process currently used for development of Jindalee Australia Air Force over-the-horizon radar signal processing and tracking algorithm. The associated software tools provide an automated testing facility for new signal processing algorithms with numerical figure-of-merit output. Users can automatically test new algorithms over a large standard set of input replay tapes to assess the performance of the signal processing sequence over a large set of environmental and target conditions.< >

关键词： Radar signal processing Radar tracking signal processing algorithms Target tracking Australia Software tools Automatic testing Software testing Surveillance Springs

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signal processing Problems and algorithms in Display Side of 3DTV

Signal Processing Problems and Algorithms in Display Side of...

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IEEE International Conference on Image processing

作者： E. Ulusoy G. B. Esmer H. M. Ozaktas L. Onural A. Gotchev V. Uzunov Department of EEE Bilkent University Bilkent Ankara Turkey Tampere University of Technology Institute of Signal Processing Tampere Finland

Two important signal processing problems in the display side of a holographic 3DTV are the computation of the diffraction field of a 3D object from its abstract representation, and determination of the best display configuration to synthesize some intended light distribution. To solve the former problem, we worked on the computation of 1D diffraction patterns from discrete data distributed over 2D space. The problem is solved using matrix pseudo-inversion which dominates the computational complexity. Then, the light field synthesis problem by a deflectable mirror array device (DMAD) is posed as a constrained linear optimization problem. The formulation makes direct application of common optimization algorithms quite easy. The simulations indicate that developed methods are promising.

关键词： signal processing algorithms Three dimensional displays Distributed computing Diffraction Holography Computer displays signal synthesis Computational complexity Mirrors Optical arrays

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Delta-operator based signal processing: fast algorithms for rapidly sampled data

Delta-operator based signal processing: fast algorithms for ...

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IEEE Conference on Decision and Control

作者： H.V. Poor Department of Electrical Engineering Princeton University Princeton NJ USA

High sampling densities often lead to numerical ill-conditioning in statistical signal processing algorithms. Reformulation of signal or system dynamics via a delta, or divided-difference, dynamical operator can provide a framework within which the amelioration of such ill-conditioning can be treated on a formal and unified basis. A number of basic signal-processing problems have been studied in his context over the past decade, and these are reviewed here.

关键词： signal processing algorithms signal sampling signal processing Bandwidth Covariance matrix Eigenvalues and eigenfunctions Filters Sampling methods Arithmetic Wideband

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Tutorial T2A: Safe Autonomous Systems: Real-Time Error Detection and Correction in Safety-Critical signal processing and Control algorithms

Tutorial T2A: Safe Autonomous Systems: Real-Time Error Detec...

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International Conference on VLSI Design

作者： Jacob Abraham Abhijit Chatterjee University of Texas at Austin Austin TX US Georgia Institute of Technology Atlanta GA US

ISBN: (纸本)9781538636930;9781538636923

While the last two decades have seen revolutions in computing and communications systems, the next few decades will see a revolution in the use of every-day robotics and artificial intelligence in broad societal applications. Examples of such systems include sensor networks, the smart power grid, self-driven cars and autonomous drones. Such systems are driven by signal processing, control and learning algorithms that process sensor data, actuate control functions and learn about the environment in which these systems operate. The trustworthiness and safety of such systems is of paramount importance and has significant impact on the commercial viability of the underlying technology. As a consequence, anomalies in system operation due to computation errors in on-board processors, degradation and failure of embedded sensors, actuators and electro-mechanical subsystems and unforeseen changes in their operation environment need to detected with minimum latency. Such anomalies also need to be mitigated in ways that ensure the safety of such systems under all possible failure scenarios. Many future systems will be selflearning in the field. It is necessary to ensure that such learning does not compromise the safety of all human personnel involved in the operation of such systems. To enable safe operation of such systems, the underlying hardware needs to be tuned in the field to maximize performance, reliability and error-resilience while minimizing power consumption. To enable such dynamic adaptation, device operating conditions and the onset of soft errors are sensed using post-manufacture and real-time checking mechanisms. These mechanisms rely on the use of built-in sensors and/or low-overhead function encoding techniques to detect anomalies in system functions. A key capability is that of being able to deduce multiple performance parameters of the system-under-test using compact optimized stimulus using learning algorithms. The sensors and function encodings assess the

关键词： signal processing algorithms Robot sensing systems signal processing Safety Real-time systems Hardware Electrical engineering

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High-Throughput Barcode Fluorescence Sensor signal Rapid processing: Integration of Multiple algorithms and Visualization Implementation

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IEEE SENSORS JOURNAL 2025年第2期25卷 3348-3357页

作者： Wang, Baole Geng, Qingfubo Zhan, Weihe Geng, Zhaoxin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China Minzu Univ China Sch Informat Engn Key Lab Ethn Language Intelligent Anal & Secur Gov Beijing 100081 Peoples R China

The field of fluorescence barcode sensor signal processing is the focus, with the development of a high-throughput fluorescence detection visualization platform that integrates multiple efficient fluorescence array localization algorithms, including manual mask positioning, an optimized k-means++ algorithm, and a projection iterative algorithm designed based on fluorescence image projection data. Comprehensive monitoring, real-time adjustment, and integrated data analysis are enabled by the platform, significantly enhancing the accuracy, speed, and convenience of fluorescence image processing. Experimental results demonstrate that the projection iterative algorithm excels in both accuracy and processing speed, achieving a mean squared error (mse) of 6.42130, which is 20.97% lower than that of the k-means++ algorithm, and a processing time of 1095 ms, which is 80.21% and 94.37% faster than the mask positioning and k-means++ algorithms, respectively. Furthermore, evaluation of different fitting algorithms shows that sigmoid curve fitting performs best across various metrics, with an R-2 value as high as 0.987480, indicating excellent data interpretability and fitting accuracy. Overall, precise, rapid, and efficient signal processing in high-throughput fluorescence detection is achieved, providing robust technical support and innovative tools for related research fields.

关键词： Fluorescence Sensors Accuracy Software systems Gray-scale Noise Clustering algorithms signal processing algorithms Sensor arrays Image sensors Barcode biochip fluorescence image high throughput sensor signal software design

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Ship Detection Software Based on High-Resolution algorithms for Primary signal processing: From Idea to Implementation in HFSWRs

Ship Detection Software Based on High-Resolution Algorithms ...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Dragan Goluboović Darko Marjanović Vlatacom Institute Belgrade Serbia Vlatacom Institute Singidunum University Belgrade Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

In recent years, maritime surveillance of ships using High Frequency Surface Wave Radars (HFSWRs) represents very important challenge, where most attention is focused on algorithms for primary signal processing and the implementation of such algorithms to make them applicable in real-time. This paper contains an overview of the high-resolution primary signal processing in HFSWRs, as well as its implementation using C programming language. All the necessary input data and parameters of the algorithms are described, then the way of executing the program, and finally the output data. A real-time operation is achieved thanks to the implementation based on multithreading by improving the existing implementation of the high-resolution range-Doppler map estimation, which is the most computationally demanding task. As a part of software infrastructure, we have developed a set of auxiliary processing components, allowing end-users graphical presentation of processed radar data. We have demonstrated the capabilities of the ship detection software which can be execute on general purpose computers, and where processing can be performed in real-time or subsequently, for the needs of which an additional program has been developed, thus forming a ship detection software package.

关键词： Computers Computer languages Software packages Surface waves Surveillance Software algorithms signal processing algorithms Radar Real-time systems Marine vehicles

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Weak signal processing in Dual-Beam Interferometric Velocity Measurements

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IEEE SENSORS JOURNAL 2025年第1期25卷 989-999页

作者： Lv, Hongjian Song, Ran Wang, Duanrui Zhu, Xingyue Lu, Chenxu Xu, Chunying Wu, Chi Shantou Univ Coll Engn Shantou 515063 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzhou Guangzhou 511458 Guangdong Peoples R China Shandong Univ Inst Marine Sci & Technol Shandong Prov Ctr Situ Marine Sensors Qingdao 266237 Shandong Peoples R China Aixsensor Co Ltd Dezhou 253500 Shandong Peoples R China

Accurate extraction of real velocity signals from Doppler velocity measurements is challenging, particularly in environments with low signal-to-noise ratios (SNRs). To address this issue, we propose a novel algorithm based on spectral peak density searching, designed for reliable Doppler signal processing under noisy conditions. First, we apply a Gaussian filter to reduce the interference of Gaussian white noise while preserving key characteristics of the Doppler signal. Next, a peak-searching operation identifies all spectral peaks in the denoised signals, and potential Doppler shift signals are selected based on a specific bandwidth threshold. The densest spectral peak band is then determined, and nonlinear least-squares smoothing is applied to precisely identify the velocity shift signal. Compared to conventional Doppler signal processing algorithms, this method significantly improves processing accuracy, reducing execution time by approximately 56%, decreasing the average relative error from 3.41% to 1.92%, and increasing the detection probability from 58% to 88%. We validated the performance of the algorithm through experiments simulating weak signals with substantial noise under conditions of low emission intensity and high-speed water flow. The results demonstrate the effectiveness and reliability of the proposed algorithm. The novelty lies in its ability to quickly transmit the processed flow velocity value back to the user interface instead of a massive amount of raw flow velocity data, emphasizing its potential for practical applications in challenging environments.

关键词： Doppler effect Velocity measurement signal processing algorithms Sensors Sea measurements Particle measurements Atmospheric measurements Accuracy Measurement by laser beam Noise Bandwidth threshold Doppler velocity measurements peak density weak signals

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signal processing-Free Intelligent Model for Power Quality Disturbances Identification

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IEEE ACCESS 2025年 13卷 9910-9922页

作者： Al-Mashdali, Mohammed F. Islam, Asif Hassan, Abdulbasit Shafiullah, Md Al-Dhaifallah, Mujahed Al Fuwail, Khalid King Fahd Univ Petr & Minerals Elect Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals High Voltage Lab Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia Air Force Inst Technol Kaduna Nigeria

Integrating different types of renewable energy sources in the power system substantially challenges the power quality (PQ), directly affecting the system's stability and service life span. The rise of power quality disturbances (PQD) generates irregularities in voltage and current waveforms, harming smart grid networks and linked devices. Traditional methods for PQD classification use complicated feature extraction techniques, which can be computationally expensive and lack scalability. This research proposes applying basic convolutional neural network (CNN) models for automated PQD detection and categorization as a prospective solution to these issues. By directly examining PQD images generated from signal data, these models reduce the requirement for human-crafted features. The study analyzes alternative CNN setups, training datasets, and disturbance types to measure model performance. The results demonstrate that these simple CNN models maintain stable accuracy values in normal and noisy environments, even with increasing classes and noise, the models managed to maintain a high-performance level with up to 99.39% accuracy for 17 classes when the Adam optimizer was used instead of RMSprop. The models could deal with noise-related disturbances, still achieving accuracy as high as 96.42% when trained by just 50% of the dataset under 30dB SNR (signal to Noise Ratio) conditions. Moreover, comparing the two frequencies on 50Hz and 60Hz performance does not show the equivalent models' robustness over different operating levels. This study highlights the potential of CNNs in boosting power quality disturbance categorization and presents paths for further inquiry in model refining and optimization. The study focuses on CNN-based models applied in power quality disturbance detection and classification research.

关键词： Convolutional neural networks Power quality Mathematical models Computational modeling Protection Feature extraction Renewable energy sources Accuracy Power system stability signal processing algorithms Convolutional neural networks (CNN) power quality (PQ) renewable energy machine learning

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Optimal Speech signal processing Based on Dynamic Multi-Scale Analysis

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IEEE ACCESS 2025年 13卷 22946-22955页

作者： Guo, Zefei Li, Xiao Zhang, Yuchen Tibet Univ Coll Informat Sci & Technol Lhasa 850000 Peoples R China

To enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distinguishing itself from previous approaches through its real-time adaptive filtering, which makes it highly adaptable to complex, non-stationary noise conditions. The proposed method is grounded in dynamic multi-scale analysis, employing multi-scale decomposition of speech signals to optimize their time-frequency characteristics and dynamic adjustments, thereby forming a new noise reduction approach, DMS. Initially, the multi-scale decomposition technique effectively captures the multi-scale features of noisy speech signals. Subsequently, optimizing the time-frequency characteristics and dynamic signal adjustments effectively removes noise while improving the signal's time-frequency resolution. Finally, the method is further enhanced through the adaptive error minimization algorithm, leading to a more pronounced noise reduction effect. Experimental results demonstrate that the proposed method outperforms the single dynamic multi-scale technique in terms of improving signal-to-noise ratio (SNR).

关键词： Noise Heuristic algorithms signal processing algorithms Noise reduction Minimization Speech enhancement Time-frequency analysis Optimization signal processing Filtering algorithms Adaptive error multi-scale analysis optimal estimation speech noise reduction time-frequency characteristic optimization

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A Pre-processing signal Modeling Approach for Multichannel ATI Digitizers

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Mei, Sitao Zhao, Yijiu Shao, E. Wang, Yusen Zhang, Yi Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Asynchronous time-interleaved (ATI) analog-to-digital converter (ADC) architecture utilizes pre-processing, low-pass filtering, and ADCs to simultaneously enhance the sampling rate and bandwidth of the acquisition system. We propose a novel approach to generate pre-processing signal in multichannel ATI architecture. The generated pre-processing signal is no longer limited to pulse wave or square wave with a fixed duty. We derive the condition for reconstructing input signal as the invertibility of the pre-processing matrix. Additionally, we analyzed the error sources in the multichannel ATI. Based on the spectral shift characteristics of the ATI, we propose an error coefficient estimation method. Using the estimated error coefficient, a digital filter can be designed to calibrate the system. A four-channel hardware prototype was developed to evaluate the performance of the proposed method, satisfactory results were obtained and the feasibility of the proposed approach was demonstrated.

关键词： Low-pass filters Calibration Mixers Estimation Frequency response Filtering algorithms Time-frequency analysis signal processing algorithms Hardware Fourier series Analog-to-digital converters (ADCs) asynchronous time-interleaved (ATI) frequency-interleaved (FI) wideband

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