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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3487 条记录，以下是3411-3420 订阅

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Adaptive Filtering for Noise Cancellation and signal Analysis in Real-time

Adaptive Filtering for Noise Cancellation and Signal Analysi...

引用

European Microwave conference

作者： Arnd Frech Markus Klugel Peter Russer GAUSS INSTRUMENTS GmbH Institute for Nanoelectronics Technische Universitat Munchen

ISBN: (纸本)9781479902644

This paper presents advanced digital signal algorithms for adaptive filtering applied for noise cancellation and signal analysis in real-time. Correlated and not-correlated signal parts are distinguished by such methods based on the statistical characteristics of the received signals. Real-time measurements of electromagnetic interference (EMI) in noisy environments are shown. An advanced digital signal processing technique for fast measurements of electromagnetic interferences is presented, carrying out the measurements in time-domain and reducing the total measurement time by orders of magnitude. For optimum noise suppression performance, different approaches of adaptive filter algorithms were investigated and enhanced with respect to implementation of the noise cancelling algorithm on field programmable gate arrays (FPGA) and enabling signal processing in real-time. Noise suppressing in real-time is shown in frequency bands of over 125 MHz at once. Several real-time bands up to 1 GHz are measured and signals completely covered by ambient noise could be successfully detected, e.g. in the DAB, the DVB-T and LTE frequency bands.

关键词： Noise Real-time systems Vectors signal processing algorithms Pollution measurement Least squares approximations Electromagnetic interference

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Wavelet analysis and applications : proceedings of an International conference on Wavelet Analysis a

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2002年

作者： International conference on Wavelet Analysis and Its Applications

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A Diverse Low Cost High Performance Platform for advanced Driver Assistance System (ADAS) Applications

A Diverse Low Cost High Performance Platform for Advanced Dr...

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IEEE conference on Computer Vision and Pattern Recognition Workshops

作者： Prashanth Viswanath Kedar Chitnis Pramod Swami Mihir Mody Sujith Shivalingappa Soyeb Nagori Manu Mathew Kumar Desappan Shyam Jagannathan Deepak Poddar Anshu Jain Hrushikesh Garud Vikram Appia Mayank Mangla Shashank Dabral Texas Instruments India Pvt Ltd Bangalore India

advanced driver assistance systems (ADAS) are becoming more and more popular. Lot of the ADAS applications such as Lane departure warning (LDW), Forward Collision Warning (FCW), Automatic Cruise Control (ACC), Auto Emergency Braking (AEB), Surround View (SV) that were present only in high-end cars in the past have trickled down to the low and mid end vehicles. Lot of these applications are also mandated by safety authorities such as EUNCAP and NHTSA. In order to make these applications affordable in the low and mid end vehicles, it is important to have a cost effective, yet high performance and low power solution. Texas Instruments (TI's) TDA3x is an ideal platform which addresses these needs. This paper illustrates mapping of different algorithms such as SV, LDW, Object detection (OD), Structure From Motion (SFM) and Camera-Monitor Systems (CMS) to the TDA3x device, thereby demonstrating its compute capabilities. We also share the performance for these embedded vision applications, showing that TDA3x is an excellent high performance device for ADAS applications.

关键词： Cameras Digital signal processing Automobiles advanced driver assistance systems Engines

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Hybrid Beamforming Design Based on Low Complexity Alternating Minimizing Solution for Millimeter Wave MIMO Systems

Hybrid Beamforming Design Based on Low Complexity Alternatin...

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International conference on advanced Electrical Engineering (ICAEE)

作者： Baghdad Hadji Abdeldjalil Aïssa-El-Bey Lamya Fergani Mustapha Djeddou Faculty of Technology Saïda University Saïda Algeria LISIC Laboratory University of Science and Technology Houari Boumediene Algiers Algeria IMT Atlantique UMR CNRS 6285 Lab-STICC Brest France Ecole Nationale Polytechnique El Harrach Algiers Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

Millimeter-wave (mmWave) bands have deservedly taken their place in the development of next-generation network communications to address the explosive growth of mobile network data traffic and high-speed communications requirements. Also, the fully connected hybrid architecture can offer a suitable beamforming gain for overcoming the limitations of mm Wave signals in outdoor communications. Consequently, for the feasibility of the hybrid architecture implementation, the designing of the optimal analog/digital beamformers is indispensable for providing good performance with the lowest complexity. Therefore, in this work, we develop a low-complexity alternating minimization design using a complete closed-form least-square method in the iterative design process to derive the optimal analog and digital beamformers by considering the total power constraint at the transmitter and the unit constant modulus constraints. Thanks to the offered global optimal solutions and the fast convergence of the least squares method, numerical results demonstrated that the proposed design attains perfect performance with the lowest computational complexity compared to the state-of-art design algorithms in terms of spectral efficiency (SE) in different scenarios.

关键词： Performance evaluation Array signal processing Transmitters Millimeter wave technology Computer architecture Minimization Hybrid power systems Computational complexity Least mean squares methods Antenna arrays

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Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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International conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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Adaptive Single Phase Autoreclosing Scheme for EHV Transmission Line

Adaptive Single Phase Autoreclosing Scheme for EHV Transmiss...

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International conference on advanced Power System Automation and Protection (APAP)

作者： Waqar Ahmad Khan Tianshu Bi Ke Jia State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing P.R.China Faculty of Engineering Lahore Leads University Lahore Pakistan

ISBN: (数字)9781728117225

ISBN: (纸本)9781728117232

Efficient protection system plays a vital role in power system stability by detecting and eliminating the faults quickly on overhead transmission lines. Temporary fault is the most frequent fault type that appears on high voltage (HV) and extra high voltage (EHV) lines which are followed by secondary arcs. Consequently adaptive single phase autorecloser (ASPAR) is the need of the time to get rid of temporary faults. Continuous wavelet transform (CWT) with fast Fourier transform (FFT) algorithm (CWTFT) is presented in this paper which utilises faulted phase voltage waveform and obtain wavelet coefficients after the circuit breaker isolate the faulty phase. Depending on the energies of the wavelet coefficients, a threshold is defined. In case of a transient fault, the coefficient energies exceeds the threshold level after the arc extinction and are well recognised by the proposed algorithm. This fact is used to initiate a safest reclosing in case of a temporary fault otherwise autoreclosing will be blocked. To appraise the efficiency of the presented algorithm, laborious simulations have been performed for a diversity of fault conditions i.e. at different fault locations, load angles and fault resistance, where in all cases, maximum consistency has been achieved.

关键词： Circuit faults Continuous wavelet transforms signal processing algorithms Power transmission lines Impedance Wavelet transforms Transforms

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Performance evaluation and comparison of filters for real time embedded system applications

Performance evaluation and comparison of filters for real ti...

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Robotics and Mechatronics conference (RobMech)

作者： Winston Dyason Theo Ian van Niekerk Russell Phillips Riaan Stopforth Advanced Mechatronics Technology Center Nelson Mandela University Port Elizabeth South Africa Stopforth Mechatronics Robotics and research Lab University of KwaZulu-Natal Durban South Africa

This paper focuses on an evaluation of an exponentially weighted moving average (EWMA) filter which has its results compared to the well known Kalman filter (KF) and a proportional (P) filter. The KF is well known and widely used in a variety of applications because of its accuracy, robustness and relative computational simplicity. Scenarios however do exist where the KF is not a suitable filter to use due to perhaps computational resource restrictions or smooth results requirements where the KF's performance can be lacking. The EWMA filter is proposed as a viable alternative to the KF, by providing a similar accuracy, improved robustness and dynamic response whilst being substantially less computationally expensive. A P filter is also presented as an effective and robust filter with minimal computational requirements. Performance comparisons are made between the three filter types and specific use cases are highlighted for that will enable designers to select the most appropriate filter for their needs based on signal-to-noise ratios.

关键词： Kalman filters signal processing algorithms Mathematical model Noise measurement Algorithm design and analysis Real-time systems Robustness

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SPIDERS: Low-Cost Wireless Glasses for Continuous In-Situ Bio-signal Acquisition and Emotion Recognition

SPIDERS: Low-Cost Wireless Glasses for Continuous In-Situ Bi...

引用

IEEE International conference on Internet-of-Things Design and Implementation (IoTDI)

作者： Jingping Nie Yigong Hu Yuanyuting Wang Stephen Xia Xiaofan Jiang Department of Electrical Engineering Columbia University

ISBN: (数字)9781728166025

ISBN: (纸本)9781728166032

We present a System for processing In-situ Bio-signal Data for Emotion Recognition and Sensing (SPIDERS)- a low-cost, wireless, glasses-based platform for continuous in-situ monitoring of user's facial expressions (apparent emotions) and real emotions. We present algorithms to provide four core functions (eye shape and eyebrow movements, pupillometry, zygomaticus muscle movements, and head movements), using the bio-signals acquired from three non-contact sensors (IR camera, proximity sensor, IMU). SPIDERS distinguishes between different classes of apparent and real emotion states based on the aforementioned four bio-signals. We prototype advanced functionalities including facial expression detection and real emotion classification with a landmarks and optical flow based facial expression detector that leverages changes in a user's eyebrows and eye shapes to achieve up to 83.87% accuracy, as well as a pupillometry-based real emotion classifier with higher accuracy than other low-cost wearable platforms that use sensors requiring skin contact. SPIDERS costs less than $20 to assemble and can continuously run for up to 9 hours before recharging. We demonstrate that SPIDERS is a truly wireless and portable platform that has the capability to impact a wide range of applications, where knowledge of the user's emotional state is critical.

关键词： Sensors Skin Biomedical monitoring Monitoring Cameras Emotion recognition Electroencephalography

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Enhancing Liver Cancer Detection with AI-Powered Image processing

Enhancing Liver Cancer Detection with AI-Powered Image Proce...

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Sustainable Expert Systems (ICSES), International conference on

作者： A. Suresh Kumar S. Rajathi Vineetha Vargheese M. Sudha Anju A P. Lokeshkiran Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Information Technology Karpagam Institute of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This project aims to identify liver tumors using deep learning techniques. The core of our approach is a sophisticated neural network trained on real CT scans of patients with liver tumors. By leveraging advanced signal-processing algorithms and deep learning models, our AI method accurately distinguishes between tumors and healthy liver tissue. Accurate identification of liver tumors on CT images is crucial for planning effective radiation therapy. CT and MRI scans provide detailed images of the internal structure of the body by analyzing tissues. Our primary objective is to develop an AI-driven neural network that can reliably identify liver tumors in these images. Given the variability in liver location among individuals, our approach employs specific techniques to ensure precision. Using U-net technology, our goal is to enhance the speed and accuracy of tumor identification, thereby improving patient access to timely and accurate diagnoses. This project underscores the significant potential of deep learning applications in health care.

关键词： Deep learning Image segmentation Accuracy Computed tomography Neural networks Liver Planning Radiation therapy Object recognition Tumors

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Comparative Study on CSP and Improved ICA algorithms in Motor Imagery

Comparative Study on CSP and Improved ICA Algorithms in Moto...

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2013 International conference on Biological,Medical and Chemical Engineering(BMCE 2013)

作者： Xiao-Xiao GONG Xiao-Jing GUO Bang-Yan ZHOU Xiao-Pei WU Lei ZHANG The Key Lab.of Intelligent Computing and Signal Processing Anhui University

Independent Component Analysis(ICA)and Common Spatial Patterns(CSP)are commonly used to find spatial filters for classification of electroencephalogram(EEG)signals in motor *** ICA needs physiological knowledge about the potential changes of task-related EEG signals to select filters manually,this paper proposes to acquire filter information from the spatial filters constructed by CSP,then the improved ICA is compared with CSP from several aspects on BCI competition III dataset IVa as well as dataset of left and right hand motor imagery from our independent *** results suggest the proposed ICA has good classification performance and is better on robustness and flexibility,while CSP is simpler and more suitable for multi-channel EEG.

关键词： Spatial Filters Electroencephalogram Motor Imagery

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