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检索条件"主题词=Signal processing algorithms"

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Sub-optimal Perceptron Controller Using WOA Algorithm for Farming Watering Mobile Robot

Sub-optimal Perceptron Controller Using WOA Algorithm for Fa...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana L. H. Oliveira J. Bautista Jesus Pinto-Lopera Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the Whale Optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital signal processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-the-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%.

关键词： Software algorithms signal processing algorithms Software Hardware Whale optimization algorithms Steady-state Mobile robots MATLAB Farming Python

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Object Action Recognition Algorithm Based on Asymmetric Fast and Slow Channel Feature Extraction

Object Action Recognition Algorithm Based on Asymmetric Fast...

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Lingchong Fan Yude Wang Ya Zhang School of Physics and Physical Engineering Qufu Normal University Qufu China School of Cyber Science and Engineering Qufu Normal University Qufu China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Aiming at the problems of insufficient video motion feature extraction and low recognition accuracy of the existing symmetric object action recognition model, we propose an asymmetric video object action recognition model that extracts content features from a single slow channel and motion features from two fast channels. A single slow channel extracts video content information; two fast channels extract the motion features of the video and uses the lateral connection to fuse the features with the slow channel. We obtain the object action recognition features after the extracted content features and motion features through global average pooling and concatenation operation, so as to realize the action recognition of motion objects. We conduct comparative experimental studies of different model architectures on the UCF101 and HMDB51 datasets. The experimental results show that the recognition results of the asymmetric object action recognition models Top1 and Top5 on UCF101 dataset are 1.32% and 1.51% higher than those of the symmetric SlowFast model, 4.44% and 4.05% higher on HMDB51 dataset, respectively. In addition, the influence of fast channels with different frame rates on the recognition results is studied in the constructed model, and the experimental results show that the recognition effect is the best when the frame rate α 1 is equal to 4 and α 2 is equal to 8. Our proposed asymmetric model achieves good performance in object action recognition, which makes a clear contribution to these improvements.

关键词： Accuracy Fuses Computational modeling signal processing algorithms Computer architecture signal processing Feature extraction Data mining

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Integrated Communication and Sensing for Meeting Automotive Requirements in 5G/6G Connected Vehicles

Integrated Communication and Sensing for Meeting Automotive ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Preeti Kumari Stelios Stefanatos Arvind Singh Naruka Hailiang Cai Avinash Kondeppady Ravi Gummadidala Abdullah Njie Arzu Karaer Arik Gubeskys Dev Putchala Kapil Gulati Gene Marsh Shailesh Patil Wireless R&D Qualcomm Technologies Inc. San Diego CA US Wireless R&D Qualcomm Technologies Inc. Bridgewater NJ USA

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Integrated sensing and communication (ISAC) is being discussed in 5G Release 19 and will likely be a critical component of 6G. In this paper we study ISAC for automotive applications. Specifically, we focus on adding sensing capability to a vehicle modem. Such additional capability will increase the value proposition of adding higher bands connectivity to vehicles. We perform a comprehensive study of challenges needed to enable automotive grade sensing in a vehicle modem, and propose solutions to those challenges. We show how cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM), which is used for communication, can be combined with novel receive processing to meet (and even exceed) the automotive sensing requirements. Furthermore, we propose a two-stage resource allocation scheme to minimize the overhead needed for sensing. This simplifies the tradeoff between communication and sensing. We also perform system level simulations to quantify the impact of sensing interference on cellular communication. Finally, we carry out measurements to identify antenna placement that can be used for both communications and sensing. Then, we prototype our proposals to demonstrate automotive level sensing using CP-OFDM.

关键词： Simulation signal processing algorithms Interference Integrated sensing and communication Radar antennas Time division multiplexing Modems Sensors Resource management Automotive engineering

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Analysis of the Accuracy of Estimating the Angle of Arrival of a signal in an Inhomogeneous Sparse Antenna Array

Analysis of the Accuracy of Estimating the Angle of Arrival ...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Olesya Bolkhovskaya Alexander Maltsev Lobachevsky University Nizhny Novgorod Russia

ISBN: (数字)9798350364989

ISBN: (纸本)9798350364996

In this paper, the characteristics analysis of a set of inhomogeneous sparse antenna arrays is carried out. The considered set of the sparse arrays is obtained by moving the antenna elements of a uniform equidistant linear array from the middle to the edges of the antenna in order to increase the accuracy of the angle-of-arrival (AoA) estimates. The theoretical analysis of the accuracies of the AoA estimates is carried out by finding the Cramer-Rao lower bounds (CRLB). The signal processing algorithm for finding the maximum likelihood (ML) estimates were implemented. For the considered set of the inhomogeneous sparse antenna arrays the careful simulations of the developed signal processing algorithm were performed. Threshold values of SNRs were obtained. It was shown that for SNRs exceed these threshold values the of the obtained estimates accuracy of the AoA practically coincides with the theoretical limits.

关键词： Maximum likelihood estimation Accuracy Transmission line matrix methods Array signal processing signal processing algorithms Receiving antennas Nonhomogeneous media

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Impact of In-Pixel processing Circuit Non-idealities on Multi-object Tracking Accuracy for Autonomous Driving

Impact of In-Pixel Processing Circuit Non-idealities on Mult...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Janak Sharda Qiucheng Wu Shiyu Chang Shimeng Yu Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Computer Science University of California at Santa Barbara Santa Barbara CA USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Deep learning algorithms are robust to a small amount of noise in the input image. Traditionally, image signal processors (ISP) are used with the CMOS image sensor (CIS) to enhance image quality which consume additional energy and latency. Here, we evaluate an ISP-Iess CIS architecture in the presence of noise and other in-pixel circuit non-idealities for the autonomous driving application. By integrating the in-pixel processing circuits to CIS, we filter out the redundant frames and only pass the critical bit information downstream to the backend processor. Such in-pixel processing does not allow ISP operations to be applied to the captured raw image. To reflect these limitations, we model and apply circuit non-idealities to the regenerated artificial raw images as an input to the QDTrack network for multi-object tracking. We evaluate the accuracy loss on the BDD100K dataset and examine its sensitivity on each of the image processing steps. We observe an overall accuracy drop of less than 1.2% in Identification Fl-score (IDFl) and 2.1 % in Multi-Object Tracking Accuracy (MOTA), suggesting that an ISP-Iess in-pixel processing circuit is feasible to reject 40% redundant frames directly on CIS.

关键词： Image quality Accuracy Program processors Sensitivity Noise signal processing algorithms Information filters

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Diabetic retinopathy detection using modified U-Net architecture and artificial metaplasticity algorithm

Diabetic retinopathy detection using modified U-Net architec...

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International Conference on Image and signal processing and their Applications (ISPA)

作者： Yasmine Benchaib Zineb Aziza Elaouaber Mohamed El Amine Lazouni Mohamed Messadi Biomedical Engineering Laboratory University of Tlemcen Algeria Biomedical Engineering Laoratory University of Tlemcen Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Diabetic retinopathy (DR) is an ocular disease caused by long-term diabetes. It is the leading cause of blindness among working-age people. DR is an asymptomatic pathology, that causes changes in the area, diameter, and tortuosity of thin blood vessels. Therefore, analyzing the morphological changes of the retinal vasculature is critical for helping ophthalmologists in the diagnosis of DR at an early stage and preventing vision loss. In this work, we present an automatic approach for diabetic retinopathy screening from color retinal images based on semantic segmentation of retinal blood vessels. First, a preprocessing and data augmentation steps are integrated to extract the green channel and increase the number of images. Then, the segmentation step is carried out using a modified U-Net network. After that, a set of geometrical parameters are calculated from segmented images such as area, perimeter and tortuosity. Finally, Artificial Metaplasticity Multi-Layer Perceptron ‘AMMLP’ classifier is employed to discriminate between healthy and DR images. The proposed method for vessel segmentation was evaluated on the DRIVE database, and it achieved an accuracy of 99.65%. For DR classification, AMMLP got a sensitivity of 98.45% on Messidor dataset. The experimental results show that our approach outperforms the existing methods, which can assist ophthalmologists in making an early and accurate DR diagnosis.

关键词： Diabetic retinopathy Pathology Sensitivity Semantic segmentation signal processing algorithms Blood vessels Retinal vessels

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Fourier Diffraction Theorem for 3d Ultrasound Imaging with a Row-Column Array

Fourier Diffraction Theorem for 3d Ultrasound Imaging with a...

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IEEE International Symposium on Biomedical Imaging

作者： Paul Hagemeyer Thomas Lisson Stefanie Dencks Georg Schmitz Chair of Medical Engineering Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Row-column arrays (RCAs) have been shown to produce high-resolution, three-dimensional ultrasound volumes while reducing the number of electronic connections. Typically, standard reconstruction methods in the time domain such as delay-and-sum (DAS) beamforming have been applied. Here, we derive the Fourier diffraction theorem for reconstructing 3D volumes from backscattered data obtained using an RCA with broadband plane wave excitation under a range of angles. Based on this theory, we propose a novel spatial frequency domain method that offers additional theoretical insight for the optimization of RCA beamforming. In comparison to time domain methods, it also promises higher computational efficiency by using the fast Fourier transform and it enables alternative signal processing and filtering schemes in the frequency domain. We validate our reconstruction algorithm with simulation data. The results demonstrate improved image quality and resolution compared to DAS beamforming.

关键词： Three-dimensional displays Ultrasonic imaging Array signal processing Diffraction signal processing algorithms Reconstruction algorithms Time-domain analysis

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Robust Adaptive Beamforming with Projection-Based Covariance Matrix Reconstruction Under Colored Noise

Robust Adaptive Beamforming with Projection-Based Covariance...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Mingzhao Rao Yuan Jiang Lei Zhao Xinghua Sun School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen China Guangdong Provincial Key Laboratory of Sea-Air-Space Communication Shenzhen China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Extant adaptive beamforming techniques employ the interference-plus-noise covariance matrix (INCM) to safe-guard the desired signal from suppression. In the presence of colored noise, null offsets may occur, resulting in degradation of beamformer performance. To bolster the beamformer's ro-bustness under colored noise, a novel reconstruction method for the INCM is proposed. This method initially estimates the steering vector of the desired signal by integrating the principal eigenvalue and projection methods. Subsequently, the colored noise covariance matrix is reconstructed utilizing the properties of the blocking matrix. Ultimately, the power of the interference signal is obtained through the projection of the received data, thereby yielding the INCM. The computational complexity of the proposed algorithm stands favorably in comparison to that of other algorithms. Simulation results demonstrate that this method exhibits strong robustness to mismatch errors under colored noise, regardless of whether the SNR is high or low, or whether the number of snapshots is high or low.

关键词： Wireless communication Array signal processing Simulation signal processing algorithms Eigenvalues and eigenfunctions Vectors Robustness Covariance matrices Colored noise signal to noise ratio

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Boosting Agricultural Productivity Using IoT and Machine Learning-Based Crop Recommendation

Boosting Agricultural Productivity Using IoT and Machine Lea...

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International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Asish Kumar Patnaik Neelamadhab Padhy Archana Patnaik Rasmita Panigrahi Department of Computer Science and Engineering GIET University Gunupur Odisha

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Crop production is crucial to the country's economic development by promoting sustainable agriculture. Integrating IoT and machine learning into crop recommendation systems widely contributes towards enhancing product yield, and minimizing farmers' efforts. The correct forecasting of crop recommendation may give a perfect outcome in analyzing the crop features based on its parameters. Objectives: This work aims to improve agricultural productivity using classification models. Our prime objective is to identify the specific parameters like soil productivity, humidity, wind velocity, rainfall, and other climatic conditions that affect the growth of crops. Classification-based prediction provides accurate results based on the optimality of crop characteristics. Materials and Methods: Our hybrid research model shows the efficiency of machine learning and IoT in the domain of crop recommendation systems. We have taken the real-time data of Nitrogen(N), Phosphorus(P), and Potassium(K) values of soil by using the NPK Soil Sensor. The real-time data collection was done at GIET University Agricultural Fields (School of Agriculture), India. The data is further converted into a. csv file for further processing. We have implemented five different types of classification techniques which include Gradient Boosting, Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine(SVM). Result Analysis: The effectiveness of all the different kinds of classification algorithms is assessed using the obtained results. We have estimated the performance metrics (accuracy, precision, F-measure, and recall) of all the classifiers.

关键词： Productivity Machine learning algorithms Crops signal processing algorithms Soil Boosting Real-time systems Classification algorithms Random forests Recommender systems

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An Approach for Rapid Online Robustness Enhancement of Distributed Power Systems using Load Classifier Forecasting

An Approach for Rapid Online Robustness Enhancement of Distr...

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International (EPE-PEMC) Power Electronics and Motion Control Conference

作者： Xin Wei Denis Sidorov Aliona Dreglea Alexey Iskakov Zongjie Wang Liguo Wang School of Electrical Engeering & Automation Harbin Institute of Technology Harbin China Energy Systems Institute of Russian Academy of Sciences Irkutsk Russia Baikal School of BRICS Irkutsk National Research Technical University Irkutsk Russia V.A.Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia Electrical and Computer Engineering Department University of Connecticut Storrs US

ISBN: (数字)9798350385236

ISBN: (纸本)9798350385243

In this paper, we propose an online approach to rapidly and efficiently improve the robustness of distributed power systems (DPSs). Based on real-time monitoring of sudden voltage sag the main work includes the following two steps: offline classifier identification and online instantaneous disturbance suppression. In order to accurately forecast load classifiers, we adopt an automatic analytical solution, Wu’s Elimination Method (WEM), to derive the expressions of differential equations that are used to describe the phenomena in DPS. Then the corresponding load classifiers have been obtained by BCU method (Boundary of stability region based Controlling Unstable equilibrium point method). The optimum switching time of the proposed Active Damping Generator can be chosen based on combining the obtained classifiers with digital signal processing. Simulation and experimental results show that through our work the suddenly changed voltage and distorted current of the DPS can be quickly recovered within 15ms which is faster than the standard IEC TS62749-2015.

关键词： Damping Power quality signal processing algorithms Power system stability Robustness Generators Real-time systems Classification algorithms Surges Standards

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