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检索条件"主题词=Filtering algorithms"

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A Review On Collaborative filtering Using Knn Algorithm

A Review On Collaborative Filtering Using Knn Algorithm

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Aniket Sharma Jerald Nirmal Kumar S Diksha Rana Sonia Setia School Of Engineering And Technology Sharda University India

Now-a-days online users are gradually increasing, which leads to the increase in the database. Collaborative filtering of the complex data is done using algorithms of time complexity. In this paper we are reviewing KNN algorithm of collaborative filtering. Collaborative filtering technique is being used for the dataset with the vast amount of data. Collaborative technique has given the most precise result in the recommendation of the data being used by the customers with same mentality and different mentalities as well. KNN algorithm helps in finding the nearest neighbor of the customer by finding the shortest distance possible between the similar users. An easy but powerful gadget getting to know technique is the k-Nearest Neighbor (kNN) set of rules. both classification and regression can be performed effectively using it. but type prediction is in which it’s miles maximum frequently utilized. The kNN identifies newly input statistics based totally on its similarity to formerly educated statistics and organizes the statistics into coherent clusters or subsets. The elegance to which the input belongs is determined by means of its closest neighbors. kNN is efficient, but it has quite a few flaws. This painting emphasizes the kNN method and its changed versions located in earlier research. those versions provide a more powerful technique at the same time as casting off the drawbacks of kNN.

关键词： Casting Databases Collaborative filtering Collaboration filtering algorithms Prediction algorithms Classification algorithms

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Filter-Based Adaptive Sliding Mode Control with Unknown Disturbance 7

Filter-Based Adaptive Sliding Mode Control with Unknown Dist...

引用

7th IEEE International Conference on Advanced Robotics and Mechatronics

作者： Zhao, Jian Shao, Ke Zhao, Jian Tan, Junbo Suzhou Coll Informat Technol Suzhou 215200 Jiangsu Peoples R China Hefei Univ Technol Sch Mech Engn Hefei 230009 Anhui Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China Jiangsu Union Tech Inst Changshu Branch Suzhou 215500 Jiangsu Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Guangdong Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

Low pass filter (LPF) is an effective approach to suppress chattering of sliding mode control (SMC). However, in current LPF-based SMC, the upper bound of the disturbance or its derivative is needed to be known a priori. As a result, due to the constant control gain, chattering still occurs on the sliding mode even under minor external disturbance. In this paper, to cancel the requirement for the upper bound of the disturbance or its derivative, a leakage-type (LT) adaptive law is developed to approximate the amplitude of the disturbance and its derivative in finite time. Thanks to the LT algorithm, the chattering is further removed when the system is on the sliding mode. By using LT adaptation, a type of LPF-based adaptive SMC is proposed. Explicitly, a linear out-layer sliding function is introduced to realize the asymptotical convergence of the system to a small neighborhood around zero. Lyapunov analysis and simulations verify the superiorities of the developed method.

关键词： Upper bound Mechatronics Low-pass filters filtering algorithms Approximation algorithms Transient analysis Nonlinear systems

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Uncertainty Quantification for the Extended and the Deterministic-Gain Kalman Filters

Uncertainty Quantification for the Extended and the Determin...

引用

American Control Conference (ACC)

作者： Wei, Shihong Spall, James C. Johns Hopkins Univ Appl Math & Stat Dept 3400 North Charles St Baltimore MD 21218 USA JHU Appl Phys Lab 3400 North Charles St Baltimore MD 21218 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

This paper is aimed at characterizing the mean square error and probabilistic uncertainty of a popular class of filtering algorithms in nonlinear systems. The state estimation error of the extended Kalman filter and the deterministic-gain Kalman filter are analyzed. We allow a vector state, but assume scalar measurements. A set of conditions for the mean square error to be upper-bounded is derived. Furthermore, the probabilistic bounds for the estimation error are computed via both the moment-based approach and the stochastic comparison analysis approach. The latter provides a formal means determining uncertainty bounds, such as statistical confidence regions.

关键词： Estimation error Uncertainty Mean square error methods filtering algorithms Probabilistic logic Kalman filters State estimation

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Improving Re-rankCCP with Rules Quality Measures 17

Improving Re-rankCCP with Rules Quality Measures

引用

17th Conference on Computer Science and Intelligence Systems (FedCSIS)

作者： Jezusek, Piotr Karpus, Aleksandra Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

Recommender Systems are software tools and techniques which aim at suggesting new items that may possibly be of interest to a user. Context-Aware Recommender Systems exploit contextual information to provide more adequate recommendations. In this paper we described a modification of an existing contextual post-filtering algorithm which uses rules-like user representation called Contextual Conditional Preferences. We extended the algorithm by taking into account rules quality measures while recommending items to a user. We proved that this modification increases the quality of recommendations, measured with precision, recall and nDCG, and has no impact on the execution time of the original algorithm.

关键词： Computer science Aggregates filtering algorithms Time measurement Business process re-engineering Software tools Recommender systems

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Improved State Propagation through AI-based Pre-processing and Down-sampling of High-Speed Inertial Data 39

Improved State Propagation through AI-based Pre-processing a...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Steinbrener, Jan Brommer, Christian Jantos, Thomas Fornasier, Alessandro Weiss, Stephan Univ Klagenfurt Control Networked Syst Grp Lakeside Pk B04a A-9020 Klagenfurt Austria

ISBN: (纸本)9781728196817

We present a novel approach to improve 6 degree-of-freedom state propagation for unmanned aerial vehicles in a classical filter through pre-processing of high-speed inertial data with AI algorithms. We evaluate both an LSTM-based approach as well as a Transformer encoder architecture. Both algorithms take as input short sequences of fixed length N of high-rate inertial data provided by an inertial measurement unit (IMU) and are trained to predict in turn one pre-processed IMU sample that minimizes the state propagation error of a classical filter across M sequences. This setup allows us to provide sufficient temporal history to the networks for good performance while maintaining a high propagation rate of pre-processed IMU samples important for later deployment on real-world systems. In addition, our network architectures are formulated to directly accept input data at variable rates thus minimizing necessary data preprocessing. The results indicate that the LSTM based architecture outperforms the Transformer encoder architecture and significantly improves the propagation error even for long IMU propagation times.

关键词： Training filtering algorithms Transformers Prediction algorithms Robot sensing systems Performance analysis History

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Heart rate detection using single-channel Doppler radar system 44

Heart rate detection using single-channel Doppler radar syst...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Sameera, Jannatun Noor Droitcour, Amy D. Boric-Lubecke, Olga Univ Hawaii Manoa Dept Elect & Comp Engn 2500 Campus Rd Honolulu HI 96822 USA Adnoviv Inc 2800 Woodlawn Dr Honolulu HI 96822 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

A number of algorithms have been developed to extract heart rate from physiological motion data with the Doppler radar system. Yet, it is very challenging to eliminate the noise associated with surroundings, especially with a single-channel Doppler radar system. However, single-channel Doppler radars provide the advantage of operating at lower power. Additionally, the heart rate extraction using single-channel Doppler radar has remained somewhat unexplored. This has motivated us to develop effective signal processing algorithms for signals received from single-channel Doppler radars. In this paper, we have proposed and studied three algorithms for estimating heart rate. The first algorithm is based on applying FFT on an FIR filtered signal. In the second algorithm, autocorrelation was performed on the filtered data. Thirdly, we used a peak finding algorithm in conjunction with a moving average preceded by a clipper to determine the heart rate. The results obtained were compared with the heart rate readings from a pulse oximeter. With a mean difference of 2.6 bpm, the heart rate from Doppler radar matched that from the pulse oximeter most frequently when the peak finding algorithm was used. The results obtained using autocorrelation and peak finding algorithm (with standard deviations of 2.6 bpm and 4.0 bpm) suggest that a single channel Doppler radar system can be a viable alternative to contact heart rate monitors in patients for whom contact measurements are not feasible.

关键词： Heart rate Pulse oximeter Finite impulse response filters Signal processing algorithms filtering algorithms Doppler radar Autocorrelation

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A Fast Converge Spectral Modulation Sensitive Active Noise Control System

A Fast Converge Spectral Modulation Sensitive Active Noise C...

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14th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Cheong, Kah-Meng Shen, Yih-Liang Chi, Tai-Shih Natl Yang Ming Chiao Tung Univ Dept Elect & Elect Engn Hsinchu Taiwan

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Psychoacoustic active noise control (PANC) systems have been proposed to improve the noise reduction performance of active noise control (ANC) systems by considering hearing properties. PANC systems are usually implemented in the filtered-x least mean square (FxLMS) architecture. In this paper, we propose a PANC system using a simple and stable fast affine projection (FAP) algorithm in the filtered-x architecture, namely the filtered-x conjugate gradient fast affine projection (FxCGFAP) PANC. The proposed FxCGFAP PANC system converges faster than typical FxLMS ANC and PANC systems. The proposed PANC system considers not only the sensitivity of human hearing to frequency but also the sensitivity to spectral modulation. Objective and subjective evaluations have been conducted. The evaluation results show that the proposed system outperforms the other three systems in terms of objective loudness scores and subjective ratings. The proposed system has been implemented on the Tensilica HiFi3 DSP platform with the fixed-point data format.

关键词： Psychoacoustics Sensitivity Frequency modulation Noise reduction Auditory system Information processing filtering algorithms

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Rejecting Impulse Artifacts from Surface EMG Signals using Real-time Cumulative Histogram filtering 43

Rejecting Impulse Artifacts from Surface EMG Signals using R...

引用

43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Yeon, Seong Ho Herr, Hugh M. MIT Program Media Arts & Sci 77 Massachusetts Ave Cambridge MA 02139 USA MIT Ctr Extreme Bion 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728111797

This paper presents a cumulative histogram filtering (CHF) algorithm to filter impulsive artifacts within surface electromyograhy (sEMG) signal for time-domain signal feature extraction. The proposed CHF algorithm filters sEMG signals by extracting a continuous subset of amplitude-sorted values within a real-time window of measured samples using information about the probabilistic distribution of sEMG amplitude. For real-time deployment of the proposed CHF algorithm on an embedded computing platform, we also present an efficient, iterative implementation of the proposed algorithm. The proposed CHF algorithm was evaluated on synthetic impulse artifacts superimposed upon undisturbed sEMG recorded from a subject with transtibial amputation. Results suggest that the CHF algorithm effectively suppresses the simulated impulse artifacts while preserving a minimum signal-to-noise ratio of 95% and an average Pearson correlation of 0.99 compared to the undisturbed sEMG recordings.

关键词： Histograms Embedded computing filtering algorithms Information filters Feature extraction Probabilistic logic Real-time systems

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Extracting Vignetting and Grain Filter Effects from Photos 22

Extracting Vignetting and Grain Filter Effects from Photos

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22nd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Abdelhamed, Abdelrahman Yim, Jonghwa Punnappurath, Abhijith Brown, Michael S. Choe, Jihwan Kim, Kihwan Samsung AI Ctr Toronto ON Canada Samsung Elect Suwon South Korea NCSOFT Seongnam Si South Korea

ISBN: (纸本)9781665409155

Most smartphones support the use of real-time camera filters to impart visual effects to captured images. Currently, such filters come preinstalled on-device or need to be downloaded and installed before use (e.g., Instagram filters). Recent work [24] proposed a method to extract a camera filter directly from an example photo that has already had a filter applied. The work in [24] focused only on the color and tonal aspects of the underlying filter. In this paper, we introduce a method to extract two spatially varying effects commonly used by on-device camera filters-namely, image vignetting and image grain. Specifically, we show how to extract the parameters for vignetting and image grain present in an example image and replicate these effects as an on-device filter. We use lightweight CNNs to estimate the filter parameters and employ efficient techniques-isotropic Gaussian filters and simplex noise-for regenerating the filters. Our design achieves a reasonable trade-off between efficiency and realism. We show that our method can extract vignetting and image grain filters from stylized photos and replicate the filters on captured images more faithfully, as compared to color and style transfer methods. Our method is significantly efficient and has been already deployed to millions of flagship smartphones.

关键词： Image color analysis Multimedia Web sites Neural networks filtering algorithms Cameras Information filters Visual effects

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LiDAR-based Obstacle Detection and Avoidance for Autonomous Vehicles using Raspberry Pi 3B 13

LiDAR-based Obstacle Detection and Avoidance for Autonomous ...

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13th UK-Automatic-Control-Council (UKACC) International Conference on Control (CONTROL)

作者： Calcroft, Max Khan, Asiya Univ Plymouth Sch Engn Comp & Math SECaM Plymouth PL4 8AA Devon England Univ Plymouth SECaM Plymouth PL4 8AA Devon England

ISBN: (纸本)9781665452007

Autonomous vehicles are redefining the transport industry - obstacle detection and avoidance are key to their operation. A number of sensor technologies have been developed and trialled. This paper presents the implementation of a Hokuyo URG-04LX Light Detection And Ranging (LiDAR) sensor on an autonomous vehicle developed with a Raspberry Pi 3B microcontroller and demonstrates its effectiveness for object detection and avoidance in varying conditions. The LiDAR sensor was integrated with the Raspberry Pi 3B using Python on LUbuntu (lightweight version of Ubuntu) and Robot Operating System (ROS). Various scenarios with low light conditions, reflective surfaces at multiple angles, simple stopping tests and different motion paths at varying speeds were tested. All tests were run at 3.2 and 4mph speed. It was found that the LiDAR sensor performed well for basic object detection but did not respond well to reflective or dark surfaces. We further compared the LiDAR's performance with ultrasonic sensors and found that it outperformed ultrasonic sensors for stopping distances. Overall, the LiDAR acts as an effective sensor for the autonomous vehicle, showing its viability in detecting objects and acting as a small scale representation of autonomous technology.

关键词： Point cloud compression Industries Laser radar Microcontrollers Operating systems Object detection filtering algorithms

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