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Journal articles on the topic 'Multi-channel SVM'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

谢, 荣生. "A Robust Image Zero-Watermarking Algorithm Based on Multi-Channel and SVM." Journal of Image and Signal Processing 09, no. 01 (2020): 65–70. http://dx.doi.org/10.12677/jisp.2020.91008.

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Peruffo Minotto, Vicente, Claudio Rosito Jung, and Bowon Lee. "Multimodal Multi-Channel On-Line Speaker Diarization Using Sensor Fusion Through SVM." IEEE Transactions on Multimedia 17, no. 10 (2015): 1694–705. http://dx.doi.org/10.1109/tmm.2015.2463722.

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Kang, Ji-yeon, Sung-yoon Cho, and Hyun-woo Oh. "Sleep Stage Evaluation System Based on Multi-Channel Sensors Using Kernel SVM." Journal of Korean Institute of Communications and Information Sciences 46, no. 1 (2021): 154–61. http://dx.doi.org/10.7840/kics.2021.46.1.154.

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Fawad, Muhammad Jamil Khan, MuhibUr Rahman, Yasar Amin, and Hannu Tenhunen. "Low-Rank Multi-Channel Features for Robust Visual Object Tracking." Symmetry 11, no. 9 (2019): 1155. http://dx.doi.org/10.3390/sym11091155.

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Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), R

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Xiong, Jiping, Lisang Cai, Fei Wang, and Xiaowei He. "SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals." Sensors 17, no. 3 (2017): 506. http://dx.doi.org/10.3390/s17030506.

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Lin, Bi, Xie Wei, and Zhao Junjie. "Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM." Computers & Geosciences 123 (February 2019): 111–20. http://dx.doi.org/10.1016/j.cageo.2018.10.008.

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Xi, Chenbo, Guangyou Yang, Lang Liu, Hongyuan Jiang, and Xuehai Chen. "A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery." Entropy 23, no. 1 (2021): 128. http://dx.doi.org/10.3390/e23010128.

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In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of

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FUTAMATA, Masachika, Kentaro NAGATA, Masafumi YAMADA, and Kazushige MAGATANI. "1P1-G04 Hand Motion Recognition System by using SVM and Multi-channel Electrodes(Non-contact Sensing)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _1P1—G04_1—_1P1—G04_2. http://dx.doi.org/10.1299/jsmermd.2011._1p1-g04_1.

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Ye, Qing, Shaohu Liu, and Changhua Liu. "A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals." Sensors 20, no. 15 (2020): 4300. http://dx.doi.org/10.3390/s20154300.

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Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic m

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Zhu, Hou Quan, Rui Ming Fang, and Chang Qing Peng. "The Application of ICA-SVM Method for Identifying Multiple Faults in Asynchronous Motors." Applied Mechanics and Materials 483 (December 2013): 405–8. http://dx.doi.org/10.4028/www.scientific.net/amm.483.405.

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Vibration signal of asynchronous motors are detected with multiple sensors and used to identifying multiple faults of motors in this paper. Independent components analysis method (ICA) is applied to compress measurements from several channels into a smaller amount of channel combinations and separate related vibration signal from interfering vibration sources, and support vector machine (SVM) based multi-class classifiers are used to identify multiple faults of asynchronous motors. Adventures of the proposed methods are that they are data-based and are not necessary to build an analytical mode

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Vu, Tuan-Hung, Jacques Boonaert, Sebastien Ambellouis, and Abdelmalik Taleb-Ahmed. "Multi-Channel Generative Framework and Supervised Learning for Anomaly Detection in Surveillance Videos." Sensors 21, no. 9 (2021): 3179. http://dx.doi.org/10.3390/s21093179.

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Recently, most state-of-the-art anomaly detection methods are based on apparent motion and appearance reconstruction networks and use error estimation between generated and real information as detection features. These approaches achieve promising results by only using normal samples for training steps. In this paper, our contributions are two-fold. On the one hand, we propose a flexible multi-channel framework to generate multi-type frame-level features. On the other hand, we study how it is possible to improve the detection performance by supervised learning. The multi-channel framework is b

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Alturki, Fahd A., Khalil AlSharabi, Akram M. Abdurraqeeb, and Majid Aljalal. "EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques." Sensors 20, no. 9 (2020): 2505. http://dx.doi.org/10.3390/s20092505.

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Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The in

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Liu, Lizheng, Jianjun Cui, Jian Niu, et al. "Design of Mirror Therapy System Base on Multi-Channel Surface-Electromyography Signal Pattern Recognition and Mobile Augmented Reality." Electronics 9, no. 12 (2020): 2142. http://dx.doi.org/10.3390/electronics9122142.

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Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern

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Liu, Mingtang, Li Wang, Zening Qin, Jiaqi Liu, Jian Chen, and Xuemei Liu. "Multi-scale feature extraction and recognition of slope damage in high fill channel based on Gabor-SVM method." Journal of Intelligent & Fuzzy Systems 38, no. 4 (2020): 4237–46. http://dx.doi.org/10.3233/jifs-190767.

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Kheira, Djelloul, and M. Beladgham. "Performance of channel selection used for Multi-class EEG signal classification of motor imagery." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1305. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1305-1312.

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<p>In this paper, a study of a non-invasive brain-machine interfaces for the classification of 4 imaginary are presented. Performance comparisons using time-frequency analysis between the Linear Discriminant Analysis motor activities (left hand, right hand, foot, tongue) with the BCI competition III dataset IIIa is (LDA), the Support Vector Machine (SVM) and the K-Nearest Neighbors (KNN) algorithms have been carried. The number and position of electrodes for each subject were investigated to provide an improvement for the classification accuracy of the algorithm. Results show that the el

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Direito, Bruno, César A. Teixeira, Francisco Sales, Miguel Castelo-Branco, and António Dourado. "A Realistic Seizure Prediction Study Based on Multiclass SVM." International Journal of Neural Systems 27, no. 03 (2017): 1750006. http://dx.doi.org/10.1142/s012906571750006x.

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A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 12

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Chen, Shi, Gong, et al. "True-Color Three-Dimensional Imaging and Target Classification Based on Hyperspectral LiDAR." Remote Sensing 11, no. 13 (2019): 1541. http://dx.doi.org/10.3390/rs11131541.

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True-color three-dimensional (3D) imaging exploits spatial and spectral information and can enable accurate feature extraction and object classification. The existing methods, however, are limited by data collection mechanisms when realizing true-color 3D imaging. We overcome this problem and present a novel true-color 3D imaging method based on a 32-channel hyperspectral LiDAR (HSL) covering a 431–751 nm spectral range. We conducted two experiments, one with nine-color card papers and the other with seven different colored objects. We used the former to investigate the effect of true-color 3D

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Yu, Dongbing, and Yu Gu. "A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose." Foods 10, no. 4 (2021): 795. http://dx.doi.org/10.3390/foods10040795.

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Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofe

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Shao, Shiliang, Ting Wang, Yun Su, Chen Yao, Chunhe Song, and Zhaojie Ju. "Multi-IMF Sample Entropy Features with Machine Learning for Surface Texture Recognition Based on Robot Tactile Perception." International Journal of Humanoid Robotics 18, no. 02 (2021): 2150005. http://dx.doi.org/10.1142/s0219843621500055.

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Discrimination of surface textures using tactile sensors has attracted increasing attention. Intelligent robotics with the ability to recognize and discriminate the surface textures of grasped objects are crucial. In this paper, a novel method for surface texture classification based on tactile signals is proposed. For the proposed method, first, the tactile signals of each channel (X, Y, Z, and S) are decomposed based on empirical mode decomposition (EMD). Then, the intrinsic mode functions (IMFs) are obtained. Second, based on the multiple IMFs, the sample entropy is calculated for each IMF.

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YANG, NAN, HU-CHUAN LU, GUO-LIANG FANG, and GANG YANG. "AN EFFECTIVE FRAMEWORK FOR AUTOMATIC SEGMENTATION OF HARD EXUDATES IN FUNDUS IMAGES." Journal of Circuits, Systems and Computers 22, no. 01 (2013): 1250075. http://dx.doi.org/10.1142/s0218126612500752.

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In this paper, we propose an effective framework to automatically segment hard exudates (HEs) in fundus images. Our framework is based on a coarse-to-fine strategy, as we first get a coarse result allowed of some negative samples, then eliminate the negative samples step by step. In our framework, we make the most of the multi-channel information by employing a boosted soft segmentation algorithm. Additionally, we develop a multi-scale background subtraction method to obtain the coarse segmentation result. After subtracting the optical disc (OD) region from the coarse result, the HEs are extra

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Nguyen, Hai Thanh, Cuong Quoc Ngo, and Hung Viet Nguyen. "PR-SVM algorithm for recognition of human hand tapping using functional near infrared spectroscopy." Science and Technology Development Journal 16, no. 3 (2013): 5–17. http://dx.doi.org/10.32508/stdj.v16i3.1607.

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Researches of human Brain Computer Interface (BCI) for the objective of diagnosis and rehabilitation have been recently increased. Cerebral oxygenation and blood flow on particular regions of human brain can be measured using a non-invasive technique – fNIRS (functional Near Infrared Spectroscopy). In this paper, a study of recognition algorithm will be described for recognizing whether one taps his/her left hand or right hand. Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky- Golay filter for getting more smoothly fNIRS data. Characte

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Parathai, Phetcharat, Naruephorn Tengtrairat, Wai Lok Woo, Mohammed A. M. Abdullah, Gholamreza Rafiee, and Ossama Alshabrawy. "Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM." Sensors 20, no. 16 (2020): 4368. http://dx.doi.org/10.3390/s20164368.

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This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector mach

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ZHANG, YUE, GANGSHENG CAO, TONGTONG ZHAO, HANYANG ZHANG, JUNTIAN ZHANG, and CHUNMING XIA. "A PILOT STUDY OF MECHANOMYOGRAPHY-BASED HAND MOVEMENTS RECOGNITION EMPHASIZING ON THE INFLUENCE OF FABRICS BETWEEN SENSOR AND SKIN." Journal of Mechanics in Medicine and Biology 20, no. 08 (2020): 2050054. http://dx.doi.org/10.1142/s0219519420500542.

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Multi-channel mechanomyography (MMG) signals were acquired from the forearm when the subjects were performing eight classes of hand movements related to rehabilitation training. Ten time domain (TD) features and wavelet packet node energy (WPNE) features were extracted from each channel of MMG, and the hand movements were classified by support vector machine (SVM), extreme learning machine (ELM), linear discriminant analysis (LDA) and [Formula: see text]-nearest neighborhood (KNN) and the classifying results of three methods of collecting MMG (sensors directly on skin, sensors on cotton fabric

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Kim, Ju-Won, Kassahun Demissie Tola, Dai Quoc Tran, and Seunghee Park. "MFL-Based Local Damage Diagnosis and SVM-Based Damage Type Classification for Wire Rope NDE." Materials 12, no. 18 (2019): 2894. http://dx.doi.org/10.3390/ma12182894.

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Wire ropes used in various applications such as elevators and cranes to safely carry heavy weights are vulnerable to breakage or cross-sectional loss caused by the external environment. Such damage can pose a serious risk to the safety of the entire structure because damage under tensile force rapidly expands due to concentration of stress. In this study, the magnetic flux leakage (MFL) method was applied to diagnose cuts, corrosion, and compression damage in wire ropes. Magnetic flux signals were measured by scanning damaged wire rope specimens using a multi-channel sensor head and a compact

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Zhang, Tao, Hong Wang, Jichi Chen, and Enqiu He. "Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms." Entropy 22, no. 11 (2020): 1248. http://dx.doi.org/10.3390/e22111248.

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Unfavorable driving states can cause a large number of vehicle crashes and are significant factors in leading to traffic accidents. Hence, the aim of this research is to design a robust system to detect unfavorable driving states based on sample entropy feature analysis and multiple classification algorithms. Multi-channel Electroencephalography (EEG) signals are recorded from 16 participants while performing two types of driving tasks. For the purpose of selecting optimal feature sets for classification, principal component analysis (PCA) is adopted for reducing dimensionality of feature sets

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Chen, Minjie, and Honghai Liu. "Robot arm control method using forearm EMG signals." MATEC Web of Conferences 309 (2020): 04007. http://dx.doi.org/10.1051/matecconf/202030904007.

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With the continuous improvement of control technology and the continuous improvement of people’s living standards, the needs of disabled people for high-quality prosthetics have become increasingly strong. A control method of robotic arm based on surface electromyography signal (sEMG) of forearm is proposed. Firstly, the 16-channel EMG data of the forearm is obtained via the multi-channel EMG acquisition instrument and the electrode cuff as input signals, the features are extracted, then the gestures are classified and identified by the support-vector machine (SVM) algorithm, and the signals a

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Saraclar, M., Y. P. Kahya, and I. Sen. "Computerized Diagnosis of Respira tory Disorders." Methods of Information in Medicine 53, no. 04 (2014): 291–95. http://dx.doi.org/10.3414/me13-02-0041.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: This work proposes an algorithm for diagnostic classification of multi-channel respiratory sounds.Methods: 14-channel respiratory sounds are modeled assuming a 250-point second order vector autoregressive (VAR) process, and the estimated model parameters are used to feed a support vector machine (SVM) classifier. Both a three-class classifier (healthy, bronchi ectasis and interstitial p

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You, Shingchern D. "Classification of Relaxation and Concentration Mental States with EEG." Information 12, no. 5 (2021): 187. http://dx.doi.org/10.3390/info12050187.

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In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency repr

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LIAQAT, AMNA, MUHAMMAD ATTIQUE KHAN, JAMAL HUSSAIN SHAH, MUHAMMAD SHARIF, MUSSARAT YASMIN, and STEVEN LAWRENCE FERNANDES. "AUTOMATED ULCER AND BLEEDING CLASSIFICATION FROM WCE IMAGES USING MULTIPLE FEATURES FUSION AND SELECTION." Journal of Mechanics in Medicine and Biology 18, no. 04 (2018): 1850038. http://dx.doi.org/10.1142/s0219519418500380.

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In the area of medical imaging and computer vision, automatic diagnosis of ulcer and bleeding from wireless capsule endoscopy images has been an active research domain. It contains several challenges including low contrast, complex background, lesion shape and color which affect its segmentation and classification accuracy. In this article, a novel method for automated detection and classification of stomach infection is implemented. The proposed method consists of four major steps including preprocessing, lesion segmentation, image representation and classification. The lesion contrast is imp

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Yao, Xudong, Qing Guo, and An Li. "Light-Weight Cloud Detection Network for Optical Remote Sensing Images with Attention-Based DeeplabV3+ Architecture." Remote Sensing 13, no. 18 (2021): 3617. http://dx.doi.org/10.3390/rs13183617.

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Clouds in optical remote sensing images cause spectral information change or loss, that affects image analysis and application. Therefore, cloud detection is of great significance. However, there are some shortcomings in current methods, such as the insufficient extendibility due to using the information of multiple bands, the intense extendibility due to relying on some manually determined thresholds, and the limited accuracy, especially for thin clouds or complex scenes caused by low-level manual features. Combining the above shortcomings and the requirements for efficiency in practical appl

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Li, Lu, Chao Wang, Hong Zhang, Bo Zhang, and Fan Wu. "Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network." Remote Sensing 11, no. 9 (2019): 1091. http://dx.doi.org/10.3390/rs11091091.

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With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression probl

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Yuan, Qi, Weidong Zhou, Fangzhou Xu, Yan Leng, and Dongmei Wei. "Epileptic EEG Identification via LBP Operators on Wavelet Coefficients." International Journal of Neural Systems 28, no. 08 (2018): 1850010. http://dx.doi.org/10.1142/s0129065718500107.

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The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time–frequency decomposition of EEG signals. After that, the “uniform” LBP operator is carried out on

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Mai, Ngoc-Dau, Boon-Giin Lee, and Wan-Young Chung. "Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device." Sensors 21, no. 15 (2021): 5135. http://dx.doi.org/10.3390/s21155135.

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In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our propo

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Xie, Dongri, Haixin Sun, and Jie Qi. "A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise." Entropy 22, no. 6 (2020): 620. http://dx.doi.org/10.3390/e22060620.

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Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN

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Lv, Jiali, Jian Wei, Zhenyu Wang, and Jin Cao. "Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network." Molecules 24, no. 24 (2019): 4590. http://dx.doi.org/10.3390/molecules24244590.

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Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of

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Bhatti, Ghulam. "Machine Learning Based Localization in Large-Scale Wireless Sensor Networks." Sensors 18, no. 12 (2018): 4179. http://dx.doi.org/10.3390/s18124179.

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The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually po

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Lee, Jung-Hwan, Eun-Seok Ryu, and Hyuck Yoo. "Multi-channel Adaptive SVC Video Streaming with ROI." Journal of Broadcast Engineering 13, no. 1 (2008): 34–42. http://dx.doi.org/10.5909/jbe.2008.13.1.34.

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Huang, Cui Cui, Li Hua Sun, and Liang Jun Yu. "Multi-Channel Stepping Motors Driven Based on SCM." Advanced Materials Research 722 (July 2013): 336–40. http://dx.doi.org/10.4028/www.scientific.net/amr.722.336.

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Stepper motor is an electromechanical device, which converts electrical pulses into discrete mechanical movements, and it is used in varies kinds of automatic control system widely. This paper introduces a driver control system of multi-channel stepping motors based on SCM, the system can realizes the controlling of at most 16 motors with two groups of SCM I/O, and in the system, the motors can work with different rate and excitation manner through the piece selecting function of 74573.

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SUN, Huan, Sheng MENG, Yan WANG, and Xiaohu YOU. "Sum-Rate Evaluation of Multi-User MIMO-Relay Channel." IEICE Transactions on Communications E92-B, no. 2 (2009): 683–86. http://dx.doi.org/10.1587/transcom.e92.b.683.

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Yoneda, E., K. I. Suto, K. Kikushima, and H. Yoshinaga. "Fully engineered multi-channel FM-SCM video distribution systems." Journal of Lightwave Technology 12, no. 2 (1994): 362–68. http://dx.doi.org/10.1109/50.350585.

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Huang, Rongsheng, Hongqiang Zhai, Chi Zhang, and Yuguang Fang. "SAM-MAC: An efficient channel assignment scheme for multi-channel ad hoc networks." Computer Networks 52, no. 8 (2008): 1634–46. http://dx.doi.org/10.1016/j.comnet.2008.02.004.

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Wen, Huan Fei, Yasuhiro Sugawara, and Yan Jun Li. "Multi-Channel Exploration of O Adatom on TiO2(110) Surface by Scanning Probe Microscopy." Nanomaterials 10, no. 8 (2020): 1506. http://dx.doi.org/10.3390/nano10081506.

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We studied the O2 dissociated state under the different O2 exposed temperatures with atomic resolution by scanning probe microscopy (SPM) and imaged the O adatom by simultaneous atomic force microscopy (AFM)/scanning tunneling microscopy (STM). The effect of AFM operation mode on O adatom contrast was investigated, and the interaction of O adatom and the subsurface defect was observed by AFM/STM. Multi-channel exploration was performed to investigate the charge transfer between the adsorbed O and the TiO2(110) by obtaining the frequency shift, tunneling current and local contact potential diff

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Yang, Shuailong, Liu Yang, Fengguang Luo, et al. "Multi-channel multi-task optical performance monitoring based multi-input multi-output deep learning and transfer learning for SDM." Optics Communications 495 (September 2021): 127110. http://dx.doi.org/10.1016/j.optcom.2021.127110.

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Wang Lijun, Xing Long, and Zhou Jianbin. "Multi-Channel Peripheral Data Acquisition System based on Labview and SCM." Journal of Convergence Information Technology 7, no. 22 (2012): 359–67. http://dx.doi.org/10.4156/jcit.vol7.issue22.42.

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45

Geraci, Giovanni, Malcolm Egan, Jinhong Yuan, Adeel Razi, and Iain B. Collings. "Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding." IEEE Transactions on Communications 60, no. 11 (2012): 3472–82. http://dx.doi.org/10.1109/tcomm.2012.072612.110686.

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46

Benachour, P., P. G. Farrell, and B. Honary. "Direct sum DC-free coding schemes for multi-user adder channel." Electronics Letters 37, no. 25 (2001): 1527. http://dx.doi.org/10.1049/el:20011038.

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47

Wang, Menghan, and Dongming Wang. "Sum-Rate of Multi-User MIMO Systems with Multi-Cell Pilot Contamination in Correlated Rayleigh Fading Channel." Entropy 21, no. 6 (2019): 573. http://dx.doi.org/10.3390/e21060573.

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This paper presents some exact results on the sum-rate of multi-user multiple-input multiple-output (MU-MIMO) systems subject to multi-cell pilot contamination under correlated Rayleigh fading. With multi-cell multi-user channel estimator, we give the lower bound of the sum-rate. We derive the moment generating function (MGF) of the sum-rate and then obtain the closed-form approximations of the mean and variance of the sum-rate. Then, with Gaussian approximation, we study the outage performance of the sum-rate. Furthermore, considering the number of antennas at base station becomes infinite, w

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Zhang, Xudong, and Haiyu Ji. "Analysis and Simulation about a Simplified Model for Modulation and Demodulation of Ship-borne Single Channel Monopulse Radar." MATEC Web of Conferences 232 (2018): 04043. http://dx.doi.org/10.1051/matecconf/201823204043.

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A simplified mathematical model for modulation and demodulation of a single channel monopulse (SCM) system is proposed, which is based on a ship-borne pulse radar S-band guided receiver. Using the proposed mathematical model, the modulation and demodulation of single-channel and multi-channel signals were simulated respectively, and the factors influencing the modulation and demodulation of the SCM system were analysed.

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Liu, Bin, Pei Kang Bai, Yu Xin Li, and He Ping Liu. "The Relationship of Processing Parameters and Surface Topography of Selective Laser Melted GH4169 Alloy." Materials Science Forum 893 (March 2017): 207–11. http://dx.doi.org/10.4028/www.scientific.net/msf.893.207.

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Selective laser melting (SLM) technology based on powder bed has been used to manufacture GH4169 samples. In the present work, taking Ni-based powders of GH4169 as experiment material, the good technological parameters of SLM were determined by analyzing the effect of the laser electric current, the scan speed ,the laser pulse width, the laser light frequency, the push powder thickness, the scan interval and the scanning way on Single-layer single-channel scanning, single-layer multi-channel scanning and multi-level multi-channel scanning. The effects of process parameters on the powder formab

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Rim, You Seung, Min-Jong Keum, Yongmin Baek, Byung Moo Lee, and Kyung Hwan Kim. "Film Density Controlled-InGaZnO Multi-Stacked Channel Based Thin-Film Transistors Using a Solution Process." Science of Advanced Materials 9, no. 9 (2017): 1578–82. http://dx.doi.org/10.1166/sam.2017.3175.

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