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https://www.readbyqxmd.com/read/29745595/-control-of-intelligent-car-based-on-electroencephalogram-and-neurofeedback
#1
Song Li, Xin Xiong, Yunfa Fu
To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online...
February 1, 2018: Sheng Wu Yi Xue Gong Cheng Xue za Zhi, Journal of Biomedical Engineering, Shengwu Yixue Gongchengxue Zazhi
https://www.readbyqxmd.com/read/29745561/-image-segmentation-and-classification-of-cytological-cells-based-on-multi-features-clustering-and-chain-splitting-model
#2
Pin Wang, Qianqian Liu, Lirui Wang, Yongming Li, Shujun Liu, Fang Yan
The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm (MFMS) was applied to localize regions of nuclei. Then, chain splitting model (CSM) containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness...
August 1, 2017: Sheng Wu Yi Xue Gong Cheng Xue za Zhi, Journal of Biomedical Engineering, Shengwu Yixue Gongchengxue Zazhi
https://www.readbyqxmd.com/read/29610069/sparse-representation-based-radiomics-for-the-diagnosis-of-brain-tumors
#3
Guoqing Wu, Yinsheng Chen, Yuanyuan Wang, Jinhua Yu, Xiaofei Lv, Xue Ju, Zhifeng Shi, Liang Chen, Zhongping Chen
Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors...
April 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/29566042/multi-features-taxi-destination-prediction-with-frequency-domain-processing
#4
Lei Zhang, Guoxing Zhang, Zhizheng Liang, Ekene Frank Ozioko
The traditional taxi prediction methods model the taxi trajectory as a sequence of spatial points. It cannot represent two-dimensional spatial relationships between trajectory points. Therefore, many methods transform the taxi GPS trajectory into a two-dimensional image, and express the spatial correlations by trajectory image. However, the trajectory image may have noise and sparsity according to trajectory data characteristics. So, we import image frequency domain processing to taxi destination prediction to reduce noise and sparsity, then propose multi-features taxi destination prediction with frequency domain processing (MTDP-FD) method...
2018: PloS One
https://www.readbyqxmd.com/read/29502452/auditory-mismatch-negativity-and-p300a-elicited-by-the-optimal-multi-feature-paradigm-in-early-schizophrenia
#5
Derek J Fisher, Debra J Campbell, Shelagh C Abriel, Emma M L Ells, Erica D Rudolph, Philip G Tibbo
The mismatch negativity (MMN) is an EEG-derived event-related potential (ERP) elicited by any violation of a predicted auditory "rule," regardless of whether one is attending to the stimuli and is thought to reflect updating of the stimulus context. Redirection of attention toward a rare, distracting stimulus event, however, can be measured by the subsequent P3a component of the P300. Chronic schizophrenia patients exhibit robust MMN deficits, as well as reductions in P3a amplitude. While, the substantial literature on the MMN in first-episode and early phase schizophrenia in this population reports reduced amplitudes, there also exist several contradictory studies...
March 1, 2018: Clinical EEG and Neuroscience: Official Journal of the EEG and Clinical Neuroscience Society (ENCS)
https://www.readbyqxmd.com/read/29382073/multi-feature-classification-of-multi-sensor-satellite-imagery-based-on-dual-polarimetric-sentinel-1a-landsat-8-oli-and-hyperion-images-for-urban-land-cover-classification
#6
Tao Zhou, Zhaofu Li, Jianjun Pan
This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy...
January 27, 2018: Sensors
https://www.readbyqxmd.com/read/29374541/healthy-full-term-infants-brain-responses-to-emotionally-and-linguistically-relevant-sounds-using-a-multi-feature-mismatch-negativity-mmn-paradigm
#7
Kaisamari Kostilainen, Valtteri Wikström, Satu Pakarinen, Mari Videman, Linnea Karlsson, Maria Keskinen, Noora M Scheinin, Hasse Karlsson, Minna Huotilainen
We evaluated the feasibility of a multi-feature mismatch negativity (MMN) paradigm in studying auditory processing of healthy newborns. The aim was to examine the automatic change-detection and processing of semantic and emotional information in speech in newborns. Brain responses of 202 healthy newborns were recorded with a multi-feature paradigm including a Finnish bi-syllabic pseudo-word/ta-ta/as a standard stimulus, six linguistically relevant deviant stimuli and three emotionally relevant stimuli (happy, sad, angry)...
March 23, 2018: Neuroscience Letters
https://www.readbyqxmd.com/read/29243947/multiple-target-based-pharmacophore-design-from-active-site-structures
#8
P Kumar, R Kaalia, A Srinivasan, I Ghosh
Health care systems have benefitted from rational drug discovery processes like vHTS, virtual high throughput screening pharmacophores and quantitative structure-activity relationships, and many challenges have been explored using such techniques: decisions on specificity and selectivity are made after screening millions of molecules for multiple targets. Recent challenges in drug research emphasize the design of drugs that bind with more than one target of interest (multi-target) and do not bind with undesirable targets...
January 2018: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/29239858/prediction-of-breast-cancer-risk-using-a-machine-learning-approach-embedded-with-a-locality-preserving-projection-algorithm
#9
Morteza Heidari, Abolfazl Zargari Khuzani, Alan B Hollingsworth, Gopichandh Danala, Seyedehnafiseh Mirniaharikandehei, Yuchen Qiu, Hong Liu, Bin Zheng
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative...
January 30, 2018: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/29220315/background-modeling-by-stability-of-adaptive-features-in-complex-scenes
#10
Dan Yang, Chenqiu Zhao, Xiaohong Zhang, Sheng Huang
The single-feature-based background model often fails in complex scenes, since a pixel is better described by several features, which highlight different characteristics of it. Therefore, the multi-feature-based background model has drawn much attention recently. In this paper, we propose a novel multi-feature-based background model, named stability of adaptive feature (SoAF) model, which utilizes the stabilities of different features in a pixel to adaptively weigh the contributions of these features for foreground detection...
March 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29201134/multi-feature-machine-learning-model-for-automatic-segmentation-of-green-fractional-vegetation-cover-for-high-throughput-field-phenotyping
#11
Pouria Sadeghi-Tehran, Nicolas Virlet, Kasra Sabermanesh, Malcolm J Hawkesford
Background: Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. Results: In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images...
2017: Plant Methods
https://www.readbyqxmd.com/read/29186756/robust-vehicle-detection-in-aerial-images-based-on-cascaded-convolutional-neural-networks
#12
Jiandan Zhong, Tao Lei, Guangle Yao
Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization...
November 24, 2017: Sensors
https://www.readbyqxmd.com/read/29186295/systematic-analysis-and-prediction-of-type-iv-secreted-effector-proteins-by-machine-learning-approaches
#13
Jiawei Wang, Bingjiao Yang, Yi An, Tatiana Marquez-Lago, André Leier, Jonathan Wilksch, Qingyang Hong, Yang Zhang, Morihiro Hayashida, Tatsuya Akutsu, Geoffrey I Webb, Richard A Strugnell, Jiangning Song, Trevor Lithgow
In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis...
November 27, 2017: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/29104279/two-stage-multi-task-representation-learning-for-synthetic-aperture-radar-sar-target-images-classification
#14
Xinzheng Zhang, Yijian Wang, Zhiying Tan, Dong Li, Shujun Liu, Tao Wang, Yongming Li
In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a ℓ 2 , 1 -norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset. The second stage of the method is to perform target images classification based on the new dictionary, utilizing multi-task collaborative representation...
November 1, 2017: Sensors
https://www.readbyqxmd.com/read/29068358/visual-localization-across-seasons-using-sequence-matching-based-on-multi-feature-combination
#15
Yongliang Qiao
Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method...
October 25, 2017: Sensors
https://www.readbyqxmd.com/read/28958416/the-functional-effects-of-prior-motion-imagery-and-motion-perception
#16
Shuai Chang, Joel Pearson
The functional sensory effects and commonalties between mental imagery of different visual features such as color, form or motion remains largely unknown. Mental imagery of static visual features, including color and orientation, can have a facilitative, priming effect on subsequent perception. However, whether motion imagery can have a similar effect remains unknown. Here we used the binocular rivalry method as a measure of motion mental imagery. After imagining or viewing motion of a particular direction, participants were required to report the dominant motion direction in a brief motion rivalry stimulus...
September 9, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28937647/identification-of-dna-binding-proteins-using-mixed-feature-representation-methods
#17
Kaiyang Qu, Ke Han, Song Wu, Guohua Wang, Leyi Wei
DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins...
September 22, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/28901574/using-a-multi-feature-paradigm-to-measure-mismatch-responses-to-minimal-sound-contrasts-in-children-with-cochlear-implants-and-hearing-aids
#18
Inger Uhlén, Elisabet Engström, Petter Kallioinen, Cecilia Nakeva von Mentzer, Björn Lyxell, Birgitta Sahlén, Magnus Lindgren, Marianne Ors
Our aim was to explore whether a multi-feature paradigm (Optimum-1) for eliciting mismatch negativity (MMN) would objectively capture difficulties in perceiving small sound contrasts in children with hearing impairment (HI) listening through their hearing aids (HAs) and/or cochlear implants (CIs). Children aged 5-7 years with HAs, CIs and children with normal hearing (NH) were tested in a free-field setting using a multi-feature paradigm with deviations in pitch, intensity, gap, duration, and location. There were significant mismatch responses across all subjects that were positive (p-MMR) for the gap and pitch deviants (F(1,43) = 5...
October 2017: Scandinavian Journal of Psychology
https://www.readbyqxmd.com/read/28866731/classification-of-focal-and-non-focal-epileptic-seizures-using-multi-features-and-svm-classifier
#19
N Sriraam, S Raghu
Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed...
September 2, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28865333/assessing-mood-symptoms-through-heartbeat-dynamics-an-hrv-study-on-cardiosurgical-patients
#20
Claudio Gentili, Simone Messerotti Benvenuti, Daniela Palomba, Alberto Greco, Enzo Pasquale Scilingo, Gaetano Valenza
BACKGROUND: Heart Rate Variability (HRV) is reduced both in depression and in coronary heart disease (CHD) suggesting common pathophysiological mechanisms for the two disorders. Within CHD, cardiac surgery patients (CSP) with postoperative depression are at greater risk of adverse cardiac events. Therefore, CSP would especially benefit from depression early diagnosis. Here we tested whether HRV-multi-feature analysis discriminates CSP with or without depression and provides an effective estimation of symptoms severity...
December 2017: Journal of Psychiatric Research
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