87,575 results match your criteria IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council[Journal]


Hidden Markov Model-Based Nonfragile State Estimation of Switched Neural Network With Probabilistic Quantized Outputs.

IEEE Trans Cybern 2019 Apr 17. Epub 2019 Apr 17.

This paper focuses on the state estimator design problem for a switched neural network (SNN) with probabilistic quantized outputs, where the switching process is governed by a sojourn probability. It is assumed that both packet dropouts and signal quantization exist in communication channels. Asynchronous estimator and quantification function are described by two different hidden Markov model between the SNNs and its estimator. Read More

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http://dx.doi.org/10.1109/TCYB.2019.2909748DOI Listing

Event-Triggered Active MPC for Nonlinear Multiagent Systems With Packet Losses.

IEEE Trans Cybern 2019 Apr 17. Epub 2019 Apr 17.

In this paper, event-triggered active model predictive control is investigated for a nonlinear multiagent system (MAS) with packet losses. By designing event-triggered mechanisms which reduce sensing cost, event-triggered conditions are detected at certain sampling instants. The prediction horizons of all agents are selected actively through the event-triggered mechanisms. Read More

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http://dx.doi.org/10.1109/TCYB.2019.2908389DOI Listing

Active Event-Triggered Control for Nonlinear Networked Control Systems With Communication Constraints.

IEEE Trans Cybern 2019 Apr 16. Epub 2019 Apr 16.

In this paper, a novel reference input and hysteresis quantizer-based active event-triggered control (RIHQAETC) scheme is proposed for nonlinear networked control systems with quantizer, networked induced delay, and packet dropout. Different from the traditional methods, such a design method is constructed involving the structure of the hysteresis quantizer. In view of the network induced delay and the potential packet dropout, our RIHQAETC method is designed to actively compensate the negative effects caused by these two issues. Read More

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http://dx.doi.org/10.1109/TCYB.2019.2907619DOI Listing

Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multiobjective Optimization Approach.

IEEE Trans Cybern 2019 Apr 16. Epub 2019 Apr 16.

Fuzzy clustering methods identify naturally occurring clusters in a dataset, where the extent to which different clusters are overlapped can differ. Most methods have a parameter to fix the level of fuzziness. However, the appropriate level of fuzziness depends on the application at hand. Read More

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http://dx.doi.org/10.1109/TCYB.2019.2907002DOI Listing

Maneuvering Target Tracking With Event-Based Mixture Kalman Filter in Mobile Sensor Networks.

IEEE Trans Cybern 2019 Apr 17. Epub 2019 Apr 17.

In this paper, the distributed remote state estimation problem for conditional dynamic linear systems in mobile sensor networks with an event-triggered mechanism is investigated. The distributed mixture Kalman filtering method is proposed to track the state of the maneuvering target, which uses particle filtering to estimate the nonlinear variables and apply Kalman filtering to estimate the linear variables. An event-based distributed filtering scheme is designed, which is an energy-efficient way to transmit data between sensors and estimators. Read More

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https://ieeexplore.ieee.org/document/8693671/
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http://dx.doi.org/10.1109/TCYB.2019.2901515DOI Listing
April 2019
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Leveraging a Big Dataset to Develop a Recurrent Neural Network to Predict Adverse Glycemic Events in Type 1 Diabetes.

IEEE J Biomed Health Inform 2019 Apr 17. Epub 2019 Apr 17.

Patients with type 1 diabetes (T1D) do not produce their own insulin. They must continuously monitor their glucose and make decisions about insulin dosing to avoid the consequences of adverse glucose excursions. Continuous glucose monitoring (CGM) systems and insulin pumps are state-of-the-art systems that can help people with T1D manage their glucose. Read More

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http://dx.doi.org/10.1109/JBHI.2019.2911701DOI Listing

Computational identification of RNA-Seq based miRNA-mediated prognostic modules in cancer.

IEEE J Biomed Health Inform 2019 Apr 16. Epub 2019 Apr 16.

Systematic identification of miRNA prognostic signature can help decipher the effects of biomarkers in cancer treatment. A number of previous studies have only characterized a single miRNA as a promising prognostic biomarker. There is currently a trend towards combining several miRNAs as a panel of prognostic signatures, but few attempt to explain the mechanism of miRNA combination. Read More

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http://dx.doi.org/10.1109/JBHI.2019.2911528DOI Listing

Novel Data Imputation for Multiple Types of Missing Data in Intensive Care Units.

IEEE J Biomed Health Inform 2019 Apr 16. Epub 2019 Apr 16.

The diversity and number of parameters monitored in an intensive care unit (ICU) make the resulting databases highly susceptible to quality issues such as missing information and erroneous data entry, which adversely affect the downstream processing and predictive modeling. Missing data interpolation and imputation techniques such as multiple imputation, expectation maximization, and hot - deck imputation techniques do not account for the type of missing data, which can lead to bias. In our study, we first model the missing data as three types: "Neglectable" also known as (a. Read More

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http://dx.doi.org/10.1109/JBHI.2018.2883606DOI Listing

A SYSTEMATIC REVIEW OF IN VITRO AND IN VIVO RADIO FREQUENCY EXPOSURE METHODS.

IEEE Rev Biomed Eng 2019 Apr 18. Epub 2019 Apr 18.

The interests in the effects of radio frequency (RF) on biological systems has increased. This interest has increased partially due to the advancements and increase implementations of RF into technology. As research in the area has progressed, the reliability and reproducibility of those experiments has not crossed multidisciplinary boundaries. Read More

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http://dx.doi.org/10.1109/RBME.2019.2912023DOI Listing

Amino acid encoding methods for protein sequences: a comprehensive review and assessment.

IEEE/ACM Trans Comput Biol Bioinform 2019 Apr 16. Epub 2019 Apr 16.

As the first step of machine-learning based protein structure and function prediction, the amino acid encoding play a fundamental role in the final success of those methods. Different with the protein sequence encoding, the amino acid encoding can be used in both residue-level and sequence-level prediction of protein properties by combining with different algorithms. However, it does not attract enough attention in the past decades, and there are no comprehensive reviews and assessments about encoding methods so far. Read More

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http://dx.doi.org/10.1109/TCBB.2019.2911677DOI Listing

Deep Robust Framework for Protein Function Prediction using Variable-Length Protein Sequences.

IEEE/ACM Trans Comput Biol Bioinform 2019 Apr 16. Epub 2019 Apr 16.

The order of amino acids in a protein sequence enables the protein to acquire a conformation suitable for performing functions, thereby motivating the need to analyse these sequences for predicting functions. Although machine learning based approaches are fast compared to methods using BLAST, FASTA, etc., they fail to perform well for long protein sequences (with more than 300 amino acids). Read More

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https://ieeexplore.ieee.org/document/8692646/
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http://dx.doi.org/10.1109/TCBB.2019.2911609DOI Listing
April 2019
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XGBoost Model for Chronic Kidney Disease Diagnosis.

IEEE/ACM Trans Comput Biol Bioinform 2019 Apr 17. Epub 2019 Apr 17.

Chronic Kidney Disease (CKD) is a menace that is affecting 10% of the world population and 15% of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial Intelligence (AI). Read More

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http://dx.doi.org/10.1109/TCBB.2019.2911071DOI Listing

Multichannel AC Biosusceptometry system to map biodistribution and assess the pharmacokinetic profile of magnetic nanoparticles by imaging.

IEEE Trans Nanobioscience 2019 Apr 18. Epub 2019 Apr 18.

In this paper, the application of a technique to evaluate in vivo biodistribution of magnetic nanoparticles (MNP) is addressed: the Multichannel AC Biosusceptometry System (MC-ACB). It allows real-time assessment of magnetic nanoparticles in both bloodstream clearance and liver accumulation, where a complex network of inter-related cells is responsible for MNP uptake. Based on the acquired MC-ACB images, we propose a mathematical model to help understand the distribution and accumulation pharmacokinetics of MNP. Read More

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http://dx.doi.org/10.1109/TNB.2019.2912073DOI Listing

A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses.

IEEE Trans Neural Syst Rehabil Eng 2019 Apr 18. Epub 2019 Apr 18.

Mild Cognitive Impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer's disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this study, we demonstrate a single-channel EEG based MCI detection method, which is cost-effective and portable, and thus suitable for regular home-based patient monitoring. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2911970DOI Listing
April 2019
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Precise tubular braid structures of ultrafine microwires as neural probes: significantly reduced chronic immune response and greater local neural survival in rat cortex.

IEEE Trans Neural Syst Rehabil Eng 2019 Apr 18. Epub 2019 Apr 18.

Braided multi-electrode probes (BMEPs) for neural interfaces comprise ultrafine microwire bundles interwoven into tubular braids. BMEPs provide highly flexible probes and tethers, and an open lattice structure with up to 24 recording/stimulating channels in precise geometries, currently all within a 150~200 μm diameter footprint. This paper compares the long-term tissue effects of BMEPs (12 × 9. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2911912DOI Listing

Quantitative Evaluation of Cerebellar Ataxia through Automated Assessment of Upper Limb Movements.

IEEE Trans Neural Syst Rehabil Eng 2019 Apr 16. Epub 2019 Apr 16.

Cerebellar damage can result in peripheral dysfunction manifesting as poor and inaccurate coordination, irregular movements and tremors. Conventionally, the severity assessment of Cerebellar ataxia (CA) is primarily based on expert clinical opinion and hence likely to be subjective. In order to establish inter rater concordance with enhanced reliability and effectiveness in the assessment of upper limb function, a novel automated system employing Microsoft Kinect is considered to capture the motion of the patient's finger for objective assessment. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2911657DOI Listing

A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System.

IEEE Trans Neural Syst Rehabil Eng 2019 Apr 16. Epub 2019 Apr 16.

Falls in! older adults are a major cause of morbidity and mortality and are a key class of preventable injuries. This paper presents a patient-specific (PS) fall prediction and detection prototype system that utilizes a single tri-axial accelerometer attached to the patient's thigh to distinguish between activities of daily living (ADL) and fall events. The proposed system consists of two modes of operation: 1) fast mode for fall predication (FMFP) predicting a fall event (300msec-700msec) before occurring, 2) slow mode for fall detection (SMFD) with a 1-sec latency for detecting a fall event. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2911602DOI Listing

Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing.

IEEE Trans Neural Syst Rehabil Eng 2019 Apr 16. Epub 2019 Apr 16.

Traditional myoelectric prostheses that employ a static pattern recognition model to identify human movement intention from surface electromyography (sEMG) signals hardly adapt to the changes in the sEMG characteristics caused by interferences from daily activities, which hinders the clinical applications of such prostheses. In this study, we focus on methods to reduce or eliminate the impacts of three types of daily interferences on myoelectric pattern recognition (MPR), i.e. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2911316DOI Listing

Automated Fine Motor Evaluation for Developmental Coordination Disorder.

IEEE Trans Neural Syst Rehabil Eng 2019 Apr 16. Epub 2019 Apr 16.

Developmental Coordination Disorder (DCD) is a type of motor learning difficulty that affects five to six percent of school-aged children, which may have a negative impact on the life of the sufferers. Timely and objective diagnosis of DCD is important for the success of the intervention. The present evaluation methods of DCD rely heavily on observational analysis of occupational therapists and physiotherapists who score the performance when children conduct some designed tasks. Read More

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http://dx.doi.org/10.1109/TNSRE.2019.2911303DOI Listing

Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes.

IEEE Trans Image Process 2019 Apr 17. Epub 2019 Apr 17.

Semantic segmentation, a pixel-level vision task, is developed rapidly by using convolutional neural networks (CNNs). Training CNNs requires a large amount of labeled data, but manually annotating data is difficult. For emancipating manpower, in recent years, some synthetic datasets are released. Read More

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http://dx.doi.org/10.1109/TIP.2019.2910667DOI Listing

Discrete Curvature Representations for Noise Robust Image Corner Detection.

IEEE Trans Image Process 2019 Apr 17. Epub 2019 Apr 17.

Image corner detection is very important in the fields of image analysis and computer vision. Curvature calculation techniques are used in many contour-based corner detectors. We identify that existing calculation of curvature is sensitive to local variation and noise in the discrete domain and does not perform well when corners are closely located. Read More

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http://dx.doi.org/10.1109/TIP.2019.2910655DOI Listing

Exploiting Typicality for Selecting Informative and Anomalous Samples in Videos.

IEEE Trans Image Process 2019 Apr 17. Epub 2019 Apr 17.

In this paper, we present a novel approach to find informative and anomalous samples in videos exploiting the concept of typicality from information theory. In most video analysis tasks, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an important problem. Furthermore, it is also useful to reduce the annotation cost as it is time-consuming to annotate unlabeled samples. Read More

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http://dx.doi.org/10.1109/TIP.2019.2910634DOI Listing

Low-Light Image Enhancement via a Deep Hybrid Network.

IEEE Trans Image Process 2019 Apr 16. Epub 2019 Apr 16.

Camera sensors often fail to capture clear images or videos in a poorly-lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the global content and salient structures of the clear image in a unified network. Read More

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http://dx.doi.org/10.1109/TIP.2019.2910412DOI Listing

Two-Level Attention Network with Multi-Grain Ranking Loss for Vehicle Re-Identification.

IEEE Trans Image Process 2019 Apr 16. Epub 2019 Apr 16.

Vehicle re-identification (re-ID) aims to identify the same vehicle across multiple non-overlapping cameras, which is a rather challenging task. On one hand, subtle changes in viewpoint and illumination condition can make the same vehicle look much different. On the other hand, different vehicles even different vehicle models may look quite similar. Read More

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http://dx.doi.org/10.1109/TIP.2019.2910408DOI Listing

Demosaicking Using a Spatial Reference Image for an Anti-Aliasing Multispectral Filter Array.

IEEE Trans Image Process 2019 Apr 16. Epub 2019 Apr 16.

Multispectral imaging with a multispectral filter array (MSFA) facilitates snapshot imaging; however, a demosaicking process is required to estimate a fully-defined multispectral image based on undersampled sensor data. Undersampling induces aliasing and adverse artifacts in the reconstructed image. To solve this problem, Jia et al. Read More

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http://dx.doi.org/10.1109/TIP.2019.2910392DOI Listing

NARROW GAP DETECTION IN MICROSCOPE IMAGES USING MARKED POINT PROCESS MODELING.

IEEE Trans Image Process 2019 Apr 16. Epub 2019 Apr 16.

Differentiating objects separated by narrow gaps is a challenging and important task in analyzing microscopic images. These small separations provide useful information for applications that require detailed boundary information and/or an accurate particle count. We present a new approach to the modeling of these gaps based on a marked point process(MPP) framework. Read More

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http://dx.doi.org/10.1109/TIP.2019.2910389DOI Listing

Three-Zone Segmentation Based Motion Compensation for Video Compression.

IEEE Trans Image Process 2019 Apr 16. Epub 2019 Apr 16.

Motion compensation has been widely employed for removing temporal redundancies in typical hybrid video coding framework. The popular video compression standards, such as H.264/AVC and HEVC, adopt the block based partitioning model to describe the motion filed due to its high compression efficiency and relatively low computational complexity. Read More

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https://ieeexplore.ieee.org/document/8692726/
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http://dx.doi.org/10.1109/TIP.2019.2910382DOI Listing
April 2019
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Deep Group-wise Fully Convolutional Network for Co-saliency Detection with Graph Propagation.

IEEE Trans Image Process 2019 Apr 15. Epub 2019 Apr 15.

A key problem in co-saliency detection is how to effectively model the interactive relationship of a whole image group and the individual perspective of each image in a united data-driven manner. In this paper, we propose a group-wise deep co-saliency detection approach to address the co-saliency object discovery problem based on the fully convolutional network (FCN). The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Read More

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http://dx.doi.org/10.1109/TIP.2019.2909649DOI Listing
April 2019
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Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net.

IEEE Trans Med Imaging 2019 Apr 16. Epub 2019 Apr 16.

Segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions and non-rigid geometrical features. To address these difficulties, we introduce a Deep Q Network(DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas. Read More

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http://dx.doi.org/10.1109/TMI.2019.2911588DOI Listing

An Improved Method of Total Variation Superiorization Applied to Reconstruction in Proton Computed Tomography.

IEEE Trans Med Imaging 2019 Apr 16. Epub 2019 Apr 16.

Previous work has shown that total variation superiorization (TVS) improves reconstructed image quality in proton computed tomography (pCT). The structure of the TVS algorithm has evolved since then and this work investigated if this new algorithmic structure provides additional benefits to pCT image quality. Structural and parametric changes introduced to the original TVS algorithm included: (1) inclusion or exclusion of TV reduction requirement, (2) a variable number, N, of TV perturbation steps per feasibility-seeking iteration, and (3) introduction of a perturbation kernel 0 < α < 1. Read More

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http://dx.doi.org/10.1109/TMI.2019.2911482DOI Listing

A Novel Dynamic Model Capturing Spatial and Temporal Patterns for Facial Expression Analysis.

IEEE Trans Pattern Anal Mach Intell 2019 Apr 17. Epub 2019 Apr 17.

Facial expression analysis could be greatly improved by incorporating spatial and temporal patterns present in facial behavior, but the patterns have not yet been utilized to their full advantage. We remedy this via a novel dynamic model - an interval temporal restricted Boltzmann machine (IT-RBM) - that is able to capture both universal spatial patterns and complicated temporal patterns in facial behavior for facial expression analysis. We regard a facial expression as a multifarious activity composed of sequential or overlapping primitive facial events. Read More

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http://dx.doi.org/10.1109/TPAMI.2019.2911937DOI Listing

Fine-grained Human-centric Tracklet Segmentation with Single Frame Supervision.

IEEE Trans Pattern Anal Mach Intell 2019 Apr 17. Epub 2019 Apr 17.

In this paper, we target at the Fine-grAined human-Centric Tracklet Segmentation (FACTS) problem, where 12 human parts, e.g., face, pants, left-leg, are segmented. Read More

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http://dx.doi.org/10.1109/TPAMI.2019.2911936DOI Listing

A Spine-Specific Phased Array for Transvertebral Ultrasound Therapy: Design & Simulation.

IEEE Trans Biomed Eng 2019 Apr 18. Epub 2019 Apr 18.

Objective: To design and simulate the performance of two spine-specific phased arrays in sonicating targets spanning the thoracic spinal canal, with the objective of efficiently producing controlled foci in the spinal canal.

Methods: Two arrays (256 elements each, 500 kHz) were designed using multi-layered ray acoustics simulation; a 4-component array with dedicated components for sonicating via the paravertebral and transvertebral paths, and a 2-component array with spine-specific adaptive focusing. Mean array efficiency (canal focus pressure/water focus pressure) was evaluated using forward simulation in neutral and flexed spines. Read More

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http://dx.doi.org/10.1109/TBME.2019.2912146DOI Listing

A Riemannian geometry approach to reduced and discriminative covariance estimation in Brain Computer Interfaces.

IEEE Trans Biomed Eng 2019 Apr 18. Epub 2019 Apr 18.

Objective: Spatial covariance matrices are extensively employed as brain activity descriptors in BCI research that, typically, involve the whole array of sensors. Here, we introduce a methodological framework for delineating the subset of sensors, the covariance structure of which offers a reduced, but more powerful, representation of brain's coordination patterns that ultimately leads to reliable mind reading.

Methods: Adopting a Riemannian geometry approach, we turn the problem of sensor selection as a maximization of a functional that is computed over the manifold of symmetric positive definite (SPD) matrices and encapsulates class separability in a way that facilitates the search among subsets of different size. Read More

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http://dx.doi.org/10.1109/TBME.2019.2912066DOI Listing
April 2019
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Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection.

IEEE Trans Biomed Eng 2019 Apr 17. Epub 2019 Apr 17.

Objective: Concealable, miniaturized electroencephalography ('mini-EEG') recording devices are crucial enablers towards long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. Read More

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http://dx.doi.org/10.1109/TBME.2019.2911728DOI Listing

Electric Stimulus-Responsive Chitosan/MNP composite Microbeads for a Drug Delivery System.

IEEE Trans Biomed Eng 2019 Apr 16. Epub 2019 Apr 16.

In the last several years, conventional Drug Delivery Systems (DDS) have evolved into DDS that are responsive to exogenous or endogenous stimuli. The objective of this work was to present a DDS that is responsive to an electric stimulus, in the form of bipolar electric pulses. The DDS structure is based on chitosan embedded with magnetic nanoparticles, and crosslinked with polyethylene glycol dimethacrylate to form microbeads. Read More

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http://dx.doi.org/10.1109/TBME.2019.2911579DOI Listing

Prospective Respiration-Gated Photoacoustic Microscopy.

IEEE Trans Biomed Eng 2019 Apr 16. Epub 2019 Apr 16.

Objective: Photoacoustic microscopy (PAM) is a promising biomedical imaging technique that relies on sequential excitation to generate three-dimensional images. It combines the high contrast of optical imaging with high penetration depth of ultrasound imaging. The normal respiration rate of mice is greater than 3 Hz, which leads to motion artifacts in most reported PAM for in-vivo imaging. Read More

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http://dx.doi.org/10.1109/TBME.2019.2911551DOI Listing

Risks and Benefits of Using a Commercially Available Ventricular Assist Device for Failing Fontan Cavopulmonary Support: A Modeling Investigation.

IEEE Trans Biomed Eng 2019 Apr 16. Epub 2019 Apr 16.

Fontan patients often develop circulatory failure and are in desperate need of a therapeutic solution. A blood pump surgically placed in the cavopulmonary pathway can substitute the function of the absent sub-pulmonary ventricle by generating a mild pressure boost. However, there is currently no commercially available device designed for the cavopulmonary application; and the risks and benefits of implanting a ventricular assist device (VAD) originally designed for the left ventricular application on the right circulation of failing Fontan patients is not yet clear. Read More

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http://dx.doi.org/10.1109/TBME.2019.2911470DOI Listing

A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities.

Sensors (Basel) 2019 Apr 15;19(8). Epub 2019 Apr 15.

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Hong Kong, China.

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. Read More

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http://dx.doi.org/10.3390/s19081796DOI Listing

Synchronization of Stochastic Complex Dynamical Networks Subject to Consecutive Packet Dropouts.

IEEE Trans Cybern 2019 Apr 12. Epub 2019 Apr 12.

This paper studies the modeling and synchronization problems for stochastic complex dynamical networks subject to consecutive packet dropouts. Different from some existing research results, both probability characteristic and upper bound of consecutive packet dropouts are involved in the proposed approach of controller design. First, an error dynamical network with stochastic and bounded delay is established by step-delay method, where the randomness of the bounded delay can be verified later by the probability theory method. Read More

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http://dx.doi.org/10.1109/TCYB.2019.2907279DOI Listing

LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices.

IEEE J Biomed Health Inform 2019 Apr 15. Epub 2019 Apr 15.

Objective: A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity.

Methods: The proposed solution employs a novel architecture consisting of wavelet transform and multiple LSTM recurrent neural networks (Fig. 1). Read More

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http://dx.doi.org/10.1109/JBHI.2019.2911367DOI Listing

Automatic Scale Severity Assessment Method in Psoriasis Skin Images using Local Descriptors.

IEEE J Biomed Health Inform 2019 Apr 15. Epub 2019 Apr 15.

Psoriasis is a chronic skin condition. Its clinical assessment involves four measures: erythema, scales, induration and area. In this paper, we introduce a scale severity scoring framework for two-dimensional psoriasis skin images. Read More

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http://dx.doi.org/10.1109/JBHI.2019.2910883DOI Listing

Marginalized Multiview Ensemble Clustering.

IEEE Trans Neural Netw Learn Syst 2019 Apr 15. Epub 2019 Apr 15.

Multiview clustering (MVC), which aims to explore the underlying cluster structure shared by multiview data, has drawn more research efforts in recent years. To exploit the complementary information among multiple views, existing methods mainly learn a common latent subspace or develop a certain loss across different views, while ignoring the higher level information such as basic partitions (BPs) generated by the single-view clustering algorithm. In light of this, we propose a novel marginalized multiview ensemble clustering (M²VEC) method in this paper. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2906867DOI Listing
April 2019
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Eliminating the Permutation Ambiguity of Convolutive Blind Source Separation by Using Coupled Frequency Bins.

IEEE Trans Neural Netw Learn Syst 2019 Apr 15. Epub 2019 Apr 15.

Blind source separation (BSS) is a typical unsupervised learning method that extracts latent components from their observations. In the meanwhile, convolutive BSS (CBSS) is particularly challenging as the observations are the mixtures of latent components as well as their delayed versions. CBSS is usually solved in frequency domain since convolutive mixtures in time domain is just instantaneous mixtures in frequency domain, which allows to recover source frequency components independently of each frequency bin by running ordinary BSS, and then concatenate them to form the Fourier transformation of source signals. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2906833DOI Listing

Toward Compact ConvNets via Structure-Sparsity Regularized Filter Pruning.

IEEE Trans Neural Netw Learn Syst 2019 Apr 12. Epub 2019 Apr 12.

The success of convolutional neural networks (CNNs) in computer vision applications has been accompanied by a significant increase of computation and memory costs, which prohibits their usage on resource-limited environments, such as mobile systems or embedded devices. To this end, the research of CNN compression has recently become emerging. In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speed up the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2906563DOI Listing
April 2019
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Deep Least Squares Fisher Discriminant Analysis.

IEEE Trans Neural Netw Learn Syst 2019 Apr 11. Epub 2019 Apr 11.

While being one of the first and most elegant tools for dimensionality reduction, Fisher linear discriminant analysis (FLDA) is not currently considered among the top methods for feature extraction or classification. In this paper, we will review two recent approaches to FLDA, namely, least squares Fisher discriminant analysis (LSFDA) and regularized kernel FDA (RKFDA) and propose deep FDA (DFDA), a straightforward nonlinear extension of LSFDA that takes advantage of the recent advances on deep neural networks. We will compare the performance of RKFDA and DFDA on a large number of two-class and multiclass problems, many of them involving class-imbalanced data sets and some having quite large sample sizes; we will use, for this, the areas under the receiver operating characteristics (ROCs) curve of the classifiers considered. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2906302DOI Listing
April 2019
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Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks.

IEEE Trans Neural Netw Learn Syst 2019 Apr 12. Epub 2019 Apr 12.

Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optimally encoded into spike trains so that the task-relevant information is retained. This paper provides a systematic quantitative and qualitative analysis and guidelines for optimal temporal encoding. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2906158DOI Listing
April 2019
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Metacognitive Octonion-Valued Neural Networks as They Relate to Time Series Analysis.

IEEE Trans Neural Netw Learn Syst 2019 Apr 12. Epub 2019 Apr 12.

In this paper, a metacognitive octonion-valued neural network (Mc-OVNN) learning algorithm and its application to diverse time series prediction are presented. The Mc-OVNN is comprised of two components: the octonion-valued neural network that represents the cognitive component and the metacognitive component that serves to self-regulate the learning algorithm. At each epoch, the metacognitive component decides if, how, and when learning occurs. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2905643DOI Listing

Greedy Projected Gradient-Newton Method for Sparse Logistic Regression.

IEEE Trans Neural Netw Learn Syst 2019 Apr 11. Epub 2019 Apr 11.

Sparse logistic regression (SLR), which is widely used for classification and feature selection in many fields, such as neural networks, deep learning, and bioinformatics, is the classical logistic regression model with sparsity constraints. In this paper, we perform theoretical analysis on the existence and uniqueness of the solution to the SLR, and we propose a greedy projected gradient-Newton (GPGN) method for solving the SLR. The GPGN method is a combination of the projected gradient method and the Newton method. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2905261DOI Listing

The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions.

IEEE Trans Neural Netw Learn Syst 2019 Apr 11. Epub 2019 Apr 11.

Although the field of learning automata (LA) has made significant progress in the past four decades, the LA-based methods to tackle problems involving environments with a large number of actions is, in reality, relatively unresolved. The extension of the traditional LA to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and so, most components of the vector will soon have values that are smaller than the machine accuracy permits, implying that they will never be chosen. Read More

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http://dx.doi.org/10.1109/TNNLS.2019.2905162DOI Listing