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Abstract: MobilePaperAccess is a wearable camera-glasses system with tangible user interface allowing mobile interaction. We access the digital information from a paper interface extending the input space. The system is devised to validate our concepts of Environment Dependent Interface (EDI) and Environment Independent Interface (EII), which focus on enabling people to access their personal data as well as public resources at any time and in any place. In this paper, we propose a continuum from physical interface to digital interface in relation with EDI and EII, and we present design, implementation and evaluation aspects of our MobilePaperAccess system. We compare two interfaces (EDI and EII) and three input techniques (finger input, mask input and page input). [Copyright &y& Elsevier]

The major hurdle in accessing laboratory experimentation is the cost of acquiring experimental scientific equipment, which is unbearable for many institutions. This paper aims to provide the community of control educators, practitioners, and researchers with an open-source low-cost experimental setup and dedicated interface, which is flexible and very easily reproducible. The proposed apparatus is a setup of four interconnected tanks. The setup is representative of real-life industrial processes and it can be adjusted to allow for several configurations for flexibility. A user-friendly dedicated MATLAB/Simulink interface with a personal computer is developed. It supports a seamless shift between a numeric simulation of the quadruple-tank process dynamics and the interface with the physical experimental plant. The CAD models, technical drawings, wiring schematics, PCB design, interface, assembly tutorials, and several examples are provided in an open-source repository. The parts are inexpensive, readily obtained, and fast to assemble. Each setup costs under 650€ and takes roughly 4 h to assemble. In this paper, several application examples are presented, not only for educational purposes, but also for the validation of a state-of-the-art decentralized control method. • The setup is representative of real-life industrial processes and very flexible. • Open-source CAD, technical drawings, wiring schematics, and software are available. • A user-friendly dedicated MATLAB/Simulink interface is developed. • The interface allows a seamless shift between simulation and experimental modes. • The setup is easily reproducible, costs under 650€, and takes 4 h to assemble. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) decoding is the core of an intelligent rehabilitation system in brain computer interface, and it has a potential advantage by using source signals, which have higher spatial resolution and the same time resolution compared to scalp electroencephalography (EEG). However, how to delve and utilize the personalized frequency characteristic of dipoles for improving decoding performance has not been paid sufficient attention. In this paper, a novel dipole feature imaging (DFI) and a hybrid convolutional neural network (HCNN) with an embedded squeeze-and-excitation block (SEB), denoted as DFI-HCNN, are proposed for decoding MI tasks. EEG source imaging technique is used for brain source estimation, and each sub-band spectrum powers of all dipoles are calculated through frequency analysis and band division. Then, the 3D space information of dipoles is retrieved, and by using azimuthal equidistant projection algorithm it is transformed to a 2D plane, which is combined with nearest neighbor interpolation to generate multi sub-band dipole feature images. Furthermore, a HCNN is designed and applied to the ensemble of sub-band dipole feature images, from which the importance of sub-bands is acquired to adjust the corresponding attentions adaptively by SEB. Ten-fold cross-validation experiments on two public datasets achieve the comparatively higher decoding accuracies of 84.23% and 92.62%, respectively. The experiment results show that DFI is an effective feature representation, and HCNN with an embedded SEB can enhance the useful frequency information of dipoles for improving MI decoding. [ABSTRACT FROM AUTHOR]

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general. [ABSTRACT FROM AUTHOR]

Brain Computer Interface (BCI) enables communication solely through mental activity. For patients who have lost complete voluntary muscle control, it serves as a potential communication and rehabilitation channel. In a number of recent investigations, BCl based on electroencephalography (EEG) has demonstrated increased reaction in nonresponsive people to interact with others. Despite the method's effectiveness, patient interaction takes way too long. However, magnetoencephalography (MEG) may shorten the training period, improving BCI reliability in the process. One of the major technological difficulties confronting MEG data collection and interpretation is that the strength of neuromagnetic fields recorded externally is considerably lower than interfering signals. There hasn't been much substantial progress on an effective MEG-BCI system because there is no large enough MEG dataset. Despite their huge potential, MEG-based BCI systems still need a lot of work in terms of signal processing algorithms that are both reliable and efficient. Unexpected head movements and changing orientation between sessions may be the cause of non-stationarity in the MEG data that was captured, changing the most effective channel selection between sessions and participants. Not only the head movement, but also non-stationarity in data can be caused by a variety of factors like user weariness, mood changes, or external noise interfering with the MEG system. This paper discusses the many types of data acquisition artifacts and review the techniques to minimize the artifacts to increase the signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Feature representation plays a pivotal role in the decoding of motor imagery electroencephalograph (MI-EEG) signals. Conventional spatial representations are often hindered by the operational time-frequency and noise interferences of MI-EEG. In response to this challenge, this paper proposed a novel method to S elective extract the R egularized S patial features and represent by the A lternating C ascaded M odel (SRS-ACM) for MI-EEG decoding. Initially, regularized common spatial patterns are derived from MI-EEG samples, capturing the relevance and redundancy between spatial features and labels. Subsequently, a global optimization framework is devised to select discriminative and comprehensive dimensions with the spatial features. Finally, an alternating cascade model, integrating sparse and collaborative representations, is developed to capture the intrinsic patterns with the selected spatial features, thereby facilitating MI-EEG decoding. Experimental evaluations were conducted across three publicly available MI-EEG datasets: BCI-III dataset 4a, BCI-IV dataset 1, and BCI-IV dataset 2a. The SRS-ACM model demonstrated notable performance improvements of 2.60 %, 6.15 %, and 1.14 % on these datasets, respectively. Ablation studies underscored the necessity and significance of the components within the SRS-ACM method, while parameter sensitivity analyses validated the robustness of the SRS-ACM approach in the development of practical MI-based brain-computer interfaces. • The selected regularized CSP representations based on supervised relevance and redundancy is applied for MI-EEG signals. • A sparse and collaborative alternating cascade model is utilized to classify selected spatial representations. • The proposed method is efficient for constructing MI-BCIs. [ABSTRACT FROM AUTHOR]

Emotion Brain Computer Interface (BCI) based on Electroencephalography (EEG) is a significant branch in the field of affective computing. However, the variability in emotional feedback among subjects facing the same emotional stimuli and the potential presence of noisy labels in the data significantly limit the effectiveness and generalizability of EEG-based emotion recognition models. In this paper, we propose the M ulti- S ource C ontrastive L earning Transfer Learning Model (MSCL) to address the emotion recognition in cross-subject scenarios. The MSCL model consists of three components: Firstly, the unsupervised and supervised contrastive learning are utilized to learn the differences and commonalities among different individuals. Secondly, domain adaptation methods and feature learning in multiple domains enhance the interaction of information between source and target domains, thereby improving the model's generalization across different individuals. Thirdly, in order to mitigate the detrimental effects of noisy labels on the model, this research dynamically allocate the weights to source domains similar to the target domain based on behavioral characteristics within the target domain and employ the corresponding noise learning methods. In our experiments, MSCL achieves excellent cross-subject emotion recognition performance on the CEED Dataset and the SEED Dataset. Our research highlights the significant capability of MSCL model in addressing the issues associated with inter-subject variability and label noise within EEG-based emotion BCI systems. • Employs a unique combination of domain adaptation and contrastive learning to manage inter-subject variability. • Utilized noise learning to minimize the impact of noisy labels. • Designed and collected emotion experiment data to ensure the reliability of model training and validation. [ABSTRACT FROM AUTHOR]

• This paper proposes a supervised CatPCA approach for performing the imagined speech classification. • The objective function of an optimization problem for designing the unitary matrix is formulated based on both the PCA algorithm and the k means algorithm. • To find the analytical solution of the optimization problem, a property on computing the singular value decomposition (SVD) of a symmetric matrix is employed. • Then, the design of the cluster centers in the k means algorithm is formulated as a quadratic programming. • Finally, the above two procedures are iterated until the algorithm converges. • The required computational power for finding the solution of the optimization problem is low. The dimension of the feature vectors for performing the imagined speech is large. Hence, it is required to reduce their dimension as well as perform the classification simultaneously. This is a categorized principal component analysis (CatPCA) problem. However, the conventional CatPCA methods are unsupervised learning methods and the design of the unitary matrix in the conventional principal component analysis (PCA) algorithms is only based on the PCA approach. Therefore, the classification performance is limited. This paper proposes a supervised CatPCA approach for performing the imagined speech classification. Also, the objective function of an optimization problem for designing the unitary matrix is formulated based on both the PCA algorithm and the k means algorithm. To find the analytical solution of the optimization problem, a property on computing the singular value decomposition (SVD) of a symmetric matrix is employed. Then, the design of the cluster centers in the k means algorithm is formulated as a quadratic programming. Finally, the above two procedures are iterated until the algorithm converges. Since the analytical solutions of these two optimization problems can be derived analytically, the required computational power for finding the solution of the optimization problem is low. To demonstrate the effectiveness of our proposed CatPCA algorithm, the Track 3 of the International brain computer interface (BCI) Competition Database is employed as the dataset for performing the performance evaluation. The computer numerical simulation results show that our proposed CatPCA algorithm yields the higher classification accuracy compared to PCA variants and the dimension reduction related algorithms. Also, the required execution time of our proposed method is lower than those of the states of the arts methods. This demonstrates the effectiveness and the efficiency of our proposed CatPCA algorithm. [ABSTRACT FROM AUTHOR]

Electroencephalographic measurement of cortical activity subserving motor behavior varies among different individuals, restricting the potential of brain computer interfaces (BCIs) based on motor imagery (MI). How to deal with this variability and thereby improve the accuracy of BCI classification remains a key issue. This paper proposes a deep learning-based approach to transfer the data distribution from BCI-friendly — "golden subjects" to the data from more typical BCI-illiterate users. In this work, we use the perceptual loss to align the dimensionality-reduced BCI-illiterate data with the data of golden subjects in low dimensions, by which a subject transfer neural network (STNN) is proposed. The network consists of two parts: 1) a generator, which generates the transferred BCI-illiterate features, and 2) a CNN classifier, which is used for the classification of the transferred features, thus outperforming traditional classification methods both in terms of accuracy and robustness. Electroencephalography (EEG) signals from 25 healthy subjects performing MI of the right hand and foot were classified with an average accuracy of 88. 2 % ± 5. 1 %. The proposed model was further validated on the BCI Competition IV dataset 2b, and was demonstrated to be robust to inter-subject variations. The advantages of STNN allow it to bridge the gap between the golden subjects and the BCI-illiterate ones, paving the way to real-time BCI applications. [ABSTRACT FROM AUTHOR]

Feature adaptation plays crucial roles in the calibration process of motor imagery brain computer interfaces (MI-BCIs). Due to the temporal varying and spatial coupling characteristics in MI-electroencephalograph (EEG), recently proposed cross-subject MI-EEG classification methods have suffered from patterns collapse and erroneous labels accumulation, evenly lower efficiency. To address these fundamental limitations, this paper proposes a novel method to represent S patial- T emporal F eatures A daptation based on D ual R egularizations (DRSTFA), and perform a simplest multi-source domain samples selection during adaptation. Specifically, the covariance centroid alignment is applied as the preprocessing, and then common spatial–temporal pattern (CSTP) is represented for aligned MI-EEG samples. Finally, the dual regularizations of cross-domain graph preservation and target domain discriminability strengthen have been incorporated into joint distribution adaptation framework for CSTP feature adaptation. The proposed method has been systematically benchmarked on three BCI competition MI-EEG datasets, and its classification performance surpasses several state-of-the-art methods. Moreover, it effectively captures meaningful temporal varying and spatially coupled features with parameter insensitivity. Our method therefore provides a novel calibration choice for newly subjects participating in MI-BCIs. [ABSTRACT FROM AUTHOR]

• The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes. • This research work is highly focused on the emotion recognition. This research work is highly meaningful, as it includes the Adaptive Search Optimizer-Based Deep Bi-LSTM using Electroencephalogram signal that helps to forecast the patient's emotions. • Moreover, to know about the most interesting works undergone in literature, we have collected a total of 16 literature papers on emotion recognition concepts. • Analyzing the past as well as recent works helps to gain more knowledge regarding the emotion techniques. In addition, it's good to learn the advantage and drawbacks of existing works, which might be a mile stone for the future researchers. Emotion is a main element of human beings, and consists of an essential impact on their daily behaviors, comprising communication, learning, etc. Emotion recognition is the main challenging task in the arena of Human-machine communication depending on physical signals, which has developed and expanded a portion of deliberation with the technologies of the brain as well as a computer interface, in which recognizing the emotions utilizing the Electroencephalogram (EEG) signal is developed seriously in improving the interaction of human and computer. In this research, deep learning (DL) enabled emotion recognition with EEG data is presented to progress the precise identification with input datasets such as DEAP and SEED IV. The frequency bands involving the alpha, gamma, delta, and so on are created for EEG signals depending on their activities. Initially, the artifacts are eradicated from the EEG signal, and then the features are extracted from the generated frequency band at the final stage. The foremost purpose of the established Adaptive search optimizer -based deep Bidirectional Long Short-term Memory (ASO-based deep Bi-LSTM) model is to forecast the patient's emotions from the EEG signals in an accurate manner, which discloses the efficiency in terms of metrics involving the specificity, accuracy, and sensitivity than the prior approaches are evaluated. Thus, the ASO-based deep Bi-LSTM model attains the maximum performance which is evaluated with an accuracy of 97.511%, sensitivity of 99.141%, and a specificity of 99.298 % for the DEAP dataset and SEED IV database concerning the Training Percentage (TP) that demonstrates the efficiency of the developed model compared with other existing techniques. [ABSTRACT FROM AUTHOR]

• A novel framework based on bispectrum, Shannon entropy and common spatial patterns. • Higher-order statistics reduce the influence of non-linear and non-Gaussian noise. • Tree-based feature selection method is used to select features. Motor imagery based brain computer interface (MI-BCI) has the advantage of strong independence that can rely on the spontaneous brain activity of the user to operate external devices. However, MI-BCI still has the problem of poor control effect, which requires more effective feature extraction algorithms and classification methods to extract distinctly separable features from electroencephalogram (EEG) signals. This paper proposes a novel framework based on Bispectrum, Entropy and common spatial pattern (BECSP). Here we use three methods of bispectrum in higher order spectra, entropy and CSP to extract MI-EEG signal features, and then select the most contributing features through tree-based feature selection algorithm. By comparing the classification results of SVM, Random Forest, Naive Bayes, LDA, KNN, Xgboost and Adaboost, we finally decide to use the SVM algorithm based on RBF kernel function which obtained the best result among them for classification. The proposed method is applied to the BCI competition IV data set 2a and BCI competition III data set IVa. On data set 2a, the highest accuracy on the evaluation data set reaches 85%. The experiment on data set IVa can also achieve good results. Compared with other algorithms that use the same data set, the performance of our algorithm has also been improved. [ABSTRACT FROM AUTHOR]

• ER needs algorithms transfering knowledge of source subjects to a customized model. • User-dependent emotion recognition with PLRSA is a semi-supervised method. • PLRSA is the first to unify the instance reweighting and feature matching paradigm. Recently, many scenarios, such as affective disorders treatment, have sparked rising needs for establishment of personalized emotion recognition (PER) models. Unfortunately, the data sparsity issue violates the basic i.i.d. assumption of supervised learning (i.e., training data and test data are independently and identically distributed). In this paper, we present a semi-supervised joint domain adaption (SSJDA) solution, aiming to inject the hidden domain knowledge from ample labeled data of multiple source individuals into the target subject's customized model. Specifically, we put forward a novel Progressive Low-Rank Subspace Alignment (PLRSA) approach, which unifies a semi-supervised instance-transfer paradigm and an unsupervised mapping-transfer learning paradigm in a single optimization framework. We leverage the boosting-based TrAdaBoost algorithm and the Transfer Component Analysis (TCA) algorithm for the implementation of instance reweighting and feature matching, respectively. Then we introduce the ℓ 2 , 1 - norm to pass feedback and make the joint learning feasible. The central idea is to progressively minimize the cross-domain distribution discrepancies to finally construct the optimal domain-invariant features. We systematically compare the PLRSA method with five state-of-the-art techniques using two public EEG datasets (DEAP and SEED). Both many-to-one and one-to-one evaluations are performed. The experimental results have confirmed the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

• The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes. • This paper devises a novel optimization driven deep model for classifying the EEG signals. In this model, the EEG signal is utilized for pre-processing discard artifacts from input signal. • The feature extraction is done to extract imperative features that include spectral-based features, like Amplitude modulation spectrogram (AMS), frequency-based features, like spectral flux, tonal power ratio, spectral centroid, spectral spread, Power Spectrum Density (PSD), logarithmic band power, and statistical features like kurtosis, entropy and skewness. • The data augmentation is performed for making the data suitable for further processing. • The motor imagery EEG signal classification is done using Deep Residual Network (DRN). The training of DRN is done using proposed Competitive Swarm Dragonfly Algorithm (CSDA). The proposed CSDA is devised by combining competitive Swarm Optimizer (CSO) and Dragonfly Algorithm (DA). • The proposed CSDA-based DRN offered enhanced performance with elevated accuracy of 91.6%, sensitivity of 92.3% and specificity of 91.9%. The brain computer interface (BCI) aimed to offer an improved and quality life for people having disabilities. Various physiological sensors are utilized for designing the BCI application. Here, the electroencephalogram (EEG) is well-known for modeling the brain signals. However, the existing techniques based on EEG signal classification are computationally expensive and not so accurate. This paper devises Competitive Swarm Dragonfly Algorithm (CSDA) for classifying the EEG signals. In this model, the input EEG signal artifacts are discarded in pre-processing phase. The feature extraction is done to extract imperative features that include spectral-based features, like Amplitude modulation spectrogram, frequency-based features, like spectral flux, tonal power ratio, spectral centroid, spectral spread, Power Spectrum Density, logarithmic band power, and statistical features like kurtosis, entropy and skewness. Here, data augmentation is performed for making the data suitable for further processing. Deep Residual Network (DRN) is used to classify the motor imagery EEG signal. The suggested CSDA is used to train DRN, which is obtained by combining the competitive Swarm Optimizer and Dragonfly Algorithm. The performance of the adapted approach is determined using motor imagery multi-class dataset and motor imagery small training sets, in which the motor imagery multi-class dataset offers the highest specificity, accuracy, and sensitivity of 91.9% 91.6%, and 92.3%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes to attempted is to manipulate an object by a set of three contacts (fingers) i.e. three end effector of the delta robots. As the point of contact with the finger and object changes dynamically, making the problem to have a large degree of variability.This proposal is limited to direct feedback of the object position with respect to the world coordinates and interfacing with the delta robot controlling in real time environment.Detection of markers by three cameras is achieved using ArUco augmented reality library. Experimental results are presented to support the approach in this paper. [ABSTRACT FROM AUTHOR]

See-through displays allows user to view the scene behind the display. See-through displays are playing a crucial role in shaping augmented reality applications. However, the cost of the transparent displays are higher than the normal displays. In this paper, we disclose a low cost digitally transparent display using a depth camera, two color cameras and a regular display. The method described in this paper tracks the eyes of the user and finds the corresponding distance from the depth sensor and renders the rear camera preview of the tablet or mobile in such a way that the user perceives it as a transparent display. This method has the potential to change the augmented reality applications in tablets or mobiles from a video see-through experience to a glass see-through experience. [ABSTRACT FROM AUTHOR]

The purpose of present review paper is to introduce the reader to key directions of manual, semi-automatic and automatic knee osteoarthritis (OA) severity classification from plain radiographs. This is a narrative review article in which we have described recent developments in severity evaluation of knee OA from X-ray images. We have primarily focussed on automatic analysis and have reviewed articles in which machine learning, transfer learning, active learning, etc. have been employed on X-ray images to access and classify the severity of knee OA. All original research articles on OA detection and classification using X-ray images published in English were searched on PubMed database, Google Scholar, RSNA radiology databases in year 2019. The search terms of "knee Osteoarthritis" were combined with search terms "Machine Learning", 'severity" and "X-ray". The initial search on various publication databases revealed a total of 743 results, out of which only 26 articles were considered relevant to radiographic knee OA severity analysis. The majority of the articles were based on automatic analysis. Manual segmentation based articles were least in numbers. Computer aided methods to diagnose knee OA are great tools to detect OA at ealry stages. Advancements in Human Computer Interface systems have led the researchers to bridge the gap between machine learning algorithms and expert healthcare professionals to provide better and timely treatment options to the knee OA affected patients. [ABSTRACT FROM AUTHOR]

Icon plays a critical role in computer interface design. Studies on icon taxonomy explain the way in which various types of icon represent the objects and provide designers creation rules by which icons are more in line with users’ cognitive psychology. However, along with larger and larger use of icons, the previous classification criterion causes the boundary between categories blur. What’s more, Single classification standard is not able to well illustrate the icons applied in today’s computer applications. The purpose of this paper is to present an objective-oriented icon taxonomy which proposes to categorize icons into action icon and knowledge icon. To assess this proposition, we analyzed a sample of icons that applied in computer interface and suggest precise application domains to both action icon and knowledge icon categories. The results of this practice manifested that action icon and knowledge icon implied a high relation with applied environment and explicated the development trace of computer icons. This work is one of the first to point out the notion of knowledge icon and to highlight the importance of objective of icon application. Findings in this paper could enrich icon use in computer interface design, especially provides possible way to improve online knowledge sharing by visual tool like icon. [ABSTRACT FROM AUTHOR]

This paper analyzes to which extent research published in Computer Standards & Interfaces (CSI) has a technical focus. We find that CSI has been following its scope very closely in the last three years and that the majority of its publications have a technical focus. Articles published in CSI constantly cite research from various technical disciplines, but there are also a limited number of references to non-technical literature. Mostly technical journals cite CSI papers, with a few exceptions of non-technical journals. We conclude that CSI stays within its scope of computer standards and interfaces interpreted in a technical sense. [ABSTRACT FROM AUTHOR]

Computer methods have opened a new era for accurate and economic analysis and design of engineering problems. They account for many significant factors such as arbitrary geometries, nonhomogeneities in material composition, complex boundary conditions, nonlinear material behavior (constitutive modeling) and complex loading conditions, which were difficult to include in conventional and closed form solution procedures. Constitutive modeling characterizes the mechanical behavior of solids and contacts (e.g. interfaces and joints), and plays perhaps the most important role for realistic solutions from procedures in computational mechanics. A great number of constitutive models, from simple to the advanced, have been proposed. Most of them account for specific characteristics of the material. However, a deforming material may experience, simultaneously, many characteristics such as elastic, plastic and creep strains, different loading (stress) paths, volume change under shear stress, microcracking leading to fracture and failure, strain softening or degradation, and healing or strengthening. Hence, there is a need for developing unified models that account for these characteristics. The main objective of these two papers is to present a brief review of the available constitutive models, and identify their capabilities and limitations; then a novel and unified approach, called the disturbed state concept (DSC) with hierarchical single surface (HISS) plasticity, is presented including its theoretical background, constitutive parameters and their determination, and validation at the test specimen and boundary value problem levels. The general capabilities of the DSC/HISS approach are emphasized by its application for a wide range of materials and contacts (interfaces and joints). Because of its generality, the DSC contains many previous models as special cases. The presentation is divided in two papers. This paper (Part 1) contains the review of various models, and then description of the DSC/HISS model and its analysis for issues such as mesh dependence and localization. Part 1 also contains the capability of the DSC/HISS model to define the behavior of both solids and contacts. Validations of the DSC/HISS model at the specimen and boundary value problem levels for a wide range of materials and contacts are included in the compendium paper, Part 2. The idea of the DSC is considered to be relatively simple, and it can be easily implemented in computer procedures. It is believed that the DSC can provide a realistic and unified approach for constitutive modeling for a wide range of materials and contacts. [ABSTRACT FROM AUTHOR]

• Rectification of problems due to sudden illumination changes and proper alignment of feature set for an effective FER. • Isolation of foreground from the background of an image to facilitate the facial key points extraction. • Elimination of unwanted noise and smoothen by using AMF filtering technique. • MATP-based texture pattern and DESURF-LOP-based facial key points to the FER outperform the traditional methods. • PPCSO-based feature selection and LSTM-CNN based classification recognize the expressions accurately. Facial Expression Recognition (FER) plays the vital role in the Human Computer Interface (HCI) applications. The illumination and pose variations affect the FER adversely. The projection of complex 3D actions on the image plane and the inaccurate alignment are the major issues in the FER process. This paper presents the novel Multi-Angle Optimal Pattern-based Deep Learning (MAOP-DL) method to rectify the problem from sudden illumination changes, find the proper alignment of a feature set by using multi-angle-based optimal configurations. The proposed method includes the five major processes as Extended Boundary Background Subtraction (EBBS), Multi-Angle Texture Pattern+STM, Densely Extracted SURF+Local Occupancy Pattern (LOP), Priority Particle Cuckoo Search Optimization (PPCSO) and Long Short-Term Memory -Convolutional Neural Network (LSTM-CNN). Initially, the EBBS algorithm subtracts the background and isolates the foreground from the images which overcome the illumination and pose variation. Then, the MATP-STM extracts the texture patterns and DESURF-LOP extracts the relevant key features of the facial points. The PPCSO algorithm selects the relevant features from the MATP-STM feature set to speed up the classification. The employment of LSTM-CNN predicts the required label for the facial expressions.The major key findings of the proposed work are clear image analysis, effective handling of pose/illumination variations and the facial alignment. The proposed MAOP-DL validates its effectiveness on two standard databases such as CK+ and MMI regarding various metrics and confirm their assurance of wide applicability in recent applications. [ABSTRACT FROM AUTHOR]

• Run-time (real-time) classification of multiple motor imagery (MI) tasks. • Filter bank based maximum-a-posteriori common spatial pattern (CSP) filtering. • Classification of tasks by detecting event-related de-synchronization in μ / β rhythm. • Superior classification and computational efficiency compared to conventional CSP. The main objective of this paper is to propose a novel technique, called filter bank maximum a-posteriori common spatial pattern (FB-MAP-CSP) algorithm, for online classification of multiple motor imagery activities using electroencephalography (EEG) signals. The proposed technique addresses the overfitting issue of CSP in addition to utilizing the spectral information of EEG signals inside the framework of filter banks while extending it to more than two conditions. The classification of motor imagery signals is based upon the detection of event-related de-synchronization (ERD) phenomena in the μ and β rhythms of EEG signals. Accordingly, two modifications in the existing MAP-CSP technique are presented: (i) The (pre-processed) EEG signals are spectrally filtered by a bank of filters lying in the μ and β brainwave frequency range, (ii) the framework of MAP-CSP is extended to deal with multiple (more than two) motor imagery tasks classification and the spatial filters thus obtained are calculated for each sub-band, separately. Subsequently, the most imperative features over all sub-bands are selected and un-regularized linear discriminant analysis is employed for classification of multiple motor imagery tasks. Publicly available dataset (BCI Competition IV Dataset I) is used to validate the proposed method i.e. FB-MAP-CSP. The results show that the proposed method yields superior classification results, in addition to be computationally more efficient in the case of online implementation, as compared to the conventional CSP based techniques and its variants for multiclass motor imagery classification. The proposed FB-MAP-CSP algorithm is found to be a potential / superior method for classifying multi-condition motor imagery EEG signals in comparison to FBCSP based techniques. [ABSTRACT FROM AUTHOR]

In this paper, hybrid BCI that merges Event Related Potentials (P300) and Steady State visual Evoked Potentials (SSVEP) simultaneously is proposed to enhance the performance of Four-choice command system. Here we integrate human facial structure in external visual stimulus to evoke stronger cortical responses in hybrid SSVEP+P300 BCI & compare it with non-face stimuli. We also addressed question of feasibility of eliciting one potential by face & other by non-face stimuli. To evoke SSVEP & P300 responses, paradigms with non-face stimuli, neutral face stimuli, and facial expression changes stimuli are proposed. We also projected additional paradigm where SSVEP is elicited by non-face and P300 by flashing different facial expressions. Results proved the last paradigm evoke stronger cortical potentials and thereby improve system accuracy and ITR than other paradigms. Author discussed external stimulus parameters that might affect simultaneous evocation of multiple brain potentials and their deterioration effect on individual potentials. [ABSTRACT FROM AUTHOR]

Brain computer interface (BCI) applications are emerging from the laboratory to the field environment with ever-increasing demands for high accuracy. However, enhancements in accuracy by employing multisensory devices or via hybridization techniques are inflicting issues like big data and soaring computation complexity. Such enhancements will further aggravate the miseries in the field environments. With more computational complexity and storage requirements, even the on-premise laboratory setups can face hardware and software renewal costs. In addition, it may also come across over-provisioning or under-provisioning of resources. Under such scenarios, computation offloading to cloud machines is gaining considerable attention from academia and industry. The key premise of cloud computing is that it involves the scope of on-demand resource availability and parallel processing. In addition, cloud resources can be accessed ubiquitously. With the advancements in internet technologies, portable BCI headsets can easily be integrated with cloud computing. However, inadvertent usage of cloud resources is neither beneficial for service users nor for service providers, as cloud services are paid. Hence, this paper chronologically investigates contemporary research solutions, considering where and how the cloud has been integrated with the BCI environments. In addition, it also puts forward the related challenges and the potential issues that demand future attention for the seamless integration of the cloud and BCI environment. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for the measurement of electrical brain activity which is extensively used in some areas of clinical practice and psychological/psychiatric research, such as epilepsy, sleep, emotion, and brain computer interfaces. Although EEG sensor do not provide actual brain localizations of the activity sources, they allow to study brain functional connectivity. In this paper we review current application of a specific family of computational methods, the Graph Neural Networks (GNN) to the analysis of EEG data. GNNs appear to be well suited to EEG data modeling as they deal with signals whose domain is defined by a graph instead of a regular lattice in Euclidean space. Readings of EEG electrodes fall in this category, hence the increasing research activity on the application of GNNs to EEG data. • EEG data analysis applications and approaches are described. • Computational approaches to EEG data analysis are discussed. • Specifically, GNN approaches and their applications to EEG are discussed in detail. [ABSTRACT FROM AUTHOR]

The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system. In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. we also design and implement a new online BCI system based on motor imagery to verify the online processing performance of the proposed method. In order to verify the effectiveness of the proposed method, we conducted offline experiments on the public dataset called BCI Competition IV Data Set 2b. Furthermore, in order to verify the processing performance of the online system, we designed 60 groups of online experiments on 12 subjects. The online experimental results show that the twelve subjects can complete the system task efficiently (the best experiment is 135.6 s with 9 trials of subject S1). This paper demonstrated that the proposed low-quality training data detection method can effectively screen out low-quality training samples, so as to improve the performance of the MI-BCI system. • A low-quality data detection method is proposed to screen low-quality training samples caused by different factors. • The possibility of reusing low-quality samples is studied. • An online asynchronous MI-BCI system is designed and implemented. [ABSTRACT FROM AUTHOR]

• The new visual stimulus paradigm using LEDs instead of computer screen. • The dynamic window strategy was introduced into CCA, and the performance of dynamic window CCA algorithm was evaluated. • The LEDs visual stimulus paradigm and dynamic window CCA algorithm were combined in online experiment. The improvements of the visual stimulus paradigm and the target recognition algorithm in the (steady-state visual evoked potential) SSVEP-based (brain computer interface) BCI have attracted widespread attention among BCI researchers. The LEDs visual stimulus paradigm was adopted in this paper, which is more portable and lighter than the widely used computer screen visual stimulus paradigm. In terms of algorithms, the 'dynamic window' strategy was introduced in canonical correlation analysis (CCA). The dynamic window CCA can automatically find the optimal window length of data in the recognition process, and this could increase the information transfer rate (ITR) of the system. An offline experiment with LEDs visual stimulus paradigm was designed to evaluate the performance of dynamic window CCA. The results showed that the LEDs visual stimulus paradigm is feasible in the SSVEP-based BCI, and the dynamic window CCA got the higher ITR compared with CCA. In this paper, an online experiment was also conducted using dynamic window CCA. The online SSVEP system used the LEDs visual stimulus paradigm to control an artificial hand. The results showed that the dynamic window CCA is well suited for online systems. [ABSTRACT FROM AUTHOR]

This paper utilizes a human-robot interface system which incorporates Kalman filter (KF) and Adaptive Multi-space Transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. KF is used to estimate the translation of the human hand. Although a KF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an Adaptive Multi-space Transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an algorithm for decomposing complex line drawings which depict connected 3D manifolds into multiple simpler drawings of individual manifolds. The decomposition process has three stages: decomposition at non-manifold vertices, along non-manifold edges and across internal faces. Once non-manifold vertices and/or edges are found, the decomposition can be performed straightforwardly. Thus the major task in this paper is decomposition across internal faces. This has two steps: basic decomposition and repair of incomplete parts. The decomposition process is performed before face identification which is computationally expensive. After decomposition, the time for face finding is much reduced and this, in turn, greatly improves the process for 3D reconstruction, which is the ultimate goal. [Copyright &y& Elsevier]

Abstract: Negotiation and collaboration issues in large organizations, with centralized control, across the globe, are becoming more complex with each passing day. The Acknowledged System of Systems (SoS) is a new approach that addresses some of these issues in a systematic, efficient manner. This paper proposes a hierarchical architectural framework to support Acknowledged SoS architecting and analysis for a Department of Defense (DoD) Acquisition process. A major challenge of the successful planning and evolution of an Acknowledged SoS is the lack of understanding of the impact of presence or absence of a system and its interface with another constituent system on the overall architecture. The agent based model (ABM) structure developed here provides Acknowledged SoS manager that has both ability to address the managerial issues as well as a decision making tool for SoS architecting. This paper offers a complete integration of the techniques used to represent SoS architectures. The work illustrates the modeling approach through a domain setting. [Copyright &y& Elsevier]

Abstract: Gesture-based interfaces to control systems using full body or upper torso motions are becoming more common, particularly in the entertainment industry but increasingly in other domains. These interfaces may represent significant hurdles for individuals with motion or balance impairments. Even for non-impaired users, commands which are not readily represented by body mimicry require users to learn new non-intuitive gestures for adding layers of complexity in commands. In this paper, a reflection and clarification of these challenges is presented, based on some sample usage cases. Then a possibly solution path is presented, resorting to an idea originating in a science fiction novel, the shamanic interface imagined by Daniel Suarez. A reflection on its feasibility and on possible paths towards its creation is presented, as well as the possible impacts to render gesture-based interfaces more accessible to all and potentially easier to learn due to enhanced cultural awareness. The basic idea presented in this reflection paper is that the creation of a concern-separation layer between the gestures being executed by the users and their interpretation by computing systems can contribute both to the access by users with special needs and to all users in general, by enabling customized approaches to gesture-based control. [Copyright &y& Elsevier]

Abstract: Large scale disasters like the earthquake and tsunami in Japan (2011) cripple the local infrastructure. Proprietary systems and protocols used today for disaster response still lack data at the high spatial and temporal resolution needed to quickly save lives and to support disaster recovery efforts. Victims are rescued after days, if not weeks; digital coordination interfaces among responders are lacking, or are based on archaic methods (pencil, paper, paint on walls); the delay in receiving vast amounts of information from the field is bounded by the time used to physically transport tapes or hard drives. In this paper we present the design, implementation and evaluation of DistressNet, a system that provides services for emergency response applications. DistressNet integrates a variety of rapidly deployable, battery powered COTS devices into a secure framework. An optimal placement of networked components allows users to quickly and reliably store and retrieve data, in a “cloud”-like manner, from a local intermittently connected “fog”. High volumes of field data are available for emergency response personnel to view on interfaces like smartphones and tablets. DistressNet is a large academic effort, proposing open systems, instead of proprietary solutions. It has been developed in collaboration with Texas Task Force 1 and its components have been evaluated for over one year in outdoor deployments that required over 1500 man hours. [Copyright &y& Elsevier]

Abstract: C++ template metaprogramming is used in various application areas, such as expression templates, static interface checking, active libraries, etc. Its recognized similarities to pure functional programming languages–like Haskell–make the adoption of advanced functional techniques possible. Such a technique is using monads, programming structures representing computations. Using them actions implementing domain logic can be chained together and decorated with custom code. C++ template metaprogramming could benefit from adopting monads in situations like advanced error propagation and parser construction. In this paper we present an approach for implementing monads in C++ template metaprograms. Based on this approach we have built a monadic framework for C++ template metaprogramming. As real world examples we present a generic error propagation solution for C++ template metaprograms and a technique for building compile-time parser generators. All solutions presented in this paper are implemented and available as an open source library. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach of structural domain partitioning for parallel processing based on an interface displacement frame method and using dual partition super-elements. Compatibility and equilibrium between the partitions is ensured at the level of the parent structure, where the dual super-elements handle the two-way communication of interface displacements/forces/stiffness and other analysis-specific entities. The proposed approach can be applied relatively easily to existing monolithic finite element analysis programs, and it offers more freedom to the user with regard to defining partitions, load balancing and isolating parts of the structure of specific interest. Several examples are used in the paper to demonstrate the benefits of the proposed approach. [Copyright &y& Elsevier]

Abstract: Application of technology systems is seen in many sectors including agriculture and food. Traditionally, agricultural industry has been solely dependent on human labor with limited application of mechanical equipment and machines. The applications of advanced technology such as embedded computing, robotics, wireless technology, GPS/GIS (Geographical Positioning System/Geographical Information System) and DBMS (Database Management System) software are seen to be recent developments. This paper reviews the applications of technology systems in agriculture and food. Because knowledge on standardization and interfacing plays a key role in using the technology systems in any application domain, this paper aims to highlight the important attributes of such an emerging research area. In particular, the paper describes soil sampling methods and technology applications; field and yield mapping with GPS and GIS; harvesters and future research in robotic-based harvesters; food processing and packaging technology such as traceability and status of RFID networking research; application of sensor network; data management and execution systems; and the automation and control standards such as fieldbus systems and OMAC guidelines. [Copyright &y& Elsevier]

Abstract: In Systems of Systems (SoS), a major challenge is to determine how to design a test suite that will check that the complete SoS mission and objectives are achieved. Combinatorial strategies that test for every interface in the SoS are not always optimal due to their exponential nature, and given the mission of the SoS, not all the constituent systems might have to actually interact with each other. To overcome these problems, this paper proposes the use of Basis Path Testing, which is a white-box software testing technique that makes use of Graph Theory to analyse the complexity of a structured system by creating a control flow graph from each of the program's functions to design an optimal test suite. This test suite is a set of paths that traverse through the functions, which are assumed linearly independent and that can be used to create a test strategy that will exercise all of the program's functions at least once to verify and validate their functionality. By applying Basis Path Testing analysis to the constituent systems in a SoS, the tester can develop an optimal test suite that will guarantee that all possible independent paths, all possible logical decisions, and all their interfaces are executed at least once. This paper presents a SoS sample architecture and show how to generate a test suite using Basis Path Testing analysis. [Copyright &y& Elsevier]

Abstract: Throughout the history of CAD, a variety of interfaces have been employed with the aim of supporting designers’ construction and manipulation of digital models. This paper explores the potential of eye tracking as a CAD interface, and presents a prototype that uses collected gaze data to support shape exploration in a two-dimensional vector-based sketch editor. The eye tracking interface uses the visual interactions of users to identify interpretations of constructed shapes, according to recognised parts identified using methods from shape grammar research. It can therefore be argued that it supports construction and manipulation of shapes according to user intent. The prototype has been evaluated in a user study where design students carried out controlled shape exploration exercises which involved interpretation and manipulation of shapes. In the paper, key concepts of eye tracking methodology are introduced; the methods used to implement the eye tracking interface are described; and the user study and its outcomes are reported. The results are positive and indicate the potential for eye tracking as an interface for supporting shape exploration in CAD. [Copyright &y& Elsevier]

Abstract: This paper presents a semi-automatic tool, called IGAnn (Interactive image ANNotation), that assists users in annotating textual labels with images. IGAnn performs an interactive retrieval-like procedure: the system presents the user with images that have higher confidences, and then the user determines which images are actually relevant or irrelevant for a specified label. By collecting relevant and irrelevant images of iterations, a hierarchical classifier associated with the specified label is built using our proposed semi-supervised approach to compute confidence values of unlabeled images. This paper describes the system interface of IGAnn and also demonstrates quantitative experiments of our proposed approach. [Copyright &y& Elsevier]

Abstract: For decision makers in the domains of agriculture and environment, for instance in government agencies, farmers, environmental NGOs and farmers'' unions, it is beneficial to evaluate ex-post or to asses ex-ante the impacts of their choices. To research these interdisciplinary relationships, models developed by different scientific disciplines and often operating at different scales can be integrated into model chains that cover processes across disciplines. In order to assemble models into an operational model chain conceptual, semantic and technical levels of integration have to be taken into account. The main focus of this paper is on technical integration to ensure repeatability and reproducibility of model chain runs and to optimize use of computer hardware for model simulations. Technical integration itself can be achieved by different approaches (i.e. manual, scripting, building or using a proprietary framework, using an open framework based on standards). From the many available modelling frameworks (e.g. OMS, TIME, KEPLER, FRAMES, MODCOM, OpenMI) the emphasis will be on OpenMI, the Open Modelling Interface and its use and usefulness as a readily available, generally accepted and open standards based framework. OpenMI is an open source software standard for dynamically linking models at runtime, which can potentially be used in many domains, but is currently mainly applied in the water and environmental domains. This paper describes and evaluates the use of OpenMI in several multi-disciplinary large projects that worked on integrated models. These projects operated in the disciplines of agriculture, land use, nitrogen cycling, forestry, hydrology and economics. To this end two workshops were organized to acquire feedback from both software developers and modellers that contributed to the aforementioned projects on the use of OpenMI. Perceived advantages and disadvantages of OpenMI differed between modellers and software engineers, although both identified the lack of standard functionality as a major disadvantage and the prescription of a way of working through OpenMI as a standard as a major advantage. In conclusion, OpenMI can be used as a standard for technical model integration across disciplines, and it is not limited to one particular discipline. [Copyright &y& Elsevier]

Abstract: Designing materials for targeted performance requirements as required in Integrated Computational Materials Engineering (ICME) demands a combined strategy of bottom–up and top–down modeling and simulation which treats various levels of hierarchical material structure as a mathematical representation, with infusion of systems engineering and informatics to deal with differing model degrees of freedom and uncertainty. Moreover, with time, the classical materials selection approach is becoming generalized to address concurrent design of microstructure or mesostructure to satisfy product-level performance requirements. Computational materials science and multiscale mechanics models play key roles in evaluating performance metrics necessary to support materials design. The interplay of systems-based design of materials with multiscale modeling methodologies is at the core of materials design. In high performance alloys and composite materials, maximum performance is often achieved within a relatively narrow window of process path and resulting microstructures. Much of the attention to ICME in the materials community has focused on the role of generating and representing data, including methods for characterization and digital representation of microstructure, as well as databases and model integration. On the other hand, the computational mechanics of materials and multidisciplinary design optimization communities are grappling with many fundamental issues related to stochasticity of processes and uncertainty of data, models, and multiscale modeling chains in decision-based design. This paper explores computational and information aspects of design of materials with hierarchical microstructures and identifies key underdeveloped elements essential to supporting ICME. One of the messages of this overview paper is that ICME is not simply an assemblage of existing tools, for such tools do not have natural interfaces to material structure nor are they framed in a way that quantifies sources of uncertainty and manages uncertainty in representing physical phenomena to support decision-based design. [Copyright &y& Elsevier]

Abstract: The subject of this paper is a framework that represents a new approach of information technology in the area of Systems Engineering. This framework enables us to specify, design, engineer, produce and maintain complex capital facilities, e.g. ships and infrastructure in an explicit and consistent way. The framework supports the needs of Systems Engineering according to ISO 15288 for unambiguous and explicit communication about such a facility between project participants, stakeholders, disciplines etc. during all life cycles by introducing an ontology for Systems Engineering. This ontology, derived from the data integration standard ISO 15926, facilitates interoperability, increases efficiency and reduces failure cost. The ontology in this paper enables model-based Systems Engineering and specifically describes a model-based approach of system breakdown structures on process level and on physical level by means of process functions, the Functional Object paradigm and a new approach of interface management by means of the port-interaction theory. The work presented in this paper is part of ongoing PhD-work by the author carried out at Delft University of Technology in the Netherlands. [Copyright &y& Elsevier]

Abstract: In-situ stress field is very important in the numerical simulation and stability analysis as well as in the engineering design and construction. So it requests an effective analysis method. The typical in-situ stress analysis method is multivariate regression, which is based on 3-D FEM direct modeling, but it may be not so accurate to obtain the distribution of in-situ stress field of the project, because when the multivariate regression is carried out, the cross influence of the main factors of in-situ stress field is ignored, and the relationship of in-situ stress field and the main factors is too complicated to express by subharmonic multivariate function. This paper presents an improved method back propagation (BP) neural network back analysis of in-situ stress field, based on the calculated results of multivariate linear regression analysis. For in the ANN (Artificial Neural Networks) method, the training samples are difficult to generate, while combining the linear regression method with ANN method this problem can be solved perfectly. Linear elastic multivariate regression is carried out to determine the general bounds of optimized paraments, and uniform design method is carried out to settle different combinations of factor levels. Training samples are gained by FEM analysis. And the visual neural network by using ActiveX technology is realized in this paper, by taking full advantages of strong capability in computing of Matlab and features of friendly interface of VB perfectly. In fact, one needs to know the status of the in-situ stress field at some important location in the engineering, by using neural network method, intelligent expression method of the in-situ stress field is presented, and the developed man-computer interface has the property of easy use in the actual engineering. [Copyright &y& Elsevier]

Abstract: Interaction with future coming Smart Environments requires research on methods for the design of a new generation of human–environment interfaces. The paper outlines an original approach to the design of multimodal applications that, while valid for the integration on today’s devices, aims also to be sufficiently flexible so as to remain consistent in view of the transition to future Smart Environments, which will likely be structured in a more complex manner, requiring that interaction with services offered by the environment is made available through the integration of multimodal/unimodal interfaces provided through objects of everyday use. In line with the most recent research tendencies, the approach is centred not only on the user interface part of a system, but on the design of a comprehensive solution, including a dialogue model which is meant to provide a robust support layer on which multimodal interaction builds upon. Specific characteristics of the approach and of a sample application being developed to validate it are discussed in the paper, along with some implementation details. [Copyright &y& Elsevier]

Abstract: This paper examines some aspects of the usefulness of interactive tabletop systems, if and how these impact collaboration. We chose creative problem solving such as brainstorming as an application framework to test several collaborative media: the use of pen-and-paper tools, the “around-the-table” form factor, the digital tabletop interface, the attractiveness of interaction styles. Eighty subjects in total (20 groups of four members) participated in the experiments. The evaluation criteria were task performance, collaboration patterns (especially equity of contributions), and users’ subjective experience. The “around-the-table” form factor, which is hypothesized to promote social comparison, increased performance and improved collaboration through an increase of equity. Moreover, the attractiveness of the tabletop device improved subjective experience and increased motivation to engage in the task. However, designing attractiveness seems a highly challenging issue, since overly attractive interfaces may distract users from the task. [Copyright &y& Elsevier]

Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI performance. We assume that the channels related to MI should contain common information when participants are executing the MI tasks. Based on this hypothesis, a correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study. The aim is to improve the classification performance of MI-based BCIs. Furthermore, a novel regularized common spatial pattern (RCSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with the Radial Basis Function (RBF) kernel is trained to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) to validate the effectiveness of the proposed methods. The results show that the CCS algorithm obtained superior classification accuracy (78% versus 56.4% for dataset1, 86.6% versus 76.5% for dataset 2 and 91.3% versus 85.1% for dataset 3) compared to the algorithm using all channels (AC), when CSP is used to extract the features. Furthermore, RCSP could further improve the classification accuracy (81.6% for dataset1, 87.4% for dataset2 and 91.9% for dataset 3), when CCS is used to select the channels. • A new correlation-based channel selection (CCS) method is proposed in this paper. • A novel regularized CSP (RCSP) is proposed to optimize the motor imagery features. • The CCS-RCSP method can significantly improve the classification accuracy. [ABSTRACT FROM AUTHOR]

Brain Computer Interface (BCI) systems have been widely used to develop sustainable assistive technology for people suffering from neurological impairments. A major limitation of current BCI systems is that they are based on Subject-dependent (SD) concept. The SD based BCI system is time consuming and inconvenient for physical or mental disables people and also not suitable for limited computer resources. In order to overcome these problems, recently subject-independent (SI) based BCI concept has been introduced to identify mental states of motor disabled people but the expected outcome of the SI based BCI has not been achieved yet. Hence this paper intends to present an efficient scheme for SI based BCI system. The goal of this research is to develop a method for classifying mental states which can be used by any user. For attaining this target, this study employs a supervised spatial filtering method with four types of feature extraction methods including Katz Fractal Dimension, Sub band Energy, Log Variance and Root Mean Square (RMS) and finally the obtained features are used as input to Linear Discriminant Analysis (LDA) classification model for identifying mental states for SI BCI system. The performance of the proposed design is evaluated in several ways such as considering different time window length; different frequency bands; different number of channels. The mean classification accuracy using Katz feature is 84.35% which is the maximum output compare to other features that outperforms the existing methods. Our proposed design will help to make a new technology for development of real-time SI based BCI systems that can be more supportive for the motor disabled patients. • A novel SI based BCI framework is introduced to identify mental states. • A new channel selection concept is also proposed for SI based BCI system. • Efficacy of the method is confirmed by statistical and graphical analyses. • The proposed method outperforms the existing methods. • This design will help to build up a new technology for development of SI based BCI. [ABSTRACT FROM AUTHOR]

In today's healthcare applications, the use of mobile technologies brings together physicians and patients for intelligent and automatic monitoring of daily clinical activities, remote life assistants, and preventive care, especially for the elderly and those under medical control. As smartphones become an important part of our everyday life, they are ever more employed in human activities recognition (HAR) including the monitoring of personal health care and wellbeing. However, HAR is complex and it is important to use the best technology and learn about human activity using machine learning. The purpose of this paper is to develop a HAR system based on the smartphone sensors' data using Bagging and Adaboost ensemble classifiers. The experimental results for the HAR data have been evaluated after performing different data mining techniques. For each subject, the total classification accuracy, the F-measure, and the ROC area were calculated. Adaboost ensemble classifiers algorithm improved significantly the performance of smartphone-based HAR, combined with SVM, it reached 97.44% accuracy compared to the rest of the classifiers. The proposed algorithm of Adaboost SVM can lead to an accurate HAR for elderly and disabled patients who need continuous care as well as it is a tool that supports the decisions of all medical practitioners. [ABSTRACT FROM AUTHOR]

• Disadvantages of current motor imagery patterns based on low-level commands. • Performance of a novel motor imagery pattern based on high-level commands. • Comparison between a classical pattern based on low-level commands and proposed pattern. Motor Imagery (MI)-based Brain Computer Interface (BCI) has received increasing attention from academicians for its practicability and convenience. With the development of classification algorithms, many traditional patterns based on low-level commands, such as the movement of hands, feet, tongue, or shoulders, can be decoded quite well. However, when encoded by low-level commands, each body part can perform only one imaginary command. This not only limits the number of tasks that a user's body can encode, but also makes the system less user-centered. In this paper, we proposed a novel pattern based on high-level commands for encoding MI BCI. This pattern combines clockwise and anticlockwise movements of both hands. Compared with a typical traditional pattern based on low-level commands, this pattern shows insignificant differences with regard to distinctiveness and stability for some of the tested subjects (p > 0.05). Thus, the proposed pattern can potentially allow additional tasks by human body without reducing their distinctiveness and stability. The results of this study also provide insights into the creation of high-level commands and improvements toward more intelligent and user-centered systems. [ABSTRACT FROM AUTHOR]

• A framework combining CSP and phase synchronization is proposed for EEG classification. • An index quantifying intrinsic phase synchronization is introduced to extract information. • Three ways of feature combinations is proposed. The common spatial patterns (CSP) approach is a classical and representative technique of optimizing spatial filters of electroencephalogram (EEG) signals in the community of brain computer interfaces (BCI). It, however, utilizes only amplitude information of the EEG signals. The phase information of the multi-channel EEG series, on the other hand, plays an important role in characterizing brain activities. In this paper, we consider enhancing the classification performance of CSP by making explicit use of information of the phase synchronization. An index, termed as rank-weighted phase lag index (rWPLI), is introduced to qualify the intrinsic phase synchronization. The rWPLI features are then incorporated into the CSP framework via three ways of feature combinations. The classification experiments on real EEG data sets of BCI competitions show the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a particulate interface model to simulate the interface by using a particulate discrete element method. Six modifications are adopted in the numerical calculations to improve the stability and accuracy. The interface model provides two failure criteria: one is the Mohr–Coulomb failure criterion and the other is the Barton–Bandis shear strength criterion. To verify the proposed model, the failure envelope, the shear-displacement curve, the closure curve, and the dilation curve were analyzed and simulated. The simulations demonstrated good agreement with the theoretical curves. The influence of the particle size was also investigated; the results proved that the quantity or size of the particle did not affect the performance of the proposed model. Finally, a rock bolt pull-out problem was simulated. The results proved that the proposed interface model can reasonably predict the shear stress acting on the interface between the rock bolt and rock. [ABSTRACT FROM AUTHOR]