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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]

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]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient's brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient's overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-EMG). The framework utilizes a combination of techniques: decoding EEG by using Graph Convolutional LSTM Networks (GCN-LSTM) to recognize the subject's motion intention, and decoding EMG by using a convolutional neural network (CNN) to accurately identify hand movements. In EEG decoding, the correlation between channels is calculated using Standardized Permutation Mutual Information (SPMI), and the decoding process is further explained by analyzing the correlation matrix. In EMG decoding, experiments are conducted on two task paradigms, both achieving promising results. The proposed framework is validated using the publicly available WAL-EEG-GAL (Wearable interfaces for hand function recovery Electroencephalography Grasp-And-Lift) dataset, where the average classification accuracies of EEG and EMG are 0.892 and 0.954, respectively. This research aims to establish an efficient and user-friendly EEG-EMG hybrid BCI, thereby facilitating the hand rehabilitation training of stroke patients. [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]

• 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]

In this paper, we will describe a method of measuring the furnace temperature of a horizontal Weiss balance using a S-type thermocouple. The cold junction of this method is referenced to the ambient temperature of the room since is measured using a Pt100 resistance thermometer. The temperatures will be calculated by an Arduino Uno microcontroller, which receives a voltage signal from two transducers: the thermocouple's transducer and a Pt100 resistive transducer. These were simulated by a SPICE simulator. The voltage output from the Pt100 transducer is read using a S analog to digital converter, the model being an MCP3551 having 22 bits resolution. This voltage is measured in terms of microvolts, so we opted for a higher resolution. The furnance temperature is thus computed by Arduino and is sent UART interface to a computer that displays real-time monitoring for the temperature function graph which is also time dependent (T = f (t)). [ABSTRACT FROM AUTHOR]

This paper relates to the subtheme of "Technological tools for enhancement of learning". Much has been said and written about the importance of accuracy in English language communication, but my professional journey suggests that teachers and teacher trainers are often unsure about what exactly it involves. In this paper I will thus address three questions. 1. What is accuracy? I will consider the concept in order to identify its key features. 2. Why is developing accuracy in communication, a valuable activity for English language teachers? Here I will outline some of the many benefits for learners, the features of accuracy offer. 3. Finally, I will provide a brief overview of the procedures and tools available on the Internet as it is the age of information technology. I will also comment on some of the web tools that can facilitate teachers' efforts to improve learners' accuracy in communication and argue that using web tools is a feasible and valuable strategy. [ABSTRACT FROM AUTHOR]

This paper analyses the development trends of intelligent instrument interfaces by applying full liquid crystal display technology. A novel reference dimension is introduced to consider related design work. In the perspective of design issues: how can we accurately analyse the requirements of drivers with various proficiency for different information, and how can instrument interfaces intelligently adapt to all kinds of drivers? This paper summarises the key points of the design work based on changing interface analysis in new automobile instruments and provides an analytical approach to targeting driving information: the index weight analysis method. A set of scientific methods to judge the degree of information requirements from various drivers in different scenarios is proposed. Therefore, it can improve the current situation that designers mainly rely on personal experience and subjective judgment when they design automobile instrument interfaces, which lacks scientific and objective judgment basis. [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]

• 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]

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]

This paper discusses the activity of interface management in the practice of systems engineering. The systems engineering manuals of three federal agencies list interface management as a technical management process. This paper outlines why each agency believes interface management is important, the interface management documentation required, and the systems engineering tools used in the process. The roles of interface management in open systems architectures and in systems of systems are discussed. Some suggestions for future work are made. [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]

The field of research of this paper combines Human Computer Interface, gesture recognition and fingertips tracking. Most gesture recognition algorithms processing color images are unable to locate folded fingers hidden inside hand contour. With use of hand landmarks detection and localization algorithm, processing directional images, the fingertips are tracked whether they are risen or folded inside the hand contour. The capabilities of the method, repeatibility and accuracy, are tested with use of 3 gestures that are recorded on the USB camera. Fingertips are tracked in gestures presenting a linear movement of an open hand, finger folding into fist and clenched fist movement. In conclusion, a discussion of accuracy in application to HCI is presented. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos de Información y Comunicación is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

• 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]

Interaction design is distinguished from most other design disciplines through its temporal form. Temporal form is the computational structure that enables and demands a temporal expression in the resulting design. Temporal form is what enables poetry. In music, temporal form is the composition of tones, pauses, and timbre arranged into harmonies and rhythms. In interaction design it is, for instance, the behaviors of pixels on a screen or the movements in shape-changing interfaces. In this paper we show the power of working explicitly with temporal form in designing computational things. We give a nuanced account of what temporal form is in interaction design, and we look at related work synthesizing what we already know of the temporal concerns in interaction design and HCI. In the second part we present a design experiment through which we explore the experiential qualities of a set of 11 simple temporal forms by letting a series of expert designers reflect upon them. We borrow a framework from Boorstin's film theory in which he distinguishes between the voyeuristic, the vicarious, and the visceral experience. We show how to use rhythms, complexity, gentle or forceful behavior, etc., to create experiences of "being alive," being entertained, or being something that we empathize with. We end the paper by arguing how the temporal form in computational things enables richer experiences than static objects do. [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]

Hallway testing, Remote Usability testing, Expert review, Automated expert review and A/B testing are the methods commonly used for Usability testing. However, there is no reliable system that integrates Brain Computer Interface (BCI) into the testing process with focus given towards user emotion analysis using electroencephalography (EEG) signals. This paper proposes a system that would be able to identify user emotions while they are conducting usability tests and the results would be able to increase the accuracy of the usability test. In the proposed system, the results of the usability test would be displayed in real time on a dashboard and a summary report can be generated for distribution. [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]

Hallway testing, Remote Usability testing, Expert review, Automated expert review and A/B testing are the methods commonly used for Usability testing. However, there is no reliable system that integrates Brain Computer Interface (BCI) into the testing process with focus given towards user emotion analysis using electroencephalography (EEG) signals. This paper proposes a system that would be able to identify user emotions while they are conducting usability tests and the results would be able to increase the accuracy of the usability test. In the proposed system the results of the usability test would be displayed in real time on a dashboard and a summary report can be generated for distribution. [ABSTRACT FROM AUTHOR]

This paper presents the evaluation of specific absorption rate for human head by designing rectangular shaped microstrip patch antenna. An antenna is a transducer that converts electrical signals into EM waves and radiates into space. The performance of an antenna is measured by its parameters namely bandwidth, gain, return loss etc. There are several types of antennas available but the microstrip patch antennas are widely used for many applications because of its low profile, light weight, low cost, feed-line flexibility, versatility, ease of fabrication etc. Even though it has several advantages, it suffers from major drawbacks namely narrow bandwidth, low gain and high return loss. In order to overcome these drawbacks, FR-4 materials are used. Specific absorption rate (SAR) is a measurement of certain amount of radiation absorbed by human body while entering into an electromagnetic field. It is defined as the power absorbed per mass of tissue and has units of a watt per kilogram. SAR is typically averaged either over the complete body or over a small sample volume (typically 1g or 10g of tissue). For evaluating the antenna performance, gain, VSWR and the return loss were calculated for the proposed antenna and the proposed antenna provides the peak the gain of 4.43dB at 10.846GHz. The proposed antenna also provides the SAR value of 0.00109W/Kg is evaluated for 1g of tissue as per the IEEE standard. The human head model and the proposed antennas are designed using CST (Computer Simulation Technology) microwave studio tool. [ABSTRACT FROM AUTHOR]

Touch interface technologies for mobile devices are essentially in use. The purpose of such touch interfaces is to run an application by touching a screen with a user's finger or to implement various functions on the device. When the user has an attempt to use the touch interface, users tend to grab the mobile device with one hand. Because of the existence of untouchable areas to which the user cannot reach with the user's fingers, it is possible to occur for a case where the user is not able to touch a specific area on the screen accurately. This results in some issues that the mobile device does not carry out the user's desired function and the execution time is delayed due to the wrong implementation. Therefore, there is a need to distinguish the area where the user can stably input the touch interface from the area where the users cannot and to overcome the problems of the unstable touch area. Furthermore, when the size of the screen increases, these issues will become more serious because of an increase in the unstable touch areas. Especially, an interface that receives position and force data like 3D-touch requires the stable area setting different from the conventional 2D-touch. In this paper, we search and analyze the stable touch areas on the large screen where the user can do 3D-touch inputs. [ABSTRACT FROM AUTHOR]

This paper seeks to present the Brain Computer Interfaces (BCI) current development stage and to analyze their progress trends in the near future. Special attention is paid to the BCIs role in improving the compatibility between humans and Artificial Intelligence (AI). [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a feed forward back-propagation neural network (FFBPNN) based method to enhance the performance of the motor imagery classification. The dataset consists of fifty nine channels of EEG signals which are first normalised using minmax method and then given as input to the FFBPNN network. Experimental outcomes of the FFBPNN are recorded in term of '0's or '1's for two classes of motor imagery signals. The accuracy of the proposed FFBPNN method has been measured using confusion matrix, mean square error and percentage accuracy. However, accuracy of the FFBPNN based method is recorded up to 99.8%. Hence the proposed method gives better accuracy of the classification which will ultimately help in designing robust BCI. [ABSTRACT FROM AUTHOR]

There are many Remote laboratories (RLs) in the world which are created mainly by schools or some organizations. But the trend suggests that in the future all of these laboratories will be merged into large grids which will be administered by Remote Laboratory Management Systems (RLMS). Such systems are composed from many parts which communicate with each other. This communication can be crucial for correct functioning of whole system. The paper intends to contribute to the standardized solutions in communication schemes of RLMSs. Each part of RLMS is described in terms of communication requirements. The largest volume of data is transmitted through the Internet. The security of this communication channel based on TCP/IP protocol is described in detail. The last part of paper describes the proposal and creation of data communication and diagnostic interface for individual remote experiments included in RLMS. [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]

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]

Nowadays, with progresses in robotic science, the design and implementation of a mechanism for human–robot interaction with a low workload is inevitable. One notable challenge in this field is the interaction between a single human and a group of robots. Therefore, we propose a new comprehensive framework for single‐human multiple‐robot remote interaction that can form an efficient intelligent adaptive interaction (IAI). Our interaction system can thoroughly adapt itself to changes in interaction context and user states. Some advantages of our devised IAI framework are lower workload, higher level of situation awareness, and efficient interaction. In this paper, we introduce a new IAI architecture as our comprehensive mechanism. In order to practically examine the architecture, we implemented our proposed IAI to control a group of unmanned aerial vehicles (UAVs) under different scenarios. The results show that our devised IAI framework can effectively reduce human workload and the level of situation awareness, and concurrently foster the mission completion percentage of the UAVs. [ABSTRACT FROM AUTHOR]

Since the emergence of brain computer interface (BCI), several methods have been applied to associate an electroencephalographic (EEG) recording with a specific mental task. Particularly, in the classification stage, several techniques such as linear Fisher discriminant (LD), feed-forward artificial neural networks (FNN) and radial basis function neural networks (RBF) have been applied successfully with BCI applications. However, in BCI applications, there is a challenge related to avoid long and tedious calibration session for users, this implies that the classification techniques used during the classification stage, have to be trained with a reduced number of EEG recordings. However, most of the classification techniques require several samples to learn accurately an association with a particular mental task. Since the spiking neural models (SNM) have shown their robustness in pattern recognition problems, this paper is focused on demonstrating that they are potential alternatives to classify EEG recordings when they are trained with a reduced number of data samples. To do that, we computed the coherence from a subset of three electrodes to obtain the feature vector of each EEG recording. Then, this information was classified using the SNM. In order to evaluate the robustness, the SNM was trained varying the number of samples. Furthermore, based on the performance and the confidence interval achieved in the classification, we developed two indexes to evaluate and compare the SNM against LD, FNN and RBF. The experimental results over the IIIa, IVa and V data sets from BCI International Competition III, suggest that the SNM are the best option to avoid long calibration sessions. [ABSTRACT FROM AUTHOR]

In this paper we present a collection of virtual interfaces used to augment a MIDI keyboard synchronized in physical and virtual space. Several virtual interfaces are developed and evaluated. Some utilize the tactility offered by the keyboard's surface, while others rely on the improved presence offered by the keyboard. An evaluation of these virtual interfaces is made with respect to learnability and utility to identify the successes and failures of these interfaces. [ABSTRACT FROM AUTHOR]

This paper focuses on the Brain Computer Interface (BCI) application and its issues. Further the attempt was made to implement left and right hand movement classification after removal of the artifacts in the acquired signals of the various hand movements. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) technology allows devices to detect each other within proximity and communicate directly. Current standards define Proximity-based Service (ProSe) which supports both direct and network supported device discovery. The paper proposes an approach to open access to ProSe functionality based on network supported discovery. The open access to ProSe functionality enables creation and deployment of 3rd party applications that initiate D2D communications in case of cellular network congestion. The 3rd party applications may access ProSe functions through Web Service interfaces. The proposed interfaces are described by information flows and actual interface definition. Models of ProSe state as seen by 3rd party application and by the network are proposed, formally described and verified. [ABSTRACT FROM AUTHOR]

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]

This paper explores the foundations regarding the systematic usage of the concept of interface in order to sketch a methodological approach, in which the fundamental perspectives that guide the abstracting of a software system solution (referred to as UP, SP and BP in the paper) are unified, with the goal to optimally derive the behaviour of the system from its structure. Moreover, this is very useful for opening new avenues in order to address the shortcomings that are provoked by changes, considering a software system that is conceived at an industrial scale. [ABSTRACT FROM AUTHOR]

In recent years, face recognition has become one of the hottest research topics aimed at biometric applications. Comparing with other biometrics recognition, face recognition provides more natural means for perceptual interface. However, face recognition algorithms weakly perform under some common conditions, which include the variation of facial expressions or lightening conditions, the occlusion of faces like wearing glasses or mask, the low resolution or noises of input images, and the like. The other problem is the recognition efficiency, especially when the facial database is tremendous. This paper presents all common subsequences (ACS) as the kernel function (similarity method) to solve the time series problem. Experiments on 4 public face databases: Caltech, Jaffe, Orl and Yale databases, demonstrate that ACS can achieve higher recognition accuracy than some classic face recognition methods, e.g. 2DPCA and 2DLDA. These instructions give you basic guidelines for preparing camera-ready papers for conference proceedings. [ABSTRACT FROM AUTHOR]

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]