We present a method for the removal of movement artifacts from the recordings of electroencephalography (EEG) signals in the context of sports health. We use a smart wearable Internet of Things-based signal recording system to record physiological human signals [EEG, electrocardiography (ECG)] in real time. Then, the movement artifacts are removed using ECG as a reference signal and the baseline estimation and denoising with sparsity (BEADS) filter algorithm for trend removal. The parameters (cut-off frequency) of the BEADS filter are optimized with respect to the number of QRS complexes detected in the reference ECG signal. Next, surrogate movement signals are generated using a linear combination of intrinsic mode functions derived from the sample movement signals by the application of empirical mode decomposition. Surrogate signals are used to test the efficiency of the BEADS method for filtering the movement-contaminated EEG signals. We provide an analysis of the efficiency of the method, extracted movement artifacts and detrended EEG signals. INDEX TERMS Mobile EEG, movement artifact removal, sports e-health, digital signal processing.
Cloud gaming provides cloud computing-based game as a service. In this paper we describe the development of a virtual reality base gliding game as a proof-of-concept. In the cloud, a cloud gaming platform is hosted on cloud servers with two principal components: game logic engaged in the implementation of game mechanics and game interactions, and video renderer that generates the game frames in real-time. The virtual gliding game was realized in the Unity gaming engine. To ensure smooth playability, and access for remote players, the computationally-intensive parts of the game were offloaded to a physically remote cloud server. To analyze the efficiency of the client-cloud interaction, three cloud servers were setup. The results of cloudification were evaluated by measuring and comparing computation offloading performance, network traffic, the probability of service drop, perceptual quality and video quality.Information 2018, 9, 293 2 of 15 (GBaaS) and Game as a Service (GaaS) [6]. Mobile backend-as-a-service (MBaaS) allows game programmers to connect the backend of their software systems to the cloud, as well as to enable integration with social networks [7]. A player can interface with the cloud-hosted application using a thin client model, which controls the displaying of the game's video frames from the cloud server and for responding to the player's commands and responding back to the cloud [8]. These cloud services aim to provide powerful real-time tools specifically dedicated for gaming, with applications emerging in other areas, such as medicine, marketing and education as well.The characteristics of mobile devices recently were rising rapidly in terms of computational abilities, memory, features, and number of apps. However, mobile apps are still relatively inferior by bounded battery life, lack of stability of wireless communications, and prone-to-interruptions connections and constrained bandwidth, and lack of local storage and computing power. By relocating (off-loading) local device tasks to the cloud servers, efficient and ambient cloud services can be accomplished, thus guaranteeing cloud gaming ecosystems with a large number of mobile game players and mobile devices working remotely, but in coordination [9]. Since mobile devices have only limited memory and computational resources, information stored on local devices is synchronized with shared data centers, therefore the system's availability is a demanding factor.The problems faced by cloud gaming include the server provisioning problem [10], with the goal of slashing server running and data storage costs, as well as measuring and reducing the latency [11]. There is also some work focusing on graphics rendering and video encoding for network bandwidth reduction in the video transmission [12]. The cloud resource allocation problems in order to minimize the interaction latency among interactive clients were researched in Reference [13]. A promising approach utilized by many authors to mitigate cloud-related problems can by implemented by computati...
Part 6: Modelling and OptimizationInternational audienceWe propose a novel method of time series decomposition based on the non-negative factorization of the Hankel matrix of time series and apply this method for time series modelling and prediction. An interim (surrogate) model of time series is built from the components of the time series using random cointegration, while the best cointegration is selected using a nature-inspired optimization method (Artificial Bee Colony). For modelling of cointegrated time series we use the ARX (AutoRegressive with eXogenous inputs) model. The results of modelling using the historical data (daily highest price) of S&P 500 stocks from 2009 are presented and compared against stand-alone ARX models. The results are evaluated using a variety of metrics (RMSE, MAE, MAPE, Pearson correlation, Nash-Suttcliffe efficiency coefficient, etc.) as well as illustrated graphically using Taylor and Target diagrams. The results show a 51–98% improvement of prediction accuracy (depending upon accuracy metric used). The proposed time series modelling method can be used for variety applications (time series denoising, prediction, etc.)
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