The brain–computer interface (BCI) system allows us to convert brain activity into meaningful con... more The brain–computer interface (BCI) system allows us to convert brain activity into meaningful control signals. This article presents an efficient BCI signal classification technique that uses median filtering and wavelet transform (WT) to improve classification performance and reduce computational complexity. In one preprocessing step, median filtering is carried out in order to attenuate noise, and WT is used to extract features that are classified by support vector machines (SVM). The database we use for this purpose is from BCI competition-II 2003 provided by the " University of Technology, Graz. " We show that using these two techniques in series, the classification accuracy can be increased up to 90 %. This method is therefore a very good approach toward designing online BCI and it is not com-putationally intensive.
Machine-to-Machine (M2M) communication is the communication between two or more devices which com... more Machine-to-Machine (M2M) communication is the communication between two or more devices which communicate with each other autonomously. The concept of mobile M2M communication has emerged due to the wide range, coverage provisioning and decreasing costs of future mobile networks. Consequently, M2M traffic poses significant challenges to mobile networks, especially due to the expected large group of devices with frequent small data transmissions. As a result, cellular-based M2M traffic could potentially degrade the performance of traditional cellular traffic due to inefficient utilization of the limited frequency spectrum. In this paper, a novel data aggregation scheme is proposed and analyzed for uplink M2M traffic in LTE-A (Long-Term-Evolution-Advanced) networks. For this purpose, an LTE-A layer 3 Inband RN (Relay Node) is used to aggregate uplink M2M traffic by sharing the PRBs (Physical Resource Blocks) among various devices. The proposed data aggregation scheme is validated through extensive computer simulations in an LTE-A implementation for the OPNET Modeler simulator.
Machine-to-machine (M2M) communication is becoming an increasingly important part of mobile traff... more Machine-to-machine (M2M) communication is becoming an increasingly important part of mobile traffic and thus also a topic of major interest for mobile communication research and telecommunication standardization bodies. M2M communication offers various ubiquitous services and is one of the main enablers of the vision inspired by the Internet of Things (IoT). The concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability, and decreasing costs of future mobile networks. Nevertheless, M2M communications pose significant challenges to mobile networks, e.g., due to the expected large number of devices with simultaneous access for sending small-sized data, and a diverse application range. This paper provides a detailed survey of M2M communications in the context of mobile networks, and thus focuses on the latest Long-Term Evolution-Advanced (LTE-A) networks. Moreover, the end-to-end network architectures and reference models for M2M communication are presented. Furthermore, a comprehensive survey is given to M2M service requirements, major current standardization efforts, and upcoming M2M-related challenges. In addition, an overview of upcoming M2M services expected in 5G networks is presented. In the end, various mobile M2M applications are discussed followed by open research questions and directions.
The brain–computer interface (BCI) system allows us to convert brain activity into meaningful con... more The brain–computer interface (BCI) system allows us to convert brain activity into meaningful control signals. This article presents an efficient BCI signal classification technique that uses median filtering and wavelet transform (WT) to improve classification performance and reduce computational complexity. In one preprocessing step, median filtering is carried out in order to attenuate noise, and WT is used to extract features that are classified by support vector machines (SVM). The database we use for this purpose is from BCI competition-II 2003 provided by the " University of Technology, Graz. " We show that using these two techniques in series, the classification accuracy can be increased up to 90 %. This method is therefore a very good approach toward designing online BCI and it is not com-putationally intensive.
Machine-to-Machine (M2M) communication is the communication between two or more devices which com... more Machine-to-Machine (M2M) communication is the communication between two or more devices which communicate with each other autonomously. The concept of mobile M2M communication has emerged due to the wide range, coverage provisioning and decreasing costs of future mobile networks. Consequently, M2M traffic poses significant challenges to mobile networks, especially due to the expected large group of devices with frequent small data transmissions. As a result, cellular-based M2M traffic could potentially degrade the performance of traditional cellular traffic due to inefficient utilization of the limited frequency spectrum. In this paper, a novel data aggregation scheme is proposed and analyzed for uplink M2M traffic in LTE-A (Long-Term-Evolution-Advanced) networks. For this purpose, an LTE-A layer 3 Inband RN (Relay Node) is used to aggregate uplink M2M traffic by sharing the PRBs (Physical Resource Blocks) among various devices. The proposed data aggregation scheme is validated through extensive computer simulations in an LTE-A implementation for the OPNET Modeler simulator.
Machine-to-machine (M2M) communication is becoming an increasingly important part of mobile traff... more Machine-to-machine (M2M) communication is becoming an increasingly important part of mobile traffic and thus also a topic of major interest for mobile communication research and telecommunication standardization bodies. M2M communication offers various ubiquitous services and is one of the main enablers of the vision inspired by the Internet of Things (IoT). The concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability, and decreasing costs of future mobile networks. Nevertheless, M2M communications pose significant challenges to mobile networks, e.g., due to the expected large number of devices with simultaneous access for sending small-sized data, and a diverse application range. This paper provides a detailed survey of M2M communications in the context of mobile networks, and thus focuses on the latest Long-Term Evolution-Advanced (LTE-A) networks. Moreover, the end-to-end network architectures and reference models for M2M communication are presented. Furthermore, a comprehensive survey is given to M2M service requirements, major current standardization efforts, and upcoming M2M-related challenges. In addition, an overview of upcoming M2M services expected in 5G networks is presented. In the end, various mobile M2M applications are discussed followed by open research questions and directions.
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