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A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user. [ABSTRACT FROM AUTHOR]

Providing complete mobility along with minimizing the poor quality of service (QoS) is one of the highest essential challenges in mobile wireless networks. Handover prediction can overcome these challenges. In this paper, two novel prediction schemes are proposed. The first, depends on scanning the quality of all signals among mobile station and all nearby stations in the surrounding area, while the second one is based on a multi-criteria prediction decision using both the signal-to-noise ratio SNR value and station’s bandwidth. Moreover, the prediction efficiency is improved by reducing the number of redundant/ unnecessary handovers. The proposed schemes are evaluated using different scenarios with several mobile stations’ numbers, different WLAN access points, LTE-base station number & location, and random mobile station movement manner. The proposed schemes achieved a success rate of 99% with the different scenarios using LTE-WLAN architecture. The performance of the proposed prediction schemes outperformed the performance of the existing prediction schemes in terms of the accuracy percentage. [ABSTRACT FROM AUTHOR]

A 26-GHz transmitter front-end is designed using 65 nm CMOS technology. The double frequency conversion transmitter consists of an intermediate frequency(IF) mixer, an millimeter-wave(mm-wave) mixer, and a pre-power amplifier. A double quadrature architecture is employed to accomplish image rejection without using an image rejection filter for the first time in the mm-wave frequency band. The IF mixer cores are designed as harmonic rejection mixers to avoid using IF filters. The measured conversion gain is 26.85±0.65 dB, with LO2 (IF LO) at 1–1.5 GHz and 26.9±0.6 dB with LO1 (mm-wave LO) at 27–29 GHz. The measured output return loss is less than -10 dB at 25.7–27.2 GHz. The output 1-dB compression point and the saturation output power measured at 26 GHz are 10 dBm and 14.1 dBm, respectively. The output-referred third-order intercept point (OIP3) measured at 26 GHz is 15.76 dBm. The third-order distortion, suppressed by the harmonic rejection mixer, is -60.5 dBc at an output power of 10 dBm. The error vector magnitude measured for OFDM 16-QAM with a 110-MHz signal bandwidth is -17.7 dB at an average output power of 3.5 dBm. The total power consumption of the proposed 26-GHz transmitter front-end is 267 mW, and it occupies a chip area of 2.31 mm2. [ABSTRACT FROM AUTHOR]

Community detection algorithms have been widely used to study the organization of complex networks like the brain. These techniques provide a partition of brain regions (or nodes) into clusters (or communities), where nodes within a community are densely interconnected with one another. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies: clusters embedded within clusters of other clusters. To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across levels of the hierarchy. We demonstrate that this method is sensitive to topological inhomogeneities of the graph’s hierarchy by providing a local measure of community stability and inter-scale reliability across topological scales. We compare the brain’s structural and functional network architectures, and we demonstrate that structural graphs display a more prominent hierarchical community organization than functional graphs. Finally, we build an explicitly multimodal multiplex graph that combines both structural and functional connectivity in a single model, and we identify the topological scales where resting state functional connectivity and underlying structural connectivity show similar versus unique hierarchical community architecture. Together, our results demonstrate the advantages of the multi-scale community detection algorithm in studying hierarchical community structure in brain graphs, and they illustrate its utility in modeling multimodal neuroimaging data. [ABSTRACT FROM AUTHOR]