Context-Aware Spectrum Coexistence of Terrestrial Beyond 5G Networks in Satellite Bands

Spectrum sharing between terrestrial 5G and incumbent networks in the satellite bands presents a promising avenue to satisfy the ever-increasing bandwidth demand of the next-generation wireless networks. However, protecting incumbent operations from harmful interference poses a fundamental challenge in accommodating terrestrial broadband cellular networks in the satellite bands. State-of-the-art spectrum-sharing policies usually consider several worst-case assumptions and ignore site-specific contextual factors in making spectrum-sharing decisions, and thus, often results in under-utilization of the shared band for the secondary licensees. To address such limitations, this paper introduces CAT3S (Context-Aware Terrestrial-Satellite Spectrum Sharing) framework that empowers the coexisting terrestrial 5G network to maximize utilization of the shared satellite band without creating harmful interference to the incumbent links by exploiting the contextual factors. CAT3S consists of the following two components: (i) context-acquisition unit to collect and process essential contextual information for spectrum sharing and (ii) context-aware base station (BS) control unit to optimize the set of operational BSs and their operation parameters (i.e., transmit power and active beams per sector). To evaluate the performance of the CAT3S, a realistic spectrum coexistence case study over the 12 GHz band is considered. Experiment results demonstrate that the proposed CAT3S achieves notably higher spectrum utilization than state-of-the-art spectrum-sharing policies in different weather contexts

Handwritten character recognition using fuzzy wavelet: a VHDL approach

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Design and Management of DOT: A Distributed OpenFlow Testbed

Abstract-With the growing adoption of Software Defined Networking (SDN), there is a compelling need for SDN emulators that facilitate experimenting with new SDN-based technologies. Unfortunately, Mininet [1], the de facto standard emulator for software defined networks, fails to scale with network size and traffic volume. The aim of this paper is to fill the void in this space by presenting a low cost and scalable network emulator called Distributed OpenFlow Testbed (DOT). It can emulate large SDN deployments by distributing the workload over a cluster of compute nodes. Through extensive experiments, we show that DOT can overcome the limitations of Mininet and emulate larger networks. We also demonstrate the effectiveness of DOT on four Rocketfuel topologies. DOT is available for public use and community-driven development at dothub.org

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Comparative LCA technology improvement opportunities for a 1.5 MW wind turbine in the context of an offshore wind farm

Wind energy is playing an increasingly important role in the development of cleaner and more efficient energy technologies leading to projections in reliability and performance of future wind turbine designs. This paper presents life cycle assessment (LCA) results of design variations for a 1.5 MW wind turbine due to the potential for advances in technology to improve the performance of a 1.5 MW wind turbine. Five LCAs have been conducted for design variants of a 1.5 MW wind turbine. The objective is to evaluate potential environmental impacts per kilowatt hour of electricity generated for a 114 MW onshore wind farm. Results for the baseline turbine show that higher contributions to impacts were obtained in the categories Ozone Depletion Potential, Marine Aquatic Eco-toxicity Potential, Human Toxicity Potential and Terrestrial Eco-toxicity Potential compared to Technology Improvement Opportunities (TIOs) 1 to 4. Compared to the baseline turbine, TIO 1 showed increased impact contributions to Abiotic Depletion Potential, Acidification Potential, Eutrophication Potential, Global Warming Potential and Photochemical Ozone Creation Potential, and TIO 2 showed an increase in contributions to Abiotic Depletion Potential, Acidification Potential and Global Warming Potential. Additionally, lower contributions to all the environmental categories were observed for TIO 3 while increased contributions towards Abiotic Depletion Potential and Global Warming Potential were noted for TIO 4. A comparative LCA study of wind turbine design variations for a particular power rating has not been explored in the literature. This study presents new insight into the environmental implications related with projected wind turbine design advancements

Brain mechanisms that underlie the effects of motivational audiovisual stimuli on psychophysiological responses during exercise

Motivational audiovisual stimuli such as music and video have been widely used in the realm of exercise and sport as a means by which to increase situational motivation and enhance performance. The present study addressed the mechanisms that underlie the effects of motivational stimuli on psychophysiological responses and exercise performance. Twenty-two participants completed fatiguing isometric handgrip-squeezing tasks under two experimental conditions (motivational audiovisual condition and neutral audiovisual condition) and a control condition. Electrical activity in the brain and working muscles was analyzed by use of electroencephalography and electromyography, respectively. Participants were asked to squeeze the dynamometer maximally for 30 s. A single-item motivation scale was administered after each squeeze. Results indicated that task performance and situational motivational were superior under the influence of motivational stimuli when compared to the other two conditions (~20% and ~25%, respectively). The motivational stimulus downregulated the predominance of low-frequency waves (theta) in the right frontal regions of the cortex (F8), and upregulated high-frequency waves (beta) in the central areas (C3 and C4). It is suggested that motivational sensory cues serve to readjust electrical activity in the brain; a mechanism by which the detrimental effects of fatigue on the efferent control of working muscles is ameliorated.This research was supported, in part, by grants from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications