A Novel De Bruijn Based Mesh Topology for Networks-on-Chip

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Design and Implementation of Efficient Smart Lighting Control System with Learning Capability for Dynamic Indoor Applications

Accurate and efficient adjustment of luminaire’s dimming level in a smart environment can be a huge challenge. Indoor lighting system as a nonlinear and time variant block, which consumes significant amount of electrical power is evaluated in this paper. In doing so, a control method is proposed to efficiently adjust luminaire’s dimming level in a smart environment and to optimize energy and user’s comfort level. The proposed control method takes advantages from neural network and its learning capabilities. In this research, photodetectors are placed at the work zones, where work zones can have different number of photodetectors without any increase in complexity and any adverse effect on the control system. The method is capable of adopting itself to daylight variations with high accuracy. A state machine is developed to implement the method. The method is implemented in MATLAB and lighting conditions are extracted in DIALux. Luminaire’s dimming levels are determined with accuracy higher than 99%. Daylight is considered as a bias to the system and thus the network does not need to be trained by any variations. In a dynamic condition, when taking into account the variation in daylight, the system mean error does not exceed 3%

Illumination Control of Smart Indoor Lighting Systems Consists of Multiple Zones

Accurate and power efficient determination of luminaires dimming level is a challenging issue in smart indoor lighting systems, since the lighting system is nonlinear and time variant. In this paper, a smart and power efficient control method is developed in order to determine luminaires dimming level in an indoor environment with multiple work zones. A positive point of the proposed control method is that photodetectors are placed at the work zones which increase the accuracy. Besides, different number of photodetectors can be placed at work zones in the proposed control method, since work zones may have different dimensions and also accuracy levels may differ. The control method takes the advantages of learning method to avoid complexity and also increase system reliability. The system can properly work with daylight variation during the daytime. Case studies are implemented in DIALux and the control method is evaluated in MATLAB. It is shown that the error for static condition is below 1% and for dynamic condition which daylight varies during daytime is increases to 5.6%

Zone Based Control Methodology of Smart Indoor Lighting Systems Using Feedforward Neural Networks

A smart, accurate, and energy efficient control strategy to adjust dimming level of luminaires in an indoor environment is proposed in this paper. The control block in lighting system is nonlinear and time variant, since multiple reflections of objects and daylight variation are related to daytime and they can directly affect the system. According to the complexity of equations which model the lighting system, a control system based on Neural Network (NN) and learning machine is developed. By considering each zone as an independent structure, occupancy in each zone is added. In addition, photodetectors are placed at the work zones and hence increasing the accuracy. The occupancy condition for other zones in the environment are considered as bias to the inputs of the system. Therefore, multiple reflections in the environment are considered in the design of the proposed control method. Accuracy and system performance is improved by separation of control block for each zone as an autonomous control unit, whereas complexity of the system is reduced. The proposed design is evaluated in test beds developed using DIALux and MATLAB. The mean error varies according to the effect of zones on each other. The method is suitable for indoor environment that zones does not have common luminaires. The mean error in the case study that is not proper for the method does not exceed 20%. Although, the error seems to be high but compared to the methods that have ceiling mount sensors is accurate and power and power efficient. Besides, the case with zones that has separated luminaires the mean error is less than 5%

Hardware architectures for deep learning

This book discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The book provides an overview of this emerging field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms

NCOD: Near-Optimum Video Compression for Object Detection

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