Figure 1: Example of SMAA 4x integrated in the Crysis 2 game. The insets show the differences between MLAA [JME * 11], our novel SMAA T2x and 4x algorithms and MSAA 8x as reference. For 1080p frames, the average cost of SMAA T2x is 1.3 ms and 2.6 ms for SMAA 4x, measured on a NVIDIA GeForce GTX 470.
AbstractWe present a new image-based, post-processing antialiasing technique, which offers practical solutions to the common, open problems of existing filter-based real-time antialiasing algorithms. Some of the new features include local contrast analysis for more reliable edge detection, and a simple and effective way to handle sharp geometric features and diagonal lines. This, along with our accelerated and accurate pattern classification allows for a better reconstruction of silhouettes. Our method shows for the first time how to combine morphological antialiasing (MLAA) with additional multi/supersampling strategies (MSAA, SSAA) for accurate subpixel features, and how to couple it with temporal reprojection; always preserving the sharpness of the image. All these solutions combine synergies making for a very robust technique, yielding results of better overall quality than previous approaches while more closely converging to MSAA/SSAA references but maintaining extremely fast execution times. Additionally, we propose different presets to better fit the available resources or particular needs of each scenario.
This work proposes an adaptive beamforming scheme applied to time domain, pre-FFT (Fast Fourier Transformation), Orthogonal Frequency-Division Multiplexing (OFDM) systems. This scheme improves the performance and the capacity of OFDM systems, using a supervised adaptive algorithm, with frequency domain multiplexed pilots of the OFDM system as a reference. The simplicity of the proposed structure, as well as the method used to obtain reference signals for the adaptive beamforming, are essential aspects that distinguish this paper from other publications. Details on the operation of the proposed scheme, as well as the performance curves, are presented in this manuscript. The proposal investigated here allows a significant reduction in the guard interval of the OFDM system, thereby increasing its robustness or transmission capacity.
The butterfly neural beamformer (NB-Butterfly) is a new adaptive multiple-antenna spatial neural filter inspired on the neural butterfly equalizer (NE-Butterfly), a filter intended to equalize any channel that has real or complex taps, whether linear or nonlinear. Due to the broad use cases of the NE-Butterfly, the objective in this paper is to introduce this novel beamforming filter, the NB-Butterfly and analyze its performance by comparing to other neural and linear beamformers, while also presenting an enhanced training strategy that wasn't present in the butterfly neural architecture before, which is called butterfly neural beamformer with joint error (NB-Butterfly-JE). The proposals are evaluated and compared for different types of channels in order to validate their performance in different use cases.INDEX TERMS Butterfly beamformer, adaptive beamforming, artificial neural networks, neural beamformer.
Optical fibers are now widely used in communica tion systems, mainly because they offer faster data transmission speeds, compared to any other type of digital communication sys tem. Despite this great advantage, problems remain that prevent the full exploitation of optical connection: by increasing transmis sion rates over longer distances, the data is affected by nonlinear inter-symbol interference caused by dispersion phenomena in the fiber. Adaptive equalizers can be used to compensate for the effects caused by nonlinear channel responses, restoring the signal originally transmitted. The present work proposes a multilayer perceptron equalizer based on artificial neural networks. The technique was validated using a simulated optical channel, and was compared with other adaptive equalization techniques.
DETI-Interact is an interactive system that offers information relevant to students in the lobby of the University of Aveiro's Department of Electronics, Telecommunications and Informatics (DETI). The project started in 2009 with a master's thesis addressing interaction with public displays through Android smartphones. Since then, it has evolved considerably; it currently allows gesture interaction based on a Kinect sensor. Meanwhile, it has involved third-year students, master's students, and undergraduate students participating in a research initiation program.
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