Channel equalizer performance analysis for Hybrid 3DTV in ATSC 3.0 standard
Recently, next generation broadcasting standardization has being conducted to transmit UHDTV contents. In this paper, an equalizer using least square estimation is designed and reception performance is analyzed for Hybrid 3DTV transceiver system. Equalizer structure for Hybrid 3DTV is designed by referring to ATSC 3.0 PHY layer. Hybrid 3DTV uses time division multiplexing for transmitting two physical layer pipes. The equalizer performance is simulated on Pedestrian Indoor channel which has long echoes. Simulation result shows that equalizer performance for Hybrid 3DTV is improved about 5 dB.
Physical Layer Framing for ATSC 3.0
An ATSC 3.0 physical layer frame consists of a bootstrap immediately followed by a preamble containing layer 1 control signaling and then one or more subframes carrying payload data. Subframes within a particular frame may be of the same or different types, with different subframe types being configured for different classes of receivers (e.g., mobile versus fixed). This paper focuses on the subframe structure, configuration, and contents, including subframe boundary symbols which carry additional pilots to assist with channel estimation at the receiver and cell multiplexing which maps physical layer pipe (PLP) payload data to physical layer resources within each subframe. Various methods for cell multiplexing multiple PLPs within a subframe are discussed and described, including time division multiplexing, frequency division multiplexing, time-frequency division multiplexing, and layered division multiplexing. Frequency interleaving, pilot insertion, and guard interval insertion are also covered, including a method of distributing extra samples to guard intervals in order to obtain frame lengths equal to an integer number of milliseconds.
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.