Contents
How the sensors communicate with the nodes?
Typically a wireless sensor network contains hundreds of thousands of sensor nodes. The sensor nodes can communicate among themselves using radio signals. A wireless sensor node is equipped with sensing and computing devices, radio transceivers and power components.
How is congestion handled in wireless sensor networks?
The congestion is detected based on the ratio of sending rate to the packet arrival rate. Nodes adjust their sending rate depending on the congestion at the node itself. PCCP tries to reduce packet loss in congestion state while achieving the weighted fairness transmission for single-path and multipath routing.
What type of processor is used in sensor node?
A specialized 32-bit nonpipelined MIPS soft-core processor has been developed for RH-node [ Figure 2]. The developed soft-core processor has a performance of 10 MIPS, which is acceptable for many sensor Network applications [9, 10].
Why do we use sensor nodes?
The sensor node consumes power for sensing, communicating and data processing. More energy is required for data communication than any other process. Current sensors are able to renew their energy from solar sources, Radio Frequency(RF), temperature differences, or vibration.
How is congestion detected?
Congestion is detected either by receipt of a duplicate acknowledgement or timeout signal. Once this happens, the TCP sender decreases the send rate by decreasing the congestion window size by a factor determined by the algorithm used.
How is deconvolution used in a neural network?
Network Deconvolution Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image. However, because of the strong correlations in real-world image data, convolutional kernels are in effect re-learning redundant data.
What are the dependencies for network deconvolution?
Network Deconvolution Install Dependencies Settings Overview 1. Running the examples from the paper 2. ImageNet dataset: 3. Semantic segmentation: Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image.
Do you need Python to use deconvolution?
Extensive experiments show that the network deconvolution operation is able to deliver performance improvement in all cases on the CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST, Cityscapes, and ImageNet datasets. This code requires the use of python3.5 or greater. We recommend using pip to install the required dependencies.
How to run deconvolution on vgg11 architecture?
As an example, to run our settings for the CIFAR-10 20-epoch run, with .001 weight decay and 128 batch size, on the vgg11 architecture, you would run: 2. ImageNet dataset: 3. Semantic segmentation: