Is it ok to mix both LSTM and GRU layers inside bidirectional enter image description here
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As a part of my project, I want to train an ASR system using teacher-student learning by the encoder of a TTS system. So, I have a TTS system that takes audio as input, not text. I try to implement an article for this purpose and I can't understand the author's meaning in this section.
The description is: " The text/speech domain discriminator takes each frame of spectral or character embeddings as input and is a 4-layer FC neural network (256→512→512→2) ".
While the student network gets audio and the teacher gets text as input and both outputs are embeddings with (#minibatch, #chars of input, #embdding dim=512).
By this description, what is the input of the discriminator? input size of the discriminator is (#minibatch, #chars of input, #embdding dim=512) or (#minibatch, #embdding dim=512)?
I attached the image of the training diagram for better understanding.
I very thankful, If somebody can help me.
While reading about the OSI and TCP/IP model for communication i came across the terms like OSI model follows vertical approach while TCP/IP model followed a horizontal approach.Can somebody explain what does vertical and horizontal approach here mean.
In the OSI model,
there are no intermediate nodes between transport layer or above layers....hence these layers communicate directly with each other....hence horizontal approch
While in TCP/IP,
each layer is traversed starting from application layer-transport layer-internet layer-host_to_network layer, and then msg is sent.
Meaning data travels each layer, accessing their functionality...only then it is sent...hence vertical approch.
Source:
Why does the OSI model follow a horizontal approach and TCP/IP follow a vertical one?
In the decoder part of Seq2seq, it is like a language modeling to be given an input word and the hidden state, to predict the next word.
How bidirectional information could be used in this mechanism?
Also, is that we also have to generate sentence words next-by-nextly in bidirectional RNN decoder?
Thank you.
Could you point me the opcodes for various mesh messaging? For example, I see that 0x8201 is the opcode for Generic OnOff get. I would like to see complete list (sensor model messaging etc).
Rgds,
Venkat.
The full list is available in the Bluetooth SIG website. Please have a look in the Mesh Model Specification v1.0.1, Chapter 7: Summary, Section 7.1: Table Summary (page 301 of 307).
How can defined topology in Castalia-3.2 for WBAN ?
How can import topology in omnet++ to casalia ?
where the topology defined in default WBAN scenario in Castalia?
with regard
thanks
Topology of a network is an abstraction that shows the structure of the communication links in the network. It's an abstraction because the notion of a link is an abstraction itself. There are no "real" links in a wireless network. The communication is happening in a broadcast medium and there are many parameters that dictate if a packet is received or not, such as the power of transmission, the path loss between transmitter and receiver, noise and interference, and also just luck. Still, the notion of a link could be useful in some circumstances, and some simulators are using it to define simulation scenarios. You might be used to simulators that you can draw nodes and then simply draw lines between them to define their links. This is not how Castalia models a network.
Castalia does not model links between the nodes, it models the channel and radios to get a more realistic communication behaviour.
Topology is often confused with deployment (I confuse them myself sometimes). Deployment is just the placement of nodes on the field. There are multiple ways to define deployment in Castalia, if you wish, but it is not needed in all scenarios (more on this later). People can confuse deployment with topology, because under very simplistic assumptions certain deployments lead to certain topologies. Castalia does not make these assumptions. Study the manual (especially chapter 4) to get a better understanding of Castalia's modeling.
After you have understood the modeling in Castalia, and you still want a specific/custom topology for some reason then you could play with some parameters to achieve your topology at least in a statistical sense. Assuming all nodes use the same radios and the same transmission power, then the path loss between nodes becomes a defining factor of the "quality" of the link between the nodes. In Castalia, you can define the path losses for each and every pair of nodes, using a pathloss map file.
SN.wirelessChannel.pathLossMapFile = "../Parameters/WirelessChannel/BANmodels/pathLossMap.txt"
This tells Castalia to use the specific path losses found in the file instead of computing path losses based on a wireless channel model. The deployment does not matter in this case. At least it does not matter for communication purposes (it might matter for other aspects of the simulation, for example if we are sampling a physical process that depends on location).
In our own simulations with BAN, we have defined a pathloss map based on experimental data, because other available models are not very accurate for BAN. For example the, lognormal shadowing model, which is Castalia's default, is not a good fit for BAN simulations. We did not want to enforce a specific topology, we just wanted a realistic channel model, and defining a pathloss map based on experimental data was the best way.
I have the impression though that when you say topology, you are not only referring to which nodes could communicate with which nodes, but which nodes do communicate with which nodes. This is also a matter of the layers above the radio (MAC and routing). For example it's the MAC and Routing that allow for relay nodes or not.
Note that in Castalia's current implementations of 802.15.6MAC and 802.15.4MAC, relay nodes are not allowed. So you can not create a mesh topology with these default implementations. Only a star topology is supported. If you want something more you'll have to implemented yourself.