Signal hidden

By reversing the order of polarization transfer steps, NOE contacts of signals hidden under the bulk of other resonances can be established in a ID TOCSY-NOESY [39] experiment (fig. 1(d)). The necessary requirement is that an efficient TOCSY transfer can be made to a particular proton from... 

Fig. 5 a, b. Illustration of the computation principles for (a) artificial neural networks with input signals (array signals), hidden nodes chosen during the training of the net, and output signals (the parameters to be predicted) and (b) principal component analysis with two principal components (PC 1 and PC 2) based on three sensor signals (represented by the x, y and z axes). Normally reduces from approximately 100 signals down to two or three PCs... 

It was found that EVLS is capable to detect (i) minor signals hidden in major ones, (ii) small changes in ODN structure and the interaction between ODN and electrode surface, and (iii) potentially closed signals (resolution of overlapped peaks). On the basis of the above-mentioned advantages, EVLS in connection with the adsorption procedure fulfills the requirements for a perspective and promising tool for qualitative and quantitative studies in bioanalysis in bio- and nanotechnologies. Therefore, the implementation of EVLS in electrochemical analyzers should be of great interest. 

Due to the limitation posed by the initial electrical leakage signal and by the chosen angle of incidence of 52 deg. diffracted signals from 8 mm deep machined notch were hidden. Defects with depth exceeding 12 mm could be detected and sized. The same difficulty was observed when the thickness of the sample was less than 30 mm. 

Another fundamental characteristic ofNDT data is that it is spatial. It is the use of an NDT signal, together with its location, which provides insight into the hidden nature of the test-piece. Any discussion of NDT inspection data assumes the spatial component is included. 

Signal-flow graphs are particularly useful in two respects. First, they make the process designer examine in considerable detail the dynamic structure and fimctioning of the process. Second, the nature of the interface between person and machine can be seen more clearly. The variables that are displayed in a system are, of course, available for study, but workers frequently respond to derivative functions of variables or "hidden" variables that must be deduced. Given that the process variables to be displayed will influence the worker s control strategy and that the number of deductions to be made will affect the mental workload involved, a process designer can select the type and amoimt of process information which will enhance performance of the task. 

The error signal from the neurons in the output layer can be easily identified. This is not so for neurons in the hidden layers. Back-propagation overcomes this difficulty by propagating the error signal backward through the network. Hence, for the hidden layers, the error signal is obtained by ... 

The general stmcture is shown in Fig. 44.9. The units are ordered in layers. There are three types of layers the input layer, the output layer and the hidden layer(s). All units from one layer are connected to all units of the following layer. The network receives the input signals through the input layer. Information is then passed to the hidden layer(s) and finally to the output layer that produces the response of the network. There may be zero, one or more hidden layers. Networks with one hidden layer make up the vast majority of the networks. The number of units in the input layer is determined by p, the number of variables in the (nxp) matrix X. The number of units in the output layer is determined by q, the number of variables in the inxq) matrix Y, the solution pattern. 

The signal propagation in the MLF networks is similar to that of the perceptron-like networks, described in Section 44.4.1. For each object, each unit in the input layer is fed with one variable of the X matrix and each unit in the output layer is intended to provide one variable of the Y table. The values of the input units are passed unchanged to each unit of the hidden layer. The propagation of the signal from there on can be summarized in three steps. 

Each hidden unit,y, receives the signals from the p units of the previous layer, the input layer. From these signals the net input is calculated ... 

The output units receive the weighted output signals of the h hidden units. The weighted sums are calculated and passed through the transfer function to yield the final output of the network. 

Taking into account that Eqs. (3.16) may also contain some unknown influences Uk which reveal themselves in interlaboratory comparisons but may normally be hidden in the term eA>, the fundamental signal model is given by... 

One layer of input nodes and another of output nodes form the bookends to one or more layers of hidden nodes Signals flow from the input layer to the hidden nodes, where they are processed, and then on to the output nodes, which feed the response of the network out to the user. There are no recursive links in the network that could feed signals from a "later" node to an "earlier" one or return the output from a node to itself. Because the messages in this type of layered network move only in the forward direction when input data are processed, this is known as a feedforward network. 

The output of hidden nodes in the immediately preceeding layer that generated the input signals into the node. 

A hidden node that sends a large signal to an output node is more responsible for any error at that node than a hidden node that sends a small signal,... 

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