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The K-band spectrum (10.9 36.0 GHz) is subdivided into the Ku-band and the Ka-band. The Ku-band is so called as it is under the center of the K-band. b The Ka-band is so called as it is above the center of the K-band.
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Decoupling network, 188 Detectors, 67 average detector, 62, 67 peak, 62, 67 quasi-peak, 62, 67 Diagnostic scanner, 341 Differential mode currents, 34, 159 measurements, 305 probes, 143 radiation, 36 Digital storage scope, 50 Dimensions, 5 Dipole antenna, 153 definition, 5 Dips, 192 Direct discharge-ESD, 43, 237 Directional coupler, 188
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To build the equation, first use the Equation button on the Tools toolbar to open the Equations dialog box. Then press the Add button to display the Add Equation dialog box. To add dimensions to the equation section, just click the dimension. You can use the keypad on the dialog box or on your keyboard to add operators and syntax. All standard rules of syntax apply for the order of operations, use of parentheses, and driving versus driven sides of the equation.
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Even symbol types that can be applied by dragging-and-dropping from the Design Library can also be loaded as styles. However, I prefer dragging from the Design Library because you get a preview of the symbol; with the styles, you just see a text tag. Blocks can also be loaded into a template or used from the Design Library as drag-and-drop items.
X-Ray uorescence at grazing-exit angles using a WDX detector is called Grazing Emission XEF (GE-XRF) ,8,16,17 Figure 5.2.5 is a schematic view of the GE-XRF setup. X-Rays from an X-ray tube directly irradiated the sample in a large area. X-Ray emissions from the sample were collimated by a double slit system and detected by the WDX detector. This method is especially useful for the trace analysis of low-Z elements.18 21 The detection limits of Si in several types of organic matrices (water, beer, urine, etc.) were at the ppb level.22 The detection limits of trace metals (Na Sr) in mineral water were determined in an intercomparison survey by several analytical methods.23 As shown in Table 5.2.1, GE-XRF with the WDX detector covered the weaknesses of TXRF, that is, the determination of lowZ elements. P rez and S nchez also developed a GE-XRF e a setup using the EDX detector.24 They carefully collimated incident and exit X-rays in the same area. In their setup, a minimum control of an exit angle of 0.03 mrad was achieved. Micro-X-ray analysis is now one of the trends in X-ray analysis. Grazing-exit X-ray spectrometry was applied to micro-XRF using a synchrotron X-ray microbeam.25 Micro-X-ray analysis can be performed in laboratories by using X-ray capillary optics. Two-dimensional scanning of the sample is used to obtain X-ray elemental mapping. Additionally, depth information is obtained by applying GE-XRS. Finally, three-dimensional X-ray analysis is performed.26
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Table 5.1 rmin 2 0 10 2 0 2 2 0 Effect of threshold on ADF and LCR NR (rmin ) 12.4 48.8 46.1 cdf (rmin ) 0.01 0.22 0.63 ADF (msec) 0.8 4.5 13.7
264 Part III Appendixes
Since there are so few bits on the PUCCH, it would be dif cult to transmit them in the standard uplink fashion and still retain good spectral ef ciency. Rather, the uplink control information from multiple users is multiplexed onto the same RB by two measures: 1. The information from different MSs are used to modulate orthogonal sequences; those orthogonal sequences have the same form as uplink pilots, i.e., phase-shifted versions of cyclically extended Zadoff Chu sequences (see Section 27.3.5). Typically, 6 different phase shifts, 2 n/6, n = 0, . . . 5 are used. Each sequence is multiplied with a BPSK or a QPSK signal, so that each sequence carries 1 or 2 bits of information. In order to randomize intercell interference, the phase shifts of the sequences are changed from symbol to symbol, where the index of the used phase is determined by a PN sequence whose initialization depends on the cell ID, as well as on the slot in which transmission takes place. 2. Block-wise spreading over the symbols in a slot is used to further enhance the multiplexing capacity. This block spreading is achieved by assigning different Walsh Hadamard sequences (see Section 18.2.5) to different users, if the desired spreading factor is 2 or 4, and DFT sequences if the spreading factor is 3. The blockwise spreading is only used for PUCCH Formats 1/1a/1b (see below). For randomization of intercell interference, the assigned spreading sequence changes (in a pseudorandom way) from slot to slot. The PUCCH signals are transmitted in the RBs at the lower and upper band edges of the system, so that they cannot disturb the contiguous assignment of RBs to the different users. From slot to
Linearity 3, 31, 63, 102 3, 288 9, 317 20, 746, 773 4, 777, 782, 788, 792 3, 798, 802 Linearization, transistor-level 317 20 LNA (Low Noise Ampli er) 3, 4, 6, 8, 10, 12, 14, 16, 18, 20, 24, 26 8, 30 42, 56, 58 60, 62 4, 66 74, 615, 698 700, 707 9, 766, 774 design 3 5, 10, 16, 27, 33 4, 36, 57, 70 for UHF band 38 9 for UWB frequency 51 for VHF band 35, 37 Load 67 70, 502 3, 512, 632, 634 5, 637 8, 640, 643, 747 52, 754 6, 812 Loop 252, 254 Low Noise Ampli er. See LNA Low-Pass Filter. See LPF Lower Speci cation Limit. See LSL LPF (Low Pass Filter) 219 Maclaurin series 132, 135 Magni cation, current and voltage 51 Manufacturability 718 20 Micro strip line 618, 632 5, 637 41, 657, 659, 661, 709, 712, 715 Miller capacitance 362 effect 503 7, 519 20, 522, 565 Mixer 75 9, 81 8, 90 2, 94 103, 106 7, 111 12, 113, 120, 133, 136, 149 51, 153, 224, 227, 231, 407, 626, 638 9, 698 9 MOB (Module as one part On Board) 602 3 Modulation 354, 776 7, 782, 784, 788, 792 Modulation-effective status level 331 Modulator 120, 133, 136, 330 2, 468 9, 477, 480 1, 483, 487 8, 492, 498 9, 707 8 IQ 469, 471 3, 475, 477, 479 85, 487, 489, 491 8 Module as one part On Board. See MOB Monte Carlo analysis 728 9 MOSFET 76 7, 83, 86, 91, 97 9, 284, 314 15, 319 20, 483, 487 8, 495, 497 500 Multiplier 250 MuRata 623, 629, 728 N-channel 270 Narrow-band 10, 52, 97 Network matching, conversion between an IT and T 430 45
which are used in a steepest descent fashion to update the array weights and are essentially identical, apart from Equation 3.96 using the current sample, :(n l),of the array's output, while Equation 3.97 using the previous sample, ~ ( n ) . These equations are identical to the update regime of Equation 3.54 used in the LMS algorithm, with the only difference being the error term. There are two conditions, which may lead to a zero-gradient situation, where the algorithm stops adapting. The first is the condition of Ig(n)l = 1, which represents the desired convergence optimum. The second is g(n) = 0, which also forces the gradient to become zero. However, fortunately this is not a practical problem, since the point g(n) = 0 is not a stable equilibrium and the system noise moves the weight vector from this zero-gradient point. A further problem in a hostile fading environmentis that the beamforrner may incorrectly select the interference as the signal to process, so as to maintain a constant modulus, rather than the desired signal. In [266] a blindarray weight adaptation techniquewas described by Laurila and Bonek, which performs joint space-time equalisation, separation and detection of multiple unsynchronised co-channel digital signals. The scheme exploits the facts that the signals are of fixed symbol rate, have a CM and a Finite Alphabet (FA) of symbols. Simulations were conducted for an eight-element Uniform Linear Array (ULA) with an element spacing of X/2 [266]. The equaliser order was five. Although the simulation parameters were not optimised, the system gaveresults demonstrating that comparable BER can be achieved, when compared to reference-assisted adaptation methods.
The non-linearity of diodes, such as a2, a4, a6, LO injection, vLO, RF signal itself, vRFo, Frequency, .
We start out with networks where each link between two nodes can be treated as essentially binary : it can either support error-free transmission of a packet (in which case there is a valid connection between the nodes), or it cannot. The network is then represented as a graph: each node corresponds to a vertex, and each (working) link corresponds to an edge. We furthermore assume that the links are reciprocal, i.e., that the transfer function from node A to node B is the same as from node B to node A; in that case the graph is undirected. Each edge has a weight: for the simple case of xed transmit power, and assuming a link either works or doesn t work, the edge weights of all working links are unity, so that the cost of transmission is the hop count. 6 In the case that different nodes can use different power (to ensure that certain links work), the edge weight could represent the power expenditure for sending a packet over a certain link. In either case, the routing problem becomes a shortest-path problem more exactly, the problem of nding the path with the smallest distance (sum of the edge weights) between two vertices in a graph. These shortest-path problems have been considered in computer science literature, and several algorithms have been developed to solve them. The Dijkstra algorithm provides a fast solution if all edge weights are positive (as they usually are); in those cases where negative edge weights need to be considered, the Bellman Ford algorithm should be used. Dijkstra Algorithm The Dijkstra algorithm nds the shortest-path weights from a source node to every node in the network, based on the principle of greedy relaxation. It proceeds in the following steps: 1. Assign the source node the distance ds = 0, and all other nodes the distance di = (note that d is the distance from the source node, and as such is a property of the nodes, not of the edges). Furthermore, mark all nodes as unvisited and declare the source node the current node. 2. Loop until all nodes are visited. (a) Consider all unvisited neighbor nodes i of the current node c, i.e., nodes with (direct) links to the current node. For each neighbor node i , compute the di = dc + wc,i where wc,i is the edge cost between nodes c and i . If di < di , replace di by di . Let the node store a pointer to the previous node on the route that led to the lowest cost. (b) Mark the current node as visited. (c) Pick the unvisited node with the smallest distance as current node. The algorithm is very ef cient; depending on the particular implementation, the runtime is proportional to O(|V |2 + |E|) or O(|V | log(|V |) + |E|), where |V | is the number of vertices, and |E| is the number of edges.
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