### Table 2: Effectiveness of complexity reduction techniques

"... In PAGE 6: ...) SuperVCC 1 1376 Enumeration 88,209 3.7E10 (estimated) Table2 shows that the complexity reduction techniques such as logic optimization and merging common IOs can tremendously reduce the complexity of the generated SuperVCC. We compared gate count (2- input NAND gate), number of internal signals and PIs.... ..."

### Table 10: Evaluation Results in OFDM Transmitter

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"... In PAGE 42: ... While BusSynth can generate a Bus Subsystem having any number of PEs according to the user options, the examples presented in detail in this section all have the same number of PEs in order to provide a basis for fair comparisons later in Section 7.3 (please note that the examples shown in Figure 7 and Figure 8 have 40MB total of non-L1 cache memory; nevertheless, the bus examples of Figures 7 and 8 do not result in the best performance as shown in Table10 of Chapter 7 Experiments and Results). In all examples in this thesis, we use the Motorola PowerPC (MPC755) for the PE core, which, however, can be changed to any other core simply by adding a CBI module for the new PE core (e.... In PAGE 121: ... Data: 2048 complex samples and 512 guard complex samples per packet 3. Each Bus System having four PowerPCs supports instruction and data cache operations Table10 shows the results of our evaluation using an OFDM transmitter that in our example has 922 lines of C code for the algorithm implementation (7,909 lines of assembly code for the algorithm implementation) and 696 lines of assembly code... In PAGE 122: ... Therefore, in SplitBA, it is more reasonable to use the FPA style. SplitBA (Case 7 in Table10 ) using the FPA style shows the best performance among the Bus Systems in our example: OFDM transmission reaches a rate of 5.1132Mbps, 16.... In PAGE 122: ...ransmission reaches a rate of 5.1132Mbps, 16.44% faster than GGBA, which we take as representative of a typical commercial bus. We can see in Table10 that the throughput of each Bus System is significantly affected by the bus types we described and programming style (PPA vs. FPA): (a) In software programming style, FPA outperforms PPA in the OFDM transmit- ter application (e.... ..."

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### Table 1. Power increase in dB of a 256-subcarrier OFDM system due to the peak power reduction by CSO.

"... In PAGE 3: ... Power increase in dB of a 256-subcarrier OFDM system due to the peak power reduction by CSO. Table1 shows the power increase of the transmitted signal... ..."

### Table 2. OFDM System parameters.

### Table 4.5 Computational complexity of the RLS algorithm and bandwidth of each subcarrier for an OFDM system with different number of subcarriers. Number of subcarriers (N) Complex multiplications per

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### Table 1 Properties of techniques for dimensionality reduction.

"... In PAGE 11: ...2. General properties In Table1 , the thirteen dimensionality reduction tech- niques are listed by four general properties: (1) the con- vexity of the optimization problem, (2) the main free... In PAGE 11: ... We discuss the four general properties below. For property 1, Table1 shows that most techniques for dimensionality reduction optimize a convex cost func- tion. This is advantageous, because it allows for find- ing the global optimum of the cost function.... In PAGE 11: ... Because of their nonconvex cost functions, autoencoders, LLC, and manifold charting may suffer from getting stuck in local optima. For property 2, Table1 shows that most nonlinear tech- niques for dimensionality reduction all have free param- eters that need to be optimized. By free parameters, we mean parameters that directly influence the cost func- tion that is optimized.... In PAGE 11: ... The main advantage of the presence of free parameters is that they provide more flexibility to the technique, whereas their main disadvantage is that they need to be tuned to optimize the performance of the di- mensionality reduction technique. For properties 3 and 4, Table1 provides insight into the computational and memory complexities of the com- putationally most expensive algorithmic components of the techniques. The computational complexity of a di- mensionality reduction technique is of importance to its applicability.... In PAGE 12: ...duction technique is determined by data properties such as the number of datapoints n, the original dimension- ality D, the target dimensionality d, and by parameters of the techniques, such as the number of nearest neigh- bors k (for techniques based on neighborhood graphs) and the number of iterations i (for iterative techniques). In Table1 , p denotes the ratio of nonzero elements in a sparse matrix to the total number of elements, m indi- cates the number of local models in a mixture of factor analyzers, and w is the number of weights in a neural network. Below, we discuss the computational complex- ity and the memory complexity of each of the entries in the table.... ..."

### Table 4.2 Spatial locations and jamming power of two jammers with respect to the power of the desired signal in the flat fading channel in the simulation of the OFDM system. Case # Type of interference Locations Jamming Power (dB)

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### TABLE I PARAMETERS FOR AN OFDM/SDMA SYSTEM.

in Effective Throughput: A UnifiedBenchmark for Pilot-Aided OFDM/SDMA Wireless Communication Systems