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reversing, and explain their exact meanings. I begin by going over the process of comparing two operands in assembly language, which is a significant building block used in almost every logical statement. I then proceed to discuss the conditional codes in IA-32 assembly language, which are employed in every conditional instruction in the instruction set.



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The vast majority of logical statements involve the comparison of two or more operands. This is usually followed by code that can act differently based on the result of the comparison. The following sections demonstrate the operand comparison mechanism in IA-32 assembly language. This process is somewhat different for signed and unsigned operands. The fundamental instruction for comparing operands is the CMP instruction. CMP essentially subtracts the second operand from the first and discards the result. The processor s flags are used for notifying the instructions that follow on the result of the subtraction. As with many other instructions, flags are read differently depending on whether the operands are signed or unsigned.

{IDCL4, Kn, [IDs10, Kn, TS, MAC, S (Aggr message)]}





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to the higher dimensional quaternionic inputs, but rather to the algebraic advantages of the geometric neurons of the net. Figure 21.9(b) shows the error curves referring to the involved normalized computational time. The demanding computer resources of the MLP with four-dimensional input vectors (MLP(4)) is almost similar to the geometric neural network, however, its performance is lower than the geometric network. The learning performance of the MLP with two inputs (MLP(2)) is still not as good as that of the geometric neural net GMLP0 2 0 . The reader should see that the learning of geometric neural nets is improved due to the Clifford product computed in a higher dimensional space. It is worth noting in Figure 21.9(b) that the MLP(4), even working in a four dimensional space, cannot perform better than the geometric neural net.

If you re not familiar with the subtleties of IA-32 flags, it is highly recommended that you go over the Arithmetic Flags section in Appendix B before reading further.

{IDs10, Kn, TS, MAC, S (message)}

Table A.1 demonstrates the behavior of the CMP instruction when comparing signed operands. Remember that the following table also applies to the SUB instruction.

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process is as follows: The base station encrypts its own ID, a current timestamp TS, and its Kn as a private key. The base station generates a random seed S and assumes itself at level 0. The packet contains the following elds:

Now we will show an application of Clifford moments and geometric MLP using real images of the objects presented in Figure 21.10. The binarized images used to generate the moments

Table A.1 Signed Subtraction Outcome Table for CMP and SUB Instructions (X represents the left operand, while Y represents the right operand) LEFT OPERAND X >= 0 RIGHT OPERAND Y >= 0 RELATION BETWEEN OPERANDS X=Y FLAGS AFFECTED

The sensor node decrypts the message received from the base station using Ks . MAC is a message authentication code for a message m. Nodes to Cluster Leader Key Calculation. When a node sends a message to cluster leader, it constructs the message as follows {IDsn , Ks , TS, MAC, S (message)}

OF = 0 SF = 0 ZF = 1 The two operands are equal, so the result is zero. OF = 0 SF = 0 ZF = 0 Flags are all zero, indicating a positive result, with no overflow.

Cl21 Cl31 are depicted in Figure 21.11. The resulting curves of the Clifford moments are presented in Figure 21.12. Using binarized views of the objects unseen before by the neural network, we test the geometric MLP. The results in Table 21.1 show that the procedure performs well.

The cluster leader checks the ID from the packet, checks if the ID in the packet matches the ID it holds, and veri es the authentication and integrity of the packet through MAC. The cluster key Kc is used for message decryption with in a cluster where a cluster leader needs to decrypt the message sent by a node and prepare that message to forward the next hop. This secure triple-key management scheme provides added resilience toward susceptible attacks on sensor networks by keeping in mind the resource-starved nature of sensor nodes.

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