By Naofumi Homma, Marcel Medwed
This e-book constitutes the completely refereed post-conference lawsuits of the 14th foreign convention on shrewdpermanent Card study and complicated functions, CARDIS 2015, held in Bochum, Germany, in November 2015.
The 17 revised complete papers awarded during this booklet have been rigorously reviewed and chosen from forty submissions. the point of interest of the convention was once on all elements of the layout, improvement, deployment, validation, and alertness of shrewdpermanent playing cards and safe parts in safe systems or systems.
Read or Download Smart Card Research and Advanced Applications: 14th International Conference, CARDIS 2015, Bochum, Germany, November 4-6, 2015. Revised Selected Papers PDF
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Additional info for Smart Card Research and Advanced Applications: 14th International Conference, CARDIS 2015, Bochum, Germany, November 4-6, 2015. Revised Selected Papers
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JD i i ). It may be observed that the ELV(αi , j1 ) ≥ ELV(αi , j2i ) ≥ · · · ≥ ELV(αi , jD sum over all the ELV(αi , j), for j ∈ [1, . . , D], equals EGV(αi ). If we operate such a sum in a cumulative way following the order provided by the sorted set J , we obtain a complete description of the trend followed by the component αi to achieve its EGV. As we can see in Fig. e. by adding the ELV contributions of much less time samples). For instance, for i = 4, the sum of the ELV(α4 , jk4 ), with k ∈ [1, .
This makes us introduce the following definition: Definition 2. The Explained Local Variance of a PC αi in a sample j, is defined by ELV(αi , j) = λi αi [j]2 = EGV(αi )αi [j]2 . r k=1 λk (13) i } ⊂ {1, 2, . . , D} be a set of indexes sorted such that Let J = {j1i , j2i , . . , jD i i ). It may be observed that the ELV(αi , j1 ) ≥ ELV(αi , j2i ) ≥ · · · ≥ ELV(αi , jD sum over all the ELV(αi , j), for j ∈ [1, . . , D], equals EGV(αi ). If we operate such a sum in a cumulative way following the order provided by the sorted set J , we obtain a complete description of the trend followed by the component αi to achieve its EGV.
7178, pp. 383–397. Springer, Heidelberg (2012) 3. : Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997) 4. : Less is more. , Handschuh, H. ) CHES 2015. LNCS, vol. 9293, pp. 22–41. Springer, Heidelberg (2015) 5. : Template attacks. , Paar, C. ) CHES 2002. LNCS, vol. 2523, pp. 13–28. Springer, Heidelberg (2003) Enhancing Dimensionality Reduction Methods 33 6. : A new LDA-based face recognition system which can solve the small sample size problem.