As introduced by Shannon in “Communication Theory of Secrecy Systems”, entropy and unicity distance are defined at a global level, under the assumption that the properties of symbols resemble that of independent random variables. However, while applying entropy and unicity to language(s), e.g., encryption and decryption, the symbols (letters) of a language are not independent. Thus, we introduce a new measure, $H_{L}(s)$, called the “local entropy” of a string s. $H_{L}(s)$ includes a priori information about the language and text at the time of application. Since the unicity distance is dependent on the entropy (because entropy is the basis of calculations for the unicity distance), local entropy leads to a local unicity distance for a string. Our local entropy measure explains why some texts are susceptible to decryption using fewer symbols than predicted by Shannon’s unicity while other texts require more. We demonstrate local entropy using a substitution cipher along with the results for an algorithm based on the principle, and show that Shannon’s unicity is an average measure rather than a lower bound; this motivates us to present a discussion on the implications of local entropy and unicity distance.

Product/Compound ciphers composed of multi-byte permutations and substitutions are generally considered to be more secure than their standalone individual component-cipher counterparts (i.e., substitution (S) and permutation (P) ciphers). In this paper, we show that a permutation-substitution-permutation (PSP) cipher that uses any form of regular byte-block boundaries, along with a regular encoding (such as ASCII), is no more secure than a multi-byte S cipher. In addition, we also show that, under certain conditions, a PSP cipher can be reduced to an S cipher. In addition to introducing the concept of isomorphic cipher reduction, we show that our theoretical findings translate into practical means by using a plaintext attack. In doing so, we shed light on the problems of encryption security that arise due to employing a product cipher in conjunction with any form of regular byte-block boundaries, and propose appropriate countermeasures.

We introduce a new class of pulse-mode circuit, called forgetful logic. Forgetful logic circuits can be used to implement more complex waveform signaling in pulse-mode artificial neural network circuits. The basic operation of forgetful logic is first explained. Its application is then illustrated by numerous examples.

An accurate printer model that is efficient enough to be used by halftoning algorithms is proposed. The proposed signal processing model (SPM) utilizes a physical model to train adaptive linear combiners (ALCs), after which the average exposure of each subpixel for any input pattern can be calculated using the optimized weight vector. The SPM can be used to model multi-level halftoning and resolution enhancement, as well as traditional halftoning. The SPM is comprised of a single ALC layer followed by a peak-to-average ratio (PAR) correction layer, which serves to produce a PAR of less than 1.5 in the modeled exposure. The PCN (PAR correction network) employs one ALC/pixel and exploits the physics governing the characteristics of exposure in small regions. A relatively small number of training patterns suffices to train the SPM.

The application of set theory methods to problems in estimation, system identification, and classification is emerging as a viable approach to a number of different problems. This correspondence studies the use of set-membership methods in symbol-by-symbol decoding of binary data in data-transmission systems. Theorems for necessary and sufficient conditions for error-free decoding are presented. Following these theorems, an algorithm is presented for selection of decode threshold levels based on set-membership properties of the system. A second algorithm is presented for automatic generation of logic truth tables for the decoder.