An internally-controlled memristor-based amplitude shift keying modulator

Purpose The purpose of this paper is to design and construct an amplitude shift keying (ASK) modulator, which, using the digital binary modulating signal, controls a floating memristor emulator (MR) internally without the need for additional control circuits to achieve the ASK modulated wave. Design/methodology/approach A binary digital unipolar signal to be modulated is converted by a pre-processor circuit into a suitable bipolar modulating direct current (DC) signal for the control of the MR state, using current conveyors the carrier signal’s amplitude is varied with the change in the memristance of the floating MR. A high pass filter is then used to remove the DC control signal (modulating signal) leaving only the modulated carrier signal. Findings The results from the experiment and simulation are in agreement showed that the MR can be switched between two states and that a change in the carrier signals amplitude can be achieved by using an MR. Thus, showing that the circuit behavior is in line with the proposed theory and validating the said theory. Originality/value In this paper, the binary signal to be modulated is modified into a suitable control signal for the MR, thus the MR relies on the internal operation of the modulator circuit for the control of its memristance. An ASK modulation can then be achieved using a floating memristor without the need for additional circuits or signals to control its memristance.

A novel ANN-based method for simultaneous estimation of thermophysical properties using experimental photothermal data

In this paper, a novel artificial neural network (ANN) based method dedicated to simultaneously estimating thermal conductivity and thermal diffusivity of CSP (concentrating solar power) plant receiver materials is presented. By monitoring the evolution of these two correlated thermophysical properties during aging cycles, CSP plants' cost efficiency could be maintained. The proposed method is based on the processing of experimental photothermal data using classification and estimation networks. All the networks are feedforward ANN trained with supervised learning algorithms. A pseudo random binary signal (PRBS) is used as excitation and the impact on performance of both the photothermal response length, which is used as model input, and the number of training examples has been evaluated. Of course, the networks' topology has been optimized, allowing the generalization ability to be controlled. Despite the lack of data, the results are promising. Mean relative errors are between 8% and 20%, and the main levers for improvement are identified. In this paper, the study deals with a large range of materials (polymers and metallic alloys).

SOFTWARE FOR EVALUATING THE EFFICIENCY OF THE SOURCE CODING ALGORITHM UNDER THE CONDITION OF CONVERTING METRIC SPACES

Предлагаются алгоритм и построенная на его основе программа оценки эффективного кодирования источника сигналов при их преобразованиях в различных метрических пространствах (в частности, для широко используемого в настоящее время аналого-цифрового преобразования непрерывного сигнала в двоичный). Алгоритм оценки учитывает показатели искажений при кодировании двоичным кодом,вероятности появления этих искажений при передаче элементов сообщения и вероятности появления сообщения в канале связи. Помимо самого алгоритма, предложены его программная реализация, созданная с помощью языка программирования 0#,а также усовершенствованный интерфейс на базе MS Excel. В программной реализации имеется графическая среда для пользователя. This paper proposes an algorithm and a program built on its basis for evaluating the effective coding of a signal source during their transformations in various metric spaces, and, in particular, for the currently widely used analog-to-digital conversion of a continuous signal to a binary signal. The estimation algorithm takes into account the indicators of distortions under binary coding, the probability of occurrence of these distortions when transmitting message elements, and the probability of a message appearing in the communication channel. In addition to the algorithm itself, its software implementation, created using the C# programming language, and an improved interface based on MS Excel are proposed. Software implementation has a graphical user environment.

RAP Model—Enabling Cross-Layer Analysis and Optimization for System-on-Chip Resilience

The Resilience Articulation Point (RAP) model aims to provision a probabilistic fault abstraction and error propagation concept for various forms of variability related faults in deep sub-micron CMOS technologies at the semiconductor material or device levels. RAP assumes that each of such physical faults will eventually manifest as a single- or multi-bit binary signal inversion or out-of-specification delay in a signal transition between bit values. When probabilistic error functions for specific fault origins are known at the bit or signal level, knowledge about the unit of design and its environment allow the transformation of the bit-related error functions into characteristic higher layer representations, such as error functions for data words, finite state machine (FSM) states, IP macro-interfaces, or software variables. Thus, design concerns can be investigated at higher abstraction layers without the necessity to further consider the full details of lower levels of design. This chapter introduces the ideas of RAP based on examples of particle strike, noise and voltage drop induced bit errors in SRAM cells. Furthermore, we show by different examples how probabilistic bit flips are systematically abstracted and propagated towards instruction and data vulnerability at MPSoC architecture level, and how RAP can be applied for dynamic testing and application-level optimizations in an autonomous robot scenario.

A functional equation of tail-balance for continuous signals in the Condorcet Jury Theorem

Consider an odd-sized jury, which determines a majority verdict between two equiprobable states of Nature. If each juror independently receives a binary signal identifying the correct state with identical probability p, then the probability of a correct verdict tends to one as the jury size tends to infinity (Marquis de Condorcet in Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix, Imprim. Royale, Paris, 1785). Recently, Alpern and Chen (Eur J Oper Res 258:1072–1081, 2017, Theory Decis 83:259–282, 2017) developed a model where jurors sequentially receive independent signals from an interval according to a distribution which depends on the state of Nature and on the juror’s “ability”, and vote sequentially. This paper shows that, to mimic Condorcet’s binary signal, such a distribution must satisfy a functional equation related to tail-balance, that is, to the ratio $$\alpha (t)$$ α ( t ) of the probability that a mean-zero random variable satisfies X$$>t$$ > t given that $$|X|>t$$ | X | > t . In particular, we show that under natural symmetry assumptions the tail-balances $$\alpha (t)$$ α ( t ) uniquely determine the signal distribution and so the distributions assumed in Alpern and Chen (Eur J Oper Res 258:1072–1081, 2017, Theory Decis 83:259–282, 2017) are uniquely determined for $$\alpha (t)$$ α ( t ) linear.