This paper presents the circuit design of a two-stage dynamic comparator optimized for high-speed, high-resolution analog-to-digital converters (ADCs). A conventional double-tail comparator is used as a reference for benchmarking. The proposed comparator is implemented using a 28 nm CMOS process and simulated using the Spectre circuit simulator under a 1 V supply. Theoretical analysis and simulation results prove that the proposed circuit is faster compared to the conventional double-tail comparator, while mantaining the same power consumption. Additionally, the design exhibits reduced noise levels, with an RMS noise voltage of less than 100 μV, making it suitable for low-noise applications. In terms of the Figure-of-Merit (FoM), the proposed comparator achieves 128 μW mV2GHz−1, showing a competitive performance compared to other state-of-the-art designs in the literature. These results highlight the effectiveness of the proposed comparator in applications requiring high-speed, low-power, and low-noise ADCs in advanced CMOS technology nodes.

The aim of this work is to analyze the chaotic behaviors in the fractional-order Chua and Chen systems via a time-domain approach. The objective is achieved using a decomposition method, which allows the solution of the fractional differential equations to be written in closed form. Specifically, by taking advantage of the capabilities given by time-domain analysis, the paper illustrates three remarkable findings: i) chaos exists in the fractional Chua system with very low order, that is, 0.03, which represents the lowest order reported in literature for any dynamical system studied so far; ii) chaos exists in the fractional Chen system with order as low as 0.24, which represents the smallest value reported in literature for the Chen system; iii) it is feasible to show the occurrence of pitchfork bifurcations and period-doubling routes to chaos in the fractional Chen system, by virtue of a systematic time-domain analysis of its dynamics.

By considering a chain of Chua's circuits, this paper focuses on the numerical study of hyperchaotic 3D-scroll attractors via the Adomian decomposition method. The objective is achieved by designing a new type of nonlinearity based on the Hermite interpolating polynomials. Several examples of 3D-scroll attractors highlight the potential of the proposed approach.

In this paper we propose to exploit cellular neural networks (CNNs) as a computational tool to obtain real-time compression of video sequences. In particular, we present a CNN-based architecture, which combines object-oriented CNN algorithms and basic coding/decoding MPEG capabilities. The proposed real-time compression architecture has been tested using standard benchmarking video sequences. Simulation results, in terms of compression ratio and peak to signal noise ratio, show that the proposed approach enables CNN-based real-time coding systems with satisfying compression ratios and good visual appearance.

In this paper the attention is focused on the complex dynamic phenomena generated by cellular neural networks (CNNs). By taking as basic cell Chua's circuit with sine-type nonlinearity, it is shown that a simple two-cell CNN is able to generate hyperchaotic behaviors. In particular, the paper shows that new multiscroll attractors can be obtained by modifying circuit parameters related to the cell nonlinearities. Finally, different examples are reported to illustrate the effectiveness and robustness of the proposed approach.

In this paper a method for designing cryptosystems driven by hyperchaotic signals is developed. The idea is to transmit a proper hyperchaotic signal, so that the receiver behaves as a linear observer for the state of the transmitter. The proposed tool proves to be: (i) rigorous, since some propositions are given for obtaining the plaintext at the receiver in a rigorous way; (ii) flexible, since a wide class of cryptosystems is designed by exploiting different hyperchaotic transmitting circuits; (iii) efficient, since the hyperchaotic carrier masks the encrypted signal, which in turn hides the message signal. The combination of hyperchaos, cryptography and complex transmitted signal makes a contribution to the development of communication systems with higher security.

This paper presents a new approach to fuzzy clustering using a membership function sensitive to density. It is a fuzzy membership function which allows the action range of the neural units matching the area they reach, even when the data set is contaminated by uniformly distributed noise points, without a need to fix a priori the number of clusters.

In this paper a new approach to information storage and retrieval using cellular neural networks is developed. The objective is achieved by considering a suitable discrete-time model of these networks and by designing them so that the input information are fed via external inputs rather than initial conditions. The technique, which exploits globally asymptotically stable networks, leads to a facilitation of their hardware implementation.