This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic Genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the Genetic Algorithm and multi-objective optimization goals. The Genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Quantum cellular automata (QCA) enables performing arithmetic and logic operations at the molecular scale. This nanotechnology promises high device density, low power consumption and high computational power. Unlike the CMOS technology where the ON and OFF states of the transistors represent binary information, in QCA, data is represented by the charge configuration. The primary and basic device in this paradigm is the three-input majority gate, thus in QCA, the conventional AND-OR mapping for implementation of logic functions is not effective. We introduce four primitive admissible geometric patterns, which aid in the identification of majority functions. For a non-majority function, a Genetic Algorithm (GA) is used to map the function to at most four majority gates in a wide range of implementations. We show that the emergence of specific genes will result in a further reduction in the number of majority gates in the network. The GA is intrinsically parallel and results in variety of implementations, which allows merging the layout and logic levels of the design and provides an important approach towards designing high-performance QCA circuits.

With the advent of Quantum computing theory and Quantum communication networks, establishing confidential and secure communication has become challenging. Quantum audio signal steganalysis is one of the interesting subfields in the field of Quantum signal processing and Quantum computing, which tries to identify hidden communications in the context of Quantum communication networks by using feature extraction techniques and Quantum machine learning Algorithms. Since steganography causes inevitable changes in the statistical characteristics of the frequency domain of the host signal, it can be used as an efficient and effective tool to build comprehensive and accurate steganalysis. So; At first, the proposed method uses Quantum Fourier transform on QRDS audio signal to extract statistical features. For this purpose, the proposed Quantum circuit network of these features, Quantum spectral center and Quantum spectral bandwidth has been designed and implemented. Finally, the Quantum Support Vector Machine (QSVM) Algorithm, using the extracted features, separates clean and stego data sets with an accuracy of more than 95%.

The entanglement classification of Quantum states is nearly related to the positive homogeneous function that is invariant under stochastic local operations and classical communication (SLOCC) transformations. In this paper, first we give a general scheme to measure the N-tangle as an entanglement measure of an arbitrary odd-N qubit pure state. Then we develop a classification Algorithm that is based on the integration between competitive learning and the computational power of Quantum computing. Using this Algorithm the entanglement measure we classify an input into one of two binary classes even if the input pattern is incomplete. The entanglement quantification and classification of Quantum states of a large number of particles becomes quickly complex. This major challenge was raised within the Quantum information science regarding the physical phenomenon of entanglement as a key resource. During the past years, the polynomial invariants such as concurrence for two-qubit states and three-tangle for pure three-qubit states was defined.