Fluctuation and Noise Letters
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Published By World Scientific

1793-6780, 0219-4775

Author(s):  
Walter C. Daugherity ◽  
Laszlo B. Kish

We point out that the exponentially fast, grounding-based search scheme in noise-based logic works mostly on core superpositions. When the superposition contains elements that are outputs of logic gate operations, the search result can be erroneous, because grounding of a reference bit can change a logic function too. Adding superpositions with a search bit of inverted signal amplitude sign (sign inversion instead of grounding) can fix the problem in special cases, but a general solution is yet to be found. Note that because phonebooks are core superpositions, the original search algorithm remains valid for phonebook lookups, for both name and number search, including fractions of names or numbers.


Author(s):  
Jiaao Song ◽  
Laszlo B. Kish

Utilizing a formerly published cold resistor circuitry, a secure key exchange system is conceived and explored. A circuit realization of the system is constructed and simulated. Similar to the Pao-Lo key exchanger, this system is secure in the steady-state limit but crackable in the transient situations.


Author(s):  
Roumen Tsekov

In this paper, the Schrödinger equation is solved for many free particles and their quantum entanglement is studied via correlation analysis. Converting the Schrödinger equation in the Madelung hydrodynamic-like form, the quantum mechanics is extended to open quantum systems by adding Ohmic friction forces. The dissipative evolution confirms the correlation decay over time, but a new integral of motion is discovered, being appropriate for storing everlasting quantum information.


Author(s):  
Everaldo Freitas Guedes

In this paper, we proposed a statistical test for the Detrending Moving-Average Cross-Correlation Coefficient ([Formula: see text]). With this methodology, it is possible to evaluate the statistical significance of [Formula: see text] for different confidence levels. The test was applied to financial market and climatological data. Findings on this research show that rejection or non-rejection of the null hypothesis of [Formula: see text] depends on the size [Formula: see text] of the series and the moving average window length [Formula: see text] evaluated. Our findings also show a behavioral pattern in the critical values of [Formula: see text]. Fixing the size of the series [Formula: see text], as the size of the moving average window length [Formula: see text] increases, the critical values tend to increase.


Author(s):  
C. Revathy ◽  
R. Sureshbabu

Speech processing is one of the required fields in digital signal processing that helps in processing the speech signals. The speech process is utilized in different fields such as emotion recognition, virtual assistants, voice identification, etc. Among the various applications, emotion recognition is one of the critical areas because it is used to recognize people’s exact emotions and eliminate physiological issues. Several researchers utilize signal processing and machine learning techniques together to find the exact human emotions. However, they fail to attain their feelings with less computational complexity and high accuracy. This paper introduces the intelligent computational technique called cat swarm optimized spiking neural network (CSSPNN). Initially, the emotional speech signal is collected from the Toronto emotional speech set (TESS) dataset, which is then processed by applying a wavelet approach to extract the features. The derived features are further examined using the defined classifier CSSPNN, which recognizes human emotions due to the effective training and learning process. Finally, the proficiency of the system is determined using experimental results and discussions. The proposed system recognizes the speech emotions up to 99.3% accuracy compared to recurrent neural networks (RNNs), deep neural networks (DNNs) and deep shallow neural networks (DSNNs).


Author(s):  
Christiana Chamon ◽  
Laszlo B. Kish

This paper introduces a new attack against the Kirchhoff–Law–Johnson-Noise (KLJN) secure key exchange scheme. The attack is based on the nonlinearity of the noise generators. We explore the effect of total distortion ([Formula: see text]) at the second order ([Formula: see text]), third order ([Formula: see text]) and a combination of the second and third orders ([Formula: see text]) on the security of the KLJN scheme. It is demonstrated that as little as 1% results in a notable power flow along the information channel, which leads to a significant information leak. We also show that decreasing the effective temperature (that is, the wire voltage) and, in this way reducing nonlinearity, results in the KLJN scheme approaching perfect security.


Author(s):  
Mohammad Reza Arab ◽  
Farbod Setoudeh ◽  
Reza Khosroabadi ◽  
Mohsen Najafi ◽  
Mohammad Bagher Tavakoli

Learning and memory involve a complex cognitive process to acquire, retain, and retrieve information in the central nervous system. However, the brain mechanism still needs to be well understood. This study aimed to examine the dynamic auditory verbal learning model of the brain mechanism involved in cognitive learning using the scale-free approach by the fractal analysis of electroencephalogram (EEG) data. This illustrates how the complexity of information processing in the brain changes while auditory and verbal learning occurs. Therefore, a standard verbal-auditory cognitive assessment test was used to create a learning paradigm. Eighteen healthy male volunteers (19–23[Formula: see text]years old) were recruited and their verbal memories were assessed using the Rey auditory verbal learning test. Fifteen unrelated words were sequentially presented to the subjects and they were asked to recall the presented words as many as possible. The experiment was repeated five times with no stop in between. EEG recording was performed before, during and after each stage. Subsequently, the Hurst exponents of EEG were calculated and their associations with the recalled words and the learning rate were estimated. The approximate entropy was intended to confirm the Hurst exponent variations of signals. The statistical analysis of the data showed that the increase in the number of the recalled words was positively correlated with an increase in the Hurst exponents of EEG signals (more significant at the temporal channels) and a decrease in the approximate entropy of EEG signals during the learning of trials. These results denoted a reduced complexity pattern in EEG signals while rehearsing auditory and verbal memories.


Author(s):  
A. Kala ◽  
S. Ganesh Vaidyanathan

Rainfall forecasting is the most critical and challenging task because of its dependence on different climatic and weather parameters. Hence, robust and accurate rainfall forecasting models need to be created by applying various machine learning and deep learning approaches. Several automatic systems were created to predict the weather, but it depends on the type of weather pattern, season and location, which leads in maximizing the processing time. Therefore, in this work, significant artificial algae long short-term memory (LSTM) deep learning network is introduced to forecast the monthly rainfall. During this process, Homogeneous Indian Monthly Rainfall Data Set (1871–2016) is utilized to collect the rainfall information. The gathered information is computed with the help of an LSTM approach, which is able to process the time series data and predict the dependency between the data effectively. The most challenging phase of LSTM training process is finding optimal network parameters such as weight and bias. For obtaining the optimal parameters, one of the Meta heuristic bio-inspired algorithms called Artificial Algae Algorithm (AAA) is used. The forecasted rainfall for the testing dataset is compared with the existing models. The forecasted results exhibit superiority of our model over the state-of-the-art models for forecasting Indian Monsoon rainfall. The LSTM model combined with AAA predicts the monsoon from June–September accurately.


Author(s):  
Debasish Panda ◽  
Amiya Ranjan Mohanty

Sonic crystals (SCs) are unique periodic structures designed to attenuate acoustic waves in tunable frequency bands known as bandgaps. Though previous works on conventional uniform SCs show good insertion loss (IL) inside the bandgaps, this work is focused on widening their bandgaps and achieving better IL inside the bandgaps by using a gradient-based sonic crystal (GBSC). The GBSC applies property gradient to the conventional SC array by varying its basic properties, i.e., the distance between the scatterers/resonators (lattice constant), and resonator dimensions between the columns and hence the name GBSC. The design of the GBSC is backed by the results of acoustic beamforming experiments conducted over the uniform SCs of hollow scatterers and Helmholtz resonators (HRs) having two-dimensional (2D) periodicity prepared by using Polyvinyl chloride (PVC) pipes without any property gradient and their respective 2D finite element (FE) studies. The experimental and FE simulation results of the uniform SCs were found to be in good agreement and therefore, the GBSC was modeled and analyzed using FE method considering the viscothermal losses inside the resonators. The results indicated that the property gradient improves both Bragg scattering and Helmholtz resonance compared to that of the uniform SCs and therefore, the GBSC exhibits wider attenuation gaps and higher attenuation levels. An array of 30 microphones was used to conduct acoustic beamforming experiments on the uniform SCs. Beamforming was found to be an advanced and fast method to perform quick measurements on the SCs.


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