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In light of the growing prevalence of Internet of Things (IoT) devices, it has become essential to incorporate cryptographic protection techniques for high-security applications. Since IoT devices are resource-constraints in terms of power and area, finding cost-effective ways to enhance their security is necessary. Physical unclonable function (PUF) is considered a trusted hardware security mechanism that generates true and intrinsic randomness by extracting the inherent process variations of circuits. In this paper, a novel pure magnetic memory-based PUF is presented. The fundamental building blocks of the proposed PUF design are magnetic devices, the so-called mCells. These magnetoresistive devices exclusively utilize Magnetic Tunnel Junction (MTJ) components. Using purely MTJ in the main memory and sense amplifier in the proposed PUF leads to high randomness, high reliability, low power, and ultra-compact occupation area. The Monte Carlo HSPICE simulation results demonstrate that the proposed PUF achieves a uniqueness of 49.89%, uniformity of 50.02 %, power consumption of 1.43 µW, and an area occupation of 0.01 µm2 per bit.

Abstract Ganzfeld conditions induce alterations in brain function and pseudo-hallucinatory experiences, particularly in people with high positive schizotypy. The current study uses graph-based parameters to investigate and classify brain networks under Ganzfeld conditions as a function of positive schizotypy. Participants from the general population (14 high schizotypy (HS), 29 low schizotypy (LS)) had an electroencephalography assessment during Ganzfeld conditions, with varying visual activation (8 frequencies of random light flicker) and soundscape-induced mood (neutral, serenity, and anxiety). Weighted functional networks were computed in six frequency sub-bands (delta, theta, alpha-low, alpha-high, beta, and gamma) as a function of light-flicker frequency and mood. The brain network was analyzed using graph theory parameters, including clustering coefficient (CC), strength, and global efficiency (GE). It was found that the LS groups had higher CC and strength than the HS groups, especially in bilateral temporal and frontotemporal brain regions. Moreover, some decreases in CC and strength measures were found in LS groups among occipital and parieto-occipital brain regions. LS groups also had significantly higher GE in all Ganzfeld conditions compared to the HS groups. The random under-sampling boosting (RUSBoost) algorithm achieved the best classification performance with an accuracy of 95.34%, specificity of 96.55%, and sensitivity of 92.85% during an anxiety-induction Ganzfeld condition. This is the first exploration of the relationship between brain functional state changes under Ganzfeld conditions in individuals who vary in positive schizotypy. The accuracy of graph-based parameters in classifying brain states as a function of schizotypy is shown, particularly for brain activity during anxiety induction, and should be investigated in psychosis.

Hardware-based security primitives like True Random Number Generators (TRNG) have become a crucial part in protecting data over communication channels. With the growth of internet and cloud storage, TRNGs are required in numerous cryptographic operations. On the other hand, the inherently dense structure and low power characteristics of emerging nanoelectronic technologies such as resistive-switching memories (RRAM) make them suitable elements in designing hardware security modules integrated in CMOS ICs. In this paper, a memristor based TRNG is presented by leveraging the high stochasticity of RRAM resistance value in OFF (High Resistive) state. In the proposal, one or two devices can be used depending on whether the objective is focused on saving area or obtaining a higher random bit frequency generation. The generated bits, based on a combination of experimental measurements and SPICE simulations, passed all 15 National Institute of Standards and Technology (NIST) tests and achieved a throughput of tens of MHz.

Altered electroencephalography (EEG) activity in schizotypal individuals is a powerful indicator of proneness towards psychosis. This alteration is beyond decreased alpha power often measured in resting state EEG. Multiscale fluctuation dispersion entropy (MFDE) measures the non-linear complexity of the fluctuations of EEGs and is a more effective approach compared to the traditional linear power spectral density (PSD) measures of EEG activity in patients with neurodegenerative disorders. In this study, we applied MFDE to EEG signals to distinguish high schizotypy (HS) and low schizotypy (LS) individuals. The study includes several trials from 29 participants psychometrically classified as HS (n=19) and LS (n=10). After preprocessing, MFDE was computed in frontal, parietal, central, temporal and occipital regions for each participant at multiple time scales. Statistical analysis and machine learning algorithms were used to calculate the differences in MFDE measures between the HS and LS groups. Our findings revealed significant differences in MFDE measures between LS and HS individuals in the delta frequency band (at time scale 100 ms). HS individuals exhibited increased complexity and irregularity compared to LS individuals in the delta frequency band particularly in the occipital region. Furthermore, the MFDE measures resulted in high accuracy (96.55%) in discriminating between HS and LS individuals and outperformed the models based on power spectrum, demonstrating the potential of MFDE as a neurophysiological marker for schizotypy traits. The increased non-linear fluctuation in delta frequency band in the occipital region of HS individuals implies the changes in cognitive functions, such as memory and attention, and has significant potential as a biomarker for schizotypy and proneness towards psychosis.