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Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. Memristors hold promise for massively-parallel computing at low power. Aguirre et al. provide a comprehensive protocol of the materials and methods for designing memristive artificial neural networks with the detailed working principles of each building block and the tools for performance evaluation. [ABSTRACT FROM AUTHOR]

Spinach seeds were irradiated with gamma-rays after that soaked in zinc oxide nanoparticles (ZnO–NPs) at 0.0, 50, 100 and 200 ppm for twenty-four hours at room temperature. Vegetative plant growth, photosynthetic pigments, and proline contents were investigated. Also, anatomical studies and the polymorphism by the SCoT technique were conducted. The present results revealed that the germination percentage was at the maximum values for the treatment of 100 ppm ZnO–NPs (92%), followed by 100 ppm ZnO–NPs + 60 Gy (90%). The application of ZnO–NPs resulted in an enhancement in the plant length. The maximum of chlorophylls and carotenoids content was recorded in the treatment, 100 ppm ZnO–NPs + 60 Gy. Meanwhile, the irradiation dose level (60 Gy) with all ZnO–NPs treatments increased proline content and reached its maximum increase to 1.069 mg/g FW for the treatment 60 Gy combined with 200 ppm ZnO–NPs. Also, the anatomical studies declared that there were variations between the treatments; un-irradiated and irradiated combined with ZnO–NPs plants which reveal that the leave epidermal tissue increased with 200 ppm ZnO–NPs in both the upper and lower epidermis. While irradiated plants with 60 Gy combined with 100 ppm ZnO–NPs gave more thickness of upper epidermis. As well as SCoT molecular marker technique effectively induced molecular alterations between the treatments. Where, SCoT primers targeted many new and missing amplicons that are expected to be associated with the lowly and highly expressed genes with 18.2 and 81.8%, respectively. Also, showed that the soaking in ZnO-NPs was helped for reducing molecular alteration rate, both spontaneous and induced by gamma irradiation. This nominates ZnO–NPs as potential nano-protective agents that can reduce irradiation-induced genetic damage. [ABSTRACT FROM AUTHOR]

Bio-impedance non-invasive measurement techniques usage is rapidly increasing in the agriculture industry. These measured impedance variations reflect tacit biochemical and biophysical changes of living and non-living tissues. Bio-impedance circuit modeling is an effective solution used in biology and medicine to fit the measured impedance. This paper proposes two new fractional-order bio-impedance plant stem models. These new models are compared with three commonly used bio-impedance fractional-order circuit models in plant modeling (Cole, Double Cole, and Fractional-order Double-shell). The two proposed models represent the characterization of the biological cellular morphology of the plant stem. Experiments are conducted on two samples of three different medical plant species from the family Lamiaceae, and each sample is measured at two inter-electrode spacing distances. Bio-impedance measurements are done using an electrochemical station (SP150) in the range of 100 Hz to 100 kHz. All employed models are compared by fitting the measured data to verify the efficiency of the proposed models in modeling the plant stem tissue. The proposed models give the best results in all inter-electrode spacing distances. Four different metaheuristic optimization algorithms are used in the fitting process to extract all models parameter and find the best optimization algorithm in the bio-impedance problems. [ABSTRACT FROM AUTHOR]

With the advent of 5G networks means for backhauling network traffic emerged as a major consideration. Densification via small cell deployment has made wired solution cost prohibitive, leading to the adoption of spectrally efficient wireless solutions, namely, Integrated Access and Backhaul (IAB). This paper discusses the different options for IAB and focuses on novel solutions for in-band IAB using Full Duplex (FD) enabled nodes. Dynamically reconfigured beam steering antennas are presented as a means of passively suppressing Self-Interference (SI). Experimental results confirm up to 65 dB of SI cancellation in a harsh environment with obstacle. Finally, robust methods for managing FD practical considerations including timing and frequency offsets are presented, with experimental results confirming performance. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid pyramid Discrete-Wavelet-Transform (DWT) Singular-Value-Decomposition (SVD) data hiding scheme for video authentication and ownership protection. The data being hidden will be in the shape of a main color logo image watermark and another secondary Black and White (B&W) logo image. The color watermark will be decomposed to Bit-Slices. A pyramid transform is performed on the Y-frames of a video stream resulting in error images; then, a Discrete Wavelet Transform (DWT) process is implemented using orthonormal filter banks on these error images, and the Bit-Slices watermarks are inserted in one or more of the resulting subbands in a way that is fully controlled by the owner; then, the watermarked video is reconstructed. SVD will be performed on the color watermark Bit-Slices. A secondary B&W watermark will be inserted in the main color watermark using another SVD process. An enhanced detection technique is developed to estimate the Singular Values to reconstruct the original color watermark image. The overall robustness of this scheme is measured when common attacks are applied to the test videos. A main contribution in this research is that the original host video is not required for the extraction process, the good robustness against different attacks specifically compression, transcoding, temporal and geometrical attacks and the duality in the hiding process. The simulation results show that the proposed algorithm achieves well under both the visual and the metric tests. Furthermore, it performed well against intentional and unintentional attacks. The reconstruction was perfect without attacks, while the average Bit-Error-Rates (BER's) achieved under attacks are in the limits of 2% for the color watermark and 5% for the secondary watermark; meanwhile, the mean Peak Signal-to-Noise Ratio (PSNR) is 57 dB. [ABSTRACT FROM AUTHOR]

In this paper, a low-voltage low-power rail-to-rail constant g m transconductance amplifier (TA) is introduced. The supply voltages are set at (±1.5 V). The circuit depends on selecting the maximum transconductance ( g m) to achieve an almost constant g m over the entire common-mode (CM) range. The circuit is then used to realize a second-order 4 MHz lowpass filter consuming 530 μW, and a fifth-order 450 kHz lowpass elliptic filter consuming 2.3 mW. Both filters can be integrated on silicon without any external connections. [ABSTRACT FROM AUTHOR]