Efficient Memristive Circuit Design of Neural Network-Based Associative Memory for Pavlovian Conditional Reflex.

The brain's learning and adaptation processes heavily rely on the concept of associative memory. One of the most basic associative learning processes is classical conditioning. This work presents a memristive neural network-based associative memory system. The system can emulate Pavlovian conditioning principles including acquisition, extension, generalization, differentiation, and spontaneous recovery that have not been considered in most of the previous counterparts. The proposed circuit can emulate these principles thanks to the resistance-changing characteristics of the memristor. Generalization has been achieved by providing both unconditional and neutral stimuli to the network to reduce the memristance of the memristor. Differentiation has been attained by employing unconditional and conditional stimuli in a training scheme to obtain a certain memristance that causes the network to respond differently to both stimuli. A revival of an exterminated stimuli is also done by increasing the synaptic weight of the system. Compared to previous designs, the proposed memristive circuit can implement all the functions of conditional reflex. Our rigorous simulations demonstrated that the proposed memristive system can condition neutral stimuli, show generalization between similar stimuli, distinguish dissimilarities between the generalized stimuli, and recover faded stimuli.

Ultra-Efficient and Robust Auto-Nonvolatile Schmitt Trigger-Based Latch Design Using Ferroelectric CNTFET Technology.

In designing ultra-efficient noise immune nanoscale circuits and systems, Schmitt triggers (STs) are vital components influencing total functionality. This article proposes an ultracompact ST using ferroelectric carbon nanotube field-effect transistors (Fe-CNTFETs) and a robust ST latch. By using the unique electrical futures of the Fe-CNTFETs, the proposed ST has been designed in a particular way to only employ two transistors similar to a conventional binary inverter. Moreover, by utilizing the negative capacitance feature of the Fe-CNTFETs, the proposed ST can perform the backup and restore operation during a scheduled power gating or a sudden power outage without imposing any additional transistors, interconnects, and control signals. The proposed ST latch is hardened to soft errors occurring due to unwanted single-event upsets (SEUs). Our extensive simulations demonstrate that our proposed ST latch offers lower transistors counts (on average 34%) and more energy savings (on average 79%). On the other hand, our design has shown 5.6 times on average higher critical charge tolerance than the previous counterparts due to its soft error hardening circuity and the superior hysteresis behavior of the proposed Fe-CNTFET-based ST. Moreover, the auto-nonvolatility of the proposed ST makes the proposed latches immune to the sudden power outage, which was not devised in the previous ST latches. Our results show new pathways in designing ultracompact and efficient nonvolatile ST latches using the Fe-CNTFET technology.

Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment.

MOTIVATION: The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time-consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA). METHOD: The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segments the far wall region. Lumen-intima (LI) and media-adventitial (MA) boundaries are then extracted from the wall region, which is then used for cIMT and PA measurement. RESULTS: Using the database of 250 carotid scans, the cIMT error using the AI model is 0.0935±0.0637 mm, which is lower than those of all previous methods. The PA error is found to be 2.7939±2.3702 mm2. The system's correlation coefficient (CC) between AI and ground truth (GT) values for cIMT is 0.99 (p < 0.0001), which is higher compared with the CC of 0.96 (p < 0.0001) shown by the earlier DL method. The CC for PA between AI and GT values is 0.89 (p < 0.0001). CONCLUSION: A novel AI-based strategy was applied to carotid US images for the joint detection of carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans.

A Novel Characterization and Performance Measurement of Memristor Devices for Synaptic Emulators in Advanced Neuro-Computing.

The advanced neuro-computing field requires new memristor devices with great potential as synaptic emulators between pre- and postsynaptic neurons. This paper presents memristor devices with TiO2 Nanoparticles (NPs)/Ag(Silver) and Titanium Dioxide (TiO2) Nanoparticles (NPs)/Au(Gold) electrodes for synaptic emulators in an advanced neurocomputing application. A comparative study between Ag(Silver)- and Au(Gold)-based memristor devices is presented where the Ag electrode provides the improved performance, as compared to the Au electrode. Device characterization is observed by the Scanning Electron Microscope (SEM) image, which displays the grown electrode, while the morphology of nanoparticles (NPs) is verified by Atomic Force Microscopy (AFM). The resistive switching (RS) phenomena observed in Ag/TiO2 and Au/TiO2 shows the sweeping mechanism for low resistance and high resistance states. The resistive switching time of Au/TiO2 NPs and Ag/TiO2 NPs is calculated, while the theoretical validation of the memory window demonstrates memristor behavior as a synaptic emulator. Measurement of the capacitor-voltage curve shows that the memristor with Ag contact is a good candidate for charge storage as compared to Au. The classification of 3 × 3 pixel black/white image is demonstrated by the 3 × 3 cross bar memristor with pre- and post-neuron system. The proposed memristor devices with the Ag electrode demonstrate the adequate performance compared to the Au electrode, and may present noteworthy advantages in the field of neuromorphic computing.

Fast and Stable Ionic Electroactive Polymer Actuators with PEDOT:PSS/(Graphene⁻Ag-Nanowires) Nanocomposite Electrodes.

Ionic electroactive polymer (IEAP) actuators that are driven by electrical stimuli have been widely investigated for use in practical applications. However, conventional electrodes in IEAP actuators have a serious drawback of poor durability under long-term actuation in open air, mainly because of leakage of the inner electrolyte and hydrated cations through surface cracks on the metallic electrodes. To overcome this problem, a top priority is developing new high-performance ionic polymer actuators with graphene electrodes that have superior mechanical, electrical conductivity, and electromechanical properties. However, the task is made difficultby issues such as the low electrical conductivity of graphene (G). The percolation network of silver nanowires (Ag-NWs) is believed to enhance the conductivity of graphene, while poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), which exhibits excellent stability under ambient conditions, is expected to improve the actuation performance of IEAP actuators. In this study, we developed a very fast, stable, and durable IEAP actuator by employing electrodes made of a nanocomposite comprising PEDOT:PSS and graphene⁻Ag-NWs (P/(G⁻Ag)). The cost-effective P/(G⁻Ag) electrodes with high electrical conductivity displayed a smooth surface resulting from the PEDOT:PSS coating, which prevented oxidation of the surface upon exposure to air, and showedstrong bonding between the ionic polymer and the electrode surface. More interestingly, the proposed IEAP actuator based on the P/G⁻Ag electrode can be used in active biomedical devices, biomimetic robots, wearable electronics, and flexible soft electronics.

Microfluidic technologies for circulating tumor cell isolation.

Metastasis is the main cause of tumor-related death, and the dispersal of tumor cells through the circulatory system is a critical step in the metastatic process. Early detection and analysis of circulating tumor cells (CTCs) is therefore important for early diagnosis, prognosis, and effective treatment of cancer, enabling favorable clinical outcomes in cancer patients. Accurate and reliable methods for isolating and detecting CTCs are necessary to obtain this clinical information. Over the past two decades, microfluidic technologies have demonstrated great potential for isolating and detecting CTCs from blood. The present paper reviews current advanced microfluidic technologies for isolating CTCs based on various biological and physical principles, and discusses their fundamental advantages and drawbacks for subsequent cellular and molecular assays. Owing to significant genetic heterogeneity among CTCs, microfluidic technologies for isolating individual CTCs have recently been developed. We discuss these single-cell isolation methods, as well as approaches to overcoming the limitations of current microfluidic CTC isolation technologies. Finally, we provide an overview of future innovative microfluidic platforms.

Implementation of an integrated op-amp based chaotic neuron model and observation of its chaotic dynamics.

This paper presents a fully integrated circuit implementation of an operational amplifier (op-amp) based chaotic neuron model with a bipolar output function, experimental measurements, and analyses of its chaotic behavior. The proposed chaotic neuron model integrated circuit consists of several op-amps, sample and hold circuits, a nonlinear function block for chaotic signal generation, a clock generator, a nonlinear output function, etc. Based on the HSPICE (circuit program) simulation results, approximated empirical equations for analyses were formulated. Then, the chaotic dynamical responses such as bifurcation diagrams, time series, and Lyapunov exponent were calculated using these empirical equations. In addition, we performed simulations about two chaotic neuron systems with four synapses to confirm neural network connections and got normal behavior of the chaotic neuron such as internal state bifurcation diagram according to the synaptic weight variation. The proposed circuit was fabricated using a 0.8-µm single poly complementary metal-oxide semiconductor technology. Measurements of the fabricated single chaotic neuron with ± 2.5 V power supplies and a 10 kHz sampling clock frequency were carried out and compared with the simulated results.