UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm.

Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.

Ultralow-power optoelectronic synaptic transistors based on polyzwitterion dielectrics for in-sensor reservoir computing.

Bio-inspired transistor synapses use solid electrolytes to achieve low-power operation and rich synaptic behaviors via ion diffusion and trapping. While these neuromorphic devices hold great promise, they still suffer from challenges such as high leakage currents and power consumption, electrolysis risk, and irreversible conductance changes due to long-range ion migrations and permanent ion trapping. In addition, their response to light is generally limited because of "exciton-polaron quenching", which restricts their potential in in-sensor neuromorphic visions. To address these issues, we propose replacing solid electrolytes with polyzwitterions, where the cation and anion are covalently concatenated via a flexible alkyl chain, thus preventing long-range ion migrations while inducing good photoresponses to the transistors via interfacial charge trapping. Our detailed studies reveal that polyzwitterion-based transistors exhibit optoelectronic synaptic behavior with ultralow-power consumption (~250 aJ per spike) and enable high-performance in-sensor reservoir computing, achieving 95.56% accuracy in perceiving the trajectory of moving basketballs.

A 2D Heterostructure-Based Multifunctional Floating Gate Memory Device for Multimodal Reservoir Computing.

The demand for economical and efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two-dimensional (2D) materials in recent years. As a rising van der Waals (vdW) p-type Weyl semiconductor with many intriguing properties, tellurium (Te) has been widely used in advanced electronics/optoelectronics. However, its application in floating gate (FG) memory devices for information processing has never been explored. Herein, an electronic/optoelectronic FG memory device enabled by Te-based 2D vdW heterostructure for multimodal reservoir computing (RC) is reported. When subjected to intense electrical/optical stimuli, the device exhibits impressive nonvolatile electronic memory behaviors including ≈108 extinction ratio, ≈100 ns switching speed, >4000 cycles, >4000-s retention stability, and nonvolatile multibit optoelectronic programmable characteristics. When the input stimuli weaken, the nonvolatile memory degrades into volatile memory. Leveraging these rich nonlinear dynamics, a multimodal RC system with high recognition accuracy of 90.77% for event-type multimodal handwritten digit-recognition is demonstrated.

Breaking the Trade-Off Between Polymer Dielectric Constant and Loss via Aluminum Oxo Macrocycle Dopants for High-Performance Neuromorphic Electronics.

The dielectric layer is crucial in regulating the overall performance of field-effect transistors (FETs), the key component in central processing units, sensors, and displays. Despite considerable efforts being devoted to developing high-permittivity (k) dielectrics, limited progress is made due to the inherent trade-off between dielectric constant and loss. Here, a solution is presented by designing a monodispersed disk-shaped Ce-Al-O-macrocycle as a dopant in polymer dielectrics. The molecule features a central Ce(III) core connected with eight Al atoms through sixteen bridging hydroxyls and eight 3-aminophenyl peripheries. The incorporation of this macrocycle in polymer dielectrics results in an up to sevenfold increase in dielectric constants and up to 89% reduction in dielectric loss at low frequencies. Moreover, the leakage-current densities decrease, and the breakdown strengths are improved by 63%. Relying on the above merits, FETs bearing cluster-doped polymer dielectrics give near three-orders source-drain current increments while maintaining low-level leakage/off currents, resulting in much higher charge-carrier mobilities (up to 2.45 cm2  V-1  s-1 ) and on/off ratios. This cluster-doping strategy is generalizable and shows great promise for ultralow-power photoelectric synapses and neuromorphic retinas. This work successfully breaks the trade-off between dielectric constant and loss and offers a unique design for polymer composite dielectrics.

Spontaneous Threshold Lowering Neuron using Second-Order Diffusive Memristor for Self-Adaptive Spatial Attention.

Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In-memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first-order dynamics. Here, a second-order memristor is experimentally demonstrated using yttria-stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second-order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL-based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second-order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high-efficiency, compact footprint, and hardware-encoded plasticity.

Electronic/Optoelectronic Memory Device Enabled by Tellurium-based 2D van der Waals Heterostructure for in-Sensor Reservoir Computing at the Optical Communication Band.

Although 2D materials are widely explored for data storage and neuromorphic computing, the construction of 2D material-based memory devices with optoelectronic responsivity in the short-wave infrared (SWIR) region for in-sensor reservoir computing (RC) at the optical communication band still remains a big challenge. In this work, an electronic/optoelectronic memory device enabled by tellurium-based 2D van der Waals (vdW) heterostructure is reported, where the ferroelectric CuInP2 S6 and tellurium channel endow this device with both the long-term potentiation/depression by voltage pulses and short-term potentiation by 1550 nm laser pulses (a typical wavelength in the conventional fiber optical communication band). Leveraging the rich dynamics, a fully memristive in-sensor RC system that can simultaneously sense, decode, and learn messages transmitted by optical fibers is demonstrated. The reported 2D vdW heterostructure-based memory featuring both the long-term and short-term memory behaviors using electrical and optical pulses in SWIR region has not only complemented the wide spectrum of applications of 2D materials family in electronics/optoelectronics but also paves the way for future smart signal processing systems at the edge.

Interaction mechanism of novel fluorescent antifolates targeted with folate receptors α and ß via molecular docking and molecular dynamic simulations.

Eight novel fluorescent antifolates were designed and docked with folate receptors FRα and FRß. The structures of the complexes were further calculated by molecular dynamic (MD) simulations. The binding energies were calculated by molecular docking and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) studies. The binding energy differences between FRα and FRß (|Ebα|-|Ebß|) values for compounds 3 and 8 were 1.3 and 1.1 kcal/mol calculated by molecular docking, and 13.9 and 10.4 kcal/mol by MM-PBSA simulation, respectively. The results indicated that compounds 3 and 8 may be the best candidates for targeted drug delivery to FRα. The binding structures, interaction residues, negatively charged pocket volume, and surface area were analyzed for all the complexes. We further calculated the root mean square displacement and secondary structural elements of the bound complexes using molecular dynamics simulations. The purpose of this study is to design novel antifolates targeted to FRα and FRß, and to further distinguish between cancer cells and inflammation.