A novel methodology in the manner of vector-matrix multiplication (VMM) architecture is suggested for intelligently determining traffic signal changes to enhance the flow of urban traffic. Unlike the conventional prediction-based traffic model, a real-time decision model considering the traffic density at each transport section is established, which simplifies the traffic signal decision process as a convolutional transformation. Compared with a periodically repetitive signal changing system, the suggested VMM system actively optimizes the signal configuration in an irregular shape according to the traffic density distribution, resulting in reduction in the time cost with highly improved decision efficiency. With this system based on particle dynamics, the travel time is reduced by approximate to 10% at the same pass ratio for different road structures (one-way, bidirectional, and intersectional transport). The pass ratio and resulting flow dynamics can be controllable using the different transformation matrix selections according to the traffic conditions. In addition, the analog conductance of the memristor device to the transformation matrix elements is applied, maintaining its reduction rate with a deviation tolerance of the VMM process up to approximate to 50%. It is believed that VMM-based signal decision platform can lead to great progress for fast and efficient transport in complex urban traffic networks.

Diffractive deep neural networks (D(2)NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all-optical object classification and perform universal linear transformations. Herein, a time-lapse image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super-resolution applications; inspired by their success, a time-lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time-lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR-10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR-10 dataset. Time-lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all-optical processors.

Herein, point light detection and ranging inertial odometry (LIO) is presented: a robust and high-bandwidth light detection and ranging (LiDAR) inertial odometry with the capability to estimate extremely aggressive robotic motions. Point-LIO has two key novelties. The first one is a point-by-point LIO framework that updates the state at each LiDAR point measurement. This framework allows an extremely high-frequency odometry output, significantly increases the odometry bandwidth, and fundamentally removes the artificial in-frame motion distortion. The second one is a stochastic process-augmented kinematic model which models the IMU measurement as an output. This new modeling method enables accurate localization and reliable mapping for aggressive motions even with inertial measurement unit (IMU) measurements saturated in the middle of the motion. Various real-world experiments are conducted for performance evaluation. Overall, Point-LIO is capable to provide accurate, high-frequency odometry (4-8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s(-1)) beyond the IMU measuring ranges. Furthermore, an exhaustive benchmark comparison is conducted. Point-LIO achieves consistently comparable accuracy and time consumption. Finally, two example applications of Point-LIO are demonstrated, one is a racing drone and the other is a self-rotating unmanned aerial vehicle, both have aggressive motions.

Light-fueled self-oscillators based on stimuli-responsive soft materials have been explored toward the realization of a myriad of nonequilibrium robotic functions, such as adaptation, autonomous locomotion, and energy conversion. However, the high energy density and unidirectionality of the light field, together with the unscalable design of the existing demonstrations, hinder their further implementation. Herein, a light-responsive lampshade-like smart material assembly as a new self-oscillator model that is unfettered by the abovementioned challenges, is introduced. Liquid crystal elastomer with low phase transition temperature is used as the photomechanical component to provide twisting movement under low-intensity incoherent light field. A spiral lampshade frame ensures an equal amount of light being shadowed as negative feedback to sustain the oscillation upon constant light field from omnidirectional excitation (0 degrees-360 degrees azimuth and 20 degrees-90 degrees zenith). Different-sized oscillators with 6, 15, and 50 mm in diameter are fabricated to prove the possibility of scaling up and down the concept. The results provide a viewpoint on the fast-growing topic of self-oscillation in soft matter and new implications for self-sustained soft robots.

Reservoir computing (RC) architecture which mimics the human brain is a fundamentally preferred method to process dynamical systems that evolve with time. However, the difficulty in generating rich reservoir states using two-terminal devices remains challenging, which hinders its hardware implementation. Herein, the 1D array of ferroelectric field-effect transistor (Fe-FET) based on alpha-In2Se3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in alpha-In2Se3 is sufficiently investigated by polarization dynamic model. The proposed Fe-FET is capable of experimentally classifying images using MNIST dataset with a high accuracy of 91%. Furthermore, time-series real-life chaotic system, for example, Earth's weather, can be accurately forecasted using our Ferro-RC based on the Jena climate dataset recorded in a 1 year period. Remarkable determination coefficient (R-2) of 0.9983 and normalized root mean square error (NRMSE) of 8.3 x 10(-3) are achieved using a minimized readout network. The demonstration of integrated memory and computation opens a route for realizing a compact RC hardware system.

The 3D variable-stiffness structure can realize shape programming, reconstruction, adaptation, and locking, and therefore, it has a wide design creation space. Accurate local stiffness control is of considerable significance to the design and application of 3D variable-stiffness structures although it is challenging. Herein, a 3D variable-stiffness structure realization scheme based on a patterned heating network is introduced. The laser-engraving and 3D-printing technologies are combined to obtain a 3D variable-stiffness structure composed of a patterned graphene-heating network (PGHN) and polylactic acid (PLA). The proposed scheme uses PGHN to accurately control the local stiffness of 3D PLA and realize programmable design and fabrication of 3D variable-stiffness structures. The torsional structure, hexagonal structure, and spring cases are used to elaborate the designability, excellent deformation and reconstruction capacity, and reasonable load bearing capacity of the PGHN/PLA variable-stiffness structure. A pneumatic disc, which is used as a reference for studies on shape control of PGHN/PLA variable-stiffness structures, is designed. Also, a pneumatic robot is designed based on the local stiffness control and shape-locking function of PGHN/PLA to achieve multimode motion control using a single air source. The PGHN/PLA variable-stiffness structure has potential applications in multimode robots, wearable devices, and deployable structures.